<<

University of South Florida Scholar Commons

Graduate Theses and Dissertations Graduate School

July 2017 Role of in Ovarian Cancer Mai Mohamed University of South Florida, [email protected]

Follow this and additional works at: http://scholarcommons.usf.edu/etd Part of the Pathology Commons

Scholar Commons Citation Mohamed, Mai, "Role of Amylase in Ovarian Cancer" (2017). Graduate Theses and Dissertations. http://scholarcommons.usf.edu/etd/6907

This Dissertation is brought to you for free and open access by the Graduate School at Scholar Commons. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected].

Role of Amylase in Ovarian Cancer

by

Mai Mohamed

A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Pathology and Cell Biology Morsani College of Medicine University of South Florida

Major Professor: Patricia Kruk, Ph.D. Paula C. Bickford, Ph.D. Meera Nanjundan, Ph.D. Marzenna Wiranowska, Ph.D. Lauri Wright, Ph.D.

Date of Approval: June 29, 2017

Keywords: ovarian cancer, amylase, computational analyses, glycocalyx, cellular invasion

Copyright © 2017, Mai Mohamed

Dedication

This dissertation is dedicated to my parents, Ahmed and Fatma, who have always stressed the importance of education, and, throughout my education, have been my strongest source of encouragement and support. They always believed in me and I am eternally grateful to them. I would also like to thank my brothers, Mohamed and Hussien, and my sister, Mariam. I would also like to thank my husband, Ahmed. You have all been a bottomless source of strength, encouragement, and love throughout my Ph.D. Thank you. I love you. I would like to thank Dr.

Kruk. Your continued support and patience are the reasons I was able to get his far. I would like to thank Stephanie Buttermore, my lab mate, for keeping me sane on my craziest days.

Acknowledgements

It is a pleasure to thank all of those who made this dissertation possible. I want to sincerely thank

Dr. Patricia Kruk for accepting me into her lab and for her guidance, support, and patience over the years. The lessons and skills I learned from her during my time her lab will be invaluable in my future endeavors. I would like to thank my committee members, Dr. Paula C. Bickford, Dr.

Meera Nanjundan, Dr. Marzenna Wiranowska, and Dr. Lauri Wright for their time and valuable insight on my dissertation research. I would also like to thank Dr. Eric Bennett for welcoming me into the program, as well as the rest of the Medical Sciences program and Pathology and Cell

Biology departmental staff who have provided support for me during my time at the University of

South Florida. Finally, thank you to all of the Kruk Lab members, past and present. Special thanks to my current fellow lab member: Stephanie Buttermore for her insight and for always making my time at the lab such a fun and entertaining experience. Finally, I would like to thank my family and friends for encouraging me over the last four years. It has been a long road that I would not have been able to travel on my own.

Table of Contents

List of Tables v

List of Figures vi

List of Abbreviations viii

Abstract xiii

Chapter 1 Overview of Ovarian Cancer Biomarkers 1 Ovarian Cancer 1 Literature-derived OC biomarkers 3 and growth factors 5 -6 (IL-6) 5 Interleukin-8 (IL-8) 5 Interleukin-10 (IL-10) 6 Prolactin (PRL) 6 Transforming beta 1 (TGF-β1) 6 alpha (TNFα) 7 Vascular endothelial growth factor (VEGF) 7 Structural and 8 Transmembrane proteins 8 Claudin-3 and -4 8 Mucin 1 (MUC1) 9 Mucin 4 (MUC4) 9 Mucin 16 (CA125) 9 Structural and matrix-related proteins 10 (COL1A1 and COL11A1) 10 Human inhibitor-1 (PAI-1) 10 (KLK-10,-11) 11 Matrix metalloproteinases (MMP-2 and -9) 12 (SPP1) 12 Stress induced phosphoprotein 1 (STIP1) 13 Metabolic regulators 13 Apolipoprotein E (ApoE) 13 synthase (FASN) 13 Receptors 14 Folate receptor-α (FLOR1) 14 Proteins of unknown function in ovarian cancer 15

i

Human epididymis protein 4 (HE4) 15 Mesothelin (MSLN) 15 Prostasin (PRSS8) 16 Shared Computational Characteristics 19 Characteristics of secreted proteins 19 Characteristics of stable proteins 20 Identifying additional key regulators of ovarian cancer 24 Rationale 31 Central Hypothesis 31 Aim 1 32 Aim 2 32 Aim 3 32

Chapter 2 Computational Analysis of Amylase Contributing to Ovarian Cancer 34 Introduction 34 History of the amylase 34 Evolution and expression of amylase genes 35 Amylase regulation 37 Differentiating amylase isozymes 38 Known amylase characteristics 39 Amylase in cancer 40 Objectives 41 Materials and methods 41 Sequences used in computational characterization 41 Amylase homology 41 Protein biochemical properties databases 41 Protein disorder 42 Secondary structure prediction 42 Posttranslational modification 43 Transcription regulation by promoter analysis 43 Mutational analysis of the amylase isozymes 43 Clinical specimens 44 Western blot 44 Results 45 Amylase isozymes are highly homologous 45 Amylase isozymes have similar molecular weights and isoelectric points 45 are highly ordered proteins with one predicted protein 45 Amylase isozymes are hydrophilic 50 Amylase is predicted to be glycosylated and phosphorylated 50 The amylase isozymes have common domains, and structural features 51 Amylase isozymes have unique binding regions for chloride, calcium and glucose 52 The amylase isozymes have similar secondary and tertiary structure profiles 54 Multiple regions of amylase are prone to aggregation 55

ii

The amylase isozymes functionally interact with metabolic proteins 56 Amylase isozyme expression may be driven by differential promotor activation 59 AMY2B overexpression is the most likely to be associated with amplification mutations in cancer 60 Clinical validation confirms elevated levels of AMY2B protein in OC 67 Discussion 69

Chapter 3 Amylase Promotes Ovarian Cancer Cell Invasion In Vitro 79 Introduction 79 Materials and methods 80 Tissue culture 80 Transmission Electron microscope (TEM) 80 Quantitative PCR 81 Western blot 82 Amylase ELISA 82 Amylase activity assay 83 Amylase transfection 83 Invasion assay 84 Glycosaminoglycan (GAG)/proteoglycan quantitative assay 84 Immunogold staining 84 Statistical analysis 85 Results 85 Confirmation of yeast-free cultures 85 OC cells overexpress amylase in vitro 87 Amylase secreted by OC cells is metabolically active 91 Inhibiting amylase decreases OC invasion 93 The gylcocalyx of OC cells is thicker than the glycocalyx of IOSE cells 94 Inhibiting amylase increases GAG production 98 Discussion 99

Chapter 4 Regulation of amylase by spirulina 103 Introduction 103 Materials and methods 104 Tissue culture 104 Microarray 105 Quantitative PCR 105 Western blot 106 Amylase ELISA 106 Invasion assay 107 MTS assay 107 Statistical analysis 107 Results 108 Amylase is a downstream target of spirulina 108 Spirulina downregulates amylase mRNA expression in OC cell lines 108 Spirulina downregulates amylase protein expression in OC cell lines 109

iii

Spirulina reduces amylase secretion by OC cell lines 112 Spirulina decreases the invasive capacity of OC cells 113 Spirulina decreased migration of OVCAR5 cells 113 Spirulina does not alter OC proliferation 115 Phycocyanin abrogates amylase RNA expression 116 Spirulina-induced inhibition of OC invasion is driven, in part, by Phycocyanin 117 Discussion 118

Chapter 5 Concluding Remarks 121

Chapter 6 References 126

Appendix I Potential novel regulators or biomarkers of OC – 683 proteins in total of non-redundant, secreted, ordered and aggregation-prone human proteins. 176

iv

List of Tables

Table 1.1 Functional and clinical characteristics of OC biomarkers 4

Table 1.2 Computational characteristics of OC biomarkers 17

Table 1.3 Proteins that interact with literature reported ovarian cancer biomarkers 28

Table 2.1 Biochemical properties of amylase determined by computational analyses 47

Table 2.2 Secondary structural and posttranslational modifications among amylase isozymes 51

Table 2.3 Proteins that functionally interact with amylase isozymes 58

Table 2.4 Distinguishing computational characteristics among amylase isozymes 72

Table 3.1 AMY1 and AMY2B are typically overexpressed in cancer cells 91

Table 4.1 Spirulina transcriptionally targets amylase 108

v

List of Figures

Figure 1.1 OC biomarkers have functional interactions 25

Figure 1.2 Summarial schematic identifying additional protein regulators of OC 26

Figure 1.3 Identification of proteins that interact with literature-derived OC biomarkers 30

Figure 2.1 Schematic of mammalian amylase gene distinction 36

Figure 2.2 Amylase proteins are highly homologous 46

Figure 2.3 Amylase isozymes are ordered proteins 48

Figure 2.4 Amylase isozymes are hydrophilic 49

Figure 2.5 Amylase isozymes share structural features 53

Figure 2.6 AMY2A has a unique 3D structure 54

Figure 2.7 Amylase isozymes have multiple aggregation prone regions 55

Figure 2.8 Amylase isozymes form functional interactions with other metabolic 57

Figure 2.9 AMY2B has unique potential transcription factors 61

Figure 2.10 Mutational events are highest in AMY2B 62

Figure 2.11 AMY2B is altered in most cancer types 63

Figure 2.12 AMY2B is the most expressed amylase isozyme in OC 66

Figure 2.13 levels of AMY2B protein are altered in OC 68

Figure 3.1 Establishing cell cultures are free of yeast 86

Figure 3.2 Amylase AMY1 and AMY2B RNA are typically expressed in OC cell lines 88

Figure 3.3 Most non-OC cell types express AMY1 and AMY2B RNA 88

vi

Figure 3.4 AMY1 and AMY2B protein are overexpressed in OC cell lines 89

Figure 3.5 OC cells produce more amylase than normal cells 92

Figure 3.6 OC cells secrete more metabolically active amylase than IOSE cells 93

Figure 3.7 Abrogation of amylase reduces invasion in OC cells 95

Figure 3.8 The glycocalyx of OC cells is thicker than IOSE cells 96

Figure 3.9 Amylase can be localized to the OC cells glycocalyx/cell surface 97

Figure 3.10 Amylase promotes GAG 98

Figure 4.1 Spirulina downregulates most amylase isozymes in cancer cells 110

Figure 4.2 Spirulina transcriptionally downregulates amylase in OC cell lines 111

Figure 4.3 Spirulina reduces amylase protein levels in OC cell lines 111

Figure 4.4 Spirulina reduces amylase secretion in OC cell lines 112

Figure 4.5 Spirulina decreased invasive capacity in OC cells 113

Figure 4.6 Spirulina reduced migration in OVCAR5 cells 114

Figure 4.7 Spirulina treatment did not alter IOSE or OC cell survival and Proliferation 115

Figure 4.8 Phycocyanin abrogates amylase expression in OC cells 116

Figure 4.9 Spirulina driven inhibition of OC invasion is partly mediated by phycocyanin 117

Figure 5.1 Schematic of the possible influence of amylase in altering the glycocalyx of OC cell and consequently lead to a more malignant phenotype. 124

vii

List of Abbreviations

AGL Glycogen debranching

AKAP1 Kinase A anchor protein 1

AKAP8 Kinase A anchor protein 8

AMY Amylase

ApoE Apolipoprotein E

ATP1A1 ATPase, Na+/K+ transporting, Alpha 1 polypeptide

ATP1A4 ATPase, Na+/K+ transporting, Alpha 4 polypeptide

BRCA1/2 BReast Cancer gene 1/2

BPI Bacterial/perimeability-increasing protein

CAC Cacodylate

CCM Concentrated conditioned media

CD2 CD molecule cDNA Complementary DNA cdx-1 Caudal Type Homeobox (transcription factor) 1

C/EBP CCAAT/enhancer-binding protein

CLDN3 Claudin-3

CLDN4 Claudin-4

COL1A1 Collagen, type I, alpha I

COL10A1 Collagen, type X, alpha 1

viii

COL11A1 Collagen, type XI, alpha 1

CPE Clostridium perfringens enterotoxin

CSSP Consensus Secondary Structure Prediction

CT Computed tomography

Ct Threshold cycle

DEPP Disorder enhanced phosphorylation predictor

ECM Extracellular matrix

ENO1 Enolase 1

EOC Epithelial ovarian cancer

EPD Eukaryotic Promoter Database

ETOH Ethanol

FASN Fatty Acid synthase

FBS fetal bovine serum

FOLR1 Folate receptor-α

FT Fallopian tube

GA Glutaraldehyde

GAG Glycosaminoglycans

GBE1 Glucan (1,4-alpha-) branching enzyme 1

GEMM Genetically engineered mouse models

GP2 2

GRAVY Grand average of hydropathy

GRE General transcriptional enhancer

HE4 Human epididymis protein-4

ix

HDL high-density

HLDL Very low-density

HOSE Human ovarian surface epithelial

HRP Horseradish peroxidase

IL-6 Interleukin-6

IL-8 Interleukin-8

IL-10 Inerleukin-10 iNOS inducible nitric oxide synthase

IOSE Human ovarian surface epithelial

IP Intraperitoneal

IP3 Inositol trisphosphate

KLK10 -10

KLK11 Kallikreins-11

LCT

LDL Low-density lipoprotein

LTR Long terminal repeat

MGAM -glucoamylase

MRI Magnetic resonance imaging

MMP-2 Metalloproteinase-2

MMP-9 Metalloproteinase-9

MSLN Mesothelin

MUC1 Mucin 1

MUC4 Mucin 4

x

MUC16 Mucin 16

NACB National Academy of Clinical Biochemistry

NADPH Nicotinamide adenine dinucleotide phosphate

NO Nitric oxide

OC Ovarian cancer

OSE Ovarian surface epithelial

OsO4/0.8% K4Fe(CN)6 Osmium tetroxide-potassium ferrocyanide

PAI-1 Human plasminogen activator inhibitor-1

PCR Polymerase chain reaction

PET Positron emission tomography

PFA Paraformaldehyde

PGM1 Phosphoglucomutase 1 pI Isoelectric point

PKC Protein kinase C

PLC C

PLCO Prostate, Lung, Colorectal and Ovarian screening trial

PNLIP Pancreatic

POU1F1 Pituitary-Specific Positive Transcription Factor 1

PRL prolactin

PRSS8 Prostasin

PVDF Polyvinylidene fluoride

PYGB Glycogen phosphorylase, brain form

PYGL Glycogen phosphorylase, form

xi

RNA Ribonucleic acid

ROS Reactive oxygen species

SDS-PAGE Sodium dodecyl sulfate polyacrylamide gel electrophoresis

SERPINE1 Human plasminogen activator inhibitor-1 sGAG Sulfated glycosaminoglycans

SI Sucrose-isomaltase

SMAD Mothers against decapentaplegic homolog 3

SPP1 Osteopontin

STAT3 Signal transducer and activator of transcription 3

STAT5A Signal transducer and activator of transcription 5A

STIP1 Tumor stress-induced phosphoprotein

TAS Transabdominal ultrasound

TBST Tween 20-Tris buffered Saline

TG

TGF Transforming growth factor

TGF-β1 Transforming growth factor beta 1

TNFα Tumor necrosis factor alpha

TSHB Thyroid Stimulating Hormone, Beta

TVS Transvaginal ultrasound

VEGF Vascular endothelial growth factor

WFDC2 WAP Four-Disulfide Core Domain 2

HE4 Human epididymis protein-4

Zic1/2 Zic Family Member 1/2

xii

Abstract

Ovarian cancer (OC) accounts for 4% of all cancer cases and 4.2% of all cancer deaths worldwide. OC is the most lethal gynecological cancer because it lacks early disease symptoms and does not have a specific diagnostic marker. As a result, more than 70% of OC patients are diagnosed in later stages when the disease has already metastasized and the 5-year survival rate has decreased to less than 20% compared with approximately 90% survival for women diagnosed with early stage disease. Therefore, I initiated my studies with a computational analysis of the 27 most commonly reported literature-derived ovarian cancer (LDOC) protein biomarkers. I found that LDOC protein biomarkers share many biochemical features including a preponderance for a stable protein structure, the ability to be secreted, and functionality related to extracellular matrix

(ECM) modification, immune response and/or energy production. Subsequently, I analyzed the human proteome to identify proteins that also share these biochemical features. Of the 70,616 proteins in the human proteome, 683 proteins were found to have similar biochemical features to the 27 LDOC proteins. I also identified a subset of 21 potential additional protein regulators of ovarian cancer (APROC) that interact with LDOCs. Three of the APROCs identified were amylase proteins AMY1A, AMY2A, and AMY2B which cleaves alpha 1, 4-glycosidic bonds in polysaccharides. Amylase is reportedly overexpressed in and secreted by ovarian tumors but its functional contribution to OC remains unknown[1]. In this thesis, I posit that amylase contributes

xiii to OC invasion. I initiated my studies by computational characterizing the different amylase isozymes to predict which amylase isozyme(s) is most likely overexpressed in and contributory to

OC invasion. I found that AMY1 and AMY2B have unique regions of disorder and unique phosphorylation sites indicating that AMY1 and AMY2B would be more likely to interact with other proteins, and to be easily secreted. Using OC patient serum samples, I was able to validate

AMY1 and AMY2B overexpression by western immunoblotting.

I then developed an in vitro model system to study the molecular contribution of amylase to OC invasion using normal ovarian surface epithelial (IOSE) and OC cell lines. I showed that

OC cells generally overexpress and secrete metabolically active amylase isozymes AMY1 and

AMY2B. Abrogating amylase activity using siRNA silencing technology decreased the capacity of OC cells to invade collagen coated Boyden chambers and increased sulfated glycosaminoglycans (sGAG) production. Since a survey of OC cell lines indicated that cancer cells have a bulkier glycocalyx compared to IOSE cells and immunogold labeling studies indicated the presence of amylase within the immediate OC microenvironment, my data suggest that, by cleaving alpha 1, 4-glycosidic bonds in glycoconjugates present within ECM, amylase may remodel the ECM to promote an invasive cancer phenotype. Amylase is therefore a target for therapeutic intervention in OC patients with hyperamylasemia. I established Spirulina, a dietary supplement, as a novel transcriptional inhibitor of amylase. Spirulina inhibited amylase expression in OC cell lines at both the message and protein levels. Spirulina reduced OC cell invasion and migration in vitro, putatively by decreasing amylase expression.

xiv

Chapter 1

Overview of Ovarian Cancer Biomarkers

Ovarian Cancer

Ovarian cancer (OC) is the 5th leading cause of cancer death in women in the US after lung, bronchial, breast, colorectal, and pancreatic cancers [2]. OC represents the most lethal gynecological cancer [3]. Approximately 22,000 new cases are diagnosed each year and approximately 14,000 women die from OC each year [2]. While the causes of OC remain unknown, risk factors for OC include Caucasian race, age, increased number of ovulatory cycles associated with early menarche, late menopause, nulliparity [4], family history of breast and/or ovarian cancer, and BReast CAncer genes 1 and 2 (BRCA1/2) mutations [5–7].

A precursor of epithelial OC has not been identified [8,9] and the site of origin of OC remains unknown [10]. OC is thought to arise from the ovarian surface epithelium [11,12], a specialized mesothelial layer of cells derived from the coelomic epithelium [10] that covers and protects the ovary [13]. The coelomic epithelium also gives rise to the epithelia of the peritoneum, the fallopian tubes (FTs) and the uterus and expresses mesenchymal markers Vimentin and N-Cadherin [14].

Further, the common embryonic origin of the epithelia lining the majority of the female reproductive tract is reflected in histologic subtypes of OC [15] such that serous, mucinous and endometrioid OCs histologically mimic the epithelium lining the FT, cervix and uterus, respectively [16]. Recent studies suggest the FT is an alternate source of OC [10], because serous

1 carcinoma, the most frequent OC subtype, resembles the FT [17]. High grade serous OCs account for much of the mortality associated with OC due to their aggressive phenotype evidenced by disease relapse following initial treatment and emergence of drug-resistant disease. [18–20].

Lastly, some OC may arise from extra-ovarian origins, i.e. clear cell OC mimics renal clear cell carcinoma and mucinous OC bears a resemblance to the gastrointestinal tract [20,21].

Symptoms of OC are generally vague and unspecific; symptoms include bloating, pelvic discomfort, gas pains and a change in urinary frequency. OC symptoms appear during early disease stages, but the symptoms are usually mistaken for benign intestinal, musculoskeletal, or gynecologic conditions. Eighty-nine percent of OC patients experience symptoms during the first stages of disease and 97% of OC patients experience symptoms in the later stages of disease.

Unfortunately, the non-specific nature of symptoms and a lack of a specific screening test results in OC generally being diagnosed when it has already metastasized [5].

Current modalities for the detection of OC include pelvic examination, transvaginal ultrasound, transabdominal ultrasound, and serum CA125 levels (see below). Other imaging methods include computed tomography, magnetic resonance imaging, and positron emission tomography.

However, none of these modalities is sufficiently sensitive or specific to identify OC, especially in early stage disease when the 5-year survival rate can be as high as 90% [4,22]. As a result, the

5-year survival for OC patients diagnosed at late stages is generally no better than 30% [23].

Consequently, identifying new screening and detection methods as well as further elucidating the etiology of OC remains imperative to improving OC patient survival [24].

2

Literature-derived OC protein biomarkers

A number of OC biomarkers has been reported to date and include biomarkers found elevated in the serum, urine and/or tissues of OC patients [25]. These biomarkers may have not only diagnostic and prognostic clinical value as indicators of survival time, drug resistance and recurrent disease, but also serve as possible therapeutic targets. OC biomarkers have been associated with molecular and cellular processes that include inflammation, cellular movement, proliferation and cell death. Many OC biomarkers also play a role in cancer development by regulating metastasis, angiogenesis, immune responses, cell cycle regulation and inflammation

[26].

In order to identify the most prominent protein biomarkers associated with OC, a review of the literature was carried out and monomer protein OC biomarkers with more than 100 publications in Pubmed or proteins explored by the Prostate, Lung, Colorectal, and Ovarian (PLCO) screening trial and/or the National Academy of Clinical Biochemistry (NACB) due to their potential diagnostic and prognostic utility were selected for further review by computational analyses. The resulting set of 27 biomarkers identified are typically overexpressed in OC patient serum and tissue and are associated with poor clinical outcome (Table 1). These biomarkers fell within the following categories:

(1) Cytokines and growth factors: interleukin-6 (IL-6), interleukin-8 (IL-8/ CXCL8), interleukin-

10 (IL-10), prolactin (PRL), transforming growth factor beta 1 (TGF-β1), tumor necrosis factor alpha (TNFα) and vascular endothelial growth factor (VEGF);

(2) Structural and Extracellular matrix (ECM) related proteins: claudin 3,4 (CLDN3,4), collagen1A1, 11A1 (COL1A1 and COL11A1), human plasminogen activator inhibitor-1

(SERPINE1), kallikreins-10, -11 (KLK-10, -11), metalloproteinase-2, -9 (MMP-2, -9), mucin 1

3

(MUC1), mucin 4 (MUC4), mucin 16 (MUC16), osteopontin (SPP1) and phosphoprotein 1

(STIP1);

(3) Metabolic regulators: apolipoprotein E (ApoE) and fatty acid synthase (FASN);

(4) Receptors: folate receptor-α (FOLR1);

(5) Proteins of unknown functions in cancer: human epididymis protein 4 (WFDC2/HE4), mesothelin (MSLN), and tumor stress-induced prostasin (PRSS8).

Table 1.1: Functional and clinical characteristics of OC biomarkers Protein expression Clinical outcomes

Protein

arker of

scites rine

References

erum

S Tissue Cystic fluid A cellsOC U Cancer cell proliferation Metastasis/ invasion Drug resistance Reduced overall survival Angiogenesis Poor prognosis M disease recurrence Cytokines and growth factors IL-6 [27–34] IL-8 [35–37] IL-10 [30,38,39] PRL [40–44] TGF-β1 [30,45–48] TNFα [49–53] VEGF [54–57] Adhesion proteins// receptors CLDN3 [58–66] CLDN4 [58–66] COL1A1 [67–69] COL11A1 [69–71] FOLR1 [72] SERPINE [73–76] KLK10 [77–91] 1KLK11 [77–91] MMP2 [92–95] MMP9 [92–95] MUC1 [96–98] MUC4 [99–101] MUC16 [102–104] SPP1 [87,105–109] STIP1 [110–117] Metabolic Regulators ApoE [89,118] FASN [119–126] Proteins of unknown function WFDC2 [87,127,128] MSLN [129–132] PRSS8 [133] Grey shading indicates notable protein expression or reported clinical outcome.

4

Herein follows a brief review of these 27 proteins describing their diagnostic, prognostic, and clinical use as well as their potential contribution and function in the etiology of OC.

Cytokines and growth factors

While both cytokines and growth factors regulate the immune response, cytokines are small polypeptides secreted from cells that stimulate proliferation, survival and differentiation of other cells through receptors. , a subtype of cytokines, attract cells to sites of inflammation, thereby influencing cell migration. Cytokines overexpressed in OC enhance cancer progression through increased cell survival, migration and chemoresistance [134].

Interleukin-6 (IL-6)

IL-6 is predominately a pro- secreted by T-cells and macrophages to stimulate the immune response [135]. However, in cancer cells, IL-6 may also promote metastasis through down-regulation of E-cadherin, thereby inducing epithelial-mesenchymal transition

(EMT) [136]. IL-6 overexpression in OC cells results in an upregulation of anti-apoptotic regulators and matrix metalloproteinase-9 leading to increased cancer cell proliferation and invasion, respectively [31,34]. Elevated serum IL-6 is also associated with higher OC stage and reduced chemotherapeutic responsiveness [32,33].

Interleukin-8 (IL-8)

IL-8 is a produced by macrophages, endothelial cells and a number of epithelial cells [137]. IL-8 overexpression increases OC cell proliferation [35] by activating the PI3K/Akt and Raf/MEK/ERK pathways and altering cell cycle regulating proteins. In addition, IL-8 overexpression appears to enhance OC invasion through an upregulation of matrix metalloproteinase (MMP2/9 – see below) expression and activity [36].

5

Interleukin-10 (IL-10)

IL-10 is an anti-inflammatory cytokine that mediates the immune response through signal transducer and activator of transcription 3 (STAT3) signaling [138] and effectively inhibits bacterial-mediated induction of pro-inflammatory cytokines [139,140]. IL-10 levels are elevated in the serum and ascites fluid of OC patients where it is thought to serve as a useful prognostic biomarker indicative of disease recurrence [30,39] and worse overall survival [38] by diminishing the immune response in OC patients.

Prolactin (PRL)

Secreted by the pituitary, PRL is best known for its role to stimulate milk production [141].

However, specific PRL isoforms secreted by monocytes have been shown to enhance the inflammatory response [142]. Further, by promoting endothelial cell migration and angiogenesis via enhanced ERK1/2 and STAT5 signaling as well as enhancing breast cancer cell migration through modulation of the actin cytoskeleton, PRL is believed to promote breast cancer progression [40,41]. Prolactin is a circulating cytokine/hormone secreted by ovarian follicular cells

[42]. PRL is significantly elevated in OC patient serum [43] where it is believed that increased

PRL expression increases OC PRL receptor expression leading to enhanced OC proliferation via activation of the MAPK/ERK1 pathway [42]. Further, PRL acts as a tumorigenic agent by activating Ras in normal ovarian epithelial cells while also protecting these cells from apoptosis following chemotherapeutic treatment [42]. Lastly, PRL overexpression can enhance OC migration via reduction of E-cadherin expression [44].

Transforming growth factor beta 1 (TGF-β1)

TGF-β1, a member of the transforming growth factor beta family, is a highly pleiotropic cytokine involved in cell growth, proliferation, differentiation and apoptosis through Mothers

6 against decapentaplegic homolog 3 (SMAD3) signaling in OC [143–145]. TGF-β1 promotes tumor cell-mediated ECM remodeling. TGF-β1 increases proteinase and expression by tumor cells which creates a positive regulatory feedback loop that increases TGF-β1 activation and release with subsequent ECM degradation. TGF-β1 expression also upregulates MMP expression leading to increased ECM remodeling [146]. Elevated TGF-β1 serum levels parallel OC stage and correlate with metastatic disease [30]. Likewise, OC patients with drug resistant disease also demonstrate elevated serum TGF-β1 [45] so that OC patients with TGF-β1 overexpression have poor prognosis [46]. Overexpressed TGF-β1 stimulates tumor growth and angiogenesis in xenograft OC models [47] and OC metastasis through MMP-2 activation [48].

Tumor necrosis factor alpha (TNFα)

TNFα is typically produced by macrophages and to control immune cells during the immune response [137]. However, TNFα is a multifunctional cytokine mediating diverse cellular effects including apoptosis, angiogenesis and cell migration [50,51]. Consequently, TNFα has been implicated in both anti- and pro-tumorigenic roles [147]. TNFα is overexpressed in OC tissue [49]. Since TNFα overexpression modulates the expression of other cytokines, including IL-

6 and IL-8, TNFα may mediate inflammatory stimuli that contribute to OC tumorigenesis [52].

TNFα can also act as an endogenous tumor promoter contributing to cellular transformation, invasion, dissemination and angiogenesis in OC [50,53].

Vascular endothelial growth factor (VEGF)

The VEGF family consists of endothelial cell mitogens that enhance endothelial cell growth and migration following binding to cell surface receptors that trigger activation of receptor tyrosine kinases [148]. Secreted VEGF recruits and promotes progenitor endothelial cell growth and migration that create leaky and permeable cancer vasculature for nutrient transportation and waste

7 removal necessary to the survival, proliferation, invasion and metastasis of cancer cells. VEGF also acts as an anti-apoptotic factor for recruited endothelial cells [54]. VEGF released by and sequestered in the ECM can be activated by extracellular cleavage by MMPs. Active VEGF then binds to ECM and proteoglycans in the ECM that then remodel the ECM to enhance vascular branching and capillary density [149]. Overexpression of VEGF in OC tissue and patient serum is associated with poor overall survival and can be indicative of tumor recurrence [55].

Serum VEGF levels have been used to predict patients’ response to anti-angiogenic treatment [56].

VEGF levels in the ovarian cyst fluids are elevated in malignant OC tumors compared with benign or borderline cysts and tumors and inhibiting VEGF expression can lead to suppression of tumor growth [57].

Structural and extracellular matrix (ECM) related proteins

Transmembrane proteins

Claudin-3 and -4

Claudins (20-27 kDa), a family of transmembrane proteins, play a central role in tight junction formation, thereby maintaining epithelial integrity [58]. Both claudin-3 and claudin-4 have been reported to be overexpressed in OC cells [58–65] and associated with increased cancer cell survival, invasion and motility which may be mediated, in part, through increased MMP-2 activity

(see below). Claudins also play a role in drug resistance since claudin-4 is overexpressed in cisplatin-resistant OC cells [66]. Claudin-3 and claudin-4 also act as specific receptors of

Clostridium perfringens enterotoxin (CPE) by mediating endotoxin-dependent cell lysis

[150,151]. Treatment of xenograft ovarian tumors with CPE-based therapies results in inhibited tumor growth suggesting that claudin-3 and claudin-4 may serve as potential targets for therapeutic intervention in OC [89,152].

8

Mucin 1 (MUC1)

MUC1, also known as CA15-3, is a highly glycosylated transmembrane glycoprotein that facilitates invasion, metastasis, evasion, drug resistance, and survival via the ErbB and β-catenin-Wnt pathways [96–98]. MUC1 is overexpressed on OC cell surfaces and in OC patient serum. Compared with normal cells, MUC1 expressed by cancer cells is structurally different – it is attached to shorter and less dense O-glycan moieties, thereby unmasking novel protein core epitopes that can be exploited to create antibodies differentiating between normal and diseased cells. MUC1 is overexpressed in over 90% of OC, but only 5% of normal ovarian tissue.

Mucin 4 (MUC4)

MUC4 is a highly glycosylated glycoprotein that lubricates epithelial cell surfaces. MUC4 is also a signal modulator that can alter tumor phenotype by reorganizing actin indirectly by HER2- mediated pathways [153]. MUC4 expression was reported to be upregulated in 100% and 88% of early (stage I and II) and advanced (stage III and IV) OC samples, respectively – with an overall incidence of 92% in OC samples. Though MUC4 plays a diagnostic role, there is no prognostic correlation of MUC4 expression with prolonged patient survival [99]. OC cells in effusions have higher MUC4 expression when compared with solid OC primary tumors and metastases [100].

MUC4-expressing OC cells demonstrate an enhanced motility due to the formation of lamellipodia, filopodia and microspikes [101].

Mucin 16 (CA125)

Mucin16, a transmembrane mucin known as CA125, is a widely used serum marker of OC

[102]. However, CA125 can be elevated in benign conditions, including menstruation, endometriosis and pregnancy as well as in malignancies of the liver, lung, bladder and contributing to false positive results. In healthy adults, CA125 can be expressed in coelomic and

9

Mullerian epithelium, as well as the pancreas, colon, gall bladder, lung, kidney, and stomach epithelia. While CA125 values below 35U/L are considered normal, CA125 is only elevated in

50% of stage I OC patients leading to many false negative results. CA125 is elevated in 75-90% of advanced stage ovarian disease and CA125 is elevated in approximately 80% of epithelial OCs.

[103,104]. CA125 plays a role in OC peritoneal metastasis by interacting with mesothelin. CA125 also plays an immunosuppressive role in OC by inhibiting natural killer cells’ cytotoxic responses

[98].

Structural and matrix-related proteins

Collagen (COL1A1 and COL11A1)

Collagens are abundant fibrous proteins found in the ECM. Collagen I (COL1A1) is the most abundant structural constituent of the ECM of OC and has been shown to enhance OC adhesion and proliferation by interacting with integrin receptors α3β1 and α6β1 that then activate the Ras,

Erk and Akt pathways in OC cell lines [154]. Collagen XI (COL11A1), which is absent in most tissues, is overexpressed in OC ECM and OC patient serum potentially serving as one of the earliest indications of cancer initiation and progression [67,69]. COL11A1 overexpression, as triggered by TGF-β1 activation and SMAD2 signaling, promotes MMP3 activation in OC [68].

COL11A1 overexpression enhances OC migration and invasion [70]. The overexpression of collagen by OC also serves to reduce chemotherapeutic drug efficacy by inhibiting drug penetration/access to OC cells, therefore, contributing to drug resistance and OC tissue survival

[71].

Human plasminogen activator inhibitor-1 (PAI-1)

PAI-1, also known as SERPINE1, is a serine inhibitor that plays a role in tissue remodeling [75]. Inactive plasminogen is converted to broad spectrum plasmin by

10 urinary (uPA) or tissue-type (tPA) plasminogen activators (PAs) which then cleave ECM components such as fibronectin, , , proteoglycans, and fibrins. PAs are regulated by plasminogen activator inhibitors (PAI) which curb plasmin generation [155]. PAIs prevent the cleavage of plasminogen by binding to the of PA [156]. PAI is overexpressed in OC patient serum [73] and tissue [74]. PAI-1 overexpression is associated with proliferation and PAI-

1 inhibitors can act as anticancer agents. PAI-1 expression is associated with poor prognosis [75] and lower overall survival [73]. Overexpressed PAI-1 has been correlated with higher grade OC tumors and a higher probability of disease recurrence [76].

Kallikrein (KLK-10, -11)

Kallikriens (KLKs) comprise a family of 15 secreted serine proteases that are dependent upon a serine in their catalytic site. Secreted into the tumor microenvironment, KLKs, which are thought to be stimulated by a hormone–driven cascade [84], are involved in tissue remodeling and neoplastic disease progression [77–79] by promoting ECM degradation, cellular invasion and metastasis [80–83]. Kallikrein-4, -5, -6, -7, -8, -10, -11, -13, -14, and -15 are overexpressed in OC tissues, serum, and cell lines at the mRNA and protein levels. Kallikrein-4, -6, and -10 are highly expressed in serous epithelial ovarian tumors, and kallikrein-5, -11, and -13 are found in non- serous ovarian tumors. Kallikrein–8, -10, -11, -13 and -14 have also been found in ascites fluid of

OC patients [84]. Further, kallikrein-10 and -11, which are promising OC serum biomarkers are, respectively, elevated in approximately 56% and 70% of OC patients serum and tissue [85–88].

Upregulation of KLKs in OC has been associated with worse overall survival, although conflicting reports indicate that further research is required [89–91].

11

Matrix metalloproteinases (MMP-2 and -9)

MMPs consist of a group of 23 proteins that play an important role in tumor progression by degrading the and ECM components; MMP2 cleaves fibronectin, vitronectin and collagen I [157] into smaller fragments that enhance adhesion of OC cells via α5β1 (fibronectin receptor) and αVβ3 (vitronectin receptor) integrin binding [158] and MMP9 degrades E-cadherin which is involved in cell-cell adhesion and differentiation [93]. MMP-2 and -9 play a role in tumor metastasis, migration, angiogenesis, cell proliferation and cell–cell interactions [92,93], therefore,

MMP-2 and -9 expression has been associated with poor survival [94]. Differential MMP expression in OC tissue can be used to distinguish different OC subtypes and determine prognosis;

MMP-2 and -9 are more highly expressed in serous OC than mucinous OC tumors. MMP-2 expression is higher in benign tumors versus borderline and malignant tumors, but MMP-9 expression is higher in malignant tumors versus borderline tumors [95].

Osteopontin (SPP1)

Osteopontin, a glycophosphoprotein found in all body fluids and expressed in the ECM, is involved in embryonic development, wound healing and tumor development. Osteopontin appears to promote tumor growth, survival, angiogenesis and metastasis by upregulation of the PI3K/Akt pathway [105] and MMP-9 activity [87,106,107]. Of clinical significance, osteopontin expression is higher in OC cells, ovarian tumor tissue and serum of OC patients when compared with healthy control patients. Osteopontin expression is higher in borderline and invasive tumors than in benign tumors and normal ovaries, which suggests that osteopontin may be effective to detect early stage

OC [108,109].

12

Stress induced phosphoprotein 1 (STIP1)

Stress induced phosphoprotein 1 (STIP1) forms nuclear scaffolds supporting formation of protein complexes that participate in transcription, protein folding, signal transduction and cell cycle regulation [159,160]. STIP1 is elevated in melanoma, glioma, hepatocellular carcinoma and and is believed to induce survival and invasion in cancer by modulating the

SMAD pathway and MMP-2 expression [110–115]. STIP1 is significantly higher in OC patients tissue and serum versus normal age-matched patients [116,117]. Analysis of STIP1 levels in OC patients with low CA125 levels revealed that STIP1 levels can be used to detect invasive OC as an alternative diagnostic marker of OC [161].

Metabolic regulators

Apolipoprotein E (ApoE)

ApoE is an essential component of the plasma lipoproteins responsible for transportation and . ApoE is mostly found in the liver, brain, adrenal glands, kidney, and macrophages and is associated with cholesterol-rich proteins [162]. ApoE expression has been associated with serous OC, but not with borderline serous tumors or normal ovarian surface epithelial. Inhibiting ApoE expression leads to G2 cell-cycle arrest and apoptosis in ApoE- expressing OC cells, indicating ApoE is important for OC proliferation and survival [89,118].

Fatty acid synthase (FASN)

FASN, a cytoplasmic enzyme, is involved in de novo fatty acid synthesis via Nicotinamide adenine dinucleotide phosphate (NADPH)-dependent condensation of malonyl-CoA and acetyl-

CoA into palmitate [119]. Normal adult tissues minimally express FASN, however, FASN overexpression has been reported in breast, colon, ovarian and [120,163–168] and is associated with metabolic dysregulation in cancer cells. FASN levels are elevated in OC

13 development, in serous carcinoma [119–121], and especially in recurrent serous carcinomas [122].

Elevated FASN levels parallel cancer aggressiveness, predict poorer overall survival as well as disease recurrence [119,120,122–126]. FASN-based therapies have been developed for recurrent

OC patients; FASN can be successfully targeted by C93, a FASN inhibitor, to induce apoptosis in drug resistant OC cells [89,169]. Since FASN is elevated in >75% of OC, the use of therapies targeting FASN has great potential, but current inhibitors of FASN have limited efficacy due to cell impermeability and solubility as well as lack of cell selectivity [170,171].

Receptors

Folate receptor-α (FLOR1)

FOLR1, a 38–40 kDa [172] glycosylphosphatidyl-inositol-membrane anchored protein, is found in the kidneys where it reabsorbs folate (vitamin B9) from the urine filtrate back into the blood. FOLR1 is normally expressed in the apical surface of epithelial cells [72], especially in the lung and kidney [172]. FOLR1 is overexpressed in epithelial cancer, including ovarian, renal, lung, and breast cancers, though its function in cancer is unknown. Overexpressed FOLR1 can be used to target cancer with therapeutic drugs or imaging agents. FOLR1 is overexpressed in 72% in primary OC and 81.5% in recurrent tumors and FOLR1 expression is equal in primary, metastatic and recurrent tumors. Over 90% of non-mucinous OCs overexpress FOLR1, and the level of

FOLR1 correlates with aggressiveness of the malignancy [72]. Secreted functional FOLR1 in the blood serves as an OC biomarker [172]. While FOLR1 is not a prognostic marker since levels of

FOLR1 do not correlate with survival or tumor response to treatment [173], FOLR1 could deliver therapeutic drugs to OC tumors. For example, BGC 945, a thymidylate synthase inhibitor, is transported into the cell via FOLR1 and, therefore, targets cells that overexpress FOLR1 [89,174].

14

Proteins of unknown function in OC

Human epididymis protein 4 (HE4)

The WAP Four-Disulfide Core Domain 2 (WFDC2) gene encodes HE4, a secreted “four- disulfide core” protein of unknown function. The four-disulfide core is a varied group of acid- and heat-stable proteins that have many proposed functions including aiding in sperm maturation and storage [127,175]. HE4 is expressed at low levels in the female reproductive tract, breast and respiratory system. HE4 is a 13KDa protein that is secreted as a 25kDa glycosylated protein [176]. The WFDC2 gene is amplified in epithelial OC (EOC) tissue and this is associated with increased HE4 in OC serum while HE4 expression remains low in normal tissue.

In comparison with CA125, serum HE4 levels have greater specificity in discriminating malignant disease from benign ovarian disease and between early and late stage disease [127]. Likewise, preoperative HE4 levels may be used to predict the extent and success of cytoreductive surgery needed for optimal debulking [87,128]. Overexpression of HE4 in OC patients correlates with lower overall survival and increased cisplatin resistance. Lastly, in vitro and in vivo HE4 overexpression increases tumor growth and cisplatin resistance in OC cells and tumors such that targeting HE4 suppresses OC cells and tumor growth. HE4 may function by interacting with

EGFR, IGF1R, and TF HIF1α [176].

Mesothelin (MSLN)

The soluble mesothelin-related peptide (SMRP) gene encodes MSLN, a 40 kDa glycosylphosphatidylinositol-anchored cell surface molecule, found on the cell surface of mesothelial cells and overexpressed in the serum and urine of patients with mesotheliomas and

OC [129–131]. MSLN is highly expressed in non-mucinous OCs, including endometrioid, clear cell, and transitional cell carcinoma as well as pancreatic adenocarcinomas, endometrium

15 carcinoma and lung carcinoma [177]. While the function of MSLN has not been fully elucidated,

MSLN binds to CA125 on tumor cell surfaces which may alter cell-to-cell adhesion and increase metastasis to the peritoneum [178]. The development of m912, a human monoclonal antibody to

MSLN, imparts tumor cell toxicity [179]. In contrast, other reports suggest that presence of MSLN after treatment in advanced-stage OC disease patients is associated with prolonged survival

[130,132].

Prostasin (PRSS8)

PRSS8, first isolated from seminal fluid, is expressed at low concentrations in a variety of tissues including salivary gland, lung, bronchus, kidney, liver, pancreas, and colon, but is absent in normal testis and ovary [180]. PRSS8 is a secreted prostatic serine protease and potential OC biomarker [181]. The function of PRSS8 is not completely understood, however, it is thought to play a role in fertilization [133,182]. Elevated PRSS8 levels in the serum of OC patients have been detected with the highest level of PRSS8 detected in stage II of the disease [133].

16

Table 1.2. Computational characteristics of OC biomarkers Biochemical Cellular Characteristi Posttranslational Modifications localization cs

Proteins

Hydrophilic Ordered Aggregated Glycosylation Phosphorylati on Sumoylation Acetylation Sulfation Ubiquitination Methylation Succinylation Palmitoylatio n Myristoylatio n Prenylation Secreted Membrane Cytoplasm Cytokines and growth factors IL-6 IL-8 IL-10 PRL TGF-β1 TNFα VEGF Adhesion proteins/proteases/ receptors CLDN3 CLDN4 COL1A1 COL11A FOLR1 SERPIN1 KLK10 KLK11E1 MMP2 MMP9 MUC1 MUC4 MUC16 SPP1 STIP1 Metabolic Regulators ApoE FASN Proteins of unknown function WFDC2 MSLN PRSS8

*The protein sequences for the OC biomarkers isolated from the literature were retrieved from Uniprot for bioinformatics analysis. The distribution of intrinsic protein disorder was analyzed using disorder predictors PONDR® VLXT [183] and PONDR® VSL2 [184,185] which identify regions of protein disorder and hydropathy. Amylpred 2 [186] was used to find protein sequences prone to form protein aggregates. Uniprot [187] was used to determine regions of protein

17 instability, signal localization, and binding sites. The predicted post-translational modifications of each protein was elucidated using different programs. Phosphorylation was predicted using PhosphoSitePlus (PSP) which provides phosphorylation, ubiquitination, acetylation and methylation information pertaining to each protein as curated from literature and low- and high- throughput data sources [188]. N-linked glycosylation was predicted using NetNGlyc 1.0 Server which predicts the acceptor sites of N-linked glycosylation at Asn-Xaa-Ser/Thr (where Xaa is not Pro) residues. NetNGlyc 1.0 Server predicts glycosylation with 76% overall accuracy [189]. O- linked glycosylation was predicted using NetOGlyc 4.0 Server which predicts the addition of a glycan moiety on Ser, Thr and Tyr residues. The NetOGlyc 4.0 determined glycosylated proteins from mapping the proteome of different human cell lines [190]. Sumoylation was predicted by SUMOplot Analysis Program. Sumoylation is the reversible attachment of a SUMO protein (11 kDa). SUMO protein is added to a peptide sequence of a hydrophobic residue bound to a lysine bound to any amino acid followed by an acidic residue [191]. Palmitoylation, the reversible attachment of a 16-carbon saturated fatty acid to a cysteine reside via a thioester linkage was predicted vis NBA-Palm [192]. Acetylation was predicted to by NetAcet 1.0 Server which predicts the addition of acetyl moiety by N-acetyltransferase A [193]. Tyrosine sulfation sites were predicted by the Sulfinator which uses four different models to recognize sulfated tyrosine residues located in the N- and C- terminals [194]. Myristoylation was predicted using Myristoylator which looks for sequences that accept the addition of a myristate to a N-terminal glycine [195]. Prenylation was predicted using PrePS which determines protein farnesylation (15 carbon polyisopren) as carried out by farnesyltransferases and geranylgeranylation (20 carbon polyisopren) as carried out by geranylgeranyltransferase 1 and Geranylgeranyltransferase 2. PrePS recognizes the motifs recognized by prenyltransfeases (the CaaX box and Rab escort protein in the C-terminal of proteins. Therefore, PrePS recognizes CaaX Farnesylation, CaaX Geranylgeranylation and Rab Geranylgeranylation [196]. Methylation was predicted using MeMo, the methylation Modification Prediction Server 2.0 which predicts methylation on lysine and arginine residues [197].

18

Shared computational characteristics among OC protein biomarkers

All these OC biomarkers have limited diagnostic usefulness. While none are effective at detecting all OCs, these OC biomarkers may contribute to malignant transformation and/or disease progression in OC. Therefore, in order to better understand the molecular mechanisms involved in

OC it would be useful to identify other proteins that may also contribute to OC based on similar biochemical protein characteristics. Therefore, to identify common biochemical features among the 27 OC biomarkers derived from literature review, computational analyses for measurements of protein order, hydropathy, posttranslational modifications, aggregation prone regions and subcellular localization were performed (Table 1.2). As outlined below, computational analyses indicated that these reported 27 OC protein biomarkers share characteristics of secreted (presence of export signal sequence, hydrophilic) and stable (ordered, aggregation prone, glycosylated, sumoylated) proteins.

Characteristics of secreted proteins - (presence of export signal sequence, hydrophilic)

The majority (22/27) of OC biomarkers examined (ApoE, COL1A1, COL11A1, FOLR1,

IL-6, IL-8, IL-10, KLK10, KLK11, MMP2, MMP9, MSLN, MUC1, MUC4, MUC16, PRL,

PRSS8, SERPINE1, SPP1, TGF-β1, VEGF, WFDC2) are secreted proteins based on the presence of an export signal sequence while 5/27 (CLDN3, CLDN4, FASN, TNF-α) and only 1/27 (STIP1) were predicted to be localized at cell membranes or within the cytoplasm, respectively.

Likewise, the vast majority (24/27) of these OC biomarkers (ApoE, COL1A1, CLDN3,

CLDN4, FASN, FOLR1, IL-6, IL-8, IL-10, KLK10, KLK11, MMP2, MMP9, MSLN, MUC1,

MUC4, MUC16, PRL, PRSS8, SERPINE1, TNF-α, TGF-β1, VEGF, WFDC2) were determined to be hydrophilic in keeping with a secretory protein profile or function within hydrophilic cytoplasmic environments. Similarly, almost half, 11/27, (COL11A1, IL-10, MUC1, MUC4,

19

MUC16, MMP2, SPP1, STIP1, TGF-β1, TNF-α, VEGF) of the OC biomarkers were predicted to be sulfated. Sulfation, the enzyme-catalyzed conjugation of a sulfo group, often to tyrosine residues, is required by many secreted proteins that traverse the Golgi apparatus for export [198].

Consequently, it is not unexpected that many of the OC biomarkers should be sulfated.

In contrast, only 8/27 (ACTH, ALB, COL1A1, COL11A1, FASN, IL-10, MMP9, TGF-β1) were predicted to be methylated. Since methylation increases protein hydrophobicity [199–201] and the vast majority of OC biomarkers are hydrophilic, it is not surprising that few are predicted to be methylated. In addition, only 4/27 biomarkers (ApoE, COL1A1, FASN, STIP1) were predicted to be succinylated. While protein succinylation at lysine residues plays a significant role in protein structure, folding and function due to the addition of a large structural moiety, succinylation also typically changes protein charge from +1 to −1 [202]. Since secreted proteins contain a positively charged amino terminal necessary for the export process, low levels of OC biomarker succinylation (4/27) are consistent with characteristics of secreted proteins [203].

Lastly, only 3/27 (CLDN3, CLDN4, PRL) are predicted to be palmitoylated and none of the

OC biomarkers are predicted to be myristoylated or prenylated. Since palmitoylation, myristoylation and prenylation anchor proteins to the membrane with potential for increased interaction with the endoplasmic reticulum [204], the few OC biomarkers predicted to undergo these posttranslational modifications suggests that they mostly operate without membrane anchorage.

Characteristics of stable proteins - (ordered, aggregation prone, glycosylated, sumoylated)

In addition to a secretory protein profile, the majority (19/27) of reported OC biomarkers

(CLDN3, CLDN4, FASN, FOLR1, IL-6, IL-8, IL-10, KLK10, KLK11, MMP2, MMP9, MSLN,

PRL, PRSS8, SERPINE1, TNF-α, TGF-β1, VEGF, WFDC2) were determined to be ordered

20 proteins with over 65% ordered content. Since highly ordered proteins have stable secondary and/or tertiary structures with low physical binding potential to other proteins, these OC biomarkers are unlikely to form larger multi-protein complexes [205]. Interestingly, 17/27

(CLDN3, CLDN4, FOLR1, IL-6, IL-8, IL-10, KLK10, KLK11, MMP2, MMP9, PRL, PRSS8,

SERPINE1, TGF-β1, TNF-α, VEGF, WFDC2) of the OC biomarkers contain amino acid sequences of which, greater than 30% are considered aggregation prone regions (APR). APRs are more likely to represent conserved regions and occur in ordered regions within close structural proximity to catalytic residues and have, therefore, been theorized to stabilize proteins and contribute to protein function [206].

With regards to possible post-translational modifications in support of stable protein structures, most (23/27) of the OC biomarkers studied (ApoE, COL1A1, COL11A1, CLDN3, FOLR1, IL-

10, KLK10, KLK11, MMP2, MMP9, MSLN, MUC1, MUC4, MUC16, PRL, PRSS8, SERPINE1,

SPP1, STIP1, TNF-α, TGF-β1, VEGF, WFDC2) are predicted to be glycosylated. Glycosylation can increase protein stability and modify protein function by increasing thermal stability that destabilizes the unfolded state of proteins [207], thereby playing a role in many cellular functions including intercellular communication, adhesion and migration, all of which are important in cancer progression. Consequently, in addition to an ordered and aggregation prone protein structure imparting a stable protein confirmation, additional protein stability would be conferred by glycosylation.

Many of the OC biomarkers (20/27) (ApoE, COL1A1, COL11A1, CLDN3, CLDN4, IL-6,

KLK10, KLK11, MMP2, MMP9, MSLN, MUC1, MUC4, MUC16, SERPINE1, SPP1, STIP1,

TNF-α, TGF-β1, VEGF) are predicted to be phosphorylated. Phosphorylation can alter protein activity, stability, function by changing serine, threonine or tyrosine residues from 0 to −2 [202]

21 as well as protein interactions [208] by activating intracellular signaling cascades [209].

Phosphorylation is predicted to be a common posttranslational modification in OC biomarkers because it affects many oncogenic processes, including transcriptional regulation, proliferation and kinase signaling [210].

Further, the vast majority (22/27) of OC biomarkers (ApoE, COL1A1, COL11A1,CLDN3,

CLDN4, FASN, FOLR1, IL-10, KLK11, MMP2, MMP9, MSLN, MUC4, MUC16, PRL, PRSS8,

SERPINE1, STIP1, TNF-α, TGF-β1, VEGF, WFDC2) are also predicted to be sumoylated.

Sumoylation is a posttranslational modification resulting from the conjugation of Small Ubiquitin- related MOdifiers (SUMO) to proteins; sumoylated proteins appear to take a part in transcription

[211,212], apoptosis [213], and signal transduction [214]. While the mechanisms that regulate sumoylation are not well understood, conjugation of SUMO to proteins can alter protein configuration potentially leading to new protein-protein interactions, inhibition of existing protein- protein interactions and/or changes to existing protein functions and activity [215], thereby enhancing functional diversity. In this way, sumoylation can increase protein stability by promoting the formation of multimeric protein complexes as well as by inhibition of ubiquitination

[216].

In contrast, only 12/27 (ApoE, CLDN3, CLDN4, COL1A1, COL11A1, FASN, MMP9,

MUC1, MUC4, MUC16, STIP1, VEGF) of OC biomarkers were predicted to be acetylated. While certain forms of protein acetylation can increase protein stability by reducing protein degradation

[217], the principal function of acetylation involves regulation of . Since the group of OC biomarkers examined do not typically include transcriptional proteins, it is not surprising that few might be acetylated. Likewise, only 10/27 (ApoE, CLDN3, CLDN4, COL11A1, FASN,

IL-6, KLK11, PRL, STIP1, TGF-β1) of OC biomarkers were predicted to be ubiquitinated.

22

Ubiquitination marks proteins for degradation. Ordered proteins have fewer ubiquitination and microRNA targeting sites, and lower mRNA decay rates. Therefore, ordered proteins can persist within cells at higher levels for longer periods before facing decay [218]. This is consistent with the prediction that most biomarkers (17/27) are not ubiquitinated.

As might be expected for disease biomarkers, computational biochemical analyses indicated that the set of published OC protein biomarkers share characteristics of secreted (export signal sequence, high hydrophilic, low hydrophobicity, low palmitoylation, myristoylation and prenylation) and stable (ordered, aggregation prone, sumoylated, glycosylated, but not acetylated or ubiquinated) proteins. However, given the functional diversity of these biomarkers as indicated above, String [219] analysis was also performed to identify a network of functional interactions among these established OC biomarkers (Figure 1.1). Interestingly, interactions among these diverse biomarkers appear functionally related to: ECM/microenvironment modifications (CLDN-

3, CLDN-4, COL1A1, COL11A1, KLK10, KLK11, MMP2, MMP9, MUC1, MUC4, MUC16,

SERPINE1, SPP1); regulation/modulation of the immune response (IL-6, IL-8, IL-10, TNFα) and; energy production (ApoE, FASN). Since the ECM/ tumor microenvironment encompasses many components, including stromal cells, ECM components (cytokines, MMPs, integrins, etc.) and exosomes, it plays an important role in OC dissemination and metastasis through a variety of mechanisms that include the activation of signaling pathways (AKT and FAK) that induce invasion and metastasis, the initiation of EMT changes, and the release of proteases and angiogenic factors that remodel ECM and stimulate microvessel formation [220]. While the immune response recognizes and eliminates tumor cells, in malignant states, immunosuppression and immunoediting often prevail leading to unhindered tumor growth. Though increased tumor- infiltrating leukocyte expression is associated with improved survival, OC is associated with an

23 immunosuppressive environment. Therefore, future therapies might aim to reverse immunosuppression and modulate the immune response to combat OC [221]. Lastly, besides endogenous energy production, OC cells induce stromal fibroblasts to secrete energy metabolites lactate and pyruvate that are then shuttled into the tricarboxylic acid (TCA) cycle in OC cell mitochondria. This shuttling promotes increased energy production contributing to accelerated tumor growth and angiogenesis [222].

Identifying additional key regulators of OC

The best OC biomarker in clinical use today is CA-125 which has a number of limitations including expression in less than 50% of stage I disease and expression that is rarely elevated in mucinous, endometrioid or clear-cell carcinomas. CA-125 is elevated in advanced-stage OC and in some benign and malignant conditions and is dependent on additional factors including age, history of hysterectomy and weight. Though many potential OC biomarkers have been identified in the literature (see above), none have overcome CA-125’s clinical limitations [26]. However, the potential biomarkers reported in literature highlight different biological pathways of ovarian tumorigenesis that could increase our understanding of the etiology of OC. Many of the OC biomarkers identified so far fall loosely within three functional groups - immune response,

ECM/microenvironment modifications, energy production and metabolism. Therefore, these protein regulators of OC may be relevant for elucidating mechanisms and pathways of disease initiation and progression.

24

Figure 1.1. OC biomarkers have functional interactions. Analysis of the interactivity of published OC biomarkers by String produced a network of predicted associations for these proteins. The network nodes are proteins, whereas the edges represent the predicted or known functional associations. An edge may be drawn with up to 7 differently colored lines that represent the existence of the seven types of evidence used in predicting the associations. A red line indicates the presence of fusion evidence; a green line - neighborhood evidence; a blue line – co-occurrence evidence; a purple line - experimental evidence; a yellow line – text mining evidence; a light blue line - database evidence; a black line – co-expression evidence.

25

70,616 non-redundant proteins

8,059 proteins have signal peptide

Literature review of monomer OC biomarkers with more than 100 1,848 proteins have signal peptides publications in PubMed and proteins and are secreted explored by PLCO screening trial and/or the NACB

1,578 proteins have signal pepties, are secreted and ordered Secreted proteins with export signal peptide sequences that are ordered, hydrophilic, and have aggregation- 758 secreted proteins with signal prone sequences peptides, are ordered and have aggregation-prone regions

683 27 literature computational derived derived proteins proteins

21 proteins that interact with literature biomarkers

Figure 1.2. Summarial schematic identifying additional protein regulators of OC. Proteins (70,616) of the human proteome were subjected to sequential computational analysis to identify proteins (21) that are secreted, ordered and aggregation-prone and which interact with previously reported OC biomarkers (27).

26

Consequently, expanding upon the characteristics of stable and secreted OC biomarkers noted above (ordered, aggregation-prone, hydrophilic, export signal peptide sequence and secreted proteins) potentially involved in immune response, ECM/microenvironment modifications, and energy production and metabolism, I explored the human proteome to identify additional protein regulators of OC (Figure 1.2). According to the Uniprot Database, the human proteome contains

70,616 non-redundant proteins, of which 8,059 proteins contain an export signal peptide and of which 1,848 proteins are secreted. Of these 1,848 proteins 1,578 proteins are ordered and 758 of the non-redundant proteins have aggregation-prone regions that cover 30% of their total amino acid composition. Of these proteins, 75 proteins are putative or uncharacterized proteins of unknown function and, therefore, were removed from the proposed list of proteins to identify 683 proteins as potential protein regulators of OC (Appendix I). Interestingly, of those 683 proteins,

243 proteins are functionally related to the regulation/modulation of the immune response, 235 proteins functionally related to ECM/microenvironment modifications, 120 proteins functionally related to energy production and metabolism and 86 proteins of unknown function. Subsequent

String analysis demonstrated possible interactions between 27 of the functionally known literature- based OC biomarkers and 21/683 putative biomarkers (Figure 1.3, Table 1.3) including three isozymes of amylase, a secreted and stable protein involved in carbohydrate metabolism.

27

Table 1.3: Proteins that interact with literature reported ovarian cancer biomarkers Protein Protein name Function in cancer CXCL12 Stromal cell-derived factor 1 (SDF-1) (hSDF-1) (C-X- Tumor growth, C motif chemokine 12) (Intercrine reduced in angiogenesis, hepatomas) (IRH) (hIRH) (Pre-B cell growth- immune functions, stimulating factor) (PBSF) [Cleaved into: SDF-1- inflammation, beta(3-72); SDF-1-alpha(3-67)] invasion [26] IL2 Interleukin-2 (IL-2) (T-cell growth factor) (TCGF) Immune response (Aldesleukin) [223] GHR Growth hormone receptor (GH receptor) (Somatotropin receptor) [Cleaved into: Growth Tumor growth and hormone-binding protein (GH-binding protein) metastasis [224] (GHBP) (Serum-binding protein)]

IFNG gamma (IFN-gamma) (Immune interferon) Immune functions, Inflammation [225] VWF Von Willebrand Factor Angiogenesis [54] KDR Kinase Insert Domain Receptor/ Vascular endothelial Angiogenesis [54] growth factor receptor 2 PLAT Tissue-type plasminogen activator (t-PA) (t- Tumor invasion [155] plasminogen activator) (tPA) (EC 3.4.21.68) (Alteplase) (Reteplase) [Cleaved into: Tissue-type plasminogen activator chain A; Tissue-type plasminogen activator chain B] PLAU -type plasminogen activator (U- Tumor invasion [155] plasminogen activator) (uPA) (EC 3.4.21.73) [Cleaved into: Urokinase-type plasminogen activator long chain A; Urokinase-type plasminogen activator short chain A; Urokinase-type plasminogen activator chain B] HPR (EC 3.2.1.166) (Endo-glucoronidase) Tumor proliferation (Heparanase-1) (Hpa1) [Cleaved into: Heparanase 8 and metastasis [226] kDa subunit; Heparanase 50 kDa subunit] TSLP Thymic stromal lymphopoietin Inflammation [227] CRLF2 -like factor 2 (Cytokine receptor-like Inflammation [227] 2) (IL-XR) (Thymic stromal lymphopoietin protein receptor) (TSLP receptor) AMY1A Alpha-amylase 1 (EC 3.2.1.1) (1,4-alpha-D-glucan Cleaves alpha 1, 4- glucanohydrolase 1) (Salivary alpha-amylase) glycosidic bonds in polysaccharides; role in cancer unknown [228] AMY2A Pancreatic alpha-amylase (PA) (EC 3.2.1.1) (1,4- Cleaves alpha 1, 4- alpha-D-glucan glucanohydrolase) glycosidic bonds in polysaccharides; role

28

in cancer unknown [228] AMY2B Alpha-amylase 2B (EC 3.2.1.1) (1,4-alpha-D-glucan Cleaves alpha 1, 4- glucanohydrolase 2B) (Carcinoid alpha-amylase) glycosidic bonds in polysaccharides; role in cancer unknown [228] TIMP1 Metalloproteinase inhibitor 1 (Erythroid-potentiating Invasion/metastases activity) (EPA) (Fibroblast collagenase inhibitor) [26] (Collagenase inhibitor) (Tissue inhibitor of metalloproteinases 1) (TIMP-1) TIMP3 Metalloproteinase inhibitor 2 (CSC-21K) (Tissue Invasion/metastases inhibitor of metalloproteinases 2) (TIMP-2) [26] LPL (LPL) (EC 3.1.1.34) , inflammation [229] APOB Apolipoprotein B (Including Ag(X) Antigen) Tumor growth [26] APOA2 Apolipoprotein A-II (Apo-AII) (ApoA-II) Tumor growth [26] (Apolipoprotein A2) [Cleaved into: Proapolipoprotein A-II (ProapoA-II); Truncated apolipoprotein A-II (Apolipoprotein A-II(1-76))] INS Insulin [Cleaved into: Insulin B chain; Insulin A Tumor growth, chain] transformation [26] LY6G5C antigen 6 complex protein G5c Unknown

29

Figure 1.3. Identification of proteins that interact with literature-derived OC biomarkers. The 27 literature OC biomarkers interact with 21 proteins from the human proteome with similar structural and functional characteristics, including the amylase isozymes AMY-1A, -2A, and -2B. Analysis of the interactivity of published OC biomarkers by String produced a network of predicted associations for these proteins. The network nodes are proteins, whereas the edges represent the predicted or known functional associations. An edge may be drawn with up to 7 differently colored lines that represent the existence of the seven types of evidence used in predicting the associations. A red line indicates the presence of fusion evidence; a green line - neighborhood evidence; a blue line – co-occurrence evidence; a purple line - experimental evidence; a yellow line – text mining evidence; a light blue line - database evidence; a black line – co-expression evidence.

30

Rationale

Given the overall poor survival associated with OC, it is imperative to identify and delineate the role of novel protein regulators of OC in order to understand their role OC progression and which could also one day serve as potential targets for therapeutic intervention. While comprising a diverse group of functional proteins, OC biomarkers share several features including a preponderance for a stable protein structure (ordered, hydrophilic, aggregation-prone, glycosylated and sumoylated), the ability to be secreted (export signal peptide sequence, hydrophilic) and functionality related to ECM modification, immune response and/or energy production. Interestingly, several amylase isozymes are noted among the potential OC protein biomarkers of interest identified for further investigation (Figure 3, Table 3). Elevated serum levels of amylase or hyperamylasemia, has been long associated with OC and has been studied as both a diagnostic and prognostic indicator even though its functional contribution to OC progression remains unknown. Amylase is also interesting because it can potentially play multiple roles in OC, including cleaving alpha 1, 4-glycosidic bonds in polysaccharides to initiate carbohydrate metabolism, thereby contributing to energy production. Amylase may also play a role in ECM remodeling by cleaving polysaccharide moieties in the tumor microenvironment; amylase has already been shown to degrade bacterial biofilm in wounds [228]. It has also been proposed that amylase is released as part of an immune response due to inflammatory microenvironment induced by OC [230].

Central Hypothesis

The objective of this study is to determine the function of amylase in OC. My central hypothesis is that increased amylase expression and secretion alters ECM structure and

31 composition to enhance OC cell invasion. This study also proposes that Spirulina is a novel transcriptional inhibitor of amylase which can decrease OC invasion. Three aims are proposed.

Aim 1: Characterize and predict which amylase isozyme(s) is overexpressed in OC.

Currently, it is unclear which amylase isozyme is overexpressed in OC. Computational analyses will be performed on the amylase isozyme (AMY1A, -1B, -1C, -2A, and -2B) sequences to determine the characteristics of each isozyme and predict which isozyme is most likely overexpressed in OC. Predicted overexpression of specific amylase isozymes will be validated in serum samples from healthy controls and patients with OC.

Aim 2: Determine the extracellular function of amylase in OC.

In order to begin to understand the role of amylase for OC progression, an in vitro model system of OC cell lines that overexpress and secrete amylase will be established, then utilized to show that by remodeling ECM, amylase may contribute to OC cell invasion.

Aim 3: Transcriptionally inhibit amylase expression using a novel agent.

Since hyperamylasemia is associated with poor clinical outcome in cancer [231], amylase may represent a novel target for therapeutic intervention. Commercially available amylase inhibitors typically inhibit amylase at the protein level. I propose to employ Spirulina, a dietary supplement, as a novel transcriptional inhibitor of amylase. Subsequently, the ability of Spirulina to inhibit OC cell migration and invasion will be determined.

32

Defining the function of amylase in OC will fill a much needed gap about the etiology of OC.

Further, these studies will establish a novel transcriptional inhibitor of amylase that may lead to more effective treatment options for OC patients with hyperamylasemia.

33

Chapter 2

Computational Analysis of Amylase Isozymes Contributing to Ovarian Cancer

Introduction

Glucose metabolism is the fundamental biochemical reaction providing energy to cells.

Whether by oxidative phosphorylation or glycolysis [232], glucose consumption is typically greater in cancer cells than their normal counterparts [233,234]. Alpha-amylase (commonly referred to as amylase) was first characterized as a starch digesting enzyme in 1913 [235]. There are three main classes of amylase (α, β and γ) that differ in their structure, mechanism of action and phylogenetic location; α-amylase is found in animals, plants, fungi, and bacteria, β-amylase is found in plant seeds and γ-amylase is found in yeast and fungi [236]. Amylase cleaves alpha 1, 4- glycosidic bonds in polysaccharides to initiate carbohydrate metabolism. Consequently, since α- amylase is restricted to mammalian cells and since starch is the major source of dietary glucose, changes in the levels of α-amylase could influence cancer cell survival.

History of the amylase genes

In 1988, cloned amylase genes examined by Southern blot indicated there are three distinct copies of the amylase genes in the (AMY1, amy2 and amy3 ) whereas previously, it was thought there is only salivary AMY1 and pancreatic AMY2 [237,238]. In 1989, amy2 and amy3 were dubbed AMY2A and AMY2B, respectively. Previously, amy3 was mostly associated

34 with carcinoid tissue because it was isolated from lung cancer tissue [239]. The AMY2B amylase gene was cloned and characterized; it was found to consist of ten exons that are highly homologous to the ten exons that make up AMY1 and AMY2A (amy2). The introns of AMY1, AMY2A, and

AMY2B were also reported to be highly homologous. However, it was found that AMY2B has two unique untranslated regions in the 5’ region, placing the promoter of the AMY2B gene farther upstream than the promoters of AMY1 and AMY2A [240]. A later report found AMY1 consists of 11 exons [241].

Evolution and expression of amylase genes

Amylase consists of a family of isozymes derived from a single ancestral gene. The evolution of amylase genes started 43 million years ago with the insertion of a γ-actin before the primordial amylase gene constituting the ancestral amylase gene (Figure. 2.1A). The ancestral gene underwent duplication. One copy of the ancestral amylase genes has maintained its structural integrity to date and is referred to as AMY-2B (Figure. 2.1A). However, approximately

39 million years ago, a retroviral insertion interrupted the actin pseudogene of the other copy of the ancestral gene and is dubbed the ‘retroviral’ amylase gene. The ‘retroviral’ amylase gene then underwent further duplication. One copy of the ‘retroviral’ amylase gene underwent long terminal repeat (LTR) recombination to become AMY-2A. Approximately a million years ago, the other

‘retroviral’ amylase gene triplicated to become AMY-1A, -1B, and -1C. Consequently, evolutionary diversification has resulted in five amylase genes (AMY-1A, -1B, -1C, AMY-2A, -

2B) clustered on the short arm of one at p21 (Figure 2.1B). These genes are characterized by three distinct gene flanking regions including a retroviral insertion (AMY1A,

35

AMY1B, and AMY1C), a 3/5’LTR recombination (AMY2A) and an actin pseudogene (AMY2B)

(Figure 2.1C) [242]. A

B

C

Figure 2.1. Schematic of mammalian amylase gene distinction. (A) The five amylase genes evolved from a single primordial gene. Adapted from [244]. (B) Current amylase genes are clustered on .p21. Figure adapted from [242]. (C) Evolution of amylase resulted in three distinct regions flanking the amylase genes characterized by a retroviral insertion (AMY1A, AMY1B, and AMY1C), a 3/5’LTR recombination (AMY2A) and an actin pseudogene (AMY2B). Figure adapted from [244].

36

The retroviral insertion is thought to have resulted in amylase tissue specificity.

Historically, AMY-1A, AMY-1B, and AMY-1C were thought to encode salivary amylase, whereas AMY2A and AMY2B encoded pancreatic amylase [243]. However, recently, AMY-2B protein has been found endogenously in many normal tissues because it does not have tissue specificity established by the retroviral insertion [243,244]. There are also reports of AMY2B protein expression in normal tissue such as the liver, gonads, fallopian tubes, ovary, leukocytes and intestinal tract. There has been evidence that AMY2B’s expression in different tissues is regulated by unique promoters. The AMY2B’s pancreatic start site differs from its hepatic start site [243]. AMY1 expression was thought to be exclusively found in salivary glands, however,

AMY1 expression has been reported in two cases of normal thyroid gland tissue, two cases of cervix tissue, and one case of fallopian tube tissue. A case of normal ovary tissue presented with

AMY1 expression, but a second case of ovarian tissue did not present with AMY1 expression

[245,246]. Both AMY2B and AMY1 expression have been isolated in normal lung tissue [246].

Total amylase levels in the serum of normal healthy human adults are elevated with age; amylase levels are elevated in women in their 30s and 40s [247].

Amylase gene regulation

The promoter of AMY-2B, the ancestral amylase gene, has not yet been identified. It is hypothesized that the retroviral insertion either activates a hidden promoter within the actin pseudogene or acts as an enhancer that drives the endogenous promoter of AMY-2B. Regardless, the retroviral insertion plays an important regulatory role in the transcription of amylase [248,249].

No work has been done on the regulation of AMY2A and the promoter of AMY2A has not been validated experimentally.

37

In contrast, it has been assumed that the promoters for AMY-1A, -1B, and -1C reside in the region flanking these genes. Studies targeting the flanking regions of AMY-1C indicate that AMY-

1C can be activated by integrin and/or growth factor stimulation [248]. Integrin activation leads to stimulation of downstream signaling cascades and an increase in intracellular calcium levels that leads to increased amylase expression. Transforming growth factor (TGF)-α activation triggers phosphatidylinositol (PLC), inositol triphosphate (IP3) and increases intracellular calcium levels. Downstream protein kinase C (PKC) and ERK1/2 phosphorylation activate the amylase 1C promoter [249,250]. Both pathways result in increased amylase 1C expression indicating that the region flanking the amylase gene may play a role in the regulation of amylase

1C expression.

It has been reported that the amylase genes contain general transcriptional enhancer (GRE) regions in their promoters that respond to glucocorticoid binding. There are two GRE regions in the promoters of the pancreatic amylase genes and five GRE regions in the promoter of the salivary amylase genes [238]. The LTRs inserted in the promoters of the salivary AMY1 and AMY2A genes contain transcriptional control elements including a TATA box, a CCAAT box, a polyadenylation signal, and GREs [244].

Differentiating amylase isozymes

The amylase enzymes are predominately secreted by the pancreas and salivary glands [251].

Amylase isozymes have been differentiated by digestion of the fluorogenic , FG5P, by salivary (AMY-1A, -1B, or -1C), pancreatic (AMY-2A) and AMY-2B amylases into FG3 and p- nitrophenyl α-maltoside or FG4 and p-nitrophenyl α-glucoside. The FG4/FG3 ratios are 1.88 for

AMY2B, 1.08 for AMY-2A and 0.52 AMY-1A, -1B, or -1C [252].

38

Amylase isozymes can also be distinguished by differences in isoelectric points (pI) and molecular weight. Experimentally, salivary amylase (AMY1A, AMY1B, and AMY1C) has a pI at

6.36 and 6.65 and pancreatic amylase has a pI at 7.4 [253]. On western blots, ovarian tumor amylase and salivary amylase have a similar migratory pattern and appear as a doublet of approximately 57 and 60 kDa. The doublet formation is believed to be due to posttranslational modifications including glycosylation and deamination. In contrast, the pancreatic amylase isoenzyme migrates as a single band at 55 kDa [253].

Known amylase characteristics

It has been reported that the amylase genes are located on the short arm of chromosome 1 and amylase proteins have an average molecular weight of 56 kDa [254]. There is also high homology among the amylase genes. The three AMY1 are more than 99.9% homologous. AMY1 is 93.2% homologous to AMY2A and 93.6% homologous to AMY2B. AMY2A and AMY2B are 94% homologous [255]. The 3D structures of the pancreatic [256] and salivary [257] α-amylase were obtained by X-ray crystallography. The resulting tertiary structures of the salivary and pancreatic amylase were revealed to be very similar. However, in these studies it was not indicated which pancreatic amylase (AMY2A or AMY2B) was used to elucidate the tertiary structure. Further, while calcium (Asn100, Arg158, Asp167, and His201) and chloride ion (Arg195, Asn298,

Arg337) binding sites of the pancreatic amylase protein have been elucidated, all amylase isozymes have three similar domains: Domain A (residues 1-99, 469-404), Domain B (residues

100-168), and Domain C (residues 405-496) with active sites at residues Asp197, Glu233, and

Asp300.

39

A trans-species comparison of human pancreatic amylase, porcine amylase, Aspergillus oryzae

“Taka” (fungus) amylase and Aspergillus niger (fungus) amylase revealed very little primary among these amylases. Yet, strong homology across species in residues 96-

101, 201, 233-236, and 294-301 confers structural homology of amylases derived from different animal species [256]. Likewise, amylase isozymes exhibit similar enzyme activity and degrees of inhibition [253]. Amylase from Aspergillus oryzae has been reported to have a single N- glycosylation site at Asn 197 [258,259].

In rats, amylase activity is affected significantly by the sex cycle; the change in amylase expression is not correlated to calcium levels in the ovary, but to sex hormones acting on the ovary.

Both salivary and pancreatic amylases were isolated in rat ovary, but only salivary amylase was isolated in rat serum [260].

Amylase in cancer

Amylase is overexpressed in and secreted by a number of tumors [261] including ovarian tumors [262]. However, it remains controversial as to which amylase gene encodes amylase isoenzymes secreted by tumors. Some suggest that amylase secreted by tumors is encoded by

AMY-2B [237,245,263–265], while others consider salivary AMY-1A, -1B, or -1C [240,246,266–

271] as the source of tumor amylases. A unique amylase isoenzyme secreted by tumors has also been suggested [253,272–274]. Further, it is unknown whether several amylase isozymes or a single isoform is secreted by tumors. As a result, disagreement and mischaracterization of tumor amylase isozyme expression is common [253,271].

40

Objectives

Hyperamylasemia is associated with disease progression although its contribution to OC is unclear [1]. Although functionally similar, differential expression of amylase isozymes in OC may be clinically significant for diagnostic and/or prognostic outcome. Since there is a controversy as to which amylase isozyme(s) is overexpressed in OC, in this chapter I sought to characterize and predict which amylase isozyme is overexpressed in OC using computational analyses (evolution, disorder, hydrophobicity, aggregation, structure, structural features, possible binding partners, mutation profile, and promoter transcription factors) of human amylase isozymes AMY-1A, -1B,

-1C, -2A, and -2B. Lastly, I sought to validate my prediction in clinical serum samples.

Materials and methods Sequences used in computational characterization

Computational amylase analyses were performed using the human AMY-1A (NCBI Reference

Sequence: NP_001008222.1); AMY-1B (NCBI Reference Sequence: NP_001008219.1); AMY-

1C (NCBI Reference sequence: NP_001008220.1); AMY-2A (NCBI Reference Sequence:

NP_000690.10) and AMY-2B (NCBI Reference Sequence: NP_066188.1) protein sequences.

Amylase isozyme homology

ClustalW2 is a program used to align multiple DNA and protein sequences using pairwise sequence alignment tools; ClustalW2 [275–277] was used to determine the percentage of homology between amylase protein sequences.

Protein biochemical properties databases

ProtParam [278] was used to define the molecular weight, theoretical pI, instability index

(distinctive dipeptides that confer instability to a protein), and GRAVY (grand average of hydropathy—the sum of hydropathy values of the entire protein, divided by the number of residues

41 in the sequence). Uniprot [187] was used to determine regions of protein instability, signal localization, and binding sites. The information gathered from Uniprot jointly with SignalP 4.1 and SBASE was also used to construct a general structure depicting the common features shared by the amylase isozymes.

SignalP 4.1 [279] was used to predict the presence and the cleavage site of a possible signal transport sequence. The protein sequences were run in SBASE [280] to determine which domains are in each amylase isozyme. Domains were confirmed with [281,282], a large collection of the protein families.

Protein disorder

The distribution of intrinsic amylase protein disorder was analyzed using disorder predictors PONDR® VLXT [183] and PONDR® VSL2 [184,185] which identify regions of protein disorder and hydropathy. PONDR® VLXT is capable of identifying potential molecular interactions motifs in disordered proteins and disordered protein regions [184] while PONDR®

VSL2 more accurately predicts general protein disorder.

Secondary structure prediction

CSSP (Consensus Secondary Structure Prediction)[283] was used to predict the consensus secondary structure of the amylase isozymes. I-TASSER was used to predict the tertiary structure of the amylase isozymes [284–287]. Amylpred 2 [186] was used to identify protein sequences prone to form protein aggregates.

Potential protein binding sites within amylase isozyme proteins were identified by the

ANCHOR algorithm [288,289] which determines protein binding motifs within regions of disordered proteins. String [219] analysis was performed to identify potential amylase isozyme binding partners by depicting a network of proteins that physically and/or functionally interact

42 with amylase. RaptorX-binding [290] analysis was performed to determine ligands that potentially bind to the amylase isozymes.

Posttranslational modification

HMMpTM [291] was used to predict phosphorylation and glycosylation sites in the amylase isozymes. Disorder Enhanced Phosphorylation Predictor (DEPP) is a PONDR phosphorylation predictor that determines possible phosphorylated amino acid residues through patterns of disorder, hydrophobicity and charge. Sumoylation was predicted by SUMOplot

Analysis Program.

Transcription regulation by promoter analysis

The promoters of the amylase isozymes have not been previously validated by experimentation. The suspected amylase promoters used were the -600 base pairs flanking the 5' transcription start site as retrieved from Ensembl genome browser [292]. The promoter regions of all five amylase isozymes were examined by the Promo database [293,294] to find possible transcription factor binding sites that could regulate amylase expression. To find other regulatory regions in the promoter, the Eukaryotic Promoter Database (EPD) [295] was utilized to find the promoter of the amylase isozymes and then find the TATA box, the GC box and the CCAAT box sequences within the promoter sequence to better distinguish the core promoter elements.

Mutational analysis of the amylase isozymes

The mutation profiles of AMY-1A, AMY-2A, and AMY-2B were found on cBioPortal

[296,297] for Cancer Genomics, a large scale cancer genomics database that determines the individual mutations in genes as well as the prevalence of mutations in cancer.

43

Clinical Specimens

With prior University of South Florida Institutional Review Board committee approval serum samples were collected at the H. Lee Moffitt Cancer Center and at the University of South

Florida. A total of 30 de-identified serum samples were collected from women with benign gynecologic disease (N = 8) as well as from patients with serous OC (N = 8), endometrial cancer

(N = 5), mucinous OC (N = 5), thecoma (N = 1), teratoma (N=1), ovarian anaplastic metastatic carcinoma (N =1) and clear cell OC (N = 1). Pooled serum (N = 4) from healthy individuals was obtained from Valley Biomedical Cat. HS1021, Lot. 7A0032 and Lot. 6K2146 (Winchester, VA) and Atlanta Biomedical Cat. S40590, Lot. M14161 and Lot. C16037 (Flowery Branch, GA) to be used as control. All serum samples were kept on ice following collection, centrifuged at 3000 g for 15 minutes at 4°C and supernatants were then aliquoted and stored at –20°C.

Western blot

Serum samples were diluted in CHAPS buffer and 120ug of serum protein were separated by a 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Proteins were transferred to Polyvinylidene fluoride (PVDF) membranes, washed in Tween 20-Tris buffered

Saline (TBST), and blocked in 5% milk in TBST. Blots were incubated in their respective primary antibodies overnight, followed by incubation with a horseradish peroxidase (HRP)-conjugated secondary antibody (Fisher, Pittsburgh, PA), and developed via FEMTO Cat. 34095 Life

Technologies (Grand Island, NY). Antibodies used were: AMY1A (1:1000) Cat.H00000276-M04

Abnova (Taipei City, Taiwan); AMY2A (1:500) LS-C114393 LSBio (Seattle, WA); AMY2B

(1:2000) Cat. LS-C173958 LSBio (Seattle, WA); ab82411 abcam (San Francisco, CA).

Densitometry was performed using ImageJ software to normalize amylase band strength to the transferrin bands in serum samples.

44

Results:

Amylase isozymes are highly homologous.

Alignment analyses were performed on amylase isozyme protein sequences using

ClustalW2 software. AMY-1A, -1B, and -1C have 100% alignment, whereas they were only 96% homologous to AMY-2A and 97% homologous to AMY-2B. Amy-2B and AMY-2A are 98% homologous (Figure 2.2). In short, all the amylase proteins are highly homologous. Henceforth,

AMY-1A, -1B, and -1C are collectively referred to as AMY1.

Amylase isozymes have similar molecular weights and isoelectric points.

While the amylase isozymes contain up to 12 exons (11 exons in AMY1 and 12 exons in

AMY2B), all the amylase isozymes contain 10 coding exons resulting in proteins of 511 amino acids (Table 2.1). Computational analysis revealed the molecular weight of salivary amylase

(AMY1) and pancreatic amylases (AMY2A and AMY2B) are 57,767.8 Da, 57,706.8 Da and

57,709.8 Da respectively (Table 2.1). Computational pI for AMY1A, AMY2A and AMY2B is

6.47, 6.60 and 6.64, respectively (Table 2.1).

Amylases are highly ordered proteins with one predicted protein binding site.

Protparam predicted that all amylases are stable (ordered) proteins, because they were beneath the threshold of 40, above of which indicates instability. In mammalian cells, all the amylase isozymes have an identical half-life of 30 hours in vitro (Table 2.1). PONDR protein disorder predictor indicated that all amylase isozymes are mostly ordered proteins with small regions of disorder. While the amylase isozymes have similar regions of disorder, AMY1 and

AMY2B have unique regions of disorder that are not found in AMY2A. AMY1 has two unique regions of disorder encompassing amino acids 51 through 63 and amino acids 375 through 384;

AMY2B also has two unique regions of disorder encompassing amino acids 51 through 63 and

45

Figure 2.2. Amylase proteins are highly homologous. The protein sequences of the amylase isozymes were subjected to ClustalW2 for alignment. AMY-1A, -1B, and -1C have 100% alignment, whereas they were only 96% homologous to AMY-2A. AMY-1A, -1B, and -1C are 97% homologous to AMY-2B. Amy-2B and AMY-2A are 98% homologous. Homologous amino acids share the same color. Clear amino acids are non-homologous.

46

Table 2.1. Biochemical properties of amylase determined by computational analyses AMY1—AMY- AMY-2A AMY-2B Significance 1A, AMY-1B, AMY-1C Exons 11 but only 10 10 12 but only All amylase coding 10 coding isozymes have 10 coding exons Length of α- 511 511 511 All amylase amylase (amino isozymes are the acids) same length Theoretical 57767.8 57706.8 57709.8 All amylase molecular isozymes have an weight (Da) average computational molecular weight of 57KDa Theoretical pI 6.47 6.60 6.64 All amylase isozymes have an average computational isoelectric point of 6.57. Instability index 23.58 23.56 25.26 All amylase isozymes are ordered Half-life The estimated half-life is: All amylase >30 hours (mammalian reticulocytes, in isozymes are stable vitro). for long periods of >20 hours (yeast, in vivo). time >10 hours (Escherichia coli, in vivo). Possible protein aa107-112 aa109-110 aa108-111 All amylase binding isozymes have one (ANCHOR) possible binding site GRAVY -0.436 -0.396 -0.417 All amylase (hydrophobicity) isozymes are hydrophilic PONDR 0.4516 0.4559 0.4537 All amylase hydropathy isozymes are hydrophilic

47

A

* *

B

C * *

Figure 2.3. Amylase isozymes are ordered proteins. Amylase isozymes AMY-1A, -1B, - 1C (A), AMY-2A (B) and AMY-2B (C) were subjected to PONDR analyses for protein disorder. AMY1 has two unique regions of disorder encompassing amino acids 51 through 63 and 375 through 384; AMY2B has two unique regions of disorder encompassing amino acids 51 through 63 and 117 through 134 (denoted by*).

48

A

B

C

Figure 2.4. Amylase isozymes are hydrophilic. Amylase isozymes AMY-1A, -1B, -1C (A), AMY-2A (B) and AMY-2B (C) were subjected to PONDR analyses for protein hydropathy. The amylase isozymes are hydrophilic according to the mean scale hydropathy. The amylase isozymes also have low absolute mean net charge values indicating that amylase isozymes are ordered.

49 amino acids 117 through 134 (Figure 2.3; denoted by *).

The ANCHOR program predicted a single potential protein binding site within the ordered protein regions, unique to each amylase isozyme. AMY1 could potentially bind another protein at amino acids 108-111; AMY2A at 109-110 and AMY2B at 107-112 (Table 2.1).

Amylase isozymes are hydrophilic.

All amylase isozymes have low negative GRAVY (grand average of hydrophobicity index) scores: AMY1 is -0.436, AMY2A is -0.396 and AMY2B is -0.417 (Table 2.1) indicating they are hydrophilic (increasing positive GRAVY values indicate higher hydrophobicity). Likewise,

PONDR predicted hydrophobicity scores of 0.4516 for AMY1, 0.4559 for AMY2A and 0.4537 for AMY2B (Figure 2.4, Table 2.1) suggesting that the amylase isozymes are hydrophilic proteins.

Amylase is predicted to be glycosylated and phosphorylated.

HMMpTM and PONDR Disorder Enhanced Phosphorylation Predictor (DEPP) database were used to identify potential posttranslational modifications, especially glycosylation and phosphorylation sites (Table 2.2). According to HMMpTM, the amylase isozymes do not differ significantly from each other with each isozyme containing 13-15 possible glycosylation and seven possible phosphorylation sites. DEPP predicted that all amylase isozymes also have two phosphorylated residues at amino acids 166 and 197. AMY2B appears to have a unique phosphorylation residue at amino acid 133 (Table 2.2, Figure 2.5). The SUMOplot Analysis

Program predicted that all amylase isozymes were sumoylated at amino acids 2, 50, 275 and 279

(Table 2.2).

50

Table 2.2 – Secondary structural and posttranslational modifications among amylase isozymes AMY1—AMY-1A, AMY-2A AMY-2B AMY-1B, AMY-1C Phosphorylation 166, 197 166, 197 133, 166, 197 (DEPP) Phosphorylation 7; residues 246, 259, 7; residues 246, 259, 7; residues 246, 259, (HMMpTM) 269, 279, 290, 291, 269, 279, 290, 291, 269, 279, 290, 291, 304 304 304 Glycosylation 14; O-linked: 58, 13; O-linked: 58, 15; O-linked: 58, 123, (HMMpTM) 127, 452, 457, 493 127, 452, 457, 493 127, 452, 457, 493 N-linked: 61, 96, N-linked: 61, 96, N-linked: 61, 96, 102, 102, 103, 165, 365, 102, 103, 165, 365, 103, 165, 365, 411, 411, 427, 476 427, 476 427, 476 Sumoylation 2,50, 275, 279 2,50, 275, 279 2,50, 275, 279 (SUMO plot) Signal peptide Yes; 1-15 Yes; 1-15 Yes; 1-15 (SignalP) Consensus Alpha helix: 15.65% Alpha helix: 16.24% Alpha helix: 16.04% Secondary beta strand: 22.30% beta strand: 24.07% beta strand: 22.11% Structure (CSSP) Aggregation 16 aggregation prone 21 aggregation prone 22 aggregation prone prone regions regions regions regions (Amylpred)

The amylase isozymes have common domains, and structural features.

According to SBASE and pfam, all the amylase isozymes have two main structural domains: an alpha amylase catalytic domain from amino acids 36 to 351 and a C-terminal all-beta domain from amino acids 421 to 510 (Figure 2.5). The C-terminal all-beta domain is unique to the glycosyl family members and is important for folding and secretion of amylase [298]. SignalP analysis predicted a transport signal cleavage site at amino acid 15 in all amylase isozymes (Table

2.2) which is in keeping with secretion of amylase out of the cell [299].

Uniprot determined possible binding sites in amylase isozymes. All amylase proteins require calcium and chloride cofactors for enzymatic function [300,301] and have calcium

51 binding sites at amino acids 115, 173, 182, and 216 and chloride binding sites at amino acids 210,

313, and 352. Amino acids 212 and 248 play an essential role in the active site of all amylase isozymes (Figure 2.5). All amylase isozymes have four disulfide bonds connecting amino acids 43 to 101, amino acids 85 to 130, amino acids 156 to 175, and amino acids 393 to 399. ProtParam also predicted that amylase requires calcium and chloride as cofactors with calcium and chloride binding sites at the same residues predicted by Uniprot (Figure 2.5).

Amylase isozymes have unique binding regions for chloride, calcium and glucose.

RaptorX binding analysis suggested that while all the amylase isozymes bind chloride, calcium, and glucose, individually, they may have additional binding residues for chloride, calcium and glucose. All amylase isozymes can bind chloride at residues R210, E248, N313, and

R352 (Figure 2.5). However, AMY2A and AMY2B could bind chloride at residue T269 and

AMY2B might additionally bind chloride at residue H216 (Figure 2.5). Likewise, all amylase isozymes could bind calcium at residues R173 and D182 (Figure 2.5). However, AMY1 can also bind calcium at residues N115 while AMY2A might bind calcium at residues N115 and H216

(Figure 2.5). All amylase isozymes appear capable of binding glucose at residues W73, W74, Y77,

Q78, H116, L177, L180, R210, D212, A213, H216, E248, I250, H314, D315, and H320 (Figure

2.5). However, AMY2A and AMY2B can also bind glucose at residues T178 and G321 (Figure

2.5). Therefore, AMY2A and AMY2B appear to share the greatest number of unique binding residues for chloride, calcium, and glucose which further supports the high homology between these isozymes.

52

Figure 2.5. Amylase isozymes share structural features. The SBASE, Uniprot, and DEPP database, Transport signal cleavage site Raptor binding, HMMpTM databases were used to Calcium binding sites define general structural features of the amylase Chloride binding sites Glucose binging sites isozymes including domains, co-factor binding sites, Active sites post -translational modifications and residues that Phosphorylation sites may play an important role in amylase catalytic O-linked glycosylation activity. The amylase isozymes have an amylase N-linked glycosylation catalytic domain from amino acid 36 to 351, a C- Disulfide bonds terminal all-beta domain from amino acid 421 to 510, AMY1 calcium binding site calcium binding sites at amino acids 173 and 182, AMY2A calcium binding site chloride binding sites at amino acids 210, 248, 313, AMY2B calcium binding site AMY1 anchor possible binding site and 352, and glucose binding sites at amino acids 73, AMY2A anchor possible binding site 74, 77, 78, 116, 117, 180, 210, 212, 213, 2116, 248, AMY2B anchor possible binding sites 250, 314, 315, and 320. The amylase isozymes also AMY2A/AMY2B glucose binding site have four disulfide bonds at amino acids 43 to 101, AMY1/AMY2B N-linked glycosylation site amino acids 85 to 130, amino acids 156 to 175, and AMY2A/AMY2B chloride binding site amino acids 393 to 399. Uniprot also determined that AMY2B phosphorylation site amino acids 212 and 248 play an essential role in the AMY2B O-linked glycosylation site active site. All amylase isozymes have phosphorylation sites at amino acids 166, 197, 246, 259, 269, 279, 290, 291, and 304. All amylase isozymes have O-linked glycosylation sites at amino acids 58, 127, 452, 457, and 493 and N-linked glycosylation sites at amino acids 61, 96, 102, 103, 165, 365, 427, and 476. B) Unique calcium, chloride, and glucose binding sites, phosphorylation and glycosylation sites and anchor potential binding sites are also depicted on the bottom of the figure.

53

The amylase isozymes have similar secondary and tertiary structure profiles. The secondary structure of amylase isozymes was examined by Consensus Secondary

Structure (CSSP) (Table 2.2). The predicted secondary structure of all amylase isozymes is composed of over 50% of coil. The other 50% is composed of alpha helices and beta sheets. AMY1 is predicted to have 15.65% alpha helical structure and 22.30% beta sheet structure. AMY2A is predicted to have 16.24% alpha helical structure and 24.07% beta sheet structure. AMY2B is predicted to have 16.04% alpha helical structure and 22.11% beta sheets. Therefore, the predicted secondary structure makeup of the amylase isozymes is very similar.

The tertiary structure as isolated by I-TASSER analysis shows that the tertiary structure of amylase isozymes AMY1 and AMY2B is identical (Figure 2.6). Though the structure of AMY2A was similar to the structure of AMY1 and AMY2B, it has looser, less organized and less defined coil structures (Figure 2.6, denoted by orange arrows).

A B C

Figure 2.6. AMY2A has a unique 3D structure. The amylase isozymes were subjected to RAPTORX to render a 3D structure. The 3D structure of AMY1 (A), AMY2A (B), and AMY2B (C) determined that the structures of AMY1 and AMY2B are identical and AMY2A has different coil structures as denoted by orange arrows.

54

Multiple regions of amylase are prone to aggregation.

The aggregation prone regions of the amylase isozymes were predicted by AmylPred2.

AMY1 has 16 aggregation prone regions. AMY2A has 21 regions of aggregation and AMY2B has

22 regions of aggregation (Table 2.2, Figure 2.7). AMY2A and AMY2B displayed unique aggregation prone regions (Figure 2.7). Unique aggregation regions within AMY2A were mapped to amino acids 2, 64-68, 97-98, 116, 118, 197, 279-281, 325-332,340-345, 355-356, 383-386, 407-

415, 439, 456, and 478-480. Likewise, unique aggregation regions within AMY2B mapped to amino acids 2, 26, 40, 97-98, 116, 176-182, 325-332, 340-345, 355-356, 383-386, 408-409, 413-

414, 439, and 464-466. While aggregation prone regions were found throughout the proteins, the isozyme unique aggregation prone regions were mostly found at the terminal ends of the protein isozymes (Figure. 2.7).

Figure 2.7. Amylase isozymes have multiple aggregation prone regions. The amylase isozymes were subjected to analysis by AmylPred2 in order to determine aggregation prone regions. AMY1 has 16 aggregation prone regions. AMY2A has 21 regions of aggregation and AMY2B has 22 regions of aggregation. AMY2A and AMY2B have unique regions of aggregation not found in AMY1 (*).

55

The amylase isozymes functionally interact with metabolic proteins According to String analysis, amylase interacts functionally with metabolic proteins

(Figure 2.8). Using a medium confidence interval of interaction, I could isolate proteins that most likely interact with amylase. Proteins that interact with all amylase isozymes are glycogen phosphorylase, brain form (PYGB), glucan (1,4-alpha-) branching enzyme 1 (GBE1), glycogen phosphorylase, liver form (PYGL), glycogen debranching enzyme (AGL), and -isomaltase

(SI). The proteins that interact with all the amylase isozymes have functions associated with carbohydrate metabolism (Table 2.3, Figure 2.8). Both AMY2A and AMY2B interact with

Thyroid Stimulating Hormone, Beta (TSHB)

However, amylase isozymes also have unique probable interactions. AMY1 interacts with lactase (LCT), kinase A anchor protein 1 (AKAP1), kinase A anchor protein 8 (AKAP8), collagen, type X, alpha 1 (COL10A1), and bacterial/permeability-increasing protein (BPI). The unique proteins associated with AMY1 are connected to carbohydrate metabolism, antibacterial functions, and extracellular matrix glycoprotein remodeling. AMY2A interacts with maltase-glucoamylase

(MGAM), glycoprotein 2 (GP2), enolase 1 (ENO1), and pancreatic lipase (PNLIP). The unique proteins associated with AMY2A are connected to carbohydrate and lipid metabolism, and immune and antibacterial functions. AMY2B interacts with phosphoglucomutase 1 (PGM1), CD molecule (CD2), ATPase, Na+/K+ transporting, Alpha 4 Polypeptide (ATP1A4), ATPase,

Na+/K+ transporting, and Alpha 1 Polypeptide (ATP1A1). The unique proteins associated with

AMY2B are connected to carbohydrate metabolism, energy production and immune functions.

56

A

B

C

Figure 2.8. Amylase isozymes form functional interactions with other metabolic enzymes. String database indicate that AMY1 (A), AMY2A (B), and AMY2B (C) have functional interactions with other starch digesting enzymes. Shown is the interaction network of the amylase isozymes at medium confidence. Green lines connect proteins which are associated by recurring conserved genomic neighborhood, red lines indicate gene-fusion events in other species, blue connections are inferred by phylogenetic co-occurrence derived from similar patterns of absence and presence of genes, black lines indicate co-expression analysis derived from similar patterns of mRNA expression, purple lines indicate high-throughput experimental data, light blue lines indicate associations in curated database, and light green lines indicate textmining or co-occurrence of gene/protein names in abstracts. 57

Table 2.3. Proteins that functionally interact with amylase isozymes Proteins that interact with all amylase isozymes AGL Amylo-alpha-1, 6-glucosidase, 4-alpha-glucanotransferase. Glycogen debrancher enzyme which is involved in glycogen degradation. PYGB Phosphorylase, glycogen; brain; Glycogen phosphorylase found predominantly in the brain as an allosteric enzyme in carbohydrate metabolism. GBE1 Glucan (1,4-alpha-), branching enzyme 1; Glycogen branching enzyme required for sufficient glycogen accumulation. PYGL Phosphorylase, glycogen, liver. This gene encodes a protein that cleaves alpha-1, 4-glucosidic bonds to release glucose-1-phosphate from liver glycogen stores. SI Sucrose-isomaltase (alpha-glucosidase). This gene encodes a sucrose-isomaltase complex essential for the final stage of digestion of dietary carbohydrates. Proteins that interact with both AMY2A and AMY2B TSHB Thyroid Stimulating Hormone, Beta is indispensable for the control of thyroid function and metabolism. Proteins that interact only with AMY1 LCT Lactase- is an integral protein in the plasma membrane that has both lactase activity and phlorizin hydrolase activity. AKAP1 A kinase (PRKA) anchor protein 1; binds to type I and II regulatory subunits of protein kinase A and anchors them to the mitochondrion. AKAP8 A kinase (PRKA) anchor protein 8; anchoring protein that mediates the compartmentation of PKA and other signaling molecules. AKAP8 may target PKA and the condensin complex to chromatin during mitosis for chromosome condensation. COL10A1 Collagen, type X, alpha 1; type X collagen is a homotrimer of hypertrophic chondrocytes during endochondral ossification BPI Bactericidal/permeability-increasing protein. This gene encodes a lipopolysaccharide binding protein associated with human neutrophil granules and has antimicrobial activity against gram-negative organisms. Proteins that interact with only AMY2A MGAM Maltase-glucoamylase (alpha-glucosidase) is a brush border membrane enzyme that plays a role in the final steps of digestion of starch. GP2 Glycoprotein 2 (zymogen granule membrane) encodes an integral membrane protein that is secreted from intracellular zymogen granules and associates with the plasma membrane via glycosylphosphatidylinositol (GPI) linkage. The encoded protein binds pathogens such as enterobacteria, thereby playing an important role in the innate immune response. ENO1 Enolase 1, (alpha). Each isoenzyme is a homodimer composed of 2 alpha, 2 gamma, or 2 beta subunits, and functions as a glycolytic enzyme. Alternative

58

splicing of this gene results in a shorter isoform that has been shown to bind to the c-myc promoter and function as a tumor suppressor. PNLIP Pancreatic lipase. It encodes a carboxyl that hydrolyzes insoluble, emulsified triglycerides, and is essential for the efficient digestion of dietary . Proteins that interact with only AMY2B PGM1 Phosphoglucomutase 1 belongs to the phosphohexose mutase family. There are several PGM isozymes, which are encoded by different genes and catalyze the transfer of phosphate between the 1 and 6 positions of glucose. In most cell types, this PGM isozyme is predominant, representing about 90% of total PGM activity. ATP1A4 ATPase, Na+/K+ Transporting, Alpha 4 Polypeptide. The protein encoded is the catalytic subunit of the subfamily of Na+/K+ -ATPases, an integral membrane protein responsible for establishing and maintaining the electrochemical gradients of Na and K ions across the plasma membrane essential for osmoregulation, for sodium-coupled transport of molecules, and for electrical excitability of nerve and muscle. ATP1A1 The catalytic component catalyzes the of ATP coupled with the exchange of sodium and potassium ions across the plasma membrane. This action creates the electrochemical gradient of sodium and potassium ions, providing the energy for active transport of various nutrients. CD2 CD2 molecule, a surface antigen of the human T-lymphocyte lineage that is expressed on all peripheral blood T cells. It is one of the earliest T-cell markers, being present on more than 95% of thymocytes and on some natural killer cells. * Protein description was retrieved from GeneCards

Amylase isozyme expression may be driven by differential promotor activation

To begin to understand the mechanisms responsible for hyperamylasemia in OC, additional

computational analyses were performed to identify potential transcription factors that might

regulate amylase expression. Promo was used to analyze the probable promoter regions of the

amylase isozymes. Promo predicted probable attractive transcription factor binding sites within

each probable promoter region. It also predicted which transcription factors would be most likely

to bind to these potential transcription factor binding sites (Figure 2.9). The binding sites of these

transcription factors are located at different regions of the isozyme promoter sequences, suggesting

differing patterns of regulation.

59

Transcription factors C/EBP alpha (regulates cell cycle regulation), C/EBP delta (regulates immune and inflammatory responses), and Zic1/2 (regulates mammalian development) were predicted to regulate AMY1 and AMY2A promoter activity. STAT5A (signal transduction and transcription factor) is a unique transcription factor of AMY1. C/EBP delta was also identified as a candidate transcription factor for AMY2B, however, POU1F1 (regulates mammalian development) and Cdx-1 (regulates intestine-specific gene expression) were uniquely predicted to regulate AMY2B (Figure 2.9).

The EPD was utilized to find potential core promoter elements such as the TATA, CG and

CCAAT boxes (Figure 2.9). AMY1 has a TATA box at residues -51 to -33 and a CG box at residues 40 to 55. AMY2A has TATA boxes at residues -52 to -21 and -17 to -5. AMY2A also has two CCAAT boxes at residues -598 to -585 and -547 to -534. AMY2B has two TATA boxes at residues -138 to -101 and -50 to -33 and a CG box at residues 15 to 28.

AMY2B overexpression is the most likely to be associated with amplification mutations in cancer cBioPortal for Cancer Genomics database determined the frequency and pattern of mutations in the amylase isozymes. The number of reported amylase genes’ mutation reflects their evolutionary history. AMY2B, the most evolutionarily conserved amylase gene, has developed the most mutations (n = 86), whereas AMY1, the most recently evolved, has developed the least amount of mutations (n = 4). All the AMY1 mutations are missense mutations; AMY2B mutations are 90.7% missense mutations, 3.5% nonsense mutations, 4.6% frameshift mutations and 1.1% splice site mutations. AMY2A has an intermediate number of mutations (n = 51). Mutations in AMY2A are

82.4% missense mutations and 17.6% nonsense mutations (Figure 2.10).

60

Figure 2.9. AMY2B has unique potential transcription factors. The potential promoters of the amylase isozymes were retrieved from Ensembl genome browser and analyzed by Promo database to identify candidate transcription factors driving amylase promoter activity.

61

A

B

C

Figure 2.10. Mutational events are highest in AMY2B. cBioPortal for Cancer Genomics database determined the pattern of mutations in the amylase isozymes AMY1 (A), AMY2A (B), and AMY2B (C). Mutations are denoted by lollipops – green lollipops denote missense mutations, red lollipops denote truncating mutations, black lollipops denote inframe deletions and insertions. Purple lollipops denote residues affected by multiple mutation types. AMY2B, the most conserved amylase gene, has developed 86 mutations, whereas AMY1, the most recently evolved, has developed 4 mutations. AMY2A developed 41 mutations.

62

A

B

Figure 2.11: AMY2B is altered in most cancer types (Continued on Next Page) 63

C

Figure 2.11. AMY2B is altered in most cancer types. Amplifications comprise the majority of amylase mutations in OC. cBioPortal for Cancer Genomics database determined the mutational profiles for amylase; AMY1 (A), AMY2A (B), and AMY2B (C).

cBioPortal for Cancer Genomics database determinations also revealed a variety of mutations

among the amylase isozymes, including non-specific mutations, deletion mutations and

amplification mutations present in many cancer types including cancers of the lung, colon, uterus

and skin (Figure 2.11). Mutations in AMY1, AMY2A, and AMY2B were found in 21, 24, and 26

cancer types, respectively. AMY1 mutations in cancers other than OC mostly arise from genetic

amplifications and deletions whereas AMY2A mutations in other cancers are comprised of a

64 mixture of deletions, amplifications and missense mutations. Likewise, AMY2B mutations in other cancers typically occur as missense mutations and amplification mutations (Figure 2.11).

The pattern of amylase isozyme mutations in OC is somewhat different from that in other cancer types. In OC, the mutational profile of amylase isozyme AMY1 consists mostly of amplifications and deletions while the mutational profile of the amylase isozyme AMY2A in OC is mostly amplifications as well as some deletions and nonsense mutations. The pattern genetic mutation of the amylase isozyme AMY2B in OC is also mostly due to amplifications although some deletions, truncations and in-frame mutations are also noted. Further, in OC, the mutation frequency of the amylase isozyme AMY1 was determined to be 2.2% amplifications and 0.3% deletions. The mutation frequency of AMY2A was 2.1% amplification, 0.3% deletion, 0.1% missense mutation and 0.1% mixed missense mutation and amplification while the mutation frequency of AMY2B was 2.2% amplifications and 0.3% deletions (Figure 2.11).

Lastly, cBioPortal for Cancer Genomics database was utilized to determine amylase isozyme mRNA expression in different cancers (Figure 2.12). AMY1 mRNA expression was low in most cancers. Only OC, lung squamous carcinoma, thyroid cancer and lung adenocarcinoma expressed relatively high AMY1 mRNA levels. AMY2A mRNA expression was almost negligible in most cancers except for its high mRNA expression in pancreatic cancer. AMY2B mRNA expression was relatively high in all cancers explored by the database. In OC, AMY2B mRNA expression levels were highest followed by AMY1 and AMY2A mRNA expression levels.

65

A

B

Figure 2.12: AMY2B is the most expressed amylase isozyme in OC (Continued on Next Page)

66

C

Figure 2.12. AMY2B is the most expressed amylase isozyme in OC. cBioPortal for Cancer Genomics database determined amylase isozyme mRNA expression in cancers at the transcriptional level; AMY1 (A), AMY2A (B), and AMY2B (C). AMY2B demonstrated the highest RNA expression in OC when compared with other amylase isozymes (denoted by boxes). AMY2B RNA is most expressed in OC followed by AMY1 and AMY2A mRNA.

Clinical validation confirms elevated levels of AMY2B protein in OC

While my computational analyses predicted elevated AMY2B RNA levels in OC, validation of elevated AMY2B protein had yet to be determined in OC. Therefore, to validate differential amylase isozyme protein expression with OC, serum samples from healthy controls, women with benign gynecologic disease and women with OC and endometrial cancer were subjected to WB analyses. Using the 95th percentile of amylase values for healthy controls as the threshold for serum

67 amylase positivity, the amount of serum AMY1, AMY2A and AMY2B was generally negligible in healthy pooled controls and individual serum samples from women with benign gynecologic disease (Figure 2.13). Likewise, negligible amounts of AMY1A and AMY2A protein were present in serum samples from endometrial cancer, clear cell carcinoma and mucinous OC. While elevated levels of AMY2B protein were noted in most endometrial cancers (4/5) and clear cell OC (1/1),

AMY2B protein levels were only elevated in 2/5 mucinous OC. AMY2B levels. In contrast, serum samples from patients with serous ovarian carcinoma revealed AMY1 (in 3/8 samples) and

AMY2B (in 7/8 samples) protein levels up to 2x greater than healthy controls.

Figure 2.13: Serum levels of AMY2B protein are altered in OC (Continued on Next Page)

68

2.5 Serum densitometry Serous OC AMY1 AMY2A AMY2B

2

Ovarian 1.5 tumor thecoma Mature Ovarian Endometrial Clear cystic Anaplastic Cancer cell OC Teratoma 1 metastatic Carcinoma Mucinous OC *

0.5 Benign gynecological disease

Ratio amylase of totransferrin

* * *

0 P90

P80

P20A 6K2146

C16037

7A0032

3119-29 3119-51 3119-53 3119-54 3119-55 3119-56 3119-62 3119-12 3119-26 3119-30 3119-40 3119-65 3119-61 3119-37 3119-44 3119-63 3119-60 3119-48 3119-59 3119-43 3119-19 3119-33 3119-15 3119-31 3119-22 3119-35 3119-17 M14161 Figure 2.13. Serum levels of AMY2B protein are altered in OC. Serum samples from normal controls (*), women with benign gynecologic disease and cancer were subjected to western immunoblotting for amylase AMY1, AMY2A, AMY2B (upper panel). Amylase band intensity was quantified and densitometry values are expressed relative to the transferrin control group (lower panel).

Discussion: The chromosomal localization of the amylase isozymes on chromosome 1p21 [302,303], evolutionary development of the amylase isozymes from a single ancestral gene [242–244], and similar biochemical properties such as the molecular weight, isoelectric point [242,304] and calcium and chloride cofactor binding features common to the amylase isozymes have been identified in the literature previously [305,306]. Further, the catalytic domain and the C-terminal domain of the amylase isozymes [307], and crystalized tertiary structure of the amylase isozymes

[257,308] have also been elucidated.

69

Given the common evolutionary background and shared sequence homology, it is not surprising that the amylase isozymes share biochemical and structural features despite differential tissue expression. Computational analyses indicated that the amylase isozymes are composed of

511 amino acids, have an average computational molecular weight of 57 kDa and an average computational isoelectric point of 6.57. The amylase isozymes also bind chloride and calcium as enzymatic cofactors. This, coupled with predictions that the amylase isozymes are hydrophilic, suggests that amylase is secreted from cells, in keeping with its known digestive function.

Structurally, the amylase isozymes share a common structure dominated by an amino terminal transport signal site and a catalytic domain. The amylase isozymes also share common phosphorylation, glycosylation, and calcium, chloride and glucose binding sites.

Computational analysis was not always in agreement with known properties of amylase reported in literature. Both computational and known literature reported the homology of the amylase isozymes as being above 90%; however, the amylase isozymes are much more homologous according to the computational analysis. Literature reported molecular weight and isoelectric point did not differentiate between the AMY2A and AMY2B making the computational analysis of the amylase isozymes size more specific. There was no agreement between the results of the computational analysis and literature reported domains of the amylase isozyme, the position of the active sites or the calcium and chloride binding sites. This discrepancy may be due to the different algorisms used by the computational programs and may not accurately depict biological conditions. It may also be that experimental validation of the new biological knowledge found by computational programs has not been done.

In order to begin to understand the role of and mechanisms contributing to hyperamylasemia seen in OC, I performed computational analyses to identify unique features of the amylase

70 isozymes in order to predict which isozyme would most likely be overexpressed in OC-associated hyperamylasemia. Computational analyses highlighted new features of the amylase isozymes not previously reported (Table 2.4). They include the following:

First, potential unique post-translational modifications, including novel glycosylation and phosphorylation sites, were identified among the amylase isozymes. Glycosylation is a regulated and reversible modification including the covalent bonding of a moiety of variable length and arrangement to a protein residue [309]. Glycosylation plays a role in folding and structural stability of protein, protein localization and interactions as well as immune responses and modifying . Glycosylation of proteins confers a more stable configuration that is less prone to degradation [310–312]. Glycan dysfunction can lead to cancer, diabetes and liver cirrhosis

[209,313,314], therefore, additional glycan sites at residues 411 for AMY1 and residues 423 and

411 for AMY2B may lead to enhanced protein stability.

Phosphorylation is a flexible, simple, reversible regulatory mechanism utilized by 30% of human proteins via protein kinases. Phosphorylation can alter protein activity, stability, function, and interactions. Abnormal phosphorylation is often a cause or consequence of human disease

[208,315]. Phosphorylation affects intracellular signaling cascades that regulate metabolism, enzyme reactions and protein degradation [209] so a unique phosphorylation site at amino acid residue 133 in AMY2B (according to DEPP) can be responsible for increased protein stability and possibly more protein-protein interactions. These potential posttranslational modifications may be key for amylase function. Together, these unique and/or aberrant posttranslational modifications could alter amylase transcription, secretion or enzymatic activity contributing to overexpression of amylase in cancer.

71

Table 2.4. Distinguishing computational characteristics among amylase isozymes AMY1 AMY2A AMY2B Tissue specificity Salivary gland Pancreas None Unique regions of Amino acid 51-63, - Amino acid 51-63 and disorder 117-134 and 375-384 117-134 Unique calcium N115 N115, H216 H216 binding residues Unique chloride - 269 T269 binding residues Unique glucose - T178, G321 V178, G321 binding residues Unique 3D structure - + - Unique regions of - 2, 64-68, 97-98, 116, 2, 26, 40, 97-98, 116, aggregation 118, 197, 279-281, 176-182, 325-332, 325-332,340-345, 340-345, 355-356, 355-356, 383-386, 383-386, 408-409, 407-415, 439, 456, 413-414, 439, 464-466 478-480 Unique - - Amino acid 133 phosphorylation sites Unique N-linked: residue 411 - N-linked: residue 411 glycosylation sites O-linked: residue 123 Unique interacting LCT, BPI, COL10A1, MGAM, TSHB, TSHB, ATP1A4, proteins AKAP1, AKAP8 GP2, PNLIP, ENO1 ATP1A1, CD2, PGM1 Unique transcription C/EBP alpha, Zic1/2, C/EBP alpha and POU1F1 and Cdx-1 factors STAT5A Zic1/2 No. of mutations 4 41 86 * Common features of the amylase isozymes are discussed in the results section. Table indicates the unique characteristics of the different amylase isozymes. Sumoylation is the reversible attachment of a Small Ubiquitin-related MOdifiers (SUMO) to proteins. SUMO protein is added to a specific peptide sequence of a hydrophobic residue bound to a lysine bound to any amino acid followed by an acidic residue [316]. Sumoylated proteins appear to take a part in transcription [211,212], apoptosis [213], and signal transduction [214].

While the mechanisms that regulate sumoylation are not well understood, conjugation of SUMO to proteins can alter protein configuration potentially leading to new protein-protein interactions,

72 inhibition of existing protein-protein interactions and/or changes to existing protein functions and activity [215], thereby enhancing functional diversity. In this way, sumoylation can increase protein stability by promoting the formation of multimeric protein complexes as well as by inhibition of ubiquitination [216]. Since all amylase isozymes are predicted to be sumoylated, it is expected that sumoylation imparts stability and may result in a longer half-life due to lack of ubiquitination and, subsequently, degradation.

Second, protein function is determined by protein structure which, in turn, is based on protein sequence. Consequently, evolutionarily related proteins have similar structural motifs and the structural similarities between the amylase isozymes are indicative of their evolutionary history

[317]. In agreement, I found that all the amylase isozymes are generally highly ordered proteins with long half-lives. Therefore, the amylase isozymes have a stable secondary and/or tertiary structure. Since ordered proteins have low binding potential to other proteins and usually do not form complexes, it is unlikely that amylase forms larger protein complexes [205]. However, I also found that AMY1 and AMY2B have unique internal regions of protein disorder. AMY1 has unique disorder regions at amino acids 51 to 63 and 375 to 384 while AMY2B has a unique disorder profile at amino acids 51 to 63 and 117 to 134. These internal regions of protein disorder in AMY1 and AMY2B represent zones of flexible protein configurations that: (1) are more prone to posttranslational modifications (such as the unique phosphorylation site at amino acid 133 in

AMY2B) that increase protein functionality; (2) may function as linkers between the two structured domains of the amylase isozymes or; (3) may confer increased binding potential to the structured domains of other proteins [317].

Third, amylase function may be related to protein aggregation inherent in amylase protein structure. Alpha-amylase was found to be more prone to aggregation as compared to β-amylase

73 due to the presence of cysteine residues on the surface of amylase which help form intermolecular sulfide bonds that covalently-link amylase dimers [318]. Computational analyses revealed that the amylase isozymes share multiple, common aggregation prone regions, but AMY2A and AMY2B have additional unique regions of aggregation. Unique aggregation prone regions were mostly found in AMY2B, indicating it is most likely to aggregate. Aggregation prone regions are more likely to be in ordered regions and stabilize proteins. Aggregation prone regions are also very likely to be found in close structural proximity to catalytic residues further indicating that aggregation prone regions contribute to protein function. Aggregation prone regions can be conserved in proteins to maintain protein stability and function [206]. AMY2A and AMY2B have unique regions of aggregation which is in keeping with their evolutionary history. Unlike AMY1, since

AMY2B is the oldest of the amylase isozymes, it has the most conserved ordered protein regions which is in keeping with the view that intrinsically ordered proteins are associated with high self- aggregability. This was further supported by analyses of tertiary amylase structure. Specifically, alpha helixes are the most common secondary structure to transverse the membrane due to their ability to maintain more polar structures inside the helix [319] while beta sheets play a large part in amyloid formation [320]. The pancreatic amylase isozyme AMY2A has more alpha helix structure indicating it may more readily transverse the membrane than AMY1 and AMY2B amylase isozymes.

The salivary glands and pancreas account for most of the amylase activity in the body.

Increased amylase expression and activity (hyperamylasemia) has been associated with insult to the pancreas or salivary glands, chronic alcoholism, amylase secreting cancers and anorexia nervosa or bulimia. Hyperamylasemia is due to an overproduction of amylase that is secreted into the serum as well as decreased amylase clearance. Decreased amylase clearance may be due to

74 macroamylasemia, an asymptomatic condition wherein amylase aggregates into high-molecular weight complexes containing immunoglobulins [321]. Reportedly, hyperamylasemia and macroamylasemia can occur as secondary conditions to cancer [322]. The amylase proteins displayed different potential aggregation-prone regions. Since AMY2A and AMY2B may be more prone to self-aggregate because they have more aggregation prone regions, they may contribute to macroamylasemia [323].

Fourth, while the ordered nature of the amylase isozymes typically precludes their physical interaction with other proteins, computational analyses predicted several potential functional amylase-protein interactions, mostly associated with carbohydrate metabolism. However, unique functional interactions among amylase isozyme family members were also noted and might represent novel functions limited to specific amylase isozymes. That is, AMY1 has unique interactions with lactase (LCT), kinase A anchor protein 1 (AKAP1), kinase A anchor protein 8

(AKAP8), collagen, type X, alpha 1 (COL10A1), and bacterial/permeability-increasing protein

(BPI). These unique interactions indicate that AMY1 might participate in a variety of established and novel functions including carbohydrate metabolism, extracellular matrix remodeling, and cell immunity. AMY2A has interactions with maltase-glucoamylase (MGAM), glycoprotein 2 (GP2), enolase 1 (ENO1), pancreatic lipase (PNLIP), and TSHB. These unique interactions indicate that

AMY2A could participate in a variety of functions including carbohydrate and lipid metabolism and cell immunity. With possible involvement in lipid metabolism, AMY2A may play a role in alternate energy production. AMY2B has interactions with phosphoglucomutase 1 (PGM1), CD molecule (CD2), ATPase, Na+/K+ transporting, Alpha 4 Polypeptide (ATP1A4), Alpha 1

Polypeptide (ATP1A1), and TSHB. These unique interactions suggest that AMY2B could also participate in carbohydrate metabolism as well as ATP production and cell immunity.

75

Interestingly, all amylase isozymes functionally interact with different immunity related proteins, indicating that amylase may modulate the immune response. These novel interactions point to potential alternative functions of the amylase isozymes. It is important to note that all the proteins that interact with amylase do not form physical interactions, but have functional interactions. There is no indication that amylase forms complexes with other metabolic enzymes. However, it has been proposed that amylase is sequestered in normal tissue and during inflammatory events, amylase is released into the blood [324]. Consequently, the high levels of tissue amylase may be related to an anti-bacterial role [325].

Fifth, unique amylase isozyme expression profiles may arise through differential gene expression regulated, in part, by different transcription factors. The current study revealed that

AMY1 transcription may be regulated by the C/EBP alpha, C/EBP delta, zic1/2, and STAT5a while AMY2A is regulated by C/EBP alpha, C/EBP delta and zic1/2 and AMY2B is regulated by

C/EBP delta, POU1F1 and Cdx-1. The function of the differentiation-inducing C/EBP transcription factor family has been found to be abrogated in hematopoietic, lung and skin cancers

[326]. C/EBP beta is overexpressed in OC epithelial cells in vivo and C/EBP alpha and delta have constant expression levels regardless of tumor stage/progression [327]. Zic1 plays a role in cancer progression in medulloblastomas, endometrial cancers, colorectal and mesenchymal neoplasms such that Zic1 is downregulated during transformation [328]. In breast cancer, loss of STAT5A is associated with poor clinical outcome and advanced tumor progression [329]. Not much is known about the role many of these transcription factors play in OC in particular though it is clear that they play a large role in cancer progression in other cancer types.

In addition to all the above, all three amylase isozymes had TATA boxes in their promoter regions while only AMY1 and AMY2B have CG boxes in their promoter regions and only

76

AMY2A has CCAAT boxes in its promoter region. Core promoter elements determine transcriptional regulations by governing which transcription factors are recruited. The TATA box is a DNA sequence that specifies where transcription initiates by binding transcription factors.

TATA boxes are typically found in highly regulated genes [330]. The GC box is usually found upstream of the TATA box and acts like a transcriptional enhancer that binds transcription factors

[331]. The CCAAT box also occurs upstream of the transcription start site and is known to be part of the core promoter. Interestingly, the C/EBP proteins are highly conserved CCAAT enhancer- binding proteins with multiple functions in proliferation, metabolism, immunity and inflammation

[332–335]. This indicates that the amylase isozymes have different core promoter and enhancer regions that recruit transcriptional factors by unique methods.

Lastly, computational analyses indicated that the number of potential mutations among amylase isozymes was greatest in AMY2B (with 86 possible mutations) compared with AMY2A

(with 41 possible mutations) and AMY1 (with 4 possible mutations). It is also interesting that the

AMY1 mutations are located at the end of the alpha-amylase catalytic domain while mutations in

AMY2A are localized mutations mostly in the N-terminal and alpha-amylase catalytic domain.

This suggests that the alpha-amylase catalytic domain may be prone to mutations. In contrast,

AMY2B has mutations uniformly distributed throughout its protein. Nonetheless, mutations of amylase isozymes often lead to amplification and could explain overexpression leading to hyperamylasemia in OC.

Despite many similarities among the amylase isozymes, computational analyses revealed significant differences among these isozymes based on regions of protein disorder, calcium binding sites, glycosylation sites, phosphorylation sites, regulatory transcription factors, tertiary structure, aggregation prone regions and susceptibility for mutational events. Consequently,

77 structural, functional and regulatory differences among the amylase isozymes may contribute to differential tissue expression in disease. The computational differences among amylase isozymes leads me to believe that AMY2A and AMY2B may be more prone to self-aggregation. AMY1 and

AMY2B have unique regions of disorder indicating these isozymes may mediate more interactions, and be more susceptible to mis-folding and aggregation. AMY1 and AMY2B have identical tertiary structures indicating AMY1 and AMY2A may have similar interactions and patterns of secretion. AMY2B is endogenously expressed in many tissues due to its lack of a retroviral insertion that induces tissue specificity. During malignant transformation, AMY2B expression may be exacerbated by genetic deregulation.

Taken together, my computational analyses predict that AMY2B and, to a lesser extent,

AMY1 may be the amylase isozymes overexpressed in OC-associated hyperamylasemia. While my computational analyses suggest that AMY2B RNA is overexpressed in OC and elevated levels of AMY2B amylase have been anecdotally reported in OC serum and tissues, information about the specific amylase isozyme overexpressed at the protein level in OC disease is lacking.

Therefore, to validate my computational predictions, serum samples from normal controls and from women with benign gynecologic disease and reproductive cancers were assessed by amylase C/EBP alpha isozyme protein expression by western immunoblotting. I was able to confirm that, compared to C/EBP delta normal controls and benign disease, AMY2B was preferentially overexpressed at the protein in STAT5A serous ovarian cancers, the most frequently occurring ovarian histologic subtype. Consequently, vitamin D computational strategies can serve as a first step to help identify novel protein regulators for OC, such as AMY2B, which can then be expanded in further studies on the molecular mechanism(s) contributing to OC progression.

78

C/EBP alpha

Chapter 3

Amylase promotes OC cell invasion in vitro

Introduction:

Hyperamylasemia has been reported in OC [262,336,337], specifically in women with serous ovarian tumors [338] and ovarian cystadenocarcinoma [230]. Zakrezewska et al. found that amylase is overexpressed in 39% of OC patients [339]. Hyperamylasemia is associated with poor prognostic outcome including rapid disease progression and increased mortality; it has been suggested that amylase is a marker of disease progression as amylase activity only decreases in response to treatment and rapidly increases when diseases relapse [340–343]. However, its contribution to OC disease etiology remains unknown [1]. Nonetheless, since hyperamylasemia in

OC patients is reduced after ovarian tumor removal or treatment, elevated serum levels of amylase in patients with hyperamylasemia are thought to result by secretion of amylase by OC cells

[269,336].

My computational analyses predicted AMY1A and AMY2B as the most likely amylase isozymes overexpressed in OC, and western blot analysis of OC patient serum samples confirmed high levels of AMY1 and AMY2B in OC (Chapter 2). To begin to determine the contribution of amylase for OC progression, in this chapter, an in vitro model of amylase secretion by OC cells will be developed. The functional contribution amylase for OC will be evaluated by measurement

79 of cellular proliferation and invasion as well as the ability for amylase to degrade ECM components. Lastly, by demonstrating amylase within the cellular glycocalyx and the ECM environment, I propose a role/mechanism by which amylase may not only drive energy production, but invasion by OC cells.

Materials and Methods: Tissue culture

The following human cell lines were used: normal human dermal fibroblasts HDF; SV 40-

Large T-Antigen transfected human ovarian surface epithelial (IOSE) cell lines MCC3, IOSE-80,

IOSE-121, and IOSE-144 derived from normal patients with no family history (NFH) of breast and/or OC [344,345]; OC cell lines CaOV3, OVCAR5, OV-90, SKOV3, TOV21G and IGROV; colorectal adenocarcinoma cancer cell lines WiDr and SW626; breast cancer cell lines MDA-MB-

231 and MCF7; head and neck cancer cell line HN5α [346]; cervical cancer cell lines Hela, C13,

OV2008; pancreatic cancer cell line Panc1; lung cancer cell lines NCI-H2009 and H69; and glioblastoma U373MG cells. Cells were cultured in Medium 199/MCDB 105 (Sigma, St. Louis,

MO) with 5% fetal bovine serum (FBS) and gentamicin and incubated at 37°C with 5% CO2.

Media was concentrated using Centriprep Centrifugal Filter Devices (Ultracel YM-3 Cat.4302

Merck Millipore Ltd, Tullagreen, Ireland).

Transmission Electron Microscopy (TEM)

Cells were grown on routine tissue culture plasticware in a monolayer on a 13mm coverslip in

6 well plates overnight. Adherent cells were fixed with 75 mM lysine in 0.075% (w/v) alcian blue,

2% paraformaldehyde (PFA), 2.5% glutaraldehyde (GA) in 0.1 M cacodylate (CAC) buffer solution, buffered at pH 7.2, for 0.5 hour at 37°C. The coverslips were then washed for 1.5 hours at room temperature followed by storage at 4°C overnight. Coverslips were then rinsed three times

80 for five minutes with 0.1 M CAC, pH 7.2 and postfixed with 1% osmium tetroxide-potassium ferrocyanide (OsO4/0.8% K4Fe(CN)6) in 0.1M CAC, pH 7.2 for 1 hr. Coverslips were then rinsed for five minutes with 0.1M CAC, pH 7.2, and washed with dH2O twice for five minutes. Coverslips were dehydrated with a graded ethanol (ETOH) series: 35%, 50%, 70%, and 95% ETOH followed by three exchanges of anhydrous 100% ETOH prior to critical point drying via three ten minute washes in 100% acetone. For resin infiltration, cells were incubated in graded acetone/Epon 812 resin solutions: 2:1 acetone to resin solution for ten min, 1:1 acetone to resin solution for 48 hours,

1:2 acetone to resin solution for two hours and pure resin twice for two hours each. The coverslips were then cut into 2mm x 6.5mm segments and baked on pure resin at 40°C for 2-4 hours and at

70°C overnight to polymerize the resin. The coverslips were then sectioned, examined and photographed on a Zeiss EM 30 electron microscope. Cell cultures were screened for yeast contamination and contaminated cultures were replaced.

Quantitative PCR

Cells were treated with TRIzol reagent from Invitrogen (Carlsbad, CA) and ribonucleic acid

(RNA) was isolated according to the manufacturer’s instructions. To generate single-stranded complementary DNA (cDNA), the Applied Biosystems GeneAmp RNA polymerase chain reaction (PCR) Core Kit (Foster City, CA) was used with 3 ug/mL total RNA using a Biometra

UNO-thermo-block and Perkin-Elmer-GeneAmp PCR system 9600. qPCR was carried out with

SYBR Green Universal Master Mix (Applied Biosystems), cDNA, and primers. Primers used were

AMY1A (GeneCopoeia: Hs-QRP-21480; Rockville, MD), AMY2A (GeneCopoeia: Hs-QRP-

21450), AMY2B (GeneCopoeia: Hs-QRP-21451; Rockville, MD), and GAPDH (GeneCopoeia:

Hs-QRP-20169; Rockville, MD) as control. Sequences for the amylase primers remain the proprietary property of GeneCopoeia. Amplification was performed with 40 cycles of denaturation

81

(95°C, 10 sec), annealing (60°C, 20 sec), and extension (72°C, 15 sec) using a Bio-RAD Chromo4

Real Time PCR Detector using Opticon Monitor 3 program. Levels of amylase were normalized to their respective GAPDH message values and the fold difference was determined by dividing the threshold cycle (Ct) value by the reference sample.

Western blot

Cells were washed in PBS, trypsinized, pelleted, rewashed with PBS and counted. Cells were lysed in CHAPS buffer for 30 minutes on ice before being centrifuged at 115,000 x g, at 4°C for 1h. Twenty ug of protein were separated by a 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Proteins were transferred to Polyvinylidene fluoride (PVDF) membranes, washed in Tween 20-Tris buffered Saline (TBST), and blocked in 5% milk in TBST.

Blots were incubated in their respective primary antibodies overnight, followed by incubation with a horseradish peroxidase (HRP)-conjugated secondary antibody (Fisher, Pittsburgh, PA), and developed via FEMTO Cat. 34095 Life Technologies (Grand Island, NY). Antibodies used were:

AMY1A (1:1000) Cat.H00000276-M04 Abnova (Taipei City, Taiwan); AMY2A (1:500) 15845-

1-AP Proteintech (Chicago, IL); AMY2B (1:2000) Cat. LS-C173958 LSBio (Seattle, WA); Beta

Actin Loading Control Antibody (BA3R) (1:10,000) Cat. # MA5-15739 Thermo Scientific

(Rockford, IL). Densitometry was performed using ImageJ software to normalize amylase band strength to the actin band.

Amylase ELISA

To measure amylase protein levels in OC samples and/or their conditioned medium, 100ul of samples corresponding to 104 cells were assayed in triplicate using the quantitative double sandwich enzyme-linked immunosorbent assay (Amy-p ELISA Kit; Cat. MBS264855;

MyBioSource, San Diego, CA). Absorbance was read on a Microplate Reader BioTek ELx800

82

(Winooski, Vermont) using Gen5 Data Analysis Software (Biotek, Winooski, Vermont). Resultant values were derived from a standard curve and expressed as the mean amylase concentration of triplicate samples.

Amylase activity assay

The Amylase Assay Kit (Cat: ab102523 Abcam, Cambridge, MA) was used to measure amylase activity according to the manufacturer’s instructions from concentrated conditioned media (CCM). The CCM corresponding to 104 cells was incubated with ethylidene-pNP-G7, a substrate that is cleaved by amylase into smaller fragments targeted by α-glucosidase to release a chromophore whose concentration was measured at 405 nm using μQuant plate reader from Bio- tek instruments, Inc. (Winooski, VT).

Amylase transfection

For silencing experiments, IOSE and OC cells were split into four groups, including a control group supplemented with only the transfection reagent, a negative control group transfected with a non-targeting control scramble siRNA and the transfection reagent, a third group supplemented separately with the three different amylase siRNAs and the transfection reagents and, lastly, a fourth group transfected with GFP to monitor transfection efficiency. Cells were transfected with ON-TARGETplus Human AMY1A siRNA-SMARTpool (Cat. L-012691-01-

0010), ON-TARGETplus Human AMY2B siRNA-SMARTpool (Cat. L-015880-01-0010), and

ON-TARGETplus Human AMY2A siRNA-SMARTpool (Cat. L-016978-01-0010) or ON-

TARGETplus Non-targeting scramble siRNAs (Cat. D-001810-01-0010) GE Healthcare

Dharmacon (Lafayette, CO). The SMARTpool is a mix of four siRNAs provided as a single reagent. Cells were plated in 6 well plates, serum starved overnight then treated with 145 pmol siRNA per well incubated with Lipofectamine 3000 Transfection Reagent (Invitrogen, Madison,

83

WI, United States) according to the manufacturer’s instructions. Transfection efficiency was determined by transfecting cells with pmaxGFP™ Control Vector (Cat. VCA-1003) Lonza (Basel,

Switzerland). Transfected cells were maintained for 48 h before further experiments, unless otherwise described.

Invasion assay

Cells were incubated on coated transwells (CytoSelect 96-Well Cell Invasion

Assay, catalog number: CBA-112-COL (San Diego, CA)) for 24 hours. Following incubation, cells that had invaded through the collagen gel and onto the transwell membrane insert were stained with 0.1% crystal violet solution and photographed using an Olympus DP20 digital camera

(Burnaby, BC, Canada) under a Leica DMIRE2 microscope. The number of cells that invaded the transwell was then counted and subjected to statistical analysis.

Glycosaminoglycan (GAG)/proteoglycan quantification assay

GAG content was determined using a Blyscan colorimetric assay (Biocolor Assays, County

Antrim, BT38 8YF, United Kingdom). After transfection with amylase siRNA, and scramble

RNA, 7x106 IOSE-121 and OVCAR5 cells were digested in a papain extraction reagent. The GAG content in each sample was detected with spectrometry at 656 nm after precipitation and dye binding of GAG according to the manufacturer’s instructions. A standard curve was created using controls according to the manufacturer’s instructions.

Immunogold staining

For immunogold staining, cells were grown on coverslips and prepared for TEM analysis as described earlier. Sectioned grids of cells were incubated in 4% w/v aqueous sodium metaperiodate for two minutes, washed multiple times in water, blocked in PBSG (PBS containing

0.001% (v/v) Tween 20 and Triton X-100 and 1% (w/v) gelatin from cold water fish) for 10

84 minutes before incubation overnight in 6ug/ml anti-salivary alpha amylase antibody (Cat. ab54765

Abcam (Cambridge, MA)). The grids were then washed twice in PBS then eight times in water before a two-hour incubation in goat-anti-mouse IgG (H&L) conjugated to 15nm colloidal gold particles 1:40 (Cat. 25133 Electron Microscopy Sciences (Hatfield, PA)). The grids were rinsed in

H2O for 30-40 sec then counterstained in uranyl acetate (saturated) in 50% methanol for 2 minutes.

To confirm the specificity of the primary antibody, non-immune goat IgG were used as negative control in place of primary antibody. The cells were then viewed by TEM.

Statistical analyses

For real time PCR, error bars illustrate RQmin and RQmax, which were calculated as: RQave divided by (standard deviation^ student’s t value at the 95% confidence interval, for 5 degrees freedom) and RQave times (standard deviation^ student’s t value at the 95% confidence interval, for 5 degrees of freedom), respectively. This range represents the 95% confidence level. For amylase activity assay, amylase ELISA, GAG assay and invasion assay, student’s t test was performed to assess statistical difference between means of triplicates ± standard error. P-values ≤

0.05 were considered statistically significant.

Results:

Confirmation of yeast-free cell cultures.

Since cell culture contaminants, including commensal yeast, can produce amylase, it was necessary to ensure that cell lines used for this study were free of microbial contaminants.

Examination of cell cultures by light microscopy did not detect presence of yeast in any cultures.

However, for more stringent examination, IOSE and OC cell lines were examined by TEM for possible yeast contamination. IOSE-121, HIO118, SKOV3, OV-90 and OVCAR5 were found to

85 be free of yeast, while yeast was found in CaOV3 and IGROV (Figure 3.1). I replaced the yeast- infected cell lines with yeast-free cell lines as validated by TEM visualization.

A. CaOV3:

B. IGROV:

Figure 3.1. Establishing cell cultures are free of yeast. Cells cultures were examined by TEM to detect microbial contamination, including yeast. Representative photographs illustrate endogenous yeast (arrows) in CaOV3 (A) and IGROV (B) ovarian cancer cell lines.

86

OC cells overexpress amylase in vitro.

Computational analyses predicted that AMY1 and AMY2B would be overexpressed in OC

(Chapter 2). A panel of cell lines was used to confirm which amylase isozymes are overexpressed in established OC cell lines as well as from other cancer types at the message level using qPCR

(Figures 3.2 and 3.3) and at the protein level using western immunoblotting (Figure 3.4). At the message level, there were variable levels of amylase expression in OC and normal cell lines (Figure

3.2). Moderate levels of AMY1 and AMY2B RNA were expressed in MCC3, while IOSE-121 essentially only expressed AMY1 RNA. As defined by the average signal ratio of amylase/GAPDH in IOSE cells, only 2/5 (40%) of OC cell lines, OVCAR5 and IGROV, overexpressed amylase AMY1 and AMY2B to a greater extent than IOSE cells. Though expressing small levels of amylase message, SKOV3, OV-90, and TOV21G were shown to express AMY1 and AMY2B RNA. Likewise, there was no clear pattern of which amylase isozyme is overexpressed in other cancer types at the RNA level (Figure 3.3). Whereas human dermal fibroblast (HDF) most notably expresses AMY1 RNA, lung cancer cells H2009 displayed an overexpression of amylase AMY1 and AMY2A while amylase RNA was not detected in H69 cells. Likewise, colorectal cancer WiDr cells displayed an overexpression of amylase AMY1 and

AMY2B while SW626 cells expressed negligible amounts of AMY1. Similarly, cervical cancer cells (Hela, C13) expressed small amounts of AMY1 and AMY2B. Cells lines representing breast cancer, head and neck cancer, glioblastoma and pancreatic cancer expressed negligible amounts of amylase although when detected, these cell lines preferentially expressed AMY1 RNA and, occasionally, AMY2B (Figure 3.3).

87

0.001 0.0009 Amylase messenger level in ovarian cells 0.0008 0.0007 0.0006 0.0005 0.0004 0.0003

Ratio of AMY/GAPDH mRNA AMY/GAPDH of Ratio 0.0002 0.0001

0

AMY1 AMY1 AMY1 AMY1 AMY1 AMY1 AMY1

AMY2B AMY2B AMY2B AMY2B AMY2B AMY2B AMY2B

AMY2A AMY2A AMY2A AMY2A AMY2A AMY2A AMY2A IOSE-121 MCC3 OVCAR5 SKOV3 OV-90 TOV21G IGROV HOSE Cells OC

Figure 3.2. Amylase AMY1 and AMY2B RNA are typically expressed in OC cell lines. Cellular RNA was collected from IOSE and OC cells and analyzed by qPCR to determine AMY1, AMY2A, and AMY2B message expression. Data are expressed as the average ratio of amylase isozyme expression to control GAPDH mRNA from triplicate samples.

0.004 0.0035 Amylase messenger level in other cancer types 0.003 0.0025 0.002 0.0015 0.001 0.0005

0

Ratio of AMY/GAPDH Ratio AMY/GAPDH of mRNA

AMY1 AMY1 AMY1 AMY1 AMY1 AMY1 AMY1 AMY1 AMY1 AMY1 AMY1 AMY1 AMY1

AMY2B AMY2B AMY2B AMY2B AMY2B AMY2B AMY2B AMY2B AMY2B AMY2B AMY2B AMY2B AMY2B

AMY2A AMY2A AMY2A AMY2A AMY2A AMY2A AMY2A AMY2A AMY2A AMY2A AMY2A AMY2A AMY2A HDF H2009 H69 WIDR SW626 MDA231 MCF7 HN5A U373MG HELA OV2008 C13 PANC1 Fibroblast Lung Ca Colorectal Ca Breast Ca H&N Ca Glioma Cervical Pancreatic Ca

Figure 3.3. Most non-OC cell types express AMY1 and AMY2B RNA. Cellular RNA was collected from HDF and representative cancer cells and analyzed by qPCR to determine AMY1, AMY2A, and AMY2B message expression. Data are expressed as the average ratio of amylase isozyme expression to control GAPDH mRNA from triplicate samples. At the protein level, all OC cell lines examined, TOV21G (8x for AMY1 and 4x for AMY2B),

OV-90 (13x for AMY1 and 8x for AMY2B), OVCAR5 (10x for AMY1 and 20x for AMY2B),

SKOV3 (5x for AMY1 and 16x for AMY2B) and IGROV (3x for AMY1 and 9x for AMY2B),

88 expressed 3x-20x more amylase AMY1 and AMY2B protein relative to IOSE-121 and IOSE-144 cells (Figure 3.4, Table 3.1). With the exception of U373MG, MDA231 and H2009 cells, cells representing other cancer types, also expressed more AMY1 and/or AMY2B protein relative to

HDF. That is, breast cancer MCF7 (7x), colorectal cancer WIDR (2x) and SW626 (12x), pancreatic cancer PANC1 (9x) and cervical cancer Hela (3x) overexpress AMY1; colon cancer cell line

SW626 (114x), and pancreatic cancer cell line PANC1 (137x) cells overexpress AMY2A. Breast cancer cell line MCF7 (214x), colorectal cancer cell lines WIDR (74x) and SW626 (475x), glioblastoma cell line U373MG (20x), pancreatic cancer cell line PANC1 (412x), and cervical cancer cell line HELA (200x) overexpressed AMY2B (Figure 3.4, Table 3.1). Overall, protein levels of the amylase isozymes were greater in cancer cell lines than normal cells and the amylase isozymes most typically expressed are AMY2B and AMY1. Analysis of amylase at the protein level showed that AMY1 and AMY2B are consistently elevated in OC cells, whereas, other cancer types don’t show consistent amylase overexpression.

144

121

- - 90 90

-

IOSE IOSE TOV21G OV OVCAR5 SKOV3 IGROV

HDF MDA231 MCF7 WIDR SW626 U373MG H2009 PANC1 HELA

AMY1A

AMY2A

AMY2B

ACTIN

Figure 3.4: AMY1 and AMY2B protein are overexpressed in OC cell lines (Continued on Next Page)

89

Densitometry of amylase in OSE and OC cells 2 1.8 AMY1 AMY2A AMY2B 1.6 1.4 1.2 1 0.8 0.6

0.4 Ratio of amylase to actin to amylaseof Ratio 0.2 0 IOSE-121 IOSE-144 TOV21G OV-90 OVCAR5 SKOV3 IGROV

0.9 Densitometry of amylase in other cancer types 0.8 AMY1 AMY2A AMY2B 0.7 0.6 0.5 0.4 0.3

Ratio of amylase to actin to amylaseof Ratio 0.2 0.1 0 HDF MDA231 MCF7 WIDR SW626 U373MG H2009 PANC1 HELA

Figure 3.4. AMY1 and AMY2B protein are overexpressed in OC cell lines. HDF, IOSE and a panel of cancer cells were subjected to western blot to determine the expression of amylase isozymes at the protein level (upper panel). Densitometry analysis of was used to determine the intensity ratio of amylase isozyme band signal to respective loading control actin bands and are shown in corresponding lower panels.

90

Table 3.1: AMY1 and AMY2B are typically overexpressed in cancer cells Cancer type Cell line Amylase isozyme overexpressed Ovarian TOV21G AMY2B Ovarian OV-90 AMY1, AMY2A and AMY2B Ovarian OVCAR5 AMY2B Ovarian IGROV AMY2B Breast MCF7 AMY1 and AMY2B Colorectal SW626 AMY1 and AMY2B Glioblastoma U373MG AMY2A Lung H2009 AMY1 and AMY2A Pancreas Panc1 AMY1 and AMY2B Cervical Hela AMY1, AMY2A and AMY2B

Amylase secreted by OC cells is metabolically active.

To determine the proportion of amylase that remains in cell lysates and the proportion that is secreted into the media, cytoplasmic lysates from IOSE-121, IOSE-144, OVCAR5 and SKOV3 cells and their respective CCM of equivalent 104 cells were subjected to an amylase ELISA.

Normal cells IOSE-121 (total 3 ng/ml) and IOSE-144 (total 2.26 ng/ml) produced negligible amounts of amylase protein in cell lysate and CCM, whereas OVCAR5 (total 47.9 ng/ml) and

SKOV3 (total 8.9 ng/ml) produced 3x to 16x more amylase than normal cells with the majority of amylase produced by OC cells secreted into their conditioned medium (Figure 3.5)

91

60

50 *

40 Cell lysate CCM

30

20 ng/ml amylase protein amylase ng/ml

10 *

0 IOSE-122 IOSE-144 OVCAR5 SKOV3

Figure 3.5. OC cells produce more amylase than normal cells. Cell lysates and their CCM were subjected to amylase elisa to determine the amounts of amylase in cell lysates and secreted into the cell media. Data are expressed as the average of triplicates ± standard deviation (*P < 0.05). To determine if secreted amylase was metabolically active, CCM from a panel of cancer cell lines was subjected to the amylase activity assay. All cancer cell lines secreted active amylase ranging from 0.7 mU/ml to 10 mU/ml. Cervical cancer cell lines secreted active amylase ranging from 0.7 mU/ml to 10.5 mU/ml, averaging 4.9 mU/ml. Lung cancer cell lines secreted active amylase ranging from 2.6 mU/ml to 3.7 mU/ml, averaging 3.2 mU/ml (Figure 3.6). Levels of amylase activity by IOSE cells ranged from 1.5 to 3.7, averaging 2.4 mU/ml. OC cells secreted metabolically active amylase to a greater extent than IOSE cells, ranging from 5.1 mU/ml to 34.2 mU/ml, averaging 11.2 mU/ml so that OC cells secreted up to 4x more active amylase compared to normal OSE cells (Figure 3.6). Interestingly, only the glioblastoma (U373MG) and a single cervical cancer cell line (C13) secreted greater amounts of metabolically active amylase (6.5 &

10.5 mU/ML) than IOSE cells. All other remaining cancer cell lines examined secreted amounts of metabolically active amylase within a similar range seen in IOSE cells.

92

40 OC 35

30

25

20

) amylase )

7 - 15 Cervical 10 cancer

mU/ml (*10 mU/ml Normal HOSE Lung 5 cancer

0

Figure 3.6. OC cells secrete more metabolically active amylase than IOSE cells. CCM, equivalent to 104 cells, was subjected to the amylase activity assay. Results are expressed as the average amylase activity in mU/ml, normalized for cell number from triplicate samples ± SE.

Inhibiting amylase decreases OC invasion.

To begin to understand how amylase promotes OC progression, I investigated whether abrogation of amylase affected the invasive capacity of OC cells using collagen coated Boyden chambers. Amylase knockdown was performed using pooled amylase siRNAs targeting all amylase isozymes. Amylase knockdown was validated by qPCR for amylase mRNA levels

(Figure 3.7A). Compared to untreated cells, siAMY abrogated AMY1 and AMY2B RNA levels in OVCAR5 cells up to 3.7x and 7.8x, respectively. While scramble RNA inhibited AMY2B to a lesser extent than siAMY, it did not alter AMY1 expression. As determined by parallel GFP signals, transfection efficiency in OVCAR5 and IOSE 121 was determined to be 80% and 76%, respectively. Amylase knockdown reduced OC cell invasion especially in SiAMY-treated OC

93 cell line OVCAR5 by 32% (Figure 3.7B). Scramble RNA did not alter invasive capacities of

OC cells. Since IOSE121 cells have little amylase expression, reduction of their amylase expression did not significantly affect IOSE121 invasion (Figure 3.7). After transfection, cell numbers did not change, therefore, amylase does not affect cell proliferation. Cells were counted after transfection with scramble RNA and amylase SiRNA for 48 hours and an insignificant percent change ranging from 0.5% to 5.3% was noted.

The glycocalyx of OC cells is thicker than the glycocalyx of IOSE cells:

Given that amylase appears to mediate OC cell invasion and that amylase is an extracellular metabolically active enzyme, I proceeded to examine whether amylase could modulate the

ECM/tumor microenvironment. To begin, IOSE121 cells and OC cells were examined by TEM to determine the thickness of their respective glycocalyces. As shown in Figure 3.8, the average glycocalyx thickness measured from 10 fields of IOSE-121, IGROV, and CAOV3, OVCAR5 and

SKOV3 was 83.7 nm, 207.4 nm, 264.8 nm, 257.2 nm, and 263 nm, respectively. The glycocalyces of OC cells, then, were 2.4x to 3.2x thicker than the glycocalyx of IOSE121 cells (Figure 3.8).

In order to localize amylase to the OC cell surface and/or its immediate ECM, immunogold labeling for amylase was performed. Immunogold labeling illustrated the presence of anti-amylase gold particles in OVCAR5 cells (Figure 3.9). Immunogold particles appeared localized at the external cell membrane and within the immediate tumor microenvironment. Control sections failed to demonstrated presence of immunogold particles.

94

A 0.0006 0.0005

0.0004

0.0003

0.0002

0.0001

0

Ratio of amylase/GAPDH mRNA amylase/GAPDHof Ratio

AMY1 AMY1 AMY1 AMY1 AMY1 AMY1

AMY2B AMY2B AMY2B AMY2B AMY2B AMY2B

AMY2A AMY2A AMY2A AMY2A AMY2A AMY2A Untreated scramble RNA Amylase Untreated scramble RNA Amylase SiRNA SiRNA IOSE-121 OVCAR5

B 40000 Abrogating amylase reduces invasion 35000 30000 25000 * 20000 15000 10000 5000

0 Invasion (relative fluorescence) (relative Invasion Untreated Scramble RNA Amylase Untreated Scramble RNA Amylase SiRNA SiRNA IOSE-121 OVCAR5

Figure 3.7. Abrogation of amylase reduces invasion in OC cells. A) IOSE121 and OVCAR5 cells were treated with siRNA targeting amylase and scramble RNA as control. Verification of siRNA efficiency was validated by qPCR. B) Treated cells were then seeded into invasion assay chambers and number of invasive cells were evaluated after 24-hour incubation. Results are expressed as the average relative fluorescence (a measure of the number of invasive cells) from triplicate samples ± SE (*P < 0.05).

95

fication

magni x 120K

8Kx

263

15Kx

257.2

OVCAR5 SKOV3

6Kx

ructural level (upper panel) and respective respective and panel) (upper level ructural

264.8 264.8

measured from 10 fields from measured

CAOV3

10Kx

207.4

x thickness (nm) xthickness

IGROV

y

l

5Kx

121

-

83.7 83.7

Average glycoca Average

OSE

Figure Figure 3.8. The glycocalyx of OC cells is thicker than IOSE cells. IOSE and examined OC cells were by TEM ultrast the at morphology OC and IOSE photographs for visualization depicted as panel). lines with glycocalyces (lower of their respective glycocalyces. Representative

96

Figure 3.9 Amylase can be localized to the OC cells glycocalyx/cell surface. OVCAR5 cells were examined by TEM following immunogold labeling with anti-AMY antibodies (upper panel) or serum controls (lower panel).

97

Inhibiting amylase increases GAG production.

In order to better understand how extracellular amylase could promote OC cell invasion,

IOSE-121 and OVCAR5 cells were treated with scramble RNA and siRNA targeting amylase and subjected to the Blyscan Sulfated Glycosaminoglycan Assay to measure sulfated glycan

(proteoglycans and glycosaminoglycans) content at the same time as my Boyden chamber experiments. Successful validation of transfection is shown in Figure 3.7A. Abrogating amylase in IOSE-121 cells slightly increased cellular GAG content over untreated IOSE-121 cells from

0.11 ug GAG to 0.33 ug GAG. In contrast, abrogating amylase increased cellular GAG levels in

OVCAR5 over untreated or scramble RNA treated cells from 1.382 ug to 3.787 ug (Figure 3.10).

4 sGAG increases after abrogating amylase expression * 3.5 Cell lysate 3

2.5

2

Totalug sGAG 1.5

1

0.5

0 Control Scramble RNA Amylase SiRNA Control Scramble RNA Amylase SiRNA IOSE-121 OVCAR5

Figure 3.10 Amylase promotes GAG digestion. IOSE 121 and OVCAR5 cells were treated with scramble RNA and amylase siRNA and subjected to the sulfated glycosaminoglycan assay. Results are expressed as the total ug of GAGs from triplicate samples ± SE (*P < 0.05).

98

Discussion:

Amylase (originally known as ptyalin) was discovered in 1831 [347] and has been extensively studied since its isolation in 1862 from pancreatic [348]. Since Weiss et al.

(1950s) reported a case of an amylase-producing tumor, multiple cases of cancer-associated hyperamylasemia have been reported although hyperamylasemia appears limited to few types of cancers [273] Nonetheless, hyperamylasemia has been strongly associated with lung cancer, ovarian cancer, multiple myeloma and pheochromocytoma cancers [349]. Hyperamylasemia is typically asymptomatic and its cause, isozyme profile and contribution to cancer progression are still unknown. Salivary amylase (AMY1) and pancreatic amylase (AMY2B) have both been proposed to be overexpressed in cancer. AMY2A has also been proposed to be a tumor suppressor gene; mutation, deletion, or silencing of AMY2A may lead to malignant transformation in gastric cancer [350]. Since OC remains the most lethal gynecologic cancer, studies on the contribution of hyperamylasemia to OC are warranted, but require the establishment of an appropriate model system.

Potentially owing to differences in reproductive physiology, humans are the only animals in which OC occurs with regular frequency. Laying hens are the only animals that spontaneously develop OC, therefore, a laying hen model of OC was characterized to determine its validity as an in vivo model of OC [351]. Laying hens spontaneously developed serous, endometrioid, mucinous and clear cell ovarian carcinomas with staging, histology and metastatic characteristics similar to human OC conditions. However, laying hens only have one ovary, making staging ovarian cancer more challenging in hens. Also, lymph nodes in chickens are not well organized so OC metastasis in laying hens is not reflective of OC in humans [351]. Rabbit models for papillogenesis may be informative for the very early stages of ovarian tumor growth, but these benign conditions

99 indicative of hyper-proliferative events do not progress to full malignant transformation [352].

Current mouse models of OC are also of limited use because they either employ intraperitoneal

(IP) or subcutaneous xenografts or are of genetic backgrounds that do not produce ovarian tumors that histologically reflect serous adenocarcinoma of the human ovary. Of note, the most common mouse models of OC employ xenograft model types which are effective in mimicking different aspects of OC growth and metastasis. However, xenograft models do not reflect the initial transformation events that lead to OC tumorigenesis. Genetically engineered mouse models

(GEMM) are important for investigating early changes that lead to OC; however, GEMMs are complicated to generate due to lack of knowledge on OC etiology [353]. Therefore, I chose to use an in vitro model system using established human cells lines to study the contribution of amylase to OC progression. While a number of OC cell lines have been established and many are commercially available, an important advantage for using an in vitro model for studies on the role of amylase for OC progression is that normal control cell lines derived from pure human ovarian surface epithelium and transfected with SV-40 large T antigen for enhanced lifespan are also available. As a result, studies using IOSE cell lines have shown the capacity of OSE cells to produce, lyse and reconstruct extracellular matrix components [344,345,354] and a demonstration that OSE has the capacity to undergo epithelial-mesenchymal conversions [355].

As predicted in chapter 2, using cell lines devoid of yeast contamination, I found increased protein levels of amylase AMY1 and AMY2B in OC cell lines. In addition, since the majority of amylase produced by OC cells was both secreted and metabolically active, so these cell lines represent a valid model system to pursue studies on the biological and molecular contribution of amylase to OC disease.

100

The glycocalyx is the outermost layer of the cell composed of a negatively charged web of enzymes, proteins, and glycoconjugates—one or more glycan units covalently bound to non- carbohydrate units. Glycoconjugates include glycosaminoglycans (GAGs), , glycolipids, and proteoglycans [356]. The glycocalyx is associated with many functions including mediating cell-to-cell and cell-to-matrix interactions [357]. It also mediates ligand-receptor interactions and other biological processes including specific recognition events. During malignant transformation, atypical glycocalyx component expression and structure influences tumor progression [356] by enhancing tumor growth, adhesion, motility, and invasion [358]. The glycocalyx is also proposed to facilitate cancer progression [356] and cancer metastasis [359].

There is a correlation between glycoconjugates present at the cell membrane and the metastatic potential of a cell [360]. A denser glycocalyx may promote more receptor interaction and lead to more adhesion and migration of tumor cells [358]. It is also believed that the bulky glycocalyx of malignant cells may serve a drug-resistant function by acting as a barrier against therapeutic agents

[358]. My survey of OC cell lines indicated that cancer cell lines have a bulkier glycocalyx compared to normal IOSE cells.

Quintarelli et al. found that amylase degraded the glycan moieties that crosslink collagen fibrils, therefore disassociating collagen fibrils and allowing for collagen degradation [361,362].

Inhibition of amylase decreased the ability of OC cells to invade collagen coated Boyden chambers, thereby identifying a functional behavior of amylase to promote OC progression.

Amylase secreted by OC cells may, therefore, degrade the glycosylation links that play a role in linking collagen fibrils to facilitate OC cell invasion. The role of amylase in the glycocalyx is further supported by the presence of amylase in the glycocalyx and ECM as indicated with immunogold studies. Amylase in the glycocalyx and ECM suggests that amylase may modify the

101

ECM to promote invasion. Amylase, which cleaves alpha 1, 4-glycosidic bonds in polysaccharides to initiate carbohydrate metabolism and contribute to energy production, can also play a role in

ECM remodeling by cleaving polysaccharide moieties in the tumor microenvironment. Amylase has already been shown to degrade connective tissue in bacterial biofilm in wounds [228].

In summary, to test the hypothesis that secreted amylase may modify the cancer cell glycocalyx by digesting α1, 4-glycosidic bonds [235] found in glycosaminoglycans, which in turn leads to altered downstream signaling that promotes proliferation, survival or migration/invasion, an in vitro model to explore the contribution of hyperamylasemia to OC was established by analyzing amylase message and protein levels from a panel of established cell lines. The activity of amylase in cell lysates and conditioned concentrated media was also investigated. Lastly, to begin to explore the contribution of hyperamylasemia to OC, the in-vitro model established was used to determine how amylase contributes to OC cell invasion.

102

Chapter 4

Regulation of Amylase by Spirulina

Introduction

Using an in vitro model system, I have been able to demonstrate that elevated levels of amylase in OC cells lines, representing hyperamylasemia, may drive disease progression, at least in part, by promoting cellular invasion related to alterations in the tumor microenvironment

(Chapter 3). Amylase, then, may serve as a novel target for therapeutic intervention in OC patients with hyperamylasemia. However, commercially available amylase inhibitors typically inhibit amylase activity at the protein level and produce a variety of adverse gastrointestinal side effects such as flatulence, abdominal distension, and diarrhea [363]. Therefore, there is a need for the development of new amylase inhibitors. Spirulina, a dietary supplement, has recently gained popularity with many health claims including that it enhances immunity, controls high blood pressure and serum cholesterol levels and has anti-tumorigenic, anti-inflammatory and anti- oxidant properties [364,365]. Spirulina is also associated with neuro-protective properties, as has been shown by reduced ischemic brain damage in rats, improved post-stroke locomotor activity and reduced levels of pro-inflammatory cytokines in the brain [366–368].

There are several species of the microalgae spirulina: Spirulina platensis, Spirulina fusiforme, and Spirulina maxima. Spirulina has been used as a food source for centuries; with the 103 earliest recorded human consumption of spirulina by the Chadiansa of Africa and Aztecs of

Mexico [369]. Spirulina is a good nutritional source because of its high protein content; 60-70% and 5-10% of spirulina’s dry weight is protein and lipid, respectively. Further, the protein in spirulina is complete, meaning it contains all the essential amino acids. Spirulina also contains many essential fatty acids (gamma-linolenic, linoleic and oleic acids) and, lastly, spirulina contains high levels of , beta-carotene, phycocyanin, superoxide dismutase, iron, calcium and phosphorous [369,370].

In this chapter, I propose employing spirulina or phycocyanin, one of the active ingredients in spirulina with high anti-inflammatory potential [371], as novel transcriptional inhibitors of amylase. By determining whether spirulina can inhibit OC cell invasion without cytotoxicity to normal cells, a novel clinical use for spirulina will be established.

Materials and Methods

Tissue Culture

The following human cell lines were used: normal human dermal fibroblasts HDF; SV 40-

Large T-Antigen transfected human ovarian surface epithelial (IOSE) cell lines MCC3, IOSE-80,

IOSE-121, and IOSE-144 [344,345]; OC cell lines CaOV3, OVCAR5, OV-90, SKOV3, TOV21G and IGROV; colorectal cancer cell lines WiDr and SW626; breast cancer cell lines MDA-MB-

231, MDA-MB-231, MCF7; head and neck cancer cell line HN5α [346]; cervical cancer cell line

Hela; pancreatic cancer cell line Panc1; lung cancer cell lines NCI-H2009 and H69; and glioblastoma U373MG cells. Cells were cultured in Medium 199/MCDB 105 (Sigma, St. Louis,

MO) with 5% fetal bovine serum (FBS) and gentamicin and incubated at 37°C with 5% CO2.

Conditioned media was concentrated (CCM) using Centriprep Centrifugal Filter Devices (Ultracel

104

YM-3 Cat.4302 Merck Millipore Ltd, Tullagreen, Ireland). Cell lines were treated with ± 0-150 ug/ml spirulina (kindly provided by Dr. P Bickford, USF) or 1-100 ug/ml phycocyanin (Catalog number: P2172-25MG; Sigma Aldrich; St. Louis, MO) for 0-72 h.

Microarray

HN5α cells were treated for 24h ± 100ug/ml spirulina, RNA collected, reverse transcribed, amplified and labeled with biotin at the H. Lee Moffitt Cancer Center Microarray core facility.

Biotin-labeled DNA was applied to Human Genome U133A Affymetrix chips and analyzed using

Affymetrix Microarray MAS 5.0 software to identify increased and decreased gene expression at p < 0.005.

Quantitative PCR

Cells were treated with TRIzol reagent from Invitrogen (Carlsbad, CA) and ribonucleic acid

(RNA) was isolated according to the manufacturer’s instructions. To generate single-stranded complementary DNA (cDNA), the Applied Biosystems GeneAmp RNA polymerase chain reaction (PCR) Core Kit (Foster City, CA) was used with 3 ug/mL total RNA using a Biometra

UNO-thermo-block and Perkin-Elmer-GeneAmp PCR system 9600. qPCR was carried out with

SYBR Green Universal Master Mix (Applied Biosystems), cDNA, and primers. Primers used were

AMY1A (GeneCopoeia: Hs-QRP-21480), AMY2A (GeneCopoeia: Hs-QRP-21450), AMY2B

(GeneCopoeia: Hs-QRP-21451), and GAPDH (GeneCopoeia: Hs-QRP-20169) as control.

Sequences for the amylase primers remain the proprietary property of GeneCopoeia. Amplification was performed with 40 cycles of denaturation (95°C, 10 sec), annealing (60°C, 20 sec), and extension (72°C, 15 sec) using a Bio-RAD Chromo4 Real Time PCR Detector using Opticon

Monitor 3 program. Levels of amylase was normalized to the GAPDH message values and the fold difference was determined by dividing the threshold cycle (Ct) value by the reference sample.

105

Western blot

Cells were washed in PBS, trypsinized, pelleted, rewashed with PBS and counted. Cells were lysed in CHAPS buffer for 30 minutes on ice before being centrifuged at 115,000 x g, at 4°C for

1h. Twenty ug of protein were separated by a 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Proteins were transferred to Polyvinylidene fluoride (PVDF) membranes, washed in Tween 20-Tris buffered Saline (TBST), and blocked in 5% milk in TBST or PBST. Blots were incubated in their respective primary antibodies overnight, followed by incubation with a horseradish peroxidase (HRP)-conjugated secondary antibody (Fisher,

Pittsburgh, PA), and developed via FEMTO Cat. 34095 Life Technologies (Grand Island, NY).

Antibodies used were: AMY1A (1:1000) Cat.H00000276-M04 Abnova (Taipei City, Taiwan);

AMY2A (1:500) 15845-1-AP Proteintech (Chicago, IL); AMY2B (1:2000) Cat. LS-C173958

LSBio (Seattle, WA); Beta Actin Loading Control Antibody (BA3R) (1:10,000) Cat. # MA5-

15739 Thermo Scientific (Rockford, IL). Densitometry was performed using ImageJ software to normalize amylase band strength to their respective actin band.

Amylase ELISA

To measure amylase protein levels, 100ul of cytoplasmic lysate and CCM of equivalent cell number (104/well) were assayed in triplicate using the quantitative double sandwich enzyme- linked immunosorbant assay (Amy-p ELISA Kit; catalog number: MBS264855; MyBioSource,

San Diego, CA) according to the manufacturer's instructions. Absorbance was read on a Microplate

Reader BioTek ELx800 (Winooski, Vermont) using Gen5 Data Analysis Software (Biotek,

Winooski, Vermont). Resultant values were derived from a standard curve and expressed as the mean amylase concentration of triplicate samples.

106

Invasion assay

Spirulina treated cells were incubated on type I collagen coated transwells (CytoSelect 96-

Well Cell Invasion Assay, catalog number: CBA-112-COL (San Diego, CA)) for 24 hours.

Following incubation, transwell filters were stained with a 0.1% crystal violet solution and photographed using an Olympus DP20 digital camera (Burnaby, BC, Canada) under a Leica

DMIRE2 microscope. The number of cells that had migrated through the collagen cell and across the transwell membrane was then counted.

MTS assay

Cell growth and cytotoxicity were determined by the MTS assay (Promega, Madison, WI) according to the manufacturer's directions. Cells (1 × 103) were seeded in 96-well plates and grown in Medium 199/MCDB 105 (Sigma, St. Louis, MO) containing 0.1% FBS. Cells were treated with varying concentrations of spirulina (0ug/ml, 50ug/ml, 100ug/ml, 150ug/ml). Absorbance was read nm using a multi-well plate reader (BioTek, Winooski, VT, USA) every 24 hours for 72 hours.

The samples were assayed in two separate experiments, each in triplicate and the results were expressed as the mean ± standard error.

Statistical Analyses

For real time PCR, error bars illustrate RQmin and RQmax, which were calculated as: RQave divided by (standard deviation^ student’s t value at the 95% confidence interval, for 5 degrees freedom) and RQave times (standard deviation^ student’s t value at the 95% confidence interval, for 5 degrees of freedom), respectively. This range represents the 95% confidence level. For amylase activity assays, ELISA, MTS, and invasion student’s t test was performed to assess statistical difference between means of triplicates ± standard error.

107

Results Amylase is a downstream target of spirulina.

In order to screen for downstream targets of spirulina, an Affymetrix microarray was performed on spirulina treated HN5α cells. The results indicated that spirulina downregulated all amylase isozyme expression at least 2.5 fold. Spirulina also suppressed expression of multiple transcription factors predicted to regulate amylase gene expression (Chapter 2) including C/EBP alpha, C/EBP delta, Zic1 and POU1F1 (Table 4.1).

Table 4.1: Spirulina transcriptionally targets amylase Gene Fold change AMY1A, AMY1B, AMY1C, AMY2A, AMY2B, ACTG1P4, AMYP1 -2.62

AMY1A, AMY1B, AMY1C, AMY2A, AMY2B -2.5 AMY1A, AMY1B, AMY1C, AMY2A, AMY2B, ACTG1P4, AMYP1 -2.5 CCAAT/enhancer binding protein (C/EBP), alpha -1.18

CCAAT/enhancer binding protein (C/EBP), delta -1.15 Zic1 -1.05 POU1F1 -1.19

Spirulina downregulates amylase mRNA expression in OC cell lines.

To confirm that spirulina inhibits amylase expression, a panel of cancer cell lines was treated ± 100ug/ml spirulina and amylase expression was measured using qPCR. While spirulina downregulated amylase expression in normal human dermal fibroblasts between 43% (AMY2B) to 95% (AMY2A) among the amylase isozymes, the pattern of spirulina-mediated inhibition of amylase expression varied among cancer cell types (Figure 4.1).

Spirulina significantly downregulated the expression of all amylase isozymes by 46% to

99% in colon cancer cells (WIDR and SW626) and by 57% to 77% in breast cancer cells MDA231, but it actually increased amylase isozyme expression by 1302% for AMY1, 79418% for AMY2A,

108 and 588% for AMY2B in MCF7 breast cancer cells. Similarly, spirulina increased amylase isozyme expression by 511% to 1875% in glioblastoma cells U373MG. Spirulina also downregulated amylase isozyme expression by approximately 80% in cervical cancer cells Hela.

Spirulina downregulated AMY1 (43%) and AMY2A (82%) expression in pancreatic cancer cells

PANC1, but increased AMY2B (200%) expression. In addition, spirulina downregulated at least two amylase isozymes in lung cancer cells (H2009) by 54%.

In contrast, spirulina consistently downregulated amylase expression in amylase positive OC cells with spirulina-mediated inhibition of AMY1 and AMY2B, ranging from 6% to 99% (Figure

4.2). However, spirulina did not significantly decrease small endogenous levels of amylase expression in IOSE-121 (Figure 4.2).

Spirulina downregulates amylase protein expression in OC cell lines.

Since RNA levels do not always accurately reflect target protein levels, to determine if spirulina-mediated inhibition of amylase could be detected at the protein level, cells were treated with 100ug/ml spirulina for 24 hours and protein amylase isozyme expression was determined using western blot. While there was no significant change in amylase protein levels in IOSE-121 cells after spirulina treatment (Figure 4.3), spirulina reduced AMY1 protein levels in OVCAR5 and OV-90 cells by 23% and 26%, respectively; spirulina reduced AMY2A protein levels in

OVCAR5 and OV-90 cells by 51% and77%, respectively and spirulina reduced AMY2B protein levels in OVCAR5 and OV-90 cells by 25% and 43%, respectively.

109 to

ssed as the ratio of amylase isozymessed signal to the GAPDH. as of amylase ratio

Figure 4.1. Spirulina downregulates most amylase isozymes in cancer cells. Cells treated ± 100 ug/ml spirulina were subjected were spirulina ug/ml 100 ± treated Cells cells. cancer in isozymes amylase most downregulates Spirulina 4.1. Figure expression. expre isozyme are Data qPCR amylase to determine

110

0.001 0.0009 0.0008 0.0007 0.0006 0.0005 0.0004 0.0003 Ratio of AMY/GAPDH Ratio AMY/GAPDH of mRNA 0.0002 0.0001

0

AMY1 AMY1 AMY1 AMY1 AMY1 AMY1 AMY1 AMY1 AMY1 AMY1

AMY2B AMY2B AMY2B AMY2B AMY2B AMY2B AMY2B AMY2B AMY2B AMY2B

AMY2A AMY2A AMY2A AMY2A AMY2A AMY2A AMY2A AMY2A AMY2A AMY2A IOSE-121 IOSE-121 + OV-90 OV-90 + TOV21G TOV21G + IGROV IGROV + OVCAR5 OVCAR5 + 100ug/ml 100ug/ml 100ug/ml 100ug/ml SPIR spirulina spirulina spirulina spirulina

Figure 4.2. Spirulina transcriptionally downregulates amylase in OC cell lines. Cells treated ± 100 ug/ml spirulina treatment were subjected to qPCR to determine amylase isozyme expression. Data are expressed as the ratio of amylase isozyme signal to GAPDH.

AMY1 AMY2A AMY2B 1

0.9 0.8

0.7

0.6

121 0.5

121 121 + spirulina

-

-

90

90 + 90 spirulina -

- 0.4

0.3

OVCAR5 + spirulina OVCAR5 IOSE IOSE IOSE OVCAR5 OV OV 0.2 AMY1A 0.1 AMY2A 0 IOSE-121 IOSE-121 OVCAR5 OVCAR5 + OV-90 OV-90 + AMY2B + 100 100ug/ml 100ug/ml Ratio amylase/actin of protein ug/ml spirulina spirulina ACTIN spirulina

Figure 4.3. Spirulina reduces amylase protein levels in OC cell lines. Cells treated ± 100 ug/ml spirulina were subjected to western blot analysis to determine amylase isozyme protein expression (left panel). Densitometric analyses representing ratio of amylase isozyme band signal to respective actin loading control band signals are shown in the right panel.

111

Spirulina reduces amylase secretion by OC cell lines.

To determine if spirulina inhibited amylase secretion, cell lysates and CCM collected from

IOSE-121, IOSE-144, OVCAR5 and SKOV3 cells treated with 100ug/ml spirulina for 24 h and then subjected to an amylase ELISA (Figure 4.4). In agreement with figure 4.3, I found 12% reduction in cellular amylase in IOSE-121 and -144, respectively. In addition, levels of secreted amylase in IOSE-121 and -144 were reduced by 28% and 18% following spirulina treatment, respectively. Also in agreement with figure 4.3, I found 72% and 16% reduction in cellular amylase in OVCAR5 and SKOV3 cells, respectively. More notably, levels of secreted amylase in OVCAR5 and SKOV3 cells were reduced by 90% and 6% following spirulina treatment, respectively. 60

50

Cell lysate CCM 40

30

20 ng/ml amylase ng/ml

10 * 0 IOSE-122 IOSE-122 + IOSE-144 IOSE-144 + OVCAR5 OVCAR5 + SKOV3 SKOV3 + 100ug/ml 100 ug/ml 100 ug/ml 100 ug/ml spirulina spirulina spirulina spirulina

Figure 4.4. Spirulina reduces amylase secretion in OC cell lines. Cell lysates and CCM from cells treated ± spirulina were subjected to amylase ELISA to determine relative amounts of cellular and secreted amylase. Data are presented as the mean of triplicates ± SE (*P<0.05).

112

Spirulina decreases the invasive capacity of OC cells.

Using Boyden chamber assays, I found that spirulina did not significantly reduce invasive capacity in IOSE-121 cells (Figure 4.5). However, spirulina significantly reduced invasive capacity of OVCAR5 by 51% compared to untreated cells.

Spirulina invasion assay 120

100

80

60 *

40

20

0 Number of invasive cells invasive of Number untreated 100ug/ml untreated 100ug/ml spirulina spirulina IOSE-121 OVCAR5

Figure 4.5. Spirulina decreased invasive capacity in OC cells. IOSE-121 and OVCAR5 cells were treated ± 100 ug/ml spirulina and assayed in Boyden chambers. Data are presented as the mean of triplicates ± S.E. (*P<0.05).

Spirulina decreased migration of OVCAR5 cells.

To confirm the inhibitory effect of spirulina for OC cell motility, OVCAR5 cells were visualized in a scratch assay for 0-24 hours ± 100 ug/ml spirulina. Untreated cells migrated to fill the scratch gap within 24 h (Figure 4.6) However, spirulina-treated cells did not migrate as quickly and had only filled in 64% of their scratch gap at 24h.

113

Day 0 Day 1

OVCAR5 control

OVCAR5 + 100 ug/ml spirulina

Spirulina decreased migration in OVCAR5 cells 120 OVCAR5 control OVCAR5 + 100 ug/ml spirulina 100

80

60 Percent Percent closure 40

20

0 DAY 0 DAY 1 Time

Figure 4.6. Spirulina reduced migration in OVCAR5 cells. OVCAR5 cells were visualized in a scratch assay for 0-24h ± 100 ug/ml spirulina. Experiment was performed in triplicate. Representative scratch assay is illustrated in the upper panel while lower panel represents percent wound closure.

114

Spirulina does not alter OC proliferation.

To determine whether spirulina is potentially cytotoxic for normal or OC cells, IOSE and

OC cell survival and growth were measured by MTS assay for 0-72 hours when treated with a

range of concentrations of spirulina (Figure 4.7). Spirulina did not significantly alter or affect

proliferation of either OSE or OC cells.

0.6 0.4 IOSE-121 IOSE-144

0.3 0.4

0.2 0ug/mL 0ug/mL 0.2 Absorbance 50ug/mL 50ug/mL 0.1 Absorbance 100ug/mL 100ug/mL 150ug/mL 150ug/mL 0 0 0 24 48 72 0 24 48 72 Time (hrs) Time (hrs) 0.4 0.3 OVCAR5 OV-90

0.3 0.2

0.2

0ug/mL 0ug/mL Absorbance 50ug/mL Absorbance 0.1 50ug/mL 0.1 100ug/mL 100ug/mL 150ug/mL 150ug/mL 0 0 0 24 Time (hrs) 48 72 0 24 Time (hrs) 48 72

Figure 4.7. Spirulina did not alter IOSE or OC cell survival and proliferation. IOSE-121, IOSE- 144, OVCAR5 and OV-90 cells were treated with varying concentrations—0ug/ml, 50ug/ml, 100ug/ml, 150ug/ml— of spirulina and proliferation was measured every 24 hours for 72 hours. Data are presented as the mean of two biological experiments, each performed in triplicates ± SE.

115

Phycocyanin abrogates amylase RNA expression.

IOSE-121 and OVCAR5 cells were treated with spirulina or phycocyanin in order to determine if transcriptional downregulation of amylase by spirulina is due to one of its major active ingredients, phycocyanin. In agreement with my earlier data (Figure 4.2), spirulina failed to suppress amylase isozyme RNA expression in IOSE-121 (Figure 4.8), but phycocyanin increased

AMY1 and AMY2B RNA expression levels in a dose related manner. In contrast, but also in agreement with my earlier data (Figure 4.2), spirulina completely inhibited AMY1 and AMY2B

RNA levels in OVCAR cells. Likewise, phycocyanin completely abrogated AMY1 and AMY2B

RNA levels in OC cells.

0.001

0.0009

0.0008

0.0007

0.0006

0.0005

0.0004

0.0003

0.0002

0.0001

Ratio of amylase/GAPDH mRNA amylase/GAPDHof Ratio 0

AMY1 AMY1 AMY1 AMY1 AMY1 AMY1 AMY1 AMY1

AMY2B AMY2B AMY2B AMY2B AMY2B AMY2B AMY2B AMY2B

AMY2A AMY2A AMY2A AMY2A AMY2A AMY2A AMY2A AMY2A Control 100ug/ml 1ug/ml 100ug/ml Control 100ug/ml 1ug/ml 100ug/ml Spirulina phycocyanin phycocyanin Spirulina phycocyanin phycocyanin IOSE-121 OVCAR5

Figure 4.8. Phycocyanin abrogates amylase expression in OC cells. IOSE-121 and OVCAR5 cells were treated with 100 ug/ml spirulina, 1ug/ml phycocyanin or 100 ug/ml phycocyanin and subjected to qPCR for amylase isozyme RNA expression. Data are expressed as the ratio of amylase isozyme signal to GAPDH.

116

Spirulina-induced inhibition of OC invasion is driven, in part, by phycocyanin.

To determine whether spirulina-mediated inhibition of OC invasive capacity was driven by phycocyanin, using Boyden chamber assays, I found that neither spirulina nor phycocyanin significantly reduced invasive capacity in IOSE-121 cells (Figure 4.9). In agreement with my earlier data (Figure 4.5), spirulina reduced the invasive capacity of OVCAR5 cells by approximately 51%. However, phycocyanin was only able to reduce the invasive capacity of

OVCAR5 cells by approximately 32%, suggesting that spirulina-induced inhibition of OC invasion is driven only in part by phycocyanin.

120 Number of invasive cells

100

80 *

60 *

40

Number of invasive cells invasive of Number 20

0 untreated 100ug/ml 1ug/ml c- untreated 100ug/ml 1ug/ml c- spirulina phycocyanin spirulina phycocyanin

IOSE-121 OVCAR5

Figure 4.9. Spirulina driven inhibition of OC invasion is partly mediated by phycocyanin. IOSE-121 and OVCAR5 cells were treated with 100 ug/ml spirulina or 1ug/ml phycocyanin and assayed in collagen-coated Boyden chambers. Data are presented as the mean of triplicates (*P<0.05).

117

Discussion

Dietary supplements are widely recognized for their medicinal benefits. Many are or contain free radical scavengers that promote improved health through anti-inflammatory activity and clinically manifest as reduced incidence of disease, reduced damage associated with disease and/or accelerated healing from disease. For example, curcumin, an active component of the spice turmeric is widely used traditional Chinese herbal medicine. It has anti-cancer properties related to suppression of the NF-κB signaling pathway which inhibits cellular proliferation and metastasis

[372]. Further, treatment of endometrial carcinoma cells, HEC-1B, with curcumin results in decreased migration and invasion by reducing the expression and activity of MMP-2/9 via suppression of the ERK signaling pathway [372,373].

Likewise, the clinical benefits of spirulina have also been investigated with regards to its anti-oxidant, anti-inflammatory and anti-cancer properties. Several preclinical and clinical studies have investigated the hypolipidemic effects of spirulina, that is, its ability to reduce cholesterol by decreasing low-density lipoproteins (LDL), very low-density lipoproteins (HLDL), and triglycerides (TG) and increase protective high-density lipoproteins (HDL). The cumulative data indicates that spirulina has hypolipidemic activity in healthy young [374] and elderly volunteers, patients with type 2 diabetes [375], patients with nephrotic syndrome [376] and patients with ischaemic heart disease [377]. However, the exact molecular mechanism by which spirulina promotes its hypolipidemic effects has not yet been elucidated [364].

The antioxidant and anti-inflammatory effects of spirulina were examined in in-vitro, pre- clinical and some clinical studies. Spirulina can decrease the inflammatory reaction induced by toxic, oxidative and physical damaging agents [378,379]. Spirulina’s antioxidant and anti- inflammatory effects have been attributed to phycocyanin and β-carotene. Phycocyanin is an

118 antioxidant that can scavenge free radicals and lower lipid peroxidation [371]. Phycocyanin has also been reported to inhibit TNF alpha and nitrile production [380]. On the other hand, the anti- oxidant properties of β-carotene are related to its ability to decrease production of nitric oxide

(NO) and PGE(2), inducible NO synthase (iNOS), cyclooxygenase-2, TNF-alpha, and IL-1beta

[381].

Lastly, the anti-cancer properties of spirulina have also been studied. Spirulina and compounds isolated from spirulina, including phycocyanobilin and chlorophyllin, tetrapyrrolic, significantly decrease proliferation of human pancreatic cancer cells in xenotransplanted nude mice [382]. Spirulina has been reported to down-regulate inflammation and carcinogenesis induced by UVB irradiation in the skin [383]. Additionally, spirulina exhibited anti-proliferative activity with low cytotoxicity in five cancer cell lines – HepG2 (liver cancer cell line), MCF-7

(breast cancer cell line), SGC-7901 (gastric cancer cell line), A549 (non-small cell lung cancer), and HT-29 (colon colorectal cancer cell line) [384]. Finally, colon colorectal cells (Caco-2) treated with spirulina display decreased proliferation, migration and increased apoptosis that may be related to increased intracellular reactive oxygen species (ROS) and nitric oxide (NO) levels [385].

In contrast, there have only been two reports investigating the role of spirulina in OC and both studies were limited to the effect of phycocyanin in a single OC cell line. By targeting mitochondrial proteins, phycocyanin was reported to inhibit cellular proliferation and induce apoptosis by increased ROS production and activation of caspase-3, -8, and -9 in SKOV3 cells.

[386]. Ying et al. (2016) suggested that the anti-proliferative action of phycocyanin in SKOV3 cells could be mediated through 18 cellular pathways [387].

In the present study, I showed that amylase is a downstream target of spirulina. Spirulina consistently downregulated amylase isozyme expression at the message and protein levels. As a

119 result, spirulina, and to a lesser degree, phycocyanin appear to abrogate OC invasion by inhibiting amylase-mediated OC cell migration and invasion. Importantly, spirulina did not appear to adversely affect normal OSE cells. Given that the high mortality associated with OC is due, to a large extent, by its propensity to metastasize early, the subset of patients whose cancers overexpress amylase may benefit from adjuvant spirulina therapy to minimize burden of disease.

120

Chapter 5

Concluding Remarks

Although its etiology is poorly understood, OC accounts for 4% of all cancer cases and

4.2% of all cancer deaths worldwide. OC is the most lethal gynecological cancer because early symptoms of the disease are generally lacking. As a result, more than 70% of OC patients are diagnosed in later stages when the disease has already metastasized. Women diagnosed in late disease stage have a 5-year survival rate of less than 20% compared with approximately 90% survival for women diagnosed with early stage disease. The current gold standard for OC detection, serum CA-125, is only elevated in 50% of stage I OC patients so that CA-125 is more useful as a prognostic rather than a diagnostic marker [388]. Therefore, there remains an urgent need to better understand the cellular and molecular regulators that drive OC progression in order to reduce metastatic spread as well as identify novel diagnostic and therapeutic targets.

A number of potential OC biomarkers have been reported to date. However, their efficacy for disease detection or functional contribution to OC progression is limited. Therefore, I initiated my studies with a computational analysis of the 27 most commonly reported literature-derived ovarian cancer (LDOC) protein biomarkers. I found that LDOC protein biomarkers share many biochemical features including a preponderance for a stable protein structure (ordered, hydrophilic, aggregation-prone, glycosylated and sumoylated), the ability to be secreted (export signal peptide

121 sequence, hydrophilic) and functionality related to ECM modification, immune response and/or energy production. Subsequently, I analyzed the human proteome to identify additional proteins that also share these biochemical features. Of the 70,616 proteins in the human proteome, 683 proteins were found to have similar biochemical features to the 27 LDOC proteins. With further computational analyses I identified a subset of 21 potential additional protein regulators of ovarian cancer (APROC) sharing similar functional and biochemical protein profiles and interactivity with

LDOCs in the humane proteome.

Three of the APROCs identified were amylase (AMY) AMY1A, AMY2A, and AMY2B, which compromise the members of the human amylase gene family. Amylase cleaves alpha 1, 4- glycosidic bonds in polysaccharides to initiate carbohydrate metabolism, thereby contributing to energy production. Amylase is reportedly overexpressed in and secreted by ovarian tumors [262].

Hyperamylasemia has been studied as both a diagnostic and prognostic indicator even though its functional contribution to OC progression remains unknown [1]. Since amylase can degrade bacterial biofilms in wounds [228], it is possible that, by cleaving polysaccharide moieties in the tumor microenvironment, amylase also plays a role in ECM remodeling. Consequently, I sought to determine whether amylase could promote OC cell migration and invasion.

I initiated my studies on the role of amylase to drive OC invasion with additional computational studies focused on characterizing the different amylase isozymes in order to predict which amylase isozyme(s) is most likely overexpressed in and contributory to OC invasion. I found that there were significant differences among the isozymes. Specifically, AMY1 and AMY2B have unique regions of disorder, unique regions of aggregation and unique phosphorylation sites indicating that AMY1 and AMY2B would be more likely to interact with other proteins, to

122 aggregate and to be easily secreted. Using serum samples from patients with OC, I was able to validate AMY1 and AMY2B overexpression by western immunoblotting.

I then developed an in vitro model system to study the molecular contribution of amylase to OC using IOSE and OC cell lines. I showed that OC cells generally overexpress and secrete metabolically active amylase isozymes AMY1 and AMY2B. Abrogating amylase activity using siRNA silencing technology decreased the capacity of OC cells to invade collagen coated Boyden chambers and increased sGAG production. Since a survey of OC cell lines indicated that cancer cells have a bulkier glycocalyx compared to IOSE cells and immunogold labeling studies indicated the presence of amylase within the immediate OC microenvironment, my data suggest that, by cleaving alpha 1, 4-glycosidic bonds in glycoconjugates (glycosaminoglycans (GAGs), glycoproteins, glycolipids, and proteoglycans) present within ECM, amylase may remodel the

ECM to promote an invasive cancer phenotype.

Further studies are needed to validate the hypothesis that amylase may promote OC invasion by altering the glycocalyx/ECM architecture in OC. Assuming that amylase maintains its traditional enzymatic activity in the OC ECM, GAGs within the glycocalyx containing α-1, 4- glycosidic bonds should be susceptible to hydrolysis by amylase enzymatic activity. By cleaving

α-1, 4-glycosidic bonds in negatively charged repellant GAGs [356], amylase may lead to proteoglycan condensation. Consequently, this proteoglycan condensation could result in integrin clustering and dimerization, leading to an altered glycocalyx characteristic of malignant cells that is associated with an invasive phenotype (Figure 5.1). Weaver et al. described metastatic tumors which overexpress bulky glycan structures and glycoproteins that alter integrin organization by funneling active integrins together. Glycocalyx –mediated integrin clustering promote phenotypes associated with cancer [389,390].

123

Figure 5.1. Schematic of the possible influence of amylase in altering the glycocalyx of OC cell and consequently lead to a more malignant phenotype. Amylase may cleave α-1, 4- glycosidic bonds in OC cell glycocalyx which leads to proteoglycan condensation and integrin clustering. The resulting altered glycocalyx promotes a malignant phenotype.

Amylase could, then, be a target for therapeutic intervention in OC patients with hyperamylasemia. However, commercially available amylase inhibitors typically inhibit amylase activity at the protein level and produce a variety of adverse gastrointestinal side effects [363]. I established Spirulina, a dietary supplement, as a novel transcriptional inhibitor of amylase.

Spirulina inhibited amylase expression in OC cell lines at both the message and protein levels.

Spirulina reduced OC cell invasion and migration in vitro, putatively by decreasing amylase expression. Phycocyanin, one of the active ingredients of spirulina, was investigated in order to determine if it was responsible for the downregulation of amylase expression and reduced invasion induced by spirulina. I found that phycocyanin reduced amylase expression in and invasive

124 behavior of OC cells, but the degree to which phycocyanin reduced amylase-mediated activities was less than that of spirulina. Therefore, multiple components of spirulina are likely responsible for regulating amylase expression.

Clearly, further studies are warranted to determine the clinical impact of amylase in OC, and, potentially other cancer types. Since lung cancer is also often clinically associated with hyperamylasemia, it would be interesting to determine whether amylase similarly mediates cellular invasion in lung cancer cells. Given the lack of sensitive and specific biomarkers for OC, it might also be clinically impactful to examine the potential for serum amylase to serve as a biomarker of disease based on amylase isozyme subtype. Furthermore, spirulina may be an attractive and effective therapy/therapeutic adjuvant to reduce the migratory and invasive capabilities of OC cells leading to decreased disease burden. Going forward, it would be important to identify and isolate the specific components of Spirulina that transcriptionally inhibit amylase as well as examine other dietary supplements for their ability to inhibit amylase alone or in concert with Spirulina. Lastly,

I have shown the power of computational analyses to provide insight about the different molecular mechanisms employed by disease regulators with shared protein profiles. I would hope that future endeavors explore the biochemical features, metabolic pathways and protein-protein networks among the remaining APROC candidates to provide additional insights about the appearance of diverse histologic subtypes, the propensity for metastatic spread and the emergence of drug resistance in OC. This, in turn, could reduce the mortality of a disease that kills thousands of women annually.

125

Chapter 6

References

1. Srivastava R, Fraser C, Gentleman D, Jamieson LA, Murphy MJ. Hyperamylasaemia: not

the usual suspects. Bmj. 2005; 331(7521):890–891. doi:10.1136/bmj.331.7521.890.

2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA. Cancer J. Clin. 2015;

65(1):5–29. doi:10.3322/caac.21254.

3. Brucks JA. Ovarian cancer. The most lethal gynecologic malignancy. Nurs. Clin. North

Am. 1992; 27(4):835–845.

4. Brown DL, Andreotti RF, Lee SI, Dejesus Allison SO, Bennett GL, Dubinsky T, et al.

ACR appropriateness criteria(c) ovarian cancer screening. Ultrasound Q. 2010;

26(4):219–223. doi:10.1097/RUQ.0b013e3181fdd604.

5. Bast RCJ, Brewer M, Zou C, Hernandez MA, Daley M, Ozols R, et al. Prevention and

early detection of ovarian cancer: mission impossible? Recent results cancer Res.

Fortschritte der Krebsforsch. Prog. dans les Rech. sur le cancer. 2007; 174:91–100.

6. Stirling D, Evans DGR, Pichert G, Shenton A, Kirk EN, Rimmer S, et al. Screening for

familial ovarian cancer: failure of current protocols to detect ovarian cancer at an early

stage according to the international Federation of gynecology and obstetrics system. J.

Clin. Oncol. 2005; 23(24):5588–5596. doi:10.1200/JCO.2005.05.097.

7. Gaarenstroom KN, van der Hiel B, Tollenaar RAEM, Vink GR, Jansen FW, van Asperen

126

CJ, et al. Efficacy of screening women at high risk of hereditary ovarian cancer: results of

an 11-year cohort study. Int. J. Gynecol. Cancer. 2006; 16 Suppl 1:54–59.

doi:10.1111/j.1525-1438.2006.00480.x.

8. Bell DA. Origins and molecular pathology of ovarian cancer. Mod. Pathol. an Off. J.

United States Can. Acad. Pathol. Inc. 2005; 18 Suppl 2:S19-32.

doi:10.1038/modpathol.3800306.

9. Chene G, Penault-Llorca F, Le Bouedec G, Mishellany F, Dauplat M-M, Jaffeux P, et al.

Ovarian epithelial dysplasia and prophylactic oophorectomy for genetic risk. Int. J.

Gynecol. Cancer. 2009; 19(1):65–72. doi:10.1111/IGC.0b013e3181990127.

10. Dietl J. Revisiting the pathogenesis of ovarian cancer: the central role of the fallopian

tube. Arch. Gynecol. Obstet. 2014; 289(2):241–246. doi:10.1007/s00404-013-3041-3.

11. Auersperg N, Wong AS, Choi KC, Kang SK, Leung PC. Ovarian surface epithelium:

biology, endocrinology, and pathology. Endocr. Rev. 2001; 22(2):255–288.

doi:10.1210/edrv.22.2.0422.

12. Dietl J, Marzusch K. Ovarian surface epithelium and human ovarian cancer. Gynecol.

Obstet. Invest. 1993; 35(3):129–135.

13. Robbins SL, Kumar V. Robbins and Cotran Pathologic Basis of Disease. 8th ed.

Philadelphia, PA: Saunders/Elsevier; 2010.

14. Blaustein A. Peritoneal mesothelium and ovarian surface cells--shared characteristics. Int.

J. Gynecol. Pathol. 1984; 3(4):361–375.

15. Dubeau L. The cell of origin of ovarian epithelial tumours. Lancet. Oncol. 2008;

9(12):1191–1197. doi:10.1016/S1470-2045(08)70308-5.

16. Marquez RT, Baggerly KA, Patterson AP, Liu J, Broaddus R, Frumovitz M, et al. Patterns

127

of gene expression in different histotypes of epithelial ovarian cancer correlate with those

in normal fallopian tube, endometrium, and colon. Clin. Cancer Res. 2005; 11(17):6116–

6126. doi:10.1158/1078-0432.CCR-04-2509.

17. Kurman RJ, Ellenson LH, Ronnett BM, editors. Blaustein’s Pathology of the Female

Genital Tract. 6th ed. Springer US; 2011.

18. Shih I-M, Kurman RJ. Ovarian tumorigenesis: a proposed model based on morphological

and molecular genetic analysis. Am. J. Pathol. 2004; 164(5):1511–1518.

19. Kurman RJ, Shih I-M. The origin and pathogenesis of epithelial ovarian cancer: a

proposed unifying theory. Am. J. Surg. Pathol. 2010; 34(3):433–443.

doi:10.1097/PAS.0b013e3181cf3d79.

20. Auersperg N. The origin of ovarian cancers--hypotheses and controversies. Front. Biosci.

(Schol. Ed). 2013; 5:709–719.

21. Kobel M, Kalloger SE, Boyd N, McKinney S, Mehl E, Palmer C, et al. Ovarian carcinoma

subtypes are different diseases: implications for biomarker studies. PLoS Med. 2008;

5(12):e232. doi:10.1371/journal.pmed.0050232.

22. van Nagell JRJ, DePriest PD, Ueland FR, DeSimone CP, Cooper AL, McDonald JM, et

al. Ovarian cancer screening with annual transvaginal sonography: findings of 25,000

women screened. Cancer. 2007; 109(9):1887–1896. doi:10.1002/cncr.22594.

23. Siegel R, Ward E, Brawley O, Jemal A. Cancer statistics, 2011: the impact of eliminating

socioeconomic and racial disparities on premature cancer deaths. CA. Cancer J. Clin.

2011; 61(4):212–236. doi:10.3322/caac.20121.

24. Gloss BS, Samimi G. Epigenetic biomarkers in epithelial ovarian cancer. Cancer Lett.

2014; 342(2):257–263. doi:10.1016/j.canlet.2011.12.036.

128

25. Mills GB, Bast RCJ, Srivastava S. Future for ovarian cancer screening: novel markers

from emerging technologies of transcriptional profiling and proteomics. J. Natl. Cancer

Inst. 2001; 93(19):1437–1439.

26. Nolen BM, Lokshin AE. Protein biomarkers of ovarian cancer: the forest and the trees.

Future Oncol. [Internet]. 2012; 8(1):55–71. doi:10.2217/fon.11.135.

27. Scambia G, Testa U, Panici PB, Martucci R, Foti E, Petrini M, et al. Interleukin-6 serum

levels in patients with gynecological tumors. Int. J. cancer. 1994; 57(3):318–323.

28. Plante M, Rubin SC, Wong GY, Federici MG, Finstad CL, Gastl GA. Interleukin-6 level

in serum and ascites as a prognostic factor in patients with epithelial ovarian cancer.

Cancer. 1994; 73(7):1882–1888.

29. van der Zee AG, de Cuyper EM, Limburg PC, de Bruijn HW, Hollema H, Bijzet J, et al.

Higher levels of interleukin-6 in cystic fluids from patients with malignant versus benign

ovarian tumors correlate with decreased hemoglobin levels and increased platelet counts.

Cancer. 1995; 75(4):1004–1009.

30. Nowak M, Glowacka E, Szpakowski M, Szyllo K, Malinowski A, Kulig A, et al.

Proinflammatory and immunosuppressive serum, ascites and cyst fluid cytokines in

patients with early and advanced ovarian cancer and benign ovarian tumors. Neuro

Endocrinol. Lett. 2010; 31(3):375–383.

31. Rabinovich A, Medina L, Piura B, Segal S, Huleihel M. Regulation of ovarian carcinoma

SKOV-3 cell proliferation and secretion of MMPs by autocrine IL-6. Anticancer Res.

2007; 27(1A):267–272.

32. Macciò A, Madeddu C. The role of interleukin-6 in the evolution of ovarian cancer:

clinical and prognostic implications---a review. J. Mol. Med. [Internet]. 2013;

129

91(12):1355–1368. doi:10.1007/s00109-013-1080-7.

33. Wang Y, Niu XL, Qu Y, Wu J, Zhu YQ, Sun WJ, et al. Autocrine production of

interleukin-6 confers cisplatin and paclitaxel resistance in ovarian cancer cells. Cancer

Lett. 2010; 295(1):110–123. doi:10.1016/j.canlet.2010.02.019.

34. Wang Y, Li L, Guo X, Jin X, Sun W, Zhang X, et al. Interleukin-6 signaling regulates

anchorage-independent growth, proliferation, adhesion and invasion in human ovarian

cancer cells. Cytokine. 2012; 59(2):228–236. doi:10.1016/j.cyto.2012.04.020.

35. Xu L, Fidler IJ. : an autocrine growth factor for human ovarian cancer.

Oncol. Res. 2000; 12(2):97–106.

36. Wang Y, Xu RC, Zhang XL, Niu XL, Qu Y, Li LZ, et al. Interleukin-8 secretion by

ovarian cancer cells increases anchorage-independent growth, proliferation, angiogenic

potential, adhesion and invasion. Cytokine [Internet]. 2012 [cited 2016 Mar 23];

59(1):145–55. doi:10.1016/j.cyto.2012.04.013.

37. Dobrzycka B, Mackowiak-Matejczyk B, Terlikowska KM, Kulesza-Bronczyk B, Kinalski

M, Terlikowski SJ. Serum levels of IL-6, IL-8 and CRP as prognostic factors in epithelial

ovarian cancer. Eur. Cytokine Netw. 2013; 24(3):106–113. doi:10.1684/ecn.2013.0340.

38. Zhao S, Wu D, Wu P, Wang Z, Huang J. Serum IL-10 Predicts Worse Outcome in Cancer

Patients: A Meta-Analysis. PLoS One [Internet]. 2015; 10(10):e0139598. Available from:

http://dx.doi.org/10.1371%252Fjournal.pone.0139598.

39. Mustea A, Konsgen D, Braicu EI, Pirvulescu C, Sun P, Sofroni D, et al. Expression of IL-

10 in patients with ovarian carcinoma. Anticancer Res. 2006; 26(2C):1715–1718.

40. Reuwer AQ, Nowak-Sliwinska P, Mans LA, van der Loos CM, von der Thüsen JH,

Twickler MTB, et al. Functional consequences of prolactin signalling in endothelial cells:

130

a potential link with angiogenesis in pathophysiology? J. Cell. Mol. Med. [Internet]. 2012;

16(9):2035–2048. doi:10.1111/j.1582-4934.2011.01499.x.

41. da Silva PL, do Amaral VC, Gabrielli V, Montt Guevara MM, Mannella P, Baracat EC, et

al. Prolactin Promotes Breast Cancer Cell Migration through Actin Cytoskeleton

Remodeling. Front. Endocrinol. (Lausanne). 2015; 6:186. doi:10.3389/fendo.2015.00186.

42. Levina V V, Nolen B, Su Y, Godwin AK, Fishman D, Liu J, et al. Biological Significance

of Prolactin in Gynecologic Cancers. Cancer Res. [Internet]. 2009; 69(12):5226–5233.

doi:10.1158/0008-5472.CAN-08-4652.

43. Mor G, Visintin I, Lai Y, Zhao H, Schwartz P, Rutherford T, et al. Serum protein markers

for early detection of ovarian cancer. Proc. Natl. Acad. Sci. U. S. A. 2005; 102(21):7677–

7682. doi:10.1073/pnas.0502178102.

44. Tan D, Chen KE, Khoo T, Walker AM. Prolactin increases survival and migration of

ovarian cancer cells: importance of prolactin receptor type and therapeutic potential of

S179D and G129R receptor antagonists. Cancer Lett. [Internet]. 2011 [cited 2016 Mar

29]; 310(1):101–8. doi:10.1016/j.canlet.2011.06.014.

45. Tas F, Karabulut S, Serilmez M, Ciftci R, Duranyildiz D. Clinical significance of serum

transforming growth factor-beta 1 (TGF-$β$1) levels in patients with epithelial ovarian

cancer. Tumor Biol. [Internet]. 2013; 35(4):3611–3616. doi:10.1007/s13277-013-1476-6.

46. Pasche B. Role of transforming growth factor beta in cancer. J. Cell. Physiol. 2001;

186(2):153–168. doi:10.1002/1097-4652(200002)186:2<153::AID-JCP1016>3.0.CO;2-J.

47. Ikushima H, Miyazono K. TGFβ signalling: a complex web in cancer progression. Nat

Rev Cancer [Internet]. 2010; 10(6):415–424. Available from:

http://dx.doi.org/10.1038/nrc2853.

131

48. Rodriguez GC, Haisley C, Hurteau J, Moser TL, Whitaker R, Bast RCJ, et al. Regulation

of invasion of epithelial ovarian cancer by transforming growth factor-beta. Gynecol.

Oncol. 2001; 80(2):245–253. doi:10.1006/gyno.2000.6042.

49. JAMMAL MP, DA SILVA AA, FILHO AM, DE CASTRO CÔBO E, ADAD SJ,

MURTA EFC, et al. Immunohistochemical staining of tumor necrosis factor-α and

interleukin-10 in benign and malignant ovarian neoplasms. Oncol. Lett. [Internet]. 2015;

9(2):979–983. doi:10.3892/ol.2014.2781.

50. Kulbe H, Thompson R, Wilson JL, Robinson S, Hagemann T, Fatah R, et al. The

Inflammatory Cytokine Tumor Necrosis Factor-α Generates an Autocrine Tumor-

Promoting Network in Epithelial Ovarian Cancer Cells. Cancer Res. [Internet]. 2007;

67(2):585–592. doi:10.1158/0008-5472.CAN-06-2941.

51. Grivennikov SI, Greten FR, Karin M. Immunity, Inflammation, and Cancer. Cell

[Internet]. 2010; 140(6):883–899. doi:10.1016/j.cell.2010.01.025.

52. Szlosarek PW, Grimshaw MJ, Kulbe H, Wilson JL, Wilbanks GD, Burke F, et al.

Expression and regulation of tumor necrosis factor α in normal and malignant ovarian

epithelium. Mol. Cancer Ther. [Internet]. 2006; 5(2):382–390. doi:10.1158/1535-

7163.MCT-05-0303.

53. Sethi G, Sung B, Aggarwal BB. TNF: a master switch for inflammation to cancer. Front.

Biosci. 2008; 13:5094–5107.

54. Bamberger ES, Perrett CW. Angiogenesis in epithelian ovarian cancer. Mol. Pathol.

[Internet]. 2002; 55(6):348–359. Available from:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1187269/.

55. Hazelton D, Nicosia RF, Nicosia S V. Vascular endothelial growth factor levels in ovarian

132

cyst fluid correlate with malignancy. Clin. Cancer Res. 1999; 5(4):823–829.

56. Yu L, Deng L, Li J, Zhang Y, Hu L. The prognostic value of vascular endothelial growth

factor in ovarian cancer: a systematic review and meta-analysis. Gynecol. Oncol. 2013;

128(2):391–396. doi:10.1016/j.ygyno.2012.11.002.

57. Kumar R, Yarmand-Bagheri R. The role of HER2 in angiogenesis. Semin. Oncol. 2001;

28(5 Suppl 16):27–32.

58. Agarwal R, D’Souza T, Morin PJ. Claudin-3 and claudin-4 expression in ovarian

epithelial cells enhances invasion and is associated with increased matrix

metalloproteinase-2 activity. Cancer Res. 2005; 65(16):7378–7385. doi:10.1158/0008-

5472.CAN-05-1036.

59. Hough CD, Sherman-Baust CA, Pizer ES, Montz FJ, Im DD, Rosenshein NB, et al.

Large-scale serial analysis of gene expression reveals genes differentially expressed in

ovarian cancer. Cancer Res. 2000; 60(22):6281–6287.

60. Lu KH, Patterson AP, Wang L, Marquez RT, Atkinson EN, Baggerly KA, et al. Selection

of potential markers for epithelial ovarian cancer with gene expression arrays and

recursive descent partition analysis. Clin. Cancer Res. 2004; 10(10):3291–3300.

doi:10.1158/1078-0432.CCR-03-0409.

61. Hibbs K, Skubitz KM, Pambuccian SE, Casey RC, Burleson KM, Oegema TRJ, et al.

Differential gene expression in ovarian carcinoma: identification of potential biomarkers.

Am. J. Pathol. 2004; 165(2):397–414. doi:10.1016/S0002-9440(10)63306-8.

62. Hough CD, Cho KR, Zonderman AB, Schwartz DR, Morin PJ. Coordinately up-regulated

genes in ovarian cancer. Cancer Res. 2001; 61(10):3869–3876.

63. Davidson B, Zhang Z, Kleinberg L, Li M, Florenes VA, Wang T-L, et al. Gene expression

133

signatures differentiate ovarian/peritoneal serous carcinoma from diffuse malignant

peritoneal mesothelioma. Clin. Cancer Res. 2006; 12(20 Pt 1):5944–5950.

doi:10.1158/1078-0432.CCR-06-1059.

64. Rangel LBA, Agarwal R, D’Souza T, Pizer ES, Alo PL, Lancaster WD, et al. Tight

junction proteins claudin-3 and claudin-4 are frequently overexpressed in ovarian cancer

but not in ovarian cystadenomas. Clin. Cancer Res. 2003; 9(7):2567–2575.

65. Bignotti E, Tassi RA, Calza S, Ravaggi A, Bandiera E, Rossi E, et al. Gene expression

profile of ovarian serous papillary carcinomas: identification of metastasis-associated

genes. Am. J. Obstet. Gynecol. 2007; 196(3):245.e1-11. doi:10.1016/j.ajog.2006.10.874.

66. Stewart JJ, White JT, Yan X, Collins S, Drescher CW, Urban ND, et al. Proteins

associated with Cisplatin resistance in ovarian cancer cells identified by quantitative

proteomic technology and integrated with mRNA expression levels. Mol. Cell.

Proteomics. 2006; 5(3):433–443. doi:10.1074/mcp.M500140-MCP200.

67. Cho A, Howell VM, Colvin EK. The Extracellular Matrix in Epithelial Ovarian Cancer –

A Piece of a Puzzle. Front. Oncol. 2015; 5. doi:10.3389/fonc.2015.00245.

68. Wu Y-H, Chang T-H, Huang Y-F, Huang H-D, Chou C-Y. COL11A1 promotes tumor

progression and predicts poor clinical outcome in ovarian cancer. Oncogene. 2014;

33(26):3432–3440. doi:10.1038/onc.2013.307.

69. Bager CL, Willumsen N, Leeming DJ, Smith V, Karsdal MA, Dornan D, et al. Collagen

degradation products measured in serum can separate ovarian and breast cancer patients

from healthy controls: A preliminary study. Cancer Biomark. 2015; 15(6):783–788.

doi:10.3233/CBM-150520.

70. Cheon D-J, Tong Y, Sim M-S, Dering J, Berel D, Cui X, et al. A collagen-remodeling

134

gene signature regulated by TGFβ signaling is associated with metastasis and poor

survival in serous ovarian cancer. Clin. Cancer Res. [Internet]. 2014; 20(3):711–723.

doi:10.1158/1078-0432.CCR-13-1256.

71. Januchowski R, Świerczewska M, Sterzyńska K, Wojtowicz K, Nowicki M, Zabel M.

Increased Expression of Several Collagen Genes is Associated with Drug Resistance in

Ovarian Cancer Cell Lines. J. Cancer [Internet]. 2016; 7(10):1295–1310.

doi:10.7150/jca.15371.

72. Kalli KR, Oberg AL, Keeney GL, Christianson TJH, Low PS, Knutson KL, et al. Folate

receptor alpha as a tumor target in epithelial ovarian cancer. Gynecol. Oncol. 2008;

108(3):619–626. doi:10.1016/j.ygyno.2007.11.020.

73. Chen S, Tai H, Tong X, Wang J, Yang F, Yang Y, et al. Variation and prognostic value of

serum plasminogen activator inhibitor-1 before and after chemotherapy in patients with

epithelial ovarian cancer. J. Obstet. Gynaecol. Res. [Internet]. 2014; 40(9):2058–2065.

doi:10.1111/jog.12474.

74. Koensgen D, Mustea A, Denkert C, Sun PM, Lichtenegger W, Sehouli J. Overexpression

of the plasminogen activator inhibitor type-1 in epithelial ovarian cancer. Anticancer Res.

2006; 26(2C):1683–1689.

75. Mashiko S, Kitatani K, Toyoshima M, Ichimura A, Dan T, Usui T, et al. Inhibition of

plasminogen activator inhibitor-1 is a potential therapeutic strategy in ovarian cancer.

Cancer Biol. Ther. 2015; 16(2):253–260. doi:10.1080/15384047.2014.1001271.

76. Tecimer C, Doering DL, Goldsmith LJ, Meyer JS, Abdulhay G, Wittliff JL. Clinical

relevance of urokinase-type plasminogen activator, its receptor and inhibitor type 1 in

ovarian cancer. Int. J. Gynecol. Cancer. 2000; 10(5):372–381.

135

77. Emami N, Diamandis EP. Utility of kallikrein-related peptidases (KLKs) as cancer

biomarkers. Clin. Chem. 2008; 54(10):1600–1607. doi:10.1373/clinchem.2008.105189.

78. Obiezu C V, Diamandis EP. Human tissue kallikrein gene family: applications in cancer.

Cancer Lett. 2005; 224(1):1–22. doi:10.1016/j.canlet.2004.09.024.

79. Luo L-Y, Katsaros D, Scorilas A, Fracchioli S, Bellino R, van Gramberen M, et al. The

serum concentration of human kallikrein 10 represents a novel biomarker for ovarian

cancer diagnosis and prognosis. Cancer Res. 2003; 63(4):807–811.

80. Borgono CA, Diamandis EP. The emerging roles of human tissue kallikreins in cancer.

Nat. Rev. Cancer. 2004; 4(11):876–890. doi:10.1038/nrc1474.

81. Michael IP, Sotiropoulou G, Pampalakis G, Magklara A, Ghosh M, Wasney G, et al.

Biochemical and enzymatic characterization of human kallikrein 5 (hK5), a novel serine

protease potentially involved in cancer progression. J. Biol. Chem. 2005; 280(15):14628–

14635. doi:10.1074/jbc.M408132200.

82. Kapadia C, Ghosh MC, Grass L, Diamandis EP. Human involvement in

extracellular matrix degradation. Biochem. Biophys. Res. Commun. 2004; 323(3):1084–

1090. doi:10.1016/j.bbrc.2004.08.206.

83. Ghosh MC, Grass L, Soosaipillai A, Sotiropoulou G, Diamandis EP. Human kallikrein 6

degrades extracellular matrix proteins and may enhance the metastatic potential of tumour

cells. Tumour Biol. 2004; 25(4):193–199. doi:10.1159/000081102.

84. Dong Y, Loessner D, Irving-Rodgers H, Obermair A, Nicklin JL, Clements JA. Metastasis

of ovarian cancer is mediated by kallikrein related peptidases. Clin. Exp. Metastasis

[Internet]. 2014; 31(1):135–147. doi:10.1007/s10585-013-9615-4.

85. Yousef GM, Diamandis EP. Tissue kallikreins: new players in normal and abnormal cell

136

growth? Thromb. Haemost. 2003; 90(1):7–16. doi:10.1267/THRO03010007.

86. Luo LY, Bunting P, Scorilas A, Diamandis EP. Human kallikrein 10: a novel tumor

marker for ovarian carcinoma? Clin. Chim. Acta. 2001; 306(1–2):111–118.

87. Das PM, Bast RCJ. Early detection of ovarian cancer. Biomark. Med. 2008; 2(3):291–303.

doi:10.2217/17520363.2.3.291.

88. Diamandis EP, Okui A, Mitsui S, Luo L-Y, Soosaipillai A, Grass L, et al. Human

kallikrein 11: a new biomarker of prostate and ovarian carcinoma. Cancer Res. 2002;

62(1):295–300.

89. Shih I-M, Davidson B. Pathogenesis of ovarian cancer: clues from selected overexpressed

genes. Future Oncol. 2009; 5(10):1641–1657. doi:10.2217/fon.09.126.

90. Yousef GM, Diamandis EP. The new human tissue kallikrein gene family: structure,

function, and association to disease. Endocr. Rev. 2001; 22(2):184–204.

doi:10.1210/edrv.22.2.0424.

91. Davidson B, Xi Z, Klokk TI, Trope CG, Dorum A, Scheistroen M, et al. Kallikrein 4

expression is up-regulated in epithelial ovarian carcinoma cells in effusions. Am. J. Clin.

Pathol. 2005; 123(3):360–368. doi:10.1309/PTBB-5BPC-KX8K-9V69.

92. John A, Tuszynski G. The role of matrix metalloproteinases in tumor angiogenesis and

tumor metastasis. Pathol. Oncol. Res. 2001; 7(1):14–23.

93. Kessenbrock K, Plaks V, Werb Z. Matrix Metalloproteinases: Regulators of the Tumor

Microenvironment. Cell [Internet]. 2010; 141(1):52–67. doi:10.1016/j.cell.2010.03.015.

94. Davidson B, Goldberg I, Gotlieb WH, Kopolovic J, Ben-Baruch G, Nesland JM, et al.

High levels of MMP-2, MMP-9, MT1-MMP and TIMP-2 mRNA correlate with poor

survival in ovarian carcinoma. Clin. Exp. Metastasis. 1999; 17(10):799–808.

137

95. Brun J-L, Cortez A, Commo F, Uzan S, Rouzier R, Darai E. Serous and mucinous ovarian

tumors express different profiles of MMP-2, -7, -9, MT1-MMP, and TIMP-1 and -2. Int. J.

Oncol. 2008; 33(6):1239–1246.

96. Deng J, Wang L, Chen H, Li L, Ma Y, Ni J, et al. The role of tumour-associated MUC1 in

epithelial ovarian cancer metastasis and progression. Cancer Metastasis Rev. [Internet].

2013; 32(3):535–551. doi:10.1007/s10555-013-9423-y.

97. Wang L, Ma J, Liu F, Yu Q, Chu G, Perkins AC, et al. Expression of MUC1 in primary

and metastatic human epithelial ovarian cancer and its therapeutic significance. Gynecol.

Oncol. [Internet]. 2007 [cited 2016 Mar 28]; 105(3):695–702.

doi:10.1016/j.ygyno.2007.02.004.

98. Chauhan SC, Kumar D, Jaggi M. Mucins in ovarian cancer diagnosis and therapy. J.

Ovarian Res. [Internet]. 2009; 2:21. doi:10.1186/1757-2215-2-21.

99. Chauhan SC, Singh AP, Ruiz F, Johansson SL, Jain M, Smith LM, et al. Aberrant

expression of MUC4 in ovarian carcinoma: diagnostic significance alone and in

combination with MUC1 and MUC16 (CA125). Mod. Pathol. an Off. J. United States

Can. Acad. Pathol. Inc. 2006; 19(10):1386–1394. doi:10.1038/modpathol.3800646.

100. Davidson B, Baekelandt M, Shih I-M. MUC4 is upregulated in ovarian carcinoma

effusions and differentiates carcinoma cells from mesothelial cells. Diagn. Cytopathol.

2007; 35(12):756–760. doi:10.1002/dc.20771.

101. Ponnusamy MP, Singh AP, Jain M, Chakraborty S, Moniaux N, Batra SK. MUC4

activates HER2 signalling and enhances the motility of human ovarian cancer cells. Br. J.

Cancer. 2008; 99(3):520–526. doi:10.1038/sj.bjc.6604517.

102. Felder M, Kapur A, Gonzalez-Bosquet J, Horibata S, Heintz J, Albrecht R, et al. MUC16

138

(CA125): tumor biomarker to cancer therapy, a work in progress. Mol. Cancer [Internet].

2014; 13:129. doi:10.1186/1476-4598-13-129.

103. Cohen JG, White M, Cruz A, Farias-Eisner R. In 2014, can we do better than CA125 in

the early detection of ovarian cancer? World J. Biol. Chem. [Internet]. 2014; 5(3):286–

300. doi:10.4331/wjbc.v5.i3.286.

104. Sundar S, Neal RD, Kehoe S. Diagnosis of ovarian cancer. BMJ [Internet]. 2015; 351.

Available from: http://www.bmj.com/content/351/bmj.h4443.abstract.

105. Dai J, Peng L, Fan K, Wang H, Wei R, Ji G, et al. Osteopontin induces angiogenesis

through activation of PI3K/AKT and ERK1/2 in endothelial cells. Oncogene. 2009;

28(38):3412–3422. doi:10.1038/onc.2009.189.

106. Song G, Cai Q-F, Mao Y-B, Ming Y-L, Bao S-D, Ouyang G-L. Osteopontin promotes

ovarian cancer progression and cell survival and increases HIF-1alpha expression through

the PI3-K/Akt pathway. Cancer Sci. 2008; 99(10):1901–1907. doi:10.1111/j.1349-

7006.2008.00911.x.

107. Song G, Ouyang G, Mao Y, Ming Y, Bao S, Hu T. Osteopontin promotes gastric cancer

metastasis by augmenting cell survival and invasion through Akt-mediated HIF-1alpha

up-regulation and MMP9 activation. J. Cell. Mol. Med. 2009; 13(8B):1706–1718.

doi:10.1111/j.1582-4934.2008.00540.x.

108. Sarojini S, Tamir A, Lim H, Li S, Zhang S, Goy A, et al. Early detection biomarkers for

ovarian cancer. J. Oncol. 2012; 2012:709049. doi:10.1155/2012/709049.

109. Kim J-H, Skates SJ, Uede T, Wong K, Schorge JO, Feltmate CM, et al. Osteopontin as a

potential diagnostic biomarker for ovarian cancer. JAMA. 2002; 287(13):1671–1679.

110. Sun W, Xing B, Sun Y, Du X, Lu M, Hao C, et al. Proteome analysis of hepatocellular

139

carcinoma by two-dimensional difference gel electrophoresis: novel protein markers in

hepatocellular carcinoma tissues. Mol. Cell. Proteomics. 2007; 6(10):1798–1808.

doi:10.1074/mcp.M600449-MCP200.

111. Erlich RB, Kahn SA, Lima FRS, Muras AG, Martins RAP, Linden R, et al. STI1 promotes

glioma proliferation through MAPK and PI3K pathways. Glia. 2007; 55(16):1690–1698.

doi:10.1002/glia.20579.

112. Carta F, Demuro PP, Zanini C, Santona A, Castiglia D, D’Atri S, et al. Analysis of

candidate genes through a proteomics-based approach in primary cell lines from

malignant melanomas and their metastases. Melanoma Res. 2005; 15(4):235–244.

113. Walsh N, O’Donovan N, Kennedy S, Henry M, Meleady P, Clynes M, et al. Identification

of pancreatic cancer invasion-related proteins by proteomic analysis. Proteome Sci. 2009;

7:3. doi:10.1186/1477-5956-7-3.

114. Walsh N, Larkin A, Swan N, Conlon K, Dowling P, McDermott R, et al. RNAi

knockdown of Hop (Hsp70/Hsp90 organising protein) decreases invasion via MMP-2

down regulation. Cancer Lett. [Internet]. 2011; 306(2):180–189.

doi:http://dx.doi.org/10.1016/j.canlet.2011.03.004.

115. Cho H, Kim S, Shin H-Y, Chung EJ, Kitano H, Hyon Park J, et al. Expression of stress-

induced phosphoprotein1 (STIP1) is associated with tumor progression and poor

prognosis in epithelial ovarian cancer. Genes, Chromosom. Cancer [Internet]. 2014;

53(4):277–288. doi:10.1002/gcc.22136.

116. Wang T-H, Chao A, Tsai C-L, Chang C-L, Chen S-H, Lee Y-S, et al. Stress-induced

phosphoprotein 1 as a secreted biomarker for human ovarian cancer promotes cancer cell

proliferation. Mol. Cell. Proteomics. 2010; 9(9):1873–1884.

140

doi:10.1074/mcp.M110.000802.

117. Kim S, Cho H, Nam EJ, Kim SW, Kim YT, Park YW, et al. Autoantibodies against stress-

induced phosphoprotein-1 as a novel biomarker candidate for ovarian cancer. Genes.

Chromosomes Cancer. 2010; 49(7):585–595. doi:10.1002/gcc.20769.

118. Chen Y-C, Pohl G, Wang T-L, Morin PJ, Risberg B, Kristensen GB, et al. Apolipoprotein

E is required for cell proliferation and survival in ovarian cancer. Cancer Res. 2005;

65(1):331–337.

119. Kuhajda FP. Fatty acid synthase and cancer: new application of an old pathway. Cancer

Res. 2006; 66(12):5977–5980. doi:10.1158/0008-5472.CAN-05-4673.

120. Gansler TS, Hardman W 3rd, Hunt DA, Schaffel S, Hennigar RA. Increased expression of

fatty acid synthase (OA-519) in ovarian neoplasms predicts shorter survival. Hum. Pathol.

1997; 28(6):686–692.

121. Alo PL, Visca P, Framarino ML, Botti C, Monaco S, Sebastiani V, et al.

Immunohistochemical study of fatty acid synthase in ovarian neoplasms. Oncol. Rep.

2000; 7(6):1383–1388.

122. Ueda SM, Yap KL, Davidson B, Tian Y, Murthy V, Wang T-L, et al. Expression of Fatty

Acid Synthase Depends on NAC1 and Is Associated with Recurrent Ovarian Serous

Carcinomas. J. Oncol. 2010; 2010:285191. doi:10.1155/2010/285191.

123. Orita H, Coulter J, Tully E, Kuhajda FP, Gabrielson E. Inhibiting fatty acid synthase for

chemoprevention of chemically induced lung tumors. Clin. Cancer Res. 2008;

14(8):2458–2464. doi:10.1158/1078-0432.CCR-07-4177.

124. Jensen V, Ladekarl M, Holm-Nielsen P, Melsen F, Soerensen FB. The prognostic value of

oncogenic antigen 519 (OA-519) expression and proliferative activity detected by

141

antibody MIB-1 in node-negative breast cancer. J. Pathol. 1995; 176(4):343–352.

doi:10.1002/path.1711760405.

125. Shurbaji MS, Kalbfleisch JH, Thurmond TS. Immunohistochemical detection of a fatty

acid synthase (OA-519) as a predictor of progression of prostate cancer. Hum. Pathol.

1996; 27(9):917–921.

126. Menendez JA, Lupu R. Fatty acid synthase and the lipogenic phenotype in cancer

pathogenesis. Nat. Rev. Cancer. 2007; 7(10):763–777. doi:10.1038/nrc2222.

127. Hellstrom I, Raycraft J, Hayden-Ledbetter M, Ledbetter JA, Schummer M, McIntosh M,

et al. The HE4 (WFDC2) protein is a biomarker for ovarian carcinoma. Cancer Res. 2003;

63(13):3695–3700.

128. Tang Z, Chang X, Ye X, Li Y, Cheng H, Cui H. Usefulness of human epididymis protein

4 in predicting cytoreductive surgical outcomes for advanced ovarian tubal and peritoneal

carcinoma. Chin. J. Cancer Res. 2015; 27(3):309–317. doi:10.3978/j.issn.1000-

9604.2015.06.01.

129. Chang K, Pastan I. Molecular cloning of mesothelin, a differentiation antigen present on

mesothelium, mesotheliomas, and ovarian cancers. Proc. Natl. Acad. Sci. U. S. A. 1996;

93(1):136–140.

130. Ho M, Hassan R, Zhang J, Wang Q-C, Onda M, Bera T, et al. Humoral immune response

to mesothelin in mesothelioma and ovarian cancer patients. Clin. Cancer Res. 2005;

11(10):3814–3820. doi:10.1158/1078-0432.CCR-04-2304.

131. Badgwell D, Lu Z, Cole L, Fritsche H, Atkinson EN, Somers E, et al. Urinary mesothelin

provides greater sensitivity for early stage ovarian cancer than serum mesothelin, urinary

hCG free beta subunit and urinary hCG beta core fragment. Gynecol. Oncol. 2007;

142

106(3):490–497. doi:10.1016/j.ygyno.2007.04.022.

132. Yen MJ, Hsu C-Y, Mao T-L, Wu T-C, Roden R, Wang T-L, et al. Diffuse mesothelin

expression correlates with prolonged patient survival in ovarian serous carcinoma. Clin.

Cancer Res. 2006; 12(3 Pt 1):827–831. doi:10.1158/1078-0432.CCR-05-1397.

133. Mok SC, Chao J, Skates S, Wong K, Yiu GK, Muto MG, et al. Prostasin, a potential

serum marker for ovarian cancer: identification through microarray technology. J. Natl.

Cancer Inst. 2001; 93(19):1458–1464.

134. Kumar J, Ward AC. Role of the receptor family in epithelial ovarian cancer

and its clinical implications. Biochim. Biophys. Acta [Internet]. 2014 [cited 2016 Mar 26];

1845(2):117–25. doi:10.1016/j.bbcan.2013.12.003.

135. Scheller J, Chalaris A, Schmidt-Arras D, Rose-John S. The pro- and anti-inflammatory

properties of the cytokine interleukin-6. Biochim. Biophys. Acta - Mol. Cell Res.

[Internet]. 2011; 1813(5):878–888. doi:http://dx.doi.org/10.1016/j.bbamcr.2011.01.034.

136. Miao J-W, Liu L-J, Huang J. Interleukin-6-induced epithelial-mesenchymal transition

through signal transducer and activator of transcription 3 in human cervical carcinoma.

Int. J. Oncol. 2014; 45(1):165–176. doi:10.3892/ijo.2014.2422.

137. Arango Duque G, Descoteaux A. Macrophage Cytokines: Involvement in Immunity and

Infectious Diseases. Front. Immunol. [Internet]. 2014; 5:491.

doi:10.3389/fimmu.2014.00491.

138. El Kasmi KC, Holst J, Coffre M, Mielke L, de Pauw A, Lhocine N, et al. General nature

of the STAT3-activated anti-inflammatory response. J. Immunol. 2006; 177(11):7880–

7888.

139. Varma TK, Toliver-Kinsky TE, Lin CY, Koutrouvelis AP, Nichols JE, Sherwood ER.

143

Cellular mechanisms that cause suppressed gamma interferon secretion in endotoxin-

tolerant mice. Infect. Immun. 2001; 69(9):5249–5263.

140. Aste-Amezaga M, Ma X, Sartori A, Trinchieri G. Molecular mechanisms of the induction

of IL-12 and its inhibition by IL-10. J. Immunol. 1998; 160(12):5936–5944.

141. Sethi BK, Chanukya G V, Nagesh VS. Prolactin and cancer: Has the orphan finally found

a home? Indian J. Endocrinol. Metab. [Internet]. 2012; 16(Suppl 2):S195–S198.

doi:10.4103/2230-8210.104038.

142. López-Rincón G, Pereira-Suárez AL, Del Toro-Arreola S, Sánchez-Hernández PE,

Ochoa-Zarzosa A, Muñoz-Valle JF, et al. Lipopolysaccharide induces the expression of an

autocrine prolactin loop enhancing inflammatory response in monocytes. J. Inflamm.

[Internet]. 2013; 10(1):1–12. doi:10.1186/1476-9255-10-24.

143. Dunfield LD, Dwyer EJC, Nachtigal MW. TGF beta-induced Smad signaling remains

intact in primary human ovarian cancer cells. Endocrinology. 2002; 143(4):1174–1181.

doi:10.1210/endo.143.4.8733.

144. de Caestecker MP, Piek E, Roberts AB. Role of Transforming Growth Factor-β Signaling

in Cancer. J. Natl. Cancer Inst. [Internet]. 2000; 92(17):1388–1402.

doi:10.1093/jnci/92.17.1388.

145. Cheng J-C, Auersperg N, Leung PCK. TGF-Beta Induces Serous Borderline Ovarian

Tumor Cell Invasion by Activating EMT but Triggers Apoptosis in Low-Grade Serous

Ovarian Carcinoma Cells. PLoS One [Internet]. 2012; 7(8):e42436. Available from:

http://dx.doi.org/10.1371%252Fjournal.pone.0042436.

146. Leivonen S-K, Kahari V-M. Transforming growth factor-beta signaling in cancer invasion

and metastasis. Int. J. cancer. 2007; 121(10):2119–2124. doi:10.1002/ijc.23113.

144

147. WANG X, LIN Y. Tumor necrosis factor and cancer, buddies or foes? Acta Pharmacol.

Sin. [Internet]. 2008; 29(11):1275–1288. Available from:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2631033/.

148. Koch S, Claesson-Welsh L. Signal Transduction by Vascular Endothelial Growth Factor

Receptors. Cold Spring Harb. Perspect. Med. [Internet]. 2012; 2(7):a006502.

doi:10.1101/cshperspect.a006502.

149. Chen TT, Luque A, Lee S, Anderson SM, Segura T, Iruela-Arispe ML. Anchorage of

VEGF to the extracellular matrix conveys differential signaling responses to endothelial

cells. J. Cell Biol. [Internet]. 2010; 188(4):595 LP-609. Available from:

http://jcb.rupress.org/content/188/4/595.abstract.

150. Katahira J, Inoue N, Horiguchi Y, Matsuda M, Sugimoto N. Molecular cloning and

functional characterization of the receptor for Clostridium perfringens enterotoxin. J. Cell

Biol. 1997; 136(6):1239–1247.

151. McClane BA. An overview of Clostridium perfringens enterotoxin. Toxicon. 1996;

34(11–12):1335–1343.

152. Santin AD, Cane S, Bellone S, Palmieri M, Siegel ER, Thomas M, et al. Treatment of

chemotherapy-resistant human ovarian cancer xenografts in C.B-17/SCID mice by

intraperitoneal administration of Clostridium perfringens enterotoxin. Cancer Res. 2005;

65(10):4334–4342. doi:10.1158/0008-5472.CAN-04-3472.

153. Singh AP, Chaturvedi P, Batra SK. Emerging roles of MUC4 in cancer: a novel target for

diagnosis and therapy. Cancer Res. 2007; 67(2):433–436. doi:10.1158/0008-5472.CAN-

06-3114.

154. Ahmed N, Riley C, Rice G, Quinn M. Role of integrin receptors for fibronectin, collagen

145

and laminin in the regulation of ovarian carcinoma functions in response to a matrix

microenvironment. Clin. Exp. Metastasis. 2005; 22(5):391–402. doi:10.1007/s10585-005-

1262-y.

155. Stoppelli MP. The Plasminogen Activation System in Cell Invasion. Madame Curie

Biosci. Database [Internet]. Landes Bio. Available from:

https://www.ncbi.nlm.nih.gov/books/NBK6146/.

156. Carter JC, Church FC. and Breast Cancer: The Roles of Peroxisome Proliferator-

Activated Receptor-γ and Plasminogen Activator Inhibitor-1. PPAR Res. [Internet]. 2009;

2009:345320. doi:10.1155/2009/345320.

157. Kenny HA, Lengyel E. MMP-2 functions as an early response protein in ovarian cancer

metastasis. Cell Cycle [Internet]. 2009; 8(5):683–688. Available from:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2840625/.

158. Kenny HA, Kaur S, Coussens LM, Lengyel E. The initial steps of ovarian cancer cell

metastasis are mediated by MMP-2 cleavage of vitronectin and fibronectin. J. Clin. Invest.

[Internet]. 2008; 118(4):1367–1379. doi:10.1172/JCI33775.

159. Odunuga OO, Longshaw VM, Blatch GL. Hop: more than an Hsp70/Hsp90 adaptor

protein. Bioessays. 2004; 26(10):1058–1068. doi:10.1002/bies.20107.

160. Longshaw VM, Chapple JP, Balda MS, Cheetham ME, Blatch GL. Nuclear translocation

of the Hsp70/Hsp90 organizing protein mSTI1 is regulated by cell cycle kinases. J. Cell

Sci. 2004; 117(Pt 5):701–710. doi:10.1242/jcs.00905.

161. Chao A, Lee L-Y, Hsueh C, Lin C-Y, Tsai C-L, Chao A-S, et al. Immunohistological

analysis of stress-induced phosphoprotein 1 in ovarian cancer patients with low serum

cancer antigen 125 levels. Taiwan. J. Obstet. Gynecol. 2013; 52(2):185–191.

146

doi:10.1016/j.tjog.2013.04.006.

162. Gliemann J. Receptors of the low density lipoprotein (LDL) receptor family in man.

Multiple functions of the large family members via interaction with complex ligands. Biol.

Chem. 1998; 379(8–9):951–964.

163. Kuhajda FP, Jenner K, Wood FD, Hennigar RA, Jacobs LB, Dick JD, et al. Fatty acid

synthesis: a potential selective target for antineoplastic therapy. Proc. Natl. Acad. Sci. U.

S. A. 1994; 91(14):6379–6383.

164. Epstein JI, Carmichael M, Partin AW. OA-519 (fatty acid synthase) as an independent

predictor of pathologic state in adenocarcinoma of the prostate. Urology. 1995; 45(1):81–

86.

165. Alo’ PL, Visca P, Marci A, Mangoni A, Botti C, Di Tondo U. Expression of fatty acid

synthase (FAS) as a predictor of recurrence in stage I breast carcinoma patients. Cancer.

1996; 77(3):474–482. doi:10.1002/(SICI)1097-0142(19960201)77:3<474::AID-

CNCR8>3.0.CO;2-K.

166. Rashid A, Pizer ES, Moga M, Milgraum LZ, Zahurak M, Pasternack GR, et al. Elevated

expression of fatty acid synthase and fatty acid synthetic activity in colorectal neoplasia.

Am. J. Pathol. 1997; 150(1):201–208.

167. Milgraum LZ, Witters LA, Pasternack GR, Kuhajda FP. Enzymes of the fatty acid

synthesis pathway are highly expressed in in situ breast carcinoma. Clin. Cancer Res.

1997; 3(11):2115–2120.

168. Pizer ES, Wood FD, Heine HS, Romantsev FE, Pasternack GR, Kuhajda FP. Inhibition of

fatty acid synthesis delays disease progression in a xenograft model of ovarian cancer.

Cancer Res. 1996; 56(6):1189–1193.

147

169. Ross J, Najjar AM, Sankaranarayanapillai M, Tong WP, Kaluarachchi K, Ronen SM.

Fatty acid synthase inhibition results in a magnetic resonance-detectable drop in

phosphocholine. Mol. Cancer Ther. 2008; 7(8):2556–2565. doi:10.1158/1535-7163.MCT-

08-0015.

170. Grunt TW, Wagner R, Grusch M, Berger W, Singer CF, Marian B, et al. Interaction

between fatty acid synthase- and ErbB-systems in ovarian cancer cells. Biochem. Biophys.

Res. Commun. [Internet]. 2009; 385(3):454–459.

doi:http://dx.doi.org/10.1016/j.bbrc.2009.05.085.

171. Bauerschlag DO, Maass N, Leonhardt P, Verburg FA, Pecks U, Zeppernick F, et al. Fatty

acid synthase overexpression: target for therapy and reversal of chemoresistance in

ovarian cancer. J. Transl. Med. [Internet]. 2015; 13:146. doi:10.1186/s12967-015-0511-3.

172. Basal E, Eghbali-Fatourechi GZ, Kalli KR, Hartmann LC, Goodman KM, Goode EL, et

al. Functional folate receptor alpha is elevated in the blood of ovarian cancer patients.

PLoS One. 2009; 4(7):e6292. doi:10.1371/journal.pone.0006292.

173. Yuan Y, Nymoen DA, Dong HP, Bjorang O, Shih I-M, Low PS, et al. Expression of the

folate receptor genes FOLR1 and FOLR3 differentiates ovarian carcinoma from breast

carcinoma and malignant mesothelioma in serous effusions. Hum. Pathol. 2009;

40(10):1453–1460. doi:10.1016/j.humpath.2009.02.013.

174. Gibbs DD, Theti DS, Wood N, Green M, Raynaud F, Valenti M, et al. BGC 945, a novel

tumor-selective thymidylate synthase inhibitor targeted to alpha-folate receptor-

overexpressing tumors. Cancer Res. 2005; 65(24):11721–11728. doi:10.1158/0008-

5472.CAN-05-2034.

175. Kirchhoff C. Molecular characterization of epididymal proteins. Rev. Reprod. 1998;

148

3(2):86–95.

176. Moore RG, Hill EK, Horan T, Yano N, Kim K, MacLaughlan S, et al. HE4 (WFDC2)

gene overexpression promotes ovarian tumor growth. Sci. Rep. 2014; 4:3574.

doi:10.1038/srep03574.

177. Ordonez NG. Application of mesothelin immunostaining in tumor diagnosis. Am. J. Surg.

Pathol. 2003; 27(11):1418–1428.

178. Rump A, Morikawa Y, Tanaka M, Minami S, Umesaki N, Takeuchi M, et al. Binding of

ovarian cancer antigen CA125/MUC16 to mesothelin mediates cell adhesion. J. Biol.

Chem. 2004; 279(10):9190–9198. doi:10.1074/jbc.M312372200.

179. Feng Y, Xiao X, Zhu Z, Streaker E, Ho M, Pastan I, et al. A novel human monoclonal

antibody that binds with high affinity to mesothelin-expressing cells and kills them by

antibody-dependent cell-mediated cytotoxicity. Mol. Cancer Ther. 2009; 8(5):1113–1118.

doi:10.1158/1535-7163.MCT-08-0945.

180. Tamir A, Gangadharan A, Balwani S, Tanaka T, Patel U, Hassan A, et al. The serine

protease prostasin (PRSS8) is a potential biomarker for early detection of ovarian cancer.

J. Ovarian Res. [Internet]. 2016; 9:20. doi:10.1186/s13048-016-0228-9.

181. Costa FP, Junior ELB, Zelmanowicz A, Svedman C, Devenz G, Alves S, et al. Prostasin,

A Potential in Ovarian Cancer- A Pilot Study. Clinics (Sao Paulo).

[Internet]. 2009; 64(7):641–644. doi:10.1590/S1807-59322009000700006.

182. Yu JX, Chao L, Chao J. Molecular cloning, tissue-specific expression, and cellular

localization of human prostasin mRNA. J. Biol. Chem. 1995; 270(22):13483–13489.

183. Romero P, Obradovic Z, Li X, Garner EC, Brown CJ, Dunker AK. Sequence complexity

of disordered protein. Proteins. 2001; 42(1):38–48.

149

184. Obradovic Z, Peng K, Vucetic S, Radivojac P, Dunker AK. Exploiting heterogeneous

sequence properties improves prediction of protein disorder. Proteins. 2005; 61 Suppl

7:176–182. doi:10.1002/prot.20735.

185. Peng K, Radivojac P, Vucetic S, Dunker AK, Obradovic Z. Length-dependent prediction

of protein intrinsic disorder. BMC Bioinformatics. 2006; 7:208. doi:10.1186/1471-2105-7-

208.

186. Tsolis AC, Papandreou NC, Iconomidou VA, Hamodrakas SJ. A consensus method for

the prediction of “aggregation-prone” peptides in globular proteins. PLoS One. 2013;

8(1):e54175. doi:10.1371/journal.pone.0054175.

187. Consortium TU. UniProt: a hub for protein information. Nucleic Acids Res [Internet].

2015; 43(D1):D204–D212. doi:10.1093/nar/gku989.

188. Hornbeck P V, Zhang B, Murray B, Kornhauser JM, Latham V, Skrzypek E.

PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 2015;

43(Database issue):D512-20. doi:10.1093/nar/gku1267.

189. Gupta R, Jung E, Brunak S. Prediction of N-glycosylation sites in human proteins. Prep.

2004;

190. Steentoft C, Vakhrushev SY, Joshi HJ, Kong Y, Vester-Christensen MB, Schjoldager KT-

BG, et al. Precision mapping of the human O-GalNAc glycoproteome through SimpleCell

technology. EMBO J. 2013; 32(10):1478–1488. doi:10.1038/emboj.2013.79.

191. Sekiyama N, Ikegami T, Yamane T, Ikeguchi M, Uchimura Y, Baba D, et al. Structure of

the Small Ubiquitin-like Modifier (SUMO)-interacting Motif of MBD1-containing

Chromatin-associated Factor 1 Bound to SUMO-3. J. Biol. Chem. [Internet]. 2008;

283(51):35966–35975. doi:10.1074/jbc.M802528200 .

150

192. Xue Y, Chen H, Jin C, Sun Z, Yao X. NBA-Palm: prediction of palmitoylation site

implemented in Naive Bayes algorithm. BMC Bioinformatics. 2006; 7:458.

doi:10.1186/1471-2105-7-458.

193. Kiemer L, Bendtsen JD, Blom N. NetAcet: prediction of N-terminal acetylation sites.

Bioinformatics. 2005; 21(7):1269–1270. doi:10.1093/bioinformatics/bti130.

194. Monigatti F, Gasteiger E, Bairoch A, Jung E. The Sulfinator: predicting tyrosine sulfation

sites in protein sequences. Bioinformatics. 2002; 18(5):769–770.

195. Bologna G, Yvon C, Duvaud S, Veuthey A-L. N-Terminal myristoylation predictions by

ensembles of neural networks. Proteomics. 2004; 4(6):1626–1632.

doi:10.1002/pmic.200300783.

196. Maurer-Stroh S, Eisenhaber F. Refinement and prediction of protein prenylation motifs.

Genome Biol. 2005; 6(6):R55. doi:10.1186/gb-2005-6-6-r55.

197. Chen H, Xue Y, Huang N, Yao X, Sun Z. MeMo: a web tool for prediction of protein

methylation modifications. Nucleic Acids Res. 2006; 34(Web Server issue):W249-53.

doi:10.1093/nar/gkl233.

198. Baeuerle PA, Huttner WB. Tyrosine sulfation is a trans-Golgi-specific protein

modification. J. Cell Biol. 1987; 105(6 Pt 1):2655–2664.

199. Aitken A, Learmonth M. The Protein Protocols Handbook [Internet]. In: Walker JM,

editor. . Totowa, NJ: Humana Press; 2002. p. 459–460. doi:10.1385/1-59259-169-8:459.

200. Jensen ON. Modification-specific proteomics: characterization of post-translational

modifications by mass spectrometry. Curr. Opin. Chem. Biol. 2004; 8(1):33–41.

doi:10.1016/j.cbpa.2003.12.009.

201. Yang X-J, Seto E. Lysine acetylation: codified crosstalk with other posttranslational

151

modifications. Mol. Cell. 2008; 31(4):449–461. doi:10.1016/j.molcel.2008.07.002.

202. Zhang Z, Tan M, Xie Z, Dai L, Chen Y, Zhao T. Identification of lysine succinylation as a

new post-translational modification. Nat. Chem. Biol. [Internet]. 2011; 7(1):58–63.

doi:10.1038/nchembio.495.

203. Iino T, Takahashi M, Sako T. Role of amino-terminal positive charge on signal peptide in

staphylokinase export across the cytoplasmic membrane of Escherichia coli. J. Biol.

Chem. 1987; 262(15):7412–7417.

204. Resh MD. Covalent Lipid Modifications of Proteins. Curr. Biol. [Internet]. 2013;

23(10):R431–R435. doi:10.1016/j.cub.2013.04.024.

205. Turoverov KK, Kuznetsova IM, Uversky VN. The Protein Kingdom Extended: Ordered

and Intrinsically Disordered Proteins, Their Folding, Supramolecular Complex Formation,

and Aggregation. Prog. Biophys. Mol. Biol. 2010; 102(2–3):73–84.

doi:10.1016/j.pbiomolbio.2010.01.003.

206. Buck PM, Kumar S, Singh SK. On the role of aggregation prone regions in protein

evolution, stability, and enzymatic : insights from diverse analyses. PLoS

Comput. Biol. [Internet]. 2013 [cited 2015 Dec 22]; 9(10):e1003291.

doi:10.1371/journal.pcbi.1003291.

207. Shental-Bechor D, Levy Y. Effect of glycosylation on protein folding: A close look at

thermodynamic stabilization. Proc. Natl. Acad. Sci. U. S. A. [Internet]. 2008;

105(24):8256–8261. doi:10.1073/pnas.0801340105.

208. Nestler EJ, Greengard P. Protein Phosphorylation is of Fundamental Importance in

Biological Regulation. 6th ed. Philadelphia: Lippincott-Raven; 1999.

209. Cheema TMK and AK. Small Changes Huge Impact: The Role of Protein

152

Posttranslational Modifications in Cellular Homeostasis and Disease. J. Amino Acids.

2011; 2011.

210. Reimand J, Wagih O, Bader GD. The mutational landscape of phosphorylation signaling

in cancer. Sci. Rep. 2013; 3:2651. doi:10.1038/srep02651.

211. Gregoire S, Yang X-J. Association with class IIa histone deacetylases upregulates the

sumoylation of MEF2 transcription factors. Mol. Cell. Biol. 2005; 25(6):2273–2287.

doi:10.1128/MCB.25.6.2273-2287.2005.

212. Girdwood DWH, Tatham MH, Hay RT. SUMO and transcriptional regulation. Semin.

Cell Dev. Biol. 2004; 15(2):201–210.

213. Besnault-Mascard L, Leprince C, Auffredou MT, Meunier B, Bourgeade MF, Camonis J,

et al. Caspase-8 sumoylation is associated with nuclear localization. Oncogene. 2005;

24(20):3268–3273. doi:10.1038/sj.onc.1208448.

214. Liang M, Melchior F, Feng X-H, Lin X. Regulation of Smad4 sumoylation and

transforming growth factor-beta signaling by protein inhibitor of activated STAT1. J.

Biol. Chem. 2004; 279(22):22857–22865. doi:10.1074/jbc.M401554200.

215. Wilkinson KA, Henley JM. Mechanisms, regulation and consequences of protein

SUMOylation. Biochem. J. 2010; 428(2):133–145. doi:10.1042/BJ20100158.

216. Miteva M, Keusekotten K, Hofmann K, Praefcke GJK, Dohmen RJ. Sumoylation as a

Signal for Polyubiquitylation and Proteasomal Degradation [Internet]. In: Groettrup M,

editor. Conjugation and Deconjugation of Ubiquitin Family Modifiers: Subcellular

Biochemistry. New York, NY: Springer New York; 2010. p. 195–214. doi:10.1007/978-1-

4419-6676-6_16.

217. Hershko A, Heller H, Eytan E, Kaklij G, Rose IA. Role of the alpha-amino group of

153

protein in ubiquitin-mediated protein breakdown. Proc. Natl. Acad. Sci. U. S. A.

[Internet]. 1984; 81(22):7021–7025. Available from:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC392068/.

218. Edwards YJK, Lobley AE, Pentony MM, Jones DT. Insights into the regulation of

intrinsically disordered proteins in the human proteome by analyzing sequence and gene

expression data. Genome Biol. [Internet]. 2009; 10(5):1–18. doi:10.1186/gb-2009-10-5-

r50.

219. Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J, et al. STRING 8--a global

view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res.

2009; 37(Database issue):D412-6. doi:10.1093/nar/gkn760.

220. Luo Z, Wang Q, Lau WB, Lau B, Xu L, Zhao L, et al. Tumor microenvironment: The

culprit for ovarian cancer metastasis? Cancer Lett. 2016; 377(2):174–182.

doi:10.1016/j.canlet.2016.04.038.

221. Turner TB, Buchsbaum DJ, Straughn JMJ, Randall TD, Arend RC. Ovarian cancer and

the immune system - The role of targeted therapies. Gynecol. Oncol. 2016; 142(2):349–

356. doi:10.1016/j.ygyno.2016.05.007.

222. Suh DH, Kim HS, Kim B, Song YS. Metabolic orchestration between cancer cells and

tumor microenvironment as a co-evolutionary source of chemoresistance in ovarian

cancer: a therapeutic implication. Biochem. Pharmacol. 2014; 92(1):43–54.

doi:10.1016/j.bcp.2014.08.011.

223. Owens OJ, Taggart C, Wilson R, Walker JJ, McKillop JH, Kennedy JH. Interleukin-2

receptor and ovarian cancer. Br. J. Cancer. 1993; 68(2):364–367.

224. Subramani R, Lopez-Valdez R, Salcido A, Boopalan T, Arumugam A, Nandy S, et al.

154

Growth hormone receptor inhibition decreases the growth and metastasis of pancreatic

ductal adenocarcinoma. Exp. Mol. Med. 2014; 46(10):e117-. doi:10.1038/emm.2014.61.

225. Gavalas NG, Karadimou A, Dimopoulos MA, Bamias A. Immune Response in Ovarian

Cancer: How Is the Immune System Involved in Prognosis and Therapy: Potential for

Treatment Utilization. Clin. Dev. Immunol. 2010; 2010. doi:10.1155/2010/791603.

226. Zheng H, Ruan J, Zhao P, Chen S, Pan L, Liu J. Heparanase is involved in proliferation

and invasion of ovarian cancer cells. Cancer Biomark. 2015; 15(5):525–534.

doi:10.3233/CBM-150459.

227. LoKuan E, Ziegler SF. Thymic Stromal Lymphopoietin (TSLP) and Cancer. J. Immunol.

2014; 193(9):4283–4288. doi:10.4049/jimmunol.1400864.

228. Fleming D, Chahin L, Rumbaugh K. Glycoside Degrade Polymicrobial

Bacterial Biofilms in Wounds. Antimicrob. Agents Chemother. 2017; 61(2).

doi:10.1128/AAC.01998-16.

229. Takasu S, Mutoh M, Takahashi M, Nakagama H. Lipoprotein lipase as a candidate target

for cancer prevention/therapy. Biochem. Res. Int. 2012; 2012:398697.

doi:10.1155/2012/398697.

230. D’Souza B, Sreekantha, D’Souza V. Hyperamylasemia in ovarian tumours – serum

amylase as a marker for ovarian cancers. Int. J. Pharma Bio Sci. 2011; 2(1):B445-449.

231. Sosnoff DR, Friend RB, Berkovic M, Rasansky RJ, Hoffman SMJ. Salivary Amylase–

Producing Multiple Myeloma: Case Report and Review of the Current Literature. J. Clin.

Oncol. [Internet]. 2013; 31(19):e309–e311. doi:10.1200/JCO.2012.46.4677.

232. Elf SE, Chen J. Targeting glucose metabolism in patients with cancer. Cancer. 2014;

120(6):774–780. doi:10.1002/cncr.28501.

155

233. Annibaldi A, Widmann C. Glucose metabolism in cancer cells. Curr. Opin. Clin. Nutr.

Metab. Care. 2010; 13(4):466–470. doi:10.1097/MCO.0b013e32833a5577.

234. Warburg O, Wind F, Negelein E. THE METABOLISM OF TUMORS IN THE BODY. J.

Gen. Physiol. 1927; 8(6):519–530.

235. Porter AE. On the Behaviour of Amylase in the Presence of a Specific Precipitate.

Biochem. J. 1913; 7(6):599–603.

236. Azzopardi E, Lloyd C, Teixeira SR, Conlan RS, Whitaker IS. Clinical applications of

amylase: Novel perspectives. Surgery. 2016; 160(1):26–37.

doi:10.1016/j.surg.2016.01.005.

237. Groot PC, Bleeker MJ, Pronk JC, Arwert F, Mager WH, Planta RJ, et al. The human α-

amylase multigene family consists of haplotypes with variable numbers of genes.

Genomics [Internet]. 1989; 5(1):29–42. doi:http://dx.doi.org/10.1016/0888-

7543(89)90083-9.

238. Gumucio DL, Wiebauer K, Caldwell RM, Samuelson LC, Meisler MH. Concerted

evolution of human amylase genes. Mol Cell Biol. 1988; 8(3):1197–1205.

239. Groot PC, Mager WH, Frants RR, Meisler MH, Samuelson LC. The human amylase-

encoding genes amy2 and amy3 are identical to AMY2A and AMY2B. Gene. 1989;

85(2):567–568.

240. Horii A, Emi M, Tomita N, Nishide T, Ogawa M, Mori T, et al. Primary structure of

human pancreatic alpha-amylase gene: its comparison with human salivary alpha-amylase

gene. Gene. 1987; 60(1):57–64.

241. Groot PC, Bleeker MJ, Pronk JC, Arwert F, Mager WH, Planta RJ, et al. Human

pancreatic amylase is encoded by two different genes. Nucleic Acids Res. 1988;

156

16(10):4724.

242. Meisler MH, Ting CN. The remarkable evolutionary history of the human amylase genes.

Crit. Rev. Oral Biol. Med. 1993; 4(3–4):503–509.

243. Samuelson LC, Wiebauer K, Gumucio DL, Meisler MH. Expression of the human

amylase genes: recent origin of a salivary amylase promoter from an actin pseudogene.

Nucleic Acids Res. 1988; 16(17):8261–8276.

244. Samuelson LC, Wiebauer K, Snow CM, Meisler MH. Retroviral and pseudogene insertion

sites reveal the lineage of human salivary and pancreatic amylase genes from a single gene

during primate evolution. Mol Cell Biol. 1990; 10(6):2513–2520.

245. Koyama I, Komine S, Iino N, Hokari S, Igarashi S, Alpers DH, et al. α-Amylase

expressed in human liver is encoded by the AMY-2B gene identified in tumorous tissues.

Clin. Chim. Acta [Internet]. 2001; 309(1):73–83. doi:http://dx.doi.org/10.1016/S0009-

8981(01)00501-0.

246. Seyama K, Nukiwa T, Takahashi K, Takahashi H, Kira S. Amylase mRNA transcripts in

normal tissues and neoplasms: the implication of different expressions of amylase

isogenes. J. Cancer Res. Clin. Oncol. [Internet]. 1994; 120(4):213–220.

doi:10.1007/BF01372559.

247. Ueda M, Araki T, Shiota T, Taketa K. Age and sex-dependent alterations of serum

amylase and levels in normal human adults. J. Gastroenterol. 1994; 29(2):189–

191.

248. Zheng C, Hoffman MP, McMillan T, Kleinman HK, O’Connell BC. Growth factor

regulation of the amylase promoter in a differentiating salivary acinar cell line. J Cell

Physiol. 1998; 177(4):628–635. doi:10.1002/(sici)1097-4652(199812)177:4<628::aid-

157

jcp13>3.0.co;2-l.

249. Jung DW, Hecht D, Ho SW, O’Connell BC, Kleinman HK, Hoffman MP. PKC and

ERK1/2 regulate amylase promoter activity during differentiation of a salivary gland cell

line. J Cell Physiol. 2000; 185(2):215–225. doi:10.1002/1097-

4652(200011)185:2<215::aid-jcp6>3.0.co;2-l.

250. Zheng C, Hoque AT, Braddon VR, Baum BJ, O’Connell BC. Evaluation of salivary gland

acinar and ductal cell-specific promoters in vivo with recombinant adenoviral vectors.

Hum. Gene Ther. 2001; 12(18):2215–2223. doi:10.1089/10430340152710559.

251. Nishide T, Emi M, Nakamura Y, Matsubara K. Corrected sequences of cDNAs for human

salivary and pancreatic alpha-amylases [corrected]. Gene. 1984; 28(2):263–270.

252. Omichi K, Hase S. Detection of human urinary alpha-amylase encoded by the AMY2B

gene using a fluorogenic substrate, FG5P. J Biochem. 1992; 112(3):303–305.

253. Zakowski JJ, Gregory MR, Bruns DE. Amylase from human serous ovarian tumors:

purification and characterization. Clin. Chem. 1984; 30(1):62–68.

254. Butterworth PJ, Warren FJ, Ellis PR. Human α-amylase and starch digestion: An

interesting marriage. Starch - Stärke [Internet]. 2011; 63(7):395–405.

doi:10.1002/star.201000150.

255. Carpenter D, Dhar S, Mitchell LM, Fu B, Tyson J, Shwan NAA, et al. Obesity, starch

digestion and amylase: association between copy number variants at human salivary

(AMY1) and pancreatic (AMY2) amylase genes. Hum. Mol. Genet. 2015; 24(12):3472–

3480. doi:10.1093/hmg/ddv098.

256. Brayer GD, Luo Y, Withers SG. The structure of human pancreatic alpha-amylase at 1.8

A resolution and comparisons with related enzymes. Protein Sci. 1995; 4(9):1730–1742.

158

doi:10.1002/pro.5560040908.

257. Ramasubbu N, Paloth V, Luo Y, Brayer GD, Levine MJ. Structure of human salivary

alpha-amylase at 1.6 A resolution: implications for its role in the oral cavity. Acta

Crystallogr. D. Biol. Crystallogr. 1996; 52(Pt 3):435–446.

doi:10.1107/S0907444995014119.

258. Minobe S, Nakajima H, Itoh N, Funakoshi I, Yamashina I. Structure of a major

oligosaccharide of Taka-amylase A. J. Biochem. 1979; 86(6):1851–1854.

259. Ji C, Wei G. Deglycosylation induces extensive dynamics changes in α-amylase revealed

by hydrogen/deuterium exchange mass spectrometry. Rapid Commun. Mass Spectrom.

[Internet]. 2013; 27(23):2625–2630. doi:10.1002/rcm.6732.

260. Kasperczyk S, Brzoza Z, Kasperczyk A, Beck B, Duiban H, Mertas A. The changes of

alpha-amylase activity in serum and different tissues of female rat during sex cycle--

isoelectrofocusing studies of alpha-amylase. Med. Sci. Monit. 2001; 7(1):49–53.

261. Weiss MJ, Edmondson HA, Wertman M. Elevated serum amylase associated with

bronchogenic carcinoma; report of case. Am J Clin Pathol. 1951; 21(11):1057–1061.

262. Kavitha S, Balasubramanian R. Elderly lady with ascites. J. Assoc. Physicians India.

2006; 54:325–326.

263. Omichi K, Hase S. Identification of the characteristic amino-acid sequence for human

alpha-amylase encoded by the AMY2B gene. Biochim. Biophys. Acta. 1993;

1203(2):224–229.

264. Sinha S, Khan H, Timms PM, Olagbaiye OA. Pancreatic-type hyperamylasemia and

hyperlipasemia secondary to ruptured ovarian cyst: a case report and review of the

literature. J. Emerg. Med. 2010; 38(4):463–466. doi:10.1016/j.jemermed.2008.04.042.

159

265. Yokouchi H, Horii A, Emi M, Tomita N, Doi S, Ogawa M, et al. Cloning and

characterization of a third type of human alpha-amylase gene, AMY2B. Gene. 1990;

90(2):281–286.

266. Flood JG, Schuerch C, Dorazio RC, Bowers GNJ. Marked hyperamylasemia associated

with carcinoma of the lung. Clin. Chem. 1978; 24(7):1207–1212.

267. Tohya T, Shimajiri S, Onoda C, Yoshimura T. Complete remission of ovarian

endometrioid adenocarcinoma associated with hyperamylasemia and liver metastasis

treated by paclitaxel and carboplatin chemotherapy: a case report. Int. J. Gynecol. Cancer

[Internet]. 2004; 14(2):378–380. doi:10.1111/j.1048-891x.2004.014225.x.

268. Sandiford JA, Chiknas SG. Hyperamylasemia and ovarian carcinoma. Clin. Chem. 1979;

25(6):948–950.

269. Kawakita T, Sasaki H, Hoshiba T, Asamoto A, Williamson E. Amylase-producing ovarian

carcinoma: A case report and a retrospective study. Gynecol. Oncol. Case Reports

[Internet]. 2012; 2(3):112–114. doi:10.1016/j.gynor.2012.06.002.

270. Takeuchi T, Fujiki H, Kameya T. Characterization of amylases produced by tumors. Clin.

Chem. 1981; 27(4):556–559.

271. Moriyama T. Sialyl salivary-type amylase associated with ovarian cancer. Clin. Chim.

Acta [Internet]. 2008; 391(1–2):106–111. doi:http://dx.doi.org/10.1016/j.cca.2008.01.025.

272. Shimamura J, Fridhandler L, Berk JE. Unusual isomaylase in cancer-associated

hyperamylasemia. Cancer. 1976; 38(5):2121–2126.

273. Berk JE, Shimamura J, Fridhandler L. Tumor-associated hyperamylasemia. Am J

Gastroenterol. 1977; 68(6):572–577.

274. Warshaw AL, Lee KH. Characteristic alterations of serum isoenzymes of amylase in

160

diseases of liver, pancreas, salivary gland, lung, and genitalia. J. Surg. Res. 1977;

22(4):362–369.

275. Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, et al.

Clustal W and Clustal X version 2.0. Bioinformatics [Internet]. 2007; 23(21):2947–2948.

doi:10.1093/bioinformatics/btm404.

276. Goujon M, McWilliam H, Li W, Valentin F, Squizzato S, Paern J, et al. A new

bioinformatics analysis tools framework at EMBL-EBI. Nucleic Acids Res [Internet].

2010; 38(Web Server issue):W695-9. doi:10.1093/nar/gkq313.

277. McWilliam H, Li W, Uludag M, Squizzato S, Park YM, Buso N, et al. Analysis Tool Web

Services from the EMBL-EBI. Nucleic Acids Res [Internet]. 2013; 41(Web Server

issue):W597-600. doi:10.1093/nar/gkt376.

278. Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, et al. Gasteiger

E., Hoogland C., Gattiker A., Duvaud S., Wilkins M.R., Appel R.D., Bairoch A. John M.

Walk. Proteomics Protoc. Handbook, Humana Press. 2005; :571–607.

279. Petersen TN, Brunak S, von Heijne G, Nielsen H. SignalP 4.0: discriminating signal

peptides from transmembrane regions. Nat Methods. 2011; 8(10):785–786.

doi:10.1038/nmeth.1701.

280. Vlahovicek K, Murvai J, Barta E, Pongor S. The SBASE protein domain library, release

9.0: an online resource for protein domain identification. Nucleic Acids Res [Internet].

2002; 30(1):273–275. doi:10.1093/nar/30.1.273.

281. Finn RD, Tate J, Mistry J, Coggill PC, Sammut SJ, Hotz H-R, et al. The Pfam protein

families database. Nucleic Acids Res [Internet]. 2008; 36(Database issue):D281-8.

doi:10.1093/nar/gkm960.

161

282. Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, et al. Pfam: the

protein families database. Nucleic Acids Res [Internet]. 2014; 42(D1):D222–D230.

doi:10.1093/nar/gkt1223.

283. Gupta A, Deshpande A, Amburi JK, Sabarinathan R, Senthilkumar R, Sekar K. CSSP

(Consensus Secondary Structure Prediction): a web-based server for structural biologists.

J. Appl. Crystallogr. [Internet]. 2009; 42(2):336–338.

doi:doi:10.1107/S0021889808043847.

284. Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y. The I-TASSER Suite: protein structure

and function prediction. Nat. Methods. 2015; 12(1):7–8. doi:10.1038/nmeth.3213.

285. Yang J, Zhang Y. I-TASSER server: new development for protein structure and function

predictions. Nucleic Acids Res. 2015; 43(W1):W174-81. doi:10.1093/nar/gkv342.

286. Roy A, Kucukural A, Zhang Y. I-TASSER: a unified platform for automated protein

structure and function prediction. Nat. Protoc. 2010; 5(4):725–738.

doi:10.1038/nprot.2010.5.

287. Zhang Y. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics.

2008; 9:40. doi:10.1186/1471-2105-9-40.

288. Mészáros B, Simon I, Dosztányi Z. Prediction of Protein Binding Regions in Disordered

Proteins. PLoS Comput. Biol. [Internet]. 2009; 5(5):e1000376.

doi:10.1371/journal.pcbi.1000376.

289. Dosztányi Z, Mészáros B, Simon I. ANCHOR: web server for predicting protein binding

regions in disordered proteins. Bioinformatics [Internet]. 2009; 25(20):2745–2746.

doi:10.1093/bioinformatics/btp518.

290. Kallberg M, Wang H, Wang S, Peng J, Wang Z, Lu H, et al. Template-based protein

162

structure modeling using the RaptorX web server. Nat Protoc. 2012; 7(8):1511–1522.

doi:10.1038/nprot.2012.085.

291. HMMpTM: Improving transmembrane protein topology prediction using phosphorylation

and glycosylation site prediction. Biochim. Biophys. Acta. 2013; 1844:316–322.

doi:10.1016/j.bbapap.2013.11.001.

292. Cunningham F, Amode MR, Barrell D, Beal K, Billis K, Brent S, et al. Ensembl 2015.

Nucleic Acids Res [Internet]. 2015; 43(D1):D662–D669. doi:10.1093/nar/gku1010.

293. Messeguer X, Escudero R, Farre D, Nunez O, Martinez J, Alba MM. PROMO: detection

of known transcription regulatory elements using species-tailored searches.

Bioinformatics. 2002; 18(2):333–334.

294. Farré D, Roset R, Huerta M, Adsuara JE, Roselló L, Albà MM, et al. Identification of

patterns in biological sequences at the ALGGEN server: PROMO and MALGEN. Nucleic

Acids Res [Internet]. 2003; 31(13):3651–3653. Available from:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC169011/.

295. Dreos R, Ambrosini G, Cavin Perier R, Bucher P. EPD and EPDnew, high-quality

promoter resources in the next-generation sequencing era. Nucleic Acids Res. 2013;

41(Database issue):D157-64. doi:10.1093/nar/gks1233.

296. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative

Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal

[Internet]. 2013. doi:10.1126/scisignal.2004088.

297. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer

genomics portal: an open platform for exploring multidimensional cancer genomics data.

Cancer Discov. 2012; 2(5):401–404. doi:10.1158/2159-8290.cd-12-0095.

163

298. Doyon Y, Home W, Daull P, LeBel D. Effect of C-domain N-glycosylation and deletion

on rat pancreatic alpha-amylase secretion and activity. Biochem. J. [Internet]. 2002;

362(Pt 1):259–264. Available from:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1222384/.

299. SQUIRES BT. Human salivary amylase secretion in relation to diet. J. Physiol. 1953;

119(2–3):153–156.

300. Aghajari N, Feller G, Gerday C, Haser R. Structural basis of alpha-amylase activation by

chloride. Protein Sci. 2002; 11(6):1435–1441. doi:10.1110/ps.0202602.

301. Morris C, Fichtel S, Taylor A. Impact of Calcium on Salivary α-Amylase Activity, Starch

Paste Apparent Viscosity, and Thickness Perception. Chemosens. Percept. [Internet].

2011; 4(3):116–122. doi:10.1007/s12078-011-9091-7.

302. Zabel BU, Naylor SL, Sakaguchi AY, Bell GI, Shows TB. High-resolution chromosomal

localization of human genes for amylase, proopiomelanocortin, somatostatin, and a DNA

fragment (D3S1) by in situ hybridization. Proc Natl Acad Sci U S A. 1983; 80(22):6932–

6936.

303. Tricoli J V, Shows TB. Regional assignment of human amylase (AMY) to p22----p21 of

chromosome 1. Somat. Cell Mol. Genet. 1984; 10(2):205–210.

304. Munke M, Lindgren V, de Martinville B, Francke U. Comparative analysis of mouse-

human hybrids with rearranged 1 by in situ hybridization and Southern

blotting: high-resolution mapping of NRAS, NGFB, and AMY on human chromosome 1.

Somat. Cell Mol. Genet. 1984; 10(6):589–599.

305. Levitzki A, Steer ML. The allosteric activation of mammalian alpha-amylase by chloride.

Eur. J. Biochem. 1974; 41(1):171–180.

164

306. Lifshitz R, Levitzki A. Identity and properties of the chloride effector binding site in hog

pancreatic alpha-amylase. Biochemistry. 1976; 15(9):1987–1993.

307. MacGregor EA, Janecek S, Svensson B. Relationship of sequence and structure to

specificity in the alpha-amylase family of enzymes. Biochim. Biophys. Acta. 2001;

1546(1):1–20.

308. Brayer GD, Luo Y, Withers SG. The structure of human pancreatic α-amylase at 1.8 Å

resolution and comparisons with related enzymes. Protein Sci. [Internet]. 1995;

4(9):1730–1742. doi:10.1002/pro.5560040908.

309. Parker CE, Mocanu V, Mocanu M et al. Mass Spectrometry for Post-Translational

Modifications [Internet]. In: O A, editor. Neuroproteomics. Boca Raton (FL): CRC

Press/Taylor & Francis; 2010. Available from:

http://www.ncbi.nlm.nih.gov/books/NBK56012/.

310. Lodish H, Berk A, Zipursky SL et al. Section 17.7Protein Glycosylation in the ER and

Golgi Complex [Internet]. In: Molecular Cell Biology. New York: W. H. Freeman and

Company.; 2000. p. Section 17.7. Available from:

http://www.ncbi.nlm.nih.gov/books/NBK21744/.

311. Rakus JF, Mahal LK. New technologies for glycomic analysis: toward a systematic

understanding of the glycome. Annu. Rev. Anal. Chem. (Palo Alto. Calif). 2011; 4:367–

392. doi:10.1146/annurev-anchem-061010-113951.

312. Varki A, Lowe JB. Biological Roles of Glycans. In: Varki A, Cummings RD, Esko JD,

Freeze HH, Stanley P, Bertozzi CR, et al., editors. . Cold Spring Harbor (NY): 2009.

313. Xue J, Zhao Q, Zhu L, Zhang W. Deglycosylation of FcalphaR at N58 increases its

binding to IgA. Glycobiology. 2010; 20(7):905–915. doi:10.1093/glycob/cwq048.

165

314. Mendez JD, Xie J, Aguilar-Hernandez M, Mendez-Valenzuela V. Trends in advanced

glycation end products research in diabetes mellitus and its complications. Mol. Cell.

Biochem. 2010; 341(1–2):33–41. doi:10.1007/s11010-010-0434-5.

315. Cohen P. The origins of protein phosphorylation. Nat Cell Biol [Internet]. 2002;

4(5):E127–E130. Available from: http://dx.doi.org/10.1038/ncb0502-e127.

316. Verger A, Perdomo J, Crossley M. Modification with SUMO: A role in transcriptional

regulation. EMBO Rep. 2003; 4(2):137–142. doi:10.1038/sj.embor.embor738.

317. van der Lee R, Buljan M, Lang B, Weatheritt RJ, Daughdrill GW, Dunker AK, et al.

Classification of intrinsically disordered regions and proteins. Chem. Rev. 2014;

114(13):6589–6631. doi:10.1021/cr400525m.

318. Liu H-L, Chen W-J, Chou S-N. Mechanisms of aggregation of alpha- and beta-amylases

in aqueous dispersions. Colloids Surfaces B Biointerfaces [Internet]. 2003; 28(2–3):215–

225. doi:http://dx.doi.org/10.1016/S0927-7765(02)00142-X.

319. Branden C, Tooze J. Introduction to Protein Structure. 2nd ed. Garland Science; 1999.

320. Rambaran RN, Serpell LC. Amyloid fibrils: Abnormal protein assembly. Prion [Internet].

2008; 2(3):112–117. Available from:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2634529/.

321. Pieper-Bigelow C, Strocchi A, Levitt MD. Where does serum amylase come from and

where does it go? Gastroenterol Clin North Am. 1990; 19(4):793–810.

322. Kazmierczak SC, Van Lente F, McHugh AM, Katzin WE. Macroamylasemia with a

markedly increased amylase clearance ratio in a patient with renal cell carcinoma. Clin.

Chem. 1988; 34(2):435–438.

323. Warshaw AL, Lee KH. Macroamylasemia and other chronic nonspecific

166

hyperamylasemias: chemical oddities or clinical entities? Am J Surg. 1978; 135(4):488–

493.

324. Otsuki M, Yuu H, Maeda M, Saeki S, Yamasaki T. Amylase in the lung. Cancer. 1977;

39(4):1656–1663.

325. Hayashi Y, Fukayama M, Koike M, Nakayama T. Amylase in human lungs and the

female genital tract. Histochemical and immunohistochemical localization.

Histochemistry. 1986; 85(6):491–496.

326. Koschmieder S, Halmos B, Levantini E, Tenen DG. Dysregulation of the C/EBPα

Differentiation Pathway in Human Cancer. J. Clin. Oncol. [Internet]. 2009; 27(4):619–

628. doi:10.1200/JCO.2008.17.9812.

327. Sundfeldt K, Ivarsson K, Carlsson M, Enerbäck S, Janson PO, Brännström M, et al. The

expression of CCAAT/enhancer binding protein (C/EBP) in the human ovary in vivo:

specific increase in C/EBPβ during epithelial tumour progression. Br. J. Cancer [Internet].

1999; 79(7–8):1240–1248. doi:10.1038/sj.bjc.6690199.

328. Gan L, Chen S, Zhong J, Wang X, Lam EKY, Liu X, et al. ZIC1 is downregulated through

promoter hypermethylation, and functions as a tumor suppressor gene in colorectal cancer.

PLoS One. 2011; 6(2):e16916. doi:10.1371/journal.pone.0016916.

329. Peck AR, Witkiewicz AK, Liu C, Klimowicz AC, Stringer GA, Pequignot E, et al. Low

levels of Stat5a protein in breast cancer are associated with tumor progression and

unfavorable clinical outcomes. Breast Cancer Res. 2012; 14(5):R130.

doi:10.1186/bcr3328.

330. Yang C, Bolotin E, Jiang T, Sladek FM, Martinez E. Prevalence of the Initiator over the

TATA box in human and yeast genes and identification of DNA motifs enriched in human

167

TATA-less core promoters. Gene. 2007; 389(1):52–65. doi:10.1016/j.gene.2006.09.029.

331. Klug WS, Cummings MR, Spencer CA, Palladina M. Concepts of Genetics. Ninth. San

Francisco: Pearson Benjamin Cummings; 2009.

332. Canettieri G, Santaguida MG, Antonucci L, Della Guardia M, Franchi A, Coni S, et al.

CCAAT/Enhancer-Binding Proteins Are Key Regulators of Human Type Two Deiodinase

Expression in a Placenta Cell Line. Endocrinology [Internet]. 2012; 153(8):4030–4038.

doi:10.1210/en.2011-2113.

333. Ramji DP, Foka P. CCAAT/enhancer-binding proteins: structure, function and regulation.

Biochem. J. 2002; 365(Pt 3):561–575. doi:10.1042/BJ20020508.

334. Cammack, Richard; Atwood, Teresa; Campbell, Peter; Parish, Howard; Smith, Anthony;

Vella, Frank; Stirling J, editor. Oxford Dictionary of Biochemistry and Molecular

Biology. 2nd ed. Oxford University Press.; 2006.

335. Mantovani R. The molecular biology of the CCAAT-binding factor NF-Y. Gene. 1999;

239(1):15–27.

336. Juang CM, Yeng MS, Twu NF, Chao GC. Hyperamylasemia associated with endometroid

carcinoma of the ovary. Zhonghua Yi Xue Za Zhi (Taipei). 2000; 63(9):710–713.

337. Schlikker I, Nakad A, Gerbaux A, Azzouzi K, Kadou J, Lezaire P, et al. Hyperamylasemia

with papillary serous cystadenocarcinoma of the ovary. Acta Clin. Belg. 1989; 44(4):255–

258.

338. Jacobs E, Jennette JC, Reavis RA. Chronic hyperamylasemia and chronic pelvic

inflammatory disease. Clin. Chem. 1983; 29(5):887–888.

339. Zakrzewska I, Pietrynczak M. The activity of alpha-amylase and its salivary isoenzymes

in serum and urine of patients with neoplastic diseases of female reproductive organs.

168

Rocz. Akad. Med. Bialymst. 1996; 41(2):492–498.

340. Hayakawa T, Kameya A, Mizuno R, Noda A, Kondo T, Hirabayashi N. Hyperamylasemia

with papillary serous cystadenocarcinoma of the ovary. Cancer. 1984; 54(8):1662–1665.

341. Hodes ME, Sisk CJ, Karn RC, Ehrlich CE, Lehrner LM, Roth LM, et al. An amylase-

producing serous cystadenocarcinoma of the ovary. Oncology. 1985; 42(4):242–247.

342. Lehrner LM, Ward JC, Karn RC, Ehrlich CE, Merritt D. An evaluation of the usefulness

of amylase isozyme differentiation in patients with hyperamylasemia. Am. J. Clin. Pathol.

1976; 66(3):576–587.

343. O’Riordan T, Gaffney E, Tormey V, Daly P. Hyperamylasemia associated with

progression of a serous surface papillary carcinoma. Gynecol. Oncol. 1990; 36(3):432–

434.

344. Kruk PA, Maines-Bandiera SL, Auersperg N. A simplified method to culture human

ovarian surface epithelium. Lab. Invest. 1990; 63(1):132–136.

345. Maines-Bandiera SL, Kruk PA, Auersperg N. Simian virus 40-transformed human ovarian

surface epithelial cells escape normal growth controls but retain morphogenetic responses

to extracellular matrix. Am. J. Obstet. Gynecol. 1992; 167(3):729–735.

346. Hanson WG, Ferguson PJ. Differential methotrexate toxicity between two human oral

squamous carcinoma cell lines. J Otolaryngol. 1993; 22(3):143–147.

347. Leuchs EF. Wirkung des Speichels auf Stärke" (Effect of saliva on starch). Ann. der Phys.

und Chemie. 1831; 98(8):623.

348. Danilewsky. “Über specifisch wirkende Körper des natürlichen und künstlichen

pancreatischen Saftes” (On the specifically-acting principles of the natural and artificial

). Virchows Arch. für Pathol. Anat. und Physiol. und für Klin. Medizin.

169

1862; 25:279–307.

349. Zhang J, Zhang L, Pan S, Gu B, Zhen Y, Yan J, et al. Amylase: sensitive tumor marker for

amylase-producing lung adenocarcinoma. J. Thorac. Dis. 2013; 5(4):E167-9.

doi:10.3978/j.issn.2072-1439.2013.08.37.

350. Kang JU, Koo SH, Kwon KC, Park JW. AMY2A: a possible tumor-suppressor gene of

1p21.1 loss in gastric carcinoma. Int. J. Oncol. 2010; 36(6):1429–1435.

351. Barua A, Bitterman P, Abramowicz JS, Dirks AL, Bahr JM, Hales DB, et al.

Histopathology of ovarian tumors in laying hens: a preclinical model of human ovarian

cancer. Int. J. Gynecol. Cancer. 2009; 19(4):531–539.

doi:10.1111/IGC.0b013e3181a41613.

352. Bai W, Oliveros-Saunders B, Wang Q, Acevedo-Duncan ME, Nicosia S V. Estrogen

stimulation of ovarian surface epithelial cell proliferation. In Vitro Cell. Dev. Biol. Anim.

2000; 36(10):657–666. doi:10.1290/1071-2690(2000)036<0657:ESOOSE>2.0.CO;2.

353. Bobbs AS, Cole JM, Cowden Dahl KD. Emerging and Evolving Ovarian Cancer Animal

Models. Cancer Growth Metastasis. 2015; 8(Suppl 1):29–36. doi:10.4137/CGM.S21221.

354. Auersperg N, Maclaren IA, Kruk PA. Ovarian surface epithelium: autonomous production

of connective tissue-type extracellular matrix. Biol. Reprod. 1991; 44(4):717–724.

355. Kruk PA, Auersperg N. Human ovarian surface epithelial cells are capable of physically

restructuring extracellular matrix. Am. J. Obstet. Gynecol. 1992; 167(5):1437–1443.

356. Fuster MM, Esko JD. The sweet and sour of cancer: glycans as novel therapeutic targets.

Nat Rev Cancer. 2005; 5(7):526–542. doi:10.1038/nrc1649.

357. Lange T, Samatov TR, Tonevitsky AG, Schumacher U. Importance of altered

glycoprotein-bound N- and O-glycans for epithelial-to-mesenchymal transition and

170

adhesion of cancer cells. Carbohydr Res. 2014; 389:39–45.

doi:10.1016/j.carres.2014.01.010.

358. Mitchell MJ, King MR. Physical Biology in Cancer. 3. The role of cell glycocalyx in

vascular transport of circulating tumor cells [Internet]. 2014.

doi:10.1152/ajpcell.00285.2013.

359. Krahling H, Mally S, Eble JA, Noel J, Schwab A, Stock C. The glycocalyx maintains a

cell surface pH nanoenvironment crucial for integrin-mediated migration of human

melanoma cells. Pflugers Arch. 2009; 458(6):1069–1083. doi:10.1007/s00424-009-0694-

7.

360. Dobrossy L, Pavelic ZP, Bernacki RJ. A correlation between cell surface sialyltransferase,

sialic acid, and glycosidase activities and the implantability of B16 murine melanoma.

Cancer Res. 1981; 41(6):2262–2266.

361. Terajima M, Perdivara I, Sricholpech M, Deguchi Y, Pleshko N, Tomer KB, et al.

Glycosylation and cross-linking in bone type I collagen. J. Biol. Chem. 2014;

289(33):22636–22647. doi:10.1074/jbc.M113.528513.

362. Quintarelli G, Dellovo MC, Balduini C, Castellani AA. The effects of alpha amylase on

collagen-proteoglycans and collagen-glycoprotein complexes in connective tissue

matrices. Histochemie [Internet]. 1969; 18(4):373–375. doi:10.1007/BF00279887.

363. Balfour JA, McTavish D. Acarbose. An update of its pharmacology and therapeutic use in

diabetes mellitus. Drugs. 1993; 46(6):1025–1054.

364. Deng R, Chow T-J. Hypolipidemic, antioxidant, and antiinflammatory activities of

microalgae Spirulina. Cardiovasc. Ther. 2010; 28(4):e33-45. doi:10.1111/j.1755-

5922.2010.00200.x.

171

365. Selmi C, Leung PSC, Fischer L, German B, Yang C-Y, Kenny TP, et al. The effects of

Spirulina on anemia and immune function in senior citizens. Cell. Mol. Immunol. 2011;

8(3):248–254. doi:10.1038/cmi.2010.76.

366. Gemma C, Mesches MH, Sepesi B, Choo K, Holmes DB, Bickford PC. Diets enriched in

foods with high antioxidant activity reverse age-induced decreases in cerebellar beta-

adrenergic function and increases pro-inflammatory cytokines. J. Neurosci. 2002;

22(14):6114–6120.

367. Stromberg I, Gemma C, Vila J, Bickford PC. Blueberry and Spirulina-enriched diets

enhance striatal dopamine recovery and induce a rapid, transient microglia activation after

injury of the rat nigrostriatal dopamine system. J. Exp. Neurol. 2005; 196(2):298–307.

368. Wang Y, Chang CF, Chou J, Chen HL, Deng X, Harvey BK, et al. Dietary

supplementation with blueberries, spinach, or spirulina reduces ischemic brain damage.

Exp Neurol [Internet]. 2005; 193(1):75–84. doi:10.1016/j.expneurol.2004.12.014.

369. Lupatini AL, Colla LM, Canan C, Colla E. Potential application of microalga Spirulina

platensis as a protein source. J. Sci. Food Agric. 2017; 97(3):724–732.

doi:10.1002/jsfa.7987.

370. Gutierrez-Salmean G, Fabila-Castillo L, Chamorro-Cevallos G. Nutritional and

Toxicological Aspects of Spirulina (Arthrospira). Nutr. Hosp. 2015; 32(1):34–40.

doi:10.3305/nh.2015.32.1.9001.

371. Romay C, Gonzalez R, Ledon N, Remirez D, Rimbau V. C-phycocyanin: a biliprotein

with antioxidant, anti-inflammatory and neuroprotective effects. Curr. Protein Pept. Sci.

2003; 4(3):207–216.

372. Chen Q, Gao Q, Chen K, Wang Y, Chen L, Li XU. Curcumin suppresses migration and

172

invasion of human endometrial carcinoma cells. Oncol. Lett. [Internet]. 2015; 10(3):1297–

1302. doi:10.3892/ol.2015.3478.

373. Gupta SC, Kim JH, Prasad S, Aggarwal BB. Regulation of survival, proliferation,

invasion, angiogenesis, and metastasis of tumor cells through modulation of inflammatory

pathways by nutraceuticals. Cancer Metastasis Rev. [Internet]. 2010; 29(3):405–434.

doi:10.1007/s10555-010-9235-2.

374. Torres-Duran P V, Ferreira-Hermosillo A, Juarez-Oropeza MA. Antihyperlipemic and

antihypertensive effects of Spirulina maxima in an open sample of Mexican population: a

preliminary report. Lipids Health Dis. 2007; 6:33. doi:10.1186/1476-511X-6-33.

375. Parikh P, Mani U, Iyer U. Role of Spirulina in the Control of Glycemia and Lipidemia in

Type 2 Diabetes Mellitus. J. Med. Food. 2001; 4(4):193–199.

doi:10.1089/10966200152744463.

376. Samuels R, Mani U V, Iyer UM, Nayak US. Hypocholesterolemic effect of spirulina in

patients with hyperlipidemic nephrotic syndrome. J. Med. Food. 2002; 5(2):91–96.

doi:10.1089/109662002760178177.

377. Ramamoorthy A, Premakumari S. Effect of supplementation of Spirulina on

hypercholesterolemic patients. Food Sci Technol. 1996; 33:124–128.

378. Khan M, Shobha JC, Mohan IK, Rao Naidu MU, Prayag A, Kutala VK. Spirulina

attenuates cyclosporine-induced nephrotoxicity in rats. J. Appl. Toxicol. 2006; 26(5):444–

451. doi:10.1002/jat.1159.

379. Germán Chamorro, Mónica Pérez-Albiter, Norma Serrano-García, José J. Mares-Sámano

PR. Spirulina maxima pretreatment partially protects against 1-methyl-4-phenyl-1,2,3,6-

tetrahydropyridine neurotoxicity. Nutr. Neurosci. [Internet]. 2006; 9(5–6):207–212.

173

doi:10.1080/10284150600929748.

380. Romay C, Delgado R, Remirez D, Gonzalez R, Rojas A. Effects of phycocyanin extract

on tumor necrosis factor-alpha and nitrite levels in serum of mice treated with endotoxin.

Arzneimittelforschung. 2001; 51(9):733–736. doi:10.1055/s-0031-1300107.

381. Bai S-K, Lee S-J, Na H-J, Ha K-S, Han J-A, Lee H, et al. beta-Carotene inhibits

inflammatory gene expression in lipopolysaccharide-stimulated macrophages by

suppressing redox-based NF-kappaB activation. Exp. Mol. Med. 2005; 37(4):323–334.

doi:10.1038/emm.2005.42.

382. Konickova R, Vankova K, Vanikova J, Vanova K, Muchova L, Subhanova I, et al. Anti-

cancer effects of blue-green alga Spirulina platensis, a natural source of -like

tetrapyrrolic compounds. Ann. Hepatol. 2014; 13(2):273–283.

383. Yogianti F, Kunisada M, Nakano E, Ono R, Sakumi K, Oka S, et al. Inhibitory effects of

dietary Spirulina platensis on UVB-induced skin inflammatory responses and

carcinogenesis. J. Invest. Dermatol. 2014; 134(10):2610–2619. doi:10.1038/jid.2014.188.

384. Wang Z, Zhang X. Inhibitory effects of small molecular peptides from Spirulina

(Arthrospira) platensis on cancer cell growth. Food Funct. 2016; 7(2):781–788.

doi:10.1039/c5fo01186h.

385. Smieszek A, Giezek E, Chrapiec M, Murat M, Mucha A, Michalak I, et al. The Influence

of Spirulina platensis Filtrates on Caco-2 Proliferative Activity and Expression of

Apoptosis-Related microRNAs and mRNA. Mar. Drugs. 2017; 15(3).

doi:10.3390/md15030065.

386. Pan R, Lu R, Zhang Y, Zhu M, Zhu W, Yang R, et al. Spirulina phycocyanin induces

differential protein expression and apoptosis in SKOV-3 cells. Int. J. Biol. Macromol.

174

2015; 81:951–959. doi:10.1016/j.ijbiomac.2015.09.039.

387. Ying J, Wang J, Ji H, Lin C, Pan R, Zhou L, et al. Transcriptome analysis of phycocyanin

inhibitory effects on SKOV-3 cell proliferation. Gene. 2016; 585(1):58–64.

doi:10.1016/j.gene.2016.03.023.

388. Zhang B, Cai FF, Zhong XY. An overview of biomarkers for the ovarian cancer diagnosis.

Eur. J. Obstet. Gynecol. Reprod. Biol. 2011; 158(2):119–123.

doi:10.1016/j.ejogrb.2011.04.023.

389. Kufe DW. Mucins in cancer: function, prognosis and therapy. Nat. Rev. Cancer. 2009;

9(12):874–885. doi:10.1038/nrc2761.

390. Paszek MJ, DuFort CC, Rossier O, Bainer R, Mouw JK, Godula K, et al. The cancer

glycocalyx mechanically primes integrin-mediated growth and survival. Nature. 2014;

511(7509):319–325. doi:10.1038/nature13535.

175

Appendix I

Potential novel regulators or biomarkers of OC – 683 proteins in total of non-redundant, secreted, ordered and aggregation-prone human proteins.

Appendix I Uniprot Uniprot Gene name Protein name entry entry name Proteins functionally related to ECM/microenvironment modifications A8K2U0 A2ML1 A2ML1 CPAMD9 Q9Y653 AGRG1 ADGRG1 GPR56 TM7LN4 TM7XN1 UNQ540/PRO1083 P01019 ANGT AGT SERPINA8 Angiotensinogen (Serpin A8) [Cleaved into: Angiotensin-1 (Angiotensin 1-10) (Angiotensin I) (Ang I); Angiotensin-2 (Angiotensin 1-8) (Angiotensin II) (Ang II); Angiotensin-3 (Angiotensin 2-8) (Angiotensin III) (Ang III) (Des-Asp[1]- angiotensin II); Angiotensin-4 (Angiotensin 3-8) (Angiotensin IV) (Ang IV); Angiotensin 1-9; Angiotensin 1-7; Angiotensin 1-5; Angiotensin 1-4] Q13740 CD166 ALCAM MEMD CD166 antigen (Activated leukocyte cell adhesion molecule) (CD antigen CD166) S4R3Y4 S4R3Y4 AMBP Protein AMBP Q15389 ANGP1 ANGPT1 KIAA0003 Angiopoietin-1 (ANG-1) O15123 ANGP2 ANGPT2 Angiopoietin-2 (ANG-2) O95841 ANGL1 ANGPTL1 ANG3 ANGPT3 Angiopoietin-related protein 1 ARP1 PSEC0154 (Angiopoietin-3) (ANG-3) UNQ162/PRO188 (Angiopoietin-like protein 1) Q9UKU9 ANGL2 ANGPTL2 ARP2 Angiopoietin-related protein 2 UNQ170/PRO196 (Angiopoietin-like protein 2)

176

P58335 ANTR2 ANTXR2 CMG2 Anthrax toxin receptor 2 (Capillary morphogenesis gene 2 protein) (CMG-2) O95393 BMP10 BMP10 Bone morphogenetic protein 10 (BMP-10) P22003 BMP5 BMP5 Bone morphogenetic protein 5 (BMP-5) P22004 BMP6 BMP6 VGR Bone morphogenetic protein 6 (BMP-6) (VG-1-related protein) (VG-1-R) (VGR-1) P18075 BMP7 BMP7 OP1 Bone morphogenetic protein 7 (BMP-7) (Osteogenic protein 1) (OP-1) (Eptotermin alfa) P59826 BPIB3 BPIFB3 C20orf185 LPLUNC3 BPI fold-containing family B member 3 (Ligand-binding protein RYA3) (Long palate, lung and nasal epithelium carcinoma- associated protein 3) Q075Z2 BSPH1 BSPH1 Binder of sperm protein homolog 1 (Bovine seminal plasma protein homolog 1) (Bovine seminal plasma protein-like 1) P35070 BTC BTC Probetacellulin [Cleaved into: Betacellulin (BTC)] Q9BXJ1 C1QT1 C1QTNF1 CTRP1 Complement C1q tumor necrosis UNQ310/PRO353 factor-related protein 1 (G protein- coupled receptor-interacting protein) (GIP) Q9BXJ0 C1QT5 C1QTNF5 CTRP5 Complement C1q tumor necrosis UNQ303/PRO344 factor-related protein 5 P60827 C1QT8 C1QTNF8 Complement C1q tumor necrosis UNQ5829/PRO19648 factor-related protein 8 (C1q/TNF- related protein 8) (CTRP8) Q8IUK8 CBLN2 CBLN2 UNQ1892/PRO4338 Cerebellin-2 P48960 CD97 CD97 Q9H5V8 CDCP1 CDCP1 TRASK UNQ2486/PRO5773 P13688 CEAM1 CEACAM1 BGP BGP1 Carcinoembryonic antigen-related cell adhesion molecule 1 (Biliary glycoprotein 1) (BGP-1) (CD antigen CD66a) Q2WEN9 CEA16 CEACAM16 CEAL2 Carcinoembryonic antigen-related cell adhesion molecule 16 (Carcinoembryonic antigen-like 2)

177

Q9UNI1 CELA1 CELA1 ELA1 -like family member 1 (EC 3.4.21.36) (Elastase-1) ( 1) P08217 CEL2A CELA2A ELA2A Chymotrypsin-like elastase family member 2A (EC 3.4.21.71) (Elastase-2A) Q9BXR6 FHR5 CFHR5 CFHL5 FHR5 Complement factor H-related protein 5 (FHR-5) Q15782 CH3L2 CHI3L2 -3-like protein 2 (Chondrocyte protein 39) (YKL- 39) Q9BU40 CRDL1 CHRDL1 NRLN1 Chordin-like protein 1 (Neuralin- 1) (Neurogenesin-1) (Ventroptin) Q9UQC9 CLCA2 CLCA2 CACC3 O43405 COCH COCH COCH5B2 Cochlin (COCH-5B2) UNQ257/PRO294 P12109 CO6A1 COL6A1 P12111 CO6A3 COL6A3 A8TX70 CO6A5 COL6A5 COL29A1 VWA4 A6NMZ7 CO6A6 COL6A6 Q8IVL8 CBPO CPO Carboxypeptidase O (CPO) (EC 3.4.17.-) O75629 CREG1 CREG1 CREG Protein CREG1 (Cellular repressor UNQ727/PRO1409 of E1A-stimulated genes 1) Q9H0B8 CRLD2 CRISPLD2 CRISP11 LCRISP2 Cysteine-rich secretory protein UNQ2914/PRO1156/PRO9783 LCCL domain-containing 2 (Cysteine-rich secretory protein 11) (CRISP-11) (LCCL domain- containing cysteine-rich secretory protein 2) O60676 CST8 CST8 CRES Cystatin-8 (Cystatin-related epididymal spermatogenic protein) Q9H4G1 CST9L CST9L CTES7B Cystatin-9 (Cystatin-like UNQ1835/PRO3543 molecule) Q8N907 DAND5 DAND5 CER2 CKTSF1B3 DAN domain family member 5 GREM3 SP1 (Cerberus-like protein 2) (Cerl-2) (Cysteine knot superfamily 1, BMP antagonist 3) (Gremlin-3) Q07507 DERM DPT Dermatopontin (Tyrosine-rich acidic matrix protein) (TRAMP) O60469 DSCAM DSCAM Q14507 EP3A EDDM3A FAM12A HE3A Epididymal secretory protein E3- alpha (Human epididymis-specific protein 3-alpha) (HE3-alpha)

178

P56851 EP3B EDDM3B FAM12B HE3B Epididymal secretory protein E3- UNQ6412/PRO21187 beta (Human epididymis-specific protein 3-beta) (HE3-beta) O43854 EDIL3 EDIL3 DEL1 EGF-like repeat and discoidin I- like domain-containing protein 3 (Developmentally-regulated endothelial cell locus 1 protein) (Integrin-binding protein DEL1) Q12805 FBLN3 EFEMP1 FBLN3 FBNL EGF-containing fibulin-like extracellular matrix protein 1 (Extracellular protein S1-5) (Fibrillin-like protein) (Fibulin-3) (FIBL-3) O95967 FBLN4 EFEMP2 FBLN4 EGF-containing fibulin-like UNQ200/PRO226 extracellular matrix protein 2 (Fibulin-4) (FIBL-4) (Protein UPH1) P20827 EFNA1 EFNA1 EPLG1 LERK1 Ephrin-A1 (EPH-related receptor TNFAIP4 tyrosine kinase ligand 1) (LERK- 1) (Immediate early response protein B61) (Tumor necrosis factor alpha-induced protein 4) (TNF alpha-induced protein 4) [Cleaved into: Ephrin-A1, secreted form] P52798 EFNA4 EFNA4 EPLG4 LERK4 Ephrin-A4 (EPH-related receptor tyrosine kinase ligand 4) (LERK- 4) Q96BH3 ESPB1 ELSPBP1 E12 Q9UJA9 ENPP5 ENPP5 UNQ550/PRO1107 Ectonucleotide pyrophosphatase/phosphodiesteras e family member 5 (E-NPP 5) (NPP-5) (EC 3.1.-.-) Q6UW88 EPGN EPGN UNQ3072/PRO9904 Epigen (Epithelial mitogen) (EPG) M5A8F1 SUPYN ERVH48-1 C21orf105 HERV- Suppressyn (Endogenous Fb1 NDUFV3-AS1 retrovirus group 48 member 1) (NDUFV3 antisense RNA 1) (endogenous retrovirus group Fb member 1) Q5T1H1 EYS EYS C6orf178 C6orf179 C6orf180 EGFL10 EGFL11 SPAM UNQ9424/PRO34591 P03951 FA11 F11 factor XI (FXI) (EC 3.4.21.27) (Plasma thromboplastin antecedent) (PTA) [Cleaved into: Coagulation factor XIa heavy

179

chain; Coagulation factor XIa light chain] P13726 TF F3 Tissue factor (TF) (Coagulation factor III) (Thromboplastin) (CD antigen CD142) P08709 FA7 F7 Coagulation factor VII P00740 FA9 F9 Coagulation factor IX (EC 3.4.21.22) (Christmas factor) (Plasma thromboplastin component) (PTC) [Cleaved into: Coagulation factor IXa light chain; Coagulation factor IXa heavy chain] Q96MK3 FA20A FAM20A UNQ9388/PRO34279 Pseudokinase FAM20A Q8IXL6 FA20C FAM20C DMP4 Extracellular serine/threonine protein kinase FAM20C (EC 2.7.11.1) (Dentin matrix protein 4) (DMP-4) (Golgi casein kinase) (Golgi-enriched fraction casein kinase) (GEF-CK) Q9Y6R7 FCGBP FCGBP P08620 FGF4 FGF4 HST HSTF1 KS3 Fibroblast growth factor 4 (FGF- 4) (Heparin secretory- transforming protein 1) (HST) (HST-1) (HSTF-1) (Heparin- binding growth factor 4) (HBGF- 4) (Transforming protein KS3) P10767 FGF6 FGF6 HST2 HSTF2 Fibroblast growth factor 6 (FGF- 6) (Heparin secretory- transforming protein 2) (HST-2) (HSTF-2) (Heparin-binding growth factor 6) (HBGF-6) P21781 FGF7 FGF7 KGF Fibroblast growth factor 7 (FGF- 7) (Heparin-binding growth factor 7) (HBGF-7) (Keratinocyte growth factor) P02679 FIBG FGG PRO2061 Fibrinogen gamma chain Q9NZU1 FLRT1 FLRT1 UNQ752/PRO1483 Leucine-rich repeat transmembrane protein FLRT1 (Fibronectin-like domain- containing leucine-rich transmembrane protein 1) O43155 FLRT2 FLRT2 KIAA0405 Leucine-rich repeat UNQ232/PRO265 transmembrane protein FLRT2 (Fibronectin-like domain-

180

containing leucine-rich transmembrane protein 2) Q9NZU0 FLRT3 FLRT3 KIAA1469 Leucine-rich repeat UNQ856/PRO1865 transmembrane protein FLRT3 (Fibronectin-like domain- containing leucine-rich transmembrane protein 3) P0C091 FREM3 FREM3 P01225 FSHB FSHB Follitropin subunit beta (Follicle- stimulating hormone beta subunit) (FSH-B) (FSH-beta) (Follitropin beta chain) Q9BTY2 FUCO2 FUCA2 PSEC0151 Plasma alpha-L- (EC UNQ227/PRO260 3.2.1.51) (Alpha-L-fucoside fucohydrolase 2) (Alpha-L- fucosidase 2) Q9UBS5 GABR1 GABBR1 GPRC3A Q9NR23 GDF3 GDF3 UNQ222/PRO248 Growth/differentiation factor 3 (GDF-3) O60383 GDF9 GDF9 Growth/differentiation factor 9 (GDF-9) Q3B7J2 GFOD2 GFOD2 UNQ9430/PRO34691 Glucose-fructose domain-containing protein 2 (EC 1.-.-.-) P10912 GHR GHR Growth hormone receptor (GH receptor) (Somatotropin receptor) [Cleaved into: Growth hormone- binding protein (GH-binding protein) (GHBP) (Serum-binding protein)] Q86XP6 GKN2 GKN2 BLOT GDDR TFIZ1 Gastrokine-2 (Blottin) (Down- UNQ465/PRO813 regulated in gastric cancer) (Trefoil factor interactions(z) 1) P0CG01 GKN3 GKN3P Gastrokine-3 Q6UWM GPRL1 GLIPR1L1 UNQ2972/PRO7434 GLIPR1-like protein 1 5 P01148 GON1 GNRH1 GNRH GRH LHRH Progonadoliberin-1 (Progonadoliberin I) [Cleaved into: Gonadoliberin-1 (Gonadoliberin I) (Gonadorelin) (Gonadotropin-releasing hormone I) (GnRH-I) (Luliberin I) (-releasing hormone I) (LH-RH I); GnRH- associated peptide 1 (GnRH- associated peptide I)]

181

P51654 GPC3 GPC3 OCI5 Glypican-3 (GTR2-2) (Intestinal protein OCI-5) (MXR7) [Cleaved into: Secreted glypican-3] O75487 GPC4 GPC4 UNQ474/PRO937 Glypican-4 (K-glypican) [Cleaved into: Secreted glypican-4] P78333 GPC5 GPC5 Glypican-5 [Cleaved into: Secreted glypican-5] Q9Y625 GPC6 GPC6 UNQ369/PRO705 Glypican-6 [Cleaved into: Secreted glypican-6] Q96T91 GPHA2 GPHA2 GPA2 ZSIG51 Glycoprotein hormone alpha-2 (Putative secreted protein Zsig51) (Thyrostimulin subunit alpha) Q86YW7 GPHB5 GPHB5 GPB5 ZLUT1 Glycoprotein hormone beta-5 (Thyrostimulin subunit beta) Q02747 GUC2A GUCA2A GUCA2 Guanylin (Guanylate cyclase activator 2A) (Guanylate cyclase- activating protein 1) (Guanylate cyclase-activating protein I) (GCAP-I) [Cleaved into: HMW- guanylin; Guanylin] Q96RW7 HMCN1 HMCN1 FIBL6 Q9Y251 HPSE HPSE HEP HPA HPA1 HPR1 Heparanase (EC 3.2.1.166) (Endo- HPSE1 HSE1 glucoronidase) (Heparanase-1) (Hpa1) [Cleaved into: Heparanase 8 kDa subunit; Heparanase 50 kDa subunit] Q8WWQ HPSE2 HPSE2 HPA2 Inactive heparanase-2 (Hpa2) 2 P02790 HEMO HPX Hemopexin (Beta-1B- glycoprotein) Q92743 HTRA1 HTRA1 HTRA PRSS11 Serine protease HTRA1 (EC 3.4.21.-) (High-temperature requirement A serine peptidase 1) (L56) (Serine protease 11) P83110 HTRA3 HTRA3 PRSP Serine protease HTRA3 (EC 3.4.21.-) (High-temperature requirement factor A3) (Pregnancy-related serine protease) P83105 HTRA4 HTRA4 Serine protease HTRA4 (EC 3.4.21.-) (High-temperature requirement factor A4) Q12794 HYAL1 HYAL1 LUCA1 -1 (Hyal-1) (EC 3.2.1.35) (Hyaluronoglucosaminidase-1)

182

(Lung carcinoma protein 1) (LuCa-1) O43820 HYAL3 HYAL3 LUCA3 Hyaluronidase-3 (Hyal-3) (EC 3.2.1.35) (Hyaluronoglucosaminidase-3) (Lung carcinoma protein 3) (LuCa-3) P35858 ALS IGFALS ALS Insulin growth factor-like family member 1 Q8N6C5 IGSF1 IGSF1 IGDC1 KIAA0364 PGSF2 P35968 VGFR2 KDR FLK1 VEGFR2 Q14667 K0100 KIAA0100 BCOX1 O43240 KLK10 KLK10 NES1 PRSSL1 Kallikrein-10 (EC 3.4.21.-) (Normal epithelial cell-specific 1) (Protease serine-like 1) Q9UKR0 KLK12 KLK12 KLKL5 Kallikrein-12 (EC 3.4.21.-) UNQ669/PRO1303 (Kallikrein-like protein 5) (KLK- L5) P07288 KLK3 KLK3 APS Prostate-specific antigen (PSA) (EC 3.4.21.77) (Gamma- seminoprotein) (Seminin) (Kallikrein-3) (P-30 antigen) () Q9Y5K2 KLK4 KLK4 EMSP1 PRSS17 PSTS Kallikrein-4 (EC 3.4.21.-) (Enamel matrix serine proteinase 1) (Kallikrein-like protein 1) (KLK-L1) (Prostase) (Serine protease 17) P49862 KLK7 KLK7 PRSS6 SCCE Kallikrein-7 (hK7) (EC 3.4.21.117) (Serine protease 6) (Stratum corneum chymotryptic enzyme) (hSCCE) Q9UKQ9 KLK9 KLK9 Kallikrein-9 (EC 3.4.21.-) (Kallikrein-like protein 3) (KLK- L3) P03952 KLKB1 KLKB1 KLK3 (EC 3.4.21.34) (Fletcher factor) (Kininogenin) (Plasma prekallikrein) (PKK) [Cleaved into: Plasma kallikrein heavy chain; Plasma kallikrein light chain] P25391 LAMA1 LAMA1 LAMA P24043 LAMA2 LAMA2 LAMM Q16787 LAMA3 LAMA3 LAMNA

183

O15230 LAMA5 LAMA5 KIAA0533 KIAA1907 Q13751 LAMB3 LAMB3 LAMNB1 Q6JVE6 LCN10 LCN10 Epididymal-specific lipocalin-10 Q6JVE9 LCN8 LCN8 LCN5 Epididymal-specific lipocalin-8 Q6P5S2 LEG1H LEG1 C6orf58 Protein LEG1 homolog O95970 LGI1 LGI1 EPT UNQ775/PRO1569 Leucine-rich glioma-inactivated protein 1 (Epitempin-1) P01229 LSHB LHB Lutropin subunit beta (Lutropin beta chain) (Luteinizing hormone subunit beta) (LH-B) (LSH-B) (LSH-beta) P02750 A2GL LRG1 LRG Leucine-rich alpha-2-glycoprotein (LRG) Q8N6Y2 LRC17 LRRC17 P37NB Leucine-rich repeat-containing UNQ3076/PRO9909 protein 17 (p37NB) P21941 MATN1 MATN1 CMP CRTM Cartilage matrix protein (Matrilin- 1) O15232 MATN3 MATN3 Matrilin-3 O95460 MATN4 MATN4 Matrilin-4 P08581 MET MET P08493 MGP MGP MGLAP GIG36 (MGP) (Cell growth-inhibiting gene 36 protein) P03956 MMP1 MMP1 CLG Interstitial collagenase (EC 3.4.24.7) (Fibroblast collagenase) (Matrix metalloproteinase-1) (MMP-1) [Cleaved into: 22 kDa interstitial collagenase; 27 kDa interstitial collagenase] P09238 MMP10 MMP10 STMY2 Stromelysin-2 (SL-2) (EC 3.4.24.22) (Matrix metalloproteinase-10) (MMP-10) (Transin-2) P39900 MMP12 MMP12 HME Macrophage metalloelastase (MME) (EC 3.4.24.65) (Macrophage elastase) (ME) (hME) (Matrix metalloproteinase- 12) (MMP-12) P51512 MMP16 MMP16 MMPX2 Matrix metalloproteinase-16 (MMP-16) (EC 3.4.24.-) (MMP- X2) (Membrane-type matrix metalloproteinase 3) (MT-MMP 3) (MTMMP3) (Membrane-type-3 matrix metalloproteinase) (MT3- MMP) (MT3MMP)

184

Q99542 MMP19 MMP19 MMP18 RASI Matrix metalloproteinase-19 (MMP-19) (EC 3.4.24.-) (Matrix metalloproteinase RASI) (Matrix metalloproteinase-18) (MMP-18) O60882 MMP20 MMP20 Matrix metalloproteinase-20 (MMP-20) (EC 3.4.24.-) (Enamel metalloproteinase) (Enamelysin) Q8N119 MMP21 MMP21 Matrix metalloproteinase-21 (MMP-21) (EC 3.4.24.-) Q9H239 MMP28 MMP28 MMP25 Matrix metalloproteinase-28 UNQ1893/PRO4339 (MMP-28) (EC 3.4.24.-) (Epilysin) P08254 MMP3 MMP3 STMY1 Stromelysin-1 (SL-1) (EC 3.4.24.17) (Matrix metalloproteinase-3) (MMP-3) (Transin-1) P22894 MMP8 MMP8 CLG1 Neutrophil collagenase (EC 3.4.24.34) (Matrix metalloproteinase-8) (MMP-8) (PMNL collagenase) (PMNL-CL) P08118 MSMB MSMB PRSP Beta-microseminoprotein (Immunoglobulin-binding factor) (IGBF) (PN44) (Prostate secreted seminal plasma protein) (Prostate secretory protein of 94 amino acids) (PSP-94) (PSP94) (Seminal plasma beta-inhibin) Q1L6U9 MSMP MSMP PSMP Prostate-associated microseminoprotein (PC3-secreted microprotein) Q99972 MYOC MYOC GLC1A TIGR (Myocilin 55 kDa subunit) (Trabecular meshwork- induced glucocorticoid response protein) [Cleaved into: Myocilin, N-terminal fragment (Myocilin 20 kDa N-terminal fragment); Myocilin, C-terminal fragment (Myocilin 35 kDa N-terminal fragment)] Q8TB73 NDNF NDNF C4orf31 Protein NDNF (Neuron-derived UNQ2748/PRO6487 neurotrophic factor) Q00604 NDP NDP EVR2 Norrin (Norrie disease protein) (X-linked exudative vitreoretinopathy 2 protein) Q15223 NECT1 NECTIN1 HVEC PRR1 PVRL1 Nectin-1 (Herpes virus entry mediator C) (Herpesvirus entry

185

mediator C) (HveC) (Herpesvirus Ig-like receptor) (HIgR) (Nectin cell adhesion molecule 1) (Poliovirus receptor-related protein 1) (CD antigen CD111) Q96NY8 NECT4 NECTIN4 LNIR PRR4 PVRL4 Nectin-4 (Ig superfamily receptor LNIR) (Nectin cell adhesion molecule 4) (Poliovirus receptor- related protein 4) [Cleaved into: Processed poliovirus receptor- related protein 4] Q8TDF5 NETO1 NETO1 BTCL1 Neuropilin and tolloid-like protein 1 (Brain-specific transmembrane protein containing 2 CUB and 1 LDL-receptor class A domains protein 1) Q6P988 NOTUM NOTUM OK/SW-CL.30 Palmitoleoyl-protein NOTUM (EC 3.1.1.98) (hNOTUM) P0C0P6 NPS NPS Neuropeptide S P47972 NPTX2 NPTX2 Neuronal pentraxin-2 (NP2) (Neuronal pentraxin II) (NP-II) Q92823 NRCAM NRCAM KIAA0343 O95156 NXPH2 NXPH2 NPH2 Neurexophilin-2 Q99784 NOE1 OLFM1 NOE1 NOEL1 Noelin (Neuronal olfactomedin- related ER localized protein) (Olfactomedin-1) Q6UX06 OLFM4 OLFM4 GW112 Olfactomedin-4 (OLM4) UNQ362/PRO698 (Antiapoptotic protein GW112) (G-CSF-stimulated clone 1 protein) (hGC-1) (hOLfD) Q6IE37 OVOS1 OVOS1 Q6IE36 OVOS2 OVOS2 P09466 PAEP PAEP Glycodelin (GD) (Placental protein 14) (PP14) (Pregnancy- associated endometrial alpha-2 globulin) (PAEG) (PEG) (Progestagen-associated endometrial protein) (Progesterone-associated endometrial protein) P0C8F1 PATE4 PATE4 Prostate and testis expressed protein 4 (PATE-like protein B) (PATE-B) Q96QU1 PCD15 PCDH15 USH1F

186

Q15113 PCOC1 PCOLCE PCPE1 Procollagen C- enhancer 1 (Procollagen COOH- terminal proteinase enhancer 1) (PCPE-1) (Procollagen C- proteinase enhancer 1) (Type 1 procollagen C-proteinase enhancer protein) (Type I procollagen COOH-terminal proteinase enhancer) Q9UKZ9 PCOC2 PCOLCE2 PCPE2 Procollagen C-endopeptidase UNQ250/PRO287 enhancer 2 (Procollagen COOH- terminal proteinase enhancer 2) (PCPE-2) (Procollagen C- proteinase enhancer 2) Q15198 PGFRL PDGFRL PRLTS Platelet-derived growth factor receptor-like protein (PDGFR-like protein) (PDGF receptor beta-like tumor suppressor) P12273 PIP PIP GCDFP15 GPIP4 Prolactin-inducible protein (Gross cystic disease fluid protein 15) (GCDFP-15) (Prolactin-induced protein) (Secretory actin-binding protein) (SABP) (gp17) Q504Y2 PKDCC PKDCC SGK493 VLK Extracellular tyrosine-protein kinase PKDCC (EC 2.7.10.2) (Protein kinase domain-containing protein, cytoplasmic) (Protein kinase-like protein SgK493) (Sugen kinase 493) (Vertebrate lonesome kinase) P00750 TPA PLAT Tissue-type plasminogen activator (t-PA) (t-plasminogen activator) (tPA) (EC 3.4.21.68) (Alteplase) (Reteplase) [Cleaved into: Tissue- type plasminogen activator chain A; Tissue-type plasminogen activator chain B] P00749 UROK PLAU Urokinase-type plasminogen activator (U-plasminogen activator) (uPA) (EC 3.4.21.73) [Cleaved into: Urokinase-type plasminogen activator long chain A; Urokinase-type plasminogen activator short chain A; Urokinase-type plasminogen activator chain B]

187

Q15195 PLGA PLGLA PLGLA1 PLGP2 Plasminogen-like protein A PRGA (Plasminogen-like protein A1) (Plasminogen-related protein A) P40967 PMEL PMEL D12S53E PMEL17 SILV Melanocyte protein PMEL (ME20-M) (ME20M) (Melanocyte protein Pmel 17) (Melanocytes lineage-specific antigen GP100) (Melanoma-associated ME20 antigen) (P1) (P100) (Premelanosome protein) (Silver locus protein homolog) [Cleaved into: M-alpha (95 kDa melanocyte-specific secreted glycoprotein) (P26) (Secreted melanoma-associated ME20 antigen) (ME20-S) (ME20S); M- beta] Q7Z5L7 PODN PODN SLRR5A Podocan UNQ293/PRO332 Q15063 POSTN POSTN OSF2 P07478 TRY2 PRSS2 TRY2 TRYP2 Trypsin-2 (EC 3.4.21.4) (Anionic ) (Serine protease 2) (Trypsin II) Q8NHM4 TRY6 PRSS3P2 T6 TRY6 Putative trypsin-6 (EC 3.4.21.4) (Serine protease 3 pseudogene 2) (Trypsinogen C) Q6UWY2 PRS57 PRSS57 PRSSL1 Serine protease 57 (EC 3.4.21.-) UNQ782/PRO1599 (Serine protease 1-like protein 1) Q16557 PSG3 PSG3 Pregnancy-specific beta-1- glycoprotein 3 (PS-beta-G-3) (PSBG-3) (Pregnancy-specific glycoprotein 3) (Carcinoembryonic antigen SG5) Q00889 PSG6 PSG6 CGM3 PSG10 PSG12 Pregnancy-specific beta-1- PSGGB glycoprotein 6 (PS-beta-G-6) (PSBG-6) (Pregnancy-specific glycoprotein 6) (Pregnancy- specific beta-1-glycoprotein 10) (PS-beta-G-10) (PSBG-10) (Pregnancy-specific glycoprotein 10) (Pregnancy-specific beta-1- glycoprotein 12) (PS-beta-G-12) (PSBG-12) (Pregnancy-specific glycoprotein 12) Q13046 PSG7 PSG7 Putative pregnancy-specific beta- 1-glycoprotein 7 (PS-beta-G-7)

188

(PSBG-7) (Pregnancy-specific glycoprotein 7) Q9UQ74 PSG8 PSG8 Pregnancy-specific beta-1- glycoprotein 8 (PS-beta-G-8) (PSBG-8) (Pregnancy-specific glycoprotein 8) Q96A98 TIP39 PTH2 TIP39 TIPF39 Tuberoinfundibular peptide of 39 residues (TIP39) ( 2) P21246 PTN PTN HBNF1 NEGF1 Pleiotrophin (PTN) (Heparin- binding brain mitogen) (HBBM) (Heparin-binding growth factor 8) (HBGF-8) (Heparin-binding growth-associated molecule) (HB- GAM) (Heparin-binding neurite outgrowth-promoting factor 1) (HBNF-1) (Osteoblast-specific factor 1) (OSF-1) P10082 PYY PYY Peptide YY (PYY) (PYY-I) (Peptide tyrosine tyrosine) [Cleaved into: Peptide YY(3-36) (PYY-II)] P20742 PZP PZP CPAMD6 P78509 RELN RELN Q6XE38 SG1D4 SCGB1D4 UNQ517/PRO812 Secretoglobin family 1D member 4 (IFN-gamma-inducible secretoglobin) (IIS) Q96PL1 SG3A2 SCGB3A2 PNSP1 UGRP1 Secretoglobin family 3A member UNQ566/PRO1128 2 (Pneumo secretory protein 1) (PnSP-1) (Uteroglobin-related protein 1) Q07699 SCN1B SCN1B Sodium channel subunit beta-1 P01009 A1AT SERPINA1 AAT PI PRO0684 Alpha-1-antitrypsin (Alpha-1 PRO2209 protease inhibitor) (Alpha-1- antiproteinase) (Serpin A1) [Cleaved into: Short peptide from AAT (SPAAT)] P29622 KAIN SERPINA4 KST PI4 Kallistatin (Kallikrein inhibitor) (Peptidase inhibitor 4) (PI-4) (Serpin A4) P05154 IPSP SERPINA5 PCI PLANH3 Plasma serine protease inhibitor PROCI (Acrosomal serine protease inhibitor) (Plasminogen activator inhibitor 3) (PAI-3) (PAI3) ( inhibitor) (PCI) (Serpin A5)

189

P01008 ANT3 SERPINC1 AT3 PRO0309 Antithrombin-III (ATIII) (Serpin C1) P05121 PAI1 SERPINE1 PAI1 PLANH1 Plasminogen activator inhibitor 1 (PAI) (PAI-1) (Endothelial plasminogen activator inhibitor) (Serpin E1) P07093 GDN SERPINE2 PI7 PN1 Glia-derived nexin (GDN) (Peptidase inhibitor 7) (PI-7) (Protease nexin 1) (PN-1) (Protease nexin I) (Serpin E2) A8MV23 SERP3 SERPINE3 Serpin E3 P36955 PEDF SERPINF1 PEDF PIG35 Pigment epithelium-derived factor (PEDF) (Cell proliferation- inducing gene 35 protein) (EPC-1) (Serpin F1) P08697 A2AP SERPINF2 AAP PLI Alpha-2-antiplasmin (Alpha-2- AP) (Alpha-2-plasmin inhibitor) (Alpha-2-PI) (Serpin F2) P05155 IC1 SERPING1 C1IN C1NH Plasma protease C1 inhibitor (C1 Inh) (C1Inh) (C1 esterase inhibitor) (C1-inhibiting factor) (Serpin G1) Q99574 NEUS SERPINI1 PI12 Neuroserpin (Peptidase inhibitor 12) (PI-12) (Serpin I1) O75830 SPI2 SERPINI2 MEPI PI14 Serpin I2 (Myoepithelium-derived serine protease inhibitor) (Pancpin) (Pancreas-specific protein TSA2004) (Peptidase inhibitor 14) (PI-14) Q15465 SHH SHH Sonic hedgehog protein (SHH) (HHG-1) [Cleaved into: Sonic hedgehog protein N-product; Sonic hedgehog protein C- product] O75093 SLIT1 SLIT1 KIAA0813 MEGF4 SLIL1 X6R3P0 X6R3P0 SLIT2 O94813 SLIT2 SLIT2 SLIL3 P55000 SLUR1 SLURP1 ARS Secreted Ly-6/uPAR-related protein 1 (SLURP-1) (ARS component B) (ARS(component B)-81/S) (Anti-neoplastic urinary protein) (ANUP) Q1W4C9 ISK13 SPINK13 HBVDNAPTP1 Serine protease inhibitor Kazal- SPINK5L3 type 13 (Hepatitis B virus DNA polymerase transactivated serine protease inhibitor) (Hespintor) 190

(Serine protease inhibitor Kazal- type 5-like 3) Q6IE38 ISK14 SPINK14 SPINK5L2 Serine protease inhibitor Kazal- type 14 P20155 ISK2 SPINK2 Serine protease inhibitor Kazal- type 2 (-) (Epididymis tissue protein Li 172) (HUSI-II) O60575 ISK4 SPINK4 Serine protease inhibitor Kazal- type 4 (Peptide PEC-60 homolog) Q6UWN8 ISK6 SPINK6 UNQ844/PRO1782 Serine protease inhibitor Kazal- type 6 (Kallikrein inhibitor) P58062 ISK7 SPINK7 ECG2 Serine protease inhibitor Kazal- UNQ745/PRO1474 type 7 (Esophagus cancer-related gene 2 protein) (ECRG-2) O60687 SRPX2 SRPX2 SRPUL Sushi repeat-containing protein SRPX2 (Sushi-repeat protein upregulated in leukemia) P02808 STAT STATH Statherin P52823 STC1 STC1 STC Stanniocalcin-1 (STC-1) Q4LDE5 SVEP1 SVEP1 C9orf13 CCP22 SELOB Q2MV58 TECT1 TCTN1 TECT1 Tectonic-1 UNQ9369/PRO34160 O75443 TECTA TECTA Q96PL2 TECTB TECTB Beta-tectorin P04155 TFF1 TFF1 BCEI PS2 Trefoil factor 1 (Breast cancer estrogen-inducible protein) (PNR- 2) (Polypeptide P1.A) (hP1.A) (Protein pS2) P01135 TGFA TGFA Protransforming growth factor alpha [Cleaved into: Transforming growth factor alpha (TGF-alpha) (EGF-like TGF) (ETGF) (TGF type 1)] P10600 TGFB3 TGFB3 Transforming growth factor beta-3 (TGF-beta-3) [Cleaved into: Latency-associated peptide (LAP)] Q9UPZ6 THS7A THSD7A KIAA0960 P01033 TIMP1 TIMP1 CLGI TIMP Metalloproteinase inhibitor 1 (Erythroid-potentiating activity) (EPA) (Fibroblast collagenase inhibitor) (Collagenase inhibitor) (Tissue inhibitor of metalloproteinases 1) (TIMP-1)

191

P35625 TIMP3 TIMP3 Metalloproteinase inhibitor 2 (CSC-21K) (Tissue inhibitor of metalloproteinases 2) (TIMP-2) Q99727 TIMP4 TIMP4 Metalloproteinase inhibitor 3 (Protein MIG-5) (Tissue inhibitor of metalloproteinases 3) (TIMP-3) P24821 TENA TNC HXB Q9UQP3 TENN TNN Q8WU66 TSEAR TSPEAR C21orf29 Thrombospondin-type laminin G domain and EAR repeat- containing protein (TSP-EAR) P04275 VWF VWF F8VWF F5GYM2 F5GYM WNT5B Protein Wnt-5b 2 D6RF94 D6RF94 WNT8A Protein Wnt-8a (Protein Wnt-8d) O60844 ZG16 ZG16 Zymogen granule membrane protein 16 (Zymogen granule protein 16) (hZG16) (Secretory lectin ZG16) P21754 ZP3 ZP3 ZP3A ZP3B ZPC Zona pellucida sperm-binding protein 3 (Sperm receptor) (ZP3A/ZP3B) (Zona pellucida glycoprotein 3) (Zp-3) (Zona pellucida protein C) [Cleaved into: Processed zona pellucida sperm- binding protein 3] Q12836 ZP4 ZP4 ZPB Zona pellucida sperm-binding protein 4 (Zona pellucida glycoprotein 4) (Zp-4) (Zona pellucida protein B) [Cleaved into: Processed zona pellucida sperm- binding protein 4] Q9BS86 ZPBP1 ZPBP ZPBP1 Zona pellucida-binding protein 1 (Sp38) Q6X784 ZPBP2 ZPBP2 ZPBPL Zona pellucida-binding protein 2 (ZPBP-like protein) P15151 PVR PVR PVS Poliovirus receptor (Nectin-like protein 5) (NECL-5) (CD antigen CD155) Q9Y5C1 ANGL3 ANGPTL3 ANGPT5 Angiopoietin-related protein 3 UNQ153/PRO179 (Angiopoietin-5) (ANG-5) (Angiopoietin-like protein 3) [Cleaved into: ANGPTL3(17- 221); ANGPTL3(17-224)]

192

Q6GPI1 CTRB2 CTRB2 Chymotrypsinogen B2 (EC 3.4.21.1) [Cleaved into: Chymotrypsin B2 chain A; Chymotrypsin B2 chain B; Chymotrypsin B2 chain C] Q99983 OMD OMD SLRR2C Osteomodulin (Keratan sulfate UNQ190/PRO216 proteoglycan osteomodulin) (KSPG osteomodulin) (Osteoadherin) (OSAD) Proteins functionally related to the regulation/modulation of the immune response Q6UW15 REG3G REG3G PAP1B Regenerating islet-derived protein UNQ429/PRO162 3-gamma (REG-3-gamma) (-associated protein 1B) (PAP-1B) (Pancreatitis- associated protein IB) (PAP IB) (Regenerating islet-derived protein III-gamma) (REG III) (Reg III- gamma) [Cleaved into: Regenerating islet-derived protein 3-gamma 16.5 kDa form; Regenerating islet-derived protein 3-gamma 15 kDa form] P07360 CO8G C8G Complement component C8 gamma chain O43699 SIGL6 SIGLEC6 CD33L CD33L1 Sialic acid-binding Ig-like lectin 6 OBBP1 (Siglec-6) (CD33 antigen-like 1) (CDw327) (Obesity-binding protein 1) (OB-BP1) (CD antigen CD327) Q9BY15 AGRE3 ADGRE3 EMR3 Adhesion G protein-coupled UNQ683/PRO1562 receptor E3 (EGF-like module receptor 3) (EGF-like module- containing mucin-like hormone receptor-like 3) P02771 FETA AFP HPAFP Alpha-fetoprotein (Alpha-1- fetoprotein) (Alpha-fetoglobulin) P28039 AOAH AOAH (EC 3.1.1.77) [Cleaved into: Acyloxyacyl hydrolase small subunit; Acyloxyacyl hydrolase large subunit] O75882 ATRN ATRN KIAA0548 MGCA P61769 B2MG B2M CDABP0092 HDCMA22P Q13072 BAGE1 BAGE BAGE1 B melanoma antigen 1 (B melanoma antigen) (Antigen

193

MZ2-BA) (Cancer/testis antigen 2.1) (CT2.1) O95972 BMP15 BMP15 GDF9B Bone morphogenetic protein 15 (BMP-15) (Growth/differentiation factor 9B) (GDF-9B) P17213 BPI BPI Bactericidal permeability- increasing protein (BPI) (CAP 57) Q9NP55 BPIA1 BPIFA1 LUNX NASG PLUNC BPI fold-containing family A SPLUNC1 SPURT member 1 (Lung-specific protein UNQ787/PRO1606 X) (Nasopharyngeal carcinoma- related protein) (Palate lung and nasal epithelium clone protein) (Secretory protein in upper respiratory tracts) (Short PLUNC1) (SPLUNC1) (Tracheal epithelium-enriched protein) (Von Ebner protein Hl) Q96DR5 BPIA2 BPIFA2 C20orf70 SPLUNC2 BPI fold-containing family A UNQ510/PRO1025 member 2 (Parotid secretory protein) (PSP) (Short palate, lung and nasal epithelium carcinoma- associated protein 2) Q8TDL5 BPIB1 BPIFB1 C20orf114 LPLUNC1 BPI fold-containing family B UNQ706/PRO1357 member 1 (Long palate, lung and nasal epithelium carcinoma- associated protein 1) (Von Ebner minor salivary gland protein) (VEMSGP) Q8NFQ6 BPIFC BPIFC BPIL2 BPI fold-containing family C protein (Bactericidal/permeability- increasing protein-like 2) (BPI- like 2) Q13410 BT1A1 BTN1A1 BTN Butyrophilin subfamily 1 member A1 (BT) Q6UWK7 CJ099 C10orf99 UNQ1833/PRO3446 Secreted protein C10orf99 (Antimicrobial peptide-57) (AP- 57) (Colon-derived SUSD2 binding factor) (CSBF) P02745 C1QA C1QA Complement C1q subcomponent subunit A P02746 C1QB C1QB Complement C1q subcomponent subunit B P02747 C1QC C1QC C1QG Complement C1q subcomponent subunit C Q9BXJ4 C1QT3 C1QTNF3 CTRP3 Complement C1q tumor necrosis UNQ753/PRO1484 factor-related protein 3

194

(Collagenous repeat-containing sequence 26 kDa protein) (CORS26) (Secretory protein CORS26) Q9BXI9 C1QT6 C1QTNF6 CTRP6 Complement C1q tumor necrosis UNQ581/PRO1151 factor-related protein 6 Q9BXJ2 C1QT7 C1QTNF7 CTRP7 Complement C1q tumor necrosis factor-related protein 7 Q9NZP8 C1RL C1RL C1RL1 C1RLP CLSPA Complement C1r subcomponent- like protein (C1r-LP) (C1r-like protein) (EC 3.4.21.-) (C1r-like serine protease analog protein) (CLSPa) P01024 CO3 C3 CPAMD1 P0C0L4 CO4A C4A CO4 CPAMD2 P0C0L5 CO4B C4B CO4 CPAMD3; C4B_2 P04003 C4BPA C4BPA C4BP C4b-binding protein alpha chain (C4bp) (Proline-rich protein) (PRP) P07358 CO8B C8B Complement component C8 beta chain (Complement component 8 subunit beta) P02748 CO9 C9 Complement component C9 [Cleaved into: Complement component C9a; Complement component C9b] P49913 CAMP CAMP CAP18 FALL39 HSD26 Cathelicidin antimicrobial peptide (18 kDa cationic antimicrobial protein) (CAP-18) (hCAP-18) [Cleaved into: Antibacterial peptide FALL-39 (FALL-39 peptide antibiotic); Antibacterial peptide LL-37] P22362 CCL1 CCL1 SCYA1 C-C motif chemokine 1 (Small- inducible cytokine A1) (T lymphocyte-secreted protein I- 309) P51671 CCL11 CCL11 SCYA11 Eotaxin (C-C motif chemokine 11) (Eosinophil chemotactic protein) (Small-inducible cytokine A11) Q99616 CCL13 CCL13 MCP4 NCC1 SCYA13 C-C motif chemokine 13 (CK- beta-10) (Monocyte chemoattractant protein 4) (Monocyte chemotactic protein 4) (MCP-4) (NCC-1) (Small- inducible cytokine A13) [Cleaved

195

into: C-C motif chemokine 13, long chain; C-C motif chemokine 13, medium chain; C-C motif chemokine 13, short chain] Q16627 CCL14 CCL14 NCC2 SCYA14 C-C motif chemokine 14 (Chemokine CC-1/CC-3) (HCC- 1/HCC-3) (HCC-1(1-74)) (NCC- 2) (Small-inducible cytokine A14) [Cleaved into: HCC-1(3-74); HCC-1(4-74); HCC-1(9-74)] Q16663 CCL15 CCL15 MIP5 NCC3 SCYA15 C-C motif chemokine 15 (Chemokine CC-2) (HCC-2) (Leukotactin-1) (LKN-1) (MIP-1 delta) (Macrophage inflammatory protein 5) (MIP-5) (Mrp-2b) (NCC-3) (Small-inducible cytokine A15) [Cleaved into: CCL15(22-92); CCL15(25-92); CCL15(29-92)] O15467 CCL16 CCL16 ILINCK NCC4 C-C motif chemokine 16 SCYA16 (Chemokine CC-4) (HCC-4) (Chemokine LEC) (IL-10- inducible chemokine) (LCC-1) (Liver-expressed chemokine) (Lymphocyte and monocyte chemoattractant) (LMC) (Monotactin-1) (MTN-1) (NCC-4) (Small-inducible cytokine A16) Q92583 CCL17 CCL17 SCYA17 TARC C-C motif chemokine 17 (CC chemokine TARC) (Small- inducible cytokine A17) (Thymus and activation-regulated chemokine) P55774 CCL18 CCL18 AMAC1 DCCK1 MIP4 C-C motif chemokine 18 PARC SCYA18 (Alternative macrophage activation-associated CC chemokine 1) (AMAC-1) (CC chemokine PARC) ( chemokine 1) (DC-CK1) (Macrophage inflammatory protein 4) (MIP-4) (Pulmonary and activation-regulated chemokine) (Small-inducible cytokine A18) [Cleaved into: CCL18(1-68); CCL18(3-69); CCL18(4-69)]

196

Q99731 CCL19 CCL19 ELC MIP3B SCYA19 C-C motif chemokine 19 (Beta- chemokine exodus-3) (CK beta- 11) (Epstein-Barr virus-induced molecule 1 ligand chemokine) (EBI1 ligand chemokine) (ELC) (Macrophage inflammatory protein 3 beta) (MIP-3-beta) (Small-inducible cytokine A19) P13500 CCL2 CCL2 MCP1 SCYA2 C-C motif chemokine 2 (HC11) (Monocyte chemoattractant protein 1) (Monocyte chemotactic and activating factor) (MCAF) (Monocyte chemotactic protein 1) (MCP-1) (Monocyte secretory protein JE) (Small-inducible cytokine A2) P78556 CCL20 CCL20 LARC MIP3A SCYA20 C-C motif chemokine 20 (Beta- chemokine exodus-1) (CC chemokine LARC) (Liver and activation-regulated chemokine) (Macrophage inflammatory protein 3 alpha) (MIP-3-alpha) (Small-inducible cytokine A20) [Cleaved into: CCL20(1-67); CCL20(1-64); CCL20(2-70)] O00626 CCL22 CCL22 MDC SCYA22 A- C-C motif chemokine 22 (CC 152E5.1 chemokine STCP-1) (MDC(1-69)) (Macrophage-derived chemokine) (Small-inducible cytokine A22) (Stimulated T-cell chemotactic protein 1) [Cleaved into: MDC(3- 69); MDC(5-69); MDC(7-69)] O00175 CCL24 CCL24 MPIF2 SCYA24 C-C motif chemokine 24 (CK- beta-6) (Eosinophil chemotactic protein 2) (Eotaxin-2) (Myeloid progenitor inhibitory factor 2) (MPIF-2) (Small-inducible cytokine A24) O15444 CCL25 CCL25 SCYA25 TECK C-C motif chemokine 25 (Chemokine TECK) (Small- inducible cytokine A25) (Thymus- expressed chemokine) Q9Y258 CCL26 CCL26 SCYA26 C-C motif chemokine 26 (CC UNQ216/PRO242 chemokine IMAC) (Eotaxin-3) (Macrophage inflammatory protein 4-alpha) (MIP-4-alpha)

197

(Small-inducible cytokine A26) (Thymic stroma chemokine-1) (TSC-1) Q9Y4X3 CCL27 CCL27 ILC SCYA27 C-C motif chemokine 27 (CC chemokine ILC) (Cutaneous T- cell-attracting chemokine) (CTACK) (ESkine) (IL-11 R- alpha-locus chemokine) (Skinkine) (Small-inducible cytokine A27) P10147 CCL3 CCL3 G0S19-1 MIP1A SCYA3 C-C motif chemokine 3 (G0/G1 switch regulatory protein 19-1) (Macrophage inflammatory protein 1-alpha) (MIP-1-alpha) (PAT 464.1) (SIS-beta) (Small- inducible cytokine A3) (Tonsillar lymphocyte LD78 alpha protein) [Cleaved into: MIP-1-alpha(4-69) (LD78-alpha(4-69))] P16619 CL3L1 CCL3L1 D17S1718 G0S19-2 C-C motif chemokine 3-like 1 SCYA3L1; CCL3L3 (G0/G1 switch regulatory protein 19-2) (LD78-beta(1-70)) (PAT 464.2) (Small-inducible cytokine A3-like 1) (Tonsillar lymphocyte LD78 beta protein) [Cleaved into: LD78-beta(3-70); LD78-beta(5- 70)] P13236 CCL4 CCL4 LAG1 MIP1B SCYA4 C-C motif chemokine 4 (G-26 T- lymphocyte-secreted protein) (HC21) (Lymphocyte activation gene 1 protein) (LAG-1) (MIP-1- beta(1-69)) (Macrophage inflammatory protein 1-beta) (MIP-1-beta) (PAT 744) (Protein H400) (SIS-gamma) (Small- inducible cytokine A4) (T-cell activation protein 2) (ACT-2) [Cleaved into: MIP-1-beta(3-69)] Q8NHW4 CC4L CCL4L1 CCL4L LAG1 C-C motif chemokine 4-like SCYA4L1; CCL4L2 CCL4L (Lymphocyte activation gene 1 SCYA4L2 protein) (LAG-1) (Macrophage inflammatory protein 1-beta) (MIP-1-beta) (Monocyte adherence-induced protein 5- alpha) (Small-inducible cytokine A4-like)

198

P13501 CCL5 CCL5 D17S136E SCYA5 C-C motif chemokine 5 (EoCP) (Eosinophil chemotactic cytokine) (SIS-delta) (Small-inducible cytokine A5) (T cell-specific protein P228) (TCP228) (T-cell- specific protein RANTES) [Cleaved into: RANTES(3-68); RANTES(4-68)] P80098 CCL7 CCL7 MCP3 SCYA6 SCYA7 C-C motif chemokine 7 (Monocyte chemoattractant protein 3) (Monocyte chemotactic protein 3) (MCP-3) (NC28) (Small-inducible cytokine A7) P80075 CCL8 CCL8 MCP2 SCYA10 SCYA8 C-C motif chemokine 8 (HC14) (Monocyte chemoattractant protein 2) (Monocyte chemotactic protein 2) (MCP-2) (Small- inducible cytokine A8) [Cleaved into: MCP-2(6-76)] Q8TD46 MO2R1 CD200R1 CD200R CRTR2 Cell surface glycoprotein CD200 MOX2R OX2R receptor 1 (CD200 cell surface UNQ2522/PRO6015 glycoprotein receptor) (Cell surface glycoprotein OX2 receptor 1) P13987 CD59 CD59 MIC11 MIN1 MIN2 CD59 glycoprotein (1F5 antigen) MIN3 MSK21 (20 kDa homologous restriction factor) (HRF-20) (HRF20) (MAC- inhibitory protein) (MAC-IP) (MEM43 antigen) (Membrane attack complex inhibition factor) (MACIF) (Membrane inhibitor of reactive lysis) (MIRL) (Protectin) (CD antigen CD59) P01732 CD8A CD8A MAL T-cell surface glycoprotein CD8 alpha chain (T-lymphocyte differentiation antigen T8/Leu-2) (CD antigen CD8a) P10966 CD8B CD8B CD8B1 T-cell surface glycoprotein CD8 beta chain (CD antigen CD8b) P00746 CFAD CFD DF PFD Complement (EC 3.4.21.46) (Adipsin) (C3 convertase activator) (Properdin factor D) P05156 CFAI CFI IF (EC 3.4.21.45) (C3B/C4B inactivator) [Cleaved into: Complement factor

199

I heavy chain; Complement factor I light chain] P36222 CH3L1 CHI3L1 Chitinase-3-like protein 1 (39 kDa synovial protein) (Cartilage glycoprotein 39) (CGP-39) (GP- 39) (hCGP-39) (YKL-40) Q2VPA4 CR1L CR1L Complement component receptor 1-like protein (Complement C4b- binding protein CR-1-like protein) Q9HC73 CRLF2 CRLF2 CRL2 ILXR TSLPR Cytokine receptor-like factor 2 (Cytokine receptor-like 2) (IL-XR) (Thymic stromal lymphopoietin protein receptor) (TSLP receptor) P02741 CRP CRP PTX1 C-reactive protein [Cleaved into: C-reactive protein(1-205)] P15509 CSF2R CSF2RA CSF2R CSF2RY Granulocyte-macrophage colony- stimulating factor receptor subunit alpha (GM-CSF-R-alpha) (GMCSFR-alpha) (GMR-alpha) (CDw116) (CD antigen CD116) P01037 CYTN CST1 Cystatin-SN (Cystain-SA-I) (Cystatin-1) (Salivary cystatin- SA-1) P01036 CYTS CST4 Cystatin-S (Cystatin-4) (Cystatin- SA-III) (Salivary acidic protein 1) O76096 CYTF CST7 Cystatin-F (Cystatin-7) (Cystatin- like metastasis-associated protein) (CMAP) (Leukocystatin) Q5W186 CST9 CST9 CLM CTES7A Cystatin-9 (Cystatin-like molecule) P02778 CXL10 CXCL10 INP10 SCYB10 C-X-C motif chemokine 10 (10 kDa -induced protein) (Gamma-IP10) (IP-10) (Small-inducible cytokine B10) [Cleaved into: CXCL10(1-73)] O14625 CXL11 CXCL11 ITAC SCYB11 C-X-C motif chemokine 11 (Beta- SCYB9B R1) (H174) (Interferon gamma- inducible protein 9) (IP-9) (Interferon-inducible T-cell alpha chemoattractant) (I-TAC) (Small- inducible cytokine B11) P48061 SDF1 CXCL12 SDF1 SDF1A SDF1B Stromal cell-derived factor 1 (SDF-1) (hSDF-1) (C-X-C motif chemokine 12) (Intercrine reduced in hepatomas) (IRH) (hIRH) (Pre- B cell growth-stimulating factor)

200

(PBSF) [Cleaved into: SDF-1- beta(3-72); SDF-1-alpha(3-67)] O43927 CXL13 CXCL13 BCA1 BLC SCYB13 C-X-C motif chemokine 13 (Angie) (B cell-attracting chemokine 1) (BCA-1) (B lymphocyte chemoattractant) (CXC chemokine BLC) (Small- inducible cytokine B13) P42830 CXCL5 CXCL5 ENA78 SCYB5 C-X-C motif chemokine 5 (ENA- 78(1-78)) (Epithelial-derived neutrophil-activating protein 78) (Neutrophil-activating peptide ENA-78) (Small-inducible cytokine B5) [Cleaved into: ENA- 78(8-78); ENA-78(9-78)] P80162 CXCL6 CXCL6 GCP2 SCYB6 C-X-C motif chemokine 6 (Chemokine alpha 3) (CKA-3) (Granulocyte chemotactic protein 2) (GCP-2) (Small-inducible cytokine B6) [Cleaved into: Small-inducible cytokine B6, N- processed variant 1; Small- inducible cytokine B6, N- processed variant 2; Small- inducible cytokine B6, N- processed variant 3] P10145 IL8 CXCL8 IL8 Q9NRR1 CYTL1 CYTL1 C4orf4 Neuferricin (Cytochrome b5 UNQ1942/PRO4425 domain-containing protein 2) P59665 DEF1 DEFA1 DEF1 DEFA2 MRS; Neutrophil 1 (Defensin, DEFA1B alpha 1) (HNP-1) (HP-1) (HP1) [Cleaved into: HP 1-56; Neutrophil defensin 2 (HNP-2) (HP-2) (HP2)] P59666 DEF3 DEFA3 DEF3 Neutrophil defensin 3 (Defensin, alpha 3) (HNP-3) (HP-3) (HP3) [Cleaved into: HP 3-56; Neutrophil defensin 2 (HNP-2) (HP-2) (HP2)] P12838 DEF4 DEFA4 DEF4 Neutrophil defensin 4 (Defensin, alpha 4) (HNP-4) (HP-4) Q01523 DEF5 DEFA5 DEF5 Defensin-5 (Defensin, alpha 5) (HD5(20-94)) [Cleaved into: HD5(23-94); HD5(29-94); HD5(56-94); HD5(63-94)] Q01524 DEF6 DEFA6 DEF6 Defensin-6 (Defensin, alpha 6)

201

P60022 DEFB1 DEFB1 BD1 HBD1 Beta-defensin 1 (BD-1) (hBD-1) (Defensin, beta 1) P81534 D103A DEFB103A BD3 DEFB103 Beta-defensin 103 (Beta-defensin DEFB3; DEFB103B 3) (BD-3) (DEFB-3) (HBD3) (hBD-3) (Defensin, beta 103) (Defensin-like protein) Q8NG35 D105A DEFB105A BD5 DEFB105 Beta-defensin 105 (Beta-defensin DEFB5; DEFB105B 5) (BD-5) (DEFB-5) (Defensin, beta 105) Q8IZN7 D107A DEFB107A DEFB107 DEFB7; Beta-defensin 107 (Beta-defensin DEFB107B 7) (BD-7) (DEFB-7) (Defensin, beta 107) Q30KR1 DB109 DEFB109P1 DEFB109 Beta-defensin 109 (Defensin, beta DEFB109A; DEFB109P1B 109) (Defensin, beta 109, pseudogene 1/1B) Q30KQ9 DB110 DEFB110 DEFB10 DEFB11 Beta-defensin 110 (Beta-defensin DEFB111 10) (DEFB-10) (Beta-defensin 11) (DEFB-11) (Beta-defensin 111) (Defensin, beta 110) (Defensin, beta 111) Q30KQ7 DB113 DEFB113 DEFB13 Beta-defensin 113 (Beta-defensin 13) (DEFB-13) (Defensin, beta 113) Q30KQ6 DB114 DEFB114 DEFB14 Beta-defensin 114 (Beta-defensin 14) (DEFB-14) (Defensin, beta 114) Q30KQ5 DB115 DEFB115 DEFB15 Beta-defensin 115 (Beta-defensin 15) (DEFB-15) (Defensin, beta 115) Q8N690 DB119 DEFB119 DEFB120 DEFB19 Beta-defensin 119 (Beta-defensin DEFB20 UNQ2449/PRO5729 120) (Beta-defensin 19) (DEFB- 19) (Beta-defensin 20) (DEFB-20) (Defensin, beta 119) (Defensin, beta 120) (ESC42-RELA) Q5J5C9 DB121 DEFB121 DEFB21 Beta-defensin 121 (Beta-defensin 21) (DEFB-21) (Defensin, beta 121) Q8N688 DB123 DEFB123 DEFB23 Beta-defensin 123 (Beta-defensin UNQ1963/PRO4485 23) (DEFB-23) (Defensin, beta 123) Q8NES8 DB124 DEFB124 DEFB24 Beta-defensin 124 (Beta-defensin 24) (DEFB-24) (Defensin, beta 124) Q9BYW3 DB126 DEFB126 C20orf8 DEFB26 Beta-defensin 126 (Beta-defensin 26) (DEFB-26) (Defensin, beta

202

126) (Epididymal secretory protein 13.2) (ESP13.2) (HBD26) Q9H1M4 DB127 DEFB127 C20orf73 DEFB27 Beta-defensin 127 (Beta-defensin UNQ1956/PRO6071 27) (DEFB-27) (Defensin, beta 127) Q7Z7B8 DB128 DEFB128 DEFB28 Beta-defensin 128 (Beta-defensin 28) (DEFB-28) (Defensin, beta 128) Q9H1M3 DB129 DEFB129 C20orf87 DEFB29 Beta-defensin 129 (Beta-defensin UNQ5794/PRO19599 29) (DEFB-29) (Defensin, beta 129) Q30KQ2 DB130 DEFB130 DEFB30; DEFB130L Beta-defensin 130 (Beta-defensin 30) (DEFB-30) (Defensin, beta 130) P59861 DB131 DEFB131 DEFB31 Beta-defensin 131 (Beta-defensin 31) (DEFB-31) (Defensin, beta 131) Q7Z7B7 DB132 DEFB132 DEFB32 Beta-defensin 132 (Beta-defensin UNQ827/PRO1754 32) (BD-32) (DEFB-32) (Defensin HEL-75) (Defensin, beta 132) Q30KQ1 DB133 DEFB133 Beta-defensin 133 (Defensin, beta 133) Q4QY38 DB134 DEFB134 Beta-defensin 134 (Defensin, beta 134) Q30KP9 DB135 DEFB135 Beta-defensin 135 (Defensin, beta 135) Q30KP8 DB136 DEFB136 Beta-defensin 136 (Defensin, beta 136) O15263 DFB4A DEFB4A DEFB102 DEFB2 Beta-defensin 4A (Beta-defensin DEFB4; DEFB4B 2) (BD-2) (hBD-2) (Defensin, beta 2) (Skin-antimicrobial peptide 1) (SAP1) Q6UX07 DHR13 DHRS13 SDR7C5 Dehydrogenase/reductase SDR UNQ419/PRO853 family member 13 (EC 1.1.-.-) (Short chain dehydrogenase/reductase family 7C member 5) Q92874 DNSL2 DNASE1L2 DHP1 DNAS1L2 -1-like 2 (EC 3.1.21.-) (DNase I homolog protein DHP1) (-like 2) (DNase I-like 2) Q9UHL4 DPP2 DPP7 DPP2 QPP Dipeptidyl peptidase 2 (EC 3.4.14.2) (Dipeptidyl aminopeptidase II) (Dipeptidyl peptidase 7) (Dipeptidyl peptidase

203

II) (DPP II) (Quiescent cell proline dipeptidase) Q14213 IL27B EBI3 IL27B Interleukin-27 subunit beta (IL-27 subunit beta) (IL-27B) (Epstein- Barr virus-induced gene 3 protein) (EBV-induced gene 3 protein) P19235 EPOR EPOR receptor (EPO-R) Q7Z5A8 F19A3 FAM19A3 TAFA3 Protein FAM19A3 (Chemokine- like protein TAFA-3) P98173 FAM3A FAM3A 2-19 2.19 Protein FAM3A (Cytokine-like protein 2-19) P24071 FCAR FCAR CD89 Immunoglobulin alpha Fc receptor (IgA Fc receptor) (CD antigen CD89) P08637 FCG3A FCGR3A CD16A FCG3 Low affinity immunoglobulin FCGR3 IGFR3 gamma Fc region receptor III-A (CD16a antigen) (Fc-gamma RIII- alpha) (Fc-gamma RIII) (Fc- gamma RIIIa) (FcRIII) (FcRIIIa) (FcR-10) (IgG Fc receptor III-2) (CD antigen CD16a) O75015 FCG3B FCGR3B CD16B FCG3 FCGR3 Low affinity immunoglobulin IGFR3 gamma Fc region receptor III-B (Fc-gamma RIII-beta) (Fc-gamma RIII) (Fc-gamma RIIIb) (FcRIII) (FcRIIIb) (FcR-10) (IgG Fc receptor III-1) (CD antigen CD16b) Q6BAA4 FCRLB FCRLB FCRL2 FCRLM2 Fc receptor-like B (Fc receptor FCRY FREB2 homolog expressed in B-cells protein 2) (FREB-2) (Fc receptor- like and mucin-like protein 2) (Fc receptor-like protein 2) (Fc receptor-related protein Y) (FcRY) Q8NFU4 FDSCP FDCSP C4orf7 Follicular dendritic cell secreted UNQ733/PRO1419 peptide (FDC secreted protein) (FDC-SP) O15520 FGF10 FGF10 Fibroblast growth factor 10 (FGF- 10) (Keratinocyte growth factor 2) Q14314 FGL2 FGL2 Fibroleukin (Fibrinogen-like protein 2) (pT49) Q9UK05 GDF2 GDF2 BMP9 Growth/differentiation factor 2 (GDF-2) (Bone morphogenetic protein 9) (BMP-9) P55259 GP2 GP2 Pancreatic secretory granule membrane major glycoprotein

204

GP2 (Pancreatic zymogen granule membrane protein GP-2) (ZAP75) P12544 GRAA GZMA CTLA3 HFSP A (EC 3.4.21.78) (CTL ) (Cytotoxic T-lymphocyte proteinase 1) (Fragmentin-1) (Granzyme-1) (Hanukkah factor) (H factor) (HF) P49863 GRAK GZMK TRYP2 Granzyme K (EC 3.4.21.-) (Fragmentin-3) (Granzyme-3) (NK-tryptase-2) (NK-Tryp-2) P51124 GRAM GZMM MET1 Granzyme M (EC 3.4.21.-) (Met-1 serine protease) (Hu-Met-1) (Met- ase) ( granular protease) A8MTL9 HMSD HMSD Serpin-like protein HMSD (Minor histocompatibility protein HMSD) (Minor histocompatibility serpin domain-containing protein) P15516 HIS3 HTN3 HIS2 Histatin-3 (Basic histidine-rich protein) (Hst) (Histidine-rich protein 3) (PB) [Cleaved into: Histatin-3; His3-(20-44)-peptide (His3 20/44) (His3-(1-25)-peptide) (His3 1/25) (Histatin-3 1/25) (Histatin-6); His3-(20-43)-peptide (His3 20/43) (His3-(1-24)-peptide) (His3 1/24) (Histatin-3 1/24) (Histatin-5); His3-(20-32)-peptide (His3 20/32) (His3-(1-13)-peptide) (His3 1/13) (Histatin-3 1/13); His3-(20-31)-peptide (His3 20/31) (His3-(1-12)-peptide) (His3 1/12) (Histatin-3 1/12); His3-(20-30)- peptide (His3 20/30) (His3-(1-11)- peptide) (His3 1/11) (Histatin-3 1/11); His3-(24-32)-peptide (His3 24/32) (His3-(5-13)-peptide) (His3 5/13) (Histatin-3 5/13); His3-(24- 31)-peptide (His3 24/31) (His3-(5- 12)-peptide) (His3 5/12) (Histatin- 11) (Histatin-3 5/12); His3-(24- 30)-peptide (His3 24/30) (His3-(5- 11)-peptide) (His3 5/11) (Histatin- 12) (Histatin-3 5/11); His3-(25- 32)-peptide (His3 25/32) (His3-(6- 13)-peptide) (His3 6/13) (Histatin-

205

3 6/13); His3-(25-30)-peptide (His3 25/30) (His3-(6-11)-peptide) (His3 6/11) (Histatin-3 6/11); His3-(26-32)-peptide (His3 26/32) (His3-(7-13)-peptide) (His3 7/13) (Histatin-3 7/13); His3-(26-31)- peptide (His3 26/31) (His3-(7-12)- peptide) (His3 7/12) (Histatin-3 7/12); His3-(26-30)-peptide (His3 26/30) (His3-(7-11)-peptide) (His3 7/11) (Histatin-3 7/11); His3-(31- 51)-peptide (His3 31/51) (His3- (12-32)-peptide) (His3 12/32) (Histatin-3 12/32) (Histatin-4); His3-(31-44)-peptide (His3 31/44) (His3-(12-25)-peptide) (His3 12/25) (Histatin-3 12/25) (Histatin-9); His3-(31-43)-peptide (His3 31/43) (His3-(12-24)- peptide) (His3 12/24) (Histatin-3 12/24) (Histatin-7); His3-(32-44)- peptide (His3 32/44) (His3-(13- 25)-peptide) (His3 13/25) (Histatin-10) (Histatin-3 13/25); His3-(32-43)-peptide (His3 32-43) (His3-(13-24)-peptide) (His3 13/24) (Histatin-3 13/24) (Histatin-8); His3-(33-44)-peptide (His3 33/44) (His3-(14-25)- peptide) (His3 14/25) (Histatin-3 14/25); His3-(33-43)-peptide (His3 33/43) (His3-(14-24)- peptide) (His3 14/24) (Histatin-3 14/24); His3-(34-44)-peptide (His3 34/44) (His3-(15-25)- peptide) (His3 15/25) (Histatin-3 15/25); His3-(34-43)-peptide (His3 34/43) (His3-(15-24)- peptide) (His3 15/24) (Histatin-3 15/24); His3-(45-51)-peptide (His3 45/51) (His3-(26-32)- peptide) (His3 26/32) (Histatin-3 26/32); His3-(47-51)-peptide (His3 47/51) (His3-(28-32)- peptide) (His3 28/32) (Histatin-3 28/32); His3-(48-51)-peptide

206

(His3 48/51) (His3-(29-32)- peptide) (His3 29/32) (Histatin-3 29/32)] Q14773 ICAM4 ICAM4 LW Intercellular adhesion molecule 4 (ICAM-4) (Landsteiner-Wiener blood group glycoprotein) (LW blood group protein) (CD antigen CD242) Q9Y6W8 ICOS ICOS AILIM Inducible T-cell costimulator (Activation-inducible lymphocyte immunomediatory molecule) (CD antigen CD278) P13284 GILT IFI30 GILT IP30 Gamma-interferon-inducible lysosomal thiol reductase (EC 1.8.-.-) (Gamma-interferon- inducible protein IP-30) (Legumaturain) P01562 IFNA1 IFNA1; IFNA13 Interferon alpha-1/13 (IFN-alpha- 1/13) (Interferon alpha-D) (LeIF D) P01566 IFN10 IFNA10 Interferon alpha-10 (IFN-alpha- 10) (Interferon alpha-6L) (Interferon alpha-C) (LeIF C) P01570 IFN14 IFNA14 Interferon alpha-14 (IFN-alpha- 14) (Interferon alpha-H) (LeIF H) (Interferon lambda-2-H) P05015 IFN16 IFNA16 Interferon alpha-16 (IFN-alpha- 16) (Interferon alpha-WA) P01571 IFN17 IFNA17 Interferon alpha-17 (IFN-alpha- 17) (Interferon alpha-88) (Interferon alpha-I') (LeIF I) (Interferon alpha-T) P01563 IFNA2 IFNA2 IFNA2A IFNA2B Interferon alpha-2 (IFN-alpha-2) IFNA2C (Interferon alpha-A) (LeIF A) P01568 IFN21 IFNA21 Interferon alpha-21 (IFN-alpha- 21) (Interferon alpha-F) (LeIF F) P05014 IFNA4 IFNA4 Interferon alpha-4 (IFN-alpha-4) (Interferon alpha-4B) (Interferon alpha-76) (Interferon alpha-M1) P05013 IFNA6 IFNA6 Interferon alpha-6 (IFN-alpha-6) (Interferon alpha-54) (Interferon alpha-K) (LeIF K) P01567 IFNA7 IFNA7 Interferon alpha-7 (IFN-alpha-7) (Interferon alpha-J) (LeIF J) (Interferon alpha-J1) (IFN-alpha- J1)

207

P32881 IFNA8 IFNA8 Interferon alpha-8 (IFN-alpha-8) (Interferon alpha-B) (LeIF B) (Interferon alpha-B2) P48551 INAR2 IFNAR2 IFNABR IFNARB Interferon alpha/beta receptor 2 (IFN-R-2) (IFN-alpha binding protein) (IFN-alpha/beta receptor 2) (Interferon alpha binding protein) (Type I interferon receptor 2) P01574 IFNB IFNB1 IFB IFNB Interferon beta (IFN-beta) (Fibroblast interferon) Q86WN2 IFNE IFNE IFNE1 UNQ360/PRO655 Interferon epsilon (IFN-epsilon) (Interferon epsilon-1) P01579 IFNG IFNG Interferon gamma (IFN-gamma) (Immune interferon) P05000 IFNW1 IFNW1 Interferon omega-1 (Interferon alpha-II-1) A6NJS3 IV1U1 IGHV1OR21-1 Putative V-set and immunoglobulin domain- containing-like protein IGHV1OR21-1 (Immunoglobulin heavy variable 1 orphon 21-1) A6NJ16 IV4F8 IGHV4OR15-8 VSIG6 Putative V-set and immunoglobulin domain- containing-like protein IGHV4OR15-8 (Immunoglobulin heavy variable 4 orphon 15-8) (Putative V-set and immunoglobulin domain- containing protein 6) A6NJ69 IGIP IGIP C5orf53 IgA-inducing protein homolog A6NGN9 IGLO5 IGLON5 IgLON family member 5 Q96ID5 IGS21 IGSF21 Immunoglobulin superfamily member 21 (IgSF21) P29459 IL12A IL12A NKSF1 Interleukin-12 subunit alpha (IL- 12A) (Cytotoxic lymphocyte maturation factor 35 kDa subunit) (CLMF p35) (IL-12 subunit p35) (NK cell stimulatory factor chain 1) (NKSF1) P29460 IL12B IL12B NKSF2 Interleukin-12 subunit beta (IL- 12B) (Cytotoxic lymphocyte maturation factor 40 kDa subunit) (CLMF p40) (IL-12 subunit p40) (NK cell stimulatory factor chain 2) (NKSF2)

208

P35225 IL13 IL13 NC30 Interleukin-13 (IL-13) Q16552 IL17 IL17A CTLA8 IL17 Interleukin-17A (IL-17) (IL-17A) (Cytotoxic T-lymphocyte- associated antigen 8) (CTLA-8) Q9NRM6 I17RB IL17RB CRL4 EVI27 IL17BR Interleukin-17 receptor B (IL-17 UNQ2501/PRO19612 receptor B) (IL-17RB) (Cytokine receptor-like 4) (IL-17 receptor homolog 1) (IL-17Rh1) (IL17Rh1) (Interleukin-17B receptor) (IL- 17B receptor) O95998 I18BP IL18BP Interleukin-18-binding protein (IL-18BP) (Tadekinig-alfa) Q9UHD0 IL19 IL19 ZMDA1 Interleukin-19 (IL-19) (Melanoma differentiation-associated protein- like protein) (NG.1) P14778 IL1R1 IL1R1 IL1R IL1RA IL1RT1 Interleukin-1 receptor type 1 (IL- 1R-1) (IL-1RT-1) (IL-1RT1) (CD121 antigen-like family member A) (Interleukin-1 receptor alpha) (IL-1R-alpha) (Interleukin- 1 receptor type I) (p80) (CD antigen CD121a) [Cleaved into: Interleukin-1 receptor type 1, membrane form (mIL-1R1) (mIL- 1RI); Interleukin-1 receptor type 1, soluble form (sIL-1R1) (sIL- 1RI)] P27930 IL1R2 IL1R2 IL1RB Interleukin-1 receptor type 2 (IL- 1R-2) (IL-1RT-2) (IL-1RT2) (CD121 antigen-like family member B) (CDw121b) (IL-1 type II receptor) (Interleukin-1 receptor beta) (IL-1R-beta) (Interleukin-1 receptor type II) (CD antigen CD121b) [Cleaved into: Interleukin-1 receptor type 2, membrane form (mIL-1R2) (mIL- 1RII); Interleukin-1 receptor type 2, soluble form (sIL-1R2) (sIL- 1RII)] Q9NPH3 IL1AP IL1RAP C3orf13 IL1R3 Interleukin-1 receptor accessory protein (IL-1 receptor accessory protein) (IL-1RAcP) (Interleukin- 1 receptor 3) (IL-1R-3) (IL-1R3) Q01638 ILRL1 IL1RL1 DER4 ST2 T1 Interleukin-1 receptor-like 1 (Protein ST2)

209

P18510 IL1RA IL1RN IL1F3 IL1RA Interleukin-1 receptor antagonist protein (IL-1RN) (IL-1ra) (IRAP) (ICIL-1RA) (IL1 inhibitor) (Anakinra) P60568 IL2 IL2 Interleukin-2 (IL-2) (T-cell growth factor) (TCGF) (Aldesleukin) Q9NYY1 IL20 IL20 ZCYTO10 Interleukin-20 (IL-20) (Cytokine UNQ852/PRO1801 Zcyto10) Q9GZX6 IL22 IL22 ILTIF ZCYTO18 Interleukin-22 (IL-22) (Cytokine UNQ3099/PRO10096 Zcyto18) (IL-10-related T-cell- derived-inducible factor) (IL-TIF) Q9NPF7 IL23A IL23A SGRF Interleukin-23 subunit alpha (IL- UNQ2498/PRO5798 23 subunit alpha) (IL-23-A) (Interleukin-23 subunit p19) (IL- 23p19) Q13007 IL24 IL24 MDA7 ST16 Interleukin-24 (IL-24) (Melanoma differentiation-associated gene 7 protein) (MDA-7) (Suppression of tumorigenicity 16 protein) Q8NEV9 IL27A IL27 IL27A IL30 Interleukin-27 subunit alpha (IL- 27 subunit alpha) (IL-27-A) (IL27-A) (Interleukin-30) (p28) Q6EBC2 IL31 IL31 Interleukin-31 (IL-31) Q6ZMJ4 IL34 IL34 C16orf77 Interleukin-34 (IL-34) P05113 IL5 IL5 Interleukin-5 (IL-5) (B-cell differentiation factor I) (Eosinophil differentiation factor) (T-cell replacing factor) (TRF) P13232 IL7 IL7 Interleukin-7 (IL-7) P16871 IL7RA IL7R Interleukin-7 receptor subunit alpha (IL-7 receptor subunit alpha) (IL-7R subunit alpha) (IL-7R- alpha) (IL-7RA) (CDw127) (CD antigen CD127) P15248 IL9 IL9 Interleukin-9 (IL-9) (Cytokine P40) (T-cell growth factor P40) Q01113 IL9R IL9R Interleukin-9 receptor (IL-9 receptor) (IL-9R) (CD antigen CD129) P08476 INHBA INHBA Inhibin beta A chain (Activin beta- A chain) (Erythroid differentiation protein) (EDF) Q8TB96 TIP ITFG1 LNKN-1 TIP CDA08 T-cell immunomodulatory protein (Protein TIP) (Integrin-alpha FG- GAP repeat-containing protein 1) (Linkin) 210

Q9BX67 JAM3 JAM3 UNQ859/PRO1868 Junctional adhesion molecule C (JAM-C) (JAM-2) (Junctional adhesion molecule 3) (JAM-3) P01591 IGJ JCHAIN IGCJ IGJ Immunoglobulin J chain (Joining chain of multimeric IgA and IgM) P21583 SCF KITLG MGF SCF Kit ligand (Mast cell growth factor) (MGF) () (SCF) (c-Kit ligand) [Cleaved into: Soluble KIT ligand (sKITLG)] Q9UEF7 KLOT KL P18428 LBP LBP Lipopolysaccharide-binding protein (LBP) Q08380 LG3BP LGALS3BP M2BP Galectin-3-binding protein (Basement membrane autoantigen p105) (Lectin galactoside-binding soluble 3-binding protein) (Mac-2- binding protein) (MAC2BP) (Mac-2 BP) (Tumor-associated antigen 90K) P15018 LIF LIF HILDA Leukemia inhibitory factor (LIF) (Differentiation-stimulating factor) (D factor) (Melanoma-derived LPL inhibitor) (MLPLI) (Emfilermin) Q8N149 LIRA2 LILRA2 ILT1 LIR7 Leukocyte immunoglobulin-like receptor subfamily A member 2 (CD85 antigen-like family member H) (Immunoglobulin-like transcript 1) (ILT-1) (Leukocyte immunoglobulin-like receptor 7) (LIR-7) (CD antigen CD85h) Q8N6C8 LIRA3 LILRA3 ILT6 LIR4 Leukocyte immunoglobulin-like receptor subfamily A member 3 (CD85 antigen-like family member E) (Immunoglobulin-like transcript 6) (ILT-6) (Leukocyte immunoglobulin-like receptor 4) (LIR-4) (Monocyte inhibitory receptor HM43/HM31) (CD antigen CD85e) P01374 TNFB LTA TNFB TNFSF1 -alpha (LT-alpha) (TNF-beta) (Tumor necrosis factor ligand superfamily member 1) Q8NDX9 LY65B LY6G5B C6orf19 G5B Lymphocyte antigen 6 complex locus protein G5b

211

Q5SRR4 LY65C LY6G5C C6orf20 G5C NG33 Lymphocyte antigen 6 complex locus protein G5c O95711 LY86 LY86 MD1 Lymphocyte antigen 86 (Ly-86) (Protein MD-1) Q9Y6Y9 LY96 LY96 ESOP1 MD2 Lymphocyte antigen 96 (Ly-96) (ESOP-1) (Protein MD-2) Q9BZG9 LYNX1 LYNX1 SLURP2 Ly-6/neurotoxin-like protein 1 (Secreted Ly-6/uPAR domain- containing protein 2) (Secreted Ly-6/uPAR-related protein 2) (SLURP-2) Q6UX82 LYPD8 LYPD8 UNQ511/PRO1026 Ly6/PLAUR domain-containing protein 8 Q6UWQ5 LYZL1 LYZL1 LYC2 -like protein 1 (EC UNQ648/PRO1278 3.2.1.17) Q7Z4W2 LYZL2 LYZL2 Lysozyme-like protein 2 (Lysozyme-2) (EC 3.2.1.17) O75951 LYZL6 LYZL6 LYC1 Lysozyme-like protein 6 (EC UNQ754/PRO1485 3.2.1.17) Q95460 HMR1 MR1 Major histocompatibility complex class I-related gene protein (MHC class I-related gene protein) (Class I histocompatibility antigen-like protein) Q969H8 MYDGF MYDGF C19orf10 IL25 Myeloid-derived growth factor (MYDGF) (Interleukin-25) (IL- 25) (Stromal cell-derived growth factor SF20) P02763 A1AG1 ORM1 AGP1 Alpha-1-acid glycoprotein 1 (AGP 1) (Orosomucoid-1) (OMD 1) P19652 A1AG2 ORM2 AGP2 Alpha-1-acid glycoprotein 2 (AGP 2) (Orosomucoid-2) (OMD 2) Q9BQ51 PD1L2 PDCD1LG2 B7DC CD273 Programmed cell death 1 ligand 2 PDCD1L2 PDL2 (PD-1 ligand 2) (PD-L2) (PDCD1 ligand 2) (Programmed death ligand 2) (Butyrophilin B7-DC) (B7-DC) (CD antigen CD273) P02776 PLF4 PF4 CXCL4 SCYB4 (PF-4) (C-X-C motif chemokine 4) (Iroplact) (Oncostatin-A) [Cleaved into: Platelet factor 4, short form] P10720 PF4V PF4V1 CXCL4V1 SCYB4V1 Platelet factor 4 variant (C-X-C motif chemokine 4 variant) (CXCL4L1) (PF4alt) (PF4var1) [Cleaved into: Platelet factor 4 variant(4-74); Platelet factor 4

212

variant(5-74); Platelet factor 4 variant(6-74)] Q96LB9 PGRP3 PGLYRP3 PGRPIA Peptidoglycan recognition protein 3 (Peptidoglycan recognition protein I-alpha) (PGLYRPIalpha) (PGRP-I-alpha) (Peptidoglycan recognition protein intermediate alpha) Q96LB8 PGRP4 PGLYRP4 PGRPIB SBBI67 Peptidoglycan recognition protein 4 (Peptidoglycan recognition protein I-beta) (PGLYRPIbeta) (PGRP-I-beta) (Peptidoglycan recognition protein intermediate beta) P19957 ELAF PI3 WAP3 WFDC14 Elafin (Elastase-specific inhibitor) (ESI) (Peptidase inhibitor 3) (PI-3) (Protease inhibitor WAP3) (Skin- derived antileukoproteinase) (SKALP) (WAP four-disulfide core domain protein 14) Q02325 PLGB PLGLB1 PLGL PRGB; Plasminogen-like protein B PLGLB2 PLGP1 (Plasminogen-related protein B) P14222 PERF PRF1 PFP Perforin-1 (P1) (Cytolysin) (Lymphocyte pore-forming protein) (PFP) P13727 PRG2 PRG2 MBP Bone marrow proteoglycan (BMPG) (Proteoglycan 2) [Cleaved into: Eosinophil granule major basic protein (EMBP) (MBP) (Pregnancy-associated major basic protein)] P01236 PRL PRL Prolactin (PRL) Q9HC23 PROK2 PROK2 BV8 Prokineticin-2 (PK2) (Protein Bv8 homolog) P07477 TRY1 PRSS1 TRP1 TRY1 TRYP1 Trypsin-1 (EC 3.4.21.4) (Beta- trypsin) (Cationic trypsinogen) (Serine protease 1) (Trypsin I) [Cleaved into: Alpha-trypsin chain 1; Alpha-trypsin chain 2] P11465 PSG2 PSG2 PSBG2 Pregnancy-specific beta-1- glycoprotein 2 (PS-beta-G-2) (PSBG-2) (Pregnancy-specific glycoprotein 2) (Pregnancy- specific beta-1 glycoprotein E) (PS-beta-E)

213

Q00888 PSG4 PSG4 CGM4 PSG9 Pregnancy-specific beta-1- glycoprotein 4 (PS-beta-G-4) (PSBG-4) (Pregnancy-specific glycoprotein 4) (Pregnancy- specific beta-1-glycoprotein 9) (PS-beta-G-9) (PSBG-9) (Pregnancy-specific glycoprotein 9) Q15238 PSG5 PSG5 Pregnancy-specific beta-1- glycoprotein 5 (PS-beta-G-5) (PSBG-5) (Pregnancy-specific glycoprotein 5) (Fetal liver non- specific cross-reactive antigen 3) (FL-NCA-3) Q96A99 PTX4 PTX4 C16orf38 Pentraxin-4 Q8TD07 N2DL4 RAET1E LETAL N2DL4 NKG2D ligand 4 (N2DL-4) ULBP4 UNQ1867/PRO4303 (NKG2DL4) (Lymphocyte effector toxicity activation ligand) (RAE-1-like transcript 4) (RL-4) (Retinoic acid early transcript 1E) Q99969 RARR2 RARRES2 TIG2 Retinoic acid receptor responder protein 2 (Chemerin) (RAR- responsive protein TIG2) (Tazarotene-induced gene 2 protein) Q06141 REG3A REG3A HIP PAP PAP1 Regenerating islet-derived protein 3-alpha (REG-3-alpha) (Hepatointestinal pancreatic protein) (HIP/PAP) (Human proislet peptide) (Pancreatitis- associated protein 1) (Regenerating islet-derived protein III-alpha) (Reg III-alpha) [Cleaved into: Regenerating islet-derived protein 3-alpha 16.5 kDa form; Regenerating islet-derived protein 3-alpha 15 kDa form] P12724 ECP RNASE3 ECP RNS3 Eosinophil cationic protein (ECP) (EC 3.1.27.-) ( 3) (RNase 3) Q96QR1 SG3A1 SCGB3A1 HIN1 PNSP2 Secretoglobin family 3A member UGRP2 UNQ629/PRO1245 1 (Cytokine HIN-1) (High in normal 1) (Pneumo secretory protein 2) (PnSP-2) (Uteroglobin- related protein 2)

214

Q8WVN6 SCTM1 SECTM1 K12 Secreted and transmembrane protein 1 (Protein K-12) O95025 SEM3D SEMA3D UNQ760/PRO1491 P01011 AACT SERPINA3 AACT GIG24 Alpha-1-antichymotrypsin (ACT) GIG25 (Cell growth-inhibiting gene 24/25 protein) (Serpin A3) [Cleaved into: Alpha-1-antichymotrypsin His-Pro-less] Q13291 SLAF1 SLAMF1 SLAM Signaling lymphocytic activation molecule (CDw150) (IPO-3) (SLAM family member 1) (CD antigen CD150) Q5VX71 SUSD4 SUSD4 UNQ196/PRO222 Sushi domain-containing protein 4 P61812 TGFB2 TGFB2 Transforming growth factor beta-2 (TGF-beta-2) (BSC-1 cell growth inhibitor) (Cetermin) (Glioblastoma-derived T-cell suppressor factor) (G-TSF) (Polyergin) [Cleaved into: Latency-associated peptide (LAP)] Q15661 TRYB1 TPSAB1 TPS1 TPS2 TPSB1 Tryptase alpha/beta-1 (Tryptase-1) (EC 3.4.21.59) (Tryptase I) (Tryptase alpha-1) P20231 TRYB2 TPSB2 TPS2 Tryptase beta-2 (Tryptase-2) (EC 3.4.21.59) (Tryptase II) Q9NP99 TREM1 TREM1 Triggering receptor expressed on myeloid cells 1 (TREM-1) (Triggering receptor expressed on monocytes 1) (CD antigen CD354) Q6UXN2 TRML4 TREML4 TLT4 Trem-like transcript 4 protein UNQ9425/PRO34675 (TLT-4) (Triggering receptor expressed on myeloid cells-like protein 4) Q969D9 TSLP TSLP Thymic stromal lymphopoietin Q96RP3 UCN2 UCN2 SRP URP Urocortin-2 (Stresscopin-related peptide) (Urocortin II) (Ucn II) (Urocortin-related peptide) Q9BZM5 N2DL2 ULBP2 N2DL2 RAET1H NKG2D ligand 2 (N2DL-2) UNQ463/PRO791 (NKG2DL2) (ALCAN-alpha) (Retinoic acid early transcript 1H) (UL16-binding protein 2) P01282 VIP VIP VIP peptides [Cleaved into: Intestinal peptide PHV-42 (Peptide histidine valine 42); Intestinal peptide PHM-27

215

(Peptide histidine methioninamide 27); Vasoactive intestinal peptide (VIP) (Vasoactive intestinal polypeptide)] Q8IW00 VSTM4 VSTM4 C10orf72 V-set and transmembrane domain- containing protein 4 [Cleaved into: Peptide Lv] P47992 XCL1 XCL1 LTN SCYC1 Lymphotactin (ATAC) (C motif chemokine 1) (Cytokine SCM-1) (Lymphotaxin) (SCM-1-alpha) (Small-inducible cytokine C1) (XC chemokine ligand 1) Q9UBD3 XCL2 XCL2 SCYC2 Cytokine SCM-1 beta (C motif chemokine 2) (XC chemokine ligand 2) P04217 A1BG A1BG Alpha-1B-glycoprotein (Alpha-1- B glycoprotein) Q86SQ3 AGRE4 ADGRE4P EMR4 EMR4P Putative adhesion G protein- GPR127 PGR16 coupled receptor E4P (EGF-like module receptor 4) (EGF-like module-containing mucin-like hormone receptor-like 4) (G- protein coupled receptor 127) (G- protein coupled receptor PGR16) Q92485 ASM3B SMPDL3B ASML3B -like 3b (ASM-like phosphodiesterase 3b) (EC 3.1.4.-) Proteins functionally related to energy production and metabolism Q96PD5 PGRP2 PGLYRP2 PGLYRPL PGRPL N-acetylmuramoyl-L-alanine UNQ3103/PRO10102 amidase (EC 3.5.1.28) (Peptidoglycan recognition protein 2) (Peptidoglycan recognition protein long) (PGRP-L) P01242 SOM2 GH2 Growth hormone variant (GH-V) (Growth hormone 2) (Placenta- specific growth hormone) P22303 ACES ACHE (AChE) (EC 3.1.1.7) Q15848 ADIPO ADIPOQ ACDC ACRP30 Adiponectin (30 kDa adipocyte APM1 GBP28 complement-related protein) (Adipocyte complement-related 30 kDa protein) (ACRP30) (Adipocyte, C1q and collagen domain-containing protein) (Adipose most abundant gene

216

transcript 1 protein) (apM-1) (Gelatin-binding protein) Q9BRR6 ADPGK ADPGK PSEC0260 ADP-dependent glucokinase (ADP-GK) (ADPGK) (EC 2.7.1.147) (RbBP-35) P43652 AFAM AFM ALB2 ALBA Afamin (Alpha-albumin) (Alpha- Alb) P04745 AMY1 AMY1A AMY1; AMY1B Alpha-amylase 1 (EC 3.2.1.1) AMY1; AMY1C AMY1 (1,4-alpha-D-glucan glucanohydrolase 1) (Salivary alpha-amylase) P19961 AMY2B AMY2B Alpha-amylase 2B (EC 3.2.1.1) (1,4-alpha-D-glucan glucanohydrolase 2B) (Carcinoid alpha-amylase) P02743 SAMP APCS PTX2 Serum amyloid P-component (SAP) (9.5S alpha-1-glycoprotein) [Cleaved into: Serum amyloid P- component(1-203)] P02652 APOA2 APOA2 Apolipoprotein A-II (Apo-AII) (ApoA-II) (Apolipoprotein A2) [Cleaved into: Proapolipoprotein A-II (ProapoA-II); Truncated apolipoprotein A-II (Apolipoprotein A-II(1-76))] P04114 APOB APOB Q13790 APOF APOF Apolipoprotein F (Apo-F) (Lipid transfer inhibitor protein) (LTIP) Q9BPW4 APOL4 APOL4 Apolipoprotein L4 (Apolipoprotein L-IV) (ApoL-IV) O95445 APOM APOM G3A NG20 HSPC336 Apolipoprotein M (Apo-M) (ApoM) (Protein G3a) Q9BUR5 MIC26 APOO FAM121B MIC23 MICOS complex subunit MIC26 MIC26 My025 (Apolipoprotein O) (MICOS UNQ1866/PRO4302 complex subunit MIC23) (Protein FAM121B) P54793 ARSF ARSF F (ASF) (EC 3.1.6.-) Q5FYB1 ARSI ARSI Arylsulfatase I (ASI) (EC 3.1.6.-) Q5FYB0 ARSJ ARSJ UNQ372/PRO708 Arylsulfatase J (ASJ) (EC 3.1.6.-) Q6UWY0 ARSK ARSK TSULF Arylsulfatase K (ASK) (EC 3.1.6.- UNQ630/PRO1246 ) (Telethon ) P06276 CHLE BCHE CHE1 (EC 3.1.1.8) (Acylcholine acylhydrolase) (Butyrylcholine esterase) (Choline esterase II) (Pseudocholinesterase)

217

P43251 BTD BTD Biotinidase (Biotinase) (EC 3.5.1.12) P23280 CAH6 CA6 6 (EC 4.2.1.1) (Carbonate dehydratase VI) (Carbonic anhydrase VI) (CA-VI) (Salivary carbonic anhydrase) (Secreted carbonic anhydrase) P08218 CEL2B CELA2B ELA2B Chymotrypsin-like elastase family member 2B (EC 3.4.21.71) (Elastase-2B) Q9UKY3 CES1P CES1P1 CES4 Putative inactive carboxylesterase 4 (Inactive carboxylesterase 1 pseudogene 1) (Placental ) (PCE-3) Q5XG92 EST4A CES4A CES8 Carboxylesterase 4A (EC 3.1.1.-) UNQ440/PRO873 P11597 CETP CETP Cholesteryl ester transfer protein (Lipid transfer protein I) Q92496 FHR4 CFHR4 CFHL4 FHR4 Complement factor H-related protein 4 (FHR-4) Q9BZP6 CHIA CHIA Acidic mammalian chitinase (AMCase) (EC 3.2.1.14) (Lung- specific protein TSA1902) Q6UXF7 CL18B CLEC18B MRLP1 C-type lectin domain family 18 UNQ306/PRO347 member B (Mannose receptor-like protein 1) Q8NCF0 CL18C CLEC18C MRLP3 C-type lectin domain family 18 member C (Mannose receptor-like protein 3) P04118 COL CLPS Q6UWE3 COLL2 CLPSL2 C6orf126 Colipase-like protein 2 UNQ3045/PRO9861 Q96KN2 CNDP1 CNDP1 CN1 CPGL2 Beta-Ala-His dipeptidase (EC UNQ1915/PRO4380 3.4.13.20) (CNDP dipeptidase 1) (Carnosine dipeptidase 1) (Glutamate carboxypeptidase-like protein 2) (Serum carnosinase) P15085 CBPA1 CPA1 CPA Carboxypeptidase A1 (EC 3.4.17.1) P48052 CBPA2 CPA2 Carboxypeptidase A2 (EC 3.4.17.15) Q8N4T0 CBPA6 CPA6 CPAH Carboxypeptidase A6 (EC 3.4.17.- ) P15086 CBPB1 CPB1 CPB PCPB Carboxypeptidase B (EC 3.4.17.2) (Pancreas-specific protein) (PASP)

218

Q96IY4 CBPB2 CPB2 Carboxypeptidase B2 (EC 3.4.17.20) (Carboxypeptidase U) (CPU) (Plasma carboxypeptidase B) (pCPB) (-activable inhibitor) (TAFI) P16870 CBPE CPE Carboxypeptidase E (CPE) (EC 3.4.17.10) (Carboxypeptidase H) (CPH) (Enkephalin convertase) (Prohormone-processing carboxypeptidase) P17538 CTRB1 CTRB1 CTRB Chymotrypsinogen B (EC 3.4.21.1) [Cleaved into: Chymotrypsin B chain A; Chymotrypsin B chain B; Chymotrypsin B chain C] Q6PKH6 DR4L2 DHRS4L2 SDR25C3 Dehydrogenase/reductase SDR family member 4-like 2 (EC 1.1.-.- ) (Short chain dehydrogenase/reductase family 25C member 3) A6NNS2 DRS7C DHRS7C SDR32C2 Dehydrogenase/reductase SDR family member 7C (EC 1.1.-.-) (Short-chain dehydrogenase/reductase family 32C member 2) Q9UGM3 DMBT1 DMBT1 GP340 O75356 ENTP5 ENTPD5 CD39L4 PCPH Ectonucleoside triphosphate diphosphohydrolase 5 (NTPDase 5) (EC 3.6.1.6) (CD39 antigen-like 4) (ER-UDPase) (Guanosine- diphosphatase ENTPD5) (GDPase ENTPD5) (EC 3.6.1.42) (Nucleoside diphosphatase) (Uridine-diphosphatase ENTPD5) (UDPase ENTPD5) Q8NAU1 FNDC5 FNDC5 FRCP2 Fibronectin type III domain- containing protein 5 (Fibronectin type III repeat-containing protein 2) [Cleaved into: Irisin] Q92820 GGH GGH Gamma-glutamyl hydrolase (EC 3.4.19.9) (Conjugase) (GH) (Gamma-Glu-X carboxypeptidase) P27352 IF GIF IFMH Gastric (Intrinsic factor) (IF) (INF) Q6UWU2 GLB1L GLB1L UNQ229/PRO262 Beta-galactosidase-1-like protein (EC 3.2.1.-)

219

Q96SL4 GPX7 GPX7 GPX6 UNQ469/PRO828 Glutathione peroxidase 7 (GPx-7) (GSHPx-7) (EC 1.11.1.9) (CL683) Q16661 GUC2B GUCA2B Guanylate cyclase activator 2B [Cleaved into: Guanylate cyclase C-activating peptide 2 (Guanylate cyclase C-activating peptide II) (GCAP-II); Uroguanylin (UGN)] P81172 HEPC HAMP HEPC LEAP1 Hepcidin (Liver-expressed UNQ487/PRO1003 antimicrobial peptide 1) (LEAP-1) (Putative liver tumor regressor) (PLTR) [Cleaved into: Hepcidin- 25 (Hepc25); Hepcidin-20 (Hepc20)] P10997 IAPP IAPP Islet amyloid polypeptide (Amylin) (Diabetes-associated peptide) (DAP) (Insulinoma amyloid peptide) P01569 IFNA5 IFNA5 Interferon alpha-5 (IFN-alpha-5) (Interferon alpha-61) (Interferon alpha-G) (LeIF G) Q6UW32 IGFL1 IGFL1 UNQ644/PRO1274 Insulin growth factor-like family member 2 Q6UWQ7 IGFL2 IGFL2 UNQ645/PRO1275 Insulin growth factor-like family member 3 Q6UXB1 IGFL3 IGFL3 UNQ483/PRO982 P01308 INS INS Insulin [Cleaved into: Insulin B chain; Insulin A chain] P04180 LCAT LCAT Phosphatidylcholine-sterol acyltransferase (EC 2.3.1.43) (Lecithin-cholesterol acyltransferase) (Phospholipid- cholesterol acyltransferase) Q6JVE5 LCN12 LCN12 Epididymal-specific lipocalin-12 Q5VSP4 LC1L1 LCN1P1 LCN1L1 Putative lipocalin 1-like protein 1 (Lipocalin 1-like pseudogene 1) Q8WX39 LCN9 LCN9 Epididymal-specific lipocalin-9 (MUP-like lipocalin) P48357 LEPR LEPR DB OBR P11150 LIPC LIPC HTGL Hepatic (HL) () (EC 3.1.1.3) (Lipase member C) P07098 LIPG LIPF Gastric triacylglycerol lipase (GL) () (EC 3.1.1.3) Q9Y5X9 LIPE LIPG UNQ387/PRO719 (EC 3.1.1.3) (Endothelial cell-derived lipase) (EDL) (EL) 220

Q8WWY LIPH LIPH LPDLR MPAPLA1 Lipase member H (LIPH) (EC 8 PLA1B 3.1.1.-) (LPD lipase-related protein) (Membrane-associated phosphatidic acid-selective -alpha) (mPA- PLA1 alpha) (Phospholipase A1 member B) Q6XZB0 LIPI LIPI LPDL PRED5 Lipase member I (LIPI) (EC 3.1.1.-) (Cancer/testis antigen 17) (CT17) (LPD lipase) (Membrane- associated phosphatidic acid- selective phospholipase A1-beta) (mPA-PLA1 beta) Q5VXJ0 LIPK LIPK LIPL2 Lipase member K (EC 3.1.1.-) (Lipase-like abhydrolase domain- containing protein 2) Q5VYY2 LIPM LIPM LIPL3 Lipase member M (EC 3.1.1.-) (Lipase-like abhydrolase domain- containing protein 3) Q5VXI9 LIPN LIPN LIPL4 Lipase member N (EC 3.1.1.-) (Lipase-like abhydrolase domain- containing protein 4) P06858 LIPL LPL LIPD Lipoprotein lipase (LPL) (EC 3.1.1.34) Q9Y2E5 MA2B2 MAN2B2 KIAA0935 Q08431 MFGM MFGE8 A6NG13 MGT4D MGAT4D Alpha-1,3-mannosyl-glycoprotein 4-beta-N- acetylglucosaminyltransferase-like protein MGAT4D (EC 2.4.1.-) O96009 NAPSA NAPSA NAP1 NAPA Napsin-A (EC 3.4.23.-) (Aspartyl protease 4) (ASP4) (Asp 4) (Napsin-1) (TA01/TA02) P61916 NPC2 NPC2 HE1 Epididymal secretory protein E1 (Human epididymis-specific protein 1) (He1) (Niemann-Pick disease type C2 protein) P0DJD8 PEPA3 PGA3 Pepsin A-3 (EC 3.4.23.1) (Pepsinogen-3) P0DJD7 PEPA4 PGA4 Pepsin A-4 (EC 3.4.23.1) (Pepsinogen-4) P0DJD9 PEPA5 PGA5 Pepsin A-5 (EC 3.4.23.1) (Pepsinogen-5) P20142 PEPC PGC Gastricsin (EC 3.4.23.3) (Pepsinogen C)

221

O43692 PI15 PI15 CRISP8 P25TI Peptidase inhibitor 15 (PI-15) (25 kDa trypsin inhibitor) (p25TI) (Cysteine-rich secretory protein 8) (CRISP-8) (SugarCrisp) Q53H76 PLA1A PLA1A NMD PSPLA1 Phospholipase A1 member A (EC 3.1.1.-) (Phosphatidylserine- specific phospholipase A1) (PS- PLA1) H3BRW4 H3BRW PLA2G10 hCG_1746186 Phospholipase A(2) (EC 3.1.1.4) 4 Q8NCC3 PAG15 PLA2G15 LYPLA3 Group XV (EC UNQ341/PRO540 2.3.1.-) (1-O-acylceramide synthase) (ACS) (LCAT-like ) (LLPL) (Lysophospholipase 3) (Lysosomal phospholipase A2) (LPLA2) P04054 PA21B PLA2G1B PLA2 PLA2A Phospholipase A2 (EC 3.1.1.4) PPLA2 (Group IB phospholipase A2) (Phosphatidylcholine 2- acylhydrolase 1B) Q5R387 PA2GC PLA2G2C Putative inactive group IIC secretory phospholipase A2 (Phosphatidylcholine 2- acylhydrolase-like protein GIIC) Q13093 PAFA PLA2G7 PAFAH Platelet-activating factor acetylhydrolase (PAF acetylhydrolase) (EC 3.1.1.47) (1- alkyl-2- acetylglycerophosphocholine esterase) (2-acetyl-1- alkylglycerophosphocholine esterase) (Group-VIIA phospholipase A2) (gVIIA-PLA2) (LDL-associated phospholipase A2) (LDL-PLA(2)) (PAF 2- acylhydrolase) P55058 PLTP PLTP Phospholipid transfer protein (Lipid transfer protein II) P16233 LIPP PNLIP Pancreatic triacylglycerol lipase (PL) (PTL) (Pancreatic lipase) (EC 3.1.1.3) P54315 LIPR1 PNLIPRP1 PLRP1 Inactive pancreatic lipase-related protein 1 (PL-RP1)

222

P54317 LIPR2 PNLIPRP2 PLRP2 Pancreatic lipase-related protein 2 (PL-RP2) (EC 3.1.1.26) (EC 3.1.1.3) () Q17RR3 LIPR3 PNLIPRP3 Pancreatic lipase-related protein 3 (PL-RP3) (EC 3.1.1.3) P27169 PON1 PON1 PON Serum / 1 (PON 1) (EC 3.1.1.2) (EC 3.1.1.81) (EC 3.1.8.1) (Aromatic esterase 1) (A-esterase 1) (K-45) (Serum 1) Q15166 PON3 PON3 Serum paraoxonase/lactonase 3 (EC 3.1.1.2) (EC 3.1.1.81) (EC 3.1.8.1) Q13162 PRDX4 PRDX4 Peroxiredoxin-4 (EC 1.11.1.15) (Antioxidant enzyme AOE372) (AOE37-2) (Peroxiredoxin IV) (Prx-IV) (Thioredoxin peroxidase AO372) (Thioredoxin-dependent peroxide reductase A0372) P04070 PROC PROC Vitamin K-dependent protein C Q9BQR3 PRS27 PRSS27 MPN Serine protease 27 (EC 3.4.21.-) UNQ1884/PRO4327 (Marapsin) (Pancreasin) P07602 SAP PSAP GLBA SAP1 Prosaposin (Proactivator polypeptide) [Cleaved into: Saposin-A (Protein A); Saposin-B- Val; Saposin-B (Cerebroside sulfate activator) (CSAct) (Dispersin) (Sphingolipid activator protein 1) (SAP-1) (Sulfatide/GM1 activator); Saposin-C (A1 activator) (Co- beta-glucosidase) ( activator) (Sphingolipid activator protein 2) (SAP-2); Saposin-D (Component C) (Protein C)] Q6NUJ1 SAPL1 PSAPL1 Proactivator polypeptide-like 1 [Cleaved into: Saposin A-like; Saposin B-Val-like; Saposin B- like; Saposin C-like; Saposin D- like] Q9NRI6 PYY2 PYY2 Putative peptide YY-2 (Putative peptide YY2) P02753 RET4 RBP4 PRO2222 Retinol-binding protein 4 (Plasma retinol-binding protein) (PRBP) (RBP) [Cleaved into: Plasma

223

retinol-binding protein(1-182); Plasma retinol-binding protein(1- 181); Plasma retinol-binding protein(1-179); Plasma retinol- binding protein(1-176)] P05451 REG1A REG1A PSPS PSPS1 REG Lithostathine-1-alpha (Islet cells regeneration factor) (ICRF) (Islet of Langerhans regenerating protein) (REG) (Pancreatic stone protein) (PSP) (Pancreatic thread protein) (PTP) (Regenerating islet- derived protein 1-alpha) (REG-1- alpha) (Regenerating protein I alpha) P35542 SAA4 SAA4 CSAA Serum amyloid A-4 protein (Constitutively expressed serum amyloid A protein) (C-SAA) Q8IW75 SPA12 SERPINA12 Serpin A12 (OL-64) (Visceral adipose tissue-derived serine protease inhibitor) (Vaspin) (Visceral adipose-specific serpin) Q9HAT2 SIAE SIAE YSG2 Sialate O- (EC 3.1.1.53) (H-Lse) (Sialic acid- specific 9-O-acetylesterase) P17405 ASM SMPD1 ASM Sphingomyelin phosphodiesterase (EC 3.1.4.12) (Acid sphingomyelinase) (aSMase) Q92484 ASM3A SMPDL3A ASML3A Acid sphingomyelinase-like phosphodiesterase 3a (ASM-like phosphodiesterase 3a) (EC 3.1.4.-) P00995 ISK1 SPINK1 PSTI Serine protease inhibitor Kazal- type 1 (Pancreatic secretory trypsin inhibitor) (Tumor- associated trypsin inhibitor) (TATI) P20061 TCO1 TCN1 TC1 -1 (TC-1) (Haptocorrin) (HC) (Protein R) (Transcobalamin I) (TC I) (TCI) P20062 TCO2 TCN2 TC2 Transcobalamin-2 (TC-2) (Transcobalamin II) (TC II) (TCII) P01222 TSHB TSHB Thyrotropin subunit beta (Thyroid-stimulating hormone subunit beta) (TSH-B) (TSH-beta) (Thyrotropin beta chain) (Thyrotropin alfa)

224

P02766 TTHY TTR PALB Transthyretin (ATTR) (Prealbumin) (TBPA) P55089 UCN1 UCN Urocortin O95399 UTS2 UTS2 UNQ525/PRO1068 Urotensin-2 (Urotensin II) (U-II) (UII) Q765I0 UTS2B UTS2B URP UTS2D Urotensin-2B (Urotensin II-related peptide) (Urotensin IIB) (U-IIB) (UIIB) (Urotensin-2 domain- containing protein) Q13231 CHIT1 CHIT1 Chitotriosidase-1 (EC 3.2.1.14) (Chitinase-1) A5D8T8 CL18A CLEC18A MRLP2 C-type lectin domain family 18 member A (Mannose receptor-like protein 2) P04746 AMYP AMY2A Pancreatic alpha-amylase (PA) (EC 3.2.1.1) (1,4-alpha-D-glucan glucanohydrolase) P80188 NGAL LCN2 HNL NGAL Neutrophil gelatinase-associated lipocalin (NGAL) (25 kDa alpha- 2-microglobulin-related subunit of MMP-9) (Lipocalin-2) (Oncogene 24p3) (Siderocalin LCN2) (p25) Q9NZK5 CECR1 CECR1 ADA2 ADGF IDGFL Adenosine deaminase CECR1 (EC 3.5.4.4) (Cat eye syndrome critical region protein 1) Proteins of unknown function Q6P093 ADCL2 AADACL2 Arylacetamide deacetylase-like 2 (EC 3.1.1.-) Q5VST6 AB17B ABHD17B C9orf77 FAM108B1 Protein ABHD17B (EC 3.-.-.-) CGI-67 (Alpha/beta hydrolase domain- containing protein 17B) (Abhydrolase domain-containing protein 17B) O43827 ANGL7 ANGPTL7 CDT6 Angiopoietin-related protein 7 UNQ313/PRO356 (Angiopoietin-like factor) (Angiopoietin-like protein 7) (Cornea-derived transcript 6 protein) P0DMC3 ELA APELA ELA TDL Apelin receptor early endogenous ligand (Protein Elabela) (Protein Toddler) Q86Y30 BAGE2 BAGE2 B melanoma antigen 2 (Cancer/testis antigen 2.2) (CT2.2) Q86Y29 BAGE3 BAGE3 B melanoma antigen 3 (Cancer/testis antigen 2.3) (CT2.3)

225

Q86Y28 BAGE4 BAGE4 MLL3P B melanoma antigen 4 (Cancer/testis antigen 2.4) (CT2.4) Q7Z5Y6 BMP8A BMP8A Bone morphogenetic protein 8A (BMP-8A) P34820 BMP8B BMP8B BMP8 Bone morphogenetic protein 8B (BMP-8) (BMP-8B) (Osteogenic protein 2) (OP-2) Q86YQ2 LATH BPIFA4P BASE LATH Putative BPIFA4P protein (BPI fold containing family A, member 4, pseudogene) (Breast cancer and salivary gland-expressed protein) (Putative latherin) Q8N4F0 BPIB2 BPIFB2 BPIL1 C20orf184 BPI fold-containing family B LPLUNC2 UNQ2489/PRO5776 member 2 (Bactericidal/permeability- increasing protein-like 1) (BPI- like 1) (Long palate, lung and nasal epithelium carcinoma- associated protein 2) (RYSR) P59827 BPIB4 BPIFB4 C20orf186 LPLUNC4 BPI fold-containing family B member 4 (Ligand-binding protein RY2G5) (Long palate, lung and nasal epithelium carcinoma- associated protein 4) Q8NFQ5 BPIB6 BPIFB6 BPIL3 BPI fold-containing family B member 6 (Bactericidal/permeability- increasing protein-like 3) Q8N8R5 CB069 C2orf69 UPF0565 protein C2orf69 P23435 CBLN1 CBLN1 Cerebellin-1 (Precerebellin) [Cleaved into: Cerebellin (CER); [des-Ser1]-cerebellin] Q6UW01 CBLN3 CBLN3 UNQ755/PRO1486 Cerebellin-3 Q9NTU7 CBLN4 CBLN4 CBLNL1 Cerebellin-4 (Cerebellin-like UNQ718/PRO1382 glycoprotein 1) Q9H6E4 CC134 CCDC134 Coiled-coil domain-containing protein 134 Q04900 MUC24 CD164 Sialomucin core protein 24 (MUC-24) (Endolyn) (Multi- glycosylated core protein 24) (MGC-24) (MGC-24v) (CD antigen CD164) Q5VXM1 CDCP2 CDCP2 CUB domain-containing protein 2 Q6NT32 EST5A CES5A CES7 Carboxylesterase 5A (EC 3.1.1.1) (Carboxylesterase-like urinary

226

excreted protein homolog) (Cauxin) P01215 GLHA CGA Glycoprotein hormones alpha chain (Anterior pituitary glycoprotein hormones common subunit alpha) (Choriogonadotropin alpha chain) (Chorionic gonadotrophin subunit alpha) (CG-alpha) (Follicle- stimulating hormone alpha chain) (FSH-alpha) (Follitropin alpha chain) (Luteinizing hormone alpha chain) (LSH-alpha) (Lutropin alpha chain) (Thyroid-stimulating hormone alpha chain) (TSH-alpha) (Thyrotropin alpha chain) H9KV56 H9KV56 CGB2 Choriogonadotropin subunit beta variant 2 P0DN87 CGB7 CGB7 Choriogonadotropin subunit beta 7 Q9BWS9 CHID1 CHID1 GL008 PSEC0104 Chitinase domain-containing SB139 protein 1 (Stabilin-1-interacting chitinase-like protein) (SI-CLP) Q9Y6N3 CLCA3 CLCA3P CLCA3 Calcium-activated chloride channel regulator family member 3 (Calcium-activated chloride channel family member 3) (hCLCA3) A2RUU4 COLL1 CLPSL1 C6orf127 Colipase-like protein 1 Q9UI42 CBPA4 CPA4 CPA3 Carboxypeptidase A4 (EC 3.4.17.- UNQ694/PRO1339 ) (Carboxypeptidase A3) Q8WXQ8 CBPA5 CPA5 Carboxypeptidase A5 (EC 3.4.17.- ) P15169 CBPN CPN1 ACBP Carboxypeptidase N catalytic chain (CPN) (EC 3.4.17.3) (Anaphylatoxin inactivator) (Arginine carboxypeptidase) (Carboxypeptidase N polypeptide 1) (Carboxypeptidase N small subunit) (Kininase-1) (Lysine carboxypeptidase) (Plasma carboxypeptidase B) (Serum carboxypeptidase N) (SCPN) P22792 CPN2 CPN2 ACBP Carboxypeptidase N subunit 2 (Carboxypeptidase N 83 kDa chain) (Carboxypeptidase N large subunit) (Carboxypeptidase N

227

polypeptide 2) (Carboxypeptidase N regulatory subunit) P24387 CRHBP CRHBP CRFBP Corticotropin-releasing factor- binding protein (CRF-BP) (CRF- binding protein) (Corticotropin- releasing hormone-binding protein) (CRH-BP) P07498 CASK CSN3 CASK CSN10 CSNK Granulocyte colony-stimulating factor (G-CSF) (Pluripoietin) () () P09228 CYTT CST2 Cystatin-SA (Cystatin-2) (Cystatin-S5) Q5W188 CST9P CST9LP1 Putative cystatin-9-like protein CST9LP1 (Cystatin-9-like pseudogene 1) Q6UWP2 DHR11 DHRS11 SDR24C1 Dehydrogenase/reductase SDR UNQ836/PRO1774 family member 11 (EC 1.1.-.-) (Short-chain dehydrogenase/reductase family 24C member 1) P24855 DNAS1 DNASE1 DNL1 DRNI Deoxyribonuclease-1 (EC 3.1.21.1) (Deoxyribonuclease I) (DNase I) (Dornase alfa) Q7Z5P4 DHB13 HSD17B13 SCDR9 SDR16C3 17-beta-hydroxysteroid HMFN0376 UNQ497/PRO1014 dehydrogenase 13 (17-beta-HSD 13) (EC 1.1.-.-) (Short chain dehydrogenase/reductase family 16C member 3) (Short-chain dehydrogenase/reductase 9) Q8N0V4 LGI2 LGI2 KIAA1916 LGIL2 Leucine-rich repeat LGI family member 2 (LGI1-like protein 2) (Leucine-rich glioma-inactivated protein 2) Q6UXI9 NPNT NPNT EGFL6L POEM Nephronectin (Preosteoblast EGF- UNQ295/PRO334 like repeat protein with MAM domain) (Protein EGFL6-like) Q9GZU5 NYX NYX CLRP Nyctalopin O95897 NOE2 OLFM2 NOE2 Noelin-2 (Olfactomedin-2) Q96PB7 NOE3 OLFM3 NOE3 Noelin-3 (Olfactomedin-3) UNQ1924/PRO4399 (Optimedin) A8MZH6 OOSP1 OOSP1 Putative oocyte-secreted protein 1 homolog Q3ZCN5 OTOGL OTOGL C12orf64 Q7RTZ1 OVCH2 OVCH2 OVTN Ovochymase-2 (EC 3.4.21.-) ()

228

Q8WXA2 PATE1 PATE1 PATE Prostate and testis expressed protein 1 Q6UY27 PATE2 PATE2 C11orf38 Prostate and testis expressed UNQ3112/PRO10144 protein 2 (PATE-like protein M) (PATE-M) B3GLJ2 PATE3 PATE3 Prostate and testis expressed protein 3 (Acrosomal vesicle protein HEL-127) (PATE-like protein DJ) (PATE-DJ) E5RFR1 E5RFR1 PENK Proenkephalin-A (Fragment) Q9HBJ0 PLAC1 PLAC1 Placenta-specific protein 1 Q6GTS8 P20D1 PM20D1 Probable carboxypeptidase PM20D1 (EC 3.4.17.-) (Peptidase M20 domain-containing protein 1) Q9BSG0 PADC1 PRADC1 C2orf7 PAP21 UNQ833/PRO1760 Q9P1C3 YN010 PRO2829 Putative uncharacterized protein PRO2829 A6NIE9 PRS29 PRSS29P ISP2 Putative serine protease 29 (EC 3.4.21.-) (Implantation serine proteinase 2-like protein) (ISP2- like protein) A4D1T9 PRS37 PRSS37 TRYX2 Probable inactive serine protease 37 (Probable inactive trypsin-X2) A1L453 PRS38 PRSS38 MPN2 Serine protease 38 (EC 3.4.21.-) (Marapsin-2) Q7Z5A4 PRS42 PRSS42 TESSP2 Serine protease 42 (EC 3.4.21.-) (Testis serine protease 2) E7EML9 PRS44 PRSS44 TESSP4 Serine protease 44 (EC 3.4.21.-) (Testis serine protease 4) (TESSP- 4) A8MTI9 PRS47 PRSS47 Putative serine protease 47 (EC 3.4.21.-) Q7RTY5 PRS48 PRSS48 ESSPL Serine protease 48 (EC 3.4.21.-) (Epidermis-specific serine protease-like protein) Q6PEW0 PRS54 PRSS54 KLKBL4 Inactive serine protease 54 (Cancer/testis antigen 67) (CT67) (Plasma kallikrein-like protein 4) Q8IYP2 PRS58 PRSS58 TRY1 TRYX3 Serine protease 58 (EC 3.4.21.4) UNQ2540/PRO6090 (Trypsin-X3) Q5JQD4 PYY3 PYY3 Putative peptide YY-3 (Putative peptide YY3) (PYY-III) P00797 RENI REN Renin (EC 3.4.23.15) (Angiotensinogenase)

229

P34096 RNAS4 RNASE4 RNS4 (RNase 4) (EC 3.1.27.-) P11684 UTER SCGB1A1 CC10 CCSP UGB Uteroglobin (Clara cell phospholipid-binding protein) (CCPBP) (Clara cells 10 kDa secretory protein) (CC10) (Secretoglobin family 1A member 1) (Urinary protein 1) (UP-1) (UP1) (Urine protein 1) Q8TD33 SG1C1 SCGB1C1 Secretoglobin family 1C member 1 (Secretoglobin RYD5) P0DMR2 SG1C2 SCGB1C2 Secretoglobin family 1C member 2 Q9HB40 RISC SCPEP1 RISC SCP1 MSTP034 Retinoid-inducible serine UNQ265/PRO302 carboxypeptidase (EC 3.4.16.-) (Serine carboxypeptidase 1) Q9UK55 ZPI SERPINA10 ZPI Protein Z-dependent protease UNQ707/PRO1358 inhibitor (PZ-dependent protease inhibitor) (PZI) (Serpin A10) P08185 CBG SERPINA6 CBG Corticosteroid-binding globulin (CBG) (Serpin A6) (Transcortin) P05543 THBG SERPINA7 TBG Thyroxine-binding globulin (Serpin A7) (T4-binding globulin) P05120 PAI2 SERPINB2 PAI2 PLANH2 Plasminogen activator inhibitor 2 (PAI-2) (Monocyte Arg-serpin) (Placental plasminogen activator inhibitor) (Serpin B2) (Urokinase inhibitor) P0C7L1 ISK8 SPINK8 Serine protease inhibitor Kazal- type 8 Q5DT21 ISK9 SPINK9 LEKTI2 Serine protease inhibitor Kazal- type 9 (Lymphoepithelial Kazal- type-related inhibitor 2) P49223 SPIT3 SPINT3 Kunitz-type protease inhibitor 3 (HKIB9) Q6UDR6 SPIT4 SPINT4 C20orf137 Kunitz-type protease inhibitor 4 P01266 THYG TG Q9P2K2 TXD16 TXNDC16 KIAA1344 P07911 UROM UMOD Uromodulin (Tamm-Horsfall urinary glycoprotein) (THP) [Cleaved into: Uromodulin, secreted form] Q6UY13 YB003 UNQ5830/PRO19650/PRO1981 Putative uncharacterized protein 6 UNQ5830/PRO19650/PRO19816

230

Q9H1F0 WF10A WFDC10A C20orf146 WAP10 WAP four-disulfide core domain protein 10A (Putative protease inhibitor WAP10A) Q8IUB3 WF10B WFDC10B WAP12 Protein WFDC10B Q8NEX5 WFDC9 WFDC9 WAP9 Protein WFDC9 P60852 ZP1 ZP1 Zona pellucida sperm-binding protein 1 (Zona pellucida glycoprotein 1) (Zp-1) [Cleaved into: Processed zona pellucida sperm-binding protein 1]

231