VU Research Portal

Signaling at the tumorigenesis-inflammation interface: developing systems immunology to resolve a paradox Abulikemu, A.

2017

document version Publisher's PDF, also known as Version of record

Link to publication in VU Research Portal

citation for published version (APA) Abulikemu, A. (2017). Signaling at the tumorigenesis-inflammation interface: developing systems immunology to resolve a paradox.

General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.

• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ?

Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

E-mail address: [email protected]

Download date: 10. Oct. 2021 Signaling at the Tumorigenesis-Inflammation Interface: developing Systems Immunology to resolve a Paradox

Ablikim Abdukerim

PhD thesis with summary in Dutch

Department of Molecular Cell Biology

Faculty of Earth and Life Sciences

VU University Amsterdam, The Netherlands

Printed by Amsterdam University Press, Amsterdam

VRIJE UNIVERSITEIT

Signaling at the Tumorigenesis-Inflammation Interface: developing Systems Immunology to resolve a Paradox

ACADEMISCH PROEFSCHRIFT

ter verkrijging van de graad Doctor aan de Vrije Universiteit Amsterdam op gezag van de rector magnificus prof.dr. V. Subramaniam, in het openbaar te verdedigen ten overstaan van de promotiecommissie van de Faculteit der Aard-en Levenswetenschappen op dinsdag 28 maart 2017 om 11.45 uur in de aula van de universiteit, De Boelelaan 1105

Door

Abulikemu Abudukelimu

geboren te Urumqi, Xinjiang, China

promotoren: prof.dr. H.V. Westerhoff

prof.dr. S.M. van Ham copromotor: dr. M. Barberis

Members of the Doctoral Examination Committee: prof. dr. F.J. Bruggeman VU University Amsterdam, the Netherlands dr. K. Krab VU University Amsterdam, the Netherlands prof. dr. R.E. Mebius VU University Medical Center , the Netherlands dr. F. A. M. Redegeld Utrecht University, the Netherlands prof. dr. J.L. Snoep University of Stellenbosch and VU University Amsterdam, South Africa and the Netherlands prof. dr. A.H.C. van Kampen University of Amsterdam, the Netherlands dr. G.J. Wolbink Amsterdam Rheumatology Center and Sanquin, the Netherlands

To my beloved parents and sister

And my niece

Table of Contents

Chapter 1: General Introduction 1.1 Inflammation and immunity are driven by various interactions in the immune system

1.1.1 Cell-mediated immunity for bacterial infection ...... 2 1.1.2 Cell-mediated immunity for viral infection ...... 4 1.2 The immune system and cancer

1.2.1 Cellular-immunity for cancer ...... 6 1.2.2 strategies to combat cancer...... 9 1.3 Cancer-related inflammatory biomarkers: TNF-α, tryptase and Ig-free light chains (FLCs) ...... 10 1.4 Mast cells

1.4.1 Mast cell activation ...... 12

1.4.2 Pro-cancer functions of mast cells ...... 15 1.4.3 Anti-cancer functions of mast cells...... 16 1.5 Mast cells, inflammation and cancer: systems biology might matter ...... 19 1.6 Aims and overview of the thesis ...... 20

Chapter 2: FLC-Mediated Mast Cell (in) Activation and Its Predictable Effects on Acute and Chronic Inflammation 2.1 Introduction ...... 22 2.2 Methods and parameters ...... 24 2.3 Results ...... 27 2.4 Discussion ...... 36

Chapter 3: Inflammation-oriented Anti-Tumor Targets in a Complex Model for Mast-cell Mediated Tumorigenesis 3.1 Introduction ...... 44 3.2 Material and Methods

3.2.1 Immunohistochemistry analysis ...... 47 3.2.2 In silico model and its analyses...... 48 3.3 Results 3.3.1 Human and murine tumor biopsies harbor disperse activated mast cell .... 51 3.3.2 Modeling the tumor-associated inflammatory network...... 55 3.4 Discussion ...... 64 3.5 Supplementary materials: in vivo validation of the in silico model

3.5.1 Anti-FLC peptides redress tumor growth in vivo...... 66 3.5.2 Supplemental experimental methods...... 67

Chapter 4: Dynamics of a Model of Innate Immunity: Complex (bi-) Stability, Flares, Irreversible Transitions, and Sensitivities 4.1 Introduction ...... 78 4.2 Methods ...... 80 4.3 Results

4.3.1 Instability, flares of infection and a therapy thereof ...... 81

4.3.2 Bimodal sensitivity analysis of innate inflammation...... 82 4.3.3 Bi-stability, irreversible transition and fata switching in innate inflammation ...... 84

4.3.4 Jumping the bi-stability barrier: fibroblast therapy of innate inflammation...... 91 4.4 Discussion

4.4.1 Diverse stabilities of a model of innate immunity ...... 99 4.4.2 Robustness of innate immunity ...... 101

4.4.3 An important role for bystander cells? ...... 102 4.5 Supplementary material ...... 103

Chapter 5: Mast Cells as Potential Therapeutic Targets for Anti-Tumor Therapy 5.1 Introduction 5.1.1 Mast Cell Biology

5.1.1.1 Origin of mast cells ...... 113 5.1.1.2 Phenotype ...... 113 5.1.1.3 The mast cell and innate immunity ...... 114

5.1.1.4 The mast cell and adaptive immunity ...... 114 5.2 Mast cells and cancer diagnosis ...... 115 5.3 Mast cells as drug target

5.3.1 Targeting mast cell degranulation ...... 118

5.3.2 Targeting mast cell tryptase ...... 119 5.3.3 Anti-IgE therapy in cancer ...... 120 5.4 (SCF) and the c-kit Mediated Signaling Pathway

5.4.1 Stem Cell Factor ...... 120 5.4.2 C-kit...... 121 5.4.3 Inhibition of SCF-C-kit signaling...... 122 5.5 Concluding remarks ...... 125

Chapter 6: Inflammation and Cancer: how could Systems Biology help unravel the Link? 6.1 The inflammation system perspective ...... 128 6.2 The cancer system perspective ...... 130 6.3 Personalized medicine ...... 132 6.4 Future directions ...... 134

Chapter 7: Further Understanding and Initial Validation Thereof (Supplemental material) S7.1 Methods ...... 136 S7.2 Results ...... 136 S7.3 Discussion ...... 137

References ...... 150 Summaries ...... 184 Abbreviations ...... 190

Acknowledgements ...... 192

Introduction to systems immunology

Chapter 1 General Introduction

1

Introduction to systems immunology

1.1 Inflammation and immunity are driven by various interactions in the immune system

Inflammation is a biological process involving morphological changes of tissues and cells due to influx of fluids and cells and the release of factors that may damage cells in the tissues that are affected by harmful pathogens and toxins [1]. In 1794, John Hunter first described the involvement of growing blood vessels in healing wounds, while the first description of the migration of white blood cells is attributed to Dutrohet in 1824. Since then, inflammation has also been analyzed critically by Rudolf Virchow and he describes that inflammation comes with the physiological symptoms redness, swelling, heat and pain [2, 3]. The phagocytes (macrophages, neutrophils, mast cells) of innate immunity defend the body against bacteria and viruses, while the secretory innate cells defend against parasites. Innate immunity by itself most often cannot fully resolve an infection; adaptive immune cells, B cells and T cells, are required to eliminate infection completely. Innate immune cells, such as macrophages and dendritic cells, digest invading pathogens and their products at the site of infection. Particularly, dendritic cells (DCs) serve as an antigen-presenting cells (APC) to activate T cells in secondary lymphoid organs, like lymph nodes. Some activated T cells quickly migrate to the infection site and further activate macrophages, attract neutrophils to the site of infection or kill infected cells, thereby supporting elimination of the pathogens. B cells take up antigen in lymph nodes and either or not with help of cognate activated T cells, then differentiate into plasma cells that make antibodies. And these antibodies also go to sites of infection to help the immune response. The ineractions between innate and adaptive immune cells create a versatile network protecting an individual against pathogens and parasites, even tumor cells. In the following paragraphs, we describe how the immune system processes and kills the infected cells in the body.

1.1.1 Cell-mediated immunity for bacterial infection

In a typical bacterial infection, resident macrophages in tissue are among the first cells to take up the pathogen and inactivate it. In many cases, the macrophage is able to kill bacterial pathogens without help of T-cell activation, but T-cell involvement is required to maintain the antimicrobial function of macrophages [4]. How does a macrophage handle infection? Macrophages do recognize and kill bacteria through complex and versatile mechanism. This mechanism involves seven processes: (1) Macrophages express pattern-recognition receptors (PRRs), such as Toll-like receptors (TLRs) for recognizing different kinds of bacterial pathogen-associated molecular patterns (PAMPs). For example, TLR5 recognizes bacterial flagella [5], whereas TLR9 recognizes unmethylated CpG dinucleotides in bacterial DNA [6]. Bacterial PAMPs trigger the rapid differentiation of monocytes to macrophages. The identification of PAMPs by pattern-recognition receptors (PRRs) sends a signal to the cell nucleus of the macrophage that activates nuclear factor kappa-light-chain-enhancer of activated B cells (NFκB), and the NFκB is responsible for production and survival of the macrophage. As a result, activated macrophages secrete various . For example, CXCL6 helps to attract neutrophils and basophils to sites of infection; othercytokines such as (IL)-1β, IL-6 and (TNF)-α trigger acute inflammation in the liver to induce effective host defense. (2) The macrophages ingest 2

Introduction to systems immunology the pathogens via recognition of specific structures by so-called scavenger receptors. Next, the complement system is activated through any of three distinct pathways that not only lead to opsonization and killing of pathogens but also to recruitment of inflammatory cells: The so-called classical pathway directs binding of complement component C1q to C-reactive protein or to immunoglobulins bound to the pathogen surface. The so-called lectin pathway is triggered by mannose-binding lectin that binds to pathogen surfaces. Finally, the so-called ‘alternative pathway’, which is triggered directly on the surface of the pathogen but neither in the host plasma nor on the surface of the host cell, involves the covalent binding of C4b to the pathogen’s surface. Importantly, opsonizing agents, such as C3b, are able to bind to the surface of microbial pathogens and this leads to production of surface bound C3-convertase by the pathogen. This enzyme exists in two forms, both of which cleave only C3b. Subsequently macrophages express additional receptors to recognize opsonins that enhance phagocytosis. Upon ingestion, the pathogen-containing phagosomes fuse with lysosomes and form a phagolysosome [7]; (3) The phagolysosome contains many proteolytic enzymes that digest and destroy the contents of invading bacteria. The digested bacterial peptides, now termed antigens, bind to a peptide binding glycoprotein known as Major Histocompatibility Complex (MHC) class II molecule and the MHC class II molecules are presented at the macrophage plasma membrane. (4) Other antigen presenting cells (APCs) like Langerhans cells and dermal dendritic cells express PRRs, such as TLRs, to recognize the protein codes of bacterial pathogens in the cutaneous epithelium of skin. Subsequently, immature DCs ingest invaded microbes and they start to mature. The DCs lose their adhesiveness and migrate to the lymphatic vessel heading towards a lymph node, where they are fully matured upon arrival. The matured DCs synthesize MHC class II in endoplasmic reticulum (ER), where bacterial peptide from digested pathogens can bind. (5) In the lymph node, both mature DCs and macrophages express MHC class II molecules, which display bacterial antigens. These antigens are recognized by a unique T-cell receptor (TCR) on naïve CD4 T cells. Engagement of TCR with its bacterial antigen is essential and regarded as ‘signal 1’, but this signal 1 is not enough to stimulate T-cell proliferation and differentiation. Therefore, (6) co-stimulatory molecules are involved in promoting the survival (signal 2) and expansion (signal 3) of T cells. Signal 2 is driven by the B7 molecule found on the surfaces of DCs binding to CD28 on T cells with the effect of promoting T-cell proliferation; signal 3 can also be delivered by cytokines such as IL-6, IL-12 and TGF-β that are secreted from APCs and these cytokines are involved in directing T-cell differentiation. (7) After T-cell differentiation, the TH1 cells migrate to the site of infection and identify the infected macrophages via presentation of the same MHC class II/antigen complexes by the infected macrophage. Subsequently they secrete pro-inflammatory cytokines, such as IFN-γ cytokine and give co-stimulation via CD40 on the macrophage. Both signals mediate further differentiation of the macrophages and additional expression of bacteriolytic proteins from the macrophages. In this way, TH1 cells enhance the function of the macrophages and help resolve the infection. TH2 or follicular T helper (Tfh) cells aid differentiation of naïve B cells by promoting B cell proliferation, somatic hypermutation and class switching of B cells to produce IgG or IgE antibodies, which aid to combat infections and take part in the inflammation process by recruiting inflammatory cells upon opsonization of the pathogens [8].

3

Introduction to systems immunology

B cells in the lymphoid follicles are particularly efficient at taking up soluble antigens, such as bacterial toxins, by the specific binding of the antigens to the B-cell receptor (BCR). The BCR is composed of a plasma membrane immunoglobulin coupled to a signaling module that is formed by the Igα–Igβ dimer, containing immunoreceptor tyrosine-based activation motifs (ITAMs). In this immunoreceptor the tyrosine residues rapidly become phosphorylated by SRC family kinases upon antigen engagement [9, 10], and this signaling pathway is essential for B-cell activation. One unique feature of B cells is that B cells form an immunological synapse where the BCR forms a central cluster surrounded by a ring of adhesion molecules, including LFA1. The synapse formation drives dynamic changes in actin cytoskeletons of B cells which help to promote the gathering and the extraction of membrane-tethered antigens. Importantly, the BCR signaling induces changes in the antigen processing that facilitate the traffic of antigen and MHC class II molecules. Bacterial antigens displayed by MHC II class molecules on B cells allow TH1, TH2 of Tfh cells to give cognate help in the B cell differentiation process. Previously activated TH cells have synthesized CD40L, which binds to CD40 on B cells. Furthermore, B-cell activating factor (BAFF) is secreted by monocytes, DCs and bone marrow stromal cells and acts as driving factor for B-cell proliferation [11, 12].

1.1.2 Cell-mediated immunity for viral infection

In viral infections, activation of T cells is thought to be almost exclusively dependent on dendritic cells (DCs). The DCs play an important role in the recognition of viral PAMPs and in the presentation of the viral antigens to T cells [13]. Once the immature DCs take up viruses, the viral proteins get digested in endosomes of the immature DCs, which readies them for loading and ready to be loaded either by MHC-class I molecule or by MHC-class II molecule that depend on the type of viral infection. Immature DCs are activated and upon maturation migrate to lymph node, where they present viral peptides on MHC-class I and II molecules on the plasma membrane [14]. Thereafter, the mature DCs engage with naïve forms of T cells (Th0) which can either be CD4+ or CD8+ CTL cells in lymph node and this process triggers T- cell activation in adaptive immunity. Moreover, mature DCs produce cytokines, such as IL-12, TNF-α, IL-1β, to help T-cell activation and proliferation. Categorically, there are two different functional classes of dendritic cells; one is called conventional DCs (cDC), which are directly participating in the activation of naïve T cells; the other one is called plasmacytoid DCs (pDCs), a unique type that produce large amounts of (IFN)-α and β in response to viral infection, but do not play a role in activating naïve T cells. The pDCs are particularly sensitive to viral infections by viral PRRs such as TLR-7 and TLR-9. pDCs present antigen only through MHC class II, whereas cDC present antigen through either MHC class I or MHC class II. Viral antigens are taken up by cDC through four different routes: (1) Virus particles can be taken up by tissue dendritic cells by macropinocytosis for delivering viral peptides MHC class II molecules and presenting them to naive CD4 T cells; (2) Viruses directly enter the cytoplasm of cDCs by the infection and synthesize viral protein using the cDCs’s protein- synthesis machinery. The MHC class I molecules present viral antigens to and activate naïve CD8 T cells; (3) Extracellular virus particles are taken up by phagocytosis or macropinocytosis through the endocytic pathway also known as cross-presentation of viral antigens. In this alternative way, the viral peptides on MHC class I molecules can still activate naïve CD8 T 4

Introduction to systems immunology cells to trigger antiviral response; (4) The viruses which are captured by immature cDCs also known as Langerhans cells, in the skin and are transported to lymph nodes. There, some captured antigen is transferred to a CD8-positive subset of cDCs resident in lymph node and this subset of cDCs are the dominant cDCc responsible for priming naïve CD8 T cells [8]. Natural killer (NK) cells which are specialized non-T, non-B cells make up 5 to 15% of the total circulating lymphocyte population. NK cells originate in CD34+ hematopoietic progenitor cells [15, 16]. In human, NK cells express CD56 which is a 140-kDa isoform of neural cell adhesion molecule (NCAM). However, murine NK cells do not express CD56 [17]. Human NK cells can be divided into two subsets: one is the CD56bright NK cell which has a high density expression of CD56 [18] and the other one is the CD56dim NK cell which is more mature and derived from the CD56bright NK cell. The CD56bright NK cells are confined to secondary lymphoid tissue (SLT), whereas CD56dim NK cells are the majority of NK circulating in the blood. Generally speaking, NK cells are able to mediate host defense against infection, particularly viral infections. In vivo studies, in mice, showed that NK cells limit the replication of cytomegalovirus [19]. In human, NK cells as part of innate immunity, fight against members of the herpes virus [20] and poxvirus [21] groups. NK cells express many cell surface receptors that can be classified into activating, inhibiting, adhesion, cytokine and chemotactic receptors. In the absence of antibodies, NK cells express activating receptors which recognize invaded viruses. For example, the Ly49H activating receptor on NK cells identifies a cytomegalovirus-encoded ligand in mouse [22], and the NKp46 activating receptor interacts with heme agglutinins derived from influenza and parainfluenza viruses [23]. Another important receptor known as killer immunoglobulin-like receptor (KIR) which is present on human NK cells has the paradoxical role to either activate or inhibit human NK cells: On the one hand, KIR suppresses killing activity of human NK cells when normal expression of MHC class I molecules on uninfected cells send negative signals to the NK cells. On the other hand, if MHC class I molecule is abnormal or ill expressed on virus-infected cells then KIR becomes an inhibitory receptor sending positive signals to the NK cell, driving apoptotic activity of NK cells onto the infected cells [24, 25]. The activation of NK cells or CTLs causes release of perforin and granzymes [26]. Perforin penetrates virus infected cells and creates a pore via which granzymes are shuttled into the cytoplasm of the infected cells. Granzymes subsequently employ parts of the basic machinery of the infected cell which gives rise to programmed cell death (apoptosis). For example, granzyme B activates caspase 3-and -7 which then cleaves and inactivates I-CAD, the inhibitor of caspase activated DNAse. The active DNAse next migrates into the nucleus and degrades the DNA of the cell, leading to apoptosis of the infected cell [27, 28]. CTLs exhibit additional mechanisms that may mediate cytotoxicity. For instance, CTLs play an important pathogenic role against hepatitis B virus (HBV), by eliminating infected cells and by yielding antiviral cytokines [29-31].

5

Introduction to systems immunology

1.2 The immune system and cancer

1.2.1 Cellular immunity for cancer

Immune cells broadly involve in cancer development, both positively and negatively [32-34]. Many cancer cells produce antigenic substances known as tumor antigens, which may be detected by immune system. Once tumor antigens detected, the immune system starts to attack cancer cells; in other words, immune cells are provoked by tumor antigens. This process, referred to as cancer surveillance, identifies and eliminates cancer cells before they turn into their malignant form. Cells of both innate and adaptive immunity have been shown to be important in this elimination. In innate immunity, NK cells are activated by the inflammatory cytokines that are released by the pre-malignant tumor cells as well by stromal cells surrounding the tumor cells. NK cells are the only cells in innate immunity to kill the cancer cell. Most tumor cells that enter circulation are destroyed during the first 24 hours and only few cells succeed in establishing themselves in organs distant from the primary tumors, in a process called metastasis. Human NK cells express activating receptors, such as NKp30, NKp44, DNAM1, and NKG2D, to recognize tumors. For instance, activated NKG2D-receptor NK cells correlated with poor prognosis in human melanoma and prostate cancer [35], and glioblastoma virotherapy shows that NKp30 and NKp46 are overexpressed on NK cells that are able to hinder the brain tumor [36]. In adaptive immunity, the tumor antigens are recognized by the immune system. Then peptides of the tumor antigens are presented to T cells by MHC molecules and this triggers a tumor-specific T-cell response. For example, HER-2/neu antigen which is presented by DCs can induce a T-cell response in patients with gastric cancer [37]. This T-cell receptor (TCR) recognizes and activates tyrosinase, which catalyzes the melamine pathway. Consequently, tyrosinase may be a valuable target in T cell-based melanoma therapy. As a result of abnormal gene expression Wilms’ tumor antigens are overexpressed in many solid tumors and leukemia. They can be recognized by CD8 and leading to increased CD95L-mediated activation-induced cell death (AICD) [38, 39]. It has also been reported that IgG produced by tumor-infiltrating B cells recognizes MAGE-B2 antigen in lung cancer [40]. In the clinic, the presence of activated CD8+ T cells both within the tumor and in peritumoral stroma has significant prognostic power for patients with colorectal cancer [41]. Histology studies showed that T-cell infiltration in other types of cancers was associated with favorable clinical outcome. This includes breast cancer, melanoma, renal cell carcinoma, ovarian cancer, pancreatic cancer and gastrointestinal stromal tumors (GIST) [42-46]. Increasing evidence shows that immune cells accumulate in the stroma surrounding tumors. On the one hand, cancer cells secrete chemotactic cytokines, i.e. that direct migration of immune cells to the site of tumorigenesis [47]. Chronic infiltration of immune cells may subvert the anti-tumor immune environment into becoming favorable for tumorigenesis (Figure 1). For example, CCL2, CCL4 and CCL5 cells induce matrix 6

Introduction to systems immunology metalloproteinase (MMP)-9 secretion from macrophages, and MMP9 is important in extracellular matrix (ECM) remodeling [48]. CXCL12 secreted by ovarian cancer hinders maturation of DC cells and antigen presentation does not occur properly in immunity [49]. On the other hand, many molecules secreted by both innate and adaptive immune cells could benefit the tumor. For example, tumor-associated macrophages (TAMs) secrete pro- angiogenic factors, thereby promoting tumorigenesis. These factors include vascular endothelial (VEGF), matrix metalloproteinases (MMPs) and [50- 53]. Several studies have suggested that TAM-derived cytokines and proteases, such as transforming growth factor-β (TGF-β), IL-10, and arginase 1, are able to suppress immune response. For instance, TGF-β inhibits antitumor activity of NK cells by suppressing the activating receptor (NKG2D) [54, 55] and inhibits cytotoxic activity of CD8+T cells by suppressing the expression of genes, including the genes encoding granzyme A and B, and IFN-γ [56]. In addition, tumor-associated neutrophils (TANs) also secrete pro-angiogenic substances: For instance, IL-6 and IL-12 secreted by TANs promote recruitment of .- inflammatory cells to the tumor microenvironment. TANs release superoxide anion (O2 ), hydrogen peroxide (H2O2) and hypochlorous acid (HOCl) and these chemicals cause DNA damage in normal cells thereby promoting the initiation of tumorigenesis. Beside immune cells, non-vascular and non-inflammatory cells such as fibroblasts are also modifiers of tumorigenesis [57, 58], and so called cancer-associated fibroblasts (CAFs) support tumorigenesis and malignant progression [59, 60]. It has recently been reported that CAFs express natriuretic peptide precursor B (NPPB), which is a novel candidate biomarker in epithelial ovarian cancer. CAF-derived NPPB was expressed in the stroma of 60% of the primary ovarian cancer tissues examined [61].

7

Introduction to systems immunology

en en The The

[47, ellular ellular

, which is supported by

and on the online source

65]

-

infiltration infiltration of both innate immune cells Figure 1.1 The such asmast and cells macrophages, accumulationand and adaptive immune cells such as T cells and B cells creates a complex network. diagram illustrates some of interactions the between c cancer and immune cells in the tumor microenvironment, which through their connections accumulate into a network with many regulatory loops. The diagram is based on information from 62 www.cancer.gov the U.S. National (NIH). Institutes The of diagram discussed Health has in an the emphasis text. indicate Full blue arrows chemokines and receptors on the cells. Red the dashed interaction lines fibroblasts. indicate betwe Red conversion secretion of cytokines. various dotted of lines indicate

8

Introduction to systems immunology

1.2.2. Immunotherapy strategies to combat cancer

During the past decade, a growing interest has evolved around the use of the immune system to treat cancer, also known as cancer immunotherapy. The immune therapy towards cancer can be divided into 4 general strategies: non-specific immune stimulation, adoptive cell transfer, immune checkpoint blockade, and vaccination strategies. The first, non-specific immune-stimulation, is used to give a general boost to the immune system in vivo. To do this, some of the many cells that make up the immune system, such as APCs, need to be activated by injecting molecules that bind to receptors in the cell membrane. The activated cell alerts other immune cells such as T cells and activated T cells are able to attack and kill cancer cells. For full activation of T cells, small intercellular signaling molecules called cytokines are needed. Two cytokines, α (IFNα) [66] and interleukine-2 (IL-2) [67], have been developed into drugs and approved for use against some forms of cancer including melanoma. Another way to stimulate immune cells in vivo is to inject bacteria. For example, Bacillus Calmette-Guérin (BCG) is normally given to children as protection against tuberculosis but scientists have found that weakened bacteria inside BCG can also help patients with bladder cancer [68]. The bacteria appear to cause inflammation which increases the number of immune cells around the cancer helping them to home into their target. Non-specific immunity can also be achieved by removing so-called immune- checkpoint blockades. These “blockades” dampen down the immune response to prevent collateral damage to healthy tissues, but to fight cancer it is necessary to remove some of these blockades to make the immune response stronger [69, 70]. For example, the antibody ipilimumab also known as Yervoy, targets a blockade molecule called CTLA4 on the surface of T cells [71]. It received FDA approval for patients with advanced stages of melanoma in 2011. Ipilimumab has also been tested in several other types of cancer. Sometimes, activating immune cells inside the body can be difficult but the next strategy is to use adoptive cell transfer, i.e., extracting the immune cells from the patient and activating them outside the body. One approach is to take immune cells directly from the tumor, amplify them in vitro and then transfer multiple cells back into the patient. The disadvantage of the method is that it is difficult to extract tumor-attached immune cells, but on the other hand these immune cells have already learned to recognize the tumor. Another approach is to take non-specific immune cells from blood and arm these cells with tumor specific receptor by using genetic engineering. Either way the cells are activated using cytokines and multiplied in petri plates before being reintroduced into the patients. The fourth strategy uses vaccination. Unlike the BCG vaccine that we mentioned above and which targets the immune system in a general way, these cancer vaccines are used to direct immune cells very specifically to the cancer tissues. Several viral vaccines have shown promising results in clinical trials. For example, a weakened version of herpes simplex virus modified to produce immune stimulating factor is being developed for melanoma and for head & neck cancer [72]. It is also possible to vaccinate with the patient’s own tumor cells. Some cells are then isolated, irradiated to stop them from spreading, and then engineered to secrete activating growth factors. When the cells are injected back into the patient, the growth factors alert the immune system to the cancer. It is also possible to vaccinate with the person’s own immune cells. For example, APCs can be taken from the patient, matured outside the body and loaded with cancer antigen. When recombinant APCs 9

Introduction to systems immunology are reintroduced into the patient, the antigen-stimulated immune cells help them to recognize the tumor. The first vaccine of this type received FDA approval in 2010 for treatment of prostate cancer and it is known as provenge or sipuleucel-T. Other vaccination approaches are still experimental [73]. The last few years have seen many promising developments in anti-cancer immunotherapy but much work remains to help get drugs from the bench through clinical trials to the bedside. A close collaboration between immunologist and oncologist is vital to find better ways to judge successive immune therapies in cancer patients and to work out the best dose. Presently, it is difficult to predict who might respond best to a particular treatment and some responses are short lived. If these obstacles can be overcome, then many more cancer patients could benefit from the strategies that boost the immune system.

1.3. Cancer-related inflammatory biomarkers: TNF-α, tryptase and Ig- free light chains (FLCs)

Tumor necrosis factor-α (TNF-α) is a pleiotropic cytokine released by immune cells and a major cause of inflammation. The TNF-α concentration in serum varies from 10 pg/ml to 150 pg/ml [74]. High levels of TNF-α correlate with mortality [75]. In innate immunity, TNF-α is mainly produced by activated macrophages [76, 77], although it is also produced by other innate immune cells such as activated mast cells [78, 79]. Overexpression of TNF-α has been reported in autoimmune disorders such as rheumatoid arthritis (RA) [80], inflammatory bowel disease (IBD) [81], multiple sclerosis [82] and asthma [83]. Anti-TNFα therapy showed etanercept to have significant therapeutic effect on patients with RA, while the monoclonal antibody infliximab is used in treatments of IBD and asthma [84, 85]. In 2008, the FDA approved the fully human monoclonal antibody adalimumab as TNF-α blocker and the latest study reported that adalimumab significantly reduces the frequency of attacks of Anterior Uvelitis (AU) with ankylosing spondylitis (AS) [86]. TNF-α over expression is apoptotic to healthy cells in brain tissues, which may contribute to dementia such as in Alzheimer’s disease [87], Parkinson’s disease [88] and vascular dementia [89]. In association with chronic inflammation, cancer often causes overproduction of TNF-α [90]. This may in turn induce NADPH oxidase Organizer 1 (Noxo 1) and guanine nucleotide binding protein (G protein) alpha 14 (Gna14) in tumor epithelial cells, thereby enhancing cancer cell proliferation [91]. In murine embryonic fibroblasts, TNF-α induced phosphorylation of transcription factor p65. Phosphorylation of p65 plays a vital role in regulating NF-kB activation and the activation of NF-kB is involved in all types of cellular processes, including cellular metabolism, chemotaxis, suggesting p65 as a potential cancer biomarker [92]. For example, p65 may play a potential role as biomarker for prostate cancer [93]. Recent studies demonstrated that TNF-α stimulated IL-6 synthesis by inducing the JAK/STAT3 and SAPK/JNK pathways in glioma cells. These pathways do not occur in non-tumor cells for providing a partial mechanism for how TNF-α may promote tumorigenesis in glioblastoma [94]. Notably, activation of STAT 3 by IL-6 causes immunosuppression, such as blockade of DCs’ maturation[95]. Conversely, STAT3 activation in myeloid cells led to increased IL-6 secretion, which mediates STAT3 activation in epithelial cells promoting tumorigenesis in pancreatic cancer [96]. Tryptase is a granule-derived serine proteinase with an important role in inflammation. The observation of increased mast cell tryptase levels both in inflammation and in cancer 10

Introduction to systems immunology patients suggest that mast cell tryptase serves as a marker in the clinic. Normally, the serum level of tryptase is lower than 12 ng/ml. Its level is elevated in diseases such as systemic mastocytosis (67 ng/ml) [97], acute allergy (>15 ng/ml) [98], chronic eosinophilic pneumonia [99], and active chronic urticarial [100]. Mast cell proteases are detectable under pathological conditions that are related to mast cell activation in the organ tissues. Overexpression of mast cell tryptase has been observed in cancer tissues from colon cancer [101, 102], lung cancer [103], skin cancer [104], and pancreatic cancer [105]. Studies also show functional roles of mast cell tryptases in which the tryptases stimulate fibroblast chemotaxis and pro-collagen mRNA synthesis [106], which might lead to augmented fibroblast proliferation. For example, rat mast cells (RMC-1) enhance fibroblast proliferation and lattice contraction by forming junctional intercellular communications [107] and co- culture study, using RMC-1 and human dermal mast cell shows that mast cells are able to induce fibroblast activities associated with hypertrophic scarring through gap junctional intercellular communications [108]. Some investigators have reported that mast cell tryptases enhanced monocyte chemoattractant protein-1 (MCP-1) and IL-8 production in human endothelial cells [109], which might lead to further pathogenesis of diseases. For example, MCP-1 expression was detected in 23 of 24 cases of Hodgkin’s disease [110] and mast-cell tryptase activates protease-activated receptor-2 (PAR-2) in cancer [102]. Plasmablasts and plasma cells generate immunoglobulins (Ig) of specific classes, such as IgA, IgE, IgG and IgM. Structurally, an immunoglobulin molecule is composed of four polypeptide chains; two identical heavy chains (H) and two identical light chains (L) linked by disulfide bonds [8]. All four chains have antigen recognition domains called Fab (fragment that is antigen binding). The heavy chains also have an FC part, which mediates effector functions, such as when the antibody is bound to a receptor on the plasma membrane of a B-cell. Immunoglobulin (Ig) light chains are another example of potential biomarkers of diseases. There are two types of Ig light chains in human and other mammals. One is kappa (κ) and other is lambda (λ). Ig secreted by a B-cell expressing only one type of Ig light chain. Differences in the ratio between κ and λ light chains may reflect the status of the B cell compartment. In healthy individuals, the total κ to λ ratio is ~ 1:3, with κ- Ig light chain ranging between 0.4 and 4.2 mg/l, and λ- Ig light chain between 1.6 and 15 mg/l [111]. In abnormal conditions, the ratio between κ and λ Ig changes to for instance 1:1.5, which is a diagnostic biomarker for patients suffering from specific malignancies. The Bence Jones Protein (BJP), a urinary paraprotein found in multiple myeloma is an Ig light chain secreted by neoplastic plasma cells. BJP was first described by the English Chemist Henry Bence Jones, who reported “a precipitate that was formed upon the addition of nitric acid to urine”. Its amino acid composition was described in 1947 [112]. In 1950, the quantitative measurement of this protein was performed by Engelfried [113]. Its chemical properties were studied as well [114]. BJP was described as an abnormal protein in serum [113]. In 1983, a negative effect of lambda (λ) BJP was reported by a case study of multiple myeloma [115]. BJP in serum is also referred to as free light chains (FLCs). In healthy individuals, the majority of immunoglobulin light chains in serum exist bound to heavy chains. However, the FLC level is increased in patient serum due to their production by mature plasma cells. Plasma cells secrete free light chains (FLCs) in excess over heavy chains into serum [116- 119]. FLCs cause kidney injury [120]. The detection of the FLCs is an important diagnostic aid for a variety of diseases, such as multiple myeloma [121, 122], AL amyloidosis [123, 11

Introduction to systems immunology

124] Waldenströms Macroglobulinaemia [125, 126], and Rheumatoid Arthritis [127]. Recent studies show that increased FLC levels can also be a predictive marker in AIDS-related lymphoma [128]. Therapeutically, anti-FLC monoclonal antibodies (mAbs) are being developed that have a high degree of reactivity and versatility towards monoclonal κ and λ FLCs produced by malignant B cells [129].

1.4. Mast cells

First described by Paul Ehrlich in 1878, mast cells have long fascinated the biomedical community. Although the function of this immune cell has been enigmatic for decades, the mast cell is regarded not only as an immune cell, but also an immunoregulatory cell. The distribution pattern of mast cells in organs and tissues [130-132] where foreign materials such as parasites, bacteria and viruses attempt to invade the host, indicates that mast cells are among the first immune cells encountering foreign antigens and protecting against diseases [133].

1.4.1. Mast cell activation

Mast cell degranulation is mainly induced by crosslinking immunoglobulin E (IgE). The Fc region of IgE binds to the α-subunit of the high affinity receptor FcɛRI on the mast cell membrane [134-137]. Classically, IgE-dependent mast cell activation proceeds through the γ subunit of the FcɛRI receptor (Fig. 2). Each FcεRI receptor has two identical γ subunits and each γ subunit contains an immunoreceptor tyrosine-based activation motif (ITAM), which contains the tyrosine residues that are phosphorylated by the protein kinase Lyn. FcɛRI activation of mast cells was considered to be Lyn-dependent, and Lyn mainly resides in lipid rafts of mast cells [138, 139]. IgE binding to FcεRI triggers Lyn to phosphorylate ITAM and subsequently, the phosphorylated ITAM binds to spleen tyrosine kinase (Syk) to phosphorylate Syk. This interaction between Lyn, ITAM and Syk is a key signaling pathway that activates the phosphorylation of the so-called ‘linker of activated T cells’ (LAT) [140]. The phosphorylation of LAT results in the recruitment of Grb2, which activates the phosphorylation of PLCɣ. Then, PLCɣ catalyzes the hydrolysis of phosphatidylinositol-4, 5- bisphosphate (ptdIns (4, 5) P2 in the plasma membrane. The first resulting product, inositol-

1, 4, 5-triphosphate (InsP3), induces mobilization of cytosolic calcium, and the other resulting product, DAG, induces activation of protein kinase C (PKC). In addition to triggering Lyn, the binding of IgE to FcεRI also activates another kinase, i.e. Fyn. Lyn- deficient mast cells show less FcεRI dependent Ca2+ mobilization and degranulation, and cytokine production is still functional [141, 142]. A possible reason for Lyn-deficient mast cell degranulation includes a Fyn kinase-dependent pathway, which is regulated via the β- subunit but not via the γ- subunits of FcɛRI receptors on mast cells [143]. The Fyn-kinase pathway requires non-T-cell activation linker (NTCL) [144] and does not activate PLCɣ, but leads to phosphorylation of P13K and Gab2. The phosphorylation of P13K amplifies a mast- cell activation that is complementary to PLCɣ-mediated mast cell activation. Taken together, the data suggest that Fyn and Lyn enable two independent signaling routes within the FcεRI pathway. Both Lyn and Fyn may be important for the coordination of downstream signaling pathways that are required for the release of pro-inflammatory compounds, including 12

Introduction to systems immunology histamine, leukotrienes C4/D4, prostaglandin D2, and a wide array of cytokines and chemokines. These bioactive molecules can drive serious allergic disorders [145]. Immunoglobulin G (IgG) can also activate mast cells for degranulation [146, 147]. Increasing evidence in animal studies shows that mast cells are involved in inflammatory diseases through an IgG-dependent activation [148-150]. Mast cells express the Fc gamma receptors FcɣRI, FcɣRII and FcɣRIII. IgG binds to FcɣRII and FcɣRIII with a low affinity, whereas FcɣRI has a strong affinity to IgG. Fcɣ receptors share a ɣ subunit with FcεRI, and, therefore, the signaling pathway in IgG-mediated mast cell activation is similar to the signaling pathway in IgE-mediated mast cell activation [151]. Recent studies show that Lyn regulates FcɣRI-induced mast cell mediator release. Little is known about any role of Syk in IgG-mediated activation [152]. In addition, mast cells express TLR 2, 4, 6 and 8 which recognize pathogen-associated molecular patterns (PAMPs) derived from bacteria [153, 154].The PAMPs binding to TLRs does not only activate mast cells but also induces T-cell activation by presenting bacterial antigen through MHC class I molecules. A study confirmed that murine mast cells can present antigen to CD8 T cells by involving a MHC class I molecule, causing IL-2, IFNγ and MIP-1α production and promoting CD8 T-cell activation and cytotoxicity [155]. The TLRs can be distinguished according to the components of the pathogen they recognize. For instance, a study showed that lipopolysaccharide (LPS) from Gram-negative bacteria initiates the TLR4-mediated activation of mast cells [156]. Yet another cause of mast cell degranulation is mediated by a 45 kD membrane protein on the surface of mast cells that can bind free immunoglobulin light chains (FLCs) to trigger mast cell activation. FLC activation is different from IgE-associated, IgG-associated and TLR-associated mechanisms [157, 158]. Passive sensitization with FLC of ɣ chain-deficient mice (FcRɣ-) in which the role of IgE was thereby compromised [159], resulted in similar inflammatory responses as monitored by ear swelling, as compared to the response in wild-type black mice (C57BL/6).

13

Introduction to systems immunology

Figure 1.2. Intracellular signal transduction involved in mast cell activation. The phosphorylation of ITAM triggers phosphorylation of signaling molecules to activate mast cell degranulation: After IgE (not shown) has bound to its receptor, FcεRI, the latter dimerizes resulting in auto-transphosphorylation. This involves the kinase LYN in neighboring lipid rafts. LYN binds to and phosphorylates ITAM domains (the boxes in the receptor structure) in the beta and gamma subunits of the receptor, which increases the affinity of the latter for LYN and SYK. The SYK recruited to the beta ITAM is phosphorylated by LYN and becomes a kinase of the transmembrane peptide LAT. As LAT gets phosphorylated its direct affinity for GRB2 is increased which leads to indirect binding of SOS and SHC to LAT. RAS. SOS activates GDP- GTP exchange of Ras. RasGTP is able to activate Raf, a first in the MAP-kinase pathway. This pathway activates transcription of genes inducing cytokines as well as activating phospholipase A2. Phosphorylated LAT also recruits phosphorylase gamma to the membrane which then hydrolyzes phosphatidylinositol-4,5-bisphosphate into inositol triphosphate and diacylglycerol (DAG). Through calcium mobilization and protein kinase C this can lead to degranulation of the mast cell resulting in an activation of tyrosine kinases with the phosphorylation of ITAM in FcεRI as a result (for review see [160]).

1.4.2. Pro-cancer functions of mast cells

There is a wealth of circumstantial evidence for the involvement of mast cells in cancer growth. One bit of such evidence is the inverse correlation of numbers of mast cells with prognosis in various tumor types. The numbers of mast cells also correlates with tumor vascularity, and the cells may promote tumor angiogenesis of squamous carcinoma [161, 162]. Mast cells correlate with poor prognosis in patients with pancreatic ductal 14

Introduction to systems immunology adenocarcinoma (PDAC) [163] and non-small cell lung cancer (NSCLC) [164]. In prostate cancer (PCa), patients with a low number of mast cells had a high recurrence-free probability (90%) and patients with high numbers of mast cells had a lower recurrence-free probability [165]. In glioblastoma, mast cells are attracted by CXCL12 which is expressed by the tumor cells [166]. In breast cancer, the metastasis of tumor cells is associated with autoimmune arthritis that reflects the increased mast cell population. Mast cells differentiating upon the interaction between C-kit on mast cells and SCF from tumor cells may enhance tumorigenesis [167]. The mast cell is a vital source of cytokines in immune response, angiogenesis, tissue- remodeling, and wound healing [168-170]. Mast cells are a source of pro-angiogenic compounds, including VEGF, bFGF, heparin, histamine, TNF-α, MMP9 and tryptase [171- 177]. A study demonstrated that tumor growth and metastasis are minimized by mast cell deficiency [178]. Moreover, mast cells enhance the level of (ILs): not only do they produce a large quantity of IL-1β by themselves, but they also stimulate the release of IL-1β from macrophages at the inflammatory sites [179, 180]. Importantly, IL-1β is strongly pro-inflammatory which associated with angiogenesis and inflammation in a mouse model [181]. A study also showed that the concentration of IL-1β is increased by the co-culturing of macrophages and a colorectal carcinoma cell line (HCT 116). In vitro, IL-1β treated HCT 116 induced pGSK3. The phosphorylation of pGSK3 activates Wnt signaling which enhances tumor cell proliferation [182]. A study has also shown that mast cell expressing mutant Nlrp3 produce IL-1β in response to lipopolysaccharide or TNF-α, and aberrant IL-1β production causes cryopyrin-associated periodic syndromes (CAPs) [183]. Therefore, overexpression of IL-1β by the local inflammatory environment may potentiate carcinogenesis, by promoting inflammation. IL-1β might also contribute to tumorigenesis [184]. Multiple chemokines from Myc-pancreatic islet tumor have been analyzed genetically in silico and classified. Among them, CCL5 is a powerful chemoattractant for mast cells [185]. In vivo, mast-cell migration is regulated by the Myc oncogene. Mast cells accumulate at the site of Myc- islet tumors but do not de-granulate. Mast cells also significantly accumulated at the site of Myc+ islet tumor and de-granulated [186]. And indeed, the Myc+ islet tumor did not expand in mast cell-deficient (KitW-sh; KitW-sh) mice, which lack endogenous mast cells. The study on unilateral ureteric obstruction (UUO) has shown that mast cell is pro-fibrotic for tubules of kidneys by secreting angiotensin (ANG II), and fibrosis is absent in UUO kidney in mast cell-deficient (KitW-sh; KitW-sh) mice unlike observed in their congenic controls (CC) [187]. These data correlate with the accumulation and degranulation of mast cells at the inflammatory site. Mast cells enhance the local production of chemokines (CCL9, CCL2 and CCL20) and leukocyte recruitment, like neutrophils and macrophages, during renal fibrosis [188-190]. In a separate study, stabilizing mast cells in C57BL/6 or BALB/C mice using cromolyn or salbutamol prevented angiogenesis and joint destruction in experimental arthritis [191]. Within the tumor microenvironment, mast cells recruit and neutrophils and activate B- and T-cell immune response [192], some clinical studies also show that mast cell infiltration around gastric cancer cells correlated with tumor angiogenesis and metastasis [193]. Recently, Ribatti [194] elucidated a beneficial role of mast cells on cancer. In his paper, he demonstrated both fundamental and clinical evidences to stress a unique role of mast cells in 15

Introduction to systems immunology cancer. As such, mast cells inevitably become important players in various diseases, including cancer [195-198]. Mast cells may also have a negative immunomodulatory function, which is to switch immune responses off. Mast cells secrete IL-10 to suppress monocytes and T-cell proliferation. Mast cells and mast-cell-derived products may have important effects on regulatory T cell (Treg) numbers and the Treg cells play important roles in immunosuppression [199]. Other studies also implied that understanding the interactions between mast cells and

Treg may offer a high potential towards unraveling the mechanism of mast-cell-derived immunosuppression [200]. For example, mast cells migrate from skin to spleen, where they enhance Treg cell activity by secreting IL-2 [201]. In turn, Treg cell secretes IL-9 to enhance mast cell function and survival within skin allograft [202]. In colorectal cancer (CRT), mast cells convert T-cell regulatory (Treg) cells from being anti-tumorigenic to being pro- tumorigenic through the release of cytokine IL6. The IL6 does not only divert the phenotype of Treg cells but also down-regulates the production of IL10 and IL17 by these cells [203]. In gastric adenocarcinoma, increased numbers of mast cells that are positive for tryptase, correlate positively with Tregs in expressing positivity on Foxp3 [204]. In H22-inoculated BALB/c mice, SCF-activated mast cells exacerbate immune suppression by releasing adenosine and by increasing the number of Treg cells. In addition, mast cells interact with other immune cells. IL-4 and IL-13 from mast cells drive M2 macrophage response which can suppress anti-cancer immunity in lung inflammation [139]. Furthermore, the mast cell is a major source of prostaglandin D2 (PGD2) and PGD2 is able to suppress cytotoxic activity of NK cells in human leukemia cell lines [205]. Another factor is that the mast cell is the main source of histamine which could activate the H4-receptor on human monocyte-derived dendritic cells (DCs). The activation of H4-receptor suppresses IL-12p70 production.

Normally, APC produces IL12p70 which is important in TH 1 type of immune response [206, 207].

1.4.3. Anti-cancer functions of mast cells Tumorigenesis appears to be associated with mast-cell activation (Table 1). Mast cells may inhibit tumorigenesis by inducing cell apoptosis through various cytokines they secrete. For example, mast cells secrete IL-4, which binds to IL-4 receptors expressed by human breast cancer. This could lead to apoptosis in breast cancer [208]. IL-6, which is secreted by mast cells, was required for this tumor inhibition [209]. Pathologically, a correlation between mast cell numbers and improved patient survival has been found in breast cancer. A decade ago, a research group detected mast cells in breast cancer patients, suggesting that mast cells might support tumorigenesis in breast cancer patients [210]. Separately, some long-term follow-up studies on breast cancer patients proved however that the number of mast cells is correlated with a good rather than a bad prognosis rate of the patients [211]. Stromal mast cells in invasive breast cancer were analyzed by using tissue microarrays (TMAs), which included a pathology analysis of 4,444 cases, and mast cells were associated with good prognosis [212]. In colorectal cancer, mast cell infiltration is associated with lower rates of lymph node and distant metastases [213]. Mast cells are important mediators of innate immunity, as they are activated by PAMPs ligand via TLRs. When TLRs are triggered mast cells produce a number of 16

Introduction to systems immunology messenger substances that regulate an immune response that optimizes host-resistance. Therapeutically, TLR-based immune therapy holds promise for the treatment of certain cancers. For instance, mast cells were proven to be decisive in TLR2-agonist (Pam3CSK4)- induced tumor inhibition [209]. It has been confirmed that imiquimod triggers TLR 7/8 on mast cells and that the chemokine CCL2 is released from mast cells. This then recruits plasmacytoid dendritic cells (pDCc) and the pDCc themselves initiate the destruction of the tumor cells, in a process that appears to be independent of the adaptive immune system (B and T cells) and proceeds without the help of NK cells [214]. In bacterial infections, mast cells may release pro-inflammatory cytokines, such as TNF-ɑ and IL-β, [215]. In addition, human skin mast cells may produce the complement factors C3 and C5 [216]. The C3 protein has been known to protect the body from pathogenic infections but has anti-cancer function as well, for example, eliminating non-small cell lung cancer cells [217]. In adaptive immunity, mast cells participate further in the modulation of the immune system. For example, the co-culture of bone-marrow mononuclear cells (BMMCs) and CD8+ T cells with antigen specific matter, resulted in the activation and proliferation of CD8+ T cells [155]. Besides, mast cells secrete biological substances such as osteopontin (OSP) in response to mast cell-CD8+ T cell interaction [218]. Mast cells produce cytokines, like IL4, IL12 and TNF-α, which are able to induce the activation of NK cells. Mast cells release CCL7, which attracts T cells and NK cells, which may enhance antitumor efficacy [219]. Mast cells mediate tumor cell cytoxicity of TNF [220, 221].

17

Introduction to systems immunology

pro cancerpro functions cells ofmast

-

and

-

proposed anti

Table 1.1

18

Introduction to systems immunology

1.5. Mast cells, inflammation and cancer: systems biology might matter

The above résumé of facts related to the function of mast cells in inflammation and cancer, was an avalanche of facts with perhaps one Leitmotif in common: complexity. This complexity does not just arise from the great many factors involved. It rather stems from (i) the many regulatory pathways that interact with each other, (ii) the occurrence of parallel pathways that are opposite in regulatory polarity, i.e. one down regulating whilst the other is upregulating, and (iii) the occurrence of entire regulatory loops arising from the sequel of forward and feedback regulation. An example of such a loop is the possibility that, by producing inflammation mast cells may compromise the viability of tissue with the consequent cell death enabling the expansion of tumor cells that themselves activate inflammation. An example of the bipolarity is that the same mast cells may also promote the death of tumor cells through their TNF-α and interferon secretion. No single molecular or cellular factor in the network of Fig. 1 has an unambiguous regulatory link with the functioning of the network. The same is true for any of the signal transduction pathways involved. The resultant appears to be a system with complex and sometimes paradoxical behavior. As was done before for the case of cancer [239], I submit that inflammation is a systems or network property and therefore requires special approaches for its understanding. Such approaches require precise, quantitative experimental determination of molecular and cellular properties [240]. After all the net effect of a positive and a negative regulatory interaction in parallel, depends on the magnitudes of both effects. They also require an integration of the experimental data that enables the reverberation of their implications through the network: the value of a parameter may have a positive direct effect but also a negative one by resetting the activity of the rest of the network. Such an integration of data can be helped by the generation of a ‘watch maker’ mathematical model [241-245]. Such watchmaker models [246] first make a diagram of all the components of the network and of all the potentially relevant interactions. Then they formulate a mathematical equation for each process that emulates the dependence of its action on the concentrations of the system components. Using available computer programs the behaviors of all the processes are then integrated in silico, producing a simulation of the network’s behavior. By determining the dependence of such simulated functional behavior, the importance of the various components and interactions for inflammation, immunity and tumorigenesis may then be assessed. What is required here ultimately, i.e. in a molecular-cellular systems immunology approach, is a formidable task that requires an integrated activity of many scientists that come from diverse backgrounds. What I intend to do in this thesis, is to (i) develop a, be it simplified, example of such a systems biology approach, (ii) demonstrate that it adds predictive value to some of the data and information that I summarized above, (iii) identify some network properties that are important for the functional behavior of the network, (iv) show that indeed the networks around mast cells should be expected to produce paradoxical behavior, (v) demonstrate that through the systems biology model, the potentially therapeutic activity of potential drugs, can be simulated and thereby assessed preliminarily.

19

Introduction to systems immunology

1.6. Aims and overview of the thesis

Today, cancer continues to be one of the most common life threatening diseases in the world. An inflammatory environment may contribute to tumor growth at the early stages of cancer. Immune cells in the inflammatory environment may affect tumor growth through acute or chronic inflammation, and thereby studying immune cells is part of a systematic approach towards combatting cancer. Targeting mast cells amongst the immune cells appears to be regressive to tumor expansion, and inhibition of mast cell activation has proven to be a therapeutic approach to cancer-related inflammation. In addition, inflammation appears to play an important role in many other multifactorial diseases. However, inflammation is a complex network phenomenon. This thesis aims to exemplify a new approach to the understanding of inflammation, i.e. one that capitalizes on the network nature of the phenomenon: systems immunology. The thesis consists of five chapters. Chapter 2 builds a systems biology model for innate immunity around mast cells, simulates the occurrence of acute and chronic inflammation, and demonstrates how the action of peptide-based drugs on chronic inflammation may be predicted. Chapter 3 extends this model to include tumor cells, assesses the occurrence of indicators of inflammation such as mast cells, FLC and TNF-α in tumor tissue, and tests a proposed drug in silico. Chapter 4 we developed analytical methods to inspect validity of our model. The related methods, such as Bi-stability for steady state and sensitivity analysis, are integrated with empirical analyses. Chapter 5 surveys the various prospects of involving mast cells in new cancer therapies and Chapter 6 discusses the further perspectives of the systems immunology we have here developed. Taken together, our findings not only imply a promising approach for targeting tumor-associated inflammation but also lead to a further support of clinical cancer therapy by systems approaches.

20

Systems immunology-inflammation

Chapter 2 FLC-Mediated Mast Cell (in) Activation and Its Predictable Effects on Acute and Chronic Inflammation

21

Systems immunology-inflammation

Summary The pathology of many diseases is co-determined by inflammation. In some cases, mast cells contribute. Upon binding of antigen-antibody complexes to immunoglobulin-like receptors on their plasma membrane, mast cells become activated and release immune mediators such as TNF-α and proteases. These cause necrosis or apoptosis of fibroblasts, which leads to release of more antigens. We here present a map of an extracellular network that may be (co-) responsible for fibroblast demise and for enhanced antigen production. We make this inflammatory map predictive by adding mathematical equations and by integrating these as a function of time and parameter values. We show that the mapped network should be able to clear bacterial infections, via acute inflammation, but may also cause chronic inflammation. In the calculations, acute inflammation flips to chronic inflammation when the antigen challenge exceeds a certain ‘On’ threshold. Subsequent reduction of the antigen load to below this ‘On threshold’ does not remove the strong pro-inflammatory phenotype unless the antigen load drops below a much lower ‘Off’ threshold. In between both thresholds, the network is caught either in a low (‘acute’) or a high (‘chronic’) inflammatory state. The predictive model may be useful for motivating the design of therapies for inflammatory diseases, such as demonstrated using an example of a peptide that binds to the free light chains of immunoglobulins. The effectiveness of such therapies is predicted to depend on the dose of administration of the anti-inflammatory drugs and may differ between individuals.

2.1 Introduction Inflammation is a first response of the immune system to pathogenic stimuli like parasites, bacteria and viruses [247-251]. Although inflammation is beneficial to the human body in the defense against pathogens, it is also associated with non-infectious diseases such as rheumatoid arthritis [252], asthma [253], diabetes [254] and cancer [255]. Inflammation is the resultant of interactive networks between innate and adaptive immune cells [256-259], tissue components like stromal fibroblasts [260], and extracellular matrix [261] the blood and vascular networks [262] and soluble molecular messengers like plasma proteins, cytokines and chemokines [263]. Together, they create the so-called inflammatory environment. Inflammation is a systemic process and has been classified into acute and chronic inflammation. Once the body is infected by pathogens, innate immune cells such as macrophages and mast cells, expresses pattern recognition receptors (PRRs) for recognizing different kind of pathogen-associated molecular patterns (PAMPs). Other factors that induce the immune system include damage-associated molecular patterns (DAPMs). These DAMPs activate dendritic cells (DC) and macrophages via Toll-like receptors (TLRs) and C-type lectin receptors (CLRs). Normally, the infection is cleared by innate immunity as an acute inflammatory response. On the one hand, acute inflammation facilitates innate and acquired immunity, which combats infection by attracting leukocytes and plasma proteins to sites of infection or injury [264, 265]. On the other hand prolonged infiltration of the inflammatory environment by various immune cells may turn acute inflammation into chronic inflammation [266, 267]. Acute inflammations persist for a couple of days or weeks, until the infection has been eliminated, i.e. they require the presence of the external stimulus. Chronic 22

Systems immunology-inflammation inflammation tends to persist over months or years, well beyond the presence of the external stimuli. In some diseases such as inflammatory arthritis [268, 269] and multiple sclerosis [270, 271] , chronic inflammation may be caused by the interplay between B cell differentiation and mast cell function. Each day in the healthy human body different types of B cells are produced that circulate through the blood and secondary lymphoid organs. Effector B cells can start secreting immunoglobulins (antibodies) when they are still small lymphocytes, but at the end stage of their maturation pathway is a large plasma cell, which secretes immunoglobulins (antibodies) at the maximum rate of about 2000 molecules per second [272]. Each B-cell has a particular receptor called B-cell receptor (BCR) on its surface that will bind to antigen [273]. A BCR has two identical light chains, two identical heavy chains, one Igα, and one Igβ. Once a B-cell recognizes an antigen using its BCR and takes an additional signal from a T helper cell (Th), it can differentiate into either of two types of cells: plasma cells and memory B cells. A plasma cell produces immunoglobulins (antibodies), which help to destroy microbes by binding to them. This opsonisation process may not only neutralize the pathogen, but also enhances their phagocytosis by professional phagocytes and induces activation of the complement system. Although antibody responses to most protein antigens are dependent on aid of the B cells by Th cells, many bacterial antigens are able to stimulate antibody production from B cells without Th cell help. For instance, bacterial polysaccharides, polymeric proteins, and lipopolysaccharides enable the antigens to stimulate naive B cells in the absence of peptide-specific T-cell help. These antigens are known as thymus-independent antigens (TI antigens). They stimulate strong antibody responses in a-thymic individuals. The contact between B cells and antigen not only induces immunoglobulin (Ig) secretion, but also causes secretion of excessive amounts of immunoglobulin free light chains (FLCs), i.e. light chains that are not bound to heavy chains [274]. Two types of immunoglobulin light chains are produced in human as well as other mammals: kappa (κ) and lambda (λ) type. Kappa (κ) exists predominantly as a monomer with a molecular weight of 22.5 kDa and FLC lambda as a dimer with a molecular weight of 45 kDa. In healthy individuals, the majority of light chains in serum exist bound to heavy chains. However, the FLC level is increased in patient serum due to their excess production over heavy chains by mature plasma cells. FLC secretion is a risk factor for various autoimmune diseases such as AL amyloidosis [275], Waldenströms macroglobulinaemia [276], and rheumatoid arthritis [277] [158, 278, 279]. FLCs also hold potential as a diagnostic marker for cancers such as hepatocellular carcinoma [280] and multiple myeloma [281]. Mast cells are effector cells of the innate immune system that are capable of producing proteolytic enzymes and a wide variety of cytokines and growth factors. Mast cells are innately present in many normal tissues and organs, and the local tissue environment matures these mast cells through various cytokines such as stem cell factor (SCF) and nerve growth factor (NGF) [282], which acts via tyrosine kinase receptors (TrkA, B, C) different from C- kit activated SCF [283]. The SCF also enhances mast cell degranulation and cytokine production through cross-linking of their high affinity surface receptors for IgE (FcεRI). Mast cells are activated by the interaction between NGF and surface lysoPS on activated platelets, thereby leading to mediator release from mast cells both in vitro and in vivo [284]. In addition to SCF and IgE, immunoglobulin free light chains, hormones, complement 23

Systems immunology-inflammation peptides (C3a, C4a, C5a) and neuropeptides are able to trigger mast cell activation. The activated mast cells secrete numerous pro-inflammatory mediators such as Tumor Necrosis Factor-α (TNF-α) [79], tryptase [285] and Vascular Endothelial Growth Factor (VEGF) [286]. FLCs might change the functional orientation of the inflammatory environment through mast cell activation. The interplay between many factors in the inflammatory network correlates with the research findings that innate immune cells and their mediators promote inflammation [287, 288]. Although it operates largely extracellularly, this network has a number of aspects in common with the various intracellular signal transduction networks that have increasingly become subject to systems biology studies: It is complex in the sense that it has many components that interact nonlinearly to produce features that are alien to the network components individually. It is co-determined by the individual’s genome in ways that can be measured quantitatively through (functional) genomics. It is also affected by environmental factors and, it exhibits memory effects, such as related to prior exposure to external antigens leading to enhanced reactivity [289-291]. Diseases related to some of these features or their absence have been recognized as systems biology diseases, where analysis of experimental data with computational methods may help both understanding and drug- target selection [239]. In this paper, we shall build and then examine a computational systems biology model of inflammation. We shall focus on FLC-mediated mast-cell activation that creates a microenvironment that may lead to inflammation. This model will make existing knowledge about the components of the network and their interactions, predictive. We will ask the network model for predictions of the response to various antigen doses. We find that the response is bi-stable, flipping between acute and chronic inflammation. A peptide drug interfering with FLCs may reset the network from a chronic inflammation state to a state that may or may not be subject to acute inflammation.

2.2 Methods and parameters The native immune network as described in Fig.1 was decomposed into its component processes and for each of the latter a characteristic rate equation was developed. This equation formulated the rate at which the process should proceed as a function of the concentrations of the components involved in the process, as depicted in Fig. 1, and kinetic parameters. When a substance was catalytically involved in the reaction, i.e. only stimulated the rate without being consumed, we wrote it on both sides of the reaction equation and as a proportional factor in both the forward and (if present) reverse rate equation. For each species in Fig.1 a balance equation was then formulated, specifying its time dependence as the difference between the rates of the reactions synthesizing the commodity and the rates of the processes degrading it. Combination of the rate and balance equations then led to differential equations, which were integrated as functions of time. This computation process was performed using COPASI. The dynamic model we made thereby depends not only on the topology of the network of Fig.1, but also on parameter values, to which we therefore had to assign values. For most of the parameters of our model accurate values are unknown. We chose parameter values that were in a realistic range. We first discuss the parameters for

24

Systems immunology-inflammation model in the operational state without inflammation, i.e. without bacteria, external CRA influx, and with negligible concentrations of TNF-α, dying fibroblasts, and drug.

Cell concentrations, total space, growth: k17,, k18 : A fibroblast has a volume of some 2 pL [292]. The volume of a Mole of such fibroblasts is 1.2 TL, giving a space completely packed with fibroblasts, a fibroblast concentration of almost 1 pM. We set the fixed concentration of B cells to 1 fM and the constant total concentration of the mast cells to 0.1fM. We assumed that in the remaining space, 1000 fM of healthy and dying fibroblasts could exist. We expressed this ‘total_space’ in terms of the concentration of subspaces of the volume of a fibroblast. In this way, the total space was 1000 fM and the concentration of healthy fibroblasts was 1000 fM or less. Free_space was assumed to equal total_space minus the concentration of fibroblasts and expressed in unit fibroblast, i.e. in fM. The fibroblast division (R18) rate was made proportional to free space at a second order rate constant of k18 = 0.485/nM/min. When all space was free space (and hence free_space=1000 fM), this corresponded to a 24h doubling time. If present, bacteria were taken to grow (R17) at a specific growth rate of k17 = 0.01/min, at a rate independent of the free space, as bacteria were assumed to fit in between the larger mammalian cells.

Association rate constants k1, k9, k14, k16, k21, k22 and k23: In water the maximum diffusion- limited rate constant for association follows the Smoluchowski equation kon=4πDr. This equation was first described by Marian von Smoluchowski in his article called “Versuch einer mathematischen Theorie der Koagulationskinetik kolloider Lösungen” published in Zeitschrift für Physikalische Chemie [293]. Here r is the sum of the radii of the two colliders and D their diffusion constant. This corresponds to 6 × 10-5 /fM/min for small molecules and some 10-5/fM/min=10/nM/min for proteins. When the two diffusing objects attract each other (or tend to remain together as in surface diffusion), the number can readily become ten times higher [294]. When the target is any of multiple receptors on a target cell, the association rate constant increases proportionally to the number of receptors per cell, but this only up to a maximum of some 5000 per cell [295]. When there are approximately 105 receptors expressed on the surface of the cell [296], any collision with the cell will be effective and then the rate constant is increased by the factor of π·rcell/rFLC≈30 000. Combining all these effects and allowing by a factor of 2 for a reduction in diffusion rate due to steric hindrances, we obtained the diffusion limited rate constant for FLC binding to an

FLC receptor (R23) on the mast cell as approximately 10 /fM/min. In the next process (R22), a CRA molecule associates with an FLC that sits on a mast cell. This may happen when that mast cell contains only one FLC, but will happen faster when the mast cell already harbors many FLCs: there are in fact many subtypes of MastCell-FLC complex. For simplicity, we took ‘the’ Mast Cell-FLC complex to be a complex of the Mast cell with some 100 FLC molecules. This required us to reduce the association rate constant by a factor of 100 to k23 =

0.1 /fM/min. The association rate constant (k22) of CRA with this “MastCells-FLC complex” should hereby become 100 times the diffusion limited value of 10-5/fM/min for larger molecules. We augmented this by another factor of 100 because of the surface diffusion effects, which we expect to be strong because CRA may already loosely associate with the heavy chains in the FLC receptors. This brought the association rate constant of R22 to k22 =

0.1/fM/min. We assumed the breakdown of CRA by MMP-8 (R1) to be a diffusion limited 25

Systems immunology-inflammation bimolecular reaction with a factor of 2 for the attractive effect [294], leading to a rate -4 constant k1 = 10 /fM/min. For B cells to produce FLCs they need to be activated by CRA binding to a receptor. We assumed some 15 CRA-receptor molecules to be present on the B cells, leading to a diffusion limited rate constant for (R9) of k9 = 0.001 /fM/min. As to the clip-off of CRA from healthy fibroblasts by MMP-7 (R14), we assumed the encounter of -5 MMP7 with a fibroblast to be diffusion limited at k14 = 10 /fM/min. This was all done to achieve a system where dying fibroblasts would only exist transiently so as to emit CRA.

The killing of healthy fibroblast by TNF-α (R16) is another bimolecular reaction. Here we assumed the TNF-α to be able to hit the fibroblast destructively at 3 sites, making the rate constant equal to k16 = 0 .0005 /fM/min. TNF-α was assumed to be released intact after its killing act. Similarly the antibacterial proteases that are secreted by the mast cells (R25) were re-released after its killing act (R21). Here the biomolecular association rate constant as taken to equal k21=0.005/fM/min, where we assumed >30 sites on each bacterial cell for the protease to attack productively.

Association rate constants k-22 and k-23: We expected the CRA and FLC concentrations to be far below 1 fM in the inactive state of the network and then to shoot through the 1 fM level when the systems moves into an active state. We therefore set the equilibrium binding dissociation constant in R22 and R23 to 1 fM. This meant that we set the corresponding dissociation rate constants k-22 and k-23 = 0.1 /min.

Washout rates: k2, k4-k8, k10 and k12: The inflammation was taken to occur in interstitial space. Lymph flow rates led to an inverse first order turnover rate constants of 1200 min in the skin of active rat [297]; for skeletal muscle this was 5000 min. Assuming increased flow at the inflammation site, we took this life time to equal 100 min: All substances other than cells were subject to efflux from the system at the same specific rate of k=1 × 10-2/ min for the reactions R2 (k2), R4 (k4), R5(k5), R6 (k6), R7(k7), R8 (k8), R10 (k10), R12 (k12).

Secretion rates and cell death rates: k13, k14, k15, k19, k20, k24, k25, k26, k27: We assumed the release of MMP-8 by healthy fibroblasts (R20) to occur in packages of 10000 enzyme molecules; the reaction stoichiometry of the product MMP-8 was set to 10000. The same cell was assumed to release MMP-7 (R19) in packages of 100 molecules at a time. The rates of these two release processes were set to k19 =0.001 /min for R19 and k20 = 0.1 /min for R20.

After antigen activation, the MastCells_FLC complex secreted TNF-α (R24) at a rate constant k24 = 5 /min. The Mast cells secreted protease (R25) at a rate constant of k25 = 65 /min per cell. The healthy and dying fibroblasts secreted CRA (R14 and R13) at rate constant of k14 = -5 10 /fM/min (see above) and k13 = 0.001 /fM/min, respectively. The dying fibroblasts then disappeared immediately. In a second process, the dying fibroblasts also disappeared at a rate independent of CRA secretion k15 = 0.2/min (R15). We assumed that dying bacteria secreted packages of 1000 CRA molecules at a rate of 10/min, k26= 10 /min (R26). Bacteria died independently of this at a rate constant k27= 0.1 /min.

Drug related rate constants k10, k11, k-11, k12: In the presence of drug in the system, R10, R11 and R12 were involved accordingly. The two washout rates constants were again taken to equal k10 = 0.0001 /min (washout of drug alone taken to be 100 times slower than standard 26

Systems immunology-inflammation washout [see above], thanks to a formulation) and k12 = 0.0001 /min for the same reason. The association rate constant was taken to be limited by diffusion and equal k11 = 10 /nM/min.

We set the dissociation rate constant of drug from the FLC _drug complex to k-11 = 0.00001/min, setting the half saturating drug concentration to 1 fM.

CRA influx rate: Reaction R3 was set to a constant level. Unless stated otherwise, this level was zero, i.e. k3 = 0 /min.

With the parameter values as stated above, the model could be run at various sets of initial conditions and relax to the same steady state. The magnitudes of all these variables were used as initial conditions for all other calculations, except where we note explicitly that a different starting point was used. For each figure and table we have a file in the supplementary material that has the definitive COPASI model with the definitive parameter values used such that it can be rerun to reproduce the figure and table. The resulting computed data were analyzed on Graphpad Prism 5.

2.3 Results Network design: Notwithstanding the whole-body nature of innate immunity, we here focus on a local environment in the mammalian body where inflammation may occur, as well as on a set of processes that are relevant for and may suffice to produce inflammation locally. These are described in Fig.1. The innate immune response is held to be activated by antigenic determinants that are shared between antigens deriving from fibroblasts, bacteria and possibly other sources. We shall refer to these antigens as Cross-Reactive Antigens

(CRA). The dotted line in Fig. 1 leading from CRA to the process denoted as R9 shows that this antigen will be modelled to activate B-cells into secreting FLC with the corresponding antigenic specificity. CRA may also flow into the network as an influx from the outside world (R3). CRA is assumed to be released by fibroblasts that are activated by TNF-α and thereby converted into what we shall call dying fibroblasts (R13). In response to CRA, B cells secrete FLC (R9), which binds to receptors on mast cells (R23). This FLC is specific to CRA, which may be released by (dying) bacteria or by the dying fibroblasts. In reaction R22 we have assumed the FLC receptor on the mast cell to be activated by the binding of CRA. We denote the secretion of CRA from the bacteria by R26. The mast cell releases mediators such as TNF-α (R24) and proteases (R25). The proteases may kill bacteria (R21). Without the proteases the bacteria would proliferate (R17). The main function of the TNF-α in this network is to turn healthy fibroblasts into dying fibroblasts (R16), which have a greatly enhanced rate of secreting CRA (R13). Healthy fibroblasts also secrete the proteases MMP-7

(R19) and MMP8 (R20), which catalyze their release of CRA (R14) and the degradation of

CRA (R1), respectively. Fibroblasts are assumed to require free space before they can divide, which is meant to reflect contact inhibition. Free space is a fixed total space minus the volume taken by healthy and dying fibroblasts. It is expressed in unit fibroblast concentration (fM). With respect to the bacterial infection we simplified into assuming that bacteria sometimes (R26) secrete CRA when they die. We assumed that bacteria are not contact- inhibited by fibroblasts.

27

Systems immunology-inflammation

. . ows

represents represents a

Ø

, the proteases

α -

Diagram of the The The symbol

7 and MMP8 and drug -

flammatory network studied here.

in destruction pool into which all Figure 2.1 Four types of cell (B cells, Mast cells, bacteria and fibroblasts) are where shown, the latter are either in a healthy or a extracellular dying molecules, we state. consider CRA, In FLC, MMP TNF terms of molecules. molecules wash out at a certain and rate cells disappear at a certain death rate. Free space refers to taken space up not by fibroblasts. Solid arr refer to conversion dashed arrows to processes secretion processes. and Both types of processes are numbered as R followed by toarrows refer influence/regulation a number. Dotted

28

Systems immunology-inflammation

Table 2.1. The processes considered in the mathematical model of Fig.1. In the first column, each process considered has been given a number which follows the symbol ‘R’ for reaction. A description name is given in the second column, whilst the third column describes the process by a chemical reaction equation. Where substances occur on both sides of the arrow, they are mere influences on the process, without themselves being consumed. This corresponds to the dotted arrows in Fig. 1. In the next column the rate equation used for the process is shown and in the final column the magnitude of the parameter values. Concentrations were expressed in fM, time in minutes.

29

Systems immunology-inflammation

Network function: We first examined whether the network we formulated in Fig. 2.1 would indeed be capable of its perhaps primary task in biology, i.e. to kill invading microorganisms with growth rates outpacing the naïve nonnative immune response. For this we needed to make the network predictive, i.e. to enable all the processes described in Fig2.1 to interact in the ways shown in Fig. 2.1,but now in a collective predictive model. For this we formulated a mathematical equation to characterize each process, most often in terms of the rate at which it would proceed (Table 1). Then we formulated balance equations describing the accumulation rate of each substance or cell type as a difference between rates of synthesis and degradation. We then integrated the differential equations produced by combining the rate and the balance equations. This then enabled prediction of how the network’s dynamics should be expected to evolve as a function of time and of the values of the various parameters. Fig.2.2 shows how, for our standard set of parameters (see supplemental material), the number of bacteria would change with time. Bacteria continue to grow in the control case where the mast cells do not secrete protease (Fig. 2.2A). When mast cells produce proteases, the number of bacteria would be reduced relatively suddenly (Fig. 2.2B); they are killed by the protease secreted by the activated mast cells (see also Fig. 2.1). The simulated mast cells succeed in restraining the microbial population to truly low levels. In our simulation, the time period of virtually disappeared bacteria (approximately 10days) was followed up by a burst of bacterial growth and a subsequent immune response removing it. We would suppose that in real life that adaptive immunity would rid the system of such subsequent bursts of microbial growth.

A B 100 300 80

200 60

40 100

Healthy Bacteria (fM) 20

Healthy Bacteria (fM) 0 0 0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5 Time (days) Time (days)

Figure 2.2 Anti-bacterial function of mast cells producing proteases. (A) Bacterial growth continues exponentially when mast cells produce no protease, i.e. k25 = 0. (B) Once activated mast cells secrete proteases bacterial growth is only transient: the curve represents the case of protease production at rates of 65 fM/min; Bacteria were seeded into the network at t=0.

Acute and chronic inflammation produced by the same network: We next focused on inflammation rather than infection. We studied the response of the network to a modest influx rate of CRA at 5 fM/min. We first ran the model until a steady state was obtained and used the resulting magnitude of all the variables as initial values for the subsequent calculations. The full lines in Fig. 2.3A-C confirm that the steady state obtained for 5 fM/minute CRA influx was stable: The number of healthy fibroblasts, as well as the TNF-α and CRA levels were independent of time, at high, low, and low levels respectively, characteristic of a hardly inflamed state. Virtually the same result was obtained for different CRA influx rates between 0 and 12 fM/min (not shown). At a CRA influx rate of 20 fM/min 30

Systems immunology-inflammation however we observed quite a different behavior (dashed line in Fig. 2.3A-C): Already immediately after the increase of the CRA influx, fibroblasts began to die (Fig.2.3A). This was accompanied by a very small but steady increase in TNF-α (Fig. 2.3B) and CRA (Fig. 2.3C) levels with progressing time. Some 8 days after the increase in CRA influx, the rate at which the fibroblasts died began to increase and the TNF-α level increased strongly and suddenly. A day later the CRA levels also increased suddenly. Only a few days later no live fibroblasts were left (Fig. 2.3A), and the TNF-α and CRA levels had become steady and high. When at time zero the CRA influx rate was increased by a further 50%, this had little additional effect on the final levels: no live fibroblasts, the same high level of TNF-α, and a steady even (50%) higher CRA level. It made a difference to the timing however: the transition from the low TNF-α, low CRA level to the high levels of these substances, now occurred three times sooner. These results suggested a bi-modality in network function. Depending on the intensity of the antigen challenge, the ultimate steady-state extent of inflammation was either minor or massive and not something in between. The dashed lines in Fig. 2.4 confirm this: they show the final steady-state levels of TNF-α, of mast cells without FLC, of free CRA as well as of healthy fibroblasts as a function of CRA influx. With all four properties, the same threshold CRA influx rate was evidenced, above which the property flipped to a level that corresponds to strong inflammation, i.e. high TNF-α, low free mast cells, high CRA levels and no healthy fibroblasts. Only for the CRA levels, the increase with CRA influx rate was, understandably, a bit more gradual. Up to this point our calculations started at steady states at low CRA influx rates (5 fM/min and 0.001 fM/min for Figs. 2.3 and 4, respectively). The bi-modality we observed above suggested that this might be a case of bi-stability, i.e. a coexistence of two different steady states for the same parameter values and external conditions; which steady state a bi- stable system settles to depends on the initial condition. To examine this possibility we performed a second set of computations, in which we started from the steady state obtained for an influx of 30 fM/min of CRA, a state of high inflammatory activity therefore (see Fig. 2.3). The solid lines in Fig. 2.4 show the results of these computations: for the entire CRA influx range between 0.05 and 11 fM/min, the steady state achieved in this case was one of strong inflammation, as judged by high levels of TNF- and CRA and low levels of un- liganded mast cells and healthy fibroblasts, whereas for the same conditions the calculations starting at 0.001 fM/min CRA influx all had led to minor inflammation. We associated this situation to a dichotomy between acute and chronic inflammation that may occur at apparently the same antigen dosage. Our calculations show that after the network of Fig. 2.1 has been taxed with an overdose of CRA it tended to remain in the fully inflamed state even if the antigen (CRA) dose was reduced drastically; this is similar to what is observed in chronic inflammation. Fig. 2.4D may be important in particular: in the conditions of chronic inflammation, there are no healthy fibroblasts left.

31

Systems immunology-inflammation

A B

HealthyFibr TNF- CRA influx 5 CRA influx 5 60 TNF- CRA influx 20 1500 HealthyFibr CRA influx 20 TNF- CRA influx 30 HealthyFibr 40 CRA influx 30

1000 (fM) 

TNF- 20 500

Healthy fibroblasts (fM) 0 0 0 5 10 15 0 5 10 15 Time (days) Time (days)

C Figure 2.3 The levels of network components

CRA involved in inflammation, including healthy 4000 CRA influx 5 CRA fibroblasts (A), TNF-α (B) and antigen (CRA) CRA influx 20 3000 CRA (C) as a function of time after increasing the CRA influx 30 2000 CRA influx level. Full line: no increase in CRA

BAFF (fM) influx, i.e. maintained at 5 fM/min. Dashed 1000 line: increase (at time zero) from 5 to 20 0 0 5 10 15 fM/min. The dotted line: increase (at time zero) Time (days) from 5 to 30 fM/min.

A B TNF- acute TNF- chronic MastCells in acute MastCells in chronic inflammation 60 inflammation inflammation inflammation 0.15

40 0.10

(fM)

TNF- 20 0.05

0 Unliganded Mast cells (fM) 0.001 0.01 0.1 1 10 100 0.00 CRA influx (fM/min) 0.001 0.01 0.1 1 10 100 CRA influx (fM/min)

C D CRA in acute CRA in chronic HealthyFibr in HealthyFibr in inflammation acute inflammation chronic inflammation 10000 inflammation 1500 1000 100 1000 10 1 CRA (fM)CRA 500 0.1

0.01 Healthy fibroblasts (fM) 0.001 0 0.001 0.01 0.1 1 10 100 0.001 0.01 0.1 1 10 100 CRA influx (fM/min) CRA influx (fM/min)

32

Systems immunology-inflammation

E FLC acute FLC chronic inflammation inflammation 300

200

FLC (fM)) 100

0 0.01 0.1 1 10 100 CRA influx (fM/min)

Figure 2.4 Steady state levels of various network components at the final steady state achieved from an initial state at very low CRA influx (dashed lines) or starting from an initial state at very high CRA influx (full lines). The former cases were calculated by starting at a steady state at 0.001 fM/min CRA influx and then increasing that influx to the level indicated on the abscissa. The latter cases were computed by starting at the steady state achieved for 30 fM/min CRA influx and by then reducing the CRA influx to the rate indicated on the abscissa (Where steady state concentrations were below 10-9, which was the resolution of COPASI computation, MMP7 and MMP8, healthy and dying fibroblasts, we re-set them to 10-9 fM). The components are, as indicated, (A) TNF-α, (B) unliganded mast cells, (C) CRA levels, and (D) healthy fibroblasts. The symbols in D refer to the CRA values for which actual calculations were carried out; the lines in between were obtained by linear interpolation.

Anti-inflammatory peptide: A possible explanation of the bi-stability might be the positive feedback loop of CRA upon itself, through activation of mast cells by CRA binding to FLC presented by those cells, TNF-α elevation and CRA production by dying fibroblasts (Fig.2.1). Such loops produce thresholds below which a system tends to one state and above which it prefers another. We wondered whether interference with that feedback loop could do away with chronic inflammation, and if so, whether that interference would have to have special qualifications. The solid line in Fig. 5A repeats that of Fig. 2.4A, i.e. the TNF-α level after reducing CRA influx rates back from 30 to the value indicated on the abscissa. The lower right-hand circle gives the TNF-α level attained (after 15 simulated days) when, to a chronic inflammation system with a continuous CRA influx of 3 fM/min (as indicated on the abscissa), a single shot of FLC-binding peptide was given (see Fig. 2.1). Fig. 2.5A shows that perhaps unexpectedly, the peptide drug was ineffective even though the CRA influx was well below the CRA-influx threshold of 12 fM/min of Fig. 2.4A. In the chronic inflammation state with a CRA influx rate of 0.1 fM/min however, the system did respond to the peptidic drug, and it did so by a switch to the low inflammation state (the lower black circle in Fig. 2.5A). We then wondered whether this flipping back by the drug to the acute inflammation state would be definitive, as expected from a bi-stable steady-state situation. We therefore followed the TNF-α level for a long time (Fig. 2.5B). We found that the transition was not definitive: after some 30 days, the system drifted back to the state of high inflammation. This was a bit odd: we seemed not to have a bi-stable state of high inflammation and low inflammation: after the history of high inflammation the low inflammation state was no longer stable, but merely transient. Apparently, the transitions from low to high inflammation at high CRA influx rates, had been irreversible.

33

Systems immunology-inflammation

We then searched for the factor that might be the culprit of the irreversibility and turned to the fibroblasts that had actually gone extinct in the high inflammation state. In our model new fibroblasts could only appear through division of existing fibroblasts, so that the killing of all fibroblasts was irreversible. In reality however, fibroblasts from neighboring tissues might come to the rescue by invading. In order to test this idea, we then added a low concentration of fibroblasts together with the drug to the model. Indeed, for the lower CRA influx rate of 0.1 fM/min, addition of very few healthy fibroblasts (5 fM) already triggered their regrowth to full confluence, as shown in Fig.2. 5C. Conversely, the levels of healthy fibroblasts went back zero at sustained CRA influx rates of 0.5 fM/min and 3.0 fM/min, as shown in Fig.5C as well. Fig. 2.5D shows with high influx rate TNF-α came back to its maximum level. The addition of more healthy fibroblasts (30 fM and 100 fM) resulted in regrowth of the fibroblasts also in the latter cases (Fig. 2.5E).

A B 3.0 CRA 60 50 CRA influx 0.5 CRA influx 3.0 0.5 CRA 40 CRA influx 0.1 0.1 CRA 40

30 (fM))

(fM)) Drug 

20 TNF-

TNF- 20 10

0 0 0.001 0.01 0.1 1 10 100 0 20 40 60 80 100 CRA influx (fM/min) Time (days)

C CRA 0.1 D fibroblast 5.0fM CRA 3.0 1000 60 fibroblasts 5.0fM

750 40 CRA 0.5

fibroblasts 5.0fM (fM)

500 

CRA 3.0 TNF- 20 250 fibroblast 5.0fM CRA 0.5 CRA 0.1

Healthy Fibroblasts (fM) fibroblast 5.0fM fibroblasts 5.0fM 0 0 0 10 20 30 40 0 10 20 30 40 Time (days) Time (days)

E F CRA 3.0 1000 fibroblasts 100fM 60

750 CRA 0.5 40 fibroblasts 30.0 fM

CRA 3.0 (fM) 500  CRA 0.5 fibroblasts 30fM fibroblasts 100.0 fM

TNF- 20 250

Healthy Fibroblasts (fM) 0 0 0 10 20 30 0 10 20 30 Time (days) Time (days)

Figure 2.5 The effect on steady state TNF-α levels and health fibroblast population of addition of FLC- binding peptide to a chronically inflamed network at various sustained rates of CRA influx. (A) The full line (blue; also in Fig. 4A) represents the steady state TNF-α levels when no peptide was added; the system had first been to the CRA influx state of 30 fM/min after which the CRA influx levels had been reduced to the number indicated on the abscissa. This produced the chronic inflammation state even for the lower CRA influx rates. The red-circles indicate the origin of the chronic inflammation state at a CRA influx rate of 0.1, 0.5 and 3.0 fM/min. The downward small black-circle shows the effect (after 15 simulated days) of then 34

Systems immunology-inflammation adding 1 nM of peptide to the case of chronic inflammation at a CRA influx 0.1 fM/min: a substantial drop in TNF-α (from 40 to 5.0). The downward mid-size black-circle shows the effect of adding the same amount of peptide to the case of chronic inflammation at a CRA influx 0.5 fM/min: only a modest drop in TNF-α (from 50 to 30). The largest black-circle shows how the chronic inflammation state persisted upon peptide addition at a CRA influx of 3 fM/min. (B) The time dependence of TNF-α after peptide inhibition to any of three chronic inflammation states, i.e. at CRA influx of 0.1, 0.5 and 3 fM/min. (C) The effect on fibroblast levels of 5 fM fibroblast addition to the system at steady state simultaneously with drug treatment of the CRA influx of the 0.1 (full red line), 0.5 (dashed blue line) and 3.0 (dashed green line) chronic states as in (A). (D) The effect on TNF-α levels of 5 fM fibroblasts added to the system at steady state simultaneously with drug treatment of the CRA influx of 0.1 (dashed red line), 0.5 (blue full line) and 3.0 (dashed green line). (E) when injecting 6 times (dashed line) and 20 times (dotted line) more healthy fibroblasts to the chronic 0.5 and de chronic 3.0 states. (F) The effect on TNF-α levels of 30.0 fM and 100.0 fibroblasts to the system at steady state simultaneously with drug treatment of the CRA influx 0.5 (red dotted line), 3.0 (blue full line).

In the situation above, peptide addition was able to switch the network back from its chronic inflammation state to a virtually un-inflamed state, either transiently (when no fibroblasts were reseeded) or persistently (when they were). We also wished to examine whether peptide addition could be used for prevention, i.e. prevent the network from turning into the chronic inflammation state altogether. We therefore set the CRA influx rate at 5 fM/min, and started the computation from the hardly-inflamed state. We added a bolus of 10 M CRA and found (Fig. 2.6A) that for initial amounts of CRA below 109 fM=1 M the system returned to the hardly-inflamed steady state characterized by low TNF-α (dashed blue line). For initial levels of CRA exceeding this 109 fM however, the system still became chronically inflamed (the full red line). The Fig. 2.6B shows that the FLC-neutralizing peptide was able to prevent the switch to full blown inflammation induced by the 10 M CRA. Specifically, the drug prevented the dying of healthy fibroblasts and brought the TNF- α level back to 0.01 or virtually zero.

A B Initial high CRA enhanced Drug effect on acute inflammation TNF- in acute inflammation 60 100 CRA influx 5.0 -drug

10 40

1

(fM) 

(fM)

 0.1 CRA influx 5.0

TNF- 20

TNF- 0.01

0.001 + drug

0.0001 0 0 5 10 15 0 5 10 15 Time (days) Time (days)

Figure 2.6 Prevention: the effect of a single dose of CRA on steady-state TNF-α level and healthy fibroblasts for the case of acute inflammation in the absence and presence of a single dose of FLC- binding peptide. (A) Simulations started at an acute inflammation steady state with 5 fM/min CRA influx (see the corresponding steady state as point on the red line of Fig. 4A) and a bolus of CRA was added at time zero. The amounts added were 0.01 fM and 10 µM for the dashed and full lines respectively. (B) 10 µM of CRA was added at time zero together with or without 1 nM of peptide. CRA influx was maintained at 5 fM/min in all cases.

35

Systems immunology-inflammation

Inter-individual differences: Although the network of Fig. 2.1 may be present in all mammals, expression levels of its components may differ between human individuals. We examined whether the dichotomy between chronic and acute inflammation predicted by Fig. 2.4 for the network of Fig. 2.1, would depend on B-cell activity, which does differ between individual humans due to differences in genome or because of different pre-exposure to CRA. Fig. 2.7A shows that for the case of acute inflammation (i.e. a calculation starting at low BAFF influx rates), the TNF-α steady state level attained is already inflamed at much lower CRA influx levels when the individual was modeled to have five times more B cells. A similar effect was computed for the dependence of TNF-α on CRA influx rate in the chronic inflammation case (Fig. 2.7B). In addition, we simulated for the bacterial growth on time course in two cases to test the strength of innate immunity in silico (Fig. 2.7C).

A B TNF- 10X B-cell TNF- B-cell 60 60 TNF- 10X B-cell TNF- B-cell

40 40 Acute inflammation

(fM)

(fM)

 Chronic inflammation

TNF- TNF- 20 20

0 0 0.1 1 10 100 0.001 0.01 0.1 1 10 100 CRA influx (fM/min) CRA influx (fM/min)

C

300 Bacterial growth weak innate immunity ( mast cell)

Bacterial growth strong innate immunity (mast cell) 200

100

Bacteria growth (fM)

0 0.0 0.5 1.0 1.5 Time (days)

Figure 2.7. Dependence of acute (A) and chronic (B) inflammation on the rate of CRA influx depends on the individual’s B-cell level; dependence of host response in silico (C) on the level of mast cells. The dashed lines in A and B refer to the standard case (Fig. 2.7A; dashed and solid lines respectively), whilst the dashed lines refer to the same calculations but for five times increased B-cell levels. The dashed line in 2.7C refers to a case in which mast does secrete protease, whereas the solid line refers to a case in which mast does NOT secrete protease.

2.4 Discussion 2.4.1. General remarks TNF-α, fibroblast killing and mast cell activation are all associated with inflammation, but inflammation itself can assume either of two dichotomous forms. The one is witnessed by a moderate increase in TNF-α and other cell lytic factors, just enough perhaps to kill a

36

Systems immunology-inflammation population of invading microorganisms or to rid the body of a damaged piece of tissue. It is also transient and disappearing when the microorganisms or damaged tissue have been removed. This is the acute type of inflammation. The other type is sustained, persisting long after the antigenic challenge has disappeared, and continuing to remove tissue, including healthy tissue of the individual itself. This chronic inflammation is associated with diseases such as rheumatoid arthritis. It may depend on self-antigen no longer being recognized as self, taking the place of CRA in our Fig. 1. The distinction between acute and chronic inflammation is a bit unclear when it is accepted that the intensity of the acute response increases with the intensity of the antigenic challenge. What then is the difference between a strong acute inflammation caused by a high amount of CRA and a perhaps weaker chronic inflammation in the presence of a lower amount of antigen? Indeed why is it that when exposed to a given antigenic dose the one person exhibits minor but useful inflammation whereas another person is subject to strong and pathological inflammation? To try to understand these paradoxical phenomena in the innate immune response a bit better vis-à-vis the confusingly high number of factors involved, we put some of the important factors together into a map of the system (Fig. 2.1) and then made this map predictive by integrating corresponding mathematical equations. The calculations we performed in this study were indeed predictive of function: they predicted that the network would act successfully against bacterial infections, that CRA would produce inflammation also in the absence of bacteria, that there are two types of inflammation, and that a peptide binding FLC could redress inflammation from chronic to acute, or could prevent chronic inflammation from occurring when the network was challenged by a high dose of CRA antigen. This success requires qualification in the sense that the results of this study are uncertain for two main reasons. The first is that the network we made predictive (i.e. Fig. 2.1) is far from completely representing all the factors involved in innate immunity and from presenting all interactions in the network properly. The second is that the parameter values inserted in the model may not have been the precise actual parameter values, because most of the latter are insufficiently known. Consequently, the present study should be seen as a mere demonstration of what networks of innate immunity are capable of. It also offers at least one innate-immunity network that exhibits the essential functional properties mentioned above. As such the network and the parameter values we used may serve future studies where parameter values are determined experimentally and then made more precisely predictive. For each figure and table we have a file in the supplementary material that has the definitive COPASI model with the definitive parameter values used, such that it can rerun to reproduce the figure and such that the model is used is completely defined there.

2.4.2. The parameter values Having written the above caveats, we would add that the parameter values used are of interest, because they were chosen and argued to be as realistic as possible with current knowledge. Cell cycle times were realistic, association rate constants were taken to reflect diffusion limitation but with corrections for multiple molecular targets on the same target cell, surface diffusion and attractive forces. Protease secretion in batches was allowed for. Our model hereby differs from other models in mathematical biology in that it is bottom-up based on realistic estimates of the parameter values rather than obtained by fitting. The observation that the model reproduces so many features of immunology, suggests that we are 37

Systems immunology-inflammation not that far away from understanding some of the networks of immunology quantitatively. Because the choices of the parameter values were made explicit, it will be possible to alter the values of the parameters whenever better information becomes available. Herewith the model constructed makes newly observed or proposed parameter values predictive of biological function. Various experimental scenarios can be used to measure the parameter values. Secretion rates of cytokines with careful ELISA. [298].Diffusion rate constants and dissociation rate constants can be measured with fluorescent techniques such as FRAP and FRET [299].

2.4.3. The CRA influxes and acute inflammation Our computational study shows that the network flipped to a state of very high inflammation at high challenges with antigen influx, with very high levels of TNF-α and extensive cell death as a result. The understanding of this complex association is perhaps best supported by Fig. 4C, where we see the predicted level of CRA as a function of CRA influx. The correlation between disease and CRA appears to run in two directions: First, increasing influx of CRA leads to little inflammation until the CRA influx exceeds a threshold, and then (see the dashed line in Fig.2.4A) only with a minor further increase in CRA influx inflammation is much stronger: high CRA influx associates with highly intensive inflammation.

2.4.4. Acute versus chronic inflammation Acute inflammation can turn into chronic inflammation [300, 301]. If an acute inflammation hasn’t resolved itself in days it can thereby be considered chronic. This phenomenon is predicted by our computations of the implications of the network of Fig. 1 with realistic parameter values: As pointed out in Figure 2. 3 and Figure 2. 4, continued acute inflammation caused by a continued influx of CRA at an intensity level above a certain threshold (12 fM/min in our computations), is predicted to lead to strong inflammation with a much elevated TNF-α (50 fM). Once steady state has been achieved for this strong and acute inflammation, return of the CRA influx to levels as low as 0.1 fM/min, fails to lead to a reduction in TNF-α levels (see Fig. 2.4A, the full line): substantial exposure of the network to external CRA above a certain threshold intensity does not only produce a strong acute inflammation but also turns the network perhaps (see below) permanently into one in a chronic inflammation state. Consequently, for the same antigenic challenge the network can turn to a high or a low inflammation state, depending on its history. This phenemonen is also similar to immunological memory in which the animal or person mount potent response to subsequent challenges with the same antigentic molecule.This suggests a mechanistic basis of the difference between acute and chronic inflammation in the bi-stability property of the network of Fig. 2.1.

2.4.5. Targeting inflammation Vis-à-vis medication, innate immunity and its active components are frequent targets [302, 303]. The mast cell contributes many different mediators, including tryptase, VEGF and TNF-α. These are contributors to tissue inflammation as well as tumorigenesis [304-308].

38

Systems immunology-inflammation

We here examined if our predictive network facility could serve in pre-validating in silico a therapy one might propose for chronic inflammation and whether an in silico study might highlight possibly complex features of such a therapy. We focused on a peptide binding to the FLC and thereby on inhibiting indirectly the activation of mast cells by CRA. Sure enough we found that such a peptide could switch the network back from a chronic to an acute inflammatory state. However, the effect of the peptide was transient only, thereby requiring multiple dosing of the peptide at subsequent points in time, or a very low drug washout rate for that effect to persist acceptably long (results not shown). At a lower CRA influx load the peptide had a longer-lasting but still no permanent effect. We found that this finding was due to the fact that in our simulations the fibroblasts had gone extinct in the chronic inflammation. Consequently, the system could simply not return to the uninflamed or acutely inflamed steady state. Why would the fibroblasts matter however? The fibroblasts themselves are not directly involved in the inflammation. The solution to this riddle was (not shown) that the fibroblasts also produce MMP8 which is a protease that removes CRA and thereby defuses the inflammation. Our simulations in which we added not only drug but also fibroblasts to the system confirmed this and suggest that the fibroblasts play an important role in keeping inflammation low by scavenging through the protease they secrete the inflammatory antigens. Economically and in view of a possible immune response against the peptide, therapy with a single dose of peptide could be preferable. Perhaps such a therapy could just consist of adding peptide and rely on fibroblasts growing back in from the surroundings of the inflamed tissue. But again perhaps, it should be advisable to try and activate this repopulation. A further analysis using patient data and our model might be able to predict whether the CRA levels in a particular patient would indeed advocate a one-time high-dose treatment with peptide rather than a continuous treatment at a lower dose or treatment combined with repopulating with fibroblasts. The observation that in the absence of fibroblast implantation, the network predicts the anti-inflammatory effect of the peptide to last only for a month or so may be important, because it could, wrongly, suggest that drug resistance has arisen. Here the mechanism is different from traditional drug resistance, as are the possible therapies preventing such apparent resistance: the mechanism is that the drug has not irreversibly flipped the network back from its chronic to its acute inflammation state. We also examined a different mode of treatment, i.e. one that might prevent acute inflammation turning into chronic inflammation. Here peptide added when the acute high antigen dose arises, was predicted to be effective. This was not what we found for all levels of CRA: In all these cases there is a threshold dependence on dose of both CRA and peptide, and there was a complex time-dependence. Deciding on the precise dosing and dynamics of the therapy and on its limitations, might benefit from having a model such as the one developed here, available.

2.4.6. Individualized disease B cells play a vital role in both acute and chronic inflammation [309-311]. This suggests that the presence of a high number of B cells could potentiate the immune response. Our model enables us to change parameters, including the number of B cells, to examine whether the network of Fig. 2.1 can already accommodate this phenomenon. We found that at a 5-fold

39

Systems immunology-inflammation increase in B-cell concentration the threshold CRA influx rate above which inflammation intensity switched from low to high, was much lower: The window of CRA influx rates where the network was bi-stable between chronic and acute inflammation was shifted to lower CRA influx rates. This illustrates that the phenomena of acute and chronic inflammation and presumably the effectiveness of therapies may differ significantly, but also predictably between individual patients. We performed a similar comparison of the level of mast cell and host response from infectious scenarios using the level of mast cell as an individual parameter. In this case, we found that an increase of mast cell enhance innate immunity to against infection, whereas decrease of mast cells weaken innate immunity in which host become vulnerable to bacterial infection.

2.4.7. Complex regulation The network in Fig. 2.1 contains a positive feedback loop, which involves CRA activation of mast cells into the secretion of TNF-α, and stimulation by TNF-α of CRA secretion by fibroblasts. A complicating matter is that the mediators of CRA secretion are themselves compromised by TNF-α. We have here shown a way to deal with the complexity of this type of regulation, i.e. by making predictive models and by simulation in silico. This has enabled us to show that the complex regulation also results in complex, yet functional properties. One such property of the network is that there is an appreciable range of CRA levels for which the inflammation remains acute. Only at the highest CRA levels, beyond an ‘On’ threshold, a switch occurs to highly intense inflammation, which may then lead to chronic inflammation when maintained sufficiently long. Conversely, we also found that there is a switch back from the chronic inflammation state to the state of acute inflammation, but that this switch requires reduction of the CRA load (as modeled by CRA influx) to an intensity below an ‘Off’ threshold that is far below the ‘On’ threshold. The “Off” threshold is around 0.1fM/min and the “On” threshold is around 17 fM/min of CRA influx. This “Off” threshold is bit more gradual. This information may re-motivate therapies of antigen removal that failed previously: because of the dual threshold effect, a slightly more intensive antigen reduction might prove more effective than a longer lasting therapy with a lower concentration of peptide. Indeed a more sophisticated approach to drug administration may be enabled by predictive modeling and this might improve the therapy of chronic inflammation. Monitoring of some of the properties involved, such as CRA and TNF-α would much empower such an approach. Our simulations also suggest that pathology and optimal therapy should be expected to differ between individual patients (our example was that of differences in B-cell activity). Also this could revamp some drug development; one should not give up on drug development if a drug only works for a fraction of the patients, but rather consider individualized dosing. Use of improved versions of our model and of biomarkers interpreted through these models may well help in such a process. Similar modeling may help interpret experiments inquiring whether a similar positive feedback loop accomplishes a specific activation of other innate immune cells such as neutrophils, macrophages and basophils and whether this may also occur in the vicinity of a tumor [312, 313].

40

Systems immunology-inflammation

2.5 Supplement By using the program Cell Designer [314], we designed an FLC-mediated inflammatory network which corresponds with the network model as shown in Fig. 2.1. This SBGN diagram is shown below.

41

Systems immunology-inflammation

42

Systems Immunology-cancer

Chapter 3 Inflammation-oriented Anti-Tumor Targets in a Complex Model for Mast- cell Mediated Tumorigenesis

43

Systems Immunology-cancer

Summary Mast cells do not only participate in inflammation, they may also play a role in cancer. Here we investigate potential pro-tumorigenic and anti-tumorigenic roles of both mast cells and the inflammation they mediate. We analyzed mast cells in human cancer tissue arrays and demonstrate that activated mast cells disperse throughout cancer tissues such as in skin, breast, pancreas, kidney and lung. We show that mast cells in murine B16 melanoma and human glioblastoma are de-granulated. Next, a dynamic representation is designed of a model of normal innate immunity that was developed in Chapter 2 for a tissue naïve in terms of tumorigenesis. To this model we merely added that the activated mast cells also secrete IL-4, which has apoptotic effects on tumor cells. In addition, the tumor cells secrete proteases that clip inflammatory antigen off of fibroblasts. The model allows a minority of tumor cells to compete with an initial majority of fibroblasts for the space freed by bout of inflammation. When we allow this model to develop in time we find that (1) under certain conditions, the FLC-mediated mast-cell activation becomes associated with tumorigenesis and (2) targeting only inflammatory components within a cancer-related network can already switch the system from tumor outgrowth to tumor containment. Collectively, the results show how the higher-order, multifactorial processes of inflammation and tumorigenesis may interact, providing important bystander roles to innate immune cells and the mediators they secrete, and suggesting new ways of interfering.

3.1 Introduction Cancer can be interpreted as a systems biology disease as it is a multifactorial process that evolves from the intricate interplay between oncogene products, growth factors and their receptors, genomic integrity, signaling pathways and molecules [5, 1]. When normal cells evolve into cancer cells, they become irresponsive to the signals within the tissue that contain them, such as signals that govern cellular differentiation, survival, proliferation and death. As a consequence, some of these cells amass within the tissue, causing local detriment and inflammation. The inflammation is known as a critical component of tumor progression in which immune-cell infiltration is associated with cancer [315-318]. Mechanistically, the inflammatory microenvironment effectuates tumors to develop a more aggressive malignant phenotype. Numerous studies have unveiled that the interaction between tumor and immune cells is a super-cellular aspect of the complex nature of cancer [239, 319], where tumor- immune cell interactions create a communication network that is both beneficial and detrimental to tumor growth. On the one hand, innate immune cells, such as resting macrophages, mast cells and neutrophils, accumulate at the site of the tumor, creating a complex “ecology” for tumorigenesis. For example, tumor-associated macrophages (TMFs) secrete angiogenesis factors such as Vascular Endothelial Growth Factor (VEGF), Interleukin (IL)-8, and Tumor necrosis factor (TNF)-α [320, 321]. TMF accumulation and infiltration is associated with tumorigenesis in human cancers [322, 323]. On the other hand, tumor cells secrete a wide variety of chemokines and some of these are potential activators for immune cells. For example, tumor cells secrete stem cell factor (SCF), which induces mast cell activation, which leads to the production of pro-angiogenic substances such as 44

Systems Immunology-cancer tryptase, which can enhance tumor cell COX activity via the PAR-2 receptor. COX-mediated PGE-2 production can activate mast cells to secrete VEGF [324]. Moreover, antigen specific- antibodies, for example, immunoglobulin (Ig)-G and -E are able to bind Fc-receptors on mast cells and this causes indirect activation of mast cells. The activated mast cells release soluble mediators such as inflammatory cytokines and chemokines, including interleukin (IL)-1 and 6, CXC-chemokine ligand 8, granulocyte/macrophage colony-stimulating factor (GM-CSF), tumor necrosis factor (TNF), and leukotriene C4 (LTC4) [325, 326]. Some of the cytokines and chemokines are beneficial to containment of the tumor cells through the induction of acute inflammation and anti-tumor effects [327]. Upon prolonged acute inflammation however, chronic inflammation ensues. Chronic inflammation may be related to more than 15% of all the deaths from cancers by supporting angiogenesis and metastasis [328-330]. As immune cells infiltrate into the tumor’s microenvironment [331, 332], these cells have become widely recognized as potential target cells in cancer therapy [333-336]. There is a growing interest in pharmaceutical industries in the development of a new generation of drugs that target tumors via immunotherapy [337]. Over the past couple of decades, the US Food and Drug Administration (FDA) has approved immunotherapy which is designed to stimulate the patient’s own immune system to mount an effective anti-tumor immune response. Such immunotherapy includes monoclonal antibodies (mAbs) such as Alemtuzumab, Ibritumomab, Brentuximab, Ado-trastuzumab, Bevacizumab, Cetuximab and Rituximab [338]. For example, Rituximab binds to a specific antigen (CD20) which is expressed on both malignant and normal B cells, and is therefore being used successfully to treat B cell related cancers such as non-Hodgkin’s lymphoma [339]. Bevacizumab is a humanized mAb which inhibits Vascular Endothelial Growth Factor (VEGF) signaling and this can stop tumor angiogenesis [340]. Belimumab binds to B cell activating factor (BAFF) [341, 342]. BAFF is usually secreted by macrophages, stromal bone marrow stromal cells, and fibroblasts during inflammatory diseases such as rheumatoid arthritis. By doing so, BAFF activates the autocatalytic feedback loop invoving fibroblasts, CRA, B-cells, and mast cells. Clinical studies show that Belimumab is the most effective drug for treatment of systemic lupus erythematosus (SLE) which is mainly caused by a B-cell malignancy [343]. Monoclonal antibodies against mast cells have been developed as well. A study also shows that mouse monoclonal antibodies (mAb 9-3 and mAb80) recognize the antigen of malignant mast cells: mAb9-3 recognized an antigen with a molecular mass of 74kD, whereas mAb80 recognized 167kD and 248kD antigens [344], and these mAbs have diagnostic effect as well therapeutic potential for mast cell tumors. Furthermore, a wide range of mast cell differentiation antigens was classified as potentially important targets in therapy against mastocytosis and mast cell leukemia [345]. The mast cell is a widely studied immune-cell, which descends from CD34+ hematopoietic stem cells (HSCs) [346]. Research data increasingly show that mast cells could be promising targets for cancer therapy [347-351], as they are early and persistent infiltrating cells in many tumors, including prostate cancer [352], breast cancer [353], colon cancer [354] skin cancer [355], pancreatic cancer [356], and lung cancer [357]. Intriguingly, the majority of studies on mast cells in cancers indicate that mast cells are predominant at the tumor periphery rather than within the tumor. Often mast cells are associated with vasculature in the healthy regions surrounding malignant tissues, suggesting that mast cells are regulators of tumor microenvironment by releasing preformed as well as de novo 45

Systems Immunology-cancer synthesized mediators, including numerous cytokines and chemokines. Histological stains ranging from toluidine blue to immunostaining for tryptase, VEGF, TNF-α and other markers, were detectable in a subset of mast cells in patients with tumor inflammation, showing that mast cell mediators are immunoreactive for antibodies. In cancer cases, the antibodies produced high staining intensities with mast cell activity, whereas in control cases staining intensity of the antibodies were consistently low or negative. Mast cell infiltration occurs at early stages in tumorigenesis where it appears able to trigger an ‘angiogenic switch’, while at later stages tumor angiogenesis and growth are regulated by tumor cells themselves and become mast cell-independent [161]. Indeed mast cell density is increased during stages of intense angiogenesis, but is inversely correlated with tumor mass itself [161]. A similar study by Soucek and colleagues [186] suggests that CCL5 secreted by pre- malignant tumors triggers mast cell accumulation at the periphery of pancreatic islet tumors. The latter study also shows that carcinoma-associated fibroblasts (CAFs) express the CXCL12 cytokine, which recruits mast cells; this is driven by oestrogen in prostate tumors [358]. Once accumulated, mast cells are activated and enhance tumor angiogenesis. As mast cells contribute to tumor growth, the mast cell activation that is provoked by Cross-Reactive Antigen (CRA) provides space for tumor growth by removing healthy fibroblasts [176, 186, 359] and this can be pro-tumorigenic. Moreover, tumor cells promote the release of CRA from fibroblasts by secreting proteases such as MMP-7, and CRA antigens are mainly produced by dying fibroblasts [145, 360]. Mast cells are activated by diverse ligands such as IgE, SCF, pathogen-associated patterns (PAMPS), sphingosine 1-phosphate (S1P) and complement component 3a (C3a) [160]. These ligands bind to their specific receptors on mast cells, which then activate signaling cascades and lead to the release of mast cell mediators. These mediators include chemokines such as MCP-1 and RANTES, cytokines such as IL-8 [361], IL-6 [362], IL-17 [363], enzymes such as chymase [364] and matrix metalloproteinases (MMPs), and growth factors such as Fibroblast Growth Factor [365] and Tumor Necrosis Factor (TNF) [366]. In contrast to their pro-tumorigenic function, mast cells may also be anti-tumorigenic: Sinnamon and colleagues demonstrated that mast cells are strongly apoptotic to cancer cells and have a preventive role in intestinal tumorigenesis in mice [367]. Mast cells can be anti- tumorigenic by secreting mediators that kill invading tumor cells and this might improve anti-tumor immunity [368]. Mast cell mediators, such as IL-4, inhibited breast cancer cell lines MCF-7 and MDA-MB-231 in vitro [208]. TGF-β released from mast cells may act as tumor suppressor through its ability to inhibit tumor cell proliferation by up-regulating cyclin kinase inhibitor [369, 370]. In addition, mast cells mediate immunoregulation/modulation; for example, IL-5, IL-6, CD40L and protease can influence B-cell development and function which might enhance IgE production by B cells [371, 372]. In addition, IgE and several mast cell activators have been shown to be important during the development of inflammatory diseases. For instance, the density of IgE receptor (FcεRI) on the surface of mast cells is upregulated in the presence of elevated free IgE levels, thus enhancing mast cell activation [373]. Furthermore, mast cells might serve as antigen presenting cells (APCs) for T cells. Gong and colleagues [374] demonstrated that IL-3 stimulated mast cells are fully capable of antigen presentation by expressing high levels of FcεRI concurrently with high levels of Major Histocompatibility Complex (MHC) class II molecule on their surface. However, it is unclear

46

Systems Immunology-cancer whether mast cells recognize and present tumor antigens to induce tumor-specific adaptive immunity. In addition to the well-known mast cell activators IgE and IgG, immunoglobulin-free light chain (FLC) has recently been attracting attention as a contributor to mast-cell activation. When B cells are activated by antigen, they proliferate and differentiate into immunoglobulin-secreting effector cells. Not only do these cells secrete complete forms of immunoglobulins such as IgA, IgE, IgG and IgM, but they also extrude monoclonal globulin- protein FLCs [375, 376]. Highly expressed in chronic diseases, the FLC is localized to the corresponding tissues, where it activates immune cells [377, 378] and triggers mast-cell activation [313] even in the absence of its heavy-chain counterpart (IgHC). Some studies confirm the presence of FLC-specific receptor on mast cells [157] and point to a direct relationship between FLC and bronchoconstriction (a later stage of airway inflammation) [158], while others indicate a proportional link between progression rate for Smoldering Multiple Myeloma (SMM) and FLC [278]. FLC binding to neutrophils led to the release of chemokines from neutrophils and less inflammation occurred when FLC was inhibited by an antagonist in chronic obstructive pulmonary disease (COPD) [312]. In this study, we investigate the potential role of mast cells in tumor cell growth and angiogenesis. We demonstrate that multiple cancers are subject to infiltration by activated mast cells. Next, experimental data are combined with in silico modeling strategies, focusing on the pro-tumorigenic and anti-tumorigenic aspects in the interplay between tumor cells and the inflammatory response. The outcome of the interaction between tumor cell growth and inflammation, depends on multiple molecular and cellular properties, the direct interactions between the mediators of both systems, and feed-forward and feedback loops that can induce a flip between positive (ongoing tumorigenesis) and negative (tumor suppression) states. We here examine whether a mathematical representation of the antiparallel model might be able to assist, in identifying potential control points in the network that might be accessible to therapeutic intervention.

3.2. Materials and Methods 3.2.1. Immunohistochemistry analysis Tissue microarray (TMA) slides from US Biomax (Rockville, MD) were de-paraffinized and rehydrated, and subjected to heat-induced epitope retrieval for MC tryptase staining in a hot water bath (90°C) for 20 minutes. IHC staining was performed on 5-µm slices of TMA with the primary antibody against mast cell tryptase (M7052, 1:200; Dako-cytomation). To visualize the intensity of the primary antibody, we used EnVisionTM G\2 System/AP kit Rabbit/Mouse (permanent red) from Dako, an Agilent Technologies Company. Slides were counterstained briefly with hematoxilin. To study mast cell de-granulation in murine tumor, tumors were fixed in 10% formalin and processed by the Tissue Embedding Pipeline (Leica TP1020/Leica 1150C, Leica microsystem GmbH, Germany). Formalin-fixed, paraffin- embedded (FFPE) sections were de-paraffinized with xylene and different grades of ethanol and stained with 1% toluidine blue in 1% sodium-chloride solution. Glioblastoma specimens were used to study the potential presence of mast cell de-granulation in a tumor with a poor prognosis. Staining with toluidine blue permits the identification of mast cells because mast cell granules stain metachronically, resulting in purplish-blue granular cytoplasmic staining. 47

Systems Immunology-cancer

Sections were photographed with a digital camera mounted on an Eclipse TE2000-U inverted microscope, using 4x to 40x objectives (all Nikon). Images were analyzed using NIS elements BR 2.3 software (Nikon). Totally, 475 patient specimens (see supplementary table 1) and more than 100 mouse specimens were used for analysis. For evaluation of IHC staining, tryptase expression levels were evaluated by multiplication of the intensity of staining (SI) with the number of positive labeled cells (PP) within the whole tissue section (IRS = SI × PP). For instance, IRS of tryptase was classified in terms of a range between 0- 12; 0-2= weak IRS, 3-6= moderate IRS, 7-10 = intense IRS. Zero percentage of positive cells (PP) means no positive cells have been detected (PP=0) and zero staining intensity means no color reaction is occurred by immunostaining (SI = 0), and therefore immunoreactive score is zero. Staining intensity (SI), (0) = no color reaction (1+) = weak reaction, (2+) = moderate reaction, and (3+) = intense reaction. PP scored as 0=1%-10% positive cells; (1+) =10%-30% positive cells; (2+) =30%-50%; (3+) =50%-80% positive cells.

3.2.2. In silico model and its analysis In keeping with the results of our experimental analysis (see Figs 1-3) and mindful of the relevant literature, we constructed the diagram of Fig. 4 as the first version of our model. The diagram shows processes (full arrows) connecting agents in the network, which can be influenced (dotted arrows) by other such agents. Agents range from various types of cell to proteins including enzymes and cytokines and ‘free space’. Each process was described by a rate equation in terms of concentrations of the agents involved and some parameter values. When a substance was catalytically involved in the reaction, i.e. only stimulated the rate without being consumed, we wrote it on both sides of the reaction equation and as a proportionality factor in both the forward and (if present) reverse rate equation. For each agent in Fig. 4 a balance equation was then formulated, specifying its time dependence as the difference between the rates of the reactions synthesizing the agent and the rates of the processes degrading it. Combination of the rate and balance equations then led to differential equations, which were integrated as functions of time. This computation process was performed using the pathway simulator COPASI [379]. The dynamic model we made thereby depends not only on the topology of the network of Fig. 4, but also on parameter values, to which we therefore had to assign values. For most of the parameters of our model, accurate values are unknown. We chose parameter values that seemed as realistic as possible. These choices are rationalized below. Experimental background of Fig. 4. Cross-Reactive Antigen (CRA) plays a vital role in proliferation and differentiation of B cells. Among the Ig’s, IgE binds to the high-affinity IgE receptor, also known as FcεRI on the membrane of mast cells. IgG can also bind to FcγR on the membrane of mast cells, and both IgE and IgG are recognized as mast cell activators. The FLC’s produced by the B cells can bind to mast cells too; incubation of mast cells with fluorescently labeled FLC validated its direct binding. In vivo experiments demonstrated an essential function of FLC in mast-cell activation [158]. In addition, Thio et al demonstrated significant binding affinity of free light chains to the antigen [313] in keeping with antigen- specificity of FLC in mast cell activation. The latter study shows that FLC and mast cells co- localize in lung cancer tissue allowing for the possibility that FLC activate mast cells in 48

Systems Immunology-cancer tumors [279]. Once activated, mast cells release mediators such as TNF-α and histamine [79, 380-382] and inflammation is monitored as an increase in TNF-α level, which is a clinical sign of inflammation [383, 384]. High levels of TNF-α are cytotoxic to healthy fibroblasts making space in the model for tumor cell growth. In contrast to this pro-cancer function of mast cells, some of mast cell-derived mediators are strongly apoptotic to tumor cells. For instance, mast cells secrete IL-4, which inhibits tumor growth in breast cancer [208] as well as in lung cancer [385]. Mast cells exhibit anti-tumorigenic effects in colorectal cancer [213] and mast cells may modulate the recruitment and activity of dendritic cells, natural killer (NK) cells, and regulatory T cells [386]. As a component of the tumor microenvironment, fibroblasts play a role in normal matrix turnover, as well as in pathological matrix degradation which can occur during inflammation and tumorigenesis [387]. Matrix metalloproteinase (MMPs) secreted by human fibroblasts derived from lung, skin and heart, such as MMP-7 [388] and MMP-8, may induce tumorigenesis and metastasis through their enzymatic actions. Specifically, MMP-7 accelerates CRA clip-off from fibroblasts and MMP-7 catalyzes CRA degradation. Initial concentration of species: we divided the process we wanted to model into an inflammatory and a tumorigenic phase. The first, inflammatory phase of five days was simulated using the ultimate model of chapter 2 at zero CRA influx, which we shall call the ‘mother model’ (Fig. S3.1) and which corresponded to the uninflamed state, but setting the steady BAF influx to 30 fM/min and the initial concentration of tumor cells to zero. For this first-phase simulation, the initial conditions of this mother model were otherwise those of Chapter 2, which had been obtained by setting CRA influx rate to zero, running a time simulation, and then taking the final state as the initial state for the subsequent simulations. (Rerunning with these initial conditions then produced no further change.) After the first- five-days’ simulation with the mother model in the presence of a steady 30 fM/min BAF influx which produced a chronically inflamed state as final state, we made the initial conditions for the subsequent simulations equal to the final concentrations of this first-phase simulation. This was the initial state for all the calculations shown in the main part of this paper. In the subsequent simulations with this ‘father model’ (Fig. S3.1) of the chronically inflamed state (cf. chapter2), we added tumor cells (0.1 fM) as well as healthy fibroblasts at the indicated concentrations at time zero, and we reduced the steady CRA influx to 0.01 fM/min. Before inflammation (i.e. in the mother model), the initial concentration of CRA was 0.01 fM; this was the steady state value of the mother model, at zero CRA influx. After the first phase inflammation computation with this mother model, the current status of the internal CRA concentration had reached 3000 fM, and (as specified above) we used this value as the initial concentration of internal CRA in the chronically inflamed model or “father model”. Similarly, the initial concentration of TNF-α was 0.0005 fM before inflammation, whilst after inflammation (in the father model), the current status of internal concentration had reached 50 fM, which turned out to be a maximum level as well as a sign of chronic inflammation in this network (see chapter 2). The protease which is secreted by mast cells in process R25 and would kill bacteria had they been present, had a low concentration (1.0 fM) before inflammation, however, this protease’s concentration had been elevated in the inflammatory phase to 650 fM, again due to mast cell activation. This

49

Systems Immunology-cancer protease is ineffective in the computations of the present chapter however, as there are no bacteria (cf. chapter 2). In the initial condition of the mother model, the healthy fibroblasts occupied almost all the free space in the network, but they were killed by TNF-α during the inflammatory response of the network in the 5-day mother simulation phase described above. Thus, the initial concentration of the healthy fibroblasts in the second phase (“father model”) was zero and subsequently the free space could be taken only by the healthy tumor cells. As one of the therapies we consider the addition of fibroblasts in some of the figures, as indicated below.

Parameter values k1-k27. All parameter values and names were the same as in the immunology model of chapter 2. This pertains to reactions R1-R28, i.e. all processes in the model of chapter 2. In the present chapter, we added processes that had to do with tumorigenesis, i.e. R29-R40.

Parameter values k28-k39. The total space that could house 1000 fM of fibroblasts in chapter 2 could now also house tumor cells: ‘free_space’ was assumed to equal total_space (1000 fM) minus the concentrations of healthy and dying fibroblasts and healthy and dying tumor cells, each of which were expressed in fM. Bacterial cell number was set to (and remained) zero. We have taken two phenotypes of tumor cells (‘healthy’ and ’mutated’) and assigned to them probabilities of taking up free space and a response to drug therapies. The tumor-cell division (R28) rate was made proportional to free space at a second order rate constant of k28 = 0.97 /nM/min (i.e. twice that of fibroblasts) for the ‘healthy’ tumor cells. We raised this rate to k28 = 9.7 /nM/min for the ‘mutated’ tumor cells. The healthy and dying tumor cells -5 secreted CRA (R30 and R29) at rate constants k30 = 10 /fM/min (see above) and k29 = 0.001 /fM/min, respectively; the same as for the fibroblasts. After the inflammation phase the ‘mother model’ had transgressed into the inflamed ‘father model’; all the fibroblasts were dead due to the strong inflammatory response, and the final concentration of both pro and anti-inflammatory substances, such as TNF-α and interleukine-4 (IL-4), were high as a result of mast cell degranulation consequent to mast cell activation: Similarly to healthy fibroblasts, with respect to the clip-off of CRA from healthy tumor cells by MMP-7 (R31), we -2 assumed the encounter of MMP7 with a tumor cell to be diffusion limited at k31 = 10

/fM/min. The killing of healthy tumor by IL-4 (R32) is a biomolecular reaction as well. Here we assumed that Interleukin-4 receptors on the tumor cell are highly expressed, making the -7 rate constant equal to k32 = 5 × 10 /fM/min. IL-4 was produced by the FLC-CRA activated mast cells at a first-order rate constant k33 of 65/min. All substances other than cells were taken to be subject to efflux from the system at the same specific rate of k=1 × 10-2 /min for the reactions R34 (k33; washout of IL-4), and R36 (k36; washout of anti-tumor drug) (but no anti-tumor drug was used in this chapter; only drug interfering with FLC). The association rate constant of anti-tumor drug to tumor cells was taken to equal k35 = 10 /nM/min. In the tumorigenesis model, healthy tumor cells also secrete MMP-7 and MMP-8 enzymes, and we set MMP-8 secretion by healthy tumor cells (R38) to occur in packages of 100 enzyme molecules; the reaction stoichiometry of the product MMP-8 was set to 100 which is the same as in reaction R20 (healthy fibroblasts). The same tumor cell was assumed to release

MMP-7 (R37) in packages of 1 molecule at a time. Because of cellular and functional differences between fibroblasts and tumor cells, the rates of these two release processes in 50

Systems Immunology-cancer

-5 tumorigenesis were set to k38 =10 /min for R38 and k37 = 0.01 /min for R37. The dying tumor cells also disappeared at a rate independent of CRA secretion k39 = 0.1 /min (R39). CRA influx rate: In the inflammatory phase (converting the ’mother model’ to the ‘father model’), the initial CRA influx rate was taken to equal a steady 30 fM/min. In the subsequent tumorigenic phase the external CRA influx (R3) was set to a steady 0.01 fM/min. Therapeutic strategies: For inflammation therapy in silico, we used only anti-FLC therapy, which was combined with cell therapy by adding small amounts of healthy fibroblasts to the system. The sets of combinational therapies are listed in supplementary table 2.

We illustrate the transition from a first phase of inflammation (‘mother model’) to a second phase of tumorigenesis (‘father model’) in supplementary Fig. S3.1. For each figure and table we have a file in the supplementary material that has the definitive COPASI model with the definitive parameter values used, such that it can be rerun to reproduce the figure and such that the model is used is completely defined there. The resulting computed data were analyzed on Graphpad Prism 5.

3.3. Results In this chapter we examine the proposed role of the innate immune system through inflammation, on tumorigenesis. This examination has two sides. First we establish whether the relevant immune cells occur in and around tumors. Second we examine whether the dynamic properties of the molecular and cellular players in inflammation suffice for such a role. 3.3.1. Human and murine tumor biopsies harbor disperse activated mast cells To examine whether mast cells might contribute to cancer development, the determined whether activated mast cells are present in cancer tissue. Immune staining of cancer tissue arrays was performed using anti-tryptase antibodies, as tryptase secretion is an indicator of mast cell activation. We observed tryptase positivity in human cancer tissues at different staining intensities between tumors. Fig. 3.1 a-d shows representative examples of the immune stained tissue slices of tumor material. Histologically, mast cells were markedly accumulated and strongly de-granulated. To identify mast cell granules, we engaged in toluidine blue staining of tissue slices, which stains granules both inside and outside of mast cells, and we observed clusters of mast cell granules in human skin cancer (Fig. 1e). In addition, mast cell granules were observed in both paraffinized (Fig 1.f-h) and frozen (Fig. 1g) sections of melanoma from C57 B/I mice, confirming that mast cells can be part of the immune infiltrates of solid tumors. Human glioblastoma was analyzed for the presence of mast cells by performing toluidine blue staining on cryostat glioma tissues. As shown in Fig. 1i and j, mast cell granules were detected in glioblastoma. Also in human pancreatic cancer (Fig. 3.1k) mast cell infiltration was apparent. As lung is a major organ for mast cell population, the occurrence of mast cell tryptase was also analyzed in sections of lung carcinoma biopsies. Fig. 3.2 a & b show that mast cell tryptase was indeed present in confined areas of lung carcinoma.

51

Systems Immunology-cancer

Systemic inflammation promotes cytokine secretion by activated macrophages and mast cells; among them, TNF-α is a member of a group cytokines that is a clinical sign of inflammation. To examine whether TNF-α may also be a molecular marker for cancer, the expression of TNF-α in tumor tissues was analyzed. We observed TNF-α expression in human lung cancer (Fig. 3.3a) as well as in murine B16 melanoma (Fig. 3.3d), whereas no TNF-α was observed in normal tissue (Fig. 3.3c). Together, these data show that mast cells are readily detectable in solid tumors like lung and skin carcinoma. Importantly, both histology (toluidine blue) and functional (tryptase) staining of human cancer biopsies indicate that the infiltrating mast cells are activated. From these experimental data however, it is unclear if mast cell activation forms an effective anti- tumorigenic immune defense or promotes tumorigenesis and metastasis.

52

Systems Immunology-cancer

53

Systems Immunology-cancer

Figure 3.1. Mast cell activity in tumor slices as sign of inflammation: staining for protease and for granules. A (a-d). Tryptase staining (red): (a) A tissue core of lung cancer tissue (human); the patient had been diagnosed with Lung Squamous Carcinoma; (b) A section of skin cancer tissue (human); the patient had been diagnosed with carcinoma; arrows indicate tryptase; (c) Section of breast cancer tissue (human); the patient had been diagnosed with invasive ductal carcinoma; circles indicate tryptase; (d) Section of colon tissue (human); diagnosed as mucinous adenocarcinoma; arrows indicate tryptase B (e-l). Toluidine blue (1%) staining: (e) A paraffin section of skin cancer tissue (human) which is comparable with panel (b); arrows indicate granules; (f) A paraffin section of intratumoral mast cells in murine B16 melanoma; arrows indicates granules; (g) A frozen section of murine B16 melanoma tissue for mast cell degranulation; arrows indicate mast cell granules; (h) A paraffin section of murine B16 melanoma tissue: a mast cell with degranulation; circle indicates a degranulated mast cell. Panel (i) and (j) show mast cells in patient brain tissue with glioblastoma. Some mast cells are highly degranulated (arrows). Panel (k) shows mast cells in human pancreatic cancer, which has been used as positive control for mast cell granules. Panel (d) is H&E staining of glioblastoma which shows morphology of glioma in the human brain. Original magnification, 4x in panel (a), 15x in panel (b), (c) and (g), 10x in panel (f) and (h), 60x in panel (i). Panel (a),(b),(c) and (d) were hematoxylin and eosin stained. All sections were 5µm thick.

Figure 3.2. Mast cell related protease in tumor tissue as sign of inflammation. Staining pattern for tryptase (red) in two different sections of lung carcinoma; arrows indicate tryptases (a) Human-lung squamous cell carcinoma with malignancy grade III; male, age 66. (b) Human lung-squamous cell carcinoma with malignancy grade II; male, age 70; circle indicates cluster of tryptase positive cells.

54

Systems Immunology-cancer

Figure 3.3. TNF-α in cancer tissue as indication of inflammation; (a) Section of lung cancer tissue (human) was immunostained red with rabbit polyclonal antibodies to TNF-α (ab6671); from patient diagnosed with lung squamous carcinoma pathologically. (b) Section of lung cancer tissue (human); no antibody used against TNF-α to serve as negative control. (c) Section of normal lung tissue (control tissue) immunostained with polyclonal antibodies to TNF-α to serve as negative control. (d) Frozen section of a tissue section of murine B16 melanoma stained for TNF-α by using same rabbit polyclonal antibodies to TNF-α (ab6671). Note: ab6671 reacts with both mouse and human tissues (http://www.abcam.com/tnf-alpha-antibody-ab6671.html)

3.3.2. Modeling the tumor-associated inflammatory network; FLC-mediated mast cell activation has paradoxical roles in tumor growth

We first collected literature information relating to the above experimental results (wet-lab data). We found ample evidence of a complex network the functioning of which was too difficult to grasp intuitively. We therefore turned to a tool that can be used to make lots of data on complex networks predictive: a corresponding mathematical model. At the first stage of model building, we identified which species are involved and need to be modeled. In a second step, we identified relevant interactions and reactions between various species. The resulting diagram is Fig. 3.4. Its basis in the experimental literature is detailed under Materials and Methods. At the third and final stage, we developed a kinetic model to express the chemical reactions into rate equations, and the time dependence of variable concentrations into balance equations (see reaction table 1). The Software (COPASI) converted the combination into differential equations and integrated these over time.

55

Systems Immunology-cancer

The issue we consider in this chapter is whether and how inflammation could influence tumorigenesis. We decided to focus further on a particular setting, i.e. one in which there would be a sudden bout of strong inflammation in a tissue in which a few surviving pre- tumor cells would be present. These pre-tumor cells would still be contact inhibited but thereby be stimulated to proliferate by the space generated by the inflammation. We call these cells ‘pre-tumor cells’ because they are still contact inhibited. On the other hand the mast cells still present in the area might secrete IL-4 and thereby kill off these pre-tumor cells. And, fibroblasts might grow into the space in a wound healing process thereby taking away the space the tumor cells might otherwise proliferate into. The scenario we are to investigate in this chapter is the one in which tumor cells that arise in an otherwise standard immunological environment develop in a way that is strongly influenced by that environment. We hereto use a standard model for innate immunity that we developed in Chapter 2, with the sole change of adding the tumor cells.

56

Systems Immunology-cancer

Figure 3.4. Schematic representation of the cancer model for mast cell mediated tumorigenesis; mast cell has been identified as a “hub” that control the modulation of the network in response to antigen and activator, and the modulation of this scale-free network is mediated by two positive feedback loops with opposite biological functions; one feedback loop is pro-tumorigenic by secretion of TNF-α and the other one is anti-tumorigenic by secretion of interleukin-4 (IL-4). Another feedback loop is either anti-infective or pro-infective by secretion of proteases. For tumor inhibition, both anti-FLC and anti-tumor drugs are presented to the network to regress tumor growth. Both single and combination therapies are examined to maximize therapeutic efficacy. Mathematically, the network was decomposed into its component processes and for each of the latter a characteristic equation was formulated, either a rate equation or an equilibrium equation. Any of the former formulated the rate at which the process should precede as a function of the concentrations of the components, such as mast cell, B-cell and tumor cell, involved in the process, as depicted in Fig. 5. Biochemically, activation by Cross Reactive Antigen (CRA), which may flux into the system form the outside (process

R3) stimulates B cells into producing FLC (R9). An uninhibited-FLC binds to mast cells (R23), but FLC may be inactivated by binding of anti-FLC peptide (R11). In a second antigen contact (R22), CRA binds to this FLC and the Mast Cell is coerced into secreting TNF-α (R24), IL-4 (R32) and protease (R25). Both IL-4 (R27) and anti-tumor peptide (R35) can turn tumor cells into dying tumor cells, which die at a constants specific rate (R26) but die at different production rate of IL-4 (R29). The TNF-α molecule can turn healthy fibroblasts into dying fibroblasts (R16) (R17) or not (R17) when they die, and create more space for tumor cells. (Free space is defined as total space minus the total number of fibroblasts and tumor cells). Healthy fibroblasts can divide at the cost of free space (R18), and the same is true for healthy tumor cells (R28). Both healthy tumor cells and healthy fibroblasts secrete two types of enzyme, i.e. MMP-7 (in processes R19 and R37, respectively) which clips off CRA from healthy fibroblasts (in process R14 and R31), and MMP-8 (R20 and R38, respectively) which degrades CRA (R1). Full arrows: conversions (gray for death); dashed arrows: synthesis or secretion; dotted arrow: influence. Bottom half of the figure: the washout reactions (R2, R4, R5, R6, R7, R8, R10, R12, R34 and R36), which all proceed at the same specific rate with rate constant called washout.

Accordingly we first modeled a 5-day phase in which due to an influx of excess antigen (CRA) the tissue became chronically (see chapter 2) inflamed. In this phase, we observed a strong increase in the level of TNF-, CRA, FLC and a decrease in MMP-7 and MMP-8, whilst the fibroblasts became extinct due to killing by TNF- (see chapter 2). After 5 days we stopped the influx of external antigen and allowed a small number of tumor cells to grow into the space liberated by the deceased fibroblasts. This will be the zero time in what we 57

Systems Immunology-cancer shall present here and below. Fig. 3.5A shows that the tumor cells populated the space within 2 days. When we enabled the mast cells to secrete IL-4 cytokine, which has an apoptotic effect on tumor cells, the growth of the tumor cells was retarded, and in particular they then failed to fill the complete space equivalent to 1000 fM (Fig. 3.5B). We also considered more aggressive tumor cells, i.e. cells that had a tenfold higher specific growth rate. They filled the space much more quickly (Fig. 3.5C), without the cytokine secretion by the mast cells having a noticeable effect (Fig. 3.5D).

58

Systems Immunology-cancer

Table 3.1 The processes considered in the mathematical model for tumorigenesis in figure 5. In the first column, each process considered has been given a number which follows the symbol ‘R’ for reaction. A description name is given in the second column, whilst the third column describes the process by a chemical reaction equation. Where substances occur on both sides of the arrow, they are mere influences on the process, without themselves being consumed. This corresponds to the dotted arrows in Fig. 5. In the next column the rate equation used for the process is shown and in the final column the magnitude of the parameter values. Concentrations were expressed in fM, time in minutes

A B 1000 1000

750 750

500 500

250 250 Healthy tumor-cells (fM)

Healthy tumor-cells (fM) 0 0 0 10 20 30 0 10 20 30 Time (days) Time (days)

C D 1000 1000

750 750

500 500

250 250

Mutated tumor-cells (fM) Mutated tumor-cells (fM) 0 0 0 10 20 30 0 10 20 30 Time (days) Time(days)

Figure 3.5. IL-4 production and tumor growth; (A) Normal type of tumor-cells (healthy tumor) with the rate of IL-4 production by mast cells set to zero; (B) Normal type of tumor-cells (healthy tumor) with various rates of IL-4 production by mast cells; (C) Mutated type of tumor-cells (mutation only increasing the growth rate 10 fold) with the rate of IL-4 production by mast cells at zero; (D) Mutated 59

Systems Immunology-cancer type of tumor-cells with various rates of IL-4 production by mast cells. In (B) and (D), the full-lines represent the tumor regression when IL-4 production rate is at 65 fM/min, the dashed lines represent the tumor regression when IL-4 production rate is at 130 fM/min and the dotted lines represent the tumor regression when IL-4 production rate is 195 fM/min. No implantation of healthy fibroblasts for any case.

Tumor therapy: A potential peptide-based therapy would bind free FLC and this should then limit mast-cell activation and hence inflammation. We added such a peptide to our computer model and then simulated what a continuous administration of a high concentration of the peptide would do to the development of the tumor. This led to the full line in Fig. 3.6A1. As compared to the dashed line in that same figure (which represents the case without peptide), we found that in silico the peptide drug enhanced tumor growth, for more than 5 months. Not only did the potential therapy fail, it also worked negatively, in silico. Re-inspection of Fig. 3.4 reminded us of findings in the experimental literature, i.e. that inflammation can have paradoxical effects on tumorigenesis. One such effect is through the IL-4 produced by the activated mast cells, which kills tumor cells. We confirmed that in our model in the absence of the anti-FLC drug, enhanced production of IL-4 by the mast cells, decreased tumor growth strongly (compare the dashed line in Fig. 3.6A2 to the dashed line in Fig. 3.6A1). And at this enhanced IL-4 production rate, the anti-FLC drug had an even worse effect on tumor growth: it basically restored it all the way, annihilating the effect of the IL4 (Fig. 3.6A2). In the other mechanism, TNF- produced by the mast cells kills the fibroblasts, creating space for tumor cell expansion. When we checked to see if the anti-FLC was successful in preventing the lysis of the fibroblasts, we found that there were no fibroblasts in the first place; in the acute inflammation that had preceded the simulation all fibroblast had died so that fibroblast regrowth was impossible (see also chapter 2). Although fibroblasts may be virtually absent from the core of realistic chronic inflammation areas, there is always the possibility of regrowth from the edges, as in wound healing. We therefore added some fibroblasts to the model at t=0 with the ability to grow. We then repeated the simulation of tumorigenesis in the absence of the anti-FLC peptide (dashed line in Fig. 3.6B1). We found that the tumor cells still grew until they filled two thirds of total space, quite similarly to what had happened in the absence of fibroblasts (the dashed line in Fig. 3.6A1). When we checked to see whether the fibroblasts could have meant anything in terms of competition with the tumor cells, we found that the implanted fibroblasts had died quickly (see the dashed line in Fig. 3.6B2) presumably due to the secretion of the TNF- by the mast cells. But when we added anti-FLC peptide as well as fibroblasts, tumorigenesis was reduced rather than stimulated by the peptide (compared the full line [with peptide] to the dashed line [without peptide] in Fig. 3.6B1. Fig. 3.6B2 confirms that the drug leads to growth of the implanted fibroblasts. We also examined the effect the peptide would have had in the absence of IL-4 production by the mast cells when only the second mechanism would be operative. Fig. 3.6C1 shows that in that case of much increased tumorigenesis, the effect of the peptide would be much stronger and opposite; it would prevent the tumor cells from colonizing the entire space because of stimulating the growth of fibroblasts (Fig. 3.6C2) by quenching the inflammation. We conclude that peptide therapy may stimulate or regress tumor development, depending on whether the tumor

60

Systems Immunology-cancer contains sufficient numbers of fibroblast that may inhibit tumor growth through contact inhibition. Of course, if the tumor cells would be mutated enough to have lost contact inhibition, the peptide effect would only be stimulatory. The effectiveness of the peptide would also depend on the dynamics of various processes. Fig. 3.7 shows the results of the same simulations but for tumor cells with a much enhanced growth rate. In that case the peptide is virtually without effect in silico.

A1 A2 + IL4 production (65) - implantation + IL4 production (195) 1000 1000 -implantation +anti-FLC 750 750 +anti-FLC -anti-FLC 500 500

250 250 Healthy tumor-cells (fM) Healthy tumor-cells (fM) -anti-FLC 0 0 0 1 2 3 4 5 0 1 2 3 4 5 Time (months) Time (months)

B1 B2 + IL-4 production (65) 1000 1000 +IL4 production (65) + implantation + implantation 750 -anti-FLC 750

+anti-FLC 500 +anti-FLC 500

250 250

Healthy tumor-cells (fM) Healthy fibroblasts (fM) -anti-FLC 0 0 0 1 2 3 4 5 0 1 2 3 4 5 Time (months) Time (months)

C1 - IL4 C2 + implantation - anti-FLC 1000 - IL4 1000 + implantation

750 750

+anti-FLC +anti-FLC 500 500

250 250

Healthy fibroblasts (fM) - anti-FLC Healthy tumor-cells (fM) 0 0 0 1 2 3 4 5 0 1 2 3 4 5 Time (months) Time (months)

Figure 3.6. FLC antagonist affecting tumor growth; (A1) Normal type of tumor-cells (healthy tumor) with the rate of IL-4 production by mast cells at 65 fM/min, in presence ( full line) and absence of anti-FLC (dashed line) (without implantation of healthy fibroblasts); (A2) as A1 but with the rate of IL-4 production by mast cells at 190 fM/min; (B1) As A1 but with implantation of fibroblasts; (B2) As B1, but showing regrowth of healthy fibroblasts after implantation; (C1) As B1 but without IL_4 production by the mast cells; (C2) As C1 but showing regrowth of healthy fibroblasts after implantation.

61

Systems Immunology-cancer

A1 A2 + anti-FLC 1000 1000 + anti-FLC - anti-FLC - anti-FLC 750 750

+IL4 production (65) + IL4 production (195) 500 - implantation 500 + implantation

250 250

Mutated tumor-cells (fM) Mutated tumor-cells (fM) 0 0 0 1 2 3 4 5 0 1 2 3 4 5 Time (months) Time (days)

B1 B2 + anti-FLC 100 1000 - anti-FLC + IL4 production (65) 75 750 + implantation

+ IL4 production (65) 50 500 + implantation

250 25

Healthy fibroblasts (fM) + anti-FLC

Muated tumor-cells (fM) - anti-FLC 0 0 0 1 2 3 4 5 0 1 2 3 4 5 Time (months) Time (months)

C1 C2 + anti-FLC 1000 100 - anti-FLC

750 75 - IL4 - IL4 + implantation + implantation 500 50

250 25

+ anti-FLC Healthy fibroblasts (fM) Mutated tumor-cells (fM) - anti-FLC 0 0 0 1 2 3 4 5 0 1 2 3 4 5 Time (months) Time (months)

Figure 3.7 FLC antagonist on mutated tumor (A1) Without implantation of healthy fibroblasts, mutated type of tumor-cells (mutated tumor) with the rate of IL-4 production by mast cells at 65 fM/min; (A2) Without implantation of healthy fibroblasts, mutated type of tumor-cells (mutated tumor) with the rate of IL-4 production by mast cells at 190 fM/min. (B1) Mutated type of tumor-cells (mutated tumor) with the rate of IL-4 production by mast cells at 65 fM/min; (B2) Regrowth of healthy fibroblasts after implantation; (C1) Mutated type of tumor-cells (mutated tumor) with the rate of IL-4 production by mast cells at zero; (C2) Regrowth of healthy fibroblasts after implantation; The full pink- lines represent the tumor growth without anti-FLC treatment, the dashed pink-lines represent the tumor regression with anti-FLC treatment. The full blue-lines represent healthy fibroblasts with anti-FLC treatment, the dashed blue-lines represent the healthy fibroblasts without anti-FLC treatment.

Sustained versus transient therapy: In the simulations discussed above, administered peptide was kept continuously kept at a high level. Such a therapy might be unsustainable for a variety of reasons. Therefore we examined the effect of a transient therapy, reflecting injection into the tumor of a single dose of peptide. To make the effect more visible, we first eliminated the IL-4 production. Fig. 3.8 shows that the effect of the peptide on tumor development depended strongly on the formulation. Should the drug be injected as such and 62

Systems Immunology-cancer be subject to normal washout rates, then one should not expect to find much of an effect on tumor development; the drug is washed out too quickly and the tumor takes over within 2 months (Fig.3.8A1). If a formulation could be found in which the drug would persist about 10 times longer, then the tumor would grow to a limited density at first and only later grow further to fill up all the space (which would happen only after 10 months (Fig. 3.8A2). Admitting IL-4 production by the mast cells maintain the success of the therapy (Fig. 3.8B1), especially if the wash-out rate of the drug could be kept small (Fig. 3.8B2). Indeed, fibroblast regrowth was the explanatory phenomenon, as shown by the blue lines in Fig. 3.8.

A1 - IL4 A2 + implantation 1000 1000

750 750

500 500

HTC vsHTC HFB (fM) 250

HTC vsHTC HFB (fM) 250

0 0 0 1 2 3 4 5 0 1 2 3 4 5 Time (months) Time (months)

B1 + IL4 B2 + IL4 1000 + implantation 1000 + implantation

750 750

500 500

HTC vsHTC HFB (fM) 250 HTC vsHTC HFB (fM) 250

0 0 0 1 2 3 4 5 0 1 2 3 4 5 Time (months) Time (months)

Figure 3.8. Tumor cell (pink) and fibroblast (blue) abundance as functions of time, IL4 production and drug washout rate. (A1) Normal type of tumor-cells (healthy tumor) with the rate of IL-4 production by mast cells at zero and the drug washout rate at 10-4 fM/min; (A2) Normal type of tumor- cells (healthy tumor) with the rate of IL-4 production by mast cells at zero and the drug washout rate at 10-5 fM/min; (B1) Normal type of tumor-cells (healthy tumor) with the rate of IL-4 production by mast cells at 65 fM/min and the drug washout rate at 10-4 fM/min; (B2) Normal type of tumor-cells (healthy tumor) with the rate of IL-4 production by mast cells at 65 fM/min and the drug washout rate at 10-5 fM/min; The pink lines represent tumor cells and the blue lines represent healthy fibroblasts. Tumor cells (0.1 fM), peptide drug (0.001 M), and where indicated, fibroblasts (100 fM) were all injected at time zero at the indicated final concentrations.

FLC-dependent tumorigenesis in silico: simulation result in Figure 3.9A showed that tumorigenesis is positively correlated with the level of FLC. In rapidly growing tumor, FLC level is quickly reached to its maximum level, whereas FLC level is slowly reached to its maximum level in the case of low tumorigenesis. The analysis is also correlated with experimental studies in which abundant expression of FLC was observed in malignant tumors (supplements). Thus, FLC maintains high expression levels in tumorigenesis and 63

Systems Immunology-cancer during its development into cancer. Figure 3.9B shows the level of FLC-binding mast cell in silico which is also highly correlated with an increased level of tumor, with further development, the level of FLC-binded mast cell has reached to its highest level where the effect of tumor is significant.

Figure 3.9. FLC level mast cell_FLC_CRA complex. (A) Yellow dashed line represents FLC level and elevation-time in normal type of tumor-cells (healthy tumor) with the rate of IL-4 production by mast cells at zero and the drug washout rate at 10-4 fM/min; Blue dashed line represents FLC level and elevation-time in mutated type of tumor-cell (mutated tumor) with the rate of IL-4 production by mast cells at zero and the drug washout rate at 10-4 fM/min. (B) shows response of mastcell_FLC_CRA complex tumor cell level in silico; the red dashed line indicates the increases of mastcell_FLC_CRA complex which is associated with the increased level of tumor cell.

3.4. Discussion Although there are many connections between cancer and inflammation, the extent to which inflammation affects the outcome of cancer remains to be defined [389-392]. For as long as the tumor cells are not yet completely cell-autonomous, an interaction network of cancer cells, fibroblasts, endothelial cells and immune cells controls tumor growth. Through its mutations and this selective environment, the tumor evolves until it ultimately escapes this control. Slowing down tumor growth by reinforcing the network that controls the growth of the early tumor cell does not only have a direct, but also a more indirect effect such as decelerating the oncogenic process, maintaining genetic stability and preventing epigenetic changes. These indirect effects reduce the target size of the mutagenesis. At least one of the recruited immune cell types, i.e. mast cells, is involved in cancer-related inflammation, which may both compromise and support tumor growth and expansion [393-395]. Along with other myeloid cells, mast cells promote tumor angiogenesis and mast cell-deficient mice exhibit a retarded rate of tumor angiogenesis [178]. It should be noted that in this study we have not yet modelled the effect run through angiogenesis. In human cancers, mast cells can be beneficial to the tumor, as has been studied in breast cancer [396], skin cancer [397] and pancreatic cancer [163]. In the present study, mast cells were found in their activated state in the majority of tumors: increased levels of expression of tryptase and TNF-α around mast cells were observed by immunostaining of tumor-biopsy sections of both human and murine origin. This suggested that the mast cells were engaged in a cancer-associated inflammation. The

64

Systems Immunology-cancer model we created contains some of the publicly available relevant information: (1) Fibroblasts secrete CRA, which is a B-cell activating factor (BAFF) [398, 399]. (2) Mast cells in the tumor microenvironment engage in both anti- and pro-tumor immunity. (3) Dysregulation of Bcells then drives secretion of excessive amounts of FLC in response to this CRA. (4) These secreted-FLCs activate mast cells. (5) The TNF-α which is released from the mast cells stimulates apoptosis of healthy fibroblasts. (6) The IL-4, which is secreted by mast cells, has an apoptotic effect on cancer cells. (7) Both healthy and dying tumors release CRA. (8) CRA is also clipped off by MMP7 that is secreted from healthy tumor cells. (9) There are less (healthy) and more aggressive (mutated) tumor genotypes. Because of the complexity of all factors involved, we felt that the design of a computational Systems Biology model for cancer-related inflammation could benefit the understanding of how inflammation may affect tumorigenesis. The mathematical model we made enabled us to confirm that the known network around mast cells may well have the effect of promoting tumorigenesis. On the one hand, the model showed that a pro-tumorigenic effect may be the result of a subtle balance between the direct anti-tumor effect of IL-4 and TNF-α secretion by activated mast cells and the indirect effect of causing inflammation, the lysis of fibroblasts and release of contact inhibition of pre- cancerous tumor cells. On the other hand, the model was still incomplete in terms of the precise identity of the rate equations and the magnitude of the parameter values we inserted. These aspects were characterized by preference for simplicity and realism, respectively. The results we obtained in our modeling should not be taken to have a precise meaning. For that, the models need to be improved by determinations of kinetic relationships and parameter values in quantitative experiments and by calibration with in vivo like experiments. Our computations did prove however that the network we modeled can have highly important effects and this may motivate such further and quantitative experimentation. Although the model contains a small portion of information about the tumor microenvironment, the connectivity of the network is informative and could be applicable in pharmaceutical research. Importantly, we have identified a possible pharmaceutical application of the network, with a “one stone two birds” approach. This approach targets both FLC-mediated chronic inflammation, and tumor growth enhancement by activated mast cells. Moreover, we are emphasizing the mast cell as a target in cancer therapy and we found that the inhibition of the activity of FLC might also be effective. It is worth noting that addition of healthy fibroblasts played a significant role. During conventional therapy, such as chemotherapy, the majority of growing cells are indiscriminately destroyed by cytotoxic agents. These agents, however, cannot distinguish between cancer and non-cancer cells and it is therefore important to reintroduce functional healthy cells from a patient to the same patient, replacing the cells lost in that patient during the treatment. The ensuing regeneration of healthy tissue within the tumor microenvironment may retard tumorigenesis. It will be important to validate the predictions made by our model; its predictions depend fairly strongly on parameter values, which we took care to identify. In a parallel activity we began to set such a validation in motion and a first report on the partial validation is in the supplemental material to this chapter. That material shows that in mouse injected with tumor cells, mast cell activity can enhance tumor growth, whilst anti-FLC peptide can eliminate this

65

Systems Immunology-cancer enhancement. Additional experimental validation will be needed, but these results constitute a sign that our model comes with predictions that make sense. Any network-based therapy could benefit directly from analyzing all positive and negative feedback loops rather than just focusing on individual parts within the loops. Clinically, Systems Biology approaches that identify FLC as a biomarker for various cancer types, may lead to the implementation of quicker, cheaper and more personalized clinical trials. They may also increase the success rate of early detection. Therapeutically, tumor growth may be inhibited either by single dose or by synergic doses of anti-inflammatory/ anti-tumor drugs. Anti-inflammatory drugs, such as anti-FLC peptide, have a potential to prevent tumor growth and to create an optimal environment for healthy cell growth. In contrast, anti-tumor drugs exert significant effects on tumor growth, but the side effects of these drugs are often quite considerable, due to extensive damage done to healthy tissues, which may then actually stimulate tumor cell regrowth. In order to minimize the side effects of drugs, synergic therapies to eliminate tumors would be an alternative in cancer therapy. From a targeting perspective, potential drugs that can inhibit FLC in mast cell activation may be a valuable therapeutic intervention. Taken together, the study presented here shows that mast cells may be involved in both cancer and inflammation, and that targeting FLC-mediated mast cell activation may constitute an added strategy in cancer therapy, provided that the consequent inactivation stimulate fibroblasts regrowth more than that it inhibits IL-4 production.

3.5. Supplemental material to Chapter 3: In vivo validation of the in silico model

3.5.1. Anti-FLC peptide redress tumor growth in vivo Mast cells can be activated by immunoglobulin light chains and this might also occur in tumorigenesis. In the model we built in Section 3.3.2, mast cells engaged in inflammation might free up space for proliferating tumor cells and thereby enhance tumorigenesis. This scenario worked in silico. But could it also work in vivo? In Section 3.3.1 we have already shown that there are mast cells in many tumor tissues, but are there also FLCs in that environment? In a parallel experimental activity we first examined the validity of the model assumption that there are: Using monoclonal antibodies directed against FLC, we stained commercially available tissue arrays of lung, skin, breast, kidney, colon, and pancreas. We found FLC-staining in tumor tissues (Fig. S3.2B-D). The FLC positive staining correlated positively with tumor malignancy (Fig. S3.2A). As required for our mathematical model of this chapter to be relevant, FLC-positive cells and tryptase-positive mast cells localized to the same area of the tissue (Fig. S3.2 F and G). We then decided to test whether mast cells are involved in tumorigenesis. To this aim we induced tumor growth in C57Bl/6 mice by injecting these mice with B16F10 B16 melanoma cells. Subsequently, the tumor volume was measured intermittently until the tumor reached a humane end point, at which point we sacrificed the mice according to a standard animal protocol. Thereafter tumor samples were subjected to pathology analysis of mast cells in tumor-stromal tissues. Fig. S3.3A and B confirm that there were toluidine blue 66

Systems Immunology-cancer staining cells, presumably mast cells, in the resulting tumor tissue. The circles in Fig. S3.3C show that the tumor grew, reaching a size of half a cubic cm after some 6 days, after which we were first able to detect it. In order to examine whether mast cells were involved in this case of tumorigenesis, we repeated the experiment in mice lacking mast cells. In these WBB6F1/J-KitW/KitW-v mice it took longer (8 days) for the tumors to grow to that same size (the open circles in Fig. S3.3C). Previous research had indicated that FLCs are involved in ear inflammation as well as asthma. They appeared to bind to specific receptors on mast cells [157, 158]. To examine whether FLCs might be involved in mast cell-mediated enhancement of tumorigenesis, we injected the C57Bl/6 mice not only with the tumor cells, but also, intermittently (once per week), with 20 µg of F991 peptide, in the vicinity of the tumor. This peptide binds strongly to and inactivates FLC and the Fig. S3.3C suggests that this effect is small in vivo and that the effect of the peptide on tumor growth through an effect on inflammation, dominated. The circles in Fig. S3.3D shows that indeed, the peptide retarded the growth of the tumor cells, virtually to the same extent as the absence of mast cells had done. Indeed, peptide injected into the tumor-growing mice without mast cells did not retard tumor growth (compare circles and blocks in Fig. S3.3E). In addition to that, tryptase positive mast cells were all expressed on human cancer specimens (Fig. S.3.4). Although we have use of the inflammatory derived predictive cancer model, in every case a close linkage is built between prediction and experimental data results from which they were obtained through studies in vitro and in vivo, and this is also notion and concept behind our model (Fig. S.3.5) Importantly, one of our suggestion is that anti-FLC peptides may be a candidate for a therapeutic target for the inhibition of tumor growth [400]. The effect of the peptide on tumor growth might also be due to a direct effect of the peptide on the tumor cells. Indeed such effects were observed in in vitro experiments (preliminary observations). Overall, experimental results confirm predictions made by our model (Chapter 3) and thereby offer a partial validation.

3.5.2. Supplemental experimental methods FLC detection in human cancer biopsies Detection of FLC-positive cells and mast cells in tissue arrays of various human tumor types. Tissue array slides from US Biomax (Rockville, MD) were stained using the Envision G2 System/AP (rabit/mouse;DakoCytomation) according to manufacturer’s instructions. Sections were stained for kappa and lambda FLCs simultaneously [24] (Fκ-C8 and Fλ-G9 purchased from Dr. A. Solomon, Tennessee; both 1 μg/mL) and mast cell tryptase (clone AA1, DakoCytomation; 0.4 μg/mL). The specific recognition of κ and λ FLCs by mAbs Fκ-C8 and Fλ-G9 in immunohistochemistry has been demonstrated before by Davern et al [24]. Primary antibodies were diluted in Tris-buffered saline containing 0.1% Triton X-100 and 1% bovine serum albumin. For tryptase staining, tissue de- paraffinization was followed by heat-induced epitope retrieval using citrate buffer (10 mM citric acid containing 0.05 % Tween-20, pH 6). Slides were counterstained with hematoxylin. Sections were viewed using an Eclipse TE2000-U inverted microscope with 4x and 40x objectives (Nikon). Images were analyzed using NIS elements BR 2.3 software (Nikon). FLC staining was differentiated as follows: – (absence of staining), + 67

Systems Immunology-cancer

(isolated cells with positive staining for FLC), ++ (clusters of ≥ 10 cells with positive staining for FLC), or +++ (clusters of cells encompassing > 10 % of the tissue core with positive staining for FLC). Staining was scored by two researchers who had no knowledge of biopsy status.

Tumorigenesis assay C57Bl/6J wild type or mast cell-deficient WBB66FI/J-Kitw/KitW-v mice that received B16F10 cells via subcutaneous flank injection were monitored for tumor growth. At the time the tumor became palpable, 25 µl PBS containing 20 µg F991 or vehicle alone was injected in the tumor vicinity. Treatment was repeated weekly. Tumor growth was monitored by measuring the largest and smallest superficial diameters of the tumors using digital calipers. The tumor volume was calculated as follows: (0.52 x largest diameter) x (smallest diameter)2. Animals were considered to have reached the endpoint of the experiment when the tumor volume measured ≥ 1.5 cm3.

FigS.3.1. Inflammation or “mother model” to tumorigenesis or “father model” is a transitional process; In the mother model, free space is occupied by healthy fibroblasts (A), however, these healthy fibroblasts are killed by inflammation in which CRA results in the elevation of TNF-α (B). In the father model, the system enters the status that all the free space is available for tumor growth (C). The addition of even a small number of tumor cells, of the cells, triggers tumorigenesis in which free space is colonized by tumor cells (D).

68

Systems Immunology-cancer

69

Systems Immunology-cancer

Figure S3.2. Immunoglobulin free light chains (FLCs) are present in breast, pancreas, lung, colon, skin and kidney cancer biopsies. (A) Percentage of FLC-positive malignant, cancer-adjacent or normal tissue biopsies (diameter 1.5 mm) from breast, pancreas, lung, colon, skin and kidney. The numbers next to each bar indicate the number of positive samples divided by the total number of individual patient biopsies examined. The color intensity of the bar corresponds to the degree of FLC staining, in terms of three categories (as illustrated in B-E): +++, the most intensive color, clusters of FLC-positive cells encompassing more than 10 % of the tissue core (B); ++, clusters of ≥ 10 FLC- positive cells (C); +, the faintest color, isolated FLC-positive cells (D); -, blank, isolated FLC-negative cells (E); (original magnification: x100). To obtain (A) biopsies were evaluated by two researchers who were naive as to the tissue status. (F-G) Expression of FLC (F) and infiltration of mast cells (tryptase staining) (G) in the same areas of tumor tissue (original magnification: x200).

70

Systems Immunology-cancer

C D C57 BI / 6 wt 2000 2000 C57 BI / 6 wt

) PBS )

3 3 1500 WBB6F1 / J-Kitw / Kitw 1500 FLC-antagonist

1000 1000

500 500 Tumor volume (mm

Tumor volume (mm

0 0 0 2 4 6 8 10 0 2 4 6 8 10 Time (days) Time (days)

E WBB6F1/ J-Kitw / Kitw 2000 PBS

)

3 1500 FLC-antagonist

1000

500

Tumor volume (mm

0 0 2 4 6 8 10 Time (days)

Figure S3.3. Mast cell activation supporting tumor growth of B16F10 melanoma. (A-B) Toluidine blue staining of mast cells in B16F10 melanoma tissue is showing peri-tumoral and intra-tumoral mast cells. All of the melanomas were isolated from B16F10 inoculated wild type mice. (C-D) Mast cell deficiency and the FLC antagonist F991 attenuate B16F10 melanoma growth. (C) Tumor growth in PBS-treated wild type C57Bl/6 (full line with circle) and mast cell-deficient WBB6F1/J-KitW/KitW-v full line with block) mice; (D) Tumor growth in wild type C57Bl/6 (full line with circle) and mast cell-deficient WBB6F1/J-KitW/KitW-v (full line with block) mice treated weekly with 20 μg F991 in PBS (blocks) or just PBS (circles) intra- tumorally. (E) Anti-FLC peptide has less or no effect on WBB6F1/J-KitW-mice.

71

Systems Immunology-cancer

72

Systems Immunology-cancer

Supplementary Table S3. 1. FLC staining in biopsies of human malignant tissue in various organs compared to staining in tissue adjacent to the tumor and normal tissue.

73

Systems Immunology-cancer

Figure S 3.4 Mast cells were found on various human cancers with increasing numbers and infiltration. Although all the cancer specimens are positive to tryptase, but strength of positivity was different. Patient samples with lung cancer show highest percentage of positivity (80%), whereas patient samples with pancreatic cancer show a lowest percentage of positivity (35%).

74

Systems Immunology-cancer

Figure S 3.5 Top-down approach used for our modeling study. We regard the link between inflammation and cancer is “a black box” in the system (top) which directs us to perform experimental studies and extensive literature mining. We break down the system according to the results of experimental studies that we carried out, subsequently refined specific subsystem (innate immunity), until its specificity is reduced to base elements such as FLC-mediated mast cell activation (down). After all, inflammation-cancer link (a black box) was able to elucidate mechanisms to realistically validate our model.

75

Systems Immunology-cancer

76

Systems Immunology-model dynamics

Chapter 4 Dynamics of a Model of Innate Immunity: Complex (bi-) Stability, Flares, Irreversible Transitions, and Sensitivities

77

Systems Immunology-model dynamics

Summary

We here implement various types of stability and sensitivity analysis to the in silico model of innate immunity that we developed in Chapter 2. Our results demonstrate that (i) the biological phenomena of acute and chronic inflammation may reflect an inherently complex bi-stability, (ii) the chronic inflammation states of the model are asymptotically stable and in some cases unique steady states, (iii) the acute- inflammation steady states of the model are asymptotically stable but not unique, (iv) some parameters impact strongly on the flipping from the acute to the chronic states but only when the system is close to the flipping point, (v) when the system is in a chronic-inflammation state, the sensitivity of TNF- to most parameters is moderate or even low and there is no transition point initiating a flip back to the acute state: the chronic inflammation branch of the model is an irrevertible attractor, (vi) adding healthy fibroblasts makes the transition from acute to chronic inflammation revertible but only at low steady activation of the innate immune response, (vii) for therapies of chronic inflammation to be definitive, fibroblast-repopulation strategies should be considered.

4.1 Introduction In engineering, modeling and simulation constitute a strong and imperative link between in vitro design and actualization. Inflammation may be one of the areas where such in silico biomedical engineering can be connected to practice, considering the multiple interventions that should be possible. Clinically, inflammation may consist of acute inflammation in which activated leukocytes perform and complete normal functions in host defense against an invading antigen. It may take this process 2 weeks to prevent and reverse disease progression [401, 402]. In some cases this acute and transient inflammation may be followed by chronic inflammation when infiltrated leukocytes continue to accumulate at the inflamed site [403- 405]. The chronic inflammation process consists first of an accumulation phase which is characterized by rapid recruitment of immune cells (i.e., macrophages, eosinophils, neutrophils, mast cells). This is followed by the activation phase, where various cytokines and chemokines are released by immune cells. Then follows a subversion phase, where the immune cells are changed in function, allowing the chronic inflammation to subside. Chronic inflammation can last many months and even years however, continuing in the absence of foreign antigen invading. This lengthy period can cause much progressive damage to normal tissues and thereby invoke numerous functional, hence financial and societal complications, involving the patient, the family, and the healthcare system [406-408]. Modeling also serves as a bridge linking theoretical immunology with its experimental outcome. For example, mathematical models based on ordinary differential equations (ODEs) have been used to study B cell responses [409, 410], T cell responses [411, 412], and Natural Killer (NK) cell mechanisms [413, 414]. From the perspective of understanding the immune response, innate immune cells have been shown to be particularly suitable for systems biology studies [415]. Functionally, the innate immune system plays an important role in effective host defense against bacterial and viral infections[416, 417]. Systems biology strategies are being developed in order to capture the roles that cellular networks and 78

Systems Immunology-model dynamics dynamical processes play in this aspect of immunity. These strategies might help to construct a systems-level understanding of the immune cells’ roles [418]. A comprehensive map of IgE-mediated mast cell activation [419] may be used to explore both intracellular and population dynamical properties of the corresponding section of the immune system. Immune cells which are involved in host defense can be isolated from living organisms, cultured in vitro, and reconstituted into another living organism in vivo. For most other multicellular systems it is much harder if not impossible to do this; for example, individual neurons can hardly be dissociated from the brain for data analysis with subsequent reconstitution into the brain in vivo. Consequently, systems biology of innate immunity may be one of the few areas where complex systems properties arising through ‘multicellular system emergence’ can be identified and understood in an integral experimental and computation analysis. Such emergent properties are likely to include the maintenance of phenotypic stability in the face of perturbations caused by infection and inflammation, resulting from both positive and negative feedback loops that together accomplish a strong yet limited response. Various software environments already exist for modeling immune system paradigms. For example, the immune cell simulator (IMMSIM) [420], the synthetic immune system [421], and the basic immune simulator [422] provide platforms for creating and then simulating the performance of virtual immune systems consisting of a variety of cell types and their interactions. Prince and Vodovotz have combined an in silico with an in vivo approach to elucidate the complexity of inflammation in CD-14 deficient mice subjected to Gram-negative lipopolysaccharide (LPS) and cannulation [423]. Nidhi et al developed a model to predict drug targets in silico. In silico identified antagonists (CCR4 inhibitor) appeared valid in an in vitro immune response model, and when subsequently tested by injection together with vaccines in vivo [424]. Rullmann et al. developed the Entelos Rheumatoid Arthritis (RA) Physiolab platform to predict therapeutic effects of drugs [425]. Separately, the National Institute of Allergy and Infectious Diseases (NIAID) established the Systems Biology for Infectious Diseases Research (SysBio) program that facilitates research lines in Systems Influenza, Systems virology, Systems Biology of Enteropathogens, and Mycobacterium tuberculosis Systems Biology [426]. Once such model simulations have been successful, the next step is to analyze their results qualitatively as well as quantitatively. The qualitative behavior of a model is often assessed by stability analysis. Parameter values and initial conditions may determine not only the dynamics of the system but also the steady state it eventually attains and therefore kinetic modelling with stability analysis is widely used for analysis of various biological models, e.g. in epidemiology, physiology and immunology. This will show whether the model predicts multiple, stable or metastable, steady, oscillatory or chaotic ‘states’. Once focus is on a certain steady state, or on the transition between such states, a sensitivity analysis will highlight whether the model’s prediction should be valid under very general conditions or only pertain to a highly specific setting, which may or may not be realistic. In addition, these two types of analysis may serve to identify the origin of the model’s non- linear behavior, at the same time detecting interactions critical to its performance. Before venturing in this direction a sufficiently detailed description of the model is required, as both stability and sensitivity analysis require differentiation of functions of state vis-à-vis

79

Systems Immunology-model dynamics parameters. Such detail is also required to make the model predictive enough to allow meaningful experimental validation. In the previous chapters we have constructed such a detailed model of innate immunity. For the purposes of this chapter, we are focusing on stability and sensitivity analyses of that model. The impact that different parameters may have on the performance of a system can be addressed by sensitivity analysis (SA). SA can thereby motivate model simplification [427], but also serves to (i) identify the factors that matter most for a specific performance measure of a network, (ii) examine for which performance function a process is most important, or (iii) determine at which time a factor is most important. It has for instance been used to evaluate the influence of maternal adaptive immunity on the time dependence of infection and its consequences for serology [428], to identify the relative importance of the molecular components in coupled MAPK and PI3K signal transduction pathways [429]. It has also been used for the NF-B signaling network [430], in an immune-based model of Helicobacter pylori infection [431], and in analyzing the T-cell response to antigen [432]. Here we present a method for performing sensitivity analyses of our kinetic model vis-a-vis the network’s response through FLC mediated innate immunity to antigen. We find a subtle balance between apparent stability and bi-stability, as well as a prevalence of moderate parameter sensitivity, except when a threshold between acute and chronic inflammation is near. We also find that fibroblasts may play a special role.

4.2. Methods Sensitivity analysis (SA): Assuming that the steady influx of CRA, maintains the network in a state that resembles an inflammatory condition, we carry out SA for the network when exposed to such a steady influx of antigen(CRA). We shall test the network’s response to changes in the level of fibroblasts and their regeneration, to the levels of cytokines, notably TNF-α, as well as to interference with immunoglobulin free light chains (FLC). Standard sensitivity analysis addresses how a single stable steady state depends on parameter (independent variables). Here we shall also address dependence on dependent variables’ initial values, multiple steady states and transitions between these [433]. We shall use standard sensitivity analysis in terms of log-log dependence of steady state variables on parameter values, as well as other ways to assess stability, including time dependence integration. Stability of steady states: In order to determine the stability of the system, numerical simulations were performed by using Copasi in two modes. In the one we asked it to compute the steady state as well as the stability properties at steady state in terms of the eigenvalues. When any eigenvalue was reported to have a positive real part, we report that the state is unstable. Every steady state computation was accompanied by a calculation of time dependence starting from that steady state as initial condition. Only when it was confirmed that the system did not evolve away from that initial state we report the state as stable. Depending on conditions we observed one or two steady states. In the latter case there often appeared to be a third, unstable steady state, which Copasi could find in its steady state mode but not in its time-dependence mode. In this chapter we are not explicit about this third state.

80

Systems Immunology-model dynamics

Often no steady state could be found due to a slow drift (e.g. Fig. 4.5A), due to stochastic re-emergence of bacterial infection (e.g. Fig. 4.1), or due to steady state variables being effectively zero, but numerically small and not zero (e.g. Fig. 4.2C&D). We did not perform a complete scan of variable initial conditions space nor of the parameter space, since it is impossible to obtain the ultimately necessary resolution when there is such high dimensionality. We can therefore not exclude that there are more than three steady states in the model for any given values of CRA influx rates. The model and a caveat: This chapter will use the model of innate immunity developed in Chapter 2. That model constitutes an attempt to be realistic in terms of elemental mechanisms and parameter values. Parameter values and kinetic equations are not phenomenological but refer to what is or could be true, or at least possible, e.g. in the sense of diffusion limitation. One of the consequences is that also in the present chapter many phenomena will be expressed in terms of apparently precise values of dependent and independent variables/parameters. These apparently precise magnitudes have no meaning other than that they enable us to be specific and clear in the presentation and discussion of our results. 4.3. Results 4.3.1. Instability, flares of infection and a therapy thereof Immunity to microbial infections is of great survival value to the human. The model of innate immunity we developed in Chapter 2 was able to deal with in silico bacterial infections (Fig. 2.2). Infecting bacteria grew quasi exponentially until they were overrun by an even faster growing immune response causing a virtually complete decrease in bacterial cell count. The model did not attain a stable steady state however (Fig. 4.1A); the steady state reported had a single real eigenvalue that was positive: 0.005 /min. This resulted in spurious resurgence of the infection in the in silico model, which only became evident when we ran the model for appreciable duration. Bursts of bacterial growth then arose at times long after the infection appeared to have been dealt with (Chapter 2). In the model the bacteria were lysed by proteases produced by activated mast cells. This activation was part of a positive feedback loop and should vanish upon disappearance of the bacteria. This produced the paradoxical situation that the regulation that was eliminating the bacteria might disappear when the density of the bacteria became low, enabling the bacterial population to re-emerge. We now focus on this type of instability and on the definitive management of such a bacterial infection. We consider a steady influx of protease as an anti-bacterial force in the system. We expect that a steady protease influx (in addition to the protease produced by activated mast cells) should reduce the maximum level of infection and get rid of it more quickly and more definitively. Fig. 4.1B-D shows that our expectations were only partly correct: the amplitude of the bacterial infection was reduced by the extra protease, but now multiple bursts of bacterial growth occurred within the time span considered, at a frequency that increased rather than decreased with the magnitude of the protease influx. The highest influx rate of protease that we tested was detrimental to the bacteria and produced a steady state without recurrence (Fig. 4.1E). At this protease influx rate, the steady state solution only had eigenvalues with negative real parts (the largest was -0.0005/min). 81

Systems Immunology-model dynamics

4.3.2. Bimodal sensitivity analysis of innate inflammation CRA is secreted by fibroblasts that are activated by TNF-.In Chapter 2, we found that up to a CRA influx rate of 17 fM/min the inflammation level increased only slightly (if at all) with increasing CRA challenge (see the red dashed line in Fig. 2.4A). At 17 fM/min it jumped up, as witnessed by a strong increase in TNF-α levels and a strong decrease in fibroblast levels. A further increase in CRA challenge hardly aggravated the inflammation. However, when the CRA influx rate was reduced, the system stayed in the high inflammatory state and only returned to longer lived states with low levels of TNF-α for very low CRA challenge (Fig. 2.4). It turned out that at some magnitudes of the CRA influx, there were two alternative stable steady states: a case of dynamic bi-stability [434, 435]. (See below for a numerical qualification of the word ‘stable’ for the acute branch of steady states).

A B P r o t e a s e in f lu x 0 ( u n s t a b le ) P r o t e a s e in f lu x 0 .0 0 1 f M /m in ( u n s t a b le )

8 0 8 0 )

) 6 0 6 0

M

M

f

f

(

(

a

a

i

i r

r 4 0 4 0

e

e

t

t

c

c

a

a

B B 2 0 2 0

0 0 0 1 2 3 4 5 0 1 2 3 4 5 T im e ( d a y s ) T im e (d a y s )

C D P r o t e a s e in f lu x 0 .0 0 3 f M /m in ( u n s t a b le ) P r o t e a s e in f lu x 0 .0 0 5 ( u n s t a b le )

8 0 8 0 )

) 6 0 6 0

M

M

f

f

(

(

a

a

i

i r

r 4 0 4 0

e

e

t

t

c

c

a

a

B B 2 0 2 0

0 0 0 1 2 3 4 5 0 1 2 3 4 5 T im e ( d a y s ) T im e ( d a y s )

Figure 4.1 Resurgence of bacterial infection E

8 0 P r o t e a s e in f lu x 1 .0 f M /m in ( s t a b l e ) in the in silico model of innate immunity: The

model that produced Fig. 2.2 was re-run at )

6 0

M f

( various magnitudes of now steady influx of a

a i r protease that killed bacteria. Protease influx

e 4 0

t c

a was (A) zero, (B) 0.001 fM/min, (C) 0.003 B 2 0 fM/min, (D) 0.005 fM/min or (E) 1.0 fM/min.

0 Only for E a stable steady state was 0 1 2 3 4 5 T im e ( d a y s ) calculated, with the largest eigenvalue equaling -0.0005 /min.

82

Systems Immunology-model dynamics

We performed a sensitivity analysis for each branch to examine if the model exhibited these features only at the particular parameter values we used when computing Fig. 2.4, or these features should be considered to be more generic. Our sensitivity analysis used Copasi and thereby presented us with the sensitivity of any steady state variable with respect to all model parameters. With one exception, we found all sensitivity coefficients referring to the dependence of fluxes on rate constants to be ‘moderate’, where ‘moderate’ refers to absolute values not far beyond 1, 1 being the magnitude of total control of any flux by all activities [436].

A B Acute mode Acute inflammation 15 15

10 10 production rate constant

5  5

_CRA influx rate constant

_TNF-  0 0

R ^TNF- 0.001 0.01 0.1 1 10 100 0 10 20 30 R^ TNF- CRA influx (fM/min) CRA influx (fM/min)

C D Chronic mode Chronic mode 2.0 2.0

1.5 1.5

1.0 1.0

production rate constant 

_CRA influx rate constant 0.5 0.5

_TNF-  0.0 0.0

R ^TNF- 0.001 0.01 0.1 1 10 100 0.001 0.01 0.1 1 10 100 R^ TNF- CRA influx (fM/min) CRA influx (fM/min)

Figure 4.2 Bimodal sensitivity analysis of the innate inflammation model. A first mode of sensitivity coefficients of steady state magnitudes of TNF-α with respect to (A) CRA influx rate and (B) TNF-α production rate constant was obtained when starting from a steady state at a very low CRA influx rate (0.001 fM/min) and then increasing the CRA influx to the level indicated on the abscissa (this procedure defines the ‘acute inflammation branch’ of the model; cf. dashed lines in Fig. 2.4). A second mode of sensitivity coefficients of steady state magnitudes of TNF-α with respect to (C) CRA influx rate (see also Chapter 2) and (D) TNF-α production rate constant, was obtained when starting from a steady state at high CRA influx rate (30 fM/min) then decreasing the CRA influx to the level indicated on the abscissa (this procedure defines the ‘chronic inflammation branch’ of the model; cf. full line in Fig. 2.4). Sensitivity coefficients were calculated for the CRA influx value corresponding to each (red) point. The (blue) straight lines connect these computed points. The exception was that for the acute mode of inflammation: Multiple sensitivity coefficients attained high absolute magnitudes at and immediately around the state corresponding to the one at 16.7 fM/min of CRA influx (see Fig. 4.2): Fig.4.2A shows that whilst TNF-α levels were starting to be sensitive to the zero order reaction rate constant of CRA influx when the influx rate ranged between 0.001 to 1.0 fM/min, they became highly sensitive when the influx rate approached its threshold level (Chapter 2) of 16.7 fM/min. At this CRA influx rate there is a singularity where the response coefficients amounted to infinity. This 83

Systems Immunology-model dynamics corresponds to the vertical jump observed for the acute inflammation branch in Fig. 2.4 A for the dashed line. For different values of some of the other parameters, this singularity should be expected to exist at different CRA influx rates. These were the results of the sensitivity analysis for the acute mode of inflammation. Fig. 4.2C shows that for the chronic mode of inflammation, the TNF-α level maintained its sensitivity to the reaction rate constant of CRA influx at levels below 2 when the influx rate ranged between 0.001 and 0.1 fM/min. Here there were no singularities. Fig. 4.2D shows that in this chronic mode, TNF-α levels are proportionally sensitive to the reaction rate constant of TNF-α production throughout the full range of CRA influx rates. Data analysis generated sensitivity coefficients for all species in the model with respect to the reaction rate constants. Supplementary Table S.1 shows the subset of sensitivity coefficients that are associated with the level of CRA influx in acute inflammation. Expected summation laws [437] were obeyed except when variables were close to zero, which caused numerical inaccuracy problems. 4.3.3. Bi-stability, irreversible transition and fata switching in innate inflammation The singularities in the response coefficients found in the previous section corresponded to a sudden transition from a hardly inflamed (‘acute’) branch of states to a highly inflamed (‘chronic’) branch of states. We showed that, for instance at CRA influx rates around 14 fM/min, the system could be attracted into either of these two types of state depending on the state they were coming from, as witnessed by the TNF- levels (Fig. 2.4A). The situation is reminiscent of a bi-stable dynamic system [436] in which there are two stable steady states for a given set of parameter values. Which of these is attained then depends on the system’s starting point. When the system resides in one state it then requires a substantial (i.e. more than infinitesimal) perturbation of the state variables [438], for the system to cross into the other steady state. Or, a parameter should be shifted down and then back up, or vice versa. We set out to examine whether this type of bi-stability pertains to our model of innate immunity. We first examined the stability of the states in both branches of Fig. 2.4A, i.e. in both the acute inflammation and the chronic inflammation branches. The stability analysis by Copasi reported that the states at the acute branch were stable. The chronic branch presented Copasi with numerical problems: no stable steady state could be found. When we analyzed this in detail we found that the fibroblast level had become extremely close to zero and was fluctuating during the calculation. To enable Copasi to compute the steady states we therefore fixed the fibroblast level to zero for the computations of the chronic branch (but see below). With this, all the states at the chronic branch were also reported by Copasi to be stable steady states. Fig. 4.3A plots the real parts of the largest of the reported eigenvalues, for both branches. They were indeed all negative, although the acute states were much less stable than the chronic branch. We then performed what we call ‘perturbation stability analysis’: we took the reigning steady state variables as initial conditions and calculated a time dependence. In all cases the system stayed put. Then we changed the TNF- levels to the value indicated by the beginning of each arrow in Fig. 4.3B and again allowed the system to evolve. The arrow head shows the TNF- level the system then evolved to. In all cases this was the original state. This was even so when we added TNF- such that the initial state had a 84

Systems Immunology-model dynamics

TNF- level of 100, i.e. well above the TNF- values associated with the chronic branch. Fig. 4.3C shows the corresponding results for the chronic branch, also confirming its stability. Even if at the steady state the TNF- level was suddenly reduced to 0.001, i.e. well below the level typical for the acute low inflammatory state, the system still evolved back to the high inflammation state. We conclude that both the acute and the chronic steady states of the model are exquisitely stable to fluctuations in the in TNF- level. For an example of a simple bi-stable system Fig. 4.3D gives the dependence of a state variable (such as a concentration) y on a bifurcation parameter p. The solid parts of the line correspond to the two stable branches and the dashed part that is not accompanied by such a solid line, to the unstable branch. Such a bi-stable system is hysteretic: If the initial steady state of the system is the one on the full line at the bottom left and the value of the parameter p is increased, then the new steady state is the point on the full line for the corresponding p- value on the abscissa. Upon iteration of the operation of increasing p and calculating the new steady state a number of times, the line turns around and becomes a line that is dashed because it refers to instability. Above that line a full line exists for the other stable branch. For further increasing values of p the system’s steady states are indicated by that full line; the steady state values of y continue to correspond to the rather high values given by that full line. ysteady state has jumped (often following the upward arrow) from fairly low to fairly high values. If one now takes one of the steady state points achieved for values of p above the ‘jump point’ and then reduces the magnitude of p, the system’s steady states slide along the upper branch of the solid line, i.e. they do not retrace the original path. Here for any single value of p two stable and one unstable steady state value exists. Where the upper line folds back, the steady state y values fall back (following the downward arrow) to the lower line and follow the lower line to the left. We wondered whether innate inflammation as captured by the model of Chapter 2, corresponds to a bi-stable system of the type of Fig. 4.3D. Fig. 2.4A is partly but not wholly similar to Fig. 4.3D: it jumps up at the right hand side, but there seems to be no drop down at the left when returning from high to low CRA influx levels following the chronic inflammation branch. Perhaps the turnaround of the upper branch only occurs theoretically, for negative values of CRA influx (as illustrated in Fig. 4.3E). Then the returning of the upper branch should not really lead to the same TNF- levels at low and even zero CRA influx as the lower branch, i.e. the system should be bi-stable also at the lowest CRA influx rates. In this case the upper branch of steady state should always reside above the lower branch (see Fig. 4.3E). In Fig. 4.3F we plot the variation of TNF- with CRA influx including for very low values of the CRA influx for both the acute and the chronic branches, and this does not confirm this interpretation: the returning of the system along the chronic branch led to the TNF- levels to become equal for the two branches at the same CRA influx rate, but then, for even lower CRA influx rates, the two branches did not merge but crossed. We conclude that the innate immune system, as modelled by us, does not correspond to classical bi-stable systems of the type of Fig. 4D-E. We then returned to the system that we had first brought to the chronic mode of inflammation by increasing the CRA influx from 0 to 30 fM/min and then brought back by switching the CRA influx back to 0.001. For that system (in silico) we then increased the CRA influx in steps, expecting the system to settle on steady states corresponding to the dashed curve of Fig. 2.4A, i.e. on the acute branch. As shown in Fig. 4.3G, it did not, 85

Systems Immunology-model dynamics however: it stuck to the chronic branch (the solid curve in Fig. 2.4A). Apparently the upper branch of chronic inflammation is not hysteretic. Once the inflammation was chronic, it was bound to stay chronic. This does not mean that the TNF- levels are always high for this branch, but it does mean that at intermediary CRA influx rates they were much higher than for the acute branch (Fig. 2.4A). What we are witnessing here, is a so-called irreversible transition [439] from one attractor (the acute branch) to another (the chronic branch) but apparently not one that follows the classical picture of Fig. 4.3D or E. What kind of dynamics is it then? The innate immunity system is complex and more than one variable is involved in the regulatory loops. As did the dashed line in Fig. 2.4D, Fig. 4.3H shows how for the acute branch the level of a second system variable, i.e. the level of fibroblasts, varies with the CRA flux. Starting from the original state where the number of fibroblasts is high, that level slowly decreases until it suddenly drops to a much smaller level, because the system cannot follow the unstable branch indicated in red. The drop from what we call the acute branch of steady states to what we call the chronic branch, coincides (occurs at the same CRA influx rate) with the sudden jump of TNF- in Fig. 2.4A and Fig. 4.3F. As did the solid line in Fig. 2.4D, the solid blue line in Fig. 4.3H shows the result obtained when we reduced the CRA level in steps back to zero, for each step again calculating the steady state fibroblast level. The fibroblast levels we calculated were zero except for numerical inaccuracies. When we again backtracked and began increasing the CRA influx, we found that the steady state fibroblast levels remained zero: also in terms of the fibroblasts, the chronic state was non-revertible, a phenomenon also noted in Fig. 4.3G whilst inspecting the TNF- level. Also here we then carried out ‘perturbation stability analysis’, i.e. we calculated the steady state fibroblast level for the acute branch and then examined the effect of a substantial perturbation in the initial fibroblast level upon the final steady state. In this case we did not fix the fibroblast levels but allowed them and all other variables to evolve. We found that the system would evolve either to the acute or to the chronic state defined by that CRA influx rate, with either of these two steady states being substantially stable to fluctuations in TNF- level. To which of these two states the system would evolve depended on the initial fibroblast concentration (all other variables’ initial conditions were kept at the original steady state value). For initial concentrations below what we shall call the ‘wobble level’ all states evolved to lower fibroblast levels, and for initial values above this wobble value, they evolved to the acute branch. For systems beginning at the acute branch the wobble states were quite close to the unstable steady states; systems in acute inflammation could be brought to chronic inflammation by fluctuations in fibroblast level that would bring them just a wee bit further down than the concentration corresponding to the unstable steady state. With respect to the transition from acute to chronic and with the fibroblast level as bifurcation variable, the system did behave like the classical bi-stable system of Fig. 4.3D. This is illustrated further by the ‘fate diagram’ of Fig. 4.3I, which indicates the evolution subsequent to a perturbation of the fibroblast concentrations in an acute steady state. For CRA influx rates below 10 fM/min, the fluctuation in fibroblast level away from the acute branch needs to be substantial for a transition to the chronic branch to ensue. For higher steady CRA influx rates, smaller fluctuations could cause the system to slip into the chronic inflammation state. For CRA influx rates above 16.8 fM/min, no acute-inflammation steady

86

Systems Immunology-model dynamics states exist anyway and all stable states correspond to chronic inflammation. A transition from the acute to the chronic inflammation branch of our model is entirely feasible. When starting from a chronic steady state the stability situation was highly different. Again we scanned the initial fibroblast concentrations for the steady state they would lead to. Now, almost all initial fibroblast levels led back to the chronic state (Fig. 4.3K). Only at very low CRA influx rates, very high increases in fibroblast levels could revert the system back from chronic to acute: the chronic inflammation state is highly robust, even to increases in fibroblast levels well beyond those of the acute branch. Taking into consideration that a 1000 fM concentration of fibroblasts corresponds to a tissue full of fibroblasts (Chapter 2), Fig. 4.3K implies that at CRA influx rates beyond 0.9 fM/min in our model, the chronic inflammation state is irreversible; fluctuations in fibroblast level that would lead back to the acute inflammation state would be higher than physically possible. As always in this study, we cannot take the values of parameters at which phenomena occur too literally, but our computation may be taken to mean that there should be a level of steady innate immune system stimulation above which a chronic inflammation becomes a self-sustaining system, a chronic inflammation indeed.

87

Systems Immunology-model dynamics

H

88

Systems Immunology-model dynamics

I

J

89

Systems Immunology-model dynamics

K

Figure 4.3. Complex stability properties of the innate inflammation model. Computations using the model of Fig. 4.2 started from the 0.001 CRA-influx state for the acute branch, or from the 30 fM/min CRA influx state for the chronic branch. The CRA influx rates were then altered to the value indicated on the abscissa and a new steady state was calculated. (A) The largest real part of all eigenvalues for the chronic and the acute mode. For the chronic mode fibroblast levels had been fixed to zero. The dotted line at the top represents zero. (B) The steady state variable levels for the acute state were taken as initial conditions with the exception of the fibroblast level, which was set to 100 or 0.00001 fM, and the system’s evolution to steady state was simulated in time, the final level of TNF-a being displayed by the arrowheads in the figure. Taking the new values as initial conditions, the steady state mode of Copasi was used to confirm it as stable and steady. (C) As B but now for the chronic states. (D) and (E) examples of standard bi-stable systems with bifurcation parameter p and bifurcation variable y. Stable steady states are covered by the solid lines, unstable steady states by the dashed line, and discrete transitions between the two by the arrows. p is usually positive definite, the area left of the ordinate in (E) is thereby unrealistic: (E) is home to an irrevertible transition from acute to chronic in that not even the maximum possible decrease of p (to zero) reverts the system from the upper to the lower branch of steady states. (F) The TNF- levels of the acute (in red) and chronic (in blue) branches computed over an extended range of CRA influx rates. (G) Lack of hysteresis within the chronic branch: The point of 0.001 CRA influx of the chronic branch was computed (by starting from the point at 30 fM/min and then reducing the CRA influx stepwise to zero (as indicated by the green dashed line; using the time dependence mode of Copasi to compute the steady state). Subsequently the CRA influx levels were increased (as indicated by the red dotted line) and steady states were again computed with the TNF- levels being shown again on the blue line. The variation of TNF- with CRA influx levels for the downward and upward trajectories of CRA influx were identical; the two lines coincide. (H) Bifurcation diagram: For acute states the steady state was calculated and the final variable values reported as the green points constituting the upper (acute) branch of the figure. It was validated by using Copasi’s steady state functionality that these states were stable. The steady state variable values were then used as initial conditions for the next calculation, except that the value for 90

Systems Immunology-model dynamics the fibroblast levels was scanned. For each of these initial fibroblast values the steady state was calculated by using Copasi’s steady state functionality. When the steady state found was reported to be unstable, it was denoted here as one of the red points that define the red line of unstable steady states. The steady state conditions were made the initial conditions and the time dependence was computed to show that the state variables did not change with time. As indicated by the arrows, it was then verified that increasing the unstable-steady-state level of the fibroblasts only a little, already produced an evolution to the upper stable steady state, whilst a minor decrease in the fibroblast level induced an evolution to the lower (zero) steady state. (I) Wobble states and steady states: As H, but now also so- called ‘’wobble’’ states were computed for the chronic branched (yielding the dashed red line) and the acute branch (yielding the red squares). Starting at the chronic steady state, only the initial value of the fibroblast level was scanned calculating the subsequent time evolution, which always ended either at the upper or at the lower stable steady state. The initial fibroblast level that demarcated the two initial fibroblast levels was noted as a ‘chronic wobble state’ (defining the red dashed line), a kind of operational unstable steady state but different from the unstable steady state calculated in H. Starting at the acute steady state, only the initial value of the fibroblast levels was scanned and the demarcation point was determined similarly and indicated by a red square. These ‘acute wobble states’ reside close to but are not identical to the unstable steady states indicated by the blue dashed line. (J) Fate diagram for perturbed acute states. The stable acute states were perturbed by changing the initial levels of fibroblasts to values indicated by the beginnings of the dashed arrows (some of these were taken just above or just below the wobble points of (I)) and the system was the allowed to evolve. The end points are indicated by the arrow heads. (K) as (J) but for the chronic states; all initial states above the red dashed line of chronic wobble states relaxed to the upper stable branch of acute inflammation, whereas all states below that dashed red line relaxed to the lower chronic branch of steady states, even if they thereby crossed the acute stable branch. In all cases, properties were calculated for various values at the abscissa and the results were connected by straight lines.

4.3.4. Jumping the bi-stability barrier: fibroblast therapy of innate inflammation In Chapter 2 we examined how anti-FLC peptide might be able to revert chronic to acute inflammation. We found that peptide added in a single dose could accomplish this, but it could only do this temporarily: after some time, the TNF- level increased back to the level of chronic inflammation. The results of the preceding section suggest an explanation: the system is trapped in the stable chronic-inflammation state. The anti-FLC peptide reduces the inflammation, but it seems impossible for the system to escape the attractor of the chronic state, as illustrated by Figs. 4.3C and 4.3K. Fig. 4.3H suggests what should be done to break this stalemate of being caught in the chronic state: add live fibroblasts to the system. Fig. 4.3K however tames the optimism: only at low CRA influx rates, this should be expected to work. Yet, Fig. 2.5E-F already showed that adding both anti-FLC peptide and fibroblasts to the chronically inflamed system could redress the inflammation definitively. The above section suggests that the addition of fibroblasts alone might be able to rid the system permanently of chronic inflammation, be it only at low CRA influx rates. In order to examine this possibility we added a sustained fibroblast influx to our model, mimicking steady infiltration of an inflamed site with fibroblasts or the division of fibroblasts at the edges of the wound. Fig. 4.4A shows that a low fibroblast influx rate (10-5 fM/min) has no effect on acute inflammation, whereas (Fig. 4.4B) a thousand-fold higher fibroblast growth rate did suppress acute inflammation. Fig. 4.4C shows that the allowing fibroblasts to grow

91

Systems Immunology-model dynamics into the system at a low rate had a temporary effect on chronic inflammation, whilst (Fig. 4.4D) a high fibroblasts ingrowth had a permanent effect on chronic inflammation. We also scanned the magnitudes of the CRA influx rate at the same time as the fibroblast ingrowth or infusion rate, again calculating the steady-state TNF- level for each combination. The results are shown in heat maps: Fig.4.4E represents TNF-α response to the ingrowth of health fibroblasts in acute inflammation and Fig.4.4F represents TNF-α response to the ingrowth of healthy fibroblasts in chronic inflammation. Understandably the effect of the fibroblasts is the greatest at the intermediate levels of CRA influx when there is a vast difference in inflammatory state (TNF-a level) between the acute and chronic inflammation states. Returning to the effect of anti-FLC peptide alone (Fig. 2.5), we checked whether indeed the state produced by addition of the peptide was stable or unstable. Fig. 4.5A is the same as Fig. 2.5A except that it now indicates this stability explicitly (by‘s’ or ‘u’). The unstable point in Fig. 4.5A shifts to a stable point in Fig. 4.5B but only after prolonged calculation when the effect of the peptide drug has evaporated and TNF- has returned to its original, high level. Figs 4.5B and Fig. 4.5D show that if also fibroblasts are added, the state that is achieved is stable early on, leading to a sustained suppressive effect of the peptide on the inflammation in silico. Figs. 4.5E and F show that steady fibroblast influx makes the drug effect even more readily persistent. We conclude that the addition of influx of fibroblasts stabilizes the state induced by the peptide drug and that peptide therapy should be combined with a therapy that stimulates fibroblast ingrowth. Alternatively, mere infiltration with healthy fibroblasts alone should be able to reduce the strength of the inflammation and even to turn chronic into acute inflammation. Of peptide addition and fibroblast activation the latter may provide the more definitive cure.

92

Systems Immunology-model dynamics

A A c u te in fla m m a tio n

+ f ib r o b la s t in g r o w t h - f ib r o b la s t in g r o w t h

6 0 )

4 0

M

f

(

- 0 .0 0 0 0 1 f M /m in

α (fM) α

-

F

N TNF T 2 0

0 0 .0 0 1 0 .0 1 0 .1 1 1 0 1 0 0 CRAB A FinfluxF in f l(fM/min)u x (fM /m in )

B A c u te in fla m m a tio n

+ f ib r o b la s t in g r o w t h - f ib r o b la s t in g r o w t h

6 0 )

4 0

M

f

(

-

 0 .0 1 fM /m in

F

N

α (fM) α -

T 2 0 TNF

0 0 .0 0 1 0 .0 1 0 .1 1 1 0 1 0 0 BCRAA F F influx in flu x (fM/min) (fM /m in )

93

Systems Immunology-model dynamics

CC C h ro n ic in fla m m a tio n

+ f ib r o b la s t in g r o w t h - f ib r o b la s t in g r o w t h 6 0

0 .0 0 0 0 1 fM /m in )

4 0

M

f

(

-

α (fM) α

-

F

N T TNF 2 0

0 0 .0 0 1 0 .0 1 0 .1 1 1 0 1 0 0 B A F FCRA in f influxlu x ( f(fM/min)M /m in )

D C h ro n ic in fla m m a tio n

+ f ib r o b la s t in g r o w t h - f ib r o b la s t in g r o w t h 6 0

0 .0 1 fM /m in

) 4 0

M

f

(

-

F

α (fM) α

- N

T 2 0 TNF

0 0 .0 0 1 0 .0 1 0 .1 1 1 0 1 0 0 B ACRAF F iinfluxn flu x (fM/min)(fM /m in )

94

Systems Immunology-model dynamics

E TNF-α

Fibroblast ingrowth and TNF-α in acute inflammation

R. CRA influx rate R.CRA influx

Influx rate of fibroblast ingrowth (Log 10)

F TNF-α

Fibroblast ingrowth and TNF-α in chronic inflammation

R. CRA influx rate R.CRA influx

Influx rate of fibroblast ingrowth (Log 10)

95

Systems Immunology-model dynamics

Figure 4.4. Healthy fibroblast therapy of inflammation, in silico. (A and B) For each CRA influx rate in the acute inflammation mode the steady-state level of TNF- was calculated (cf. Fig. 2.4A, dashed line), but now in the presence of an extra influx of healthy fibroblasts at a rate of (A) 110-5 fM/min or (B) 0.01 fM/min, or in the absence thereof (the blue curves). (C and D): as above but then for the chronic inflammation branch. (E) Steady state calculations for the acute inflammation branch for various fixed rates of fibroblast influx and CRA influx, represented as heat map, the color reflecting the steady state level of TNF-. (F) As E but for the chronic inflammation branch.

A C h r o n i c i n f l a m m a t i o n

D r u g

6 0 3 . 0 i n f l u x 0 . 5 i n f l u x s

0 . 1 i n f l u x )

) s

4 0 s

M

f

(

-

α (fM) α -

F u s

N TNF T 2 0

u s

0 0 . 0 0 1 0 . 0 1 0 . 1 1 1 0 1 0 0 B CRAA F F influx i n f l u (fM/min)x ( f M / m i n )

B C h r o n i c i n f l a m m a t i o n

D r u g + f i b r o b l a s t

6 0 3 . 0 i n f l u x 0 . 5 i n f l u x s

0 . 1 i n f l u x )

) s

4 0 s

M

f

(

- 

F u s N

T 2 0

s 0 0 . 0 0 1 0 . 0 1 0 . 1 1 1 0 1 0 0 B A CRAF F iinfluxn f l u x (fM/min) ( f M / m i n )

96

Systems Immunology-model dynamics

C C h r o n i c i n f l a m m a t i o n

D r u g 6 0 3 . 0 i n f l u x 0 . 5 i n f l u x s s 0 . 1 i n f l u x

) s ) s s

4 0

M f

( s

-

α (fM) α

F

-

N α (fM) α

T 2 0

TNF

- TNF

0 0 . 0 0 1 0 . 0 1 0 . 1 1 1 0 1 0 0 B A FCRAF i influxn f l u x (fM/min)( f M / m i n )

D C h r o n i c i n f l a m m a t i o n

D r u g + f i b r o b l a s t f o r l o n g e r p e r i o d 6 0 3 . 0 i n f l u x 0 . 5 i n f l u x s s 0 . 1 i n f l u x

) s ) s s

4 0

M

f

(

-

F

α (fM) α

- N

T 2 0 TNF

s 0 0 . 0 0 1 0 . 0 1 0 . 1 1 1 0 1 0 0 B ACRAF F influx i n f l u x(fM/min) ( f M / m i n )

97

Systems Immunology-model dynamics

E C h r o n i c i n f l a m m a t i o n

F i b r o b l a s t i n g r o w t h + d r u g

6 0 3 . 0 i n f l u x 0 . 5 i n f l u x s

0 . 1 i n f l u x s

) )

4 0 s

M

f

(

-

F

α (fM) α

- N

T 2 0 TNF

s s s 0 0 . 0 0 1 0 . 0 1 0 . 1 1 1 0 1 0 0 B A CRAF F iinfluxn f l u x (fM/min) ( f M / m i n )

F C h r o n i c i n f l a m m a t i o n

F i b r o b l a s t i n g r o w t h f o r a l o n g e r p e r i o d

6 0 3 . 0 i n f l u x 0 . 5 i n f l u x s s

0 . 1 i n f l u x )

) s

4 0

M

f

(

-

F

α (fM) α

N -

T 2 0 TNF

s s s 0 0 . 0 0 1 0 . 0 1 0 . 1 1 1 0 1 0 0 B ACRAF F influxi n f l u x(fM/min) ( f M / m i n )

Figure 4.5. Time dependence of anti-FLC peptide therapy with and without fibroblast supplementation. The steady-state chronic-inflammation level of TNF-α and all other systems variables were computed for CRA influxes of 0.1 fM/min, 0.5 fM/min and 3.0 fM/min each time starting from the initial state at 30 fM/min (see Fig. 2.4). Then drug (anti-FLC peptide) was added to a concentration of 1nM as indicated fibroblasts were added to a concentration of 5fM, or fibroblast influx as started at a rate of 0.01fM, and the time evaluation of the system was followed and the TNF-a level attained after 15 days (A, C, E) and after 2 months (B, D, F) were calculated and reported in the figure. If the TNF- level subsequently changed with time, the level was annotated by ‘us’ for ‘unstable’. If it did not change with time and the same value was attained after taking the variable values as initial values and performing a steady state computation with Copasi reporting the steady 98

Systems Immunology-model dynamics state to be stable, it was annotated with ‘s’. (A) Monotherapy, the use of single anti-FLC treatment, TNF-a as computed after 15 days; (B) Dual therapy, anti-FLC drug plus the implantation of healthy fibroblasts; (C) Monotherapy as in (A) but after the longer simulation; (D) Synergic therapy after a longer simulation, anti-FLC drug plus the implantation of healthy fibroblasts; (E) Synergistic therapy, anti-FLC drug plus ingrowth of healthy fibroblasts; (F) Synergic therapy after a longer simulation, anti-FLC drug plus an ingrowth of healthy fibroblasts; The red open circles represent TNF-α levels before drug treatment and the black open circles represent TNF-α levels after drug treatment. The green arrows indicate the change of TNF-α level caused by the drug. The blue line was computed as in Fig. 2.4D and represents the chronic-inflammation steady-states of the model.

4.4. Discussion In Chapter 2 we developed a model that describes the role played by the innate immune system in inflammation. The model appeared to have a preference for either of two families (branches) of states, which we associated with acute and chronic inflammation. These states were characterized by low and high levels of TNF- respectively. The system we modelled exhibited little preference to persist outside of these states; it avoided intermediate levels of TNF-. In the present chapter, we employed stability and sensitivity analysis to examine what determines these preferences, how strong they are, what they imply with respect to dynamic and hysteretic properties of inflammation-associated disease, and whether these preferences and their implications are realistic. A pertinent example of how stability analysis can help understand immune-system related pathologies is the work by Murase et al, which reported that pathogens can destabilize the interior balance of a model that represented pathogen-immune interaction dynamics [440]. In pharmacokinetics, steady state drug effect models have been used to estimate robustness of predicted for drug-dose response relationships, and this step was considered essential to Phase 2 drug development [441].

4.4.1. Diverse stabilities of a model of innate immunity Chapter 2’s model of innate immunity exhibited various features that were at odds as compared with the usual ‘watchmaker’ models of metabolic pathways [241, 243, 245, 442]. For instance, for the same parameter set, more than one steady state was found when the CRA influx rate was around 10 mM/min and treatment with anti-FLC peptide drug appeared to be effective in a first virtually-steady state, whilst the therapy was modelled to be ineffective after a much longer time. The present chapter has clarified this: First, we verified that the model is home to two branches of stable steady states, which indeed overlap in terms of corresponding parameter values, i.e. bi-stability was occurring. Second, we found that the model is somewhat out of the ordinary. Redressing the CRA influx, which at high magnitudes had caused a jump in TNF- level to a magnitude characteristic of the chronic inflammation branch of states, all the way back to zero, did not cause the system to jump back to the acute branch. The transition from acute to chronic is what we call an irrevertible transition (which in the literature tends to be called an irreversible transition, which is a term that does not comply with the non-equilibrium thermodynamics meaning of the word; [438]. Such irrevertible transitions are known to occur in some bi-stable systems [443] such as the one depicted in Fig. 4.3E. We established however, that our innate inflammation model did not correspond to the case of Fig. 4.3E: the level of TNF- for the acute branch model and 99

Systems Immunology-model dynamics that for the chronic branch neither merged at low values of CRA influx as they do in simple case of bi-stability (Fig. 4.3D), nor stayed apart from each other as they do in the simple case of bi-stability with an irreversible transition (Fig. 4.3E). They actually crossed each other (See Fig. 4.3F). Looking at how for unstable steady states the fibroblast level varied with CRA influx rate for the model, we found that this was more similar to the case of the irreversible transition of Fig. 4.3E (see Fig. 4.3H and J). The complexity of the irreversible transition was greater than this however. The actual transition point form acute to chronic and much more so form chronic to acute did not correspond to the magnitude of the fibroblast concentration at the unstable state. It was different certainly for the latter transition; we called these transition points ‘wobble points’ (Figs 4.3I). The reason behind the transition from chronic back to acute being determined neither by the TNF-α nor by the fibroblast unstable steady state value, is that the bifurcation is determined by multiple state variables rather than one: we witness a complexity in our innate immunity model that derives from its multidimensionality, with multiple dimensions determining fate. In the computations of the chronic branch of states, a numerical problem had been occurring. The Copasi software reported that it could not compute the steady states. Accordingly, we could not use its stability analysis facility either. We then calculated the evolution of the system with time and found that it always evolved to the same values for the state variables TNF- and fibroblast level. The fibroblast level was extremely close to zero, but of course in a digital computer it is possible but unlikely to arrive at the true zero in a computation. Concluding that the steady state value of fibroblasts should be equal to a true zero, we fixed it to true zero in most of our subsequent computations. We did the same for the dying fibroblasts. This then enabled Copasi’s computation of the steady states which it reported to be stable, calculating the eigenvalues. (To enable Copasi to compute the steady states, it sufficed to set the relevant variable to zero; there was no need to fix them, but for clarity we did). This procedure however, might have compromised our inference that the chronic branch consisted of stable steady states. We therefore took to an alternative method of validating the stability of these steady states: we computed the steady state, took the values of all variables as initial conditions for our subsequent calculations, then perturbed the fibroblast level away from zero, and calculated the subsequent time dependence. For small perturbations of the fibroblast level, we observed that the model returned asymptotically to the zero fibroblast state, making that state asymptotically stable in the sense of Lyapunoff [438], i.e. confirming the stability of that state (Fig. 4.3I). For more substantial perturbations of the fibroblast level the system evolved to the steady state for that CRA influx that resides on the upper, chronic branch (See Fig. 4.3J). Now the system became more similar to standard bi-stable systems of the types illustrated by Figs. 4.3D and E, with a branch of states (the red wobble states in Fig. 4.3J) in between, above which the system evolved to the chronic branch. This line connecting the red wobble squares in Fig. 4.3J, almost corresponds to the unstable steady states (the dashed blue line). For the transition from the chronic back to the acute state however, the corresponding wobble points were far away from the unstable steady states, again (see above) witnessing the complexity of the model and perhaps a complexity of innate immunity itself. We conclude that the system of innate immunity is one of a complex type of bi-stability: The transitions between the two branches are not completely irrevertible. Certain variables more and not others can be used to push the system back from the chronic to the acute state 100

Systems Immunology-model dynamics and then only to a limited extent. The fibroblast level was one of the former types of variables and the TNF- appeared to be one of the latter types. The chronic inflammation branch of states is robust however to ordinary fluctuations and perturbations, requiring very special perturbations for therapies to be successful (see Chapter 2). To the extent that our model corresponds to reality, this conclusion could have important implications for the management of innate immunity and for potential therapies of its diseases. It shows that of multiple therapies that act temporarily, only some might work definitively. In addition the modeling should be capable of discriminating between the two types of therapy, transient and permanent. For instance, the model shows that anti FLC peptide therapy may work temporarily but not definitively, whilst sufficiently intensive fibroblast therapy should cure definitively, provided that the CRA influx is limited. This was indeed observed in the simulations in Chapter 2, but without the further foundation provided by the present Chapter. Our prediction that anti-FLC therapy might not work ultimately, may not quite be realistic: in actual cases there will always be ingrowth of fibroblasts and therefore one would obtain in any practice the situation of a combined therapy with fibroblast ingrowth and anti- FLC peptide. Our conclusions should still be taken to mean then that any activation of fibroblast ingrowth (or in practice activation of corresponding stem cells), should be considered a strong candidate adjuvant therapy, on first principles. Another ramification is of course, that with time after the first challenge, the adaptive immunity system will become active and take over much of the role of the innate immune system. Therefore the flares of microbial growth and the switch to chronic inflammation may be moderated by the adaptive immune system taking away the antigenic stimulation that lies at their bases.

4.4.2. Robustness of innate immunity The model discussed in chapter 2 was constructed by our bottom-up methodology. We translated the individual processes of the presumed network into reaction equations and rate equations. We then used Copasi to integrate these equations. We thereby found dependencies at steady state, as well as the trajectories that the model followed through time. We found that the type, more than the magnitude, of the inflammatory response depended on the influx of CRA protein: Below a certain threshold level, the protein did not elicit much of an inflammatory response, i.e. the steady-state TNF- and fibroblast levels were low and high respectively, and hardly affected by the CRA influx rate. Above the threshold level at some 17 mM/min of CRA influx, varying the CRA influx did not much affect the steady state magnitudes of these two variables either, but now they were low and high, respectively: at the transition there was a very sudden increase in TNF- level and an equally sudden drop in fibroblast level. The sensitivity analysis for the acute branch confirmed this: the TNF- and fibroblast levels were fairly insensitive to parameter changes throughout, but were ultrasensitive at and around the transition point. The sensitivities were quite different for the chronic branch: there they were small throughout; there was no ultra-sensitivity anywhere. Our heat map of the dependence of the TNF- levels on both the CRA influx rate and the fibroblast influx rate confirmed the step transition now as a function of both the CRA influx rate and the fibroblast influx rate, as well as the lack of ultra-sensitivity in the chronic inflammation.

101

Systems Immunology-model dynamics

We conclude that the chronic inflammation branch of the model is highly robust all over. The acute branch is also robust, except near and beyond the critical CRA influx where it transits to the chronic inflammation branch. We are here finding the emergence of phenotypic stability in the face of perturbations of physiological states, with the special property that there are two alternative ones, i.e. acute and chronic inflammation; phenotypic bi-stability therefore. An exception was found for the case where the model’s innate immunity was activated by a bacterial infection. There we found a virtually stable state after the immunity had dealt with the infection, but every now and then bacteremia seemed to recur. When we tried to cure this (as always: in silico) with a sustained influx of antibacterial protease, we found that this did not seem to help much: the frequency of the spurious emergence of bacteria increased rather than decreased although indeed the amplitude decreased. Only at high protease influx rates, the pulses of bacterial growth vanished. Here it turned out that the model of innate immunity was marginally stable except when there was excess protease. This may be of interest when trying to understand the reemergence of bacterial pathogens after apparent silence, such as in the case of tuberculosis. These cases of reemergence occurred without drug resistance arising and without antigenic variation [444].

4.4.3. An important role for bystander cells? The stroma of tissues contains fibroblasts. These cells might seem to serve household functions only: they manufacture protein fibers, and synthesize extracellular matrix and collagen. They also fill up the space of the tissue, and when there is empty such space, they grow until they become contact inhibited again, ensuring filling of the tissue space. In this process, the fibroblasts secrete and respond to growth factors. In Chapter 2 we made this tissue-regeneration process operational in silico, by including the simulation of the regrowth of healthy fibroblasts. The other side of the same coin, i.e. the innate immune system’s response to the growth and influx of healthy fibroblasts is a novel topic, which we also dealt with. In the simulations done in the present chapter, a paradox emerged. Rather than being a background/household factor healthy fibroblasts seemed to be essential for switching the system from a disease state to a healthy state. Without their activity, anti-FLC, which was considered requisite for a therapy in silico, did not produce a permanent effect. For the effect of anti-FLC to be permanent, healthy fibroblasts had to be fluxed into the system or a sufficiently high single dose of these cells had to be given. Because our model was also simulating the lymph system and its tendency to cleanse tissue, the anti-FLC peptide was only present temporarily in the system, whereas even a single dose of fibroblasts generated progeny and thereby provided for a continuous influx. The resolution of the paradox we mentioned above is complex but may reside in the fact that healthy fibroblasts secrete both anti-inflammatory (such as MMP-8) and pro-inflammatory (such as MMP-7) factors, whereas dying fibroblasts secrete more pro-inflammatory factors [445, 446]. Inflammation causes the number of dying fibroblasts to increase, which then puts in motion a positive feedback loop (see Fig. 2.1). More systems biology research should put the details in this explanation in place, or replace the explanation with a better alternative. Herewith healthy fibroblasts are predicted to have a therapeutic effect: healthy fibroblasts alone should be able to cure disease, but only when the immune disease is present. In our model ingrowth of fibroblasts had little effect when the system was in the acute inflammation 102

Systems Immunology-model dynamics branch. The acute mode of inflammation is saturated with healthy fibroblasts and this is why the curves with and without fibroblast ingrowth show overlapping TNF-a activity (Fig. 4.4A). However, when TNF-α is there as output response of the chronic inflammation, the TNF-α curves with and without fibroblast ingrowth show a different outcome at most CRA influx rates (Fig. 4.4B).

4.5. Supplementary material

Species Time course (4 days) Steady-state Protease Protease influx 1.0 influx 1.0

Healthy Bacteria 0 0

Dying Bacteria 0 0

TNF-α 0.000498884 0.000498883

Healthy Fibroblasts 999.486 999.484

Dying Fibroblasts 0.00124037 0.00124036

CRA 0.00999386 0.00999387

MMP-8 999484 999486

MMP-7 99.9486 99.9484

FLC 0.000999387 0.000999386

Protease 100.0060 100.0060

103

Systems Immunology-model dynamics

Table S4.1. Concentration of various species in steady state and time course simulation

CRA influx 0 (R1_CRA_degradation).k1 (R2_CRA_washout).k1 (R3_CRAinflux).v (R4_FLC_washout).k1 [CRA] -1.00 0.00 0.00 0.00 [MMP8] 0.00 0.00 0.00 0.00 [washout] 0.00 0.00 0.00 0.00 [FLC] -1.00 0.00 0.00 -1.00 [MMP7] 0.00 0.00 0.00 0.00 [Protease] -1.99 0.00 0.00 -1.00 [DyingFibr] -1.99 0.00 0.00 -1.00 [HealthyFibr] 0.00 0.00 0.00 0.00 [TNFalpha] -1.99 0.00 0.00 -1.00 [free_space] -1.99 0.00 0.00 -1.00 [MastCells_FLC] -1.00 0.00 0.00 -1.00 [MastCells_FLC_CRA] -1.99 0.00 0.00 -1.00 [MastCells] 0.00 0.00 0.00 0.00

CRA influx 0 (R5_MMP7_washout).k1 (R6_MMP8_washout).k1 (R7_Protease_washout).k1 (R8_TNFalpha_washout).k1 [CRA] -1.00 1.00 0.00 0.00 [MMP8] 0.00 -1.00 0.00 0.00 [washout] 0.00 0.00 0.00 0.00 [FLC] -1.00 1.00 0.00 0.00 [MMP7] -1.00 0.00 0.00 0.00 [Protease] -1.99 2.00 -1.00 0.00 [DyingFibr] -1.99 2.00 0.00 -1.00 [HealthyFibr] 0.00 0.00 0.00 0.00 [TNFalpha] -1.99 2.00 0.00 -1.00 [free_space] -1.99 2.00 0.00 -1.00 [MastCells_FLC] -1.00 1.00 0.00 0.00 [MastCells_FLC_CRA] -1.99 2.00 0.00 0.00 [MastCells] 0.00 0.00 0.00 0.00

CRA influx 0 (R9_FLC_production).k1 (R10_drug_washout).k1 (R11_FLC_drug_binding).k1 (R11_FLC_drug_binding).k2 [CRA] 0.00 0.00 0.00 0.00 [MMP8] 0.00 0.00 0.00 0.00 [washout] 0.00 0.00 0.00 0.00 [FLC] 1.00 0.00 0.00 0.00 [MMP7] 0.00 0.00 0.00 0.00 [Protease] 1.00 0.00 0.00 0.00 [DyingFibr] 1.00 0.00 0.00 0.00 [HealthyFibr] 0.00 0.00 0.00 0.00 [TNFalpha] 1.00 0.00 0.00 0.00 [free_space] 1.00 0.00 0.00 0.00 [MastCells_FLC] 1.00 0.00 0.00 0.00 [MastCells_FLC_CRA] 1.00 0.00 0.00 0.00 [MastCells] 0.00 0.00 0.00 0.00

CRA influx 0 (R12_FLC_drug_washout).k1 (R13_CRA_Secretion_DyingFibr) (R14_CRAClipOffHealthyFibr).k (R15_DyingFibroblast_death).k1 [BAFF] 0.00 0.00 1.00 0.00 [MMP8] 0.00 0.00 0.00 0.00 [washout] 0.00 0.00 0.00 0.00 [FLC] 0.00 0.00 1.00 0.00 [MMP7] 0.00 0.00 0.00 0.00 [Protease] 0.00 0.00 2.00 0.00 [DyingFibr] 0.00 0.00 2.00 -0.99 [HealthyFibr] 0.00 0.00 0.00 0.00 [TNFalpha] 0.00 0.00 2.00 0.00 [free_space] 0.00 0.00 2.00 0.00 [MastCells_FLC] 0.00 0.00 1.00 0.00 [MastCells_FLC_CRA] 0.00 0.00 2.00 0.00 [MastCells] 0.00 0.00 0.00 0.00

CRAinflux 0 (R16_Healthy_to_Dying_fibrobla (R17_HealthyBacteriaProduction (R18_HealthyFibProduction).k1 (R19_MMP7_release_HealthyFibr) [CRA] 0.00 0.00 0.00 1.00 [MMP8] 0.00 0.00 0.00 0.00 [washout] 0.00 0.00 0.00 0.00 [FLC] 0.00 0.00 0.00 1.00 [MMP7] 0.00 0.00 0.00 1.00 [Protease] 0.00 0.00 0.00 2.00 [DyingFibr] 1.00 0.00 0.00 2.00 [HealthyFibr] 0.00 0.00 0.00 0.00 [TNFalpha] 0.00 0.00 0.00 2.00 [free_space] 1.00 0.00 -1.00 2.00 [MastCells_FLC] 0.00 0.00 0.00 1.00 [MastCells_FLC_CRA] 0.00 0.00 0.00 2.00 [MastCells] 0.00 0.00 0.00 0.00

CRA influx 0 (R20_MMP8_release_HealthyFibr (R21_Healthy_to_Dying_Bacteria (R22_CRA_binding).k1 (R22_CRA_binding).k2 [CRA] -1.00 0.00 0.00 0.00 [MMP8] 1.00 0.00 0.00 0.00 [washout] 0.00 0.00 0.00 0.00 [FLC] -1.00 0.00 0.00 0.00 [MMP7] 0.00 0.00 0.00 0.00 [Protease] -1.99 0.00 1.00 -1.00 [DyingFibr] -1.99 0.00 1.00 -1.00 [HealthyFibr] 0.00 0.00 0.00 0.00 [TNFalpha] -1.99 0.00 1.00 -1.00 [free_space] -1.99 0.00 1.00 -1.00 [MastCells_FLC] -1.00 0.00 0.00 0.00 [MastCells_FLC_CRA] -1.99 0.00 1.00 -1.00 [MastCells] 0.00 0.00 0.00 0.00

BAFF influx 0 (R23_FLC_binding).k1 (R23_FLC_binding).k2 (R24_TNFalpha_production).k1 (R25_Protease_production).k1 [CRA] 0.00 0.00 0.00 0.00 [MMP8] 0.00 0.00 0.00 0.00 [washout] 0.00 0.00 0.00 0.00 [FLC] 0.00 0.00 0.00 0.00 [MMP7] 0.00 0.00 0.00 0.00 [Protease] 1.00 -1.00 0.00 1.00 [DyingFibr] 1.00 -1.00 1.00 0.00 [HealthyFibr] 0.00 0.00 0.00 0.00 [TNFalpha] 1.00 -1.00 1.00 0.00 [free_space] 1.00 -1.00 1.00 0.00 [MastCells_FLC] 1.00 -1.00 0.00 0.00 [MastCells_FLC_CRA] 1.00 -1.00 0.00 0.00 [MastCells] 0.00 0.00 0.00 0.00

104

Systems Immunology-model dynamics

CRA influx 16.7 (R1_CRA_degradation).k1 (R2_CRA_washout).k1 (R3_CRAinflux).v (R4_FLC_washout).k1 [CRA] -5.90 0.00 6.33 -2.49 [MMP8] 5.22 0.00 -5.63 2.64 [washout] 0.00 0.00 0.00 0.00 [FLC] -5.90 0.00 6.33 -3.48 [MMP7] 5.22 0.00 -5.63 2.64 [Protease] -11.54 0.00 12.47 -5.84 [DyingFibr] -6.39 0.00 6.77 -3.22 [HealthyFibr] 5.22 0.00 -5.63 2.64 [TNFalpha] -11.54 0.00 12.47 -5.84 [free_space] -11.54 0.00 12.47 -5.84 [MastCells_FLC] -5.68 0.00 6.10 -3.36 [MastCells_FLC_CRA] -11.54 0.00 12.47 -5.84 [MastCells] 0.21 0.00 -0.23 0.12

CRA influx 16.7 (R5_MMP7_washout).k1 (R6_MMP8_washout).k1 (R7_Protease_washout).k1 (R8_TNFalpha_washout).k1 [CRA] -0.17 6.52 0.00 -2.56 [MMP8] 0.15 -6.80 0.00 2.72 [washout] 0.00 0.00 0.00 0.00 [FLC] -0.17 6.52 0.00 -2.56 [MMP7] -0.85 -5.80 0.00 2.72 [Protease] -0.33 12.84 -1.00 -5.02 [DyingFibr] -0.18 6.96 0.00 -3.32 [HealthyFibr] 0.15 -5.80 0.00 2.72 [TNFalpha] -0.33 12.84 0.00 -6.02 [free_space] -0.33 12.84 0.00 -6.02 [MastCells_FLC] -0.16 6.28 0.00 -2.47 [MastCells_FLC_CRA] -0.33 12.84 0.00 -5.02 [MastCells] 0.01 -0.24 0.00 0.09

CRAinflux 16.7 (R9_FLC_production).k1 (R10_drug_washout).k1 (R11_FLC_drug_binding).k1 (R11_FLC_drug_binding).k2 [CRA] 2.63 0.00 0.00 0.00 [MMP8] -2.78 0.00 0.00 0.00 [washout] 0.00 0.00 0.00 0.00 [FLC] 3.64 0.00 0.00 0.00 [MMP7] -2.78 0.00 0.00 0.00 [Protease] 6.15 0.00 0.00 0.00 [DyingFibr] 3.36 0.00 0.00 0.00 [HealthyFibr] -2.78 0.00 0.00 0.00 [TNFalpha] 6.15 0.00 0.00 0.00 [free_space] 6.15 0.00 0.00 0.00 [MastCells_FLC] 3.51 0.00 0.00 0.00 [MastCells_FLC_CRA] 6.15 0.00 0.00 0.00 [MastCells] -0.13 0.00 0.00 0.00

CRA influx 16.7 (R12_FLC_drug_washout).k1 (R13_CRA_Secretion_DyingFibr) (R14_CRAClipOffHealthyFibr).k (R15_DyingFibroblast_death).k1 [CRA] 0.00 0.00 0.17 0.00 [MMP8] 0.00 0.00 -0.15 0.00 [washout] 0.00 0.00 0.00 0.00 [FLC] 0.00 0.00 0.17 0.00 [MMP7] 0.00 0.00 -0.15 0.00 [Protease] 0.00 0.00 0.34 -0.01 [DyingFibr] 0.00 0.00 0.18 -1.00 [HealthyFibr] 0.00 0.00 -0.15 0.00 [TNFalpha] 0.00 0.00 0.34 -0.01 [free_space] 0.00 0.00 0.34 -0.01 [MastCells_FLC] 0.00 0.00 0.16 0.00 [MastCells_FLC_CRA] 0.00 0.00 0.34 -0.01 [MastCells] 0.00 0.00 -0.01 0.00

CRA influx 16.7 (R16_Healthy_to_Dying_fibrobla (R17_HealthyBacteriaProduction (R18_HealthyFibProduction).k1 (R19_MMP7_release_HealthyFibr) [CRA] 2.72 0.00 -2.56 0.17 [MMP8] -2.87 0.00 2.71 -0.15 [washout] 0.00 0.00 0.00 0.00 [FLC] 2.72 0.00 -2.56 0.17 [MMP7] -2.87 0.00 2.71 0.85 [Protease] 5.35 0.00 -5.02 0.34 [DyingFibr] 3.46 0.00 -2.31 0.18 [HealthyFibr] -2.87 0.00 2.71 -0.15 [TNFalpha] 5.35 0.00 -5.02 0.34 [free_space] 6.35 0.00 -6.01 0.34 [MastCells_FLC] 2.62 0.00 -2.46 0.16 [MastCells_FLC_CRA] 5.35 0.00 -5.02 0.34 [MastCells] -0.10 0.00 0.09 -0.01

CRA influx 16.7 (R20_MMP8_release_HealthyFibr (R21_Healthy_to_Dying_Bacteria (R22_CRA_binding).k1 (R22_CRA_binding).k2 [CRA] -5.90 0.00 2.70 -2.55 [MMP8] 6.22 0.00 -2.85 2.70 [washout] 0.00 0.00 0.00 0.00 [FLC] -5.90 0.00 2.70 -2.55 [MMP7] 5.22 0.00 -2.85 2.70 [Protease] -11.54 0.00 6.31 -5.98 [DyingFibr] -6.39 0.00 3.44 -3.30 [HealthyFibr] 5.22 0.00 -2.85 2.70 [TNFalpha] -11.54 0.00 6.31 -5.98 [free_space] -11.54 0.00 6.31 -5.98 [MastCells_FLC] -5.68 0.00 2.60 -2.45 [MastCells_FLC_CRA] -11.54 0.00 6.31 -5.98 [MastCells] 0.21 0.00 -0.10 0.10

CRA influx 16.7 (R23_FLC_binding).k1 (R23_FLC_binding).k2 (R24_TNFalpha_production).k1 (R25_Protease_production).k1 [CRA] 2.63 -2.49 2.72 0.00 [MMP8] -2.78 2.64 -2.87 0.00 [washout] 0.00 0.00 0.00 0.00 [FLC] 2.63 -2.49 2.72 0.00 [MMP7] -2.78 2.64 -2.87 0.00 [Protease] 6.15 -5.84 5.35 1.00 [DyingFibr] 3.36 -3.22 3.46 0.00 [HealthyFibr] -2.78 2.64 -2.87 0.00 [TNFalpha] 6.15 -5.84 6.35 0.00 [free_space] 6.15 -5.84 6.35 0.00 [MastCells_FLC] 3.51 -3.36 2.62 0.00 [MastCells_FLC_CRA] 6.15 -5.84 5.35 0.00 [MastCells] -0.13 0.12 -0.10 0.00

105

Systems Immunology-model dynamics

CRA influx 30.0 (R1_CRA_degradation).k1 (R2_CRA_washout).k1 (R3_CRAinflux).v (R4_FLC_washout).k1 [CRA] 0.00 -1.00 1.00 0.00 [MMP8] -1088.87 -970.15 -1029.66 -1245.04 [washout] 0.00 0.00 0.00 0.00 [FLC] 0.00 -1.00 1.00 -1.00 [MMP7] 2491.53 185.72 -1798.63 3090.17 [Protease] 0.00 0.00 0.00 0.00 [DyingFibr] -1013.75 -996.30 -1003.98 -1033.48 [HealthyFibr] -1099.69 -964.05 -1033.52 -1276.91 [TNFalpha] 0.00 0.00 0.00 0.00 [free_space] 0.00 0.00 0.00 0.00 [MastCells_FLC] 0.00 1.00 -1.00 0.00 [MastCells_FLC_CRA] 0.00 0.00 0.00 0.00 [MastCells] 0.00 2.00 -2.00 1.00

CRA influx 30.0 (R5_MMP7_washout).k1 (R6_MMP8_washout).k1 (R7_Protease_washout).k1 (R8_TNFalpha_washout).k1 [CRA] 0.00 0.00 0.00 0.00 [MMP8] -991.23 -1089.18 -996.74 -1002.23 [washout] 0.00 0.00 0.00 0.00 [FLC] 0.00 0.00 0.00 0.00 [MMP7] -197.01 1251.92 -1796.61 271.75 [Protease] 0.00 0.00 -1.00 0.00 [DyingFibr] -1003.35 -1012.27 -999.56 -1000.25 [HealthyFibr] -990.64 -1098.57 -996.14 -1003.16 [TNFalpha] 0.00 0.00 0.00 -1.00 [free_space] 0.00 0.00 0.00 0.00 [MastCells_FLC] 0.00 0.00 0.00 0.00 [MastCells_FLC_CRA] 0.00 0.00 0.00 0.00 [MastCells] 0.00 0.00 0.00 0.00

CRA influx 30.0 (R9_FLC_production).k1 (R10_drug_washout).k1 (R11_FLC_drug_binding).k1 (R11_FLC_drug_binding).k2 [CRA] 0.00 0.00 0.00 0.00 [MMP8] -1079.35 8.08 0.00 0.00 [washout] 0.00 0.00 0.00 0.00 [FLC] 1.00 0.00 0.00 0.00 [MMP7] -2337.97 2.97 0.00 0.00 [Protease] 0.00 0.00 0.00 0.00 [DyingFibr] -1010.91 -279.95 0.00 0.00 [HealthyFibr] -1090.07 3.30 0.00 0.00 [TNFalpha] 0.00 0.00 0.00 0.00 [free_space] 0.00 0.00 0.00 0.00 [MastCells_FLC] 0.00 0.00 0.00 0.00 [MastCells_FLC_CRA] 0.00 0.00 0.00 0.00 [MastCells] -1.00 0.00 0.00 0.00

CRA influx 30.0 (R12_FLC_drug_washout).k1 (R13_CRA_Secretion_DyingFibr) (R14_CRAClipOffHealthyFibr).k (R15_DyingFibroblast_death).k1 [CRA] 0.00 0.00 0.00 0.00 [MMP8] 0.00 -999.29 -971.48 -925.06 [washout] 0.00 0.00 0.00 0.00 [FLC] 0.00 0.00 0.00 0.00 [MMP7] 0.00 491.40 50.74 3125.17 [Protease] 0.00 0.00 0.00 0.00 [DyingFibr] 0.00 -999.97 -996.18 -990.59 [HealthyFibr] 0.00 -999.78 -968.82 -917.43 [TNFalpha] 0.00 0.00 0.00 0.00 [free_space] 0.00 0.00 0.00 0.00 [MastCells_FLC] 0.00 0.00 0.00 0.00 [MastCells_FLC_CRA] 0.00 0.00 0.00 0.00 [MastCells] 0.00 0.00 0.00 0.00

CRA influx 30.0 (R16_Healthy_to_Dying_fibrobla (R17_HealthyBacteriaProduction (R18_HealthyFibProduction).k1 (R19_MMP7_release_HealthyFibr) [CRA] 0.00 0.00 0.00 0.00 [MMP8] -1178.35 0.00 -993.32 -1002.64 [washout] 0.00 0.00 0.00 0.00 [FLC] 0.00 0.00 0.00 0.00 [MMP7] 260.43 0.00 -896.97 -1186.02 [Protease] 0.00 0.00 0.00 0.00 [DyingFibr] -1027.09 0.00 -999.03 -1001.65 [HealthyFibr] -1203.71 0.00 -991.78 -1005.46 [TNFalpha] 0.00 0.00 0.00 0.00 [free_space] 0.00 0.00 0.00 0.00 [MastCells_FLC] 0.00 0.00 0.00 0.00 [MastCells_FLC_CRA] 0.00 0.00 0.00 0.00 [MastCells] 0.00 0.00 0.00 0.00

CRA influx 30.0 (R20_MMP8_release_HealthyFibr (R21_Healthy_to_Dying_Bacteria (R22_CRA_binding).k1 (R22_CRA_binding).k2 [BAFF] 0.00 0.00 0.00 0.00 [MMP8] -1007.90 8.08 -1519.59 544.28 [washout] 0.00 0.00 0.00 0.00 [FLC] 0.00 0.00 0.00 0.00 [MMP7] -1200.44 2.97 -2048.41 -2083.45 [Protease] 0.00 0.00 0.00 0.00 [DyingFibr] -1001.12 -279.95 -1071.49 26698.50 [HealthyFibr] -1009.35 3.30 -1594.45 -883.47 [TNFalpha] 0.00 0.00 0.00 0.00 [free_space] 0.00 0.00 0.00 0.00 [MastCells_FLC] 0.00 0.00 -1.00 1.00 [MastCells_FLC_BAFF] 0.00 0.00 0.00 0.00 [MastCells] 0.00 0.00 -1.00 1.00

CRA influx 30.0 (R23_FLC_binding).k1 (R23_FLC_binding).k2 (R24_TNFalpha_production).k1 (R25_Protease_production).k1 [CRA] 0.00 0.00 0.00 0.00 [MMP8] -1023.49 -1076.65 -1031.44 -968.20 [washout] 0.00 0.00 0.00 0.00 [FLC] 0.00 0.00 0.00 0.00 [MMP7] -1848.37 -2741.97 -238.38 -2278.25 [Protease] 0.00 0.00 0.00 1.00 [DyingFibr] -1003.38 -1013.60 -1004.71 -995.03 [HealthyFibr] -1026.73 -1088.78 -1034.91 -963.16 [TNFalpha] 0.00 0.00 1.00 0.00 [free_space] 0.00 0.00 0.00 0.00 [MastCells_FLC] 0.00 0.00 0.00 0.00 [MastCells_FLC_CRA] 0.00 0.00 0.00 0.00 [MastCells] -1.00 1.00 0.00 0.00

106

Systems Immunology-model dynamics

Table S4.2. Sensitivity coefficients for acute inflammation at CRA influx of 0, 16.7 and 30.0 fM/min: the sensitivity coefficients that have been indicated by colors represent the key species in the network.

CRA influx 0 (R1_CRA_degradation).k1 (R2_CRA_washout).k1 (R3_CRAinflux).v (R4_FLC_washout).k1 [CRA] 0 -1117.87 0 -896.73 [MMP8] 0 -1003.13 0 -936.15 [washout] 0 0.00 0 0.00 [FLC] 0 -1116.61 0 -901.47 [MMP7] 0 0.00 0 0.00 [Protease] 0 -750.00 0 -1000.00 [TNFalpha] -1 -1.00 -1 1.00 [free_space] 0 0.00 0 0.00 [MastCells_FLC] 0 -1116.90 0 -905.09 [MastCells_FLC_CRA] 0 -812.50 0 -875.00 [MastCells] 0 0.00 0 0.00

CRA influx 0 (R5_MMP7_washout).k1 (R6_MMP8_washout).k1 (R7_Protease_washout).k1 (R8_TNFalpha_washout).k1 [CRA] 0.00 -245.28 2163.01 -2311.02 [MMP8] 0.00 -470.08 -147.94 -2055.65 [washout] 0.00 0.00 0.00 0.00 [FLC] 0.00 -273.74 1831.37 -2250.98 [MMP7] 0.00 0.00 0.00 0.00 [Protease] 0.00 500.00 3000.00 -4000.00 [TNFalpha] -1.00 -1.00 1.00 1.00 [free_space] 0.00 0.00 0.00 0.00 [MastCells_FLC] 0.00 -305.56 1622.69 -2203.70 [MastCells_FLC_CRA] 0.00 -500.00 3000.00 -1000.00 [MastCells] 0.00 0.00 0.00 0.00

CRA influx 0 (R9_FLC_production).k1 (R10_drug_washout).k1 (R11_FLC_drug_binding).k1 (R11_FLC_drug_binding).k2 [CRA] -2207.16 0 0 0 [MMP8] -1931.91 0 0 0 [washout] 0.00 0 0 0 [FLC] -2192.16 0 0 0 [MMP7] 0.00 0 0 0 [Protease] -3000.00 0 0 0 [TNFalpha] -1.00 -1 -1 -1 [free_space] 0.00 0 0 0 [MastCells_FLC] -2194.44 0 0 0 [MastCells_FLC_CRA] -2000.00 0 0 0 [MastCells] 0.00 0 0 0

CRA influx 0 (R12_FLC_drug_washout).k1 (R13_CRA_Secretion_DyingFibr).k1 (R14_CRAClipOffHealthyFibr).k1 (R15_DyingFibroblast_death).k1 [CRA] 0 0 0 0 [MMP8] 0 0 0 0 [washout] 0 0 0 0 [FLC] 0 0 0 0 [MMP7] 0 0 0 0 [Protease] 0 0 0 0 [TNFalpha] -1 -1 -1 -1 [free_space] 0 0 0 0 [MastCells_FLC] 0 0 0 0 [MastCells_FLC_CRA] 0 0 0 0 [MastCells] 0 0 0 0

CRA influx 0 (R16_Healthy_to_Dying_fibroblast).k1 (R17_HealthyBacteriaProduction).k1 (R18_HealthyFibProduction).k1 (R19_MMP7_release_HealthyFibr).k1 [CRA] 0 0 0 0 [MMP8] 0 0 0 0 [washout] 0 0 0 0 [FLC] 0 0 0 0 [MMP7] 0 0 0 0 [Protease] 0 0 0 0 [TNFalpha] -1 -1 -1 -1 [free_space] 0 0 0 0 [MastCells_FLC] 0 0 0 0 [MastCells_FLC_CRA] 0 0 0 0 [MastCells] 0 0 0 0

CRA influx 0 (R20_MMP8_release_HealthyFibr ).k1 (R21_Healthy_to_Dying_Bacteria).k1 (R22_CRA_binding).k1 (R22_CRA_binding).k2 [CRA] 0 0 2065.89 2104.75 [MMP8] 0 0 670.23 -1841.68 [washout] 0 0 0.00 0.00 [FLC] 0 0 1901.96 1735.29 [MMP7] 0 0 0.00 0.00 [Protease] 0 0 9000.00 -1000.00 [TNFalpha] -1 -1 -1.00 1.00 [free_space] 0 0 0.00 0.00 [MastCells_FLC] 0 0 1777.78 1543.98 [MastCells_FLC_CRA] 0 0 7000.00 0.00 [MastCells] 0 0 0.00 0.00

CRA influx 0 (R23_FLC_binding).k1 (R23_FLC_binding).k2 (R24_TNFalpha_production).k1 (R25_Protease_production).k1 [CRA] -765.16 1688.25 -1339.93 -1370.14 [MMP8] -887.63 1604.58 695.50 -861.53 [washout] 0.00 0.00 0.00 0.00 [FLC] -790.20 1552.94 -1163.73 -1349.02 [MMP7] 0.00 0.00 0.00 0.00 [Protease] -250.00 -1000.00 -4000.00 0.00 [TNFalpha] -1.00 1.00 1.00 -1.00 [free_space] 0.00 0.00 0.00 0.00 [MastCells_FLC] -805.56 1490.74 -1083.33 -1343.75 [MastCells_FLC_CRA] -750.00 1000.00 -3000.00 -2000.00 [MastCells] 0.00 0.00 0.00 0.00

107

Systems Immunology-model dynamics

CRA influx 0.1 (R1_CRA_degradation).k1 (R2_CRA_washout).k1 (R3_CRA influx).v (R4_FLC_washout).k1 [CRA] 0.00 -1.00 1.00 0.00 [MMP8] -1000.02 -999.89 -1000.04 -1000.01 [washout] 0.00 0.00 0.00 0.00 [FLC] 0.00 -1.00 1.00 -1.00 [MMP7] -1000.00 -1000.00 -1000.00 -1000.00 [Protease] 0.00 -0.25 0.25 -0.08 [TNFalpha] 0.00 -0.25 0.25 -0.08 [free_space] 0.00 0.00 0.00 0.00 [MastCells_FLC] 0.00 0.75 -0.75 -0.08 [MastCells_FLC_CRA] 0.00 -0.25 0.25 -0.08 [MastCells] 0.00 1.75 -1.75 0.92

CRA influx 0.1 (R5_MMP7_washout).k1 (R6_MMP8_washout).k1 (R7_Protease_washout).k1 (R8_TNFalpha_washout).k1 [CRA] 0.00 0.00 0.00 0.00 [MMP8] 83.49 -854.52 -853.85 -1000.00 [washout] 0.00 0.00 0.00 0.00 [FLC] 0.00 0.00 0.00 0.00 [MMP7] -1000.00 -357371.00 -1000.00 -1000.00 [Protease] 0.00 0.00 -1.00 0.00 [TNFalpha] 0.00 0.00 0.00 -1.00 [free_space] 0.00 0.00 0.00 0.00 [MastCells_FLC] 0.00 0.00 0.00 0.00 [MastCells_FLC_CRA] 0.00 0.00 0.00 0.00 [MastCells] 0.00 0.00 0.00 0.00

CRA influx 0.1 (R9_FLC_production).k1 (R10_drug_washout).k1 (R11_FLC_drug_binding).k1 (R11_FLC_drug_binding).k2 [CRA] 0.00 0.00 0.00 0.00 [MMP8] -999.87 0.00 0.00 0.00 [washout] 0.00 0.00 0.00 0.00 [FLC] 1.00 0.00 0.00 0.00 [MMP7] -1000.00 0.00 0.00 0.00 [Protease] 0.08 0.00 0.00 0.00 [TNFalpha] 0.08 0.00 0.00 0.00 [free_space] 0.00 0.00 0.00 0.00 [MastCells_FLC] 0.08 0.00 0.00 0.00 [MastCells_FLC_CRA] 0.08 0.00 0.00 0.00 [MastCells] -0.92 0.00 0.00 0.00

CRA influx 0.1 (R12_FLC_drug_washout).k1 (R13_BAFF_Secretion_DyingFibr).k1 (R14_CRAClipOffHealthyFibr).k1 (R15_DyingFibroblast_death).k1 [CRA] 0.00 0.00 0.00 0.00 [MMP8] 0.00 0.00 0.00 0.00 [washout] 0.00 0.00 0.00 0.00 [FLC] 0.00 0.00 0.00 0.00 [MMP7] 0.00 0.00 0.00 0.00 [Protease] 0.00 0.00 0.00 0.00 [TNFalpha] 0.00 0.00 0.00 0.00 [free_space] 0.00 0.00 0.00 0.00 [MastCells_FLC] 0.00 0.00 0.00 0.00 [MastCells_FLC_CRA] 0.00 0.00 0.00 0.00 [MastCells] 0.00 0.00 0.00 0.00

CRA influx 0.1 (R16_Healthy_to_Dying_fibroblast).k1 (R17_HealthyBacteriaProduction).k1 (R18_HealthyFibProduction).k1 (R19_MMP7_release_HealthyFibr).k1 [CRA] 0.00 0.00 0.00 0.00 [MMP8] 0.00 0.00 0.00 0.00 [washout] 0.00 0.00 0.00 0.00 [FLC] 0.00 0.00 0.00 0.00 [MMP7] 0.00 0.00 0.00 0.00 [Protease] 0.00 0.00 0.00 0.00 [TNFalpha] 0.00 0.00 0.00 0.00 [free_space] 0.00 0.00 0.00 0.00 [MastCells_FLC] 0.00 0.00 0.00 0.00 [MastCells_FLC_CRA] 0.00 0.00 0.00 0.00 [MastCells] 0.00 0.00 0.00 0.00

CRA influx 0.1 (R20_MMP8_release_HealthyFibr ).k1 (R21_Healthy_to_Dying_Bacteria).k1 (R22_CRA_binding).k1 (R22_CRA_binding).k2 [CRA] 0.00 0.00 0.00 0.00 [MMP8] 0.00 0.00 -999.97 -521.21 [washout] 0.00 0.00 0.00 0.00 [FLC] 0.00 0.00 0.00 0.00 [MMP7] 0.00 0.00 -1000.00 -1000.00 [Protease] 0.00 0.00 0.17 -0.17 [TNFalpha] 0.00 0.00 0.17 -0.17 [free_space] 0.00 0.00 0.00 0.00 [MastCells_FLC] 0.00 0.00 -0.83 0.83 [MastCells_FLC_CRA] 0.00 0.00 0.17 -0.17 [MastCells] 0.00 0.00 -0.83 0.83

CRA influx 0.1 (R23_FLC_binding).k1 (R23_FLC_binding).k2 (R24_TNFalpha_production).k1 (R25_Protease_production).k1 [CRA] 0.00 0.00 0.00 0.00 [MMP8] -999.98 -1000.33 -999.93 -868.54 [washout] 0.00 0.00 0.00 0.00 [FLC] 0.00 0.00 0.00 0.00 [MMP7] -1000.00 -1000.00 -1000.00 -1000.00 [Protease] 0.08 -0.08 0.00 1.00 [TNFalpha] 0.08 -0.08 1.00 0.00 [free_space] 0.00 0.00 0.00 0.00 [MastCells_FLC] 0.08 -0.08 0.00 0.00 [MastCells_FLC_CRA] 0.08 -0.08 0.00 0.00 [MastCells] -0.92 0.92 0.00 0.00

108

Systems Immunology-model dynamics

CRA influx 1.0 (R1_BAFF_degradation).k1 (R2_CRA_washout).k1 (R3_CRAinflux).v (R4_FLC_washout).k1 [CRA] 0 -1.00 1.00 0.00 [MMP8] 0 -750.56 -658.77 -94433.20 [washout] 0 0.00 0.00 0.00 [FLC] 0 -1.00 1.00 -1.00 [MMP7] -1 -1.00 -1.00 -1.00 [Protease] 0 -0.01 0.01 0.00 [TNFalpha] 0 -0.01 0.01 0.00 [free_space] 0 0.00 0.00 0.00 [MastCells_FLC] 0 0.99 -0.99 0.00 [MastCells_FLC_BAFF] 0 -0.01 0.01 0.00 [MastCells] 0 1.99 -1.99 1.00

CRA influx 1.0 (R5_MMP7_washout).k1 (R6_MMP8_washout).k1 (R7_Protease_washout).k1 (R8_TNFalpha_washout).k1 [CRA] 0 0.00 0.00 0.00 [MMP8] 0 7028.37 -474.21 340488.00 [washout] 0 0.00 0.00 0.00 [FLC] 0 0.00 0.00 0.00 [MMP7] -1 -1.00 -1.00 -1.00 [Protease] 0 0.00 -1.00 0.00 [TNFalpha] 0 0.00 0.00 -1.00 [free_space] 0 0.00 0.00 0.00 [MastCells_FLC] 0 0.00 0.00 0.00 [MastCells_FLC_CR%A] 0 0.00 0.00 0.00 [MastCells] 0 0.00 0.00 0.00

CRA influx 1.0 (R9_FLC_production).k1 (R10_drug_washout).k1 (R11_FLC_drug_binding).k1 (R11_FLC_drug_binding).k2 [CRA] 0.00 0 0 0 [MMP8] 20.59 0 0 0 [washout] 0.00 0 0 0 [FLC] 1.00 0 0 0 [MMP7] -1.00 -1 -1 -1 [Protease] 0.00 0 0 0 [TNFalpha] 0.00 0 0 0 [free_space] 0.00 0 0 0 [MastCells_FLC] 0.00 0 0 0 [MastCells_FLC_CRA] 0.00 0 0 0 [MastCells] -1.00 0 0 0

BAFF influx 1.0 (R12_FLC_drug_washout).k1 (R13_CRA_Secretion_DyingFibr).k1 (R14_CRAClipOffHealthyFibr).k1 (R15_DyingFibroblast_death).k1 [CRA] 0 0 0 0 [MMP8] 0 0 0 0 [washout] 0 0 0 0 [FLC] 0 0 0 0 [MMP7] -1 -1 -1 -1 [Protease] 0 0 0 0 [TNFalpha] 0 0 0 0 [free_space] 0 0 0 0 [MastCells_FLC] 0 0 0 0 [MastCells_FLC_CRA] 0 0 0 0 [MastCells] 0 0 0 0

CRA influx 1.0 (R16_Healthy_to_Dying_fibroblast).k1 (R17_HealthyBacteriaProduction).k1 (R18_HealthyFibProduction).k1 (R19_MMP7_release_HealthyFibr).k1 [CRA] 0 0 0 0 [MMP8] 0 0 0 0 [washout] 0 0 0 0 [FLC] 0 0 0 0 [MMP7] -1 -1 -1 -1 [Protease] 0 0 0 0 [TNFalpha] 0 0 0 0 [free_space] 0 0 0 0 [MastCells_FLC] 0 0 0 0 [MastCells_FLC_CRA] 0 0 0 0 [MastCells] 0 0 0 0

CRA influx 1.0 (R20_MMP8_release_HealthyFibr ).k1 (R21_Healthy_to_Dying_Bacteria).k1 (R22_CRA_binding).k1 (R22_CRA_binding).k2 [CRA] 0 0 0.00 0.00 [MMP8] 0 0 6275.54 -11827.50 [washout] 0 0 0.00 0.00 [FLC] 0 0 0.00 0.00 [MMP7] -1 -1 -1.00 -1.00 [Protease] 0 0 0.01 -0.01 [TNFalpha] 0 0 0.01 -0.01 [free_space] 0 0 0.00 0.00 [MastCells_FLC] 0 0 -0.99 0.99 [MastCells_FLC_CRA] 0 0 0.01 -0.01 [MastCells] 0 0 -0.99 0.99

CRA influx 1.0 (R23_FLC_binding).k1 (R23_FLC_binding).k2 (R24_TNFalpha_production).k1 (R25_Protease_production).k1 [CRA] 0.00 0.00 0.00 0.00 [MMP8] -291.33 -645753.00 -1951.82 169028.00 [washout] 0.00 0.00 0.00 0.00 [FLC] 0.00 0.00 0.00 0.00 [MMP7] -1.00 -1.00 -1.00 -1.00 [Protease] 0.00 0.00 0.00 1.00 [TNFalpha] 0.00 0.00 1.00 0.00 [free_space] 0.00 0.00 0.00 0.00 [MastCells_FLC] 0.00 0.00 0.00 0.00 [MastCells_FLC_CRA] 0.00 0.00 0.00 0.00 [MastCells] -1.00 1.00 0.00 0.00

109

Systems Immunology-model dynamics

Table S4.3. Sensitivity coefficients of the chronic inflammation state at the three CRA influx rates. CRA influx rates were of 0, 0.1 and 1.0 fM/min.

CRA influx 0 Sum of coefficient CRA influx 0.1 Sum of coefficient CRA influx 1.0 Sum of coefficient [CRA] -2231.39 [CRA] 0.00 [CRA] 0.00 [MMP8] -7165.386 [MMP8] -13014.67 [MMP8] -233299.89 [washout] 0 [washout] 0.00 [washout] 0.00 [FLC] -3016.351 [FLC] 0.00 [FLC] 0.00 [MMP7] 0 [MMP7] -371371.00 [MMP7] -28.00 [Protease] -2500 [Protease] 0.00 [Protease] 0.00 [TNFalpha] -16 [TNFalpha] 0.00 [TNFalpha] 0.00 [free_space] 0 [free_space] 0.00 [free_space] 0.00 [MastCells_FLC] -3523.135 [MastCells_FLC] 0.00 [MastCells_FLC] 0.00 [MastCells_FLC_CRA] 62.5 [MastCells_FLC_CRA] 0.00 [MastCells_FLC_CRA] 0.00 [MastCells] 0 [MastCells] 0.00 [MastCells] 0.01

110

Systems Immunology-mast cell

Chapter 5 Mast Cells as Potential Target for Anti- Tumor Therapy

111

Systems Immunology-mast cell

Summary Mast cells are primarily known for their role as initiators of innate immunity in response to pathogens, particularly parasites and bacteria, and for triggering of allergic immune response. In addition to their direct function, mast cells recruit other innate and adaptive immune cells to the site of inflammation and this indirect function is able to “tune” the immune response. However, increasing evidence shows that accumulation of mast cells in the stroma surrounding tumors might contribute to early tumor development. Some human and murine tumors contain high numbers of mast cells and mast activation is observed within the tumors. Activated mast cells may promote tumorigenesis by secreting mediators that enhance tumor inflammation, as well as by interacting with other immune cells. In this review, we relate the biological functions of mast cells to their potentially supportive or inhibitory roles in tumorigenesis, describe mast cell markers with diagnostic potential, and finally focus on possibilities for therapeutic targeting of mast cells to combat cancer via stabilization of mast cell activity. This might be achieved by small drug compounds interfering with the network around the mast cells or by specific kinase inhibitors inhibiting mast cell degranulation.

5.1 Introduction A century ago, Paul Ehrlich first described Mästzellen, or mast cells in his thesis as a medical student. His finding was that mast cells are widely present around developing tissues and that the morphology of a mast cell is characterized by its degranulation. To date his discovery still plays a significant role in mast cell biology. Mast cells play significant roles in maintaining a healthy physiology of tissues, as witnessed by their conservation in evolution, and there has never been a report of a human lacking mast cells. The most compelling evidence of the importance of mast cells in health and disease is their roles in wound healing and angiogenesis and in the defense against a whole host of pathogens. Mast cells participate in both innate and adaptive immunity. Mast cells also contribute to inflammatory processes, attracting various leukocytes, such as neutrophils, eosinophils, and basophils, to the site of injury. In chronic inflammation, evidence of mast cell activation was demonstrated at the sites of inflammation. This presence and the fact that diverse mediators, such as cytokines and growth factors that can be secreted by activated mast cells, display a pro-inflammatory function suggest a wide range of possible roles for mast cells in promoting chronic inflammation. In cancer, various observations suggest that mast cells either contribute to anti-tumor immunity [368, 447] or promote tumor angiogenesis, tumor tumor invasion of tissues, and immune suppression, for example via release of Vascular Endothelial Growth Factor (VEGF) [448], Matrix Metalloproteinases (MMPs) [449], angiopoietin-1 [450], sphingosine-1-phosphate [451] and proteases [452].

112

Systems Immunology-mast cell

5.1.1. Mast Cell Biology 5.1.1.1 Origin of mast cell Mast cells originate in hematopoietic stem cells (HSCs) in the bone marrow. Their progenitors were first identified in mouse fetal blood [453]. In the human, mast cells can be found in the endothelial cell wall of blood and lymphatic vessels, in skin, and in respiratory and gastrointestinal systems. In mice, mast cells are especially found in skin, tongue, skeletal muscle and stomach. Small numbers of mast cells are also found in mouse lung, intestine, spleen and kidney. In zebra fish, mast cells are found in the intestines. The zebra fish mast cell is functionally and structurally similar to its mammalian counterparts [454, 455]. Unlike other leukocytes, mature mast cells cannot be recovered from the blood stream [456]. Instead, immature mast cells move into peripheral tissues, where they undergo local maturation under the control of local cytokines and stem cell factor (SCF) from the surrounding environment [148]. This allows mast cells to proliferate. Addition of bioactive metabolites, such as sphingosine-1-phosphate, to SCF accelerates the maturation of human cord-blood derived mast cells. Finally, mast cell maturation and function is affected by fibroblast-derived prostaglandin synthase, that is coupled to local PGD2 and MCDP1 (prostaglandin receptor) expression [457].

5.1.1.2. Phenotype Mast cells are secretory cells that are phenotypically distinct from other innate immune cells based on their distribution over organs and tissues. In mice, mast cells in the skin and the peritoneal cavity are named connective tissue mast cells (CTMCs) and mast cells in the intestinal sub-mucosa are known as mucosal mast cells (MMCs). CTMCs are typical mast cells, whereas MMCs are atypical. MMCs express mast cell protease (MMCP)-1 and-2, whereas CTMCs express MMCP-4,-5,-6 and carboxypeptidase [458, 459]. CTMCs exhibit T-cell independent functions in which the proliferation of CTMCs is mostly regulated by SCF. For instance, the incubation of mouse CTMCs with recombinant (r) SCF give rise to CTMCs-like colonies, suggesting SCF stimulates CTMCs proliferation [460]. In contrast, MMCs is dependent on T-cell-derived cytokines: IL-3 and IL-4 for example, support survival and proliferation of MMCs [461, 462]. Bone marrow-derived mast cells (BMMC) are stimulated to proliferate by T lymphocytes and WEH1-3B cells [463, 464]. Apart from distinct mast cell subtypes, the mast cell phenotype was demonstrated to be influenced by the genetic background of mice. Separate studies in BALB/C and C57BL/6 mice showed that Natural Killer (NK) cell-derived IL-9 regulates the influx of mast cell progenitors in BALB/C, while T-regulatory (Treg) cells promote the influx of mast cell progenitors into lung tissue in C57BL/6 [465]. In the human, mast cell subtypes are distinguished by their cytoplasmic granular content. The granules contain tryptase (T) and chymase (C) and cells named doubly positive mast cells may be so for T and C or. Such ‘MCTC‘ are mainly found in the skin. In the small intestinal sub-mucosa MCTC seem to be the human counterpart of mouse MMCs [466]. The cells that contain granules that are positive for tryptase (T) but negative for chymase, are named singly positive mast cell for T or MCT. The MCT are predominantly present in lung alveoli and in the small intestine mucosa. In terms of tissue localization, the MCT shares similarity with mouse CTMCs. 113

Systems Immunology-mast cell

5.1.1.3. The mast cell and innate immunity Some functions of mast cells are still unknown. By distinction through their high affinity to IgE/FcεRI receptors and their innate capacity to secrete inflammatory mediators, mast cells are known as the primary effector cells involved in the pathogenesis of type 1 hypersensitivity reactions [467]. The role of mast cells in the immune system and their protective functions during the different types of infection are well recognized [386], in line with their wide-spread, distribution patterns in many organs and tissues [224]. In order to respond to stimuli, mast cells express a wide variety of pattern recognition receptors (PRR) that enable them to recognize pathogens. For example, in vitro expression of Toll-Like Receptors (TLRs)-3,7 and 9 on skin mast cells grown on murine connective tissue, mediated cytokine and chemokine production upon activation [468]. Next, mast cell mediators, including tryptases and chymases, can attract antigen presenting cells (APCs) to the sites of infection [469]. Once attracted, mast cell-derived TNF-α can induce subsequent maturation and lymph node migration of dendritic cells (DC) in mice [470]. In addition, mast cells have a potent activity to recruit other innate immune cells to the infected site, intensively being commissioned to initiating innate immunity. For instance, an in vivo study showed that mast cell-derived leukotriene (LT) B4 is a chemoattractant for neutrophils that are moved to the site bacterial infection (FimH-expressing E.coli), and that this mechanism enhances bacterial clearance in normal congenic control (WBB6F1 -+/+) but not in mast cell-deficient mice v (WBB6F1-W/W ) [471]. A subsequent study showed that the mast cell-derived proteases can contribute to host defense against bacterial pathogens. Chymase and tryptase for instance, degrade eotaxin-3 (CCL26) and the CCL29 that neutralizes LPS-properties of bacteria [472]. In addition, mast cells play a protective role in viral infection. For example, mast cell- secreted CXCL-8 chemokine is a chemoattractant for NK-cells in viral infection. In vivo studies revealed that draining lymph nodes are highly vulnerable to dengue virus (DENV) in v mast cell-deficient mice (WBB6F1-W/W ) [473], and that draining lymph nodes are highly v vulnerable to dengue virus (DENV) in mast cell-deficient mice (WBB6F1-W/W ) [474].

5.1.1.4. The mast cell and adaptive immunity In adaptive immunity, mast cells may play a role as antigen presenting cells (APCs): they present pathogen peptides to T cells. CD34 (+) stem cell derived mast cells expressed both HLA-DR, which is a MHC class II cell surface receptor, and CD80 in tonsil, suggesting that mast cells present antigen to CD4 T cells [475]. An early report indicated that mast cells synthesize Major Histocompatibility Complex (MHC) class II, which is accumulated in secretory lysosomes of mast cells [476]. Banovac and colleagues first showed that 10 % of rat mast cells isolated from the pleural cavity displayed MHC class II antigen on their plasma membrane. Addition of gamma interferon (IFN-γ) enhanced the expression of MHC class II antigen in as much as 80 % of the cells [477]. In vitro studies showed that mast cells are able to interact with T cell receptors (TCRs) so as to induce clonal expansion of T cells [478]. In addition, bone marrow- derived mast cells (BMCMCs) treated with granulocyte-macrophage colony-stimulating factor (GM-CSF) expressed the co-stimulatory molecules CD80 and CD86 required as secondary activation signal for CD4 T cells [479]. In addition to this direct mode of antigen-presentation and CD4 T cell activation, mast cell-secreted TNF-α and

114

Systems Immunology-mast cell histamine can induce maturation migration of dendritic cells (DCs) to lymph nodes [480, 481] and thus stimulate antigen presentation by DCs to CD4 and CD8 T cells. Next to a role in antigen presentation and T cell activation, mast cells produce chemotactic factors such as IL-16, XCL1 (also called lymphotactin), CCL2, CCL3, CCL4, CCL5, CCL20, CXCL10 [467, 482, 483]. These chemokines promote recruitment of effector T cells to local inflammation sites. Interestingly, T cell-derived cytokines, such as β- chemokines MCP-1 and MIP-1α, may directly trigger mast cell degranulation in rat mouse models [484, 485], suggesting the existence of a complex network of cellular crosstalk. Finally, mast cells may affect the proliferation and differentiation of B cells. Immunohistological staining of secondary lymphoid tissues for mast cell tryptase and CD20 B-cell marker showed that mast cells and B cells co-localize in paracortical and medullary areas of lymph nodes and surrounding follicles, suggesting that mast cells and B cells may communicate [486]. Indeed, human mast cells express CD154 which is a ligand for the CD40 costimulatory molecule on B cells. Activation of CD40 on B cells by CD154 was shown to induce IgE production in the presence of IL-4 or adenosine [371, 487]. Moreover, in the presence of IL-4, IgE and IgG1 production by B cells are upregulated by mast cell protease-1 [488]. Again, extensive crosstalk between B cells and mast cells may exist, as a recent study showed that malignant B cells secrete excessive amounts of SCF [489], which may recruit mast cells to the sites of neoplasia.

5.2. Mast cells and cancer diagnosis A correlation between mast cell activation markers and tumor growth has drawn attention in cancer studies [186, 490, 491]. Soucek and colleagues [186] analyzed and classified multiple chemokines from Myc pancreatic islet tumors. CCL5, a powerful chemoattractant for mast cells, was present. In vivo, mast cell migration is regulated by the Myc oncogene and mast cells are significantly present and degranulated at the site of Myc+ islet tumors. A significant role for mast cells in tumor formation has also been reported in lung cancer [164], breast cancer [492] and skin cancer [161] in human. The early detection of cancer is of key importance for effective tumor therapy in patients. Mast cells may provide diagnostic and prognostic information to clinicians. In cancer, not only mast cell infiltration but also mast cell degranulation in and around tumors may have a diagnostic potential in certain tumors as they were shown to contribute to the tumor growth by enhancing angiogenesis and metastasis [450, 493, 494]. In mast cell leukemia (MCL), observing the morphology of mast cells plays a diagnostic role; for example, the morphology of mast cells in a patient with acute MCL differs from the one with chronic MCL. Clinically, the patients diagnosed with the acute type of mast cell leukemia contain mostly immature mast cells in the blood, which causes organ damage, drug resistance, and poor prognosis [495]. Mast cell degranulation and infiltration can change depending on the progression of disease [496]. In an animal study, mast cell infiltration was primarily associated with the pathological sites of dysplasia and hyperkeratosis after 8 weeks, and associated with skin carcinogenesis within 10-14 weeks. By electron microscopy, mast cell degranulation was observed at the site of carcinoma [497]. In human, mast cell infiltration was significantly enriched in patients with ulcerative colitis and colonic primary cancer, whereas mast cell

115

Systems Immunology-mast cell infiltration and numbers remained low in healthy adjacent tissue [498]. In addition, mast cell infiltration has been observed in adenomatous polyps from which adenocolon cancer develops [499]. Finally, mast cell degranulation is associated with the progression of angiosarcoma [500]. Next to quantification of mast cell infiltrates in tumor, mast cell activation and degranulation analyses may hold diagnostic potential. Clinically, serum tryptase determination is a marker for the diagnosis or monitoring of mast cell disorders including mast cell activation-induced anaphylaxis, mastocytosis and a number of myeloproliferative conditions with mast cell lineage involvement [501]. In cancer biopsies, tryptase and chymase-containing mast cells (MCTC) are significantly high in malignant carcinoma (Fig. 5.1A and B), and tryptase was shown to promote the invasion and migration of breast cancer cells [502, 503]. Similarly, in patients with pancreatic cancer the number of tryptase-positive mast cells and serum tryptase activity are both higher than in healthy controls [356]. The latest study shows that both mast cell density positive to protease (MCDPT) and microvescular density (MVD) are correlated with tumorigenesis in breast cancer patients [504]. Mast cells have been shown to be a rich source of vascular endothelial growth factor (VEGF) and to involve angiogenesis [171, 308]. Thus, immune-detection of tryptase and VEGF in mast cell activation can be useful for the diagnosis of inflammatory conditions in cancer patients. Mast cells are identified classically by toluidine-blue staining, which is a conventional method for observing mast cell granules (Fig. 5.1C and D). In addition, these granules include various types of mast cell mediators which can be used for diagnostic purposes. For instance, elevated expression of TNFα has the potential to indicate mast cell activation in cancer, although macrophage is the main source of TNF-α (Fig. 5.2A and B). To analyze mast cell infiltration, the exact quantification of mast cells plays a significant role. Computer-automated quantification of mast cell numbers based on staining of their specific granules has been performed in mastocytosis biopsies and non-malignant skin disorders [505]. In basal cell carcinoma (BCC), mast cells were quantitatively analyzed based on their morphological features and shown to be different in numbers as compared to normal skin [506]. Amgen Inc. has successfully applied a laser scanning cytometer technique for identifying and quantifying mast cells in cancer biopsies. These quantitative studies underline a potential diagnostic value of the assessment of mast cell infiltration in tumor biopsies.

116

Systems Immunology-mast cell

Figure 5.1. Tryptase-positive mast cells are widely present in human cancer tissue. Panel A and B are mast cell detection in cancer specimens (Biomax. Inc); Panel C and D show that mast cells are detected in murine melanoma tissue. Blue spots are mast cell granules in which toluidine blue selectively stains acidic tissue components. Original magnification, 15X for panel A, 4X for panel B, 4X for panel C and 40X for panel D.

Figure 5.2. Microscopic images of lung cancer tissues (Biomax). Tissue sections (A) and (B) were stained for TNF-α by using rabbit polyclonal antibodies against TNF-α (ab6671). The clusters of red- color points indicates that TNF-α positive cells are widely present in the tumor.

117

Systems Immunology-mast cell

5.3. Mast cells as drug target In tumor microenvironments mast cells have been observed to secrete wide range of mediators. On the one hand, mast cells secrete pro-tumorigenic substances as mast cell infiltration positively correlates with tumorigenesis [507, 508]; on the other hand, mediators released from mast cells may have anti-tumor effects; for example, heparin secreted by mast cells can inhibit human breast cancer cell in the presence of fibroblasts [509]. Pharmacologically, targeting of mast cell degranulation in general or inhibition of the action of the pro-tumorigenic factors by small drug compounds may be explored as anti-tumor therapy, either alone or in combination with other anti-cancer therapies.

5.3.1. Targeting mast cell degranulation Upon activation, via the linking of affinity to a wide range of cell surface receptors, mast cells release mediators. These mediators are classified into two types. The first type of mediator-class consists of preformed mediators such as histamine, which are secreted within minutes after mast cell activation. The second mediator class is formed by newly synthesized mediators, including a wide range of cytokines and chemokines, which are secreted over a more sustained time period, like between 3-12 hours after mast cell activation. Moreover, mast cell degranulation is strictly controlled, the process of degranulation can be fast or slow, the latter providing for a more selective process and for piecemeal degranulation. Over 20 compounds have been identified that inhibit mast cell activation and to maintain mast cell stability. This then prevents mast cell degranulation by blocking calcium channels and thereby preventing the release of preformed substances such as histamine and tryptase. Cromolyn is an anti-inflammatory medication. The drug stabilizes mast cells by preventing the release of mediators that cause inflammation. Clinically, cromolyn was first introduced as an anti-asthma drug and has been used for treatment for asthma more than three decades. The latest report on Chlamydia pneumonia lung infection again shows that cromolyn-treated mice displayed reduced BALF cell number and neutrophil numbers, and inhibiting mast cell degranulation is detrimental for Chlamydia pneumonia replication [510]. Introduced in 1984 [511] nedocromil is considered as an inhibitor of mast cell degranulation that prevents the release of substances that cause inflammation. Similarly, both cromolyn and nedocromil are sodium cromoglycate and these drugs inhibit the allergen-induced early phase and late phase responses in the upper and lower airways and conjunctiva, where mucosal mast cells are critically involved in the allergic responses [512]. Functionally, either nedocromil or cromolyn inhibit the flux of chloride ions in mast cells, epithelial cells and neurons and increase their threshold for activation [513]. Apart from anti-inflammatory therapy, cromolyn binds to S100P which is overexpressed in more than 90 % of pancreatic cancers. The interaction between cromolyn and S100P has the ability to block S100P function and to improve its therapeutic effect on cancer [514, 515]. Olopatadine also demonstrates a mast cell stabilizing activity, but in addition acts as an anti-histaminic in which Olopatadine binds to the histamine H1 receptor to block the action of endogenous histamine [516]. Ketotifen is a not her mast cell stabilizer used to treat asthma. Its ability to affect the underlying pathology of asthma includes the inhibition

118

Systems Immunology-mast cell of the development of airway hyper-reactivity associated with activation of platelets by PAF (Platelet Activating Factor), inhibition of PAF-induced accumulation of eosinophils and platelets in the airways, suppression of the priming of eosinophils by human recombinant cytokines, and antagonism of bronchoconstriction due to leukotrienes. Moreover, increased numbers of mast cells raises questions concerning the source of histamine in breast cancer tissues and treatment of breast carcinoma with cimetine, a H2 receptor antagonist, may influence tumor proliferation by blocking tumor-growth promoting effects of histamine [517]. JNJ 7777120 is a new H4 receptor antagonist. H4 receptor activation on human mast cells induces calcium mobilization which triggers signal transduction pathways within the cell, thereby causing mast cell degranulation, but it does not affect IgE crosslinking-induced degranulation [518]. Drugs as azelastine and episastine have been examined to inhibit IgE-mediated phosphorylation of FcRI protein which blocks mast cell activation to degranulation [519]. Many other compounds such as syk- kinase inhibitors [520, 521], MAP kinase inhibitors, ion channel antagonists [522, 523] and sphingosine kinase inhibitors [524] could be used to block mast cell activation, but in most cases other cells are likely to be affected as well.

5.3.2. Targeting mast cell tryptase Tryptase is considered to be a pro-inflammatory mediator. In serum, the total tryptase level reflects a burden of neoplastic mast cells [97, 525]. In various tissues, elevated tryptase content serves as a marker for mast-cell degranulation and thereby of their activation [526, 527]. Chemically, tryptase is a neutral serine protease with trypsin-like specificity, hydrolyzing peptide bonds on the carboxy-terminus of basic residues such as arginine or lysine, with a molecular weight of 134 kDa and with a tetrameric structure consisting of non-covalently linked subunits. From early reports human mast cell tryptase emerged as a new target for therapeutic intervention in systemic anaphylaxis and other hypersensitivity and autoimmune disorders [528, 529]. Upon mast cell degranulation, tryptase is secreted along with histamine, chymase and proteoglycans into extracellular space [530]. Tryptase levels can be quantified both in serum and tissues. This enzyme has been used for monitoring pathogenesis of a variety of human diseases, such as asthma and rheumatoid arthritis [531, 532]. Tryptase-like enzymes take part in the activation and internalization of pathogenic viruses such as influenza virus, Sendai virus and human immunodeficiency virus (HIV) [533, 534]. In cancer, proteolytic activities of tryptase cause degradation of extracellular matrix components. Extracellular matrix degradation is a crucial step for angiogenesis as well as during invasion and metastasis of tumor cells. Mast cell-derived tryptase stimulates the proliferation of vascular endothelial cells and breast cancer cells through activation of the proteinase-activated receptor-2 (PAR-2) on these cells [535]. Increasing numbers of tryptase- positive mast cells are positively associated with tumorigenesis such as human malignant melanoma [536], endometrial carcinoma [537], breast cancer [538], gastric cancer [539], and pancreatic ductal adenocarcinoma [540]. Therapeutically, mast cell tryptase can be targeted by various tryptase inhibitors. Notable compounds include guanidino dipeptide, guanidino dimers, delta inhibitors, benzamidine dimers. These drugs are at the stage of preclinical application only [529]. A separate study shows that aerosol administration of the tryptase inhibitor APC 366, abolished late-phase 119

Systems Immunology-mast cell bronchoconstriction and the associated airway hyperresponsiveness that ensues subsequent airway antigen provocation [541]. A subsequent study showed histamine release to be decreased significantly by using the tryptase inhibitor APC 366v [542]. Recently, molecular mechanisms of tryptase inhibitors such as gabexate mesylate, nafamsotat mesylate and tranilast, have been described, their prospective roles in cancer being discussed as well [543]. The specific drug target of nafamsotat mesylate is implicated by blocking nuclear factor kappa-B (Nf-κB) of the cells in pancreatic cancer [544], which is mediated by tryptase through protease activated receptor-2 (PAR-2).

5.3.3. Anti-IgE therapy in cancer Interaction of IgE with its high affinity receptor FcRI without receptor crosslinking by antigen, has been shown to increase the survival of mast cells. Therefore, in theory, anti- IgE treatment may reduce the number of mast cells in inflammatory conditions. Indeed, TM omaluzimab (Xolair ), an IgE inhibitor that blocks mast cell degranulation, lessened systematic mast cell activation disease (MCAD) in the clinic [545]. The efficacy of omaluzimab has been demonstrated in asthma in which the drug has reduced normal exacerbation rate by 26 % (0.68 versus 0.91), severe exacerbation rates by 50 % (0.24 versus 0.48) and emergency visit rates by 44 % (0.24 versus 0.43), and significantly improved asthma-related quality of life as compared with placebo [546]. Although there is no direct link between IgE level and malignancy, IgE is demonstrated to target tumor cells specifically and to activate tumoricidal effector cells, such as eosinophils, mast cells and macrophages [547, 548]. Therefore, anti-IgE therapy might be considered as anti-cancer therapy although it has not been applied for cancer patients so far.

5.4. Stem Cell Factor (SCF) and the c-kit Mediated Signaling Pathways Mast cell degranulation can be i nfluenced by stem cell factor (SCF) and its cognate receptor c-Kit. Therefore, pharmaceuticals interfering with SCF/c-Kit or their down-stream signals may alter mediator release (patterns) by mast cells.

5.4.1. Stem Cell Factor (SCF) SCF can be expressed in two active isoforms. One isoform is a 248 amino acid-long protein, known as soluble SCF (sSCF) which has a molecular weight of 18.5kDa. The second isoform is a 220 amino acid-long protein, known as membrane SCF (mSCF). In fact, both mSCF and sSCF are membrane-bound, but the presence of a specific cleavage site in sSCF causes production of the soluble form of SCF, whereas mSCF lacks this cleavage site and remains membrane bound. In vitro and in vivo data have provided evidence that mSCF plays a more important role than sSCF in mast cell development [549-551]. SCF is produced in the bone marrow stroma by hematopoietic cells [552], but also by intestinal epithelial cells [553], by cells of the central nervous system [554] and by skin keratinocytes [555]. Importantly, mast cells are strongly chemotactic vis-à-vis chemoattractants such as IgE, adenosine, laminin and TGF-β, but also to SCF [556]. SCF is therefore important in mast cell biology to direct infiltration of mast cell progenitors chemotactically to specific anatomical locations [557-559].

120

Systems Immunology-mast cell

Mast cell progenitors express chemokine receptors to regulate their migration in a tissue specific homing-manner, which plays an important role in innate and adaptive immune responses [560]. Once mast cell progenitors enter peripheral tissues, they differentiate into mast cells under the influence of mainly SCF and locally produced proteins like Interleukin (IL)-3, 4, 10 and Nerve growth factor (NGF). SCF together with IL-3 are required for migration, maturation, proliferation and differentiation of mast cells. Early studies have shown that IL-3 is vital for mast cell proliferation [561-563], whereas SCF is important in maintaining mast cell viability and to promote mast cell maturation [308, 564-566]. As such, mast cell survival is dependent on SCF [560]. Whereas CD34+ progenitor cells stimulated by rhIL-3 produced only small numbers of mast cells, the numbers of mast cells were highly increased upon addition of human recombinant SCF and rhIL-3 to the mast cell-progenitors in vitro [567]. Finally, optimal antigen-mediated mast cell activation is affected by SCF, albeit that data are currently inconclusive. SCF was reported to induce elevated cytokine expression in mast cells upon their antigen-mediated activation [568]. However, prolonged exposure of mast cells to SCF in vivo conversely attenuated IgE-mediated mast cell activation which significantly reduced the capacity of mast cells to degranulate and to release cytokine [569, 570]. All data together, however, show that SCF is not only important for mast cell infiltration, but also crucial for mast cell survival and maturation. Therefore, SCF may be an interesting target to counter undesired mast cell activities.

5.4.2. C-kit The SCF receptor C-kit is distributed widely amongst hematopoietic cells [571]. Interaction between SCF and C-kit is crucial for the biology of mast cells [572, 573]. While activation of the SCF/C-kit signaling pathway promotes mast cell development, inhibition of this pathway leads to mast cell apoptosis [574-576]. Upon binding of its ligand, SCF, C-kit dimerizes and induces tyrosine kinase activity. This process is accompanied by the fusion of the split catalytic domain of each C-kit monomer in the cytosolic domain and results in phosphorylation of tyrosine residues present in the cytosol [577]. Phosphorylation by the fused and activated cytosolic domain of C-kit regulates mast cell function via recruitment and phosphorylation of the tyrosine kinases Lyn and Fyn. Binding of Lyn to the cytosolic domain of C-kit is vital for SCF-mediated proliferation of mast cells [578]. Fyn is required for Rac activation to regulate chemotaxis of mast cells [579]. Further signal transduction in mast cells is achieved via phosphorylation of the adaptor molecules Grb2 and Grb7 (growth factor receptor-bound proteins 2 and 7), and the SH2- containing transforming protein C1 (Shc) [580]. Grb2 and Grb7 are associated with the phosphorylation of tyrosine (Y703 and Y936 in human) in C-kit. The association occurs when Grb2 and Grb7 bound strongly to Y936 in C-kit/SCF, however, Grb2 not Grb7 bound to Y703 in C-kit/SCF. The adaptor protein Grb2 is involved in signal transduction and cell communication, while Grb7 complexes promote cell migration. The presence of Grb2 and Grb7 links SCF signaling to Ras/mitogen-activated protein kinase pathways [580]. Shc is recruited by tyrosine phosphorylation (Y568/570 in human) to signaling pathways, but has so far unknown biological functions. Downstream signaling is also mediated by phosphoinositide

3’-kinase (P13K) and phospholipase Cɣ (PLCɣ). P13K has been implicated in the regulation of DNA synthesis and cell survival. PLCɣ is recruited by phosphorylation of Y730 in mouse 121

Systems Immunology-mast cell and Y936 in human. PLCɣ is involved in proliferation and survival. Collectively, P13K and

PLCɣ activate Ras-Raf-mitogen-activated protein kinase (MAPK), Jak2 and the STAT1/3/5 pathways for mast cell growth, survival, migration, adhesion (Fig.5. 3) [160, 581-585].

4.3. Inhibition of SCS-C-kit signaling C-kit activity can be blocked by specific tyrosine kinase inhibitors. Imatinib mesylate (also known as STI 571/Gleevec/Glivec) is a compound blocking c-Kit catalytic activity. Specifically, imatinib mesylate blocks c-Kit signalling within mast cells and mastocytoma cells, preventing cell proliferation and survival [586]. Clinically, imatinib has already been used for treatment of patients with chronic myeloid leukemia (CML) [587] and gastrointestinal stromal tumors (GISTs) [588]. Nilotinib (AMN107) is an orally available signal transduction inhibitor of c-Kit, Bcr-Abl, and PDGF receptor (PDGFR) tyrosine kinases [589]. Nilotinib inhibits both wild-type c-Kit and c-Kit carrying mutations [590], except for the D816V mutation and could therefore inhibit activation and proliferation of mast cells [591]. PI3K appears to be a major signaling-protein in tumors and is immediately downstream of c-Kit in a signaling pathway also known as the “survival” kinase pathway [592, 593]. Downstream of c-Kit are signaling pathways involving PI3K and other important kinases called AKT and mTOR. Currently, there are drugs in clinical trials that inhibit the PI3K downstream pathway. However, blocking of PI3K may induce side effects such as inhibition of insulin function and heart-failure [594, 595]. Moreover, the activity of bruton tyrosine kinase (Btk) play a functional role in mast cell activation in which Btk is activated upon FcεRI cross-linking to be fully expressed in signal transduction in mast cells [596]. A novel inhibitor of Btk, PCI-32765, shows that the kinase inhibitor blocked the release of granula-derived hexosaminidase from mast cells in vitro; in collagen-induced arthritis model, PCI-32765 is able to suppress disease progression. In insulinoma-bearing mice, PCI-32765 inhibits Btk, blocks mast cell degranulation, and actuates collapse of tumor vasculature and tumor regression [597, 598]. Taken together, targeting mast cell function is an emerging and practical strategy for cancer therapy and clinical application of kinase inhibitor compounds towards mast cells function may ultimately prove beneficial for cancer patients.

122

Systems Immunology-mast cell

Figure 5.3. Schematic representation of signaling pathways; signaling cascades activated in mast cells after SCF binding to the c-Kit receptor. The SCF-mediated signaling pathway is crucial for mast cell function such as growth, proliferation and migration. SCF binds to its receptor, the binding leads to dimerization of C-kit which induces auto phosphorylation of C-kit recruits and activates signaling molecules including Lyn, Fyn and Grb2 as well as P13K, Raf. Activation of these pathways results in multiple functional effects, like increased cell proliferation, differentiation and survival. For detailed information about these signaling cascades see review [160].

123

Systems Immunology-mast cell

d

e

s

u

d

l

ou

c

s

or

it

b

i

nh

i

se

n

a

o

n

ti

i

a

k

v

i

t

e

c

n

i

a

s

e

h

t

yro

t

n

i

e e

v

i

s

t

t

c

e

e

l

rg

e

a

s

t

i i

r

t

e

ul

h

t

m

o

r

e

o

b

o

t

d

e

r

a

e

vi

n

o

i

t

a

v

y

i

t

il

c

r

a

a

d

m

n

a

pri

n

e e

r

o

i

a

t

t

a

r

a

e

h

f

t

i

s

prol

l

l

e

pound

c

m

t

o

s

a

C

m

.

t

1

i

5. b

i

nh

i

o

Table t

124

Systems Immunology-mast cell

5.5. Concluding remarks Mast cells may play a role in tumor development that subtracts from their anti-tumor activities. Mast cell infiltration and degranulation may enhance tumorigenesis. Histological quantification of mast cell infiltration into tumors and their surrounding stroma, together with markers that pinpoint their degranulation status, may therefore have a diagnostic potential in tumor pathology. The tumor promoting effects of mast cells are mainly mediated by secretion of pro-angiogenic factors and by tissue remodeling, enhancement of tumor cell proliferation, and immunosuppression. In clinical situations however, the role of mast cells in tumor formation, maintenance and metastasis has not yet drawn enough effort to unravel the potential of anti-mast cell therapy in cancer either by itself or in combination with other tumor targeting therapies. Further research in preclinical tumor models is necessary to delineate the potential value of the mast cell as a therapeutic target cell for the treatment of cancer.

125

Systems Immunology-mast cell

126

Systems Immunology-future perspective

Chapter 6 Inflammation and Cancer: how could Systems Biology help unravel the Link?

127

Systems Immunology-future perspective

Summary One of the major driving forces of biomedical research is the desire to understand the various factors that influence health and the pathology of disease. Inflammation is such a factor in many diseases. From a historical point of view, Metchnikoff and Ehrlich shared the Nobel Prize in 1908 because they had discovered the inflammatory response to bacterial infection, in terms of antibody production. Since then, it has become clear that inflammation plays a role in many, if not almost all, diseases. Inflammation is a systemic process that involves networks of interactions between various cells and proteins. It assists in tissue repair after injury and tissue remodeling. Chronic inflammation appears essential to arthritis and conducive to various types of cancer. The presence of extensive networks of inflammatory cells and molecules (for instance, chemokines, cytokines and prostaglandins) in tumor tissues may facilitate tissue remodeling and angiogenesis in favor of tumorigenesis in some cases, even though they are normally part of chronic inflammatory responses and tissue repair. We here propose the term immune system biology to indicate an approach to inflammation that focuses on the roles played by networking. Such an approach may help develop an integrated framework of factors ranging from genes to tissues, enabling the integration of dynamic information in ways that make that information computable and hence predictive. Upon experimental validation of predictions this may lead to an improved understanding of some of the links between inflammation and various diseases, including cancer. With the evolution of molecular biology into system biology, biology has gained new technological and conceptual tools for investigating, modeling and understanding living organisms at the system level. The new tools combine experimental and computational approaches and implement advanced quantitative techniques and/or large-scale measurement methodologies. Also clinically, translation of biological data into systemic understanding may enhance more targeted and individualized therapies of chronic diseases including cancer. Recently, the US National Institutes Health (NIH) set a roadmap to apply systems biology to the exploration of inflammation, emphasizing the importance of mathematics and engineering for biomedical research.

6.1.The inflammation systems perspective Inflammation is a dynamic process involved in many complex diseases [389, 616-618]. Both cellular and molecular processes regulate inflammation. They do this through complex interactions, mostly between proteins. This is important if not vital for understanding the pathophysiology of many diseases [619-621]. And then there is the paradox: on the one hand, inflammation favors tissue repair and regeneration after injury, whilst on the other hand, inflammation is a prime source of many chronic diseases (Fig.6.1). The latter include asthma, Alzheimer’s, trauma, sepsis, inflammatory bowel disease, and various types of cancer. Despite tremendous efforts, numerous issues encumber an operational understanding of how the immune system contributes to those diseases, i.e. an understanding that supports therapeutic intervention. Innate immune cells are the major participants in both acute and chronic inflammation. Accordingly, there continues to be a need to apply laboratory, clinical 128

Systems Immunology-future perspective and systems biology in order to pattern the mechanisms of innate immunity in response to inflammation. By using systems biology approaches, biologists and physicians aiming to gain insights into the biology of inflammation combine experimental discovery with mathematical modeling [622]. This newly-recognized field is likely to provide a platform for a new generation of drugs, which will target inflammation based on interference with the molecular and cellular network rather than with single molecular species. New and more effective therapies may emerge that take into consideration how biological components interact with each other to produce emergent behaviors. Online resources, such as http://systems.genetics.ucla.edu/data and www.jjj.bio.vu.nl, provide genetic and kinetic models that elucidate disease mechanisms and can help identify network targets also outside the immediate realm of inflammation. For instance, Bakker et al. presented a kinetic model of glycolysis in the bloodstream of the mammalian parasite Trypanosoma brucei as a JWS-online model [242]. A systems approach to inflammation should not shy away from the complexity of addressing integral dynamic, distributed processes either [623, 624]. For instance, Altboum and colleagues used digital cell quantification (DCQ) to study immune cell dynamics in mouse lungs during days of influenza infection [625]. A system model quantitatively described immune cell interactions via cytokine signaling in a study of inflammation in the human skin [626]. Borghans and colleagues developed a model to study the mechanisms of T-cell vaccination (TCV) [627] and to estimate T- and B-cell receptors diversities by using Amplicot (concentration × time) values [628]. Naq and colleagues constructed a predictive model for how an IgE-Fc epsilon RI complex might enhance Syc kinase phosphorylation in mast cell activation [629]. Callender and colleagues presented mathematical modelling and analysis of the G-protein signaling pathway in macrophage [630]. A subsequent study developed a mathematical model of the phenotypic switches among three phenotypes of helper T-cell (Th1, Th2, and Th17) in response to airway exposure to lipopolysaccharides (LPS) [631]. System genetics in a mouse setting has unraveled the macrophage response to inflammation [632]. In targeting inflammation, immunological modeling offers the potential to tune pharmaceutical processes such as in target prioritization [633, 634]. This includes efforts aimed at optimizing treatment schedules of and at personalizing treatment approaches towards, inflammatory diseases and cancer [635-637]. In silico disease modeling provides predictive information to accelerate and rationalize drug discovery. It may also help in the identification of clinically relevant biomarkers of inflammatory diseases [638, 639]; in silico modeling has been applied to studying the process of infectious diseases, for instance in the analysis of a mathematical model of HIV revealing dynamical features of the disease which might provide therapies for this infection [640-642]. A mathematical model of TNF-α dynamics revealed that TNF-α blockade should offer high drug efficacy in rheumatoid patients but limited efficacy on systemic inflammatory response syndrome [643].

129

Systems Immunology-future perspective

Figure 6. 1. The various factors that trigger inflammation. Inflammation possesses “a double-edged sword”: while acute inflammation has positive effects on health, chronic inflammation has negative effects, which might also promote tumorigenesis.

6.2.The cancer system perspective Over the past decades, cancer immunology has developed as a unique branch of the biomedical sciences. Cancer is a network disease [239]. Indeed, cancer requires, at the same time, the sustaining of proliferative signaling, the evasion of growth suppressors, the resisting of cell death, angiogenesis, and the activation of invasion and metastasis [644]. A cancer microenvironment constitutes blood vessels, immune cells, fibroblasts, signaling molecules, the extracellular matrix (ECM) and mechanical cues that promote tumorigenesis. Immune cells are a critical component of the cancer microenvironment which makes their networking a most relevant research objective in system-based immunology. The immune system includes both adaptive and innate immunity, and indeed, intricate combinations thereof. In adaptive immunity, B cells and T cells are important for orchestrating the antigen specific immune responses to foreign materials such as bacteria and viruses [645]. B cells produce the immunoglobulins, which mostly consist of a complex of two heavy chain 130

Systems Immunology-future perspective polypeptides with two light chain polypeptides, all of which have antigen recognitions parts. B cells can also secrete the light chains independently, as so-called Free Light Chains (FLCs). Innate immune cells include mast cells, macrophages, basophils, neutrophils, and dendritic cells. Although these cells provide immediate defense against infection and injury by acute inflammation, they may somehow lead to chronic inflammation upon prolonged inflammation. Studies show that chronic inflammation is associated with a wide range of malignancies. For example, chronic skin inflammation is associated with skin squamous cell carcinomas (SCCs) and inflammation is induced by epidermal c-Fos in mouse skin [646]. In human, prolonged activation of the host immune system by parasitic, viral, and bacterial agents has been shown to promote tumorigenesis in bladder [647], liver [648], and stomach [649]. A classical biology that focuses on an individual cell, or a molecular biology focusing on a molecule, has limited ability to deliver comprehensive information about cancer development. Traditional systems biology approaches would add a repertoire of tools helping to construct a model of the network of intracellular components and protein molecules that capture the intrinsic properties of tumor cells [650]. For tumor immunology one needs to proceed even further however, so as also to address cell-cell interactions. This is important, for instance, when considering how B-cell-secreted FLC initiates mast cell activation during tumorigenesis and when outlining network-based drug targeting in cancer-specific microenvironments. That is, one needs to look at molecular as well as at cellular networking and in fact at both in integration. It is this crossroads between intracellular biology, transcellular biology and systems biology that I have been trying to develop for cancer immunology in this thesis. When expounding the network, the analysis in this thesis considered that TNF-α released by mast cells might be pro-tumorigenic due to the elimination of healthy tissues from the inflammatory environment, creating space for tumor expansion. As a result of tumor-cell activation, a positive feedback loop could “kick in” stimulating FLC-mediated mast cell activation. As this would explain the dying of healthy tissues during chronic inflammation, this was also taken into account. A corollary of a loop is that intervention at any point in the loop may affect its function. Hence, also mast cells should constitute a target in this network, i.e. inhibiting mast cell activation could be a strategy for enhancing the efficacy of cancer treatment. Indeed the addition of a specific inhibitor of FLC was then predicted to regress tumor growth by blocking mast cell activation. These predicted cellular events seemed to align with a mechanistic principle of “more activated mast cell = higher tumorigenesis” and “less activated mast cell = lower tumorigenesis”. Herewith our network-based approach identified FLC as a rational drug target and helped analyze its interaction mechanism with immune cells in operational terms, i.e. in terms that might assist rational therapy design. In the supplemental material to chapter 3, I described a preliminary experimental test of this possibility, which I carried out in collaboration with others. Evidence in the literature indicates that FLC is not only an activator of mast cells but also an activator of neutrophils in chronic inflammation [312], even though clinical studies have shown that free light chain in serum is a biomarker for patients with B-cell non-Hodgkin lymphoma and chronic leukemia [651]. Network-based FLC therapy should be expected to affect network flexibility, including positive feedback loops through cells and molecules in the network and this could be a promising strategy for enhancing the therapeutic efficacy towards chronic inflammatory diseases as well as cancer. Recent studies indicated that 131

Systems Immunology-future perspective network-based drug design may help advance treatment methods by outlining personalized medicine for patients suffering from complex diseases [652-655]. Such personalized medicine may help provide the right patient with the right drug at the right time at the right dose. Cresci and colleagues reported that personalized therapy reduced mortality in heart attack patients treated with anti-clotting drug by focusing on p450 gene variants [656]. Thiele et al mapped metabolic data information onto a genome sequence based framework (‘metabolic map’) predicting biomarkers at 77% accuracy when compared to experimental data [657]. Geenen and colleagues developed an experiment-based mathematical model of glutathione metabolism in human liver, predicting the efficacy of biomarkers [658]. Both these approaches can be personalized readily. If the models of inflammation developed in this thesis can be linked through to specific proteins and thence to genes, a similar such personalized approach should become possible.

6.3.Personalized medicine In the 18th century, Caleb Parry wrote that “it is much more important to know what kind of patient has a disease than to know what kind of disease a patient has”. François-Marie Arouet has been quoted as saying that “Doctors are men who prescribe medicines of which they know little, to cure diseases of which they know less, in human beings of whom they know nothing”. In the late 1990s, the notion of personalized medicine has been introduced to the biomedical community. More predictive and preventive strategies are lying at the heart of personalized medicine, which confers the concept of “the right drug at the right dose at the right time’’ and optimizes drug prescription by following patients individually. Designing drug tailored to genomic profiles and targeting individual genetic variations, may establish a new doctrine in biomedical research. More than almost 40% of drug compounds in phase I and nearly 70% of drug compounds in phase II have failed in trials [659]. Thus, there is an urgent need to tailor medication, including prevention, diagnosis and treatment. A prime way in which medicine will grow to defeat human diseases more definitively, is for system approaches to elucidate disease mechanisms, and for personalized medicine to jump-increase therapy efficacy. In the past decade, significant progress has been made towards personalized medicine. Despite a mire of obstacles to the practical application of personalized medicine, the National Institutes of Health (NIH) and the Food and Drug Administration (FDA) set a goal for future medicine to deliver benefit to patients. Researchers identified genetic variability as a key issue which might not only make fundamental causes of human illness less elusive, but also make patients’ responses to therapies more manageable. We also recognize more paradoxical challenges, such as a policy that is required for securing the patient’s privacy. On the one hand, regulatory agencies and policy makers are encouraging de novo co-development of drug-genetic test combinations by industry [660]. On the other hand, scientists and politicians are working together to protect people’s genetic information. In 2008, the US senate passed the Genetic Information Non-discrimination Act (GINA) to ban US employers from using genetic information in hiring, firing, and deciding about promotion, as well as from collecting genetic information from employees. In 2010, Senator Barack Obama’s bill also known as the Genomics and Personalized Medicine Act (GPMA) was resubmitted to the US congress. It aimed to set new directions for the

132

Systems Immunology-future perspective regulation of personalized medicine. In the European Union, academia and research institutes set objectives to analyze definitions of genetic tests. Under the framework of EU-funded projects, Lehrach, Zatloukal, Westerhoff and their colleagues launched a research consortium to work towards the virtual patient and elucidate more of the nature of disease, as the second important step of understanding of the human genome. To legalize individual treatment, the European Union Law passed the legislation for a Biomedical convention in which article 4 obliges professionals in the health field to follow certain rules [661]. In 2013, the Health Directorate of the European Commission published “staff working documents” that defined the positive role of personalized medicine and the challenges and opportunities that it presents to healthcare systems. Internationally, The Personalized Medicine Coalition (PMC) was established to present new approaches towards reforming healthcare systems. These approaches include the adoption of personalized medicine concepts and products, for the benefit of patients. The Systems Biology technologies also enable traditional medicine to re-flourish. For example, Wang and colleagues summarized new trends in and potential limitations of Systems Biology in Traditional Chinese Medicine (TCM) [662]. In the Netherlands, the Sino-Dutch Center for Preventive and Personalized Medicine (SD PPM) was launched to bridge between philosophies of Western and Chinese medicine, based on a Systems Biology approach. Van Wietmarschen and colleagues used Systems Biology tools to analyze rheumatoid arthritis (RA) patients which are classified as cold and hot in Chinese medical practice [663]. Similar to other traditional medicine, Uyghur traditional medicine along with Western medicine still plays a significant role in the Xinjiang region in China. The basic principle of Uyghur medicine is that a doctor first checks a patient’s pulse and symptoms, and then writes a personalized prescription which is based on the disease history of the individual patient. Uyghur medicine has the potential to re-flourish by using the Systems Biology approach. Today, research activities addressing personalized medicine cover a broad spectrum of various diseases. Recently, the FDA published a report about personalized medicine in which 33% of the activity focuses on oncology, 21% of the activity is about cardiorenal research, 16% of the research activity addresses personalized antiviral medicine and 11% of the research activity is based on neurology. More than 100 approved drugs contain information on genomic biomarkers. For instance, vemurafenib approved by the FDA in 2012 selectively binds to the ATP-binding site of BRAF kinase and inhibits its activity [664]. Panitumumab, a product of Amgen inc, is an inhibitor for epidermal growth factor receptor (EGFR) and the drug already entered the clinics treating patients with colorectal cancer. Overall, real progress will come when clinically beneficial new products and approaches are incorporated into translational systems biology in the clinic. As systems biology advances, we expect to see more individualized clinical trials based on a more thorough understanding of the dynamic features of disease. Because there is a strong clinical need to accelerate research on personalized medicine, future studies on drug discovery and targeting should also consider whether manipulating specific targets for specific individuals during disease progression can have a positive effect on drug therapy. With substantial investment and broad collaboration, personalized medicine may well bring better outcomes and better health to patients.

133

Systems Immunology-future perspective

6.4.Future directions In the search for new therapeutic strategies and in our venture towards a personalized medicine maximizing therapeutic efficacy, the system biology of inflammation and cancer may be of help. This study may serve as a first step: it presented a comprehensive view of a network-based cancer-associated inflammation. The combination of experimental and theoretical systems biology may be strength of our study, as limited as the experimental validation may still be: experimental validation of the models of systems biology is always more difficult than can be accommodated in a single study. The simulation results from inflammatory and cancer networks that were discussed in previous chapters did correspond with data from published experimental studies on human and murine systems and from our own studies in the supplementary material of chapter 3. A next goal is to determine which components and species within the network need detailing in the model so as to achieve quantitative as well as qualitative outcomes that reflect the experimental data more precisely. And in future work, we will investigate how to scale this model to predict chronic inflammation which might lead to tumorigenesis. This will require close collaboration with clinicians, oncologists and immunologists. In addition, we will investigate further both the pro- and the anti-cancer functions of mast cells. As future research resolves issues, enhanced understanding of the pharmacological modification of mast cell networks may ultimately enable the stabilization of mast cells in the tumor microenvironment, and thereby the destabilization of tumor progression.

134

Systems Immunology- supplements

Chapter 7 Further understanding and initial validation thereof (supplemental)

135

Systems Immunology- supplements

Summary

This chapter describes work that is supplemental to this thesis. It identifies important feedback loops in the innate immune response network and then shows the results of modelling‐accompanied experiments that validate these feedback loops, their effects and associated therapies.

S7.1. Methods Some aspects of the model of Chapter 2 were verified experimentally. Through microscopy we found evidence that mast cells bound FLC and IgE and took up anti‐FLC drug (Fig. S7.1). Other aspects were based on common literature knowledge, as reviewed in Chapter 2. We next used the scheme of the model (Fig.2.1 in Chapter 2) to decipher feedback loops (Supplementary Fig. S2‐6). We modelled the effect of increasing FLC levels and the prediction was validated by an experimental study (Supplementary Fig.7). To predict therapeutic effect of anti‐FLC, we incorporated anti‐FLC peptide into the model and validated the prediction accordingly (Supplementary Fig. 8). Similarly, we incorporated anti‐ TNF‐α antibody into the model and referred to validation in clinics (Supplementary Fig. 9). To validate mast cells as a main source of TNF‐α, we performed ELISA study to quantify the TNF‐α level on tissue sections containing tumor cells surrounded by an inflammatory environment (Supplementary Fig.10). Chapter 2 and 3 report further predictions of the model and show that they correspond with observations reported in the experimental literature. Notwithstanding these efforts, the complexity of the model is so large that it will require a multitude of additional experiments for complete validation. In terms of its validation, the status of the models used here should be classified as preliminary.

S7.2. Results We validated one of the mechanistic aspects of the model, i.e. that FLC binds to mast cells (Fig. S7.1). Fig.S7.2‐S7.6 show the network motifs that appear to play roles in innate immunity based inflammation. Fig. S7.2 shows the naive network in the presence of bacteria but without the positive feedback loop that these bacteria introduced into the network (Fig. S7.3). Fig.S7.4 shows that by secreting molecules that activate the innate immune system and that we here simulate as CRA, the bacteria initiate a second positive feedback loop. These two positive feedback loops amplify each other. Fig.S7.5 emphasizes that other phenomena may also stimulate the latter feedback loop, essentially by also providing antigens that activate the loop. Fig.S7.6 shows how essentially in our models we have summarized all these causes into a single CRA influx from the outside world. We have also begun some experimental validations of the importance of these loops, by interfering with them. A model of the complexity of the model we have constructed for innate immunity requires a great many experiments for validation, only some of which could be carried out by us. We did show (Fig.S7.7) that FLC itself can induce inflammation in mice, as observed in an ear swelling assay and that intraperitoneal injection with anti‐FLC peptide could strongly reduce this inflammation (Fig.S7.8). Experimental results had been predicted by our modelling. The peptide also reduced the observation of TNF‐α and histamine at inflammation sites (Fig.S7.9).

136

Systems Immunology- supplements

S7.3. Discussion In this chapter some initial work on understanding the causal chains in innate immunity, as well as some experimental validations were reported. They show that our model of innate immunity is useful for developing and then modelling ideas, and that some of these ideas are validated by experiments. Many more experiments will be needed before our model can be considered to be robust or falsified and replaced by a better model.

A

Supplementary figure S7.1. Immune validation of proposed FLC binding to mast cells (A; left hand side) Binding of fluorescein (FITC)‐labeled FLC to mast cells; (B; right‐hand

side) Binding of fluorescein (FITC)‐ labeled IgE to mast cells as positive B control.

137

Systems Immunology- supplements

innate

get species of

The The scheme shows CRA

Figure S7.2. System model scheme for immune response containing FLC‐mediated mast cell activation. stimulated B‐cells secreting FLC; interaction between FLC and mast cell causing mediator release by a second CRA contact (simulating contact with antigen that CRA) with the is FLC‐mast cell complex; healthy covariant with bacteria as target species of protease secreted by the activated mast cells; tar TNF‐α secreted by the mast cells being healthy fibroblasts; B‐cell activating by foursecreted typescell all being represented molecules is by CRA: healthy and dying bacteria, as well as dyinghealthy fibroblasts.and

Dying bacteria

Dying

fibroblast

Healthy Healthy bacteria

138

Systems Immunology- supplements

bacteria

Healthy

fibrobla

Healthy

bacteria

Dying

ewr. h seai rfr t te ae in case the to refers scenario The network. in silico. in protease secrete not do cells mast the which flow the indicates arrowhead the and loop, feedback repres texture is which arrow The arrow. U‐turn the by indicated as division response bacterial for scenario the represents scheme 1. immune loop feedback positive innate containing for scheme model System S7.3. figure Supplementary

the towards molecules CRA of positive the of effect an ents

ild ih papyrus with filled

The

139

Systems Immunology- supplements

CRA

indicated indicated by the U‐turn arrow;

protease to combat bacterial e e arrows indicate directions of the

The scheme represents a scenario for the

scheme for elucidating innate positive feedback loops 1 immune and 2. response anti‐infectious role of mast cells. Bacterial division is the blu second loop; the two arrows that are filled with papyrus texture represent effect of this positive feedback loop; the first effect is to change the bacterial status from healthy to dying species and the second effect is secretion from the arrowhead dying indicates bacteria, the the molecules into flow the network. of This represents the CRA scheme case in which mast cells do secrete infection silico. in

Supplementary figure S7.4. System model

Dying bacteria

140

Systems Immunology- supplements

bacteria

Healthy

bacteria

Dying

ak e arw TFα production, TNF‐α arrow, red dark is system the When and chronicinflammatory diseas and TNF‐α of production excessive fibroblasts feedback to lead can this which loop, of effect side the indicates figure supplementary in described two fee positive the of effects The loop. feedback arrows second the of TNF‐α. directions indicate arrows blue The secrete arrow. cells is division Bacterial mast rhe allergies, as such states positive theimmunity. innate of effects side for scenario of 2. response loop effects feedback immune side innate showing for scheme model System S7.5. figure Supplementary

filled with papyrus texture represent represent texture papyrus with filled

a represents scheme The

indicated by the U‐turn U‐turn the by indicated

umatoid arthritis or or arthritis umatoid

xrse t disease to expressed

dback loop as they as loop dback of death excessive

e.

S4.3; the S4.3;

141

Systems Immunology- supplements

System System

into the α α

ections of the positive

The scheme represents a .

silico. Supplementary Supplementary figure S7.6. model scheme for response in innate the absence immune of bacterial infection showing inflammation in scenario for constant towards the network. When the system CRA influx switches from anti‐infectious to pro‐ inflammation, the secreted TNF‐α from mast cell turns healthy fibroblasts to dying fibroblasts. The blue indicate arrows dir feedback loop created; which the is filled arrow with papyrus indicates texture influx of molecules, and the red arrow indicates external CRA the feeding of CRA and TNF‐ network

Dying Dying fibroblast

142

Systems Immunology- supplements

Dyingn g fibrobl ast

143

Systems Immunology- supplements

Supplementary figure S7.7. Activation of the innate immune response by high amounts of FLC. The scheme represents a scenario for obtaining an FLC‐sensitized network with antigen specificity. When increased amount of FLC present in the network, the system should be highly sensitive to an antigen molecule. In the scheme, FLC is enclosed by a red circle to indicate its increase by injection. (A) The curves show the simulated evolution of TNF‐α levels for the case in which there was a high amount of FLC in the system (solid blue line), and the control simulation with little FLC in the system (red dashed line). For positive control, we used a Copasi model that contains a high initial level of FLC. For negative control, we used a Copasi model that contains a low initial level of FLC. (B) Inflammation (ear swelling) induced by FLC (=IgLC) injection into mice. In mice injected with phosphate‐buffered saline (PBS) instead, inflammation did not occur.

144

Systems Immunology- supplements

145

Systems Immunology- supplements

146

Systems Immunology- supplements

Supplementary figure S7.8. Intervention by high anti‐FLC peptide with ear inflammation in mouse. The scheme represents a scenario for the anti‐FLC therapy of inflammation. When anti‐FLC peptide is presented to the network, the drug interferes with the pathways related to FLC‐mediated mast cell activation as indicated by the red crosses. CRA influx to the system would continue B‐cell activation as indicated by a blue arrow. (A) Model simulation of TNF‐α for (blue) when a high amount of FLC transfers antigen‐sensitivity in the system and (red) when there is little FLC in the system. (B) The curve shows a simulated line (blue) in which the network contains anti‐FLC peptide known as F991 and the red circle indicates the level of TNF‐α without that therapy in silico. As experimental validation, (C) mice ear swelling induced by antigen‐specific FLC was treated by intraperitoneal injection of anti‐FLC peptide (F991) after antigen challenge in vivo.

147

Systems Immunology- supplements

Supplementary figure S7.9. Effect of anti‐FLC peptide on inflammation. (A) and (B) show that anti‐FLC peptide reduces the concentrations of TNF‐α and histamine in the inflammation around a tumor in vivo, and these samples were taken from wild‐type mice treated with anti‐FLC peptide (F991) or Phosphate‐buffered saline (PBS). Separately, the samples were also taken from mast‐cell deficient mice. Treating with PBS and using of mast cell deficient mice was used as positive and negative controls, respectively. Both wild‐type and mast‐cell deficient mice were inoculated by murine B16 melanoma.

148

Systems Immunology-list of references

References

149

Systems Immunology-list of references

1. Benaroyo, L., How do we define inflammation?. Praxis (Bern 1994), 1994. 83 (48): P. 1343-7 ). 2. Heidland, A., Klassen, A., Rutkowski, P. & Bahner, U. (2006) The contribution of Rudolf Virchow to the concept of inflammation: what is still of importance?, J Nephrol. 19 Suppl 10, S102-9. 3. Virchow, R. C. (2006) Report on the typhus epidemic in Upper Silesia. 1848, Am J Public Health. 96, 2102-5. 4. O'Riordan, M., Yi, C. H., Gonzales, R., Lee, K. D. & Portnoy, D. A. (2002) Innate recognition of bacteria by a macrophage cytosolic surveillance pathway, Proc Natl Acad Sci U S A. 99, 13861-6. 5. Smith, K. D., Andersen-Nissen, E., Hayashi, F., Strobe, K., Bergman, M. A., Barrett, S. L., Cookson, B. T. & Aderem, A. (2003) Toll-like receptor 5 recognizes a conserved site on flagellin required for protofilament formation and bacterial motility, Nat Immunol. 4, 1247-53. 6. Hemmi, H., Takeuchi, O., Kawai, T., Kaisho, T., Sato, S., Sanjo, H., Matsumoto, M., Hoshino, K., Wagner, H., Takeda, K. & Akira, S. (2000) A Toll-like receptor recognizes bacterial DNA, Nature. 408, 740-5. 7. Wellek, B., Hahn, H. & Opferkuch, W. (1976) Opsonizing activities of IgG, IgM antibodies and the C3b inactivator-cleaved third component of complement in macrophage phagocytosis, Agents Actions. 6, 260-2. 8. Murphy, K. P., Travers, P., Walport, M., Janeway, C. & Ralph Erskine Conrad Memorial Fund. (2008) Janeway's immunobiology, 7th edn, Garland Science, New York. 9. Cambier, J. C., Pleiman, C. M. & Clark, M. R. (1994) Signal transduction by the B cell antigen receptor and its coreceptors, Annu Rev Immunol. 12, 457-86. 10. Reth, M. & Wienands, J. (1997) Initiation and processing of signals from the B cell antigen receptor, Annu Rev Immunol. 15, 453-79. 11. Mueller, C. G., Boix, C., Kwan, W. H., Daussy, C., Fournier, E., Fridman, W. H. & Molina, T. J. (2007) Critical role of monocytes to support normal B cell and diffuse large B cell lymphoma survival and proliferation, J Leukoc Biol. 82, 567-75. 12. Huard, B., Arlettaz, L., Ambrose, C., Kindler, V., Mauri, D., Roosnek, E., Tschopp, J., Schneider, P. & French, L. E. (2004) BAFF production by antigen-presenting cells provides T cell co-stimulation, Int Immunol. 16, 467-75. 13. Freer, G. & Matteucci, D. (2009) Influence of dendritic cells on viral pathogenicity, PLoS Pathog. 5, e1000384. 14. Trombetta, E. S., Ebersold, M., Garrett, W., Pypaert, M. & Mellman, I. (2003) Activation of lysosomal function during dendritic cell maturation, Science. 299, 1400-3. 15. Galy, A., Travis, M., Cen, D. & Chen, B. (1995) Human T, B, natural killer, and dendritic cells arise from a common bone marrow progenitor cell subset, Immunity. 3, 459-73. 16. Meek, B., Cloosen, S., Borsotti, C., Van Elssen, C. H., Vanderlocht, J., Schnijderberg, M. C., van der Poel, M. W., Leewis, B., Hesselink, R., Manz, M. G., Katsura, Y., Kawamoto, H., Germeraad, W. T. & Bos, G. M. (2010) In vitro-differentiated T/natural killer-cell progenitors derived from human CD34+ cells mature in the thymus, Blood. 115, 261-4. 17. Poli, A., Michel, T., Theresine, M., Andres, E., Hentges, F. & Zimmer, J. (2009) CD56bright natural killer (NK) cells: an important NK cell subset, Immunology. 126, 458-65. 18. Cooper, M. A., Fehniger, T. A., Turner, S. C., Chen, K. S., Ghaheri, B. A., Ghayur, T., Carson, W. E. & Caligiuri, M. A. (2001) Human natural killer cells: a unique innate immunoregulatory role for the CD56(bright) subset, Blood. 97, 3146-51. 19. van Dommelen, S. L., Tabarias, H. A., Smyth, M. J. & Degli-Esposti, M. A. (2003) Activation of natural killer (NK) T cells during murine cytomegalovirus infection enhances the antiviral response mediated by NK cells, J Virol. 77, 1877-84. 20. Bishop, G. A., Marlin, S. D., Schwartz, S. A. & Glorioso, J. C. (1984) Human natural killer cell recognition of herpes simplex virus type 1 glycoproteins: specificity analysis with the use of monoclonal antibodies and antigenic variants, J Immunol. 133, 2206-14. 21. Burshtyn, D. N. (2013) NK cells and poxvirus infection, Front Immunol. 4, 7. 22. Arase, H., Mocarski, E. S., Campbell, A. E., Hill, A. B. & Lanier, L. L. (2002) Direct recognition of cytomegalovirus by activating and inhibitory NK cell receptors, Science. 296, 1323-6. 23. Mandelboim, O., Lieberman, N., Lev, M., Paul, L., Arnon, T. I., Bushkin, Y., Davis, D. M., Strominger, J. L., Yewdell, J. W. & Porgador, A. (2001) Recognition of haemagglutinins on virus- infected cells by NKp46 activates lysis by human NK cells, Nature. 409, 1055-60.

150

Systems Immunology-list of references

24. Moretta, A., Bottino, C., Mingari, M. C., Biassoni, R. & Moretta, L. (2002) What is a natural killer cell?, Nat Immunol. 3, 6-8. 25. Ivarsson, M. A., Michaelsson, J. & Fauriat, C. (2014) Activating killer cell Ig-like receptors in health and disease, Front Immunol. 5, 184. 26. van Dommelen, S. L., Sumaria, N., Schreiber, R. D., Scalzo, A. A., Smyth, M. J. & Degli-Esposti, M. A. (2006) Perforin and granzymes have distinct roles in defensive immunity and immunopathology, Immunity. 25, 835-48. 27. Lord, S. J., Rajotte, R. V., Korbutt, G. S. & Bleackley, R. C. (2003) Granzyme B: a natural born killer, Immunol Rev. 193, 31-8. 28. Farhadi, N., Lambert, L., Triulzi, C., Openshaw, P. J., Guerra, N. & Culley, F. J. (2014) Natural killer cell NKG2D and granzyme B are critical for allergic pulmonary inflammation, J Allergy Clin Immunol. 133, 827-35 e3. 29. Guidotti, L. G. (2002) The role of cytotoxic T cells and cytokines in the control of hepatitis B virus infection, Vaccine. 20 Suppl 4, A80-2. 30. Phillips, S., Chokshi, S., Riva, A., Evans, A., Williams, R. & Naoumov, N. V. (2010) CD8(+) T cell control of hepatitis B virus replication: direct comparison between cytolytic and noncytolytic functions, J Immunol. 184, 287-95. 31. Busca, A. & Kumar, A. (2014) Innate immune responses in hepatitis B virus (HBV) infection, Virology journal. 11, 22. 32. de Visser, K. E., Eichten, A. & Coussens, L. M. (2006) Paradoxical roles of the immune system during cancer development, Nat Rev Cancer. 6, 24-37. 33. Green, C. E., Liu, T., Montel, V., Hsiao, G., Lester, R. D., Subramaniam, S., Gonias, S. L. & Klemke, R. L. (2009) Chemoattractant signaling between tumor cells and macrophages regulates cancer cell migration, metastasis and neovascularization, PLoS One. 4, e6713. 34. Gorgun, G. T., Whitehill, G., Anderson, J. L., Hideshima, T., Maguire, C., Laubach, J., Raje, N., Munshi, N. C., Richardson, P. G. & Anderson, K. C. (2013) Tumor-promoting immune-suppressive myeloid-derived suppressor cells in the multiple myeloma microenvironment in humans, Blood. 121, 2975-87. 35. Terme, M., Ullrich, E., Delahaye, N. F., Chaput, N. & Zitvogel, L. (2008) Natural killer cell- directed therapies: moving from unexpected results to successful strategies, Nat Immunol. 9, 486-94. 36. Alvarez-Breckenridge, C. A., Yu, J., Price, R., Wojton, J., Pradarelli, J., Mao, H., Wei, M., Wang, Y., He, S., Hardcastle, J., Fernandez, S. A., Kaur, B., Lawler, S. E., Vivier, E., Mandelboim, O., Moretta, A., Caligiuri, M. A. & Chiocca, E. A. (2012) NK cells impede glioblastoma virotherapy through NKp30 and NKp46 natural cytotoxicity receptors, Nat Med. 18, 1827-34. 37. Kono, K., Takahashi, A., Sugai, H., Fujii, H., Choudhury, A. R., Kiessling, R. & Matsumoto, Y. (2002) Dendritic cells pulsed with HER-2/neu-derived peptides can induce specific T-cell responses in patients with gastric cancer, Clin Cancer Res. 8, 3394-400. 38. Van Driessche, A., Gao, L., Stauss, H. J., Ponsaerts, P., Van Bockstaele, D. R., Berneman, Z. N. & Van Tendeloo, V. F. (2005) Antigen-specific cellular immunotherapy of leukemia, Leukemia. 19, 1863- 71. 39. Bourkoula, K., Englert, C., Giaisi, M., Kohler, R., Krammer, P. H. & Li-Weber, M. (2014) The Wilms' tumor suppressor WT1 enhances CD95L expression and promotes activation-induced cell death in leukemic T cells, Int J Cancer. 134, 291-300. 40. Mizukami, M., Hanagiri, T., Baba, T., Fukuyama, T., Nagata, Y., So, T., Ichiki, Y., Sugaya, M., Yasuda, M., Takenoyama, M., Sugio, K. & Yasumoto, K. (2005) Identification of tumor associated antigens recognized by IgG from tumor-infiltrating B cells of lung cancer: correlation between Ab titer of the patient's sera and the clinical course, Cancer Sci. 96, 882-8. 41. Pages, F., Berger, A., Camus, M., Sanchez-Cabo, F., Costes, A., Molidor, R., Mlecnik, B., Kirilovsky, A., Nilsson, M., Damotte, D., Meatchi, T., Bruneval, P., Cugnenc, P. H., Trajanoski, Z., Fridman, W. H. & Galon, J. (2005) Effector memory T cells, early metastasis, and survival in colorectal cancer, N Engl J Med. 353, 2654-66. 42. Azimi, F., Scolyer, R. A., Rumcheva, P., Moncrieff, M., Murali, R., McCarthy, S. W., Saw, R. P. & Thompson, J. F. (2012) Tumor-infiltrating lymphocyte grade is an independent predictor of sentinel lymph node status and survival in patients with cutaneous melanoma, J Clin Oncol. 30, 2678-83. 43. Ali, H. R., Provenzano, E., Dawson, S. J., Blows, F. M., Liu, B., Shah, M., Earl, H. M., Poole, C. J., Hiller, L., Dunn, J. A., Bowden, S. J., Twelves, C., Bartlett, J. M., Mahmoud, S. M., Rakha, E., Ellis,

151

Systems Immunology-list of references

I. O., Liu, S., Gao, D., Nielsen, T. O., Pharoah, P. D. & Caldas, C. (2014) Association between CD8+ T-cell infiltration and breast cancer survival in 12 439 patients, Ann Oncol. 25, 1536-43. 44. Zhang, L., Conejo-Garcia, J. R., Katsaros, D., Gimotty, P. A., Massobrio, M., Regnani, G., Makrigiannakis, A., Gray, H., Schlienger, K., Liebman, M. N., Rubin, S. C. & Coukos, G. (2003) Intratumoral T cells, recurrence, and survival in epithelial ovarian cancer, N Engl J Med. 348, 203-13. 45. Ino, Y., Yamazaki-Itoh, R., Shimada, K., Iwasaki, M., Kosuge, T., Kanai, Y. & Hiraoka, N. (2013) Immune cell infiltration as an indicator of the immune microenvironment of pancreatic cancer, Br J Cancer. 108, 914-23. 46. Rusakiewicz, S., Semeraro, M., Sarabi, M., Desbois, M., Locher, C., Mendez, R., Vimond, N., Concha, A., Garrido, F., Isambert, N., Chaigneau, L., Le Brun-Ly, V., Dubreuil, P., Cremer, I., Caignard, A., Poirier-Colame, V., Chaba, K., Flament, C., Halama, N., Jager, D., Eggermont, A., Bonvalot, S., Commo, F., Terrier, P., Opolon, P., Emile, J. F., Coindre, J. M., Kroemer, G., Chaput, N., Le Cesne, A., Blay, J. Y. & Zitvogel, L. (2013) Immune infiltrates are prognostic factors in localized gastrointestinal stromal tumors, Cancer Res. 73, 3499-510. 47. Balkwill, F. (2004) Cancer and the chemokine network, Nat Rev Cancer. 4, 540-50. 48. Robinson, S. C., Scott, K. A. & Balkwill, F. R. (2002) Chemokine stimulation of monocyte matrix metalloproteinase-9 requires endogenous TNF-alpha, Eur J Immunol. 32, 404-12. 49. Zou, W., Machelon, V., Coulomb-L'Hermin, A., Borvak, J., Nome, F., Isaeva, T., Wei, S., Krzysiek, R., Durand-Gasselin, I., Gordon, A., Pustilnik, T., Curiel, D. T., Galanaud, P., Capron, F., Emilie, D. & Curiel, T. J. (2001) Stromal-derived factor-1 in human tumors recruits and alters the function of plasmacytoid precursor dendritic cells, Nat Med. 7, 1339-46. 50. Granata, F., Frattini, A., Loffredo, S., Staiano, R. I., Petraroli, A., Ribatti, D., Oslund, R., Gelb, M. H., Lambeau, G., Marone, G. & Triggiani, M. (2010) Production of vascular endothelial growth factors from human lung macrophages induced by group IIA and group X secreted phospholipases A2, J Immunol. 184, 5232-41. 51. Lamagna, C., Aurrand-Lions, M. & Imhof, B. A. (2006) Dual role of macrophages in tumor growth and angiogenesis, J Leukoc Biol. 80, 705-13. 52. Guo, C., Buranych, A., Sarkar, D., Fisher, P. B. & Wang, X. Y. (2013) The role of tumor- associated macrophages in tumor vascularization, Vasc Cell. 5, 20. 53. Zhao, W., Wang, L., Zhang, L., Yuan, C., Kuo, P. C. & Gao, C. (2010) Differential expression of intracellular and secreted osteopontin isoforms by murine macrophages in response to toll-like receptor agonists, J Biol Chem. 285, 20452-61. 54. Flavell, R. A., Sanjabi, S., Wrzesinski, S. H. & Licona-Limon, P. (2010) The polarization of immune cells in the tumour environment by TGFbeta, Nat Rev Immunol. 10, 554-67. 55. Castriconi, R., Cantoni, C., Della Chiesa, M., Vitale, M., Marcenaro, E., Conte, R., Biassoni, R., Bottino, C., Moretta, L. & Moretta, A. (2003) Transforming growth factor beta 1 inhibits expression of NKp30 and NKG2D receptors: consequences for the NK-mediated killing of dendritic cells, Proc Natl Acad Sci U S A. 100, 4120-5. 56. Thomas, D. A. & Massague, J. (2005) TGF-beta directly targets cytotoxic T cell functions during tumor evasion of immune surveillance, Cancer Cell. 8, 369-80. 57. Tlsty, T. D. & Hein, P. W. (2001) Know thy neighbor: stromal cells can contribute oncogenic signals, Curr Opin Genet Dev. 11, 54-9. 58. Karagiannis, G. S., Poutahidis, T., Erdman, S. E., Kirsch, R., Riddell, R. H. & Diamandis, E. P. (2012) Cancer-associated fibroblasts drive the progression of metastasis through both paracrine and mechanical pressure on cancer tissue, Mol Cancer Res. 10, 1403-18. 59. Mueller, M. M. & Fusenig, N. E. (2004) Friends or foes - bipolar effects of the tumour stroma in cancer, Nat Rev Cancer. 4, 839-49. 60. Kalluri, R. & Zeisberg, M. (2006) Fibroblasts in cancer, Nat Rev Cancer. 6, 392-401. 61. Lawrenson, K., Grun, B., Lee, N., Mhawech-Fauceglia, P., Kan, J., Swenson, S., Lin, Y. G., Pejovic, T., Millstein, J. & Gayther, S. A. (2015) NPPB is a Novel Candidate Biomarker Expressed by Cancer-Associated Fibroblasts In Epithelial Ovarian Cancer, Int J Cancer. 136 (6): 1390-401 62. Tarin, D. (2012) Clinical and biological implications of the tumor microenvironment, Cancer Microenviron. 5, 95-112. 63. Unsworth, A., Anderson, R. & Britt, K. (2014) Stromal Fibroblasts and the Immune Microenvironment: Partners in Mammary Gland Biology and Pathology?, J Mammary Gland Biol Neoplasia.

152

Systems Immunology-list of references

64. Pittet, M. J. (2009) Behavior of immune players in the tumor microenvironment, Curr Opin Oncol. 21, 53-9. 65. Gorbachev, A. V. & Fairchild, R. L. (2014) Regulation of chemokine expression in the tumor microenvironment, Crit Rev Immunol. 34, 103-20. 66. Lens, M. B. & Dawes, M. (2002) Interferon alfa therapy for malignant melanoma: a systematic review of randomized controlled trials, J Clin Oncol. 20, 1818-25. 67. Payne, R., Glenn, L., Hoen, H., Richards, B., Smith, J. W., 2nd, Lufkin, R., Crocenzi, T. S., Urba, W. J. & Curti, B. D. (2014) Durable responses and reversible toxicity of high-dose interleukin-2 treatment of melanoma and renal cancer in a Community Hospital Biotherapy Program, J Immunother Cancer. 2, 13. 68. Kawai, K., Miyazaki, J., Joraku, A., Nishiyama, H. & Akaza, H. (2013) Bacillus Calmette-Guerin (BCG) immunotherapy for bladder cancer: current understanding and perspectives on engineered BCG vaccine, Cancer Sci. 104, 22-7. 69. Pardoll, D. M. (2012) The blockade of immune checkpoints in cancer immunotherapy, Nat Rev Cancer. 12, 252-64. 70. Naidoo, J., Page, D. B. & Wolchok, J. D. (2014) Immune checkpoint blockade, Hematol Oncol Clin North Am. 28, 585-600. 71. Lipson, E. J. & Drake, C. G. (2011) Ipilimumab: an anti-CTLA-4 antibody for metastatic melanoma, Clin Cancer Res. 17, 6958-62. 72. Sivendran, S., Pan, M., Kaufman, H. L. & Saenger, Y. (2010) Herpes simplex virus oncolytic vaccine therapy in melanoma, Expert Opin Biol Ther. 10, 1145-53. 73. Gardner, T. A., Elzey, B. D. & Hahn, N. M. (2012) Sipuleucel-T (Provenge) autologous vaccine approved for treatment of men with asymptomatic or minimally symptomatic castrate-resistant metastatic prostate cancer, Hum Vaccin Immunother. 8, 534-9. 74. Murch, S. H., Lamkin, V. A., Savage, M. O., Walker-Smith, J. A. & MacDonald, T. T. (1991) Serum concentrations of tumour necrosis factor alpha in childhood chronic inflammatory bowel disease, Gut. 32, 913-7. 75. Rink, L. & Kirchner, H. (1996) Recent progress in the tumor necrosis factor-alpha field, Int Arch Allergy Immunol. 111, 199-209. 76. Riches, D. W., Chan, E. D. & Winston, B. W. (1996) TNF-alpha-induced regulation and signalling in macrophages, Immunobiology. 195, 477-90. 77. Agbanoma, G., Li, C., Ennis, D., Palfreeman, A. C., Williams, L. M. & Brennan, F. M. (2012) Production of TNF-alpha in macrophages activated by T cells, compared with lipopolysaccharide, uses distinct IL-10-dependent regulatory mechanism, J Immunol. 188, 1307-17. 78. Woolley, D. E. & Tetlow, L. C. (2000) Mast cell activation and its relation to proinflammatory cytokine production in the rheumatoid lesion, Arthritis Res. 2, 65-74. 79. Bischoff, S. C., Lorentz, A., Schwengberg, S., Weier, G., Raab, R. & Manns, M. P. (1999) Mast cells are an important cellular source of tumour necrosis factor alpha in human intestinal tissue, Gut. 44, 643-52. 80. Bryl, E., Vallejo, A. N., Matteson, E. L., Witkowski, J. M., Weyand, C. M. & Goronzy, J. J. (2005) Modulation of CD28 expression with anti-tumor necrosis factor alpha therapy in rheumatoid arthritis, Arthritis Rheum. 52, 2996-3003. 81. Komatsu, M., Kobayashi, D., Saito, K., Furuya, D., Yagihashi, A., Araake, H., Tsuji, N., Sakamaki, S., Niitsu, Y. & Watanabe, N. (2001) Tumor necrosis factor-alpha in serum of patients with inflammatory bowel disease as measured by a highly sensitive immuno-PCR, Clin Chem. 47, 1297- 301. 82. Spuler, S., Yousry, T., Scheller, A., Voltz, R., Holler, E., Hartmann, M., Wick, M. & Hohlfeld, R. (1996) Multiple sclerosis: prospective analysis of TNF-alpha and 55 kDa TNF receptor in CSF and serum in correlation with clinical and MRI activity, J Neuroimmunol. 66, 57-64. 83. Thomas, P. S. (2001) Tumour necrosis factor-alpha: the role of this multifunctional cytokine in asthma, Immunol Cell Biol. 79, 132-40. 84. Taille, C., Poulet, C., Marchand-Adam, S., Borie, R., Dombret, M. C., Crestani, B. & Aubier, M. (2013) Monoclonal Anti-TNF-alpha Antibodies for Severe Steroid-Dependent Asthma: A Case Series, Open Respir Med J. 7, 21-5. 85. Su, C. G., Judge, T. A. & Lichtenstein, G. R. (2001) The role of biological therapy in inflammatory bowel disease, Drugs Today (Barc). 37, 121-133.

153

Systems Immunology-list of references

86. van Denderen, J. C., Visman, I. M., Nurmohamed, M. T., Suttorp-Schulten, M. S. & van der Horst- Bruinsma, I. E. (2014) Adalimumab Significantly Reduces the Recurrence Rate of Anterior Uveitis in Patients with Ankylosing Spondylitis, J Rheumatol. 41(9): 1843-8. 87. Tobinick, E., Gross, H., Weinberger, A. & Cohen, H. (2006) TNF-alpha modulation for treatment of Alzheimer's disease: a 6-month pilot study, Med.Gen.Med. 8, 25. 88. Mogi, M., Togari, A., Kondo, T., Mizuno, Y., Komure, O., Kuno, S., Ichinose, H. & Nagatsu, T. (2000) Caspase activities and tumor necrosis factor receptor R1 (p55) level are elevated in the substantia nigra from parkinsonian brain, J Neural Transm. 107, 335-41. 89. Tarkowski, E., Tullberg, M., Fredman, P. & Wikkelso, C. (2003) Correlation between intrathecal sulfatide and TNF-alpha levels in patients with vascular dementia, Dement Geriatr Cogn Disord. 15, 207-11. 90. Szlosarek, P. W., Grimshaw, M. J., Kulbe, H., Wilson, J. L., Wilbanks, G. D., Burke, F. & Balkwill, F. R. (2006) Expression and regulation of tumor necrosis factor alpha in normal and malignant ovarian epithelium, Mol Cancer Ther. 5, 382-90. 91. Oshima, H., Ishikawa, T., Yoshida, G. J., Naoi, K., Maeda, Y., Naka, K., Ju, X., Yamada, Y., Minamoto, T., Mukaida, N., Saya, H. & Oshima, M. (2013) TNF-alpha/TNFR1 signaling promotes gastric tumorigenesis through induction of Noxo1 and Gna14 in tumor cells, Oncogene. 92. Okazaki, T., Sakon, S., Sasazuki, T., Sakurai, H., Doi, T., Yagita, H., Okumura, K. & Nakano, H. (2003) Phosphorylation of serine 276 is essential for p65 NF-kappaB subunit-dependent cellular responses, Biochem Biophys Res Commun. 300, 807-12. 93. Gannon, P. O., Lessard, L., Stevens, L. M., Forest, V., Begin, L. R., Minner, S., Tennstedt, P., Schlomm, T., Mes-Masson, A. M. & Saad, F. (2013) Large-scale independent validation of the nuclear factor-kappa B p65 prognostic biomarker in prostate cancer, Eur J Cancer. 49, 2441-8. 94. Tanabe, K., Matsushima-Nishiwaki, R., Yamaguchi, S., Iida, H., Dohi, S. & Kozawa, O. (2010) Mechanisms of tumor necrosis factor-alpha-induced interleukin-6 synthesis in glioma cells, J Neuroinflammation. 7, 16. 95. Sansone, P. & Bromberg, J. (2012) Targeting the interleukin-6/Jak/stat pathway in human malignancies, J Clin Oncol. 30, 1005-14. 96. Lesina, M., Kurkowski, M. U., Ludes, K., Rose-John, S., Treiber, M., Kloppel, G., Yoshimura, A., Reindl, W., Sipos, B., Akira, S., Schmid, R. M. & Algul, H. (2011) Stat3/Socs3 activation by IL-6 transsignaling promotes progression of pancreatic intraepithelial neoplasia and development of pancreatic cancer, Cancer Cell. 19, 456-69. 97. Sperr, W. R., Jordan, J. H., Fiegl, M., Escribano, L., Bellas, C., Dirnhofer, S., Semper, H., Simonitsch-Klupp, I., Horny, H. P. & Valent, P. (2002) Serum tryptase levels in patients with mastocytosis: correlation with mast cell burden and implication for defining the category of disease, International archives of allergy and immunology. 128, 136-41. 98. Lin, R. Y., Schwartz, L. B., Curry, A., Pesola, G. R., Knight, R. J., Lee, H. S., Bakalchuk, L., Tenenbaum, C. & Westfal, R. E. (2000) Histamine and tryptase levels in patients with acute allergic reactions: An emergency department-based study, J Allergy Clin Immunol. 106, 65-71. 99. Bargagli, E., Bigliazzi, C., Leonini, A., Nikiforakis, N., Perari, M. G. & Rottoli, P. (2005) Tryptase concentrations in bronchoalveolar lavage from patients with chronic eosinophilic pneumonia, Clin Sci (Lond). 108, 273-6. 100. Ferrer, M., Nunez-Cordoba, J. M., Luquin, E., Grattan, C. E., De la Borbolla, J. M., Sanz, M. L. & Schwartz, L. B. (2010) Serum total tryptase levels are increased in patients with active chronic urticaria, Clin Exp Allergy. 40, 1760-6. 101. Wang, H. W., McNeil, H. P., Husain, A., Liu, K., Tedla, N., Thomas, P. S., Raftery, M., King, G. C., Cai, Z. Y. & Hunt, J. E. (2002) Delta tryptase is expressed in multiple human tissues, and a recombinant form has proteolytic activity, J Immunol. 169, 5145-52. 102. Malfettone, A., Silvestris, N., Saponaro, C., Ranieri, G., Russo, A., Caruso, S., Popescu, O., Simone, G., Paradiso, A. & Mangia, A. (2013) High density of tryptase-positive mast cells in human colorectal cancer: a poor prognostic factor related to protease-activated receptor 2 expression, J Cell Mol Med. 17, 1025-37. 103. Ibaraki, T., Muramatsu, M., Takai, S., Jin, D., Maruyama, H., Orino, T., Katsumata, T. & Miyazaki, M. (2005) The relationship of tryptase- and chymase-positive mast cells to angiogenesis in stage I non-small cell lung cancer, Eur J Cardiothorac Surg. 28, 617-21.

154

Systems Immunology-list of references

104. Kambe, N., Kambe, M., Kochan, J. P. & Schwartz, L. B. (2001) Human skin-derived mast cells can proliferate while retaining their characteristic functional and protease phenotypes, Blood. 97, 2045- 52. 105. Strouch, M. J., Cheon, E. C., Salabat, M. R., Krantz, S. B., Gounaris, E., Melstrom, L. G., Dangi- Garimella, S., Wang, E., Munshi, H. G., Khazaie, K. & Bentrem, D. J. (2010) Crosstalk between mast cells and pancreatic cancer cells contributes to pancreatic tumor progression, Clin Cancer Res. 16, 2257-65. 106. Gruber, B. L., Kew, R. R., Jelaska, A., Marchese, M. J., Garlick, J., Ren, S., Schwartz, L. B. & Korn, J. H. (1997) Human mast cells activate fibroblasts: tryptase is a fibrogenic factor stimulating collagen messenger ribonucleic acid synthesis and fibroblast chemotaxis, J Immunol. 158, 2310-7. 107. Foley, T. T., Saggers, G. C., Moyer, K. E. & Ehrlich, H. P. (2011) Rat mast cells enhance fibroblast proliferation and fibroblast-populated collagen lattice contraction through gap junctional intercellular communications, Plast Reconstr Surg. 127, 1478-86. 108. Foley, T. T. & Ehrlich, H. P. (2013) Through gap junction communications, co-cultured mast cells and fibroblasts generate fibroblast activities allied with hypertrophic scarring, Plast Reconstr Surg. 131, 1036-44. 109. Kinoshita, M., Okada, M., Hara, M., Furukawa, Y. & Matsumori, A. (2005) Mast cell tryptase in mast cell granules enhances MCP-1 and interleukin-8 production in human endothelial cells, Arterioscler Thromb Vasc Biol. 25, 1858-63. 110. Luciani, M. G., Stoppacciaro, A., Peri, G., Mantovani, A. & Ruco, L. P. (1998) The monocyte chemotactic protein a (MCP-1) and (IL-8) in Hodgkin's disease and in solid tumours, Mol Pathol. 51, 273-6. 111. Nelson, M., Brown, R. D., Gibson, J. & Joshua, D. E. (1992) Measurement of free kappa and lambda chains in serum and the significance of their ratio in patients with multiple myeloma, Br J Haematol. 81, 223-30. 112. Papastamatis, S. C., Kench, J. E. & Wilkinson, J. F. (1949) Amino acid composition of Bence- Jones protein, Nature. 164, 961. 113. Engelfried, J. J. (1950) The quantitative estimation of Bence Jones proteins in urine, J Lab Clin Med. 36, 137-9. 114. Jirgensons, B., Landua, A. J. & Awapara, J. (1952) Study of the physicochemical and chemical properties of Bence-Jones protein from multiple myeloma, Biochim Biophys Acta. 9, 625-32. 115. Isobe, T., Itoh, T., Tomon, M., Okimura, Y., Kinoshita, K. & Fujita, T. (1983) Plasma cell myeloma showing progressive bone destruction during long-term clinical remission, Jpn J Med. 22, 245-8. 116. Van Loveren, H., Ratzlaff, R. E., Kato, K., Meade, R., Ferguson, T. A., Iverson, G. M., Janeway, C. A. & Askenase, P. W. (1986) Immune serum from mice contact-sensitized with picryl chloride contains an antigen-specific T cell factor that transfers immediate cutaneous reactivity, Eur J Immunol. 16, 1203-8. 117. Hopper, J. E. & Papagiannes, E. (1986) Evidence by radioimmunoassay that mitogen-activated human blood mononuclear cells secrete significant amounts of light chain Ig unassociated with heavy chain, Cell Immunol. 101, 122-31. 118. Hannam-Harris, A. C. & Smith, J. L. (1981) Free immunoglobulin light chain synthesis by human foetal liver and cord blood lymphocytes, Immunology. 43, 417-23. 119. Bradwell, A. R., Carr-Smith, H. D., Mead, G. P., Tang, L. X., Showell, P. J., Drayson, M. T. & Drew, R. (2001) Highly sensitive, automated immunoassay for immunoglobulin free light chains in serum and urine, Clin Chem. 47, 673-80. 120. Basnayake, K., Stringer, S. J., Hutchison, C. A. & Cockwell, P. (2011) The biology of immunoglobulin free light chains and kidney injury, Kidney Int. 79, 1289-301. 121. Mead, G. P., Carr-Smith, H. D., Drayson, M. T., Morgan, G. J., Child, J. A. & Bradwell, A. R. (2004) Serum free light chains for monitoring multiple myeloma, Br J Haematol. 126, 348-54. 122. Drayson, M., Tang, L. X., Drew, R., Mead, G. P., Carr-Smith, H. & Bradwell, A. R. (2001) Serum free light-chain measurements for identifying and monitoring patients with nonsecretory multiple myeloma, Blood. 97, 2900-2. 123. Lachmann, H. J., Gallimore, R., Gillmore, J. D., Carr-Smith, H. D., Bradwell, A. R., Pepys, M. B. & Hawkins, P. N. (2003) Outcome in systemic AL amyloidosis in relation to changes in concentration of circulating free immunoglobulin light chains following chemotherapy, Br J Haematol. 122, 78-84.

155

Systems Immunology-list of references

124. Abraham, R. S., Katzmann, J. A., Clark, R. J., Bradwell, A. R., Kyle, R. A. & Gertz, M. A. (2003) Quantitative analysis of serum free light chains. A new marker for the diagnostic evaluation of primary systemic amyloidosis, Am J Clin Pathol. 119, 274-8. 125. Itzykson, R., Le Garff-Tavernier, M., Katsahian, S., Diemert, M. C., Musset, L. & Leblond, V. (2008) Serum-free light chain elevation is associated with a shorter time to treatment in Waldenstrom's macroglobulinemia, Haematologica. 93, 793-4. 126. Leleu, X., Xie, W., Bagshaw, M., Banwait, R., Leduc, R., Roper, N., Weller, E. & Ghobrial, I. M. (2011) The role of serum immunoglobulin free light chain in response and progression in waldenstrom macroglobulinemia, Clin Cancer Res. 17, 3013-8. 127. Gottenberg, J. E., Aucouturier, F., Goetz, J., Sordet, C., Jahn, I., Busson, M., Cayuela, J. M., Sibilia, J. & Mariette, X. (2007) Serum immunoglobulin free light chain assessment in rheumatoid arthritis and primary Sjogren's syndrome, Ann Rheum Dis. 66, 23-7. 128. Landgren, O., Goedert, J. J., Rabkin, C. S., Wilson, W. H., Dunleavy, K., Kyle, R. A., Katzmann, J. A., Rajkumar, S. V. & Engels, E. A. (2010) Circulating serum free light chains as predictive markers of AIDS-related lymphoma, J Clin Oncol. 28, 773-9. 129. Davern, S., Tang, L. X., Williams, T. K., Macy, S. D., Wall, J. S., Weiss, D. T. & Solomon, A. (2008) Immunodiagnostic capabilities of anti-free immunoglobulin light chain monoclonal antibodies, Am J Clin Pathol. 130, 702-11. 130. Metz, M., Grimbaldeston, M. A., Nakae, S., Piliponsky, A. M., Tsai, M. & Galli, S. J. (2007) Mast cells in the promotion and limitation of chronic inflammation, Immunol Rev. 217, 304-28. 131. Galli, S. J., Nakae, S. & Tsai, M. (2005) Mast cells in the development of adaptive immune responses, Nat Immunol. 6, 135-42. 132. Grimbaldeston, M. A., Metz, M., Yu, M., Tsai, M. & Galli, S. J. (2006) Effector and potential immunoregulatory roles of mast cells in IgE-associated acquired immune responses, Curr Opin Immunol. 18, 751-60. 133. Tsai, M., Grimbaldeston, M. & Galli, S. J. Mast cells and immunoregulation/immunomodulation, Adv Exp Med Biol. 716, 186-211. 134. Kalesnikoff, J. & Galli, S. J. (2008) New developments in mast cell biology, Nature immunology. 9, 1215-23. 135. Daeron, M. (1997) Fc receptor biology, Annual review of immunology. 15, 203-34. 136. Saito, H., Ishizaka, T. & Ishizaka, K. (2013) Mast cells and IgE: from history to today, Allergol Int. 62, 3-12. 137. Galli, S. J. & Tsai, M. (2012) IgE and mast cells in allergic disease, Nat Med. 18, 693-704. 138. Humphrey, M. B., Lanier, L. L. & Nakamura, M. C. (2005) Role of ITAM-containing adapter proteins and their receptors in the immune system and bone, Immunological reviews. 208, 50-65. 139. Kovarova, M., Tolar, P., Arudchandran, R., Draberova, L., Rivera, J. & Draber, P. (2001) Structure-function analysis of Lyn kinase association with lipid rafts and initiation of early signaling events after Fcepsilon receptor I aggregation, Mol Cell Biol. 21, 8318-28. 140. Siraganian, R. P. (2003) Mast cell signal transduction from the high-affinity IgE receptor, Current opinion in immunology. 15, 639-46. 141. Nishizumi, H. & Yamamoto, T. (1997) Impaired tyrosine phosphorylation and Ca2+ mobilization, but not degranulation, in lyn-deficient bone marrow-derived mast cells, J Immunol. 158, 2350-5. 142. Kawakami, Y., Kitaura, J., Satterthwaite, A. B., Kato, R. M., Asai, K., Hartman, S. E., Maeda- Yamamoto, M., Lowell, C. A., Rawlings, D. J., Witte, O. N. & Kawakami, T. (2000) Redundant and opposing functions of two tyrosine kinases, Btk and Lyn, in mast cell activation, J Immunol. 165, 1210- 9. 143. Parravicini, V., Gadina, M., Kovarova, M., Odom, S., Gonzalez-Espinosa, C., Furumoto, Y., Saitoh, S., Samelson, L. E., O'Shea, J. J. & Rivera, J. (2002) Fyn kinase initiates complementary signals required for IgE-dependent mast cell degranulation, Nat Immunol. 3, 741-8. 144. Tkaczyk, C., Horejsi, V., Iwaki, S., Draber, P., Samelson, L. E., Satterthwaite, A. B., Nahm, D. H., Metcalfe, D. D. & Gilfillan, A. M. (2004) NTAL phosphorylation is a pivotal link between the signaling cascades leading to human mast cell degranulation following Kit activation and Fc epsilon RI aggregation, Blood. 104, 207-14. 145. Groot Kormelink, T., Abudukelimu, A. & Redegeld, F. A. (2009) Mast cells as target in cancer therapy, Curr Pharm Des. 15, 1868-78.

156

Systems Immunology-list of references

146. Woolhiser, M. R., Okayama, Y., Gilfillan, A. M. & Metcalfe, D. D. (2001) IgG-dependent activation of human mast cells following up-regulation of FcgammaRI by IFN-gamma, European journal of immunology. 31, 3298-307. 147. Ott, V. L. & Cambier, J. C. (2000) Activating and inhibitory signaling in mast cells: new opportunities for therapeutic intervention?, The Journal of allergy and clinical immunology. 106, 429- 40. 148. Metcalfe, D. D., Baram, D. & Mekori, Y. A. (1997) Mast cells, Physiol Rev. 77, 1033-79. 149. Kumar, R. K., Temelkovski, J., McNeil, H. P. & Hunter, N. (2000) Airway inflammation in a murine model of chronic asthma: evidence for a local humoral immune response, Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology. 30, 1486-92. 150. Sylvestre, D. L. & Ravetch, J. V. (1996) A dominant role for mast cell Fc receptors in the Arthus reaction, Immunity. 5, 387-90. 151. Dombrowicz, D., Flamand, V., Miyajima, I., Ravetch, J. V., Galli, S. J. & Kinet, J. P. (1997) Absence of Fc epsilonRI alpha chain results in upregulation of Fc gammaRIII-dependent mast cell degranulation and anaphylaxis. Evidence of competition between Fc epsilonRI and Fc gammaRIII for limiting amounts of FcR beta and gamma chains, The Journal of clinical investigation. 99, 915-25. 152. Pullen, N. A., Falanga, Y. T., Morales, J. K. & Ryan, J. J. (2012) The Fyn-STAT5 Pathway: A New Frontier in IgE- and IgG-Mediated Mast , Frontiers in immunology. 3, 117. 153. Dietrich, N., Rohde, M., Geffers, R., Kroger, A., Hauser, H., Weiss, S. & Gekara, N. O. (2010) Mast cells elicit proinflammatory but not type I interferon responses upon activation of TLRs by bacteria, Proceedings of the National Academy of Sciences of the United States of America. 107, 8748- 53. 154. Supajatura, V., Ushio, H., Nakao, A., Akira, S., Okumura, K., Ra, C. & Ogawa, H. (2002) Differential responses of mast cell Toll-like receptors 2 and 4 in allergy and innate immunity, The Journal of clinical investigation. 109, 1351-9. 155. Stelekati, E., Bahri, R., D'Orlando, O., Orinska, Z., Mittrucker, H. W., Langenhaun, R., Glatzel, M., Bollinger, A., Paus, R. & Bulfone-Paus, S. (2009) Mast cell-mediated antigen presentation regulates CD8+ T cell effector functions, Immunity. 31, 665-76. 156. Chung, S. H., Kweon, M. N., Lee, H. K., Choi, S. I., Yang, J. Y. & Kim, E. K. (2009) Toll-like receptor 4 initiates an innate immune response to lipopolysaccharide in human conjunctival epithelial cells, Exp Eye Res. 88, 49-56. 157. Redegeld, F. A., van der Heijden, M. W., Kool, M., Heijdra, B. M., Garssen, J., Kraneveld, A. D., Van Loveren, H., Roholl, P., Saito, T., Verbeek, J. S., Claassens, J., Koster, A. S. & Nijkamp, F. P. (2002) Immunoglobulin-free light chains elicit immediate hypersensitivity-like responses, Nat Med. 8, 694-701. 158. Kraneveld, A. D., Kool, M., van Houwelingen, A. H., Roholl, P., Solomon, A., Postma, D. S., Nijkamp, F. P. & Redegeld, F. A. (2005) Elicitation of allergic asthma by immunoglobulin free light chains, Proc Natl Acad Sci U S A. 102, 1578-83. 159. Takai, T., Li, M., Sylvestre, D., Clynes, R. & Ravetch, J. V. (1994) FcR gamma chain deletion results in pleiotrophic effector cell defects, Cell. 76, 519-29. 160. Gilfillan, A. M. & Tkaczyk, C. (2006) Integrated signalling pathways for mast-cell activation, Nat Rev Immunol. 6, 218-30. 161. Coussens, L. M., Raymond, W. W., Bergers, G., Laig-Webster, M., Behrendtsen, O., Werb, Z., Caughey, G. H. & Hanahan, D. (1999) Inflammatory mast cells up-regulate angiogenesis during squamous epithelial carcinogenesis, Genes Dev. 13, 1382-97. 162. Ribatti, D. & Crivellato, E. (2011) Mast cells, angiogenesis and cancer, Advances in experimental medicine and biology. 716, 270-88. 163. Chang, D. Z., Ma, Y., Ji, B., Wang, H., Deng, D., Liu, Y., Abbruzzese, J. L., Liu, Y. J., Logsdon, C. D. & Hwu, P. (2011) Mast cells in tumor microenvironment promotes the in vivo growth of pancreatic ductal adenocarcinoma, Clin Cancer Res. 17, 7015-23. 164. Stoyanov, E., Uddin, M., Mankuta, D., Dubinett, S. M. & Levi-Schaffer, F. (2012) Mast cells and histamine enhance the proliferation of non-small cell lung cancer cells, Lung Cancer. 75, 38-44. 165. Nonomura, N., Takayama, H., Nishimura, K., Oka, D., Nakai, Y., Shiba, M., Tsujimura, A., Nakayama, M., Aozasa, K. & Okuyama, A. (2007) Decreased number of mast cells infiltrating into needle biopsy specimens leads to a better prognosis of prostate cancer, British journal of cancer. 97, 952-6.

157

Systems Immunology-list of references

166. Polajeva, J., Sjosten, A. M., Lager, N., Kastemar, M., Waern, I., Alafuzoff, I., Smits, A., Westermark, B., Pejler, G., Uhrbom, L. & Tchougounova, E. (2011) Mast cell accumulation in glioblastoma with a potential role for stem cell factor and chemokine CXCL12, PLoS One. 6, e25222. 167. Das Roy, L., Curry, J. M., Sahraei, M., Besmer, D. M., Kidiyoor, A., Gruber, H. E. & Mukherjee, P. (2013) Arthritis augments breast cancer metastasis: role of mast cells and SCF/c-Kit signaling, Breast Cancer Res. 15. 168. Gordon, J. R., Burd, P. R. & Galli, S. J. (1990) Mast cells as a source of multifunctional cytokines, Immunology today. 11, 458-64. 169. Galli, S. J., Gordon, J. R. & Wershil, B. K. (1991) Cytokine production by mast cells and basophils, Current opinion in immunology. 3, 865-72. 170. Galli, S. J. (1993) New concepts about the mast cell, The New England journal of medicine. 328, 257-65. 171. Grutzkau, A., Kruger-Krasagakes, S., Baumeister, H., Schwarz, C., Kogel, H., Welker, P., Lippert, U., Henz, B. M. & Moller, A. (1998) Synthesis, storage, and release of vascular endothelial growth factor/vascular permeability factor (VEGF/VPF) by human mast cells: implications for the biological significance of VEGF206, Molecular biology of the cell. 9, 875-84. 172. Qu, Z., Huang, X., Ahmadi, P., Stenberg, P., Liebler, J. M., Le, A. C., Planck, S. R. & Rosenbaum, J. T. (1998) Synthesis of basic fibroblast growth factor by murine mast cells. Regulation by transforming growth factor beta, tumor necrosis factor alpha, and stem cell factor, International archives of allergy and immunology. 115, 47-54. 173. Jorpes, J. E. (1959) Heparin: a mucopolysaccharide and an active antithrombotic drug, Circulation. 19, 87-91. 174. Theoharides, T. C., Bondy, P. K., Tsakalos, N. D. & Askenase, P. W. (1982) Differential release of serotonin and histamine from mast cells, Nature. 297, 229-31. 175. Okayama, Y., Ono, Y., Nakazawa, T., Church, M. K. & Mori, M. (1998) Human skin mast cells produce TNF-alpha by substance P, International archives of allergy and immunology. 117 Suppl 1, 48-51. 176. Kanbe, N., Tanaka, A., Kanbe, M., Itakura, A., Kurosawa, M. & Matsuda, H. (1999) Human mast cells produce matrix metalloproteinase 9, Eur J Immunol. 29, 2645-9. 177. Sperr, W. R., Jordan, J. H., Baghestanian, M., Kiener, H. P., Samorapoompichit, P., Semper, H., Hauswirth, A., Schernthaner, G. H., Chott, A., Natter, S., Kraft, D., Valenta, R., Schwartz, L. B., Geissler, K., Lechner, K. & Valent, P. (2001) Expression of mast cell tryptase by myeloblasts in a group of patients with acute myeloid leukemia, Blood. 98, 2200-9. 178. Starkey, J. R., Crowle, P. K. & Taubenberger, S. (1988) Mast-cell-deficient W/Wv mice exhibit a decreased rate of tumor angiogenesis, Int J Cancer. 42, 48-52. 179. Bochner, B. S., Charlesworth, E. N., Lichtenstein, L. M., Derse, C. P., Gillis, S., Dinarello, C. A. & Schleimer, R. P. (1990) Interleukin-1 is released at sites of human cutaneous allergic reactions, J Allergy Clin Immunol. 86, 830-9. 180. Nigrovic, P. A., Binstadt, B. A., Monach, P. A., Johnsen, A., Gurish, M., Iwakura, Y., Benoist, C., Mathis, D. & Lee, D. M. (2007) Mast cells contribute to initiation of autoantibody-mediated arthritis via IL-1, Proc Natl Acad Sci U S A. 104, 2325-30. 181. Nakao, S., Kuwano, T., Tsutsumi-Miyahara, C., Ueda, S., Kimura, Y. N., Hamano, S., Sonoda, K. H., Saijo, Y., Nukiwa, T., Strieter, R. M., Ishibashi, T., Kuwano, M. & Ono, M. (2005) Infiltration of COX-2-expressing macrophages is a prerequisite for IL-1 beta-induced neovascularization and tumor growth, J Clin Invest. 115, 2979-91. 182. Kaler, P., Augenlicht, L. & Klampfer, L. (2009) Macrophage-derived IL-1beta stimulates Wnt signaling and growth of colon cancer cells: a crosstalk interrupted by vitamin D3, Oncogene. 28, 3892- 902. 183. Nakamura, Y., Franchi, L., Kambe, N., Meng, G., Strober, W. & Nunez, G. Critical role for mast cells in interleukin-1beta-driven skin inflammation associated with an activating mutation in the nlrp3 protein, Immunity. 37, 85-95. 184. Apte, R. N. & Voronov, E. (2008) Is interleukin-1 a good or bad 'guy' in tumor immunobiology and immunotherapy?, Immunol Rev. 222, 222-41. 185. Fischer, M., Juremalm, M., Olsson, N., Backlin, C., Sundstrom, C., Nilsson, K., Enblad, G. & Nilsson, G. (2003) Expression of CCL5/RANTES by Hodgkin and Reed-Sternberg cells and its possible role in the recruitment of mast cells into lymphomatous tissue, International journal of cancer Journal international du cancer. 107, 197-201.

158

Systems Immunology-list of references

186. Soucek, L., Lawlor, E. R., Soto, D., Shchors, K., Swigart, L. B. & Evan, G. I. (2007) Mast cells are required for angiogenesis and macroscopic expansion of Myc-induced pancreatic islet tumors, Nat Med. 13, 1211-8. 187. Veerappan, A., Reid, A. C., O'Connor, N., Mora, R., Brazin, J. A., Estephan, R., Kameue, T., Chen, J., Felsen, D., Seshan, S. V., Poppas, D. P., Maack, T. & Silver, R. B. (2012) Mast cells are required for the development of renal fibrosis in the rodent unilateral ureteral obstruction model, Am J Physiol Renal Physiol. 302, F192-204. 188. Summers, S. A., Gan, P. Y., Dewage, L., Ma, F. T., Ooi, J. D., O'Sullivan, K. M., Nikolic- Paterson, D. J., Kitching, A. R. & Holdsworth, S. R. Mast cell activation and degranulation promotes renal fibrosis in experimental unilateral ureteric obstruction, Kidney international. 82, 676-85. 189. Miyazawa, S., Hotta, O., Doi, N., Natori, Y. & Nishikawa, K. (2004) Role of mast cells in the development of renal fibrosis: use of mast cell-deficient rats, Kidney international. 65, 2228-37. 190. Jones, S. E., Kelly, D. J., Cox, A. J., Zhang, Y., Gow, R. M. & Gilbert, R. E. (2003) Mast cell infiltration and chemokine expression in progressive renal disease, Kidney international. 64, 906-13. 191. Kneilling, M., Hultner, L., Pichler, B. J., Mailhammer, R., Morawietz, L., Solomon, S., Eichner, M., Sabatino, J., Biedermann, T., Krenn, V., Weber, W. A., Illges, H., Haubner, R. & Rocken, M. (2007) Targeted mast cell silencing protects against joint destruction and angiogenesis in experimental arthritis in mice, Arthritis and rheumatism. 56, 1806-16. 192. Kinet, J. P. (2007) The essential role of mast cells in orchestrating inflammation, Immunol Rev. 217, 5-7. 193. Yano, H., Kinuta, M., Tateishi, H., Nakano, Y., Matsui, S., Monden, T., Okamura, J., Sakai, M. & Okamoto, S. (1999) Mast cell infiltration around gastric cancer cells correlates with tumor angiogenesis and metastasis, Gastric Cancer. 2, 26-32. 194. Ribatti, D. (2013) Mast cells and macrophages exert beneficial and detrimental effects on tumor progression and angiogenesis, Immunol Lett. 152, 83-8. 195. Murdoch, C., Muthana, M., Coffelt, S. B. & Lewis, C. E. (2008) The role of myeloid cells in the promotion of tumour angiogenesis, Nature reviews Cancer. 8, 618-31. 196. Ribatti, D., Vacca, A., Nico, B., Crivellato, E., Roncali, L. & Dammacco, F. (2001) The role of mast cells in tumour angiogenesis, British journal of haematology. 115, 514-21. 197. Crivellato, E., Nico, B. & Ribatti, D. (2008) Mast cells and tumour angiogenesis: new insight from experimental carcinogenesis, Cancer Lett. 269, 1-6. 198. Ribatti, D., Crivellato, E., Roccaro, A. M., Ria, R. & Vacca, A. (2004) Mast cell contribution to angiogenesis related to tumour progression, Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology. 34, 1660-4. 199. Beissert, S., Schwarz, A. & Schwarz, T. (2006) Regulatory T cells, J Invest Dermatol. 126, 15-24. 200. Frossi, B., Gri, G., Tripodo, C. & Pucillo, C. (2010) Exploring a regulatory role for mast cells: 'MCregs'?, Trends Immunol. 31, 97-102. 201. Hershko, A. Y. & Rivera, J. (2010) Mast cell and T cell communication; amplification and control of adaptive immunity, Immunol Lett. 128, 98-104. 202. Lu, L. F., Lind, E. F., Gondek, D. C., Bennett, K. A., Gleeson, M. W., Pino-Lagos, K., Scott, Z. A., Coyle, A. J., Reed, J. L., Van Snick, J., Strom, T. B., Zheng, X. X. & Noelle, R. J. (2006) Mast cells are essential intermediaries in regulatory T-cell tolerance, Nature. 442, 997-1002. 203. Blatner, N. R., Bonertz, A., Beckhove, P., Cheon, E. C., Krantz, S. B., Strouch, M., Weitz, J., Koch, M., Halverson, A. L., Bentrem, D. J. & Khazaie, K. (2010) In colorectal cancer mast cells contribute to systemic regulatory T-cell dysfunction, Proceedings of the National Academy of Sciences of the United States of America. 107, 6430-5. 204. Zhao, Y., Wu, K., Cai, K., Zhai, R., Tao, K., Wang, G. & Wang, J. (2012) Increased numbers of gastric-infiltrating mast cells and regulatory T cells are associated with tumor stage in gastric adenocarcinoma patients, Oncol Lett. 4, 755-758. 205. Chen, Y., Perussia, B. & Campbell, K. S. (2007) Prostaglandin D2 suppresses human NK cell function via signaling through D prostanoid receptor, J Immunol. 179, 2766-73. 206. Gutzmer, R., Diestel, C., Mommert, S., Kother, B., Stark, H., Wittmann, M. & Werfel, T. (2005) Histamine H4 receptor stimulation suppresses IL-12p70 production and mediates chemotaxis in human monocyte-derived dendritic cells, J Immunol. 174, 5224-32. 207. Kalinski, P., Hilkens, C. M., Wierenga, E. A. & Kapsenberg, M. L. (1999) T-cell priming by type-1 and type-2 polarized dendritic cells: the concept of a third signal, Immunol Today. 20, 561-7.

159

Systems Immunology-list of references

208. Gooch, J. L., Lee, A. V. & Yee, D. (1998) inhibits growth and induces apoptosis in human breast cancer cells, Cancer Res. 58, 4199-205. 209. Oldford, S. A., Haidl, I. D., Howatt, M. A., Leiva, C. A., Johnston, B. & Marshall, J. S. (2010) A critical role for mast cells and mast cell-derived IL-6 in TLR2-mediated inhibition of tumor growth, J Immunol. 185, 7067-76. 210. Aaltomaa, S., Lipponen, P., Papinaho, S. & Kosma, V. M. (1993) Mast cells in breast cancer, Anticancer Res. 13, 785-8. 211. Dabiri, S., Huntsman, D., Makretsov, N., Cheang, M., Gilks, B., Bajdik, C., Gelmon, K., Chia, S. & Hayes, M. (2004) The presence of stromal mast cells identifies a subset of invasive breast cancers with a favorable prognosis, Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc. 17, 690-5. 212. Rajput, A. B., Turbin, D. A., Cheang, M. C., Voduc, D. K., Leung, S., Gelmon, K. A., Gilks, C. B. & Huntsman, D. G. (2008) Stromal mast cells in invasive breast cancer are a marker of favourable prognosis: a study of 4,444 cases, Breast cancer research and treatment. 107, 249-57. 213. Tan, S. Y., Fan, Y., Luo, H. S., Shen, Z. X., Guo, Y. & Zhao, L. J. (2005) Prognostic significance of cell infiltrations of immunosurveillance in colorectal cancer, World J Gastroenterol. 11, 1210-4. 214. Drobits, B., Holcmann, M., Amberg, N., Swiecki, M., Grundtner, R., Hammer, M., Colonna, M. & Sibilia, M. (2012) Imiquimod clears tumors in mice independent of adaptive immunity by converting pDCs into tumor-killing effector cells, J Clin Invest. 122, 575-85. 215. Dietrich, N., Rohde, M., Geffers, R., Kroger, A., Hauser, H., Weiss, S. & Gekara, N. O. (2010) Mast cells elicit proinflammatory but not type I interferon responses upon activation of TLRs by bacteria, Proceedings of the National Academy of Sciences of the United States of America. 107, 8748- 53. 216. Fukuoka, Y., Hite, M. R., Dellinger, A. L. & Schwartz, L. B. (2013) Human Skin Mast Cells Express Complement Factors C3 and C5, J Immunol. 191 (4): 1827-34. 217. Okroj, M., Hsu, Y. F., Ajona, D., Pio, R. & Blom, A. M. (2008) Non-small cell lung cancer cells produce a functional set of complement factor I and its soluble cofactors, Mol Immunol. 45, 169-79. 218. Nagasaka, A., Matsue, H., Matsushima, H., Aoki, R., Nakamura, Y., Kambe, N., Kon, S., Uede, T. & Shimada, S. (2008) Osteopontin is produced by mast cells and affects IgE-mediated degranulation and migration of mast cells, European journal of immunology. 38, 489-99. 219. Wetzel, K., Struyf, S., Van Damme, J., Kayser, T., Vecchi, A., Sozzani, S., Rommelaere, J., Cornelis, J. J. & Dinsart, C. (2007) MCP-3 (CCL7) delivered by parvovirus MVMp reduces tumorigenicity of mouse melanoma cells through activation of T lymphocytes and NK cells, International journal of cancer Journal international du cancer. 120, 1364-71. 220. Benyon, R. C., Bissonnette, E. Y. & Befus, A. D. (1991) Tumor necrosis factor-alpha dependent cytotoxicity of human skin mast cells is enhanced by anti-IgE antibodies, J Immunol. 147, 2253-8. 221. Dery, R. E., Lin, T. J., Befus, A. D., Milne, C. D., Moqbel, R., Menard, G. & Bissonnette, E. Y. (2000) Redundancy or cell-type-specific regulation? Tumour necrosis factor in alveolar macrophages and mast cells, Immunology. 99, 427-34. 222. Wimberly, A. L., Forsyth, C. B., Khan, M. W., Pemberton, A., Khazaie, K. & Keshavarzian, A. (2013) Ethanol-induced mast cell-mediated inflammation leads to increased susceptibility of intestinal tumorigenesis in the APC Delta468 min mouse model of colon cancer, Alcohol Clin Exp Res. 37 Suppl 1, E199-208. 223. Ma, Y. & Ullrich, S. E. (2013) Intratumoral mast cells promote the growth of pancreatic cancer, Oncoimmunology. 2, e25964. 224. Urb, M. & Sheppard, D. C. (2012) The role of mast cells in the defence against pathogens, PLoS Pathog. 8, e1002619. 225. Della Rovere, F., Granata, A., Monaco, M. & Basile, G. (2009) Phagocytosis of cancer cells by mast cells in breast cancer, Anticancer Res. 29, 3157-61. 226. Smith, H. A. & Kang, Y. (2013) The metastasis-promoting roles of tumor-associated immune cells, J Mol Med (Berl). 91, 411-29. 227. Fenger, J. M., Bear, M. D., Volinia, S., Lin, T. Y., Harrington, B. K., London, C. A. & Kisseberth, W. C. (2014) Overexpression of miR-9 in mast cells is associated with invasive behavior and spontaneous metastasis, BMC Cancer. 14, 84. 228. Murahata, R. I. & Mitchell, M. S. (1982) Modulation of cell-mediated alloimmunity by BCG. II. Induction of specific, nonadherent, non-T-killer cells by BCG and alloantigen, J Natl Cancer Inst. 69, 613-8.

160

Systems Immunology-list of references

229. Marichal, T., Tsai, M. & Galli, S. J. (2013) Mast cells: potential positive and negative roles in tumor biology, Cancer Immunol Res. 1, 269-79. 230. Piconese, S., Gri, G., Tripodo, C., Musio, S., Gorzanelli, A., Frossi, B., Pedotti, R., Pucillo, C. E. & Colombo, M. P. (2009) Mast cells counteract regulatory T-cell suppression through interleukin-6 and OX40/OX40L axis toward Th17-cell differentiation, Blood. 114, 2639-48. 231. Dundar, E., Oner, U., Peker, B. C., Metintas, M., Isiksoy, S. & Ak, G. (2008) The significance and relationship between mast cells and tumour angiogenesis in non-small cell lung carcinoma, The Journal of international medical research. 36, 88-95. 232. Mangia, A., Malfettone, A., Rossi, R., Paradiso, A., Ranieri, G., Simone, G. & Resta, L. (2011) Tissue remodelling in breast cancer: human mast cell tryptase as an initiator of myofibroblast differentiation, Histopathology. 58, 1096-106. 233. Gulubova, M. & Vlaykova, T. (2009) Prognostic significance of mast cell number and microvascular density for the survival of patients with primary colorectal cancer, Journal of gastroenterology and hepatology. 24, 1265-75. 234. Carvalho, R. F., Nilsson, G. & Harvima, I. T. (2010) Increased mast cell expression of PAR-2 in skin inflammatory diseases and release of IL-8 upon PAR-2 activation, Experimental dermatology. 19, 117-22. 235. della Rovere, F., Granata, A., Familiari, D., D'Arrigo, G., Mondello, B. & Basile, G. (2007) Mast cells in invasive ductal breast cancer: different behavior in high and minimum hormone-receptive cancers, Anticancer research. 27, 2465-71. 236. Welsh, T. J., Green, R. H., Richardson, D., Waller, D. A., O'Byrne, K. J. & Bradding, P. (2005) Macrophage and mast-cell invasion of tumor cell islets confers a marked survival advantage in non- small-cell lung cancer, Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 23, 8959-67. 237. Chan, J. K., Magistris, A., Loizzi, V., Lin, F., Rutgers, J., Osann, K., DiSaia, P. J. & Samoszuk, M. (2005) Mast cell density, angiogenesis, blood clotting, and prognosis in women with advanced ovarian cancer, Gynecol Oncol. 99, 20-5. 238. Fleischmann, A., Schlomm, T., Kollermann, J., Sekulic, N., Huland, H., Mirlacher, M., Sauter, G., Simon, R. & Erbersdobler, A. (2009) Immunological microenvironment in prostate cancer: high mast cell densities are associated with favorable tumor characteristics and good prognosis, The Prostate. 69, 976-81. 239. Hornberg, J. J., Bruggeman, F. J., Westerhoff, H. V. & Lankelma, J. (2006) Cancer: a Systems Biology disease, Biosystems. 83, 81-90. 240. Alberghina, L. & Westerhoff, H. V. (2005) Systems biology: definitions and perspectives, Springer, Berlin ; New York. 241. Teusink, B., Passarge, J., Reijenga, C. A., Esgalhado, E., van der Weijden, C. C., Schepper, M., Walsh, M. C., Bakker, B. M., van Dam, K., Westerhoff, H. V. & Snoep, J. L. (2000) Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry, Eur J Biochem. 267, 5313-29. 242. Bakker, B. M., Michels, P. A., Opperdoes, F. R. & Westerhoff, H. V. (1997) Glycolysis in bloodstream form Trypanosoma brucei can be understood in terms of the kinetics of the glycolytic enzymes, J Biol Chem. 272, 3207-15. 243. Bruggeman, F. J., Boogerd, F. C. & Westerhoff, H. V. (2005) The multifarious short-term regulation of ammonium assimilation of Escherichia coli: dissection using an in silico replica, FEBS J. 272, 1965-85. 244. Kouril, T., Esser, D., Kort, J., Westerhoff, H. V., Siebers, B. & Snoep, J. L. (2013) Intermediate instability at high temperature leads to low pathway efficiency for an in vitro reconstituted system of gluconeogenesis in Sulfolobus solfataricus, FEBS J. 280, 4666-80. 245. Geenen, S., du Preez, F. B., Reed, M., Nijhout, H. F., Kenna, J. G., Wilson, I. D., Westerhoff, H. V. & Snoep, J. L. (2012) A mathematical modelling approach to assessing the reliability of biomarkers of glutathione metabolism, Eur J Pharm Sci. 46, 233-43. 246. Snoep, J. L., Bruggeman, F., Olivier, B. G. & Westerhoff, H. V. (2006) Towards building the silicon cell: a modular approach, Biosystems. 83, 207-16. 247. Cucchiara, S., Stronati, L. & Aloi, M. Interactions between intestinal microbiota and innate immune system in pediatric inflammatory bowel disease, J Clin Gastroenterol. 46 Suppl, S64-6.

161

Systems Immunology-list of references

248. Chotirmall, S. H., Al-Alawi, M., Mirkovic, B., Lavelle, G., Logan, P. M., Greene, C. M. & McElvaney, N. G. (2013) Aspergillus-associated airway disease, inflammation, and the innate immune response, Biomed Res Int., 723129. 249. Abraham, C. & Medzhitov, R. (2011) Interactions between the host innate immune system and microbes in inflammatory bowel disease, Gastroenterology. 140, 1729-37. 250. Mogensen, T. H. (2009) Pathogen recognition and inflammatory signaling in innate immune defenses, Clin Microbiol Rev. 22, 240-73, Table of Contents. 251. Busca, A. & Kumar, A. (2014) Innate immune responses in hepatitis B virus (HBV) infection, Virol J. 11, 22. 252. De Cata, A., D'Agruma, L., Tarquini, R. & Mazzoccoli, G. (2014) Rheumatoid arthritis and the biological clock, Expert Rev Clin Immunol. 10, 687-95. 253. Murdoch, J. R. & Lloyd, C. M. (2010) Chronic inflammation and asthma, Mutat Res. 690, 24-39. 254. Luft, V. C., Schmidt, M. I., Pankow, J. S., Couper, D., Ballantyne, C. M., Young, J. H. & Duncan, B. B. (2013) Chronic inflammation role in the obesity-diabetes association: a case-cohort study, Diabetol Metab Syndr. 5, 31. 255. Coffelt, S. B. & de Visser, K. E. (2014) Cancer: Inflammation lights the way to metastasis, Nature. 507, 48-9. 256. Liaskou, E., Wilson, D. V. & Oo, Y. H. (2012) Innate immune cells in liver inflammation, Mediators Inflamm., 949157. 257. Lobo, P. I., Brayman, K. L. & Okusa, M. D. (2014) Natural IgM Anti-leucocyte Autoantibodies (IgM-ALA) Regulate Inflammation Induced by Innate and Adaptive Immune Mechanisms, J Clin Immunol. 34 Suppl: S22-9. 258. Kannarkat, G. T., Boss, J. M. & Tansey, M. G. (2013) The role of innate and adaptive immunity in Parkinson's disease, J Parkinsons Dis. 3, 493-514. 259. Deretic, V., Saitoh, T. & Akira, S. (2013) Autophagy in infection, inflammation and immunity, Nat Rev Immunol. 13, 722-37. 260. Naylor, A. J., Filer, A. & Buckley, C. D. (2013) The role of stromal cells in the persistence of chronic inflammation, Clin Exp Immunol. 171, 30-5. 261. Korpos, E., Wu, C. & Sorokin, L. (2009) Multiple roles of the extracellular matrix in inflammation, Curr Pharm Des. 15, 1349-57. 262. Lusis, A. J. (2012) Life after GWAS: functional genomics in vascular biology, Arterioscler Thromb Vasc Biol. 32, 169. 263. Buhrmann, C., Shayan, P., Aggarwal, B. B. & Shakibaei, M. (2013) Evidence that TNF-beta ( alpha) can activate the inflammatory environment in human chondrocytes, Arthritis Res Ther. 15, R202. 264. Easmon, C. S. & Glynn, A. A. (1975) The role of humoral immunity and acute inflammation in protection against staphyloccocal dermonecrosis, Immunology. 29, 67-74. 265. Kumar, R., Clermont, G., Vodovotz, Y. & Chow, C. C. (2004) The dynamics of acute inflammation, J Theor Biol. 230, 145-55. 266. Lowe, D. B. & Storkus, W. J. (2011) Chronic inflammation and immunologic-based constraints in malignant disease, Immunotherapy. 3, 1265-74. 267. Takase, O., Iwabuchi, K. & Quigg, R. J. (2014) Immunoregulation of inflammation in chronic kidney disease, J Immunol Res. , 897487. 268. Kiener, H. P., Baghestanian, M., Dominkus, M., Walchshofer, S., Ghannadan, M., Willheim, M., Sillaber, C., Graninger, W. B., Smolen, J. S. & Valent, P. (1998) Expression of the C5a receptor (CD88) on synovial mast cells in patients with rheumatoid arthritis, Arthritis Rheum. 41, 233-45. 269. Edwards, J. C. & Cambridge, G. (2006) B-cell targeting in rheumatoid arthritis and other autoimmune diseases, Nat Rev Immunol. 6, 394-403. 270. Ibrahim, M. Z., Reder, A. T., Lawand, R., Takash, W. & Sallouh-Khatib, S. (1996) The mast cells of the multiple sclerosis brain, J Neuroimmunol. 70, 131-8. 271. Monson, N. L., Cravens, P. D., Frohman, E. M., Hawker, K. & Racke, M. K. (2005) Effect of rituximab on the peripheral blood and cerebrospinal fluid B cells in patients with primary progressive multiple sclerosis, Arch Neurol. 62, 258-64. 272. Alberts, B. (2002) Molecular biology of the cell, 4th edn, Garland Science, New York. 273. Zhong, Y., Byrd, J. C. & Dubovsky, J. A. (2014) The B-cell receptor pathway: a critical component of healthy and malignant immune biology, Semin Hematol. 51, 206-18.

162

Systems Immunology-list of references

274. Ambrosino, D. M., Kanchana, M. V., Delaney, N. R. & Finberg, R. W. (1991) Human B cells secrete predominantly lambda L chains in the absence of H chain expression, J Immunol. 146, 599-602. 275. Lachmann, H. J., Gallimore, R., Gillmore, J. D., Carr-Smith, H. D., Bradwell, A. R., Pepys, M. B. & Hawkins, P. N. (2003) Outcome in systemic AL amyloidosis in relation to changes in concentration of circulating free immunoglobulin light chains following chemotherapy, Br J Haematol. 122, 78-84. 276. Abraham, R. S., Katzmann, J. A., Clark, R. J., Bradwell, A. R., Kyle, R. A. & Gertz, M. A. (2003) Quantitative analysis of serum free light chains. A new marker for the diagnostic evaluation of primary systemic amyloidosis, Am J Clin Pathol. 119, 274-8. 277. Gottenberg, J. E., Aucouturier, F., Goetz, J., Sordet, C., Jahn, I., Busson, M., Cayuela, J. M., Sibilia, J. & Mariette, X. (2007) Serum immunoglobulin free light chain assessment in rheumatoid arthritis and primary Sjogren's syndrome, Ann Rheum Dis. 66, 23-7. 278. Dispenzieri, A., Kyle, R. A., Katzmann, J. A., Therneau, T. M., Larson, D., Benson, J., Clark, R. J., Melton, L. J., 3rd, Gertz, M. A., Kumar, S. K., Fonseca, R., Jelinek, D. F. & Rajkumar, S. V. (2008) Immunoglobulin free light chain ratio is an independent risk factor for progression of smoldering (asymptomatic) multiple myeloma, Blood. 111, 785-9. 279. Groot Kormelink, T., Pardo, A., Knipping, K., Buendia-Roldan, I., Garcia-de-Alba, C., Blokhuis, B. R., Selman, M. & Redegeld, F. A. (2011) Immunoglobulin free light chains are increased in hypersensitivity pneumonitis and idiopathic pulmonary fibrosis, PLoS One. 6, e25392. 280. Teng, M., Pirrie, S., Ward, D. G., Assi, L. K., Hughes, R. G., Stocken, D. & Johnson, P. J. (2014) Diagnostic and mechanistic implications of serum free light chains, albumin and alpha-fetoprotein in hepatocellular carcinoma, Br J Cancer. 110, 2277-82. 281. Larsen, J. T., Kumar, S. K., Dispenzieri, A., Kyle, R. A., Katzmann, J. A. & Rajkumar, S. V. (2013) Serum free light chain ratio as a biomarker for high-risk smoldering multiple myeloma, Leukemia. 27, 941-6. 282. Aloe, L. & Levi-Montalcini, R. (1977) Mast cells increase in tissues of neonatal rats injected with the nerve growth factor, Brain Res. 133, 358-66. 283. Tam, S. Y., Tsai, M., Yamaguchi, M., Yano, K., Butterfield, J. H. & Galli, S. J. (1997) Expression of functional TrkA receptor tyrosine kinase in the HMC-1 human mast cell line and in human mast cells, Blood. 90, 1807-20. 284. Kawamoto, K., Aoki, J., Tanaka, A., Itakura, A., Hosono, H., Arai, H., Kiso, Y. & Matsuda, H. (2002) Nerve growth factor activates mast cells through the collaborative interaction with lysophosphatidylserine expressed on the membrane surface of activated platelets, J Immunol. 168, 6412-9. 285. Hermes, B., Feldmann-Boddeker, I., Welker, P., Algermissen, B., Steckelings, M. U., Grabbe, J. & Henz, B. M. (2000) Altered expression of mast cell chymase and tryptase and of c-Kit in human cutaneous scar tissue, J Invest Dermatol. 114, 51-5. 286. Aoki, M., Pawankar, R., Niimi, Y. & Kawana, S. (2003) Mast cells in basal cell carcinoma express VEGF, IL-8 and RANTES, Int Arch Allergy Immunol. 130, 216-23. 287. Barton, G. M. (2008) A calculated response: control of inflammation by the innate immune system, J Clin Invest. 118, 413-20. 288. Newton, K. & Dixit, V. M. (2012) Signaling in innate immunity and inflammation, Cold Spring Harb Perspect Biol. 4. 289. Grindstaff, J. L., Hasselquist, D., Nilsson, J. K., Sandell, M., Smith, H. G. & Stjernman, M. (2006) Transgenerational priming of immunity: maternal exposure to a bacterial antigen enhances offspring humoral immunity, Proc Biol Sci. 273, 2551-7. 290. Harada, K. & Ikegami, T. (2000) Evolution of specificity in an immune network, J Theor Biol. 203, 439-49. 291. Souwer, Y., Griekspoor, A., de Wit, J., Martinoli, C., Zagato, E., Janssen, H., Jorritsma, T., Bar- Ephraim, Y. E., Rescigno, M., Neefjes, J. & van Ham, S. M. (2012) Selective infection of antigen- specific B lymphocytes by Salmonella mediates bacterial survival and systemic spreading of infection, PLoS One. 7, e50667. 292. Uhal, B. D., Ramos, C., Joshi, I., Bifero, A., Pardo, A. & Selman, M. (1998) Cell size, cell cycle, and alpha-smooth muscle actin expression by primary human lung fibroblasts, Am J Physiol. 275, L998-L1005. 293. Shoup, D. E. (2014) Diffusion-controlled reaction rates for two active sites on a sphere, BMC Biophys. 7, 3.

163

Systems Immunology-list of references

294. Koenig, S. H. & Brown, R. D., 3rd (1972) H 2 CO 3 as substrate for carbonic anhydrase in the dehydration of HCO 3, Proc Natl Acad Sci U S A. 69, 2422-5. 295. DeLisi, C. & Wiegel, F. W. (1981) Effect of nonspecific forces and finite receptor number on rate constants of ligand--cell bound-receptor interactions, Proc Natl Acad Sci U S A. 78, 5569-72. 296. Schodin, B. A., Tsomides, T. J. & Kranz, D. M. (1996) Correlation between the number of T cell receptors required for T cell activation and TCR-ligand affinity, Immunity. 5, 137-46. 297. Reed, R. K., Johansen, S. & Noddeland, H. (1985) Turnover rate of interstitial albumin in rat skin and skeletal muscle. Effects of limb movements and motor activity, Acta Physiol Scand. 125, 711-8. 298. Mishima, Y., Ishihara, S., Hansen, J. J. & Kinoshita, Y. (2014) TGF-beta detection and measurement in murine B cells: pros and cons of the different techniques, Methods Mol Biol. 1190, 71- 80. 299. Ishikawa-Ankerhold, H. C., Ankerhold, R. & Drummen, G. P. (2012) Advanced fluorescence microscopy techniques--FRAP, FLIP, FLAP, FRET and FLIM, Molecules. 17, 4047-132. 300. Pawelec, G., Goldeck, D. & Derhovanessian, E. (2014) Inflammation, ageing and chronic disease, Curr Opin Immunol. 29C, 23-28. 301. Rhoades, R. & Bell, D. R. Medical physiology : principles for clinical medicine, 4th edn, Wolters Kluwer Health/Lippincott Williams & Wilkins, Philadelphia. 302. Gaestel, M., Kotlyarov, A. & Kracht, M. (2009) Targeting innate immunity protein kinase signalling in inflammation, Nat Rev Drug Discov. 8, 480-99. 303. Chakravarthy, K. V., Davidson, B. A., Helinski, J. D., Ding, H., Law, W. C., Yong, K. T., Prasad, P. N. & Knight, P. R. (2011) Doxorubicin-conjugated quantum dots to target alveolar macrophages and inflammation, Nanomedicine. 7, 88-96. 304. Castells, M. (2006) Mast cell mediators in allergic inflammation and mastocytosis, Immunol Allergy Clin North Am. 26, 465-85. 305. Hirai, S., Ohyane, C., Kim, Y. I., Lin, S., Goto, T., Takahashi, N., Kim, C. S., Kang, J., Yu, R. & Kawada, T. (2014) Involvement of mast cells in adipose tissue fibrosis, Am J Physiol Endocrinol Metab. 306, E247-55. 306. Skaper, S. D., Facci, L. & Giusti, P. (2014) Mast cells, glia and neuroinflammation: partners in crime?, Immunology. 141, 314-27. 307. Payne, V. & Kam, P. C. (2004) Mast cell tryptase: a review of its physiology and clinical significance, Anaesthesia. 59, 695-703. 308. Boesiger, J., Tsai, M., Maurer, M., Yamaguchi, M., Brown, L. F., Claffey, K. P., Dvorak, H. F. & Galli, S. J. (1998) Mast cells can secrete vascular permeability factor/ vascular endothelial cell growth factor and exhibit enhanced release after immunoglobulin E-dependent upregulation of fc epsilon receptor I expression, J Exp Med. 188, 1135-45. 309. Cain, D., Kondo, M., Chen, H. & Kelsoe, G. (2009) Effects of acute and chronic inflammation on B-cell development and differentiation, J Invest Dermatol. 129, 266-77. 310. Kuenz, B., Lutterotti, A., Ehling, R., Gneiss, C., Haemmerle, M., Rainer, C., Deisenhammer, F., Schocke, M., Berger, T. & Reindl, M. (2008) Cerebrospinal fluid B cells correlate with early brain inflammation in multiple sclerosis, PLoS One. 3, e2559. 311. Kelly-Scumpia, K. M., Scumpia, P. O., Weinstein, J. S., Delano, M. J., Cuenca, A. G., Nacionales, D. C., Wynn, J. L., Lee, P. Y., Kumagai, Y., Efron, P. A., Akira, S., Wasserfall, C., Atkinson, M. A. & Moldawer, L. L. (2011) B cells enhance early innate immune responses during bacterial sepsis, J Exp Med. 208, 1673-82. 312. Braber, S., Thio, M., Blokhuis, B. R., Henricks, P. A., Koelink, P. J., Groot Kormelink, T., Bezemer, G. F., Kerstjens, H. A., Postma, D. S., Garssen, J., Kraneveld, A. D., Redegeld, F. A. & Folkerts, G. (2012) An association between neutrophils and immunoglobulin free light chains in the pathogenesis of chronic obstructive pulmonary disease, Am J Respir Crit Care Med. 185, 817-24. 313. Thio, M., Groot Kormelink, T., Fischer, M. J., Blokhuis, B. R., Nijkamp, F. P. & Redegeld, F. A. (2012) Antigen binding characteristics of immunoglobulin free light chains: crosslinking by antigen is essential to induce allergic inflammation, PLoS One. 7, e40986. 314. Choi, S. & SpringerLink (Online service) Systems Biology for Signaling Networks in Systems Biology, pp. XVI, 908p. 326 illus., 10 illus. in color 315. Guven Maiorov, E., Keskin, O., Gursoy, A. & Nussinov, R. (2013) The structural network of inflammation and cancer: merits and challenges, Semin Cancer Biol. 23, 243-51. 316. Bryant, T. (1868) Remarks on Some Cases of Inflammation of the Breast Simulating Cancer, Br Med J. 2, 608-9.

164

Systems Immunology-list of references

317. Kidane, D., Chae, W. J., Czochor, J., Eckert, K. A., Glazer, P. M., Bothwell, A. L. & Sweasy, J. B. (2014) Interplay between DNA repair and inflammation, and the link to cancer, Crit Rev Biochem Mol Biol. 318. Donohoe, C. L., O'Farrell, N. J., Doyle, S. L. & Reynolds, J. V. (2014) The role of obesity in gastrointestinal cancer: evidence and opinion, Therap Adv Gastroenterol. 7, 38-50. 319. Berezhnaya, N. M. (2010) Interaction between tumor and immune system: the role of tumor cell biology, Exp Oncol. 32, 159-66. 320. Noy, R. & Pollard, J. W. (2014) Tumor-Associated Macrophages: From Mechanisms to Therapy, Immunity. 41, 49-61. 321. Ribatti, D., Nico, B., Crivellato, E. & Vacca, A. (2007) Macrophages and tumor angiogenesis, Leukemia. 21, 2085-9. 322. Bowman, R. L. & Joyce, J. A. (2014) Therapeutic targeting of tumor-associated macrophages and microglia in glioblastoma, Immunotherapy. 6, 663-6. 323. Colvin, E. K. (2014) Tumor-associated macrophages contribute to tumor progression in ovarian cancer, Front Oncol. 4, 137. 324. Ryan, J. J., Morales, J. K., Falanga, Y. T., Fernando, J. F. & Macey, M. R. (2009) Mast cell regulation of the immune response, World Allergy Organ J. 2, 224-32. 325. Brabers, N. A. & Nottet, H. S. (2006) Role of the pro-inflammatory cytokines TNF-alpha and IL- 1beta in HIV-associated dementia, Eur J Clin Invest. 36, 447-58. 326. Dinarello, C. A. (2000) Proinflammatory cytokines, Chest. 118, 503-8. 327. Lorenz, O., Parzefall, W., Kainzbauer, E., Wimmer, H., Grasl-Kraupp, B., Gerner, C. & Schulte- Hermann, R. (2009) Proteomics reveals acute pro-inflammatory and protective responses in rat Kupffer cells and hepatocytes after chemical initiation of liver cancer and after LPS and IL-6, Proteomics Clin Appl. 3, 947-67. 328. Mantovani, A., Allavena, P., Sica, A. & Balkwill, F. (2008) Cancer-related inflammation, Nature. 454, 436-44. 329. Walkley, C. R., Olsen, G. H., Dworkin, S., Fabb, S. A., Swann, J., McArthur, G. A., Westmoreland, S. V., Chambon, P., Scadden, D. T. & Purton, L. E. (2007) A microenvironment- induced myeloproliferative syndrome caused by retinoic acid receptor gamma deficiency, Cell. 129, 1097-110. 330. Coussens, L. M. & Werb, Z. (2002) Inflammation and cancer, Nature. 420, 860-7. 331. Vesely, M. D., Kershaw, M. H., Schreiber, R. D. & Smyth, M. J. (2011) Natural innate and adaptive immunity to cancer, Annu Rev Immunol. 29, 235-71. 332. Grivennikov, S. I., Greten, F. R. & Karin, M. (2010) Immunity, inflammation, and cancer, Cell. 140, 883-99. 333. Gao, F., Liang, B., Reddy, S. T., Farias-Eisner, R. & Su, X. (2014) Role of Inflammation- Associated Microenvironment in Tumorigenesis and Metastasis, Curr Cancer Drug Targets. 334. Hamada, S., Masamune, A. & Shimosegawa, T. (2014) Inflammation and pancreatic cancer: disease promoter and new therapeutic target, J Gastroenterol. 335. Rech, A. J. & Vonderheide, R. H. (2013) Dynamic Interplay of Oncogenes and T Cells Induces PD-L1 in the Tumor Microenvironment, Cancer Discov. 3, 1330-2. 336. Gajewski, T. F., Schreiber, H. & Fu, Y. X. (2013) Innate and adaptive immune cells in the tumor microenvironment, Nat Immunol. 14, 1014-22. 337. Forde, P. M., Reiss, K. A., Zeidan, A. M. & Brahmer, J. R. (2013) What lies within: novel strategies in immunotherapy for non-small cell lung cancer, Oncologist. 18, 1203-13. 338. Scott, A. M., Wolchok, J. D. & Old, L. J. (2012) Antibody therapy of cancer, Nat Rev Cancer. 12, 278-87. 339. Read, E. D., Eu, P., Little, P. J. & Piva, T. J. (2014) The status of radioimmunotherapy in CD20+ non-Hodgkin's lymphoma, Target Oncol. 340. Eremina, V., Jefferson, J. A., Kowalewska, J., Hochster, H., Haas, M., Weisstuch, J., Richardson, C., Kopp, J. B., Kabir, M. G., Backx, P. H., Gerber, H. P., Ferrara, N., Barisoni, L., Alpers, C. E. & Quaggin, S. E. (2008) VEGF inhibition and renal thrombotic microangiopathy, N Engl J Med. 358, 1129-36. 341. Bossen, C. & Schneider, P. (2006) BAFF, APRIL and their receptors: structure, function and signaling, Semin Immunol. 18, 263-75. 342. Runkel, L. & Stacey, J. (2014) Lupus clinical development: will belimumab's approval catalyse a new paradigm for SLE drug development?, Expert Opin Biol Ther. 14, 491-501.

165

Systems Immunology-list of references

343. Kim, S. S., Kirou, K. A. & Erkan, D. (2012) Belimumab in systemic lupus erythematosus: an update for clinicians, Ther Adv Chronic Dis. 3, 11-23. 344. Sasai, K., Katoh, M., Fujita, D., Yamashita, M., Constantinoiu, C. C., Matsubayashi, M., Tani, H. & Baba, E. (2007) Monoclonal antibodies for the diagnosis of canine mastocytoma, Hybridoma (Larchmt). 26, 162-7. 345. Valent, P. (1995) 1995 Mack-Forster Award Lecture. Review. Mast cell differentiation antigens: expression in normal and malignant cells and use for diagnostic purposes, Eur J Clin Invest. 25, 715- 20. 346. Ribatti, D., Moschetta, M. & Vacca, (2014) A. Microenvironment and multiple myeloma spread, Thromb Res. 133 Suppl 2, S102-6. 347. Theoharides, T. C. & Conti, P. (2004) Mast cells: the Jekyll and Hyde of tumor growth, Trends Immunol. 25, 235-41. 348. Molin, D., Edstrom, A., Glimelius, I., Glimelius, B., Nilsson, G., Sundstrom, C. & Enblad, G. (2002) Mast cell infiltration correlates with poor prognosis in Hodgkin's lymphoma, Br J Haematol. 119, 122-4. 349. Maltby, S., Khazaie, K. & McNagny, K. M. (2009) Mast cells in tumor growth: angiogenesis, tissue remodelling and immune-modulation, Biochim Biophys Acta. 1796, 19-26. 350. Wood, S. L., Pernemalm, M., Crosbie, P. A. & Whetton, A. D. (2014) The role of the tumor- microenvironment in lung cancer-metastasis and its relationship to potential therapeutic targets, Cancer Treat Rev. 351. Ribatti, D. (2013) Mast cells and macrophages exert beneficial and detrimental effects on tumor progression and angiogenesis, Immunol Lett. 152, 83-8. 352. Michalaki, V., Syrigos, K., Charles, P. & Waxman, J. (2004) Serum levels of IL-6 and TNF-alpha correlate with clinicopathological features and patient survival in patients with prostate cancer, Br J Cancer. 90, 2312-6. 353. Kamel, M., Shouman, S., El-Merzebany, M., Kilic, G., Veenstra, T., Saeed, M., Wagih, M., Diaz- Arrastia, C., Patel, D. & Salama, S. (2012) Effect of tumour necrosis factor-alpha on estrogen metabolic pathways in breast cancer cells, J Cancer. 3, 310-21. 354. Marech, I., Ammendola, M., Gadaleta, C., Zizzo, N., Oakley, C., Gadaleta, C. D. & Ranieri, G. (2014) Possible biological and translational significance of mast cells density in colorectal cancer, World J Gastroenterol. 20, 8910-8920. 355. Hart, P. H., Grimbaldeston, M. A. & Finlay-Jones, J. J. (2001) Sunlight, immunosuppression and skin cancer: role of histamine and mast cells, Clin Exp Pharmacol Physiol. 28, 1-8. \356. Strouch, M. J., Cheon, E. C., Salabat, M. R., Krantz, S. B., Gounaris, E., Melstrom, L. G., Dangi- Garimella, S., Wang, E., Munshi, H. G., Khazaie, K. & Bentrem, D. J. (2010) Crosstalk between mast cells and pancreatic cancer cells contributes to pancreatic tumor progression, Clin Cancer Res. 16, 2257-65. 357. Tomita, M., Matsuzaki, Y. & Onitsuka, T. (2000) Effect of mast cells on tumor angiogenesis in lung cancer, Ann Thorac Surg. 69, 1686-90. 358. Ellem, S. J., Taylor, R. A., Furic, L., Larsson, O., Frydenberg, M., Pook, D., Pedersen, J., Cawsey, B., Trotta, A., Need, E., Buchanan, G. & Risbridger, G. P. (2014) A pro-tumourigenic loop at the human prostate tumour interface orchestrated by oestrogen, CXCL12 and mast cell recruitment, J Pathol. 359. Hiromatsu, Y. & Toda, S. (2003) Mast cells and angiogenesis, Microsc Res Tech. 60, 64-9. 360. Huang, B., Lei, Z., Zhang, G. M., Li, D., Song, C., Li, B., Liu, Y., Yuan, Y., Unkeless, J., Xiong, H. & Feng, Z. H. (2008) SCF-mediated mast cell infiltration and activation exacerbate the inflammation and immunosuppression in tumor microenvironment, Blood. 112, 1269-79. 361. Salamon, P., Shoham, N. G., Gavrieli, R., Wolach, B. & Mekori, Y. A. (2005) Human mast cells release Interleukin-8 and induce neutrophil chemotaxis on contact with activated T cells, Allergy. 60, 1316-9. 362. Conti, P., Kempuraj, D., Di Gioacchino, M., Boucher, W., Letourneau, R., Kandere, K., Barbacane, R. C., Reale, M., Felaco, M., Frydas, S. & Theoharides, T. C. (2002) Interleukin-6 and mast cells, Allergy Asthma Proc. 23, 331-5. 363. Lin, A. M., Rubin, C. J., Khandpur, R., Wang, J. Y., Riblett, M., Yalavarthi, S., Villanueva, E. C., Shah, P., Kaplan, M. J. & Bruce, A. T. (2011) Mast cells and neutrophils release IL-17 through extracellular trap formation in psoriasis, J Immunol. 187, 490-500.

166

Systems Immunology-list of references

364. Brown, J. K., Knight, P. A., Wright, S. H., Thornton, E. M. & Miller, H. R. (2003) Constitutive secretion of the granule chymase mouse mast cell protease-1 and the chemokine, CCL2, by mucosal mast cell homologues, Clin Exp Allergy. 33, 132-46. 365. Qu, Z., Liebler, J. M., Powers, M. R., Galey, T., Ahmadi, P., Huang, X. N., Ansel, J. C., Butterfield, J. H., Planck, S. R. & Rosenbaum, J. T. (1995) Mast cells are a major source of basic fibroblast growth factor in chronic inflammation and cutaneous hemangioma, Am J Pathol. 147, 564- 73. 366. Zhang, B., Alysandratos, K. D., Angelidou, A., Asadi, S., Sismanopoulos, N., Delivanis, D. A., Weng, Z., Miniati, A., Vasiadi, M., Katsarou-Katsari, A., Miao, B., Leeman, S. E., Kalogeromitros, D. & Theoharides, T. C.(2011) Human mast cell degranulation and preformed TNF secretion require mitochondrial translocation to exocytosis sites: relevance to atopic dermatitis, J Allergy Clin Immunol. 127, 1522-31 e8. 367. Sinnamon, M. J., Carter, K. J., Sims, L. P., Lafleur, B., Fingleton, B. & Matrisian, L. M. (2008) A protective role of mast cells in intestinal tumorigenesis, Carcinogenesis. 29, 880-6. 368. Dalton, D. K. & Noelle, R. J. (2012) The roles of mast cells in anticancer immunity, Cancer Immunol Immunother. 61, 1511-20. 369. Li, J. M., Nichols, M. A., Chandrasekharan, S., Xiong, Y. & Wang, X. F. (1995) Transforming growth factor beta activates the promoter of cyclin-dependent kinase inhibitor p15INK4B through an Sp1 consensus site, J Biol Chem. 270, 26750-3. 370. Gitig, D. M. & Koff, A. (2001) Cdk pathway: cyclin-dependent kinases and cyclin-dependent kinase inhibitors, Mol Biotechnol. 19, 179-88. 371. Gauchat, J. F., Henchoz, S., Mazzei, G., Aubry, J. P., Brunner, T., Blasey, H., Life, P., Talabot, D., Flores-Romo, L., Thompson, J. & et al. (1993) Induction of human IgE synthesis in B cells by mast cells and basophils, Nature. 365, 340-3. 372. Tkaczyk, C., Frandji, P., Botros, H. G., Poncet, P., Lapeyre, J., Peronet, R., David, B. & Mecheri, S. (1996) Mouse bone marrow-derived mast cells and mast cell lines constitutively produce B cell growth and differentiation activities, J Immunol. 157, 1720-8. 373. Stone, K. D., Prussin, C. & Metcalfe, D. D. (2010) IgE, mast cells, basophils, and eosinophils, J Allergy Clin Immunol. 125, S73-80. 374. Gong, J., Yang, N. S., Croft, M., Weng, I. C., Sun, L., Liu, F. T. & Chen, S. S. (2010) The antigen presentation function of bone marrow-derived mast cells is spatiotemporally restricted to a subset expressing high levels of cell surface FcepsilonRI and MHC II, BMC Immunol. 11, 34. 375. Honjo, T., Alt, F. W. & Neuberger, M. S. (2004) Molecular biology of B cells, Elsevier, Amsterdam ; Boston. 376. Stevenson, F. K., Gregg, E. O., Smith, J. L. & Stevenson, G. T. (1984) Secretion of immunoglobulin by neoplastic B lymphocytes from lymph nodes of patients with lymphoma, Br J Cancer. 50, 579-86. 377. Takabayashi, T., Kato, A., Peters, A. T., Suh, L. A., Carter, R., Norton, J., Grammer, L. C., Tan, B. K., Chandra, R. K., Conley, D. B., Kern, R. C., Fujieda, S. & Schleimer, R. P. (2012) Glandular mast cells with distinct phenotype are highly elevated in chronic rhinosinusitis with nasal polyps, J Allergy Clin Immunol. 130, 410-20 e5. 378. Rijnierse, A., Redegeld, F. A., Blokhuis, B. R., Van der Heijden, M. W., Te Velde, A. A., Pronk, I., Hommes, D. W., Nijkamp, F. P., Koster, A. S. & Kraneveld, A. D. (2010) Ig-free light chains play a crucial role in murine mast cell-dependent colitis and are associated with human inflammatory bowel diseases, J Immunol. 185, 653-9. 379. Hoops, S., Sahle, S., Gauges, R., Lee, C., Pahle, J., Simus, N., Singhal, M., Xu, L., Mendes, P. & Kummer, U. (2006) COPASI--a COmplex PAthway SImulator, Bioinformatics. 22, 3067-74. 380. Zhao, Y., Burbach, J. A., Roby, K. F., Terranova, P. F. & Brannian, J. D. (1998) Macrophages are the major source of tumor necrosis factor alpha in the porcine corpus luteum, Biol Reprod. 59, 1385-91. 381. Stoyanov, E., Uddin, M., Mankuta, D., Dubinett, S. M. & Levi-Schaffer, F. (2012) Mast cells and histamine enhance the proliferation of non-small cell lung cancer cells, Lung Cancer. 75, 38-44. 382. Zwadlo-Klarwasser, G., Braam, U., Muhl-Zurbes, P. & Schmutzler, W. (1994) Macrophages and lymphocytes: alternative sources of histamine, Agents Actions. 41 Spec No, C99-100. 383. Bradley, J. R. (2008) TNF-mediated inflammatory disease, J Pathol. 214, 149-60. 384. Lukacs, N. W., Strieter, R. M., Chensue, S. W., Widmer, M. & Kunkel, S. L. (1995) TNF-alpha mediates recruitment of neutrophils and eosinophils during airway inflammation, J Immunol. 154, 5411-7.

167

Systems Immunology-list of references

385. Topp, M., Koenigsmann, M., Miresluis, A., Oberberg, D., Eitelbach, F., Vonmarschall, Z., Notter, M., Reufi, B., Stein, H., Thiel, E. & Berdel, W. (1993) Interleukin-4 inhibits growth of a human lung- tumor cell-line in-vitro and has therapeutic activity against xenografts of this cell-line in-vivo, Int J Oncol. 3, 167-70. 386. Galli, S. J. & Tsai, M. (2010) Mast cells in allergy and infection: versatile effector and regulatory cells in innate and adaptive immunity, Eur J Immunol. 40, 1843-51. 387. Lindner, D., Zietsch, C., Becher, P. M., Schulze, K., Schultheiss, H. P., Tschope, C. & Westermann, D. (2012) Differential expression of matrix metalloproteases in human fibroblasts with different origins, Biochem Res Int. 2012, 875742. 388. Yeh, S. I., Han, K. Y., Sabri, A., Rosenblatt, M. I., Azar, D. T., Jain, S. & Chang, J. H. (2010) MMP-7 knock-in corneal fibroblast cell lines secrete MMP-7 with proteolytic activity towards collagen XVIII, Curr Eye Res. 35, 799-805. 389. Lu, H., Ouyang, W. & Huang, C. (2006) Inflammation, a key event in cancer development, Mol Cancer Res. 4, 221-33. 390. Coffelt, S. B. & de Visser, K. E. (2014) Cancer: Inflammation lights the way to metastasis, Nature. 507, 48-9. 391. Rakoff-Nahoum, S. (2006) Why cancer and inflammation?, Yale J Biol Med. 79, 123-30. 392. Twu, O., Dessi, D., Vu, A., Mercer, F., Stevens, G. C., de Miguel, N., Rappelli, P., Cocco, A. R., Clubb, R. T., Fiori, P. L. & Johnson, P. J. (2014) Trichomonas vaginalis homolog of macrophage migration inhibitory factor induces prostate cell growth, invasiveness, and inflammatory responses, Proc Natl Acad Sci U S A. 393. Schmid, M. C. & Varner, J. A. (2010) Myeloid cells in the tumor microenvironment: modulation of tumor angiogenesis and tumor inflammation, J Oncol, 201026. 394. Ma, Y. & Ullrich, S. E. (2013) Intratumoral mast cells promote the growth of pancreatic cancer, Oncoimmunology. 2, e25964. 395. Lorusso, G. & Ruegg, C. (2008) The tumor microenvironment and its contribution to tumor evolution toward metastasis, Histochem Cell Biol. 130, 1091-103. 396. Marech, I., Ammendola, M., Sacco, R., Capriuolo, G. S., Patruno, R., Rubini, R., Luposella, M., Zuccala, V., Savino, E., Gadaleta, C. D., Ribatti, D. & Ranieri, G. (2014) Serum tryptase, mast cells positive to tryptase and microvascular density evaluation in early breast cancer patients: possible translational significance, BMC cancer. 14, 534. 397. Sarchio, S. N., Kok, L. F., O'Sullivan, C., Halliday, G. M. & Byrne, S. N. (2012) Dermal mast cells affect the development of sunlight-induced skin tumours, Exp Dermatol. 21, 241-8. 398. Schneider, P., MacKay, F., Steiner, V., Hofmann, K., Bodmer, J. L., Holler, N., Ambrose, C., Lawton, P., Bixler, S., Acha-Orbea, H., Valmori, D., Romero, P., Werner-Favre, C., Zubler, R. H., Browning, J. L. & Tschopp, J. (1999) BAFF, a novel ligand of the tumor necrosis factor family, stimulates B cell growth, J Exp Med. 189, 1747-56. 399. Alsaleh, G., Francois, A., Knapp, A. M., Schickel, J. N., Sibilia, J., Pasquali, J. L., Gottenberg, J. E., Wachsmann, D. & Soulas-Sprauel, P. (2011) Synovial fibroblasts promote immunoglobulin class switching by a mechanism involving BAFF, Eur J Immunol. 41, 2113-22. 400. Groot Kormelink, T., Powe, D. G., Kuijpers, S. A., Abudukelimu, A., Fens, M. H., Pieters, E. H., Kassing van der Ven, W. W., Habashy, H. O., Ellis, I. O., Blokhuis, B. R., Thio, M., Hennink, W. E., Storm, G., Redegeld, F. A. & Schiffelers, R. M. (2014) Immunoglobulin free light chains are biomarkers of poor prognosis in basal-like breast cancer and are potential targets in tumor-associated inflammation, Oncotarget. 5, 3159-67. 401. Melnicoff, M. J., Horan, P. K. & Morahan, P. S. (1989) Kinetics of changes in peritoneal cell populations following acute inflammation, Cell Immunol. 118, 178-91. 402. Wu, Q., Feng, Y., Yang, Y., Jingliu, Zhou, W., He, P., Zhou, R., Li, X. & Zou, J. (2004) Kinetics of the phenotype and function of murine peritoneal macrophages following acute inflammation, Cell Mol Immunol. 1, 57-62. 403. Thomson, A. J., Telfer, J. F., Young, A., Campbell, S., Stewart, C. J., Cameron, I. T., Greer, I. A. & Norman, J. E. (1999) Leukocytes infiltrate the myometrium during human parturition: further evidence that labour is an inflammatory process, Hum Reprod. 14, 229-36. 404. Demeestere, D., Libert, C. & Vandenbroucke, R. E. (2015) Clinical implications of leukocyte infiltration at the choroid plexus in (neuro)inflammatory disorders, Drug Discov Today. 20, 928-41. 405. Carlos, T. M. (2001) Leukocyte recruitment at sites of tumor: dissonant orchestration, J Leukoc Biol. 70, 171-84.

168

Systems Immunology-list of references

406. Berk, M., Williams, L. J., Jacka, F. N., O'Neil, A., Pasco, J. A., Moylan, S., Allen, N. B., Stuart, A. L., Hayley, A. C., Byrne, M. L. & Maes, M. (2013) So depression is an inflammatory disease, but where does the inflammation come from?, BMC Med. 11, 200. 407. Jeon, Y. H., Essue, B., Jan, S., Wells, R. & Whitworth, J. A. (2009) Economic hardship associated with managing chronic illness: a qualitative inquiry, BMC Health Serv Res. 9, 182. 408. Verhaak, P. F., Heijmans, M. J., Peters, L. & Rijken, M. (2005) Chronic disease and mental disorder, Soc Sci Med. 60, 789-97. 409. Lee, H. Y., Topham, D. J., Park, S. Y., Hollenbaugh, J., Treanor, J., Mosmann, T. R., Jin, X., Ward, B. M., Miao, H., Holden-Wiltse, J., Perelson, A. S., Zand, M. & Wu, H. (2009) Simulation and prediction of the adaptive immune response to influenza A virus infection, J Virol. 83, 7151-65. 410. Shahaf, G., Johnson, K. & Mehr, R. (2006) B cell development in aging mice: lessons from mathematical modeling, Int Immunol. 18, 31-9. 411. Antia, R., Bergstrom, C. T., Pilyugin, S. S., Kaech, S. M. & Ahmed, R. (2003) Models of CD8+ responses: 1. What is the antigen-independent proliferation program, J Theor Biol. 221, 585-98. 412. Wodarz, D. & Thomsen, A. R. (2005) Effect of the CTL proliferation program on virus dynamics, Int Immunol. 17, 1269-76. 413. Watzl, C., Sternberg-Simon, M., Urlaub, D. & Mehr, R. (2012) Understanding natural killer cell regulation by mathematical approaches, Front Immunol. 3, 359. 414. Scherbakova, A., Lust, H., Everaus, H. & Aints, A. (2013) A mathematical model of natural killer cell activity, Cytometry A. 83, 585-91. 415. Zak, D. E. & Aderem, A. (2009) Systems biology of innate immunity, Immunol Rev. 227, 264-82. 416. Uthaisangsook, S., Day, N. K., Bahna, S. L., Good, R. A. & Haraguchi, S. (2002) Innate immunity and its role against infections, Ann Allergy Asthma Immunol. 88, 253-64; quiz 265-6, 318. 417. Reddy, N. M., Suryanarayana, V., Kalvakolanu, D. V., Yamamoto, M., Kensler, T. W., Hassoun, P. M., Kleeberger, S. R. & Reddy, S. P. (2009) Innate immunity against bacterial infection following hyperoxia exposure is impaired in NRF2-deficient mice, J Immunol. 183, 4601-8. 418. Maurya, M. R., Benner, C., Pradervand, S., Glass, C. & Subramaniam, S. (2007) Systems biology of macrophages, Adv Exp Med Biol. 598, 62-79. 419. Niarakis, A., Bounab, Y., Grieco, L., Roncagalli, R., Hesse, A. M., Garin, J., Malissen, B., Daeron, M. & Thieffry, D. (2014) Computational modeling of the main signaling pathways involved in mast cell activation, Curr Top Microbiol Immunol. 382, 69-93. 420. Celada, F. & Seiden, P. E. (1992) A computer model of cellular interactions in the immune system, Immunol Today. 13, 56-62. 421. Mata, J. & Cohn, M. (2007) Cellular automata-based modeling program: synthetic immune system, Immunol Rev. 216, 198-212. 422. Folcik, V. A., An, G. C. & Orosz, C. G. (2007) The Basic Immune Simulator: an agent-based model to study the interactions between innate and adaptive immunity, Theor Biol Med Model. 4, 39. 423. Prince, J. M., Levy, R. M., Bartels, J., Baratt, A., Kane, J. M., 3rd, Lagoa, C., Rubin, J., Day, J., Wei, J., Fink, M. P., Goyert, S. M., Clermont, G., Billiar, T. R. & Vodovotz, Y. (2006) In silico and in vivo approach to elucidate the inflammatory complexity of CD14-deficient mice, Mol Med. 12, 88-96. 424. Bayry, J., Tchilian, E. Z., Davies, M. N., Forbes, E. K., Draper, S. J., Kaveri, S. V., Hill, A. V., Kazatchkine, M. D., Beverley, P. C., Flower, D. R. & Tough, D. F. (2008) In silico identified CCR4 antagonists target regulatory T cells and exert adjuvant activity in vaccination, Proc Natl Acad Sci U S A. 105, 10221-6. 425. Rullmann, J. A., Struemper, H., Defranoux, N. A., Ramanujan, S., Meeuwisse, C. M. & van Elsas, A. (2005) Systems biology for battling rheumatoid arthritis: application of the Entelos PhysioLab platform, Syst Biol (Stevenage). 152, 256-62. 426. Aderem, A., Adkins, J. N., Ansong, C., Galagan, J., Kaiser, S., Korth, M. J., Law, G. L., McDermott, J. G., Proll, S. C., Rosenberger, C., Schoolnik, G. & Katze, M. G. (2011) A systems biology approach to infectious disease research: innovating the pathogen-host research paradigm, MBio. 2, e00325-10. 427. Kremling, A., Bettenbrock, K., Laube, B., Jahreis, K., Lengeler, J. W. & Gilles, E. D. (2001) The organization of metabolic reaction networks. III. Application for diauxic growth on glucose and lactose, Metab Eng. 3, 362-79. 428. Chapman, J. D., Chappell, M. J. & Evans, N. D. (2011) The use of a formal sensitivity analysis on epidemic models with immune protection from maternally acquired antibodies, Comput Methods Programs Biomed. 104, 37-49.

169

Systems Immunology-list of references

429. Hu, D. & Yuan, J. M. (2006) Time-dependent sensitivity analysis of biological networks: coupled MAPK and PI3K signal transduction pathways, J Phys Chem A. 110, 5361-70. 430. Joo, J., Plimpton, S., Martin, S., Swiler, L. & Faulon, J. L. (2007) Sensitivity analysis of a computational model of the IKK NF-kappaB IkappaBalpha A20 signal transduction network, Ann N Y Acad Sci. 1115, 221-39. 431. Alam, M., Deng, X., Philipson, C., Bassaganya-Riera, J., Bisset, K., Carbo, A., Eubank, S., Hontecillas, R., Hoops, S., Mei, Y., Abedi, V. & Marathe, M. (2015) Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection, PLoS One. 10, e0136139. 432. George, A. J., Stark, J. & Chan, C. (2005) Understanding specificity and sensitivity of T-cell recognition, Trends Immunol. 26, 653-9. 433. Reijenga, K. A., van Megen, Y. M., Kooi, B. W., Bakker, B. M., Snoep, J. L., van Verseveld, H. W. & Westerhoff, H. V. (2005) Yeast glycolytic oscillations that are not controlled by a single oscillophore: a new definition of oscillophore strength, J Theor Biol. 232, 385-98. 434. Glansdorff, P. & Prigogine, I. (1978) Thermodynamic theory of structure, stability and fluctuations, Wiley-Interscience, London, New York,. 435. Tanaka, R. J., Ono, M. & Harrington, H. A. (2011) Skin barrier homeostasis in atopic dermatitis: feedback regulation of kallikrein activity, PLoS One. 6, e19895. 436. Westerhoff, H. V. & Dam, K. v. (1987) Thermodynamics and control of biological free-energy transduction, Elsevier ;Sole distributors for the USA and Canada, Elsevier Science Pub. Co., Amsterdam ; New York, NY, USA. 437. Westerhoff, H. V., Kolodkin, A., Conradie, R., Wilkinson, S. J., Bruggeman, F. J., Krab, K., van Schuppen, J. H., Hardin, H., Bakker, B. M., Mone, M. J., Rybakova, K. N., Eijken, M., van Leeuwen, H. J. & Snoep, J. L. (2009) Systems biology towards life in silico: mathematics of the control of living cells, J Math Biol. 58, 7-34. 438. Westerhoff, H. V., Plomp, P. J., Groen, A. K., Wanders, R. J., Bode, J. A. & van Dam, K. (1987) On the origin of the limited control of mitochondrial respiration by the adenine nucleotide translocator, Arch Biochem Biophys. 257, 154-69. 439. Henrich, C. C., Brookmeyer, K. A., Shrier, L. A. & Shahar, G. (2006) Supportive relationships and sexual risk behavior in adolescence: an ecological-transactional approach, J Pediatr Psychol. 31, 286-97. 440. Murase, A., Sasaki, T. & Kajiwara, T. (2005) Stability analysis of pathogen-immune interaction dynamics, J Math Biol. 51, 247-67. 441. Miller, R., Ewy, W., Corrigan, B. W., Ouellet, D., Hermann, D., Kowalski, K. G., Lockwood, P., Koup, J. R., Donevan, S., El-Kattan, A., Li, C. S., Werth, J. L., Feltner, D. E. & Lalonde, R. L. (2005) How modeling and simulation have enhanced decision making in new drug development, J Pharmacokinet Pharmacodyn. 32, 185-97. 442. van Eunen, K., Rossell, S., Bouwman, J., Westerhoff, H. V. & Bakker, B. M. (2011) Quantitative analysis of flux regulation through hierarchical regulation analysis, Methods Enzymol. 500, 571-95. 443. Heinrich, R., Schuster, S. & SpringerLink (Online service) (1996) The Regulation of Cellular Systems in pp. 1 online resource., Springer US,, Boston, MA. 444. Figueiredo, L. M., Janzen, C. J. & Cross, G. A. (2008) A histone methyltransferase modulates antigenic variation in African trypanosomes, PLoS Biol. 6, e161. 445. Olgart, C. & Frossard, N. (2001) Human lung fibroblasts secrete nerve growth factor: effect of inflammatory cytokines and glucocorticoids, Eur Respir J. 18, 115-21. 446. Pilling, D., Vakil, V., Cox, N. & Gomer, R. H. (2015) TNF-alpha-stimulated fibroblasts secrete lumican to promote fibrocyte differentiation, Proc Natl Acad Sci U S A. 112, 11929-34. 447. Wasiuk, A., Dalton, D. K., Schpero, W. L., Stan, R. V., Conejo-Garcia, J. R. & Noelle, R. J. (2012) Mast cells impair the development of protective anti-tumor immunity, Cancer Immunol Immunother. 61, 2273-82. 448. Shaik-Dasthagirisaheb, Y. B., Varvara, G., Murmura, G., Saggini, A., Potalivo, G., Caraffa, A., Antinolfi, P., Tete, S., Tripodi, D., Conti, F., Cianchetti, E., Toniato, E., Rosati, M., Conti, P., Speranza, L., Pantalone, A., Saggini, R., Theoharides, T. C. & Pandolfi, F. (2013) Vascular endothelial growth factor (VEGF), mast cells and inflammation, Int J Immunopathol Pharmacol. 26, 327-35. 449. Wang, M. F., Lu, C. Y. & Lai, S. C. (2013) Up-regulation of matrix metalloproteinases-2 and -9 via an Erk1/2/NF-kappaB pathway in murine mast cells infected with Toxoplasma gondii, J Comp Pathol. 149, 146-55.

170

Systems Immunology-list of references

450. Nakayama, T., Yao, L. & Tosato, G. (2004) Mast cell-derived angiopoietin-1 plays a critical role in the growth of plasma cell tumors, J Clin Invest. 114, 1317-25. 451. Kunkel, G. T., Maceyka, M., Milstien, S. & Spiegel, S. (2013) Targeting the sphingosine-1- phosphate axis in cancer, inflammation and beyond, Nat Rev Drug Discov. 12, 688-702. 452. Reber, L. L., Daubeuf, F., Pejler, G., Abrink, M. & Frossard, N. (2014) Mast cells contribute to bleomycin-induced lung inflammation and injury in mice through a chymase/mast cell protease 4- dependent mechanism, J Immunol. 192, 1847-54. 453. Rodewald, H. R., Dessing, M., Dvorak, A. M. & Galli, S. J. (1996) Identification of a committed precursor for the mast cell lineage, Science. 271, 818-22. 454. Da'as, S., Teh, E. M., Dobson, J. T., Nasrallah, G. K., McBride, E. R., Wang, H., Neuberg, D. S., Marshall, J. S., Lin, T. J. & Berman, J. N. (2011) Zebrafish mast cells possess an FcvarepsilonRI-like receptor and participate in innate and adaptive immune responses, Dev Comp Immunol. 35, 125-34. 455. Dobson, J. T., Seibert, J., Teh, E. M., Da'as, S., Fraser, R. B., Paw, B. H., Lin, T. J. & Berman, J. N. (2008) Carboxypeptidase A5 identifies a novel mast cell lineage in the zebrafish providing new insight into mast cell fate determination, Blood. 112, 2969-72. 456. Jamur, M. C., Grodzki, A. C., Berenstein, E. H., Hamawy, M. M., Siraganian, R. P. & Oliver, C. (2005) Identification and characterization of undifferentiated mast cells in mouse bone marrow, Blood. 105, 4282-9. 457. Taketomi, Y., Ueno, N., Kojima, T., Sato, H., Murase, R., Yamamoto, K., Tanaka, S., Sakanaka, M., Nakamura, M., Nishito, Y., Kawana, M., Kambe, N., Ikeda, K., Taguchi, R., Nakamizo, S., Kabashima, K., Gelb, M. H., Arita, M., Yokomizo, T., Nakamura, M., Watanabe, K., Hirai, H., Nakamura, M., Okayama, Y., Ra, C., Aritake, K., Urade, Y., Morimoto, K., Sugimoto, Y., Shimizu, T., Narumiya, S., Hara, S. & Murakami, M. (2013) Mast cell maturation is driven via a group III phospholipase A2-prostaglandin D2-DP1 receptor paracrine axis, Nat Immunol. 14, 554-63. 458. Moon, T. C., St Laurent, C. D., Morris, K. E., Marcet, C., Yoshimura, T., Sekar, Y. & Befus, A. D. (2010) Advances in mast cell biology: new understanding of heterogeneity and function, Mucosal Immunol. 3, 111-28. 459. Xing, W., Austen, K. F., Gurish, M. F. & Jones, T. G. (2011) Protease phenotype of constitutive connective tissue and of induced mucosal mast cells in mice is regulated by the tissue, Proc Natl Acad Sci U S A. 108, 14210-5. 460. Takagi, M., Nakahata, T., Kubo, T., Shiohara, M., Koike, K., Miyajima, A., Arai, K., Nishikawa, S., Zsebo, K. M. & Komiyama, A. (1992) Stimulation of mouse connective tissue-type mast cells by hemopoietic stem cell factor, a ligand for the c-kit receptor, J Immunol. 148, 3446-53. 461. Nakahata, T., Kobayashi, T., Ishiguro, A., Tsuji, K., Naganuma, K., Ando, O., Yagi, Y., Tadokoro, K. & Akabane, T. (1986) Extensive proliferation of mature connective-tissue type mast cells in vitro, Nature. 324, 65-7. 462. Tsuji, K., Nakahata, T., Takagi, M., Kobayashi, T., Ishiguro, A., Kikuchi, T., Naganuma, K., Koike, K., Miyajima, A., Arai, K. & et al. (1990) Effects of interleukin-3 and interleukin-4 on the development of "connective tissue-type" mast cells: interleukin-3 supports their survival and interleukin-4 triggers and supports their proliferation synergistically with interleukin-3, Blood. 75, 421- 7. 463. Nabel, G., Galli, S. J., Dvorak, A. M., Dvorak, H. F. & Cantor, H. (1981) Inducer T lymphocytes synthesize a factor that stimulates proliferation of cloned mast cells, Nature. 291, 332-4. 464. Schrader, J. W., Lewis, S. J., Clark-Lewis, I. & Culvenor, J. G. (1981) The persisting (P) cell: histamine content, regulation by a T cell-derived factor, origin from a bone marrow precursor, and relationship to mast cells, Proc Natl Acad Sci U S A. 78, 323-7. 465. Jones, T. G., Finkelman, F. D., Austen, K. F. & Gurish, M. F. (2010) T regulatory cells control antigen-induced recruitment of mast cell progenitors to the lungs of C57BL/6 mice, J Immunol. 185, 1804-11. 466. Schwartz, L. B. (2006) Analysis of MC(T) and MC(TC) mast cells in tissue, Methods Mol Biol. 315, 53-62. 467. Galli, S. J., Nakae, S. & Tsai, M. (2005) Mast cells in the development of adaptive immune responses, Nat Immunol. 6, 135-42. 468. Matsushima, H., Yamada, N., Matsue, H. & Shimada, S. (2004) TLR3-, TLR7-, and TLR9- mediated production of proinflammatory cytokines and chemokines from murine connective tissue type skin-derived mast cells but not from bone marrow-derived mast cells, J Immunol. 173, 531-41.

171

Systems Immunology-list of references

469. Huang, C., De Sanctis, G. T., O'Brien, P. J., Mizgerd, J. P., Friend, D. S., Drazen, J. M., Brass, L. F. & Stevens, R. L. (2001) Evaluation of the substrate specificity of human mast cell tryptase beta I and demonstration of its importance in bacterial infections of the lung, J Biol Chem. 276, 26276-84. 470. Otsuka, A., Kubo, M., Honda, T., Egawa, G., Nakajima, S., Tanizaki, H., Kim, B., Matsuoka, S., Watanabe, T., Nakae, S., Miyachi, Y. & Kabashima, K. (2011) Requirement of interaction between mast cells and skin dendritic cells to establish contact hypersensitivity, PLoS One. 6, e25538. 471. Malaviya, R. & Abraham, S. N. (2000) Role of mast cell leukotrienes in neutrophil recruitment and bacterial clearance in infectious peritonitis, J Leukoc Biol. 67, 841-6. 472. Gela, A., Kasetty, G., Jovic, S., Ekoff, M., Nilsson, G., Morgelin, M., Kjellstrom, S., Pease, J. E., Schmidtchen, A. & Egesten, A. (2014) Eotaxin-3 (CCL26) exerts innate host defense activities that are modulated by mast cell proteases, Allergy. 473. Tavora, F., Cresswell, N., Li, L., Ripple, M. & Burke, A. (2010) Immunolocalisation of fibrin in coronary atherosclerosis: implications for necrotic core development, Pathology. 42, 15-22. 474. St John, A. L., Rathore, A. P., Yap, H., Ng, M. L., Metcalfe, D. D., Vasudevan, S. G. & Abraham, S. N. (2011) Immune surveillance by mast cells during dengue infection promotes natural killer (NK) and NKT-cell recruitment and viral clearance, Proc Natl Acad Sci U S A. 108, 9190-5. 475. Suurmond, J., van Heemst, J., van Heiningen, J., Dorjee, A. L., Schilham, M. W., van der Beek, F. B., Huizinga, T. W., Schuerwegh, A. J. & Toes, R. E. (2013) Communication between human mast cells and CD4(+) T cells through antigen-dependent interactions, Eur J Immunol. 43, 1758-68. 476. Raposo, G., Tenza, D., Mecheri, S., Peronet, R., Bonnerot, C. & Desaymard, C. (1997) Accumulation of major histocompatibility complex class II molecules in mast cell secretory granules and their release upon degranulation, Mol Biol Cell. 8, 2631-45. 477. Banovac, K., Neylan, D., Leone, J., Ghandur-Mnaymneh, L. & Rabinovitch, A. (1989) Are the mast cells antigen presenting cells?, Immunol Invest. 18, 901-6. 478. Mekori, Y. A. & Metcalfe, D. D. (1999) Mast cell-T cell interactions, J Allergy Clin Immunol. 104, 517-23. 479. Frandji, P., Tkaczyk, C., Oskeritzian, C., David, B., Desaymard, C. & Mecheri, S. (1996) Exogenous and endogenous antigens are differentially presented by mast cells to CD4+ T lymphocytes, Eur J Immunol. 26, 2517-28. 480. Jawdat, D. M., Albert, E. J., Rowden, G., Haidl, I. D. & Marshall, J. S. (2004) IgE-mediated mast cell activation induces Langerhans cell migration in vivo, J Immunol. 173, 5275-82. 481. Suto, H., Nakae, S., Kakurai, M., Sedgwick, J. D., Tsai, M. & Galli, S. J. (2006) Mast cell- associated TNF promotes dendritic cell migration, J Immunol. 176, 4102-12. 482. Lin, T. J., Maher, L. H., Gomi, K., McCurdy, J. D., Garduno, R. & Marshall, J. S. (2003) Selective early production of CCL20, or macrophage inflammatory protein 3alpha, by human mast cells in response to Pseudomonas aeruginosa, Infect Immun. 71, 365-73. 483. Rajakulasingam, K., Hamid, Q., O'Brien, F., Shotman, E., Jose, P. J., Williams, T. J., Jacobson, M., Barkans, J. & Durham, S. R. (1997) RANTES in human allergen-induced rhinitis: cellular source and relation to tissue eosinophilia, Am J Respir Crit Care Med. 155, 696-703. 484. Alam, R., Kumar, D., Anderson-Walters, D. & Forsythe, P. A. (1994) Macrophage inflammatory protein-1 alpha and monocyte chemoattractant peptide-1 elicit immediate and late cutaneous reactions and activate murine mast cells in vivo, J Immunol. 152, 1298-303. 485. Wang, H. W., Tedla, N., Lloyd, A. R., Wakefield, D. & McNeil, P. H. (1998) Mast cell activation and migration to lymph nodes during induction of an immune response in mice, J Clin Invest. 102, 1617-26. 486. Merluzzi, S., Frossi, B., Gri, G., Parusso, S., Tripodo, C. & Pucillo, C. (2010) Mast cells enhance proliferation of B lymphocytes and drive their differentiation toward IgA-secreting plasma cells, Blood. 115, 2810-7. 487. Ryzhov, S., Goldstein, A. E., Matafonov, A., Zeng, D., Biaggioni, I. & Feoktistov, I. (2004) Adenosine-activated mast cells induce IgE synthesis by B lymphocytes: an A2B-mediated process involving Th2 cytokines IL-4 and IL-13 with implications for asthma, J Immunol. 172, 7726-33. 488. Yoshikawa, T., Imada, T., Nakakubo, H., Nakamura, N. & Naito, K. (2001) Rat mast cell protease-I enhances immunoglobulin E production by mouse B cells stimulated with interleukin-4, Immunology. 104, 333-40. 489. Fox, M. F., Pontier, A., Gurbuxani, S. & Sipkins, D. A. (2013) Stem cell factor expression in B cell malignancies is influenced by the niche, Leuk Lymphoma. 54, 2274-80.

172

Systems Immunology-list of references

490. Khazaie, K., Blatner, N. R., Khan, M. W., Gounari, F., Gounaris, E., Dennis, K., Bonertz, A., Tsai, F. N., Strouch, M. J., Cheon, E., Phillips, J. D., Beckhove, P. & Bentrem, D. J. (2011) The significant role of mast cells in cancer, Cancer Metastasis Rev. 30, 45-60. 491. Ribatti, D. & Crivellato, E. (2009) The controversial role of mast cells in tumor growth, Int Rev Cell Mol Biol. 275, 89-131. 492. Roy, L. D., Curry, J. M., Sahraei, M., Besmer, D. M., Kidiyoor, A., Gruber, H. E. & Mukherjee, P. (2013) Arthritis augments breast cancer metastasis: role of mast cells and SCF/c-Kit signaling, Breast Cancer Res. 15, R32. 493. Coussens, L. M. & Werb, Z. (2001) Inflammatory cells and cancer: think different!, J Exp Med. 193, F23-6. 494. Ch'ng, S., Wallis, R. A., Yuan, L., Davis, P. F. & Tan, S. T. (2006) Mast cells and cutaneous malignancies, Mod Pathol. 19, 149-59. 495. Valent, P., Sotlar, K., Sperr, W. R., Reiter, A., Arock, M. & Horny, H. P. (2015) Chronic mast cell leukemia: a novel leukemia-variant with distinct morphological and clinical features, Leuk Res. 39, 1-5. 496. Dvorak, A. M. (2005) Ultrastructural studies of human basophils and mast cells, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society. 53, 1043-70. 497. Flynn, E. A., Schwartz, J. L. & Shklar, G. (1991) Sequential mast cell infiltration and degranulation during experimental carcinogenesis, Journal of cancer research and clinical oncology. 117, 115-22. 498. Kashiwase, Y., Inamura, H., Morioka, J., Igarashi, Y., Kawai-Kowase, K. & Kurosawa, M. (2008) Quantitative analysis of mast cells in benign and malignant colonic lesions: immunohistochemical study on formalin-fixed, paraffin-embedded tissues, Allergologia et immunopathologia. 36, 271-6. 499. Gounaris, E., Erdman, S. E., Restaino, C., Gurish, M. F., Friend, D. S., Gounari, F., Lee, D. M., Zhang, G., Glickman, J. N., Shin, K., Rao, V. P., Poutahidis, T., Weissleder, R., McNagny, K. M. & Khazaie, K. (2007) Mast cells are an essential hematopoietic component for polyp development, Proceedings of the National Academy of Sciences of the United States of America. 104, 19977-82. 500. Demitsu, T., Inoue, T., Kakurai, M., Kiyosawa, T., Yoneda, K. & Manabe, M. (2002) Activation of mast cells within a tumor of angiosarcoma: ultrastructural study of five cases, The Journal of dermatology. 29, 280-9. 501. Vitte, J. (2013) Human mast cell tryptase in biology and medicine, Mol Immunol. 502. Kankkunen, J. P., Harvima, I. T. & Naukkarinen, A. (1997) Quantitative analysis of tryptase and chymase containing mast cells in benign and malignant breast lesions, International journal of cancer Journal international du cancer. 72, 385-8. 503. Xiang, M., Gu, Y., Zhao, F., Lu, H., Chen, S. & Yin, L. (2010) Mast cell tryptase promotes breast cancer migration and invasion, Oncology reports. 23, 615-9. 504. Marech, I., Ammendola, M., Sacco, R., Capriuolo, G. S., Patruno, R., Rubini, R., Luposella, M., Zuccala, V., Savino, E., Gadaleta, C. D., Ribatti, D. & Ranieri, G. (2014) Serum tryptase, mast cells positive to tryptase and microvascular density evaluation in early breast cancer patients: possible translational significance, BMC Cancer. 14, 534. 505. Wilkinson, B., Jones, A. & Kossard, S. (1992) Mast cell quantitation by image analysis in adult mastocytosis and inflammatory skin disorders, Journal of cutaneous pathology. 19, 366-70. 506. Janowski, P., Strzelecki, M., Brzezinska-Blaszczyk, E. & Zalewska, A. (2001) Computer analysis of normal and basal cell carcinoma mast cells, Medical science monitor : international medical journal of experimental and clinical research. 7, 260-5. 507. Takanami, I., Takeuchi, K. & Naruke, M. (2000) Mast cell density is associated with angiogenesis and poor prognosis in pulmonary adenocarcinoma, Cancer. 88, 2686-92. 508. Ribatti, D., Vacca, A., Ria, R., Marzullo, A., Nico, B., Filotico, R., Roncali, L. & Dammacco, F. (2003) Neovascularisation, expression of fibroblast growth factor-2, and mast cells with tryptase activity increase simultaneously with pathological progression in human malignant melanoma, Eur J Cancer. 39, 666-74. 509. Samoszuk, M., Kanakubo, E. & Chan, J. K. (2005) Degranulating mast cells in fibrotic regions of human tumors and evidence that mast cell heparin interferes with the growth of tumor cells through a mechanism involving fibroblasts, BMC Cancer. 5, 121.

173

Systems Immunology-list of references

510. Chiba, N., Shimada, K., Chen, S., Jones, H. D., Alsabeh, R., Slepenkin, A. V., Peterson, E., Crother, T. R. & Arditi, M. (2015) Mast Cells Play an Important Role in Chlamydia pneumoniae Lung Infection by Facilitating Immune Cell Recruitment into the Airway, J Immunol. 511. Edwards, A. M. & Howell, J. B. (2000) The chromones: history, chemistry and clinical development. A tribute to the work of Dr R. E. C. Altounyan, Clin Exp Allergy. 30, 756-74. 512. Bradding, P., Walls, A. F. & Holgate, S. T. (2006) The role of the mast cell in the pathophysiology of asthma, J Allergy Clin Immunol. 117, 1277-84. 513. Norris, A. A. & Alton, E. W. (1996) Chloride transport and the action of sodium cromoglycate and nedocromil sodium in asthma, Clin Exp Allergy. 26, 250-3. 514. Arumugam, T. & Logsdon, C. D. (2011) S100P: a novel therapeutic target for cancer, Amino Acids. 41, 893-9. 515. Arumugam, T., Ramachandran, V., Sun, D., Peng, Z., Pal, A., Maxwell, D. S., Bornmann, W. G. & Logsdon, C. D. (2013) Designing and developing S100P inhibitor 5-methyl cromolyn for pancreatic cancer therapy, Mol Cancer Ther. 12, 654-62. 516. Rosenwasser, L. J., O'Brien, T. & Weyne, J. (2005) Mast cell stabilization and anti-histamine effects of olopatadine ophthalmic solution: a review of pre-clinical and clinical research, Curr Med Res Opin. 21, 1377-87. 517. Bowrey, P. F., King, J., Magarey, C., Schwartz, P., Marr, P., Bolton, E. & Morris, D. L. (2000) Histamine, mast cells and tumour cell proliferation in breast cancer: does preoperative cimetidine administration have an effect?, Br J Cancer. 82, 167-70. 518. Zhang, M., Thurmond, R. L. & Dunford, P. J. (2007) The histamine H(4) receptor: a novel modulator of inflammatory and immune disorders, Pharmacol Ther. 113, 594-606. 519. Hanashiro, K., Sunagawa, M., Nakasone, T., Nakamura, M. & Kosugi, T. (2007) Inhibition of IgE-mediated phosphorylation of FcepsilonRIgamma protein by antiallergic drugs in rat basophilic leukemia (RBL-2H3) cells: a novel action of antiallergic drugs, Int Immunopharmacol. 7, 994-1002. 520. Masuda, E. S. & Schmitz, J. (2008) Syk inhibitors as treatment for allergic rhinitis, Pulm Pharmacol Ther. 21, 461-7. 521. Wong, B. R., Grossbard, E. B., Payan, D. G. & Masuda, E. S. (2004) Targeting Syk as a treatment for allergic and autoimmune disorders, Expert Opin Investig Drugs. 13, 743-62. 522. Barnes, G. R. & Collins, C. J. (2008) The influence of briefly presented randomized target motion on the extraretinal component of ocular pursuit, J Neurophysiol. 99, 831-42. 523. Duan, W. & Wong, W. S. (2006) Targeting mitogen-activated protein kinases for asthma, Curr Drug Targets. 7, 691-8. 524. Melendez, A. J. (2008) Allergy therapy: the therapeutic potential of targeting sphingosine kinase signalling in mast cells, Eur J Immunol. 38, 2969-74. 525. Savini, P., Rondoni, M., Poletti, G., Lanzi, A., Quercia, O., Soverini, S., De Benedittis, C., Musardo, G., Martinelli, G. & Stefanini, G. F. (2015) Serum total tryptase level confirms itself as a more reliable marker of mast cells burden in mast cell leukaemia (aleukaemic variant), Case Rep Hematol. 2015, 737302. 526. Hogan, A. D. & Schwartz, L. B. (1997) Markers of mast cell degranulation, Methods. 13, 43-52. 527. Harvima, I. T., Karkola, K., Harvima, R. J., Naukkarinen, A., Neittaanmaki, H., Horsmanheimo, M. & Fraki, J. E. (1989) Biochemical and histochemical evaluation of tryptase in various human tissues, Arch Dermatol Res. 281, 231-7. 528. Buckley, M. G., Walters, C., Wong, W. M., Cawley, M. I., Ren, S., Schwartz, L. B. & Walls, A. F. (1997) Mast cell activation in arthritis: detection of alpha- and beta-tryptase, histamine and cationic protein in synovial fluid, Clin Sci (Lond). 93, 363-70. 529. Cairns, J. A. (2005) Inhibitors of mast cell tryptase beta as therapeutics for the treatment of asthma and inflammatory disorders, Pulm Pharmacol Ther. 18, 55-66. 530. Schwartz, L. B., Lewis, R. A., Seldin, D. & Austen, K. F. (1981) Acid hydrolases and tryptase from secretory granules of dispersed human lung mast cells, J Immunol. 126, 1290-4. 531. Shin, K., Nigrovic, P. A., Crish, J., Boilard, E., McNeil, H. P., Larabee, K. S., Adachi, R., Gurish, M. F., Gobezie, R., Stevens, R. L. & Lee, D. M. (2009) Mast cells contribute to autoimmune inflammatory arthritis via their tryptase/heparin complexes, J Immunol. 182, 647-56. 532. Swystun, V. A., Gordon, J. R., Davis, E. B., Zhang, X. & Cockcroft, D. W. (2000) Mast cell tryptase release and asthmatic responses to allergen increase with regular use of salbutamol, J Allergy Clin Immunol. 106, 57-64.

174

Systems Immunology-list of references

533. Sakai, K., Kohri, T., Tashiro, M., Kishino, Y. & Kido, H. (1994) Sendai virus infection changes the subcellular localization of tryptase Clara in rat bronchiolar epithelial cells, Eur Respir J. 7, 686-92. 534. Kido, H., Niwa, Y., Beppu, Y. & Towatari, T. (1996) Cellular proteases involved in the pathogenicity of enveloped animal viruses, human immunodeficiency virus, influenza virus A and Sendai virus, Adv Enzyme Regul. 36, 325-47. 535. Stack, M. S. & Johnson, D. A. (1994) Human mast cell tryptase activates single-chain urinary- type plasminogen activator (pro-urokinase), J Biol Chem. 269, 9416-9. 536. Ribatti, D., Ennas, M. G., Vacca, A., Ferreli, F., Nico, B., Orru, S. & Sirigu, P. (2003) Tumor vascularity and tryptase-positive mast cells correlate with a poor prognosis in melanoma, Eur J Clin Invest. 33, 420-5. 537. Benitez-Bribiesca, L., Wong, A., Utrera, D. & Castellanos, E. (2001) The role of mast cell tryptase in neoangiogenesis of premalignant and malignant lesions of the uterine cervix, J Histochem Cytochem. 49, 1061-2. 538. Ranieri, G., Ammendola, M., Patruno, R., Celano, G., Zito, F. A., Montemurro, S., Rella, A., Di Lecce, V., Gadaleta, C. D., Battista De Sarro, G. & Ribatti, D. (2009) Tryptase-positive mast cells correlate with angiogenesis in early breast cancer patients, Int J Oncol. 35, 115-20. 539. Ammendola, M., Sacco, R., Sammarco, G., Donato, G., Zuccala, V., Romano, R., Luposella, M., Patruno, R., Vallicelli, C., Verdecchia, G. M., Cavaliere, D., Montemurro, S. & Ranieri, G. (2013) Mast Cells Positive to Tryptase and c-Kit Receptor Expressing Cells Correlates with Angiogenesis in Gastric Cancer Patients Surgically Treated, Gastroenterol Res Pract. 2013, 703163. 540. Esposito, I., Menicagli, M., Funel, N., Bergmann, F., Boggi, U., Mosca, F., Bevilacqua, G. & Campani, D. (2004) Inflammatory cells contribute to the generation of an angiogenic phenotype in pancreatic ductal adenocarcinoma, J Clin Pathol. 57, 630-6. 541. Clark, J. M., Abraham, W. M., Fishman, C. E., Forteza, R., Ahmed, A., Cortes, A., Warne, R. L., Moore, W. R. & Tanaka, R. D. (1995) Tryptase inhibitors block allergen-induced airway and inflammatory responses in allergic sheep, Am J Respir Crit Care Med. 152, 2076-83. 542. He, S., Aslam, A., Gaca, M. D., He, Y., Buckley, M. G., Hollenberg, M. D. & Walls, A. F. (2004) Inhibitors of tryptase as mast cell-stabilizing agents in the human airways: effects of tryptase and other agonists of proteinase-activated receptor 2 on histamine release, J Pharmacol Exp Ther. 309, 119-26. 543. Ammendola, M., Leporini, C., Marech, I., Gadaleta, C. D., Scognamillo, G., Sacco, R., Sammarco, G., De Sarro, G., Russo, E. & Ranieri, G. (2014) Targeting mast cells tryptase in tumor microenvironment: a potential antiangiogenetic strategy, Biomed Res Int. 2014, 154702. 544. Fujiwara, Y., Furukawa, K., Haruki, K., Shimada, Y., Iida, T., Shiba, H., Uwagawa, T., Ohashi, T. & Yanaga, K. (2011) Nafamostat mesilate can prevent adhesion, invasion and peritoneal dissemination of pancreatic cancer thorough nuclear factor kappa-B inhibition, J Hepatobiliary Pancreat Sci. 18, 731-9. 545. Molderings, G. J., Raithel, M., Kratz, F., Azemar, M., Haenisch, B., Harzer, S. & Homann, J. (2011) Omalizumab treatment of systemic mast cell activation disease: experiences from four cases, Intern Med. 50, 611-5. 546. Buhl, R., Barth, H., Dorner, L., Nabavi, A., Rohr, A. & Mehdorn, H. M. (2007) De novo development of intraosseous cavernous hemangioma, J Clin Neurosci. 14, 289-92. 547. Jensen-Jarolim, E. & Pawelec, G. (2012) The nascent field of AllergoOncology, Cancer Immunol Immunother. 61, 1355-7. 548. Jensen-Jarolim, E., Achatz, G., Turner, M. C., Karagiannis, S., Legrand, F., Capron, M., Penichet, M. L., Rodriguez, J. A., Siccardi, A. G., Vangelista, L., Riemer, A. B. & Gould, H. (2008) AllergoOncology: the role of IgE-mediated allergy in cancer, Allergy. 63, 1255-66. 549. Ashman, L. K. (1999) The biology of stem cell factor and its receptor C-kit, Int J Biochem Cell Biol. 31, 1037-51. 550. Reber, L., Da Silva, C. A. & Frossard, N. (2006) Stem cell factor and its receptor c-Kit as targets for inflammatory diseases, European journal of pharmacology. 533, 327-40. 551. Smith, M. A., Pallister, C. J. & Smith, J. G. (2001) Stem cell factor: biology and relevance to clinical practice, Acta haematologica. 105, 143-50. 552. Sasaki, T., Mizuochi, C., Horio, Y., Nakao, K., Akashi, K. & Sugiyama, D. (2010) Regulation of hematopoietic cell clusters in the placental niche through SCF/Kit signaling in embryonic mouse, Development. 137, 3941-52.

175

Systems Immunology-list of references

553. Rosbottom, A., Knight, P. A., McLachlan, G., Thornton, E. M., Wright, S. W., Miller, H. R. & Scudamore, C. L. (2002) Chemokine and cytokine expression in murine intestinal epithelium following Nippostrongylus brasiliensis infection, Parasite immunology. 24, 67-75. 554. Sun, L., Lee, J. & Fine, H. A. (2004) Neuronally expressed stem cell factor induces neural stem cell migration to areas of brain injury, The Journal of clinical investigation. 113, 1364-74. 555. Morita, E., Lee, D. G., Sugiyama, M. & Yamamoto, S. (1994) Expression of c-kit ligand in human keratinocytes, Archives of dermatological research. 286, 273-7. 556. Nilsson, G., Butterfield, J. H., Nilsson, K. & Siegbahn, A. (1994) Stem cell factor is a chemotactic factor for human mast cells, J Immunol. 153, 3717-23. 557. Tsai, M., Shih, L. S., Newlands, G. F., Takeishi, T., Langley, K. E., Zsebo, K. M., Miller, H. R., Geissler, E. N. & Galli, S. J. (1991) The rat c-kit ligand, stem cell factor, induces the development of connective tissue-type and mucosal mast cells in vivo. Analysis by anatomical distribution, histochemistry, and protease phenotype, The Journal of experimental medicine. 174, 125-31. 558. Tsai, M., Takeishi, T., Thompson, H., Langley, K. E., Zsebo, K. M., Metcalfe, D. D., Geissler, E. N. & Galli, S. J. (1991) Induction of mast cell proliferation, maturation, and heparin synthesis by the rat c-kit ligand, stem cell factor, Proceedings of the National Academy of Sciences of the United States of America. 88, 6382-6. 559. Halova, I., Draberova, L. & Draber, P. Mast cell chemotaxis - chemoattractants and signaling pathways, Front Immunol. 3, 119. 560. Okayama, Y. & Kawakami, T. (2006) Development, migration, and survival of mast cells, Immunologic research. 34, 97-115. 561. Sakai, H., Toyota, N., Ito, F., Takahashi, H., Hashimoto, Y. & Iizuka, H. (1999) Glucocorticoids inhibit proliferation and adhesion of the IL-3-dependent mast cell line, MC/9, to NIH/3T3 fibroblasts, with an accompanying decrease in IL-3 receptor expression, Arch Dermatol Res. 291, 224-31. 562. Itakura, A., Miura, Y., Hikasa, Y., Kiso, Y. & Matsuda, H. (2001) Interleukin-3 and stem cell factor modulate cell cycle regulatory factors in mast cells: negative regulation of p27Kip1 in proliferation of mast cells induced by interleukin-3 but not stem cell factor, Exp Hematol. 29, 803-11. 563. Bianchine, P. J., Burd, P. R. & Metcalfe, D. D. (1992) IL-3-dependent mast cells attach to plate- bound vitronectin. Demonstration of augmented proliferation in response to signals transduced via cell surface vitronectin receptors, J Immunol. 149, 3665-71. 564. Blechman, J. M., Lev, S., Givol, D. & Yarden, Y. (1993) Structure-function analyses of the kit receptor for the steel factor, Stem Cells. 11 Suppl 2, 12-21. 565. Shiohara, M. & Koike, K. (2005) Regulation of mast cell development, Chem Immunol Allergy. 87, 1-21. 566. Peters, E. M., Maurer, M., Botchkarev, V. A., Jensen, K., Welker, P., Scott, G. A. & Paus, R. (2003) Kit is expressed by epithelial cells in vivo, J Invest Dermatol. 121, 976-84. 567. Kirshenbaum, A. S., Goff, J. P., Kessler, S. W., Mican, J. M., Zsebo, K. M. & Metcalfe, D. D. (1992) Effect of IL-3 and stem cell factor on the appearance of human basophils and mast cells from CD34+ pluripotent progenitor cells, J Immunol. 148, 772-7. 568. Hundley, T. R., Gilfillan, A. M., Tkaczyk, C., Andrade, M. V., Metcalfe, D. D. & Beaven, M. A. (2004) Kit and FcepsilonRI mediate unique and convergent signals for release of inflammatory mediators from human mast cells, Blood. 104, 2410-7. 569. Ito, T., Smrz, D., Jung, M. Y., Bandara, G., Desai, A., Smrzova, S., Kuehn, H. S., Beaven, M. A., Metcalfe, D. D. & Gilfillan, A. M. (2012) Stem cell factor programs the mast cell activation phenotype, Journal of immunology. 188, 5428-37. 570. Ando, A., Martin, T. R. & Galli, S. J. (1993) Effects of chronic treatment with the c-kit ligand, stem cell factor, on immunoglobulin E-dependent anaphylaxis in mice. Genetically mast cell-deficient Sl/Sld mice acquire anaphylactic responsiveness, but the congenic normal mice do not exhibit augmented responses, J Clin Invest. 92, 1639-49. 571. Ullrich, A. & Schlessinger, J. (1990) Signal transduction by receptors with tyrosine kinase activity, Cell. 61, 203-12. 572. Huang, E., Nocka, K., Beier, D. R., Chu, T. Y., Buck, J., Lahm, H. W., Wellner, D., Leder, P. & Besmer, P. (1990) The hematopoietic growth factor KL is encoded by the Sl locus and is the ligand of the c-kit receptor, the gene product of the W locus, Cell. 63, 225-33. 573. Kitamura, Y. & Go, S. (1979) Decreased production of mast cells in S1/S1d anemic mice, Blood. 53, 492-7.

176

Systems Immunology-list of references

574. Mekori, Y. A., Oh, C. K. & Metcalfe, D. D. (1993) IL-3-dependent murine mast cells undergo apoptosis on removal of IL-3. Prevention of apoptosis by c-kit ligand, Journal of immunology. 151, 3775-84. 575. Yee, N. S., Paek, I. & Besmer, P. (1994) Role of kit-ligand in proliferation and suppression of apoptosis in mast cells: basis for radiosensitivity of white spotting and steel mutant mice, The Journal of experimental medicine. 179, 1777-87. 576. Finotto, S., Mekori, Y. A. & Metcalfe, D. D. (1997) Glucocorticoids decrease tissue mast cell number by reducing the production of the c-kit ligand, stem cell factor, by resident cells: in vitro and in vivo evidence in murine systems, The Journal of clinical investigation. 99, 1721-8. 577. Mol, C. D., Lim, K. B., Sridhar, V., Zou, H., Chien, E. Y., Sang, B. C., Nowakowski, J., Kassel, D. B., Cronin, C. N. & McRee, D. E. (2003) Structure of a c-kit product complex reveals the basis for kinase transactivation, The Journal of biological chemistry. 278, 31461-4. 578. Shivakrupa, R. & Linnekin, D. (2005) Lyn contributes to regulation of multiple Kit-dependent signaling pathways in murine bone marrow mast cells, Cellular signalling. 17, 103-9. 579. Samayawardhena, L. A., Kapur, R. & Craig, A. W. (2007) Involvement of Fyn kinase in Kit and integrin-mediated Rac activation, cytoskeletal reorganization, and chemotaxis of mast cells, Blood. 109, 3679-86. 580. Thommes, K., Lennartsson, J., Carlberg, M. & Ronnstrand, L. (1999) Identification of Tyr-703 and Tyr-936 as the primary association sites for Grb2 and Grb7 in the c-Kit/stem cell factor receptor, Biochem J. 341 ( Pt 1), 211-6. 581. Ronnstrand, L. (2004) Signal transduction via the stem cell factor receptor/c-Kit, Cellular and molecular life sciences : CMLS. 61, 2535-48. 582. Ito, T., Smrz, D., Jung, M. Y., Bandara, G., Desai, A., Smrzova, S., Kuehn, H. S., Beaven, M. A., Metcalfe, D. D. & Gilfillan, A. M. (2012) Stem Cell Factor Programs the Mast Cell Activation Phenotype, Journal of immunology. 583. Roskoski, R., Jr. (2005) Signaling by Kit protein-tyrosine kinase--the stem cell factor receptor, Biochemical and biophysical research communications. 337, 1-13. 584. Jensen, B. M., Akin, C. & Gilfillan, A. M. (2008) Pharmacological targeting of the KIT growth factor receptor: a therapeutic consideration for mast cell disorders, British journal of pharmacology. 154, 1572-82. 585. Miyazawa, K. (2012) Phosphorylation in the activation loop as the finishing touch in c-Kit activation, Journal of biochemistry. 151, 457-9. 586. Heinrich, M. C., Griffith, D. J., Druker, B. J., Wait, C. L., Ott, K. A. & Zigler, A. J. (2000) Inhibition of c-kit receptor tyrosine kinase activity by STI 571, a selective tyrosine kinase inhibitor, Blood. 96, 925-32. 587. Deininger, M., Buchdunger, E. & Druker, B. J. (2005) The development of imatinib as a therapeutic agent for chronic myeloid leukemia, Blood. 105, 2640-53. 588. Kitamura, Y. & Hirotab, S. (2004) Kit as a human oncogenic tyrosine kinase, Cell Mol Life Sci. 61, 2924-31. 589. Weisberg, E., Manley, P., Mestan, J., Cowan-Jacob, S., Ray, A. & Griffin, J. D. (2006) AMN107 (nilotinib): a novel and selective inhibitor of BCR-ABL, Br J Cancer. 94, 1765-9. 590. Blay, J. Y. & von Mehren, M. (2011) Nilotinib: a novel, selective tyrosine kinase inhibitor, Semin Oncol. 38 Suppl 1, S3-9. 591. Gleixner, K. V., Mayerhofer, M., Aichberger, K. J., Derdak, S., Sonneck, K., Bohm, A., Gruze, A., Samorapoompichit, P., Manley, P. W., Fabbro, D., Pickl, W. F., Sillaber, C. & Valent, P. (2006) PKC412 inhibits in vitro growth of neoplastic human mast cells expressing the D816V-mutated variant of KIT: comparison with AMN107, imatinib, and cladribine (2CdA) and evaluation of cooperative drug effects, Blood. 107, 752-9. 592. Engelman, J. A., Chen, L., Tan, X., Crosby, K., Guimaraes, A. R., Upadhyay, R., Maira, M., McNamara, K., Perera, S. A., Song, Y., Chirieac, L. R., Kaur, R., Lightbown, A., Simendinger, J., Li, T., Padera, R. F., Garcia-Echeverria, C., Weissleder, R., Mahmood, U., Cantley, L. C. & Wong, K. K. (2008) Effective use of PI3K and MEK inhibitors to treat mutant Kras G12D and PIK3CA H1047R murine lung cancers, Nat Med. 14, 1351-6. 593. Marone, R., Cmiljanovic, V., Giese, B. & Wymann, M. P. (2008) Targeting phosphoinositide 3- kinase: moving towards therapy, Biochim Biophys Acta. 1784, 159-85. 594. von Haehling, S., Lainscak, M., Springer, J. & Anker, S. D. (2009) Cardiac cachexia: a systematic overview, Pharmacol Ther. 121, 227-52.

177

Systems Immunology-list of references

595. Zhao, J. J. & Roberts, T. M. (2006) PI3 kinases in cancer: from oncogene artifact to leading cancer target, Sci STKE. 2006, pe52. 596. Kawakami, Y., Yao, L., Miura, T., Tsukada, S., Witte, O. N. & Kawakami, T. (1994) Tyrosine phosphorylation and activation of Bruton tyrosine kinase upon Fc epsilon RI cross-linking, Mol Cell Biol. 14, 5108-13. 597. Honigberg, L. A., Smith, A. M., Sirisawad, M., Verner, E., Loury, D., Chang, B., Li, S., Pan, Z., Thamm, D. H., Miller, R. A. & Buggy, J. J. (2010) The Bruton tyrosine kinase inhibitor PCI-32765 blocks B-cell activation and is efficacious in models of autoimmune disease and B-cell malignancy, Proc Natl Acad Sci U S A. 107, 13075-80. 598. Soucek, L., Buggy, J. J., Kortlever, R., Adimoolam, S., Monclus, H. A., Allende, M. T., Swigart, L. B. & Evan, G. I. (2011) Modeling pharmacological inhibition of mast cell degranulation as a therapy for insulinoma, Neoplasia. 13, 1093-100. 599. Molderings, G. J., Brettner, S., Homann, J. & Afrin, L. B. (2011) Mast cell activation disease: a concise practical guide for diagnostic workup and therapeutic options, J Hematol Oncol. 4, 10. 600. Shah, N. P., Lee, F. Y., Luo, R., Jiang, Y., Donker, M. & Akin, C. (2006) Dasatinib (BMS- 354825) inhibits KITD816V, an imatinib-resistant activating mutation that triggers neoplastic growth in most patients with systemic mastocytosis, Blood. 108, 286-91. 601. Gleixner, K. V., Mayerhofer, M., Sonneck, K., Gruze, A., Samorapoompichit, P., Baumgartner, C., Lee, F. Y., Aichberger, K. J., Manley, P. W., Fabbro, D., Pickl, W. F., Sillaber, C. & Valent, P. (2007) Synergistic growth-inhibitory effects of two tyrosine kinase inhibitors, dasatinib and PKC412, on neoplastic mast cells expressing the D816V-mutated oncogenic variant of KIT, Haematologica. 92, 1451-9. 602. Fabbro, D., Ruetz, S., Bodis, S., Pruschy, M., Csermak, K., Man, A., Campochiaro, P., Wood, J., O'Reilly, T. & Meyer, T. (2000) PKC412--a protein kinase inhibitor with a broad therapeutic potential, Anticancer Drug Des. 15, 17-28. 603. Patnaik, M. M., Tefferi, A. & Pardanani, A. (2007) Kit: molecule of interest for the diagnosis and treatment of mastocytosis and other neoplastic disorders, Curr Cancer Drug Targets. 7, 492-503. 604. Camacho, R., Staruch, M. J., DaSilva, C., Koprak, S., Sewell, T., Salituro, G. & Dumont, F. J. (1999) Hypothemycin inhibits the proliferative response and modulates the production of cytokines during T cell activation, Immunopharmacology. 44, 255-65. 605. Jensen, B. M., Beaven, M. A., Iwaki, S., Metcalfe, D. D. & Gilfillan, A. M. (2008) Concurrent inhibition of kit- and FcepsilonRI-mediated signaling: coordinated suppression of mast cell activation, J Pharmacol Exp Ther. 324, 128-38. 606. Schirmer, A., Kennedy, J., Murli, S., Reid, R. & Santi, D. V. (2006) Targeted covalent inactivation of protein kinases by resorcylic acid lactone polyketides, Proc Natl Acad Sci U S A. 103, 4234-9. 607. Huang, M., Dorsey, J. F., Epling-Burnette, P. K., Nimmanapalli, R., Landowski, T. H., Mora, L. B., Niu, G., Sinibaldi, D., Bai, F., Kraker, A., Yu, H., Moscinski, L., Wei, S., Djeu, J., Dalton, W. S., Bhalla, K., Loughran, T. P., Wu, J. & Jove, R. (2002) Inhibition of Bcr-Abl kinase activity by PD180970 blocks constitutive activation of Stat5 and growth of CML cells, Oncogene. 21, 8804-16. 608. Prenen, H., Cools, J., Mentens, N., Folens, C., Sciot, R., Schoffski, P., Van Oosterom, A., Marynen, P. & Debiec-Rychter, M. (2006) Efficacy of the kinase inhibitor SU11248 against gastrointestinal stromal tumor mutants refractory to imatinib mesylate, Clin Cancer Res. 12, 2622-7. 609. Chen, Y. B. & LaCasce, A. S. (2008) Enzastaurin, Expert Opin Investig Drugs. 17, 939-44. 610. Vignot, S., Soria, J. C., Spano, J. P. & Mounier, N. (2008) [Protein kinases C: a new cytoplasmic target], Bull Cancer. 95, 683-9. 611. Kosmider, O., Denis, N., Dubreuil, P. & Moreau-Gachelin, F. (2007) Semaxinib (SU5416) as a therapeutic agent targeting oncogenic Kit mutants resistant to imatinib mesylate, Oncogene. 26, 3904-8. 612. Liu, X., Huang, A., Ding, C. & Chu, P. K. (2006) Bioactivity and cytocompatibility of zirconia (ZrO(2)) films fabricated by cathodic arc deposition, Biomaterials. 27, 3904-11. 613. Srkalovic, G., Hussein, M. A., Hoering, A., Zonder, J. A., Popplewell, L. L., Trivedi, H., Mazzoni, S., Sexton, R., Orlowski, R. Z. & Barlogie, B. (2014) A phase II trial of BAY 43-9006 (sorafenib) (NSC-724772) in patients with relapsing and resistant multiple myeloma: SWOG S0434, Cancer Med. 614. Sonpavde, G. & Hutson, T. E. (2007) Pazopanib: a novel multitargeted tyrosine kinase inhibitor, Curr Oncol Rep. 9, 115-9.

178

Systems Immunology-list of references

615. Nechushtan, H. (2010) The complexity of the complicity of mast cells in cancer, Int J Biochem Cell Biol. 42, 551-4. 616. Osborn, O. & Olefsky, J. M. (2012) The cellular and signaling networks linking the immune system and metabolism in disease, Nat Med. 18, 363-74. 617. Wyss-Coray, T. (2006) Inflammation in Alzheimer disease: driving force, bystander or beneficial response?, Nat Med. 12, 1005-15. 618. Medzhitov, R. (2008) Origin and physiological roles of inflammation, Nature. 454, 428-35. 619. Jostins, L.Ripke, S.Weersma, R. K.Duerr, R. H.McGovern, D. P.Hui, K. Y.Lee, J. C.Schumm, L. P.Sharma, Y.Anderson, C. A.Essers, J.Mitrovic,195 M.Ning, K.Cleynen, I.Theatre, E.Spain, S. L.Raychaudhuri, S.Goyette, P.Wei, Z.Abraham, C.Achkar, J. P.Ahmad, T.Amininejad, L.Ananthakrishnan, A. N.Andersen, V.Andrews, J. M.Baidoo, L.Balschun, T.Bampton, P. A.Bitton, A.Boucher, G.Brand, S.Buning, C.Cohain, A.Cichon, S.D'Amato, M.De Jong, D.Devaney, K. L.Dubinsky, M.Edwards, C.Ellinghaus, D.Ferguson, L. R.Franchimont, D.Fransen, K.Gearry, R.Georges, M.Gieger, C.Glas, J.Haritunians, T.Hart, A.Hawkey, C.Hedl, M.Hu, X.Karlsen, T. H.Kupcinskas, L.Kugathasan, S.Latiano, A.Laukens, D.Lawrance, I. C.Lees, C. W.Louis, E.Mahy, G.Mansfield, J.Morgan, A. R.Mowat, C.Newman, W.Palmieri, O.Ponsioen, C. Y.Potocnik, U.Prescott, N. J.Regueiro, M.Rotter, J. I.Russell, R. K.Sanderson, J. D.Sans, M.Satsangi, J.Schreiber, S.Simms, L. A.Sventoraityte, J.Targan, S. R.Taylor, K. D.Tremelling, M.Verspaget, H. W.De Vos, M.Wijmenga, C.Wilson, D. C.Winkelmann, J.Xavier, R. J.Zeissig, S.Zhang, B.Zhang, C. K.Zhao, H.International, I. B. D. G. C.Silverberg, M. S.Annese, V.Hakonarson, H.Brant, S. R.Radford-Smith, G.Mathew, C. G.Rioux, J. D., et al. (2012) Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease, Nature. 491, 119-24. 620. Xie, L., Liu, C., Wang, L., Gunawardena, H. P., Yu, Y., Du, R., Taxman, D. J., Dai, P., Yan, Z., Yu, J., Holly, S. P., Parise, L. V., Wan, Y. Y., Ting, J. P. & Chen, X. (2013) Protein phosphatase 2A catalytic subunit alpha plays a MyD88-dependent, central role in the gene-specific regulation of endotoxin tolerance, Cell Rep. 3, 678-88. 621. Erle, D. J. & Sheppard, D. (2014) The cell biology of asthma, J Cell Biol. 205, 621-631. 622. Zhang, Y., Fan, H., Xu, J., Xiao, Y., Xu, Y., Li, Y. & Li, X. (2013) Network analysis reveals functional cross-links between disease and inflammation genes, Sci Rep. 3, 3426. 623. Vodovotz, Y., Csete, M., Bartels, J., Chang, S. & An, G. (2008) Translational systems biology of inflammation, PLoS Comput Biol. 4, e1000014. 624. Lavrik, I. N. (2014) Systems biology of death receptor networks: live and let die, Cell Death Dis. 5, e1259. 625. Altboum, Z., Steuerman, Y., David, E., Barnett-Itzhaki, Z., Valadarsky, L., Keren-Shaul, H., Meningher, T., Mendelson, E., Mandelboim, M., Gat-Viks, I. & Amit, I. (2014) Digital cell quantification identifies global immune cell dynamics during influenza infection, Mol Syst Biol. 10, 720. 626. Valeyev, N. V., Hundhausen, C., Umezawa, Y., Kotov, N. V., Williams, G., Clop, A., Ainali, C., Ouzounis, C., Tsoka, S. & Nestle, F. O. (2010) A systems model for immune cell interactions unravels the mechanism of inflammation in human skin, PLoS Comput Biol. 6, e1001024. 627. Borghans, J. A., De Boer, R. J., Sercarz, E. & Kumar, V. (1998) T cell vaccination in experimental autoimmune encephalomyelitis: a mathematical model, J Immunol. 161, 1087-93. 628. Baltcheva, I., Veel, E., Volman, T., Koning, D., Brouwer, A., Le Boudec, J. Y., Tesselaar, K., de Boer, R. J. & Borghans, J. A. (2012) A generalized mathematical model to estimate T- and B-cell receptor diversities using AmpliCot, Biophys J. 103, 999-1010. 629. Nag, A., Monine, M. I., Blinov, M. L. & Goldstein, B. (2010) A detailed mathematical model predicts that serial engagement of IgE-Fc epsilon RI complexes can enhance Syk activation in mast cells, J Immunol. 185, 3268-76. 630. Callender, H. L. & Ann Horn, M. (2010) Mathematical modelling and analysis of cellular signalling in macrophages, J Biol Dyn. 4, 12-27. 631. Lee, S., Hwang, H. J. & Kim, Y. (2014) Modeling the role of TGF-beta in regulation of the Th17 phenotype in the LPS-driven immune system, Bull Math Biol. 76, 1045-80. 632. Orozco, L. D., Bennett, B. J., Farber, C. R., Ghazalpour, A., Pan, C., Che, N., Wen, P., Qi, H. X., Mutukulu, A., Siemers, N., Neuhaus, I., Yordanova, R., Gargalovic, P., Pellegrini, M., Kirchgessner, T. & Lusis, A. J. (2012) Unraveling inflammatory responses using systems genetics and gene- environment interactions in macrophages, Cell. 151, 658-70.

179

Systems Immunology-list of references

633. Forst, C. V. (2002) Modeling systems biology for research and target prioritization, Pharmacogenomics. 3, 739-43. 634. Muhammad, S. A., Ahmed, S., Ali, A., Huang, H., Wu, X., Yang, X. F., Naz, A. & Chen, J. (2014) Prioritizing drug targets in Clostridium botulinum with a computational systems biology approach, Genomics. 104 (1): 24-35 635. Agur, Z. & Vuk-Pavlovic, S. (2012) Mathematical modeling in immunotherapy of cancer: personalizing clinical trials, Mol Ther. 20, 1-2. 636. Mi, Q., Li, N. Y., Ziraldo, C., Ghuma, A., Mikheev, M., Squires, R., Okonkwo, D. O., Verdolini- Abbott, K., Constantine, G., An, G. & Vodovotz, Y. (2010) Translational systems biology of inflammation: potential applications to personalized medicine, Per Med. 7, 549-559. 637. Brodin, P., Valentini, D., Uhlin, M., Mattsson, J., Zumla, A. & Maeurer, M. J. (2013) Systems level immune response analysis and personalized medicine, Expert Rev Clin Immunol. 9, 307-17. 638. Ekins, S., Mestres, J. & Testa, B. (2007) In silico pharmacology for drug discovery: applications to targets and beyond, Br J Pharmacol. 152, 21-37. 639. Lu, P., Hontecillas, R., Horne, W. T., Carbo, A., Viladomiu, M., Pedragosa, M., Bevan, D. R., Lewis, S. N. & Bassaganya-Riera, J. (2012) Computational modeling-based discovery of novel classes of anti-inflammatory drugs that target lanthionine synthetase C-like protein 2, PLoS One. 7, e34643. 640. Perelson, A. S. (2002) Modelling viral and immune system dynamics, Nat Rev Immunol. 2, 28-36. 641. Perelson, A. S., Neumann, A. U., Markowitz, M., Leonard, J. M. & Ho, D. D. (1996) HIV-1 dynamics in vivo: virion clearance rate, infected cell life-span, and viral generation time, Science. 271, 1582-6. 642. Perelson, A. S. & Ribeiro, R. M. (2013) Modeling the within-host dynamics of HIV infection, BMC Biol. 11, 96. 643. Jit, M., Henderson, B., Stevens, M. & Seymour, R. M. (2005) TNF-alpha neutralization in cytokine-driven diseases: a mathematical model to account for therapeutic success in rheumatoid arthritis but therapeutic failure in systemic inflammatory response syndrome, Rheumatology (Oxford). 44, 323-31. 644. Hanahan, D. & Weinberg, R. A. (2000) The hallmarks of cancer, Cell. 100, 57-70. 645. Alberts, B. (2002) Molecular biology of the cell, 4th edn, Garland Science, New York. 646. Briso, E. M., Guinea-Viniegra, J., Bakiri, L., Rogon, Z., Petzelbauer, P., Eils, R., Wolf, R., Rincon, M., Angel, P. & Wagner, E. F. (2013) Inflammation-mediated skin tumorigenesis induced by epidermal c-Fos, Genes Dev. 27, 1959-73. 647. Badawi, A. F., Mostafa, M. H., Probert, A. & O'Connor, P. J. (1995) Role of schistosomiasis in human bladder cancer: evidence of association, aetiological factors, and basic mechanisms of carcinogenesis, Eur J Cancer Prev. 4, 45-59. 648. Lake-Bakaar, G., Jacobson, I. & Talal, A. (2012) B cell activating factor (BAFF) in the natural history of chronic hepatitis C virus liver disease and mixed cryoglobulinaemia, Clin Exp Immunol. 170, 231-7. 649. Blaser, M. J. (2000) Linking Helicobacter pylori to gastric cancer, Nat Med. 6, 376-7. 650. Hornberg, J. J., Binder, B., Bruggeman, F. J., Schoeberl, B., Heinrich, R. & Westerhoff, H. V. (2005) Control of MAPK signalling: from complexity to what really matters, Oncogene. 24, 5533-42. 651. Martin, W., Abraham, R., Shanafelt, T., Clark, R. J., Bone, N., Geyer, S. M., Katzmann, J. A., Bradwell, A., Kay, N. E. & Witzig, T. E. (2007) Serum-free light chain-a new biomarker for patients with B-cell non-Hodgkin lymphoma and chronic lymphocytic leukemia, Transl Res. 149, 231-5. 652. Erler, J. T. & Linding, R. (2012) Network medicine strikes a blow against breast cancer, Cell. 149, 731-3. 653. Robin, X., Creixell, P., Radetskaya, O., Santini, C. C., Longden, J. & Linding, R. (2013) Personalized network-based treatments in oncology, Clin Pharmacol Ther. 94, 646-50. 654. Creixell, P., Schoof, E. M., Erler, J. T. & Linding, R. (2012) Navigating cancer network attractors for tumor-specific therapy, Nat Biotechnol. 30, 842-8. 655. Chen, D., Zhang, F., Tang, S., Chen, Y., Lu, P., Wen, S., Zhang, H., Liu, X., Chao, E. & Yang, H. (2013) A network-based systematic study for the mechanism of the treatment of zhengs related to cough variant asthma, Evid Based Complement Alternat Med. 595924. 656. Cresci, S., Depta, J. P., Lenzini, P. A., Li, A. Y., Lanfear, D. E., Province, M. A., Spertus, J. A. & Bach, R. G. (2014) Cytochrome p450 gene variants, race, and mortality among clopidogrel-treated patients after acute myocardial infarction, Circ Cardiovasc Genet. 7, 277-86.

180

Systems Immunology-list of references

657. Thiele, I., Swainston, N., Fleming, R. M., Hoppe, A., Sahoo, S., Aurich, M. K., Haraldsdottir, H., Mo, M. L., Rolfsson, O., Stobbe, M. D., Thorleifsson, S. G., Agren, R., Bolling, C., Bordel, S., Chavali, A. K., Dobson, P., Dunn, W. B., Endler, L., Hala, D., Hucka, M., Hull, D., Jameson, D., Jamshidi, N., Jonsson, J. J., Juty, N., Keating, S., Nookaew, I., Le Novere, N., Malys, N., Mazein, A., Papin, J. A., Price, N. D., Selkov, E., Sr., Sigurdsson, M. I., Simeonidis, E., Sonnenschein, N., Smallbone, K., Sorokin, A., van Beek, J. H., Weichart, D., Goryanin, I., Nielsen, J., Westerhoff, H. V., Kell, D. B., Mendes, P. & Palsson, B. O. (2013) A community-driven global reconstruction of human metabolism, Nat Biotechnol. 31, 419-25. 658. Geenen, S., du Preez, F. B., Snoep, J. L., Foster, A. J., Sarda, S., Kenna, J. G., Wilson, I. D. & Westerhoff, H. V. (2013) Glutathione metabolism modeling: a mechanism for liver drug-robustness and a new biomarker strategy, Biochim Biophys Acta. 1830, 4943-59. 659. Hay, M., Thomas, D. W., Craighead, J. L., Economides, C. & Rosenthal, J. (2014) Clinical development success rates for investigational drugs, Nat Biotechnol. 32, 40-51. 660. Lesko, L. J. & Schmidt, S. (2014) Clinical implementation of genetic testing in medicine: a US regulatory science perspective, Br J Clin Pharmacol. 77, 606-11. 661. Soini, S. (2012) Genetic testing legislation in Western Europe-a fluctuating regulatory target, J Community Genet. 662. Wang, X., Zhang, A., Sun, H. & Wang, P. (2012) Systems biology technologies enable personalized traditional Chinese medicine: a systematic review, Am J Chin Med. 40, 1109-22. 663. van Wietmarschen, H. A., Dai, W., van der Kooij, A. J., Reijmers, T. H., Schroen, Y., Wang, M., Xu, Z., Wang, X., Kong, H., Xu, G., Hankemeier, T., Meulman, J. J. & van der Greef, J. (2012) Characterization of rheumatoid arthritis subtypes using symptom profiles, clinical chemistry and metabolomics measurements, PLoS One. 7, e44331. 664. Swaika, A., Crozier, J. A. & Joseph, R. W. (2014) Vemurafenib: an evidence-based review of its clinical utility in the treatment of metastatic melanoma, Drug Des Devel Ther. 8, 775-787.

181

Systems Immunology-list of references

182

Systems Immunology-summaries and acknowledgements

Summaries and acknowledgements

183

Systems Immunology-summaries and acknowledgements

In order to provide a proper theoretical framework for targeting disease-associated mast cells, chapter 1 describes basic mechanisms of both innate and adaptive immunity. Diverse roles played by cellular immunity in response to bacterial and viral infections are summarized, and the clinical relevance of immune therapy is highlighted. Paradoxical roles of mast cells are described, and strategies for modulating functional roles of mast cells are addressed. Systems Biology approaches towards the elucidation of the complexity of cancer- related inflammation and targeting strategies based on such approaches, are sketched. The insights provided in this chapter illustrate that cancer is a systems biology disease in which immune cells play bi-functional roles within the tumor microenvironment. Although it is widely accepted that the immune response inhibits tumorigenesis, immune evasion by tumor cells and negative performance of immune cells, can be beneficial for tumor growth by supporting tumor inflammation.

In chapter 2, we first summarize mechanisms by which antigens elicit inflammatory effects, and outline the application of peptidic drugs and the combination of cellular modulation with implantation of healthy fibroblasts. We draw a formalized network diagram of many of the processes involved with the aim of integrating immunobiology with experimental data in a formal, consistent manner. Here a bacterial antigen is the invigorator of the inflammatory network, with cross-reactive antigen (CRA) as the factor activating B cells. Once activated, B cells normally secrete the complete form of antibodies which has two immunoglobulin (Ig) heavy chains and two Ig light chains covalently boud to each other by cystine bridges. However, the activated B-cell also secretes excessive amounts of so-called free light chains of these Igs (FLCs). These mostly antigen specific molecules are used as biomarkers of autoimmune diseases such as asthma and rheumatoid arthritis. The FLCs bind to mast cells and make these activatable by antigens. Activated mast cells attack the invading bacteria but also damage the fibroblasts around them by secreting TNF-. In innate immunity, the inflammatory network that is triggered by the bacterial antigen mounts an immediate response in the sense of an acute inflammation, which is driven by mast cells and should be detrimental to the invading bacterial pathogen. The same network can however also engage in chronic inflammation, which is a pathological condition. Because of the complexity of the network with a plethora of positive and negative feedback loops, it is difficult to understand (i) what may cause it to switch from acute to chronic inflammation and (ii) if and (iii) how it could be switched back. We propose that in silico simulation of this inflammatory network might help to understand system behaviors of inflammation and to generate some answers to these questions. We thereto provide the network diagram that we had already made, with rate and balance equations and integrate these as functions of time and parameter values which we estimated on the basis of the scientific literature and physical chemical considerations. The in silico simulation showed that excessive antigen influx into the network may turn acute inflammation into chronic inflammation, that this mechanism was depending on the ratio between healthy and dying fibroblasts, and that the fibroblasts are crucial in wound healing. This model provides a potent example of how systems immunology may help to explore cellular immunity in an integrative way. With the models we present, it is not only possible to illustrate the known biology, but also to extrapolate behaviors to therapeutically targeted

184

Systems Immunology-summaries and acknowledgements environments such as that of chronic inflammation treated with anti TNF-. It might be attractive for the pharmaceutical industry to develop new generations of drugs based on such, more integrative, approaches. Furthermore, chapter 2 highlights the potential of individualized Medicine based on the quantity of adaptive immune cells such as B-cells: the number of B cells impacts the antigen threshold between acute and chronic inflammation.

Chapter 3 describes a possible pro-cancer function of mast cells. It reports on mouse and human studies highlighting activation of mast cells, as evidenced by tryptase secretion, in various cancer contexts. Increased numbers of mast cells (as evidenced by toluidine blue staining) as well as tryptase expression (functional staining) are associated with the pathophysiology of tumor progression, including human skin, lung, colon, and pancreas tumors and murine melanoma. In mouse samples, mast cells are shown to be degranulated. When considering the pro-tumorigenic effect of mast cell degranulation, it is important that mast cell granules possess a high content of bioactive substances some of which could contribute to tumor growth, such as tryptase, TNF-α, VEGF, and histamine. Mast cells may not only be a diagnostic marker for cancer but also constitute a therapeutic target in cancer therapy. The release of mediators by mast cells in response to tumor cell growth may be a doubly edged sword. On the one hand, mast cells secrete cytokines, such as IL-4, that kill tumor cells, but on the other hand mast cells secrete TNF-α, which may promote tumor inflammation by killing healthy tissue cells and creating space and resource access for tumor cells. Again a balance between processes with positive and negative regulatory loops seems to determine whether mast cell activation promotes or retards tumorigenesis. We therefore set out to extend the systems-biology approach of innate immunity of chapter 2 towards addressing the role of innate immunity in tumorigenesis. Chapter 3 thereby links the innate sensing of tumor cells by the mast cell with its activation that may well promote tumor pathogenesis. Conventional and cellular targeting strategies of mast cells are highlighted and implantation of fibroblasts is simulated by using COPASI-software to make our cancer model predictive. Therapeutically, considering anti-FLC drugs in the cancer model suggested in an in silico treatment aimed at achieving tumor stabilization and re-growth of fibroblasts. In supplemental material to this chapter (presented as Chapter 7), we present experimental data that suggest that Ig free-light chains (FLCs) are indeed tumor growth modulators in cancer. Both human and mouse studies suggest that targeting FLC has potential for inhibiting tumor inflammation by its interference with the activation of mast cells. Taken together, experimental, modeling and literature studies now suggest that mast cells are involved in tumor inflammation and progression.

In chapter 4 we deployed analytical methods to inspect the fundamental characteristics and actionability of our model: stability analysis and sensitivity analysis were implemented in conjunctions with empirical analyses in silico. The stability of the model’s fixed points were assessed by linear stability analysis based on the eigenvalues of the Jacobian matrix. This information is crucial for stability of steady state under perturbations by the system’s inherent dynamics. Chapter 4 also describes the robustness and tolerance of our model when it comes to significant changes in concentrations of reactants and parameters. It addresses

185

Systems Immunology-summaries and acknowledgements the local robustness of steady states and whether any bi-stability is maintained. Sensitivity analysis is used to quantify which species and parameters have the largest impact on the model’s behavior, both for acute and for chronic inflammation. A major outcome of this chapter is that in the absence of fibroblast ingrowth, our model is not really bistable: the transition from acute to chronic inflammation is then irreversible. Treatment with anti-FLC peptide only transiently eliminates the model’s chronic inflammation. Fibroblast implantation or the stimulation of stem cells that generate fibroblasts, is essential for the actute to chronic transition to become reversible and for the systems to become bistable in the sense that also the acute state reached after the chronic state, becomes stable.

In chapter 5 we outline the various functional roles of mast cells in the immune system and highlight their diagnostic potential in cancer. We also discuss the potential of considering mast cells as drug target in cancer therapy. We give pathology evidence demonstrating mast cell degranulation as well as illustrating targeting strategies such as anti-IgE therapy. We describe the Stem Cell Factor (SCF)-mediated signaling pathway which is responsible for the development of mast cells, and discuss whether the targeting of signaling molecules involved in this development by kinase inhibitors blocking mast cell activation, might offer therapeutic potential.

In chapter 6 we discuss how Systems Biology may be used to analyze the link between chronic inflammation and tumor growth. Given the tremendous complexity of cancer, are we ready to study the inflammatory nature of cancer? Are we sufficiently equipped, both in terms of the appropriate quantitative experiments and in terms of multi-scale modelling, to use modeling approaches to predict and to monitor diseases as complex as cancer? Before 2000, it would have been impossible to answer in the affirmative. Systems analysis was invigorated however by the completion of the human genome sequence in 2001. Together with the advent of protein interaction maps and their online application by 2003, new technologies such as mass spectrometry proteomics and metabolomics have launched a next phase of systems approaches towards complex organisms like the human. Initially systems biology focused on intracellular networks, shying away from multicellular networks such as the immune cell environment and the tumor milieu. Ambitiously, we have here looked at multicellular immunity when developing a semi-artificial inflammatory network for computer simulation. There is much to be learned from immunobiology which is vital for curing many human diseases definitively. And there may even be more to be learned when this is put into the quantitative network perspective of a new Systems Immunology. As challenging as it may be, such learning should lead to more rational and science-based therapies of the many multifactorial diseases mankind still suffers from, at last.

In supplementary chapter 7 we summarize examples of other work that has been done by us in immunology and drug targeting. We discuss the model validation based on these experimental studies and the extent to which the model can be used to predict disease development in silico.

186

Systems Immunology-summaries and acknowledgements

Om een theoretisch raamwerk te fourneren rondom het richten van geneesmiddelen op aan ziekte geassocieerde mestcellen, worden in hoofdstuk 1 de basismechanismen van zowel aangeboren als verworven immuniteit beschreven. Infecties door bacteriën en virussen worden door het immuunsysteem herkend en bestreden. Hoewel de klinische relevantie van immuuntherapie voor infectieziektes daarmee helder is, behoeft hij toch verfijning. De dubbelzinnige rol die mestcellen spelen, wordt daarom beschreven, alsook strategieën voor het moduleren van die rol. Systeembiologie ontrafelt complexiteit van intra- and paracellulaire systemen. Kanker-gerelateerde ontstekingen en geneesmiddelstrategieën worden dan ook in dit kader geschetst. De inzichten die in dit hoofdstuk naar voren gehaald worden, illustreren dat kanker een systeem-biologische ziekte is waarbij immuun-cellen een bi-functionele rol zouden kunnen spelen in de directe omgeving van de tumor. Hoewel het vrij algemeen aanvaard is dat het immuunsysteem schadelijk is voor kanker, lichten we toe hoe negatieve effecten van immuuncellen op het weefsel rond tumorcellen de tumorgroei juist zouden kunnen bevorderen.

In hoofdstuk 2 vatten we eerst de onderliggende mechanismen samen waarmee antigenen ontstekkingseffecten ontlokken aan het lichaam en geven we vervolgens een overzicht over toepassingen van zowel therapeutische peptides als combinaties van cellulaire modulatie met fibroblastimplantatie. We tekenen een geformaliseerd netwerk van veel van de processen die betrokken zijn bij de aangeboren immuunreactie. Hierin voorziet bacterieel antigeen het inflammatoire netwerk van het kruisreagerende antigeen (CRA) dat B-cellen activeert. Eens geactiveerd scheiden B-cellen een antilichaam uit dat bestaat uit twee ‘zware’ immunoglobuline (Ig) ketens en twee lichte, allen aan elkaar verbonden door cystinebruggen. Geactiveerde B-cellen scheiden ook grote hoeveelheden uit van zogenaamde ‘vrije lichte ketens’ (FLCs). De meeste FLCs zijn antigeenspecifiek. Zij worden benut als diagnosticum van auto-immuunziekten zoals astma en reumatoïde artritis. De FLCs binden aan mestcellen en het FLC-mestcel complex wordt weer geactiveerd door het met het FLC corresponderende antigeen. Geactiveerde mestcellen vallen niet alleen de binnendringende bacteriën aan, maar ook gezonde weefselcellen zoals fibroblasten door het afscheiden van TNF-α. In de aangeboren immuniteit brengt het door bacterieel antigeen geactiveerde inflammatoire netwerk een onmiddellijke reactie teweeg in de vorm van een acute ontsteking die dodelijk is voor de bacteriële pathogenen. Hetzelfde netwerk kan echter ook een chronische ontsteking opwekken en dan ontaardt het process in een pathologische aandoening. Vanwege de complexiteit van het netwerk met zijn overvloed aan positieve en negatieve terugkoppelingen, is het een lastige taak te begrijpen (i) wat het overschakelen van acute naar chronische ontsteking veroorzaakt, en (ii) of en (iii) hoe kan worden teruggeschakeld. We stellen dat in silico simulatie van dit inflammatoire netwerk zou kunnen helpen om het systeemgedrag van de ontsteking te begrijpen en om te komen tot een aantal antwoorden op bovenstaande vragen. We versterken daartoe het netwerkschema dat we reeds hadden gemaakt met snelheid- en balansvergelijkingen en integreren deze als functie van de tijd en van parameterwaardes. De in silico simulaties lieten zien dat (i) overvloedige antigeeninstroom een acute ontsteking om kan zetten in een chronische, (ii) het mechanisme afhankelijk is van de verhouding van gezonde tot stervende fibroblasten, en (iii) de fibroblasten cruciaal zijn voor definitieve genezing van de chronische ontsteking. Ons

187

Systems Immunology-summaries and acknowledgements wiskundige model verschaft ons een krachtige representatie van dit immunologische systeem, en biedt daarmee de mogelijkheid om netwerkaspecten van cellulaire immuniteit te ontdekken. Bij de modellen die we hier verder nog presenteren gaat het niet alleen om illustratie van bekende biologische effecten, maar ook om extrapolatie van het systeemgedrag tot therapeutisch gerichte situaties zoals een met anti-TNF-α behandelde chronische ontsteking. Het zou aantrekkelijk kunnen zijn voor de farmaceutische industrie om nieuwe generaties geneesmiddelen op basis van zulke meer integratieve benaderingen te ontwikkelen. Voorts wijst hoofdstuk 2 op de potentie van geïndividualiseerde therapie op basis van de hoeveelheid adaptieve immuuncellen zoals B-cellen: het aantal B-cellen in een individu zal de antigene drempelwaarde beinvloeden die het verschil tussen acute en chronische ontstekingen uitmaakt.

In hoofdstuk 3 stellen we voor dat mestcellen een rol spelen in een kankerbevorderend mechanisme. Verhoogde aantallen mestcellen (toluidine blauwkleuring) en tryptase activiteit (functionele kleuring) lijken te correleren met de progressie van huid-, long-, darm-, en pancreastumoren van mens en van melanoma van muis. Mestcelactivatie wordt aangetoond in biopten van verschillende vormen van kanker in mens en muis; in muizenmonsters wordt mestceldegranulatie waargenomen. Om een mogelijk tumorigeen effect van mestceldegranulatie te apprecieren is het belangrijk te beseffen dat mestcellen korrels bevatten met een hoog gehalte aan bioactieve stoffen als tryptase, TNF-α, VEGF en histamine, die een aanzienlijk bijdrage zouden kunnen leveren aan tumorgroei. Mestcellen zijn daarom niet alleen een diagnosticum voor kanker maar ook een therapeutisch doelwit bij kankertherapie. De door mastcellen in antwoord op tumorcelgroei geproduceerde mediatoren kunnen een “dubbel zwaard” vormen: enerzijds scheiden mestcellen cytokinen, zoals IL-4, uit die de tumorcellen schaden, anderzijds exporteren ze TNF-α, dat de ontsteking kan bevorderen; door dood van gezonde cellen en weefsels creeërt het dan ruimte voor tumorcellen. Wederom lijkt een balans tussen positieve en negatieve terugkoppelingen te bepalen of mestcelactivatie het ontstaan en de proliferatie van tumoren bevordert of vertraagt. Gezien deze betrokkenheid van regulatoire netwerken breiden we ons onderzoek vervolgens uit met de systeembiologische aanpak van aangeboren immuniteit die in hoofdstuk 2 werd beschreven, nu in de richting van de rol die aangeboren immuniteit zou kunnen spelen bij tumorgroei. Hoofdstuk 3 verbindt daarmee de aangeboren immuundetectie en verwijdering van tumorcellen met de activatie van mestcellen die tumorpathogenese juist zou kunnen bevorderen. Conventionele en cellulaire behandelingsstrategieën gericht op mestcellen worden aangeroerd. Om het model van aangeboren immuniteit en kanker meer voorspellend te maken, werd het effect van fibroblastimplantatie gesimuleerd door gebruik te maken van de Copasi-software. In dit kanker model resulteerde anti-FLC peptide in tumor stabilisatie en in het hergroeien van fibroblasten. Aanvullende resultaten gepresenteerd in hoofdstuk 7 suggereren dat Ig vrije lichte ketens (FLCs) modulatoren zijn van kankergroei. Zowel humane als muisstudies suggereren dat remming door FLC potentie heeft voor het afremmen van tumorgroei, door te intervenieren in de activering van mestcellen. Tezamen met experimentele modellering in silico en literatuur studies, suggereert dit een concept waarin mestcellen betrokken zijn bij tumor-geasocieerde ontstekingen en tumorprogressie.

188

Systems Immunology-summaries and acknowledgements

In hoofdstuk 4 rollen we analytische methoden uit om ons model verder te karakteriseren in termen van zijn dynamische eigenschappen. Stabiliteits- en gevoeligheidsanalyse worden geïntegreerd met een meer empirische analyse in silico. Op basis van de eigenwaarden van de Jacobiaan wordt de stabiliteit van het model gespecificeerd middels lineaire stabiliteitsanalyse. Deze informatie is belang voor het begrijpen of een in de wiskundige zin tijdsonafhankelijke toestand ook in de werkelijkheid stabiel en constant is, nu ten aanzien van intrinsieke verstoringen (‘ruis’) die voortkomen uit het netwerk zelf. Middels gevoeligheidsanalyse zochten we uit welke celtypes en parameters de grootste invloed op het resultaat van het model hebben. Belangrijker nog, we bepaalden hoe sommige, daarmee cruciale, parameters in het model bij erg geringe verandering toch soms tot ernstige verstoring van het systeem kunnen leiden. Verder hebben we de parametergevoeligheid van de acute en de chronische ontsteking apart uitgerekend. Verder in hoofdstuk 4 wordt ook de robuustheid en tolerantie van ons model beschreven waar het gaat om aanzienlijke veranderingen in de concentraties van reactanten, waarbij het behoud van (bi-)stabiliteit in het gedrang kan komen. Een belangrijke resultaat van hoofdstuk 4 is verder dat in afwezigheid van fibroblastproliferatie het gemodelleerde systeem niet echt bistabiel is: het herbergt een onomkeerbare overgang waardoor zowel de aanvankelijk acute ontsteking en de eventueel daaropvolgende chronische ontsteking beide wel stabiel zijn, maar de eventueel daaropvolgende acute onsteking niet. Het door middel van anti-FLC peptide terugbrengen van de chronische ontsteking naar een acute ontsteking is dan slechts tijdelijk tenzij nieuwe en groeiende fibroblasten worden ingebracht. Verder bediscussiëren we de modelvalidatie op basis van experimenteel onderzoek (zie ook het supplementaire hoofdstuk 7). Het gaat hier mede om de bevestiging van voorspellingen van de ontwikkeling van de ziekte in silico.

In hoofdstuk 5 beschrijven we de verschillende functionele rollen die mestcellen in het immuunsysteem spelen en bespreken we de daaraan verbonden diagnostische mogelijkheden bij kanker. We bespreken ook de mogelijkheid om mestcellen als doelwit te gebruiken bij kankertherapie. We laten pathologiestudies zien waarin mestceldegranulatie alsmede effecten van anti-IgE-therapieen zichtbaar zijn gemaakt. We beschrijven de door stamcel factor (SCF)- gemedieerde signaleringsroute die verantwoordelijk is voor de ontwikkeling van mestcellen, alsmede het tot doelwit maken van signaalmoleculen betrokken bij de ontwikkeling van de mestcel. Dat kinaseremmers mestcelactivering blokkeren, duidt op therapeutisch potentieel.

In hoofdstuk 6 bespreken we hoe systeembiologische benaderingen optimaal gebruikt zouden kunnen worden om het verband tussen chronische ontsteking en tumorgroei te analyseren. Gezien de enorme complexiteit van kanker zal dit geen sinecure zijn. Is men wel bereid om de inflammatoire aard van kanker te bestuderen of vindt men dat dit afleidt van de veronderstelde hoofdzaak of gewoon te veel gedoe is? Zijn we voldoende voorbereid, zowel in termen van de juiste kwantitatieve experimenten als op het gebied van modelleren op verschillende tijd- en ruimteschalen, om ziektes zo ingewikkeld als kanker te gaan voorspellen, onder controle te houden, of zelfs robuust te genezen anders dan door chirurgie? Vóór 2000 zou het onbestaanbaar zijn geweest om een bevestigend antwoord te geven. Maar de systeemanalyse werd versneld door de voltooing van het menselijk genoom in 2001. Samen met de opkomst van eiwitinteractiekaarten en hun internettoepassing in en na 2003

189

Systems Immunology-summaries and acknowledgements hebben nieuwe technologieën, zoals massaspectrometrie, proteomics en metabolomics een volgende groeifase ervan gelanceerd ook voor organismen die zo ingewikkelde zijn als de mens. Aanvankelijk richtte de systeembiologie zich voornamelijk op intracellulaire netwerken en schuwde het multicellulaire netwerken zoals de immuun- en de tumormilieus. Ambitieus hebben we hier echter al vooruit gekeken naar multicellulaire immuniteit bij de ontwikkeling van een inflammatoir netwerk en naar een computersimulatie ervan. Er moet nog veel geleerd worden aangaande die heel complexe immunologie die zo van belang is voor de genezing van menselijke ziektes. En er valt nog veel meer te leren wanneer dit in een kwantitatief netwerkperspectief van een nieuwe systeemimmunologie wordt geplaatst. Maar zulke lering zal leiden tot veel rationelere en wetenschappelijk onderbouwde therapien. Het wordt tijd.

List of abbreviations Abs Antibodies APC Antigen presenting cell BCR B-cell receptor

BMMC Bone marrow mononuclear cell

C3a Complement component 3a

CAF Carcinoma-associated fibroblast

CCL Chemokine (C-C motif) ligand

CLR C-type lectin receptor

CTMC Connective tissue mast cell

COPASI Complex pathway simulator, a softare for modelling

COPD Chronic obstructive pulmonary disease

CRA Cross-reacting antigen

DAMP Damage-associated molecular pattern

DC Dendritic cells

DENV Dengue virus

FDA US food and drug administration

FLC Free light chain fM femtoMolar (10-15M)

TL TeraLiter (1012 litres)

GM-CSF Granulocyte/macrophage colony-stimulating factor

190

Systems Immunology-summaries and acknowledgements

HSC Hematopoietic stem cell

IFN Interferon

Ig Immunoglobulin

IL Interleukin

IRS Immunoreactive score

ITAM Immunoreceptor tyrosine-based activation motif

LT Leukotriene mAbs Monoclonal antibodies

MCL Mast cell leukemia

MCP Methyl-accepting chemotaxis protein

MCTC Chymase-containing mast cell

MHC Major histocompatibility complex

MMC Mucosal mast cell

MMCP Mast cell protease

MMP Matrix metalloproteinase

NGF Nerve growth factor

NK Natural killer cell

NFκB Nuclear factor kappa-light-chain enhancer of activated B-cell

OPN Osteopontin

PAMP Pathogen-associated molecular pattern

PP Percentage of positive cell

PRR Pattern recognition receptor

S1P Sphingosine 1-phosphate

SCF Stem cell factor

SI Staining intensity

SMM Smoldering Multiple Myeloma

TCR T-cell receptor

Tfh T helper cell

191

Systems Immunology-summaries and acknowledgements

TGF Transforming growth factor

Th0 Naïve T-cell

TLR Toll-like receptor

TMA Tissue microarray

TMF Tumor-associated macrophage

TNF Tumor necrosis factor

VEGF Vascular endothelial growth factor

Acknowledgements

None of the work described in this thesis would have been possible without the help of different people and the support of multiple research groups. The funding agencies that have enabled both and experimental and modelling part of this work, I should like to thank very much for their support. They include the Netherlands Organization for Scientific Research (NWO), the H2020 program Corbel (NFRADEV-4-2014-2015 #654248), and the University of Amsterdam Systems Biology priority area. The people that have helped, assisted and supported me are acknowledged below. I should also like to thanks the Department of Cell Biology, the Amsterdam Institute for Molecules Medicines and Systems of the VU University, as well as the Department of Synthetic Systems Biology and Nuclear Organization of the Swammerdam Institute for Life Science of the University of Amsterdam for their kind hospitality.

First of all, I’d like to thank my promotors Hans and Marieke and my co-promotor Matteo.

Hans: many thanks for giving me opportunity to complete my PhD thesis under your guidance. Even though you tend to be very much in favor of pure systems biology, you were willing to supervise a thesis focusing almost exclusively on immunology, and even though your agenda was always very full, you have always managed to make some time (sometimes a couple of hours, sometimes many hours) to discuss manuscripts, problems and potential future projects. I furthermore very much appreciate the amount of freedom you have given me, both with regard to the modelling studies performed, and with regard to the experimental data to be integrated into the models during my time as a research fellow at both the VU and the UVA. I’d finally also like to thank you simply for being such a nice person, who is always willing to answer questions, to listen to concerns, and to share some wise words, both from a professional and from a personal point of view. Finally, I’ve enjoyed the time we spent together at conferences and meetings, and maybe even more, the time at hotel restaurants and bar afterwards, discussing both professional and private things. I sincerely

192

Systems Immunology-summaries and acknowledgements hope that we can continue to cooperate for many years to come, and I’d like to wish you all theAcknowledgement best for the future.s

Marieke: I very much appreciate your guidance, both from a scientific and personal point of view. You were always there when I needed help or advice, and your reaction is always positive, honest but also very critical, and compassionate and caring. I’d furthermore like to thank you for showing interest in my work, as well as for your insightful and useful comments on my work. I sincerely hope that you will manage to keep up the good work for many more years to come, and that we can continue to cooperate in the future.

Matteo: I very much appreciate the support and help I received from you at the University of Amsterdam. You are open and sincere to discuss work-related issues. I’d again like to thank you for showing your interest in my project. Socially, I enjoyed the time we spent at meetings, seminars and lunch breaks at the UvA, chatting about both Systems Biology and Cappuccino. I sincerely wish you all the best for your work and your life.

Jeannet: you essentially facilitated all the administrative procedures. Without your guidance, it is hard to imagine me arranging official things at the University. I am very grateful to you for your help. I sincerely hope that you will continue to do well for years to come, and I’d like to wish you all the best!!

Frank: I’d again to thank you for becoming a member of my Ph.D. committee and for showing your interest. Moreover, I’ve enjoyed the time we spent at meetings and conferences and, maybe even more, the BBQ at your place in Zeist, chatting both professional and personal matters over one or two beers. I sincerely wish you all the best with your work and family.

Nilgün: you are definitely among the two or three people who deserve the most credit with regard to the modelling part of this thesis. We spent many hours together at the department, and occasionally on workshops and courses in Amsterdam. Besides, I’ve always enjoyed the conversation that we’ve had in Turkish. I am really very grateful for your efforts, your enthusiasm, your excellent skills, and I sincerely hope that you will continue to do well for years to come.

Samrina, Stefania and Thierry: I was happy to participate in the courses in Amsterdam, Utrecht and Maastricht. You are kind and professional. I sincerely wish you all the best and success with your projects.

Marcel and Roel: you’re probably the ones who learned me most how to work with cells and microscopes, I mean properly. You were always helpful, and you always made sure that everything in the cell culture lab and the microscope room ran the way it should. Without

193

Systems Immunology-summaries and acknowledgements your aid, it would have been hard to include the microscope pictures that are presented in this thesis.

Fred and Martijn: even though you haven’t been directly involved in this thesis. I’d like to thank you for always helping me out with computer and printer problems, and for discussing international news and issues. I hope that you will remain to be around at the VU or at least in Amsterdam for many years, and that you will continue to do well in the future.

Klaas, Marijke and Martin: in spite of the fact that you haven’t been directly involved in the generation of this thesis, I’d like to thank you for helping me on a number of different issues, and simply for being around. Furthermore, we knew each other well for a long time and I felt not new or a stranger while I‘ve been at the department.

Finally, I’d like to thank my family and friends.

Mam: thank you very, very, very much for always having been there for me, either in person, or on the phone or via skype. You came to the Netherlands many times to visit me. You’ve always helped and supported me wherever you could, and you’ve always shown interest in my work. I am really grateful to you for everything you have done for me, and I sincerely hope that you will be around many, many more years.

Pap: you’ve shaped me to become the way I am, teaching me to always aim for the highest, and to always be positive about things. I sincerely thank you for supporting me wherever you could; together with mam, you supported me both mentally and financially. I deeply appreciate the fact that you’ve always been there for me whenever I needed help, and I sincerely hope you will remain around for many, many more years.

Güli: dear sister!!; many thanks for always having been there for me, for taking care of everything while I am away. I’ve always enjoyed the time we spent together in childhood, as teenager and at university while we both studying in Beijing. I appreciate your kind and pleasant nature, and I sincerely wish all the best to you, for having a happy family.

Anneke: you are a very kind and thoughtful person. Besides, I really enjoyed Christmas dinners at your place in Amsterdam. I sincerely wish all the best for your family, especially for you, Hans and Emilia.

Sameh: as a very devoted friend, we always keep in good contact. In the timeframe in which we’ve been studying in Amsterdam, you were my good friend. I deeply appreciate your kind and friendly nature, and I sincerely wish all the best for your family, especially for Petra and Kean.

Anita: many thanks for being there. I really deeply appreciate you kind and uncomplicated nature. I hope that you will remain to be around for many more years. 194

Systems Immunology-summaries and acknowledgements

Ivo: we always stay in contact. I really like your kind and open character. Besides, I really enjoyed international meetings and activities that we are both participating in as the members of the “30up group”.

Ans and Anneke: you essentially helped me to overcome practical difficulties in the Netherlands. I am highly grateful to you for all of your help, and I sincerely hope that you will remain to be around many more years.

List of publication and conference presentations

Groot Kormelink T, Abudukelimu A, Redegeld FA. Mast cells as target in cancer therapy. Curr Pharm Des. 2009;15(16):1868-78.

Groot Kormelink T, Powe DG, Kuijpers SA, Abudukelimu A, Fens MH, Pieters EH, Kassing van der Ven WW, Habashy HO, Ellis IO, Blokhuis BR, Thio M, Hennink WE, Storm G, Redegeld FA, Schiffelers RM.. Immunoglobulin free light chains are biomarkers of poor prognosis in basal-like breast cancer and are potential targets in tumor-associated inflammation. Oncotarget. 2014 May 30;5(10):3159-67.

Ablikim Abulikemu. Study on Adaptability of Tamed Variety of Pistia Stratioties L in Wenquan County, Xinjiang” Journal of Arid Zone Research Sep 2001, Vol. 18 No.3 (31- 38)

Abulikemu Abudukelimu, Thierry D.G.A. Mondeel, Matteo Barberis and Hans V. Westerhoff. Learning to read and write in evolution: from static pseudoenzymes and pseudosignalers to dynamic gear shifters. Biochemical Society Signal Transduction 2017; revised and resubmitted.

Abulikemu Abudukelimu, Matteo Barberis, Frank Redegeld, Nilgun Sahin, Marieke van Ham and Hans V. Westerhoff. FLC-Mediated Mast Cell (in) Activation and Its Predictable Effects on Acute and Chronic Inflammation. 2017; submitted for publication

195

Systems Immunology-summaries and acknowledgements

6th Conference on Systems Biology of Mammalian Cells, Munich, Germany, April, 2016

Open Bridges for life science data, Welcome Genome Campus, Hinxton, UK, November, 2015

SB@ NL 2014 Systems Biology Conference, a selected abstract, Maastricht, December 2014, The Netherlands

Jubelium Xinjiang Universit, an invited speaker, Urumqi, China, October 2014; Topic: “Systems Immunology-Inflammation and Cancer”

Interdisciplinary signaling conference, an invited speaker, Visegrad in Hungary, July 2014; Topic: “Systems Biology of Inflammation”

International Conference, Berlin in Germany, December 2010; a selected poster: “Mast Cell and Cancer”

PharmScifair, Nice, June 2009. Press and presentation: “Mast Cell as a Target in Cancer Therapy”

European Mast Cell Network Symposium, Stuttgart, July 2008

196