PROTEIN-PROTEIN INTERACTIONS with and WITHIN the NLRP3 INFLAMMASOME Evidence from STRING and Literature Studies

PROTEIN-PROTEIN INTERACTIONS WITH AND WITHIN THE NLRP3 INFLAMMASOME Evidence from STRING and literature studies

Bachelor Degree Project in Bioscience G2E level, 30 ECTS Spring term 2020

Supervisor: Mikael Ejdebäck m [email protected]

Examiner: Erik Gustafsson e [email protected]

School of Bioscience University of Skövde Box 408 541 28 Skövde Abstract Inflammasomes are multiprotein complexes that play a role in the innate immune system. One inflammasome is the NLRP3 inflammasome, which can be activated and primed by different stimuli that bind to pattern recognition receptors (PRRs). There are many theories of how the NLRP3 inflammasome can be regulated, one of which is deubiquitination by deubiquitinating enzymes (DUBs). The NLRP3 inflammasome is also involved in many diseases, for example, diabetes, cancer and neurodegenerative diseases. The main aim of this study is to increase knowledge of the protein-protein interactions with and within the NLRP3 inflammasome. Thus, this study will give further insight into NLRP3 inflammasome pathways and can lead to novel treatment targets for different NLRP3-associated diseases in the future. The NLRP3 inflammasome and its regulation were described in this study and protein-protein interaction (PPI) networks of the individual NLRP3 inflammasome key components (NLRP3, PYCARD, caspase-1) were obtained from STRING for human, mouse and macaque orthologs. The obtained PPI networks were then compared. The types of PPI in all PPI networks, either functional or physical, were verified by KEGG or research literature, respectively. Mass spectrometry data of unstimulated and stimulated THP-1 cells were also analyzed. During this study the BRISC complex and its members, a DUB, was also further explored. All in all, the study increased the knowledge about the protein-protein interactions with and within the NLRP3 inflammasome. Further research can aid in the discovery of novel treatment targets of diseases related to inflammasomes.

List of abbreviations CASP1 Caspase-1

Co-IP Co-immunoprecipitation

DAMP Damage-associated molecular pattern

DSB Double strand break

DUB Deubiquitinating enzyme

IL Interleukin

KEGG Kyoto Encyclopedia of Genes and Genomes

LPS Lipopolysaccharide

MS Mass spectrometry

NLRP3 Nod-like receptor 3

PAMP Pathogen-associated molecular pattern

PPI Protein-protein interaction

PYCARD Pyrin-containing CARD domain

STRING Search Tool for the Retrieval of Interacting

Genes/Proteins

Table of Contents Introduction ...... 1 Methods ...... 5 Results ...... 6 Discussion ...... 21 Conclusion and future perspectives ...... 23 Acknowledgements ...... 24 References ...... 25 Appendix 1 – Schematic NLRP3 inflammasome construction reaction ...... 32 Appendix 2 – MS data analysis ...... 33

Introduction The immune system is a system to protect from pathogens and other harmful substances. There are two types of immunity: innate immunity and adaptive immunity (Iwasaki & Medzhitov, 2015). Innate immunity is the first line of defense and consists of mechanical barriers, like the skin, and cellular components and processes. After the initial response of the innate immunity, the adaptive immunity is triggered. Adaptive immunity, also called acquired immunity, is a system with an immunological memory that includes specialized cells: T cells and B cells (Bonilla & Oettgen, 2010). The T cells are activated by antigen-presenting macrophages. Subsequently, T cells activate killer cells, which phagocytize the pathogen. The B cells are also activated and either become plasma cells that produce antibodies that suppress the foreign antigen or they become memory cells. One part of the innate immune system against harmful substances is inflammation. Inflammation is characterized by heat (calor), pain (dolor), rubor (redness), swelling (tumor) and function loss (fuctio laesia) as described by Calsus and Galen. There are two major types of inflammation. Acute inflammation is inflammation that lasts a relatively short time and chronic inflammation can last for months or even years (Pahwa, Goyal, Bansal & Jialal, 2020). Acute inflammation follows some basic steps (Medzhitov, 2008). After exposure to pathogens, or due to tissue or cellular damage, plasma and leukocytes are delivered to the area of injury or infection. Mast cells then produce inflammatory biomolecules, including cytokines that signal for vasodilation, which leads to transport of plasma and leukocytes to the area of injury or infection. This is called leukocyte extravasation (Vestweber, 2015). Exits of red blood cells from the area of injury are also blocked. Subsequently, the neutrophils are activated directly or indirectly, i.e. by cytokines. Finally, the activated neutrophils kill the pathogen by producing granules, which contain components that can lyse the pathogen (Brostjan & Oehler, 2020). Injury or infection also activate inflammasomes. Inflammasomes are multiprotein complexes that are components of the innate immune system (Martinon, Burns & Tschopp, 2002). Inflammasomes contain pro-caspases, which are activated and are important in inflammation. Inflammasomes cause pyroptosis upon activation. Pyroptosis was first defined as inflammatory caspase-mediated cell death (Man, Karki & Kanneganti, 2017). However, recent discoveries have changed the definition to gasdermin D (GSDMD)-mediated cell death instead (Shi, Gao & Shao, 2017). Pyroptosis is activated by either damage-associated molecular patterns (DAMPs) or pathogen-associated molecular patterns (PAMPs). These patterns are recognized by transmembrane proteins, like toll-like receptors (TLRs) or cytoplasmic receptors. The transmembrane and cytoplasmic receptors are collectively called pattern recognition receptors (PRRs) (Takeuchi & Akira, 2010). The NLRP1 inflammasome is part of the NLR family of inflammasomes and was the first inflammasome discovered and described in the scientific article of Martinon, Burns and Tschopp (2002). There are different types of inflammasomes known, including NLRP1 (Nucleotide-binding oligomerization domain (NOD), leucine-rich repeat (LRR)-containing (NLR) family pyrin domain containing 1), NLRP3, NLRC4, absent in melanoma 2 (AIM2) and Pyrin. These are the canonical inflammasomes. Furthermore, there are also non-canonical inflammasomes. All of the previously mentioned inflammasomes differ in molecular structure and in activation (Rathinam & Fitzgerald, 2016). The focus will be on the related NLRP3 inflammasome.

NLRP3 inflammasome Just like NLRP1, the NLRP3 inflammasome is part of the NLR family of inflammasomes. There is a consensus that there is a two-step activation: a priming signal and an activation signal needed to construct an active inflammasome (He, Hara & Núñez, 2016). First, the priming signal originates

Page: 1 from binding of different stimuli to the PRRs. The signaling activates nuclear factor kappa B (NF- -IL- -IL18 and NLRP3 transcripts amongst others. An overview of the different PAMP signals that cause the priming signalκB), which can be is founda transcription in Table 1. factor and increased synthesis of pro 1β, pro Table 1. PAMP signals that cause the NLRP3 priming signal

Stimuli Research paper

Nigericin Perregaux & Gabel (1994)

Maitotoxin Mariathesan et al. (2006)

Viruses, e.g. influenza A Ichinohe, Pang & Iwasaki (2010)

Bacteria, e.g. Staphylococcus aureus McGillan et al. (2013)

LPS Kayagaki et al. (2013)

The second signal is activation. The activation step activates the inflammasome, can be triggered by numerous, structurally different stimuli, the DAMP stimuli (Place & Kanneganti, 2020). There is no general consensus of the exact mechanism of activation. An overview of DAMP stimuli and connected research papers are listed in Table 1. Table 2. DAMP stimuli that affect NLRP3 activation

Stimuli Research paper Silica Hornung et al. (2008) Pathogen-driven ligands, e.g. pore-forming toxins

Endogenous danger signals, e.g. serum amyloid A, Mariathasan et al. (2006) ATP

K+ efflux Pétrilli et al. (2007) Lysosomal disturbance Wang, Yuan, Chen & Wang (2019) Signaling by calcium Lee et al. (2012)

Mitochondria-derived factors, e.g. oxidized Shimada et al. (2012) mitochondrial DNA Reactive oxygen species and cardioplin Pétrilli, Dostert, Muruve, & Tschopp (2007) Microtubule-driven spatial arrangement of Misawa et al. (2013) mitochondria

There are several theories for the negative regulation of NLRP3. Firstly, on the priming signal level, microRNA-233 (miRNA-233) could interact with NLRP3 mRNA and target it for destruction (Sutterwala, Haasken & Cassel, 2014). Secondly, also on the priming level, inflammatory cytokine IL-10 can be signaled by type 1 interferons (IFNs) and STAT3 to decrease expression of pro-IL- and pro-IL- -10 via the transcription factor STAT1, which in turn signals via STAT3 for a reduction of pro-IL- -IL- 1β decreased NLRP31α (Guarda expression. et al., 2011). On inflammasome Namely, type activation1 IFNs induce level, IL there are also several possible 1α and pro 1β. This leads to Page: 2 but not exclusive theories. The first theory by Mishra and colleagues (2012) propose that nitric oxide leads to S-Nitrosylation of NLRP3, which hinders NLRP3 inflammasome construction. The other theories proposed by Mehto and colleagues (2019) and Nabar and Kehrl (2019) are related to immunity-related GTPase M (IRGM). IRGM can bind to the NACHT domain of NLRP3 and to the PYD of ASC. This restricts the oligmerizations that constructs the NLRP3 inflammasome. IRGM could also target NLRP3 and ASC for autophagy. Another mechanism of regulation of the priming signal and thus the NLRP3 inflammasome, is deubiquitination. Ubiquitination is the process of adding an ubiquitin-group to proteins, which will then be promoted to be degraded (Grumati & Dikic, 2017). There are several steps from ubiquitination of proteins to degradation. Firstly, the process begins with the activation with ATP of the C-terminal glycine residue of the ubiquitin and then ubiquitin is covalently conjugated to the lysine residue of a protein by an ubiquitin-proteasome (UPS). In the ubiquitin conjugation step, in subsequent order, E1 (ubiquitin-activating enzyme), E2 (ubiquitin-conjugating enzyme) and E3 (ubiquitin ligases) are involved. Either one or more ubiquitinations can take place with one protein. Finally, the ubiquitinated protein is degraded by the 26S proteasome (Hershko & Ciachanover, 1998). Ubiquitination is a reversible process, thus proteins can also be deubiquitinated by deubiquitinating enzymes (DUBs), (Reyes-Turcu, Ventii & Wilkinson, 2009). One DUB is called the BRCC36 isopeptidase complex (BRISC) (Cooper et al., 2009). BRISC contains four stoichiometric proteins: BRISC and BRCA1 A complex member 1 (BABAM1), BRISC and BRCA A complex member 2 (BABAM2), abraxas 2, BRISC complex unit (ABRAXAS2) and BRCA1/BRCA2- containing complex subunit 3 (BRCC3) (HGNC database, n.d). DUBs are very specific and BRISC specifically deubiquitinates lysine-63 of a polyubiquitin chain. For example, ubiquitination at a lysine-48 of the ubiquitin, making a polyubiquitin chain, signals the protein for degradation by 26 proteasome. However, lysine-63 ubiquitination, does not signal for protein degradation, but signals for other processes e.g. DNA repair (Huang & D’Andrea, 2006) and transcription (Weake & Workman, 2008). BRISC is not only involved in deubiquitination, but also mitotic processes (Yan et al., 2015). BRISC can also form a complex with serine hydroxymethyltransferase (SHMT) and be part of interferon response regulation (Zheng et al., 2013). Zheng and colleagues (2013) propose that the BRISC-SHMT2 deubiquitinates the type 1 interferon receptor chain 1 (IFNAR1) which leads to decreased endocytosis. It has also be found that NLRP3 can be signaled to be deubiquitinated by MyD88 (Juliana et al., 2012). When NLRP3 is deubiquitylated, it is activated. All of this enables NLRP3 inflammasome to be open to different stimuli. As mentioned earlier, inflammasomes can cause pyroptosis. There are several steps in the signal cascade that leads to pyroptosis where the NLRP3 inflammasome is involved. Step 1: priming by PAMPs and activation by DAMPs leads to the construction of an active NLRP3 inflammasome. Step 2: pro-caspase 1 is cleaved into active caspase-1. Step 3a: caspase-1 cleaves pro-IL- -IL- 18, which become active IL- -18, respectively. Step 3b: caspase-1 cleaves GSDMD, which becomes GSDMD N-terminal. Step 4: GSDMD N-terminal forms pores in the lipid bilayer.1β and proStep 5: pyroptosis occurs and simultaneously1β and IL IL- -18 are released from the cell. A schematic overview of NLRP3-mediated pyroptosis can be seen in Figure 1. However, pyroptosis is not required for the release of the interleukins,1β only and the IL pore formation by GSDMD (Evavold et al., 2018). Appendix 1 shows the schematic overview of the NLRP3 inflammasome constructions step and subsequent dimerization.

Diseases related to the NLRP3 inflammasome There are many diseases related to the NLRP3 inflammasome. Dysregulation in NLRP3 signaling pathways can give rise to different diseases, including neurological disorders and metabolic diseases. Examples of neurological disorders potentially caused by NLRP3 inflammasome dysregulation are Alzheimer’s disease (Saresella et al., 2016) and Parkinson’s disease (Zhou et al., Page: 3

2016). Additionally, examples of metabolic diseases are diabetes type 2 (Donath & Shoelson, 2011) and atherosclerosis obesity (Vandanmagsar et al., 2011). Mutations in the NLRP3 gene are also associated with a group of rare diseases called cryopyrin-associated periodic syndrome (Yu & Leslie, 2011).

Figure 1. A schematic overview of the steps in NLRP3-mediated pyroptosis. (1) Priming by PAMPs and activation by DAMPs leads to the construction of an active NLRP3 inflammasome. (2) Pro-caspase 1 is cleaved into active caspase-1. (3a) Caspase-1 cleaves pro-IL- -IL-18, which become active IL- and IL-18, respectively. (3b) Caspase-1 cleaves GSDMD, which becomes GSDMD N-terminal. (4) GSDMD N- terminal forms pores in the lipid bilayer. (5) Pyroptosis occurs1β and and pro simultaneously IL- -18 are1β released from the cell. 1β and IL Protein-protein interaction studies In order to understand the mechanism behind inflammasome formation and activation and to increase the knowledge about diseases and find potential treatment targets, biological pathways need to be studied with research on protein-protein interactions. Protein-protein interactions are crucial for all biological processes that take place (Peng, Wang, Peng, Wu & Pan, 2017). There are two types of protein-protein interactions. One interaction is direct and thus called a physical interaction and the other interaction is indirect and thus called a functional interaction. Physical interactions are when proteins are directly bound to each other. On the other hand, in functional interactions the proteins do not interact directly, but rather are part of the same pathway and thus interact indirectly (De Las Rivas & Fontanillo, 2012). Physical interactions can be determined by different methods, i.e. co-immunoprecipitation and pull-down assay. Functional interactions are determined by knockdown or knockout of gene, i.e. by CRISPR. An overview of the different methods is given below. One of the methods of determining physical protein-protein interactions is co- immunoprecipitation (Lee, 2007). There are several steps performed in co-immunoprecipitation (Lin & Lai, 2017). Sepharose beads coupled with antibodies via a bacterial protein called Protein A or G are used. The antibody that is coupled to the bead is specific to the protein of interest. the protein sample (for example a cell lysis extract) and beads are mixed and incubated. The protein complexes attach to the antibodies on the beads and the mix is centrifuged and washed. The bound protein complexes are thereby specifically enriched on the and can be eluted in a pure form. The purified protein complexes can then be analyzed as intact complexes or its individual parts by different biochemical techniques, like SDS-PAGE followed by Western blot (using an antibody directed to another protein in the complex) or Mass spectrometry (Iqbal, Akins & Kenedy, 2018).

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Another method for detecting physical protein-protein interactions is the pull-down assay (Jain, Liu, Xiang & Ha, 2012). The pull-down method uses a bait protein, instead of antibody, which is tagged (examples: GST, biotin) and which will bind to any interacting proteins. The prey protein is any protein that will bind to the bait protein as soon as sample containing a protein of interest is added and the mix is incubated. Subsequently, the protein complexes are eluted with an elution buffer. Lastly, SDS-PAGE followed by western blot is used to visualize the protein complex (Louche, Salcedo & Bigot, 2017). For studies of functional interactions, a gene knockout (Kim et al., 2019) or gene knockdown (Summerton, 2007) can be used. Gene knockout is a technique that aims to completely disrupt a gene and gene knockdown is a technique that aims to reduce expression of a gene. One tool that can be used for both gene knockout and knockdown is CRISPR-Cas9 (Deyell, Ameta & Nghe, 2019). The effects CRISPR-Cas9 mediated knockout or knockdown can then be studied to see what kind of effects the knockout or knockdown of one gene has on other genes and proteins. All by all, understanding how proteins interact with each other leads to increased knowledge about biological pathways work and helps finding novel treatment targets of diseases.

Aim and objectives The main aim of this study is to increase knowledge of the protein-protein interactions with and within the NLRP3 inflammasome. Thus, this study will give further insight into NLRP3 inflammasome pathways and can aid in discovery of novel treatment targets for different NLRP3- associated diseases in the future. Objectives:

• To describe the NLRP3 inflammasome and its regulation. • To collect protein-protein interaction (PPI) networks of human, mouse and macaque orthologs of NLRP3 inflammasome key components (NLRP3, PYCARD, caspase-1) from the STRING database version 11. • To compare the collected data between the different animal orthologs. • To determine the types of protein-protein interactions, either physical or functional, in all PPI networks based on evidence in research literature and Kyoto Encyclopedia of Genes and Genomes (KEGG) database release 93.0. • To analyze mass spectrometry data of non-stimulated and stimulated THP-1 cells.

Methods Individual searches in the STRING (Szklarczyk et al., 2018) database version 11 on standard settings were performed with NLRP3, PYCARD and CASP1 (NLRP3 inflammasome key components) as search queries for the human, mouse and macaque orthologs. There was also a search performed with human NLRP3 on STRING version 11, with 50 interactors to be shown in the protein-protein interaction (PPI) network instead of the standard 10 interactors, other settings were still standard. The PPI were analyzed and compared. Kyoto Encyclopedia of Genes and Genomes (KEGG) release 93.0 (Kanehisa & Groto, 2000; Kanehisa, 2019; Kanehisa, Sato, Furumichi, Moshima & Tanabe, 2018) was used to determine the functional interactions between proteins. Furthermore, the type of protein-protein interactions was determined based on scientific evidence that was linked in STRING. A proteins list of mass spectrometry data from stimulated and non-stimulated THP1 cells was obtained and used for comparisons between theoretical findings and the experimental findings.

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Results The obtained PPI networks from STRING can be found in Figure 2a-c, Figure 4a-c, Figure 5a-c for human, mouse and macaque, respectively. The types of interactions between the NLRP3 inflammasome key components and other proteins in their respective networks were obtained directly from STRING. In this study, a functional interaction is defined as a non-physical (indirect) interaction between the query protein and other proteins that are found in the same KEGG pathway. A physical interaction is defined as a direct interaction that has been experimentally determined to be physical by for example co-immunoprecipitation. Some interactions may show up in the PPI network but are found to be neither physical nor functional according to the results from the STRING analysis.

Human NLRP3 inflammasome key components Using standard settings on STRING and human NLRP3, PYCARD and CASP1 as search queries, three different PPI networks were obtained (Figure 2). Further investigations using linked research articles and the nod-like receptor signaling pathway in KEGG, reveal the way that other proteins interact with the search query proteins (Table 3, 4 and 5) in either a physical or functional way.

a. b.

c. Figure 2. PPI networks obtained from STRING with human NLRP3 (a), PYCARD (b) and CASP1 (c) as search queries. The searches were performed on standard settings. The interaction between two proteins is visualized by a line which has a specific color. The colors indicate the type of evidence and can be split up in three main groups. The first group is the know interactions, which contains interactions from curated databases (light blue) and experimentally determined interactions (purple). The second group is predicted interactions, which contains gene neighborhood evidence (green), gene fusion evidence (red), and gene occurrence evidence (dark blue). The third group is other types of evidence, which contains textmining (where two proteins are mentioned together in the text) (yellow), co-expression (black), and protein homology (purple).

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Table 3. Types of interactions between human NLRP3 and other proteins in the PPI network Query Interacting Type of Method/Source Reference protein protein interaction NLRP3 CASP1 Functional KEGG pathway NLR SP NLRP3 PYCARD Physical Co-IP Dowds et al. (2004) NLRP3 IL18 Functional KEGG pathway NLR SP NLRP3 TXNIP Physical Co-IP Zhou et al. (2009) NLRP3 CARD8 Physical Co-IP Cheung et al. (2017) NLRP3 AIM2 Functional KEGG pathway NLR SP NLRP3 NLCR4 Functional KEGG pathway NLR SP NLRP3 DHX33 Functional KEGG pathway NLR SP NLRP3 CASP5 Functional KEGG pathway NLR SP NLRP3 NEK7 Physical Co-IP He et al. (2016) Co-IP = co-immunoprecipitation; NLR SP = nod-like receptor signaling pathway

Table 4. Types of interactions between human PYCARD and other proteins in the PPI network Query protein Interacting protein Type of Method/Source Reference interaction PYCARD CASP1 Physical Co-IP Lin et al. (2015) PYCARD NLRP3 Physical Co-IP Dowds et al. (2004) PYCARD AIM2 Physical Co-IP Fernandes- Alnemri et al. (2009) PYCARD CASP8 Physical Co-IP Gringhuis et al. (2012) PYCARD CASP5 Functional KEGG pathway NLR SP PYCARD NLRC4 Physical Co-IP Geddes et al. (2001) PYCARD IL1B Functional KEGG pathway NLR SP PYCARD MNDA None N/a N/a PYCARD IL18 Functional KEGG pathway NLR SP PYCARD CARD8 Functional KEGG Pathway NLR SP Co-IP = co-immunoprecipitation; NLR SP = nod-like receptor signaling pathway Table 5. Types of interactions between human CASP1 and other proteins in the PPI network

Query protein Interacting Type of Method/Source Reference protein interaction CASP1 IL18 Physical In vitro assay, in vivo Akita et al. (1997) assay CASP1 NLRC4 Physical Co-IP Geddes et al. (2001) CASP1 NLRP3 Functional KEGG pathway NLR SP CASP1 PYCARD Physical Co-IP Lin et al. (2015) CASP1 IL1B Physical In vitro assay, in vivo Howard et al. assay (1991) CASP1 CARD8 Physical Co-IP Razmara et al. (2002) CASP1 NLRP1 Physical Co-IP Hsu et al. (2008) CASP1 AIM2 Physical Co-IP Lu et al. (2014) CASP1 NOD1 Physical Co-IP Yoo et al. (2002) CASP1 CASP8 Functional KEGG pathway NLR SP Co-IP = co-immunoprecipitation; NLR SP = nod-like receptor signaling pathway

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By increasing the number of interactors to 40 in STRING with NLRP3 as a search query, the PPI network obtained (Figure 3) revealed a multiprotein complex called the BRISC complex. The BRISC complex may be interesting for further research on its effects on the NLRP3 inflammasome and associated pathways. The four members of the BRISC complex, BABAM1, BRE, BRCC3 and FAM175B, can be seen in the red box in Figure 3.

Figure 3. PPI network obtained from STRING with human NLRP3 as a search query. The number of interactors in the settings was increased to 50. Other settings were standard. Members of the BRISC complex can be seen in the red box: BABAM1, BRE, BRCC3 and FAM175B.

Mouse NLRP3 inflammasome key components Using standard settings on STRING and mouse NLRP3, PYCARD and CASP1 as search queries, three different PPI networks were obtained (Figure 4). Further investigations using linked research articles and the nod-like receptor signaling pathway in KEGG, reveal the way that other proteins interact with the search query proteins (Table 6, 7 and 8) in either a physical or functional way. Two proteins (Malt1 and Serpina3g) were determined to have neither physical nor functional interactions with Pycard.

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a. b.

Figure 4. PPI networks obtained from STRING with mouse Nlrp3 (a), Pycard (b) and Casp1 (c) as search queries. The searches were performed on standard settings.

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Table 6. Types of interactions between mouse Nlrp3 and other proteins in the PPI network Query protein Interacting Type of Method/Source Reference protein interaction Nlrp3 Casp1 Functional KEGG pathway NLR SP Nlrp3 Pycard Physical Co-IP Lee et al. (2012) Nlrp3 P2rx7 Functional KEGG pathway NLR SP Nlrp3 Txnip Functional KEGG pathway NLR SP Nlrp3 Il1b Functional KEGG pathway NLR SP Nlrp3 Nek7 Physical Co-IP He et al. (2016) Nlrp3 Mavs Physical Co-IP Subramanian et al. (2013) Nlrp3 Panx1 Functional KEGG pathway NLR SP Nlrp3 Ctsb Functional KEGG pathway NLR SP Nlrp3 Il18 Functional KEGG pathway NLR SP Co-IP = co-immunoprecipitation; NLR SP = nod-like receptor signaling pathway Table 7. Types of interactions between mouse Pycard and other proteins in the PPI network Query protein Interacting Type of Method/Source Reference protein interaction Pycard Casp1 Physical Co-IP Lin et al. (2015) Pycard Nlrp3 Physical Co-IP Lee et al. (2012) Pycard Aim2 Physical Co-IP Kim et al. (2015) Pycard Nlrc4 Physical Co-IP Qu et al. (2015) Pycard Il1b Functional KEGG pathway NLR SP Pycard Casp8 Functional KEGG pathway NLR SP Pycard Il18 Functional KEGG pathway NLR SP Pycard Mefv Functional KEGG pathway NLR SP Pycard Malt1 None N/a N/a Pycard Serpina3g None N/a N/a Co-IP = co-immunoprecipitation; NLR SP = nod-like receptor signaling pathway Table 8. Types of interactions between mouse Casp1 and other proteins in the PPI network Query protein Interacting Type of Method/Source Reference protein interaction Casp1 Nlrc4 Physical Co-IP Qu et al. (2012) Casp1 Il18 Functional KEGG pathway NLR SP Casp1 Pycard Physical Co-IP Lin et al. (2015) Casp1 Nlrp3 Functional KEGG pathway NLR SP Casp1 Il1b Physical Co-IP Duong et al. (2015) Casp1 Aim2 Functional KEGG pathway NLR SP Casp1 Naip5 Functional KEGG pathway NLR SP Casp1 Nod1 Functional KEGG pathway NLR SP Casp1 Naip2 Functional KEGG pathway NLR SP Casp1 Gsdmd Functional KEGG pathway NLR SP Co-IP = co-immunoprecipitation; NLR SP = nod-like receptor signaling pathway

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Macaque NLRP3 inflammasome key components Using standard settings on STRING and using macaque orthologs of NLRP3, PYCARD and CASP1 as search queries, three different PPI networks were obtained (Figure 5). Further investigations using only the nod-like receptor signaling pathway in the KEGG database, reveal the way that other proteins interact with the search query proteins (Table 9, 10 and 11) in just a functional way. Thus, no physical interactions were found between any of the proteins, including between proteins where physical interactions were found in human and mouse orthologs. However, most of the proteins of the PPI networks do show up in the nod-like receptor signaling pathway in KEGG and those interactions were thus considered functional. Only one protein (APAF1) does not show up in the KEGG pathway database and has no physical interaction. APAF1 is therefore labeled as having neither a physical nor a functional interaction with macaque CASP1.

a. b.

Figure 5. PPI networks obtained from STRING with macaque NLRP3 (a), PYCARD (b) and CASP1 (c) as search queries. The searches were performed on standard settings.

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Table 9. Types of interactions between macaque NLRP3 and other proteins in the PPI network

Query protein Interacting Type of Method/Source Reference protein interaction NLRP3 PYCARD Functional KEGG pathway NLR SP NLRP3 IL1B Functional KEGG pathway NLR SP NLRP3 TXNIP Functional KEGG pathway NLR SP NLRP3 CARD8 Functional KEGG pathway NLR SP NLRP3 NEK7 Functional KEGG pathway NLR SP NLRP3 MAVS Functional KEGG pathway NLR SP NLRP3 CTSB Functional KEGG pathway NLR SP NLRP3 IL18 Functional KEGG pathway NLR SP NLRP3 CASP1 Functional KEGG pathway NLR SP NLRP3 MLKL Functional KEGG pathway NLR SP Co-IP = co-immunoprecipitation; NLR SP = nod-like receptor signaling pathway Table 10. Types of interactions between macaque PYCARD and other proteins in the PPI network Query protein Interacting protein Type of Method/Source Reference interaction

PYCARD NLRP3 Functional KEGG pathway NLR SP PYCARD MEFV Functional KEGG pathway NLR SP PYCARD NLRC4 Functional KEGG pathway NLR SP PYCARD AIM2 Functional KEGG pathway NLR SP PYCARD NLRP12 Functional KEGG pathway NLR SP PYCARD IL18 Functional KEGG pathway NLR SP PYCARD NLRP1 Functional KEGG pathway NLR SP PYCARD IL1B Functional KEGG pathway NLR SP PYCARD NLRP6 Functional KEGG pathway NLR SP PYCARD CASP1 Functional KEGG pathway NLR SP Co-IP = co-immunoprecipitation; NLR SP = nod-like receptor signaling pathway Table 11. Types of interactions between macaque CASP1 and other proteins in the PPI network

Query protein Interacting Type of Method/Source Reference protein interaction CASP1 PYCARD Functional KEGG pathway NLR SP CASP1 NLRP3 Functional KEGG pathway NLR SP CASP1 IL18 Functional KEGG pathway NLR SP CASP1 NLRC4 Functional KEGG pathway NLR SP CASP1 CASP8 Functional KEGG pathway NLR SP CASP1 APAF1 None N/a N/a CASP1 NOD2 Functional KEGG pathway NLR SP CASP1 IL1B Functional KEGG pathway NLR SP CASP1 NLRP1 Functional KEGG pathway NLR SP CASP1 CARD8 Functional KEGG pathway NLR SP Co-IP = co-immunoprecipitation; NLR SP = nod-like receptor signaling pathway

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Human – mouse – macaque NLRP3 orthologs PPI networks of the different species were compared and it was found that there was an overlap of five proteins (CASP1, IL1B, NEK7, PYCARD and TXNIP) being interactors with NLRP3 in all three species. Four proteins (AIM2, NLRC4, DHX33 and CASP5) were found to only interact with human NLRP3. Two proteins (P2rx7 and Panx1) were found to only interact with mouse Nlrp3. One protein (MLKL) was found to only interact with macaque NLRP3. Figure 6 shows the protein names and their overlaps. There was an overlap of one protein (CARD8) between human and macaque NLRP3 orthologs and there was also an overlap of three proteins (CTSB, IL18 and MAVS) between macaque and mouse NLRP3 orthologs.

AIM2 NLCR4 DHX33 CASP5

CARD8 CASP1, IL1B,

NEK7, PYCARD, TXNIP P2rx7

MLKL CTSB Panx1

IL18 MAVS

Figure 6. Overlap of results with NLRP3 as a query in the STRING database for humans (blue), Mus musculus (red) and Macaca mulatta (gray). Four interactions were only found with human NLRP3. Two interactions were only found in mouse Nlrp3. One interaction was only found with macaque NLRP3. There was an overlap of five proteins between the three orthologs. There was an overlap of one protein between human and macaque NLRP3 orthologs. There was an overlap of three proteins between macaque and mouse NLRP3 orthologs.

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Human – mouse – macaque PYCARD orthologs An overlap of six proteins (AIM2, CASP1, IL18, IL1B, NLRC4 and NLRP3) interacting with PYCARD in all three species was found. Three proteins (CASP5, MNDA and CARD8) were found to only interact with human PYCARD. Two proteins (Malt1 and Serpina3g) were found to only interact with mouse Pycard. Three proteins (NLRP12, NLRP1 and NLRP6) were found to only interact with macaque PYCARD. Figure 7 shows the protein names and their overlaps. There was an overlap of one protein (CASP8) between human and mouse PYCARD orthologs and there was also an overlap of only one protein (MEFV) between macaque and mouse NLRP3 orthologs.

CASP5 MNDA CARD8

CASP8 AIM2, CASP1, IL18, IL1B, NLRC4, NLRP3 NLRP12 NLRP1 Malt1 NLRP6 MEFV Serpina3g

Figure 7. Overlap of results with PYCARD as a query in the STRING database for humans (blue), Mus musculus (red) and Macaca mulatta (gray). Three interactions were only found with human PYCARD. Two interactions were only found in mouse Pycard. Three interactions were only found with macaque PYCARD. There was an overlap of six proteins between the three orthologs. There was an overlap of one protein between human and mouse PYCARD orthologs. There was an overlap of one protein between macaque and mouse PYCARD orthologs.

Human – mouse – macaque CASP1 orthologs There was an overlap of five proteins (IL18, IL1B, NLRC4, NLRP3 and PYCARD) in all three species. No proteins were found to only interact with human CASP1. Three proteins (Naip5, Naip2 and Page: 14

Gsdmd) were found to only interact with mouse Casp1 and two proteins (APAF1 and NOD2) were found to only interact with macaque CASP1. Figure 8 shows the protein names and their overlaps. An overlap of two proteins (AIM2 and NOD1) between human and mouse CASP1 orthologs was found. There was also an overlap found of three proteins (CARD8, CASP8 and NLRP1) between macaque and human CASP1 orthologs.

CARD8 AIM2 IL18, IL1B, CASP8 NOD1 NLRP1 NLRC4, NLRP3, PYCARD

APAF1 Naip5 Naip2 NOD2 Gsdmd

Figure 8. Overlap of results with CASP1 as a query in the STRING database for humans (blue), Mus musculus (red) and Macaca mulatta (gray). No interactions were only found with human CASP1. Three interactions were only found in mouse Casp1. Two interactions were only found with macaque CASP1. There was an overlap of six proteins between the three orthologs. There was an overlap of two proteins between human and mouse CASP1 orthologs. There was an overlap of three proteins between macaque and human CASP1 orthologs.

Proteins of interest Table 12 shows a list of proteins of interest for the NLRP3 inflammasome and if they are found in the different protein-protein interaction (PPI) networks of human, mouse and macaque NLRP3 inflammasome key components. This list includes truncated inflammasome inhibitors and potential (novel) members of the NLRP3 inflammasome.

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Table 12. Proteins of interest and occurrence in PPI networks of human, mouse and macaque NLRP3 inflammasome key components.

Proteins of interest Extra information Found in Found in Found in human PPI mouse PPI macaque of… of… PPI of… NLRC4 PYCARD and Pycard and PYCARD CASP1 Casp1 and CASP1 CARD8 NLRP3, - NLRP3 PYCARD and and CASP1 CASP1 CARD16 - - - CARD17 Truncated inflammasome - - - inhibitor CARD18 Truncated inflammasome - - - inhibitor CARD19 Truncated inflammasome - - - inhibitor CASP4 Possibly part of NLRP3 - - - inflammasome CASP5 Possibly part of NLRP3 NLRP3 and - - inflammasome PYCARD CASP8 PYCARD and Pycard - CASP1

The bioinformatical information in the databases was complemented by experimental data from scientific reports. An overview of the mass spectrometry (MS) data analysis of NLRP3 inflammasome key components and proteins of interest can be found in Appendix 2 and information of the samples can be found in Table 13. From the NRLP3 inflammasome key components, only PYCARD was found in nine out of twelve samples. Furthermore, CARD8, CARD19 and BABAM1 were all found in just one sample. Both CARD8 and BABAM1 were only found in sample 12. CARD19 was only found in sample 4. Interestingly, there were samples with immunoprecipitation against NLRP3, NLRP3 was not found in any of the samples. Table 13. Sample information.

Sample number Tube tag Sample information 1 IP sample Ctrl 4/9 Immunoprecipitation control, total lysate 2 IP sample 1-4 4/9 Immunoprecipitation against NLRP3, stimulated, total lysate 3 IP sample 2-5 4/9 Immunoprecipitation against NLRP3, stimulated, total lysate 4 IP sample 3-6 4/9 Immunoprecipitation against NLRP3, stimulated, total lysate 5 ZVAD Stim Total lysate, stimulated, including caspase inhibitor 6 ZVAD Ctrl Total lysate, non-stimulated, including caspase inhibitor 7 Stim 2 Stimulated 8 Ctrl 2 Control 9 GFP test 300g Pellet from 300g centrifugation for 10 minutes 10 GFP test 5000g Pellet from 300g centrifugation (pellet removed) for 10 minutes and after that, 5000g centrifugation for 10 minutes 11 GFP test 25000g pellet Pellet from first 300g centrifugation (pellet removed), 5000g centrifugation (pellet removed) and then 25000g centrifugation for 10 minutes 12 GFP test 25000g super Supernatant after 25000g centrifugation

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BRISC complex The BRISC complex was found in the STRING PPI network of human NLRP3 with 50 interactors, as can be seen in the red box in Figure 3. The BRISC complex is composed of ABRAXAS2, BRCC3, BABAM1 and BABAM2. Its function is deubiquitination of different proteins and BRISC is also involved mitotic processes and other cellular processes (Cooper et al., 2009; Yan et al., 2015). An overview of the different BRISC complex members is described below. General information about the BRISC complex members and potential protein-protein interactions found with NLRP3 inflammasome key components can be found in Table 14. An overview of the protein-protein interactions found between BRISC complex members can be found in Table 15.

ABRAXAS2 ABRAXAS 2 (UniProt entry identifier: Q15018) is also known as KIAA0157, FAM175B and ABRO1. The ABRAXAS2 gene is located on chromosome position 10q.26.13. ABRAXAS2 acts like a scaffold protein of BRCC3 and determines its location (Feng, Wang & Chen, 2010). ABRAXAS2 is also involved in tumorigenesis and DNA damage (Zhang et al., 2014). There has only been one report on the effects of ABRAXAS2 on the NLRP3 inflammasome pathways (Ren et al., 2019). Ren and colleagues (2019) propose that ABRAXAS2 is essential for NLRP3-Asc, Asc oligomerization, caspase-1 activation, IL-18 production and IL-1β production. The mechanism was described in five steps. Step 1: priming signals induce phosphorylation at S194 and the first deubiquitination of NLRP3. Step 2: ABRAXAS2 binds to the phosphorylated and deubiquitinated NLRP3 and subsequently recruits the other BRISC complex members to the NLRP3. Step 3: due to activation signals, a conformational change happens with NLRP3. This leads to exposure of the LRR domain of the NLRP3 protein. Step 4: the BRISC complex deubiquitinates K63 ubiquitin chains from the LRR domain. Step 5: the BRISC complex disassociates from the NLRP3 protein and the activated NLRP3 inflammasome is constructed by recruitment of PYCARD and pro-caspase-1. This leads to activation of caspase-1 and subsequent maturation of pro-IL-18 and pro-IL-1β into IL-18 and IL- 1β which are secreted. Furthermore, ABRAXAS2 deficiency in sepsis mice decreased the sepsis severity. Finally, the knockout of ABRAXAS2 provided similar results as the knockout of BRCC3. In Figure 10 the interaction network from STRING of human ABRAXAS2 can be seen. ABRAXAS2 (FAM17B in Figure 10) has protein-protein interactions with the other BRISC complex members. There have been physical protein-protein interactions found with BABAM1 (Huttlin et al., 2015; Zheng et al., 2013), BABAM2 (BRE in Figure 8) (Zheng et al., 2013) and BRCC3 (Huttlin et al., 2015; Zheng et al., 2013). A physical protein-protein interaction as demonstrated by affinity capture- western is also found between ABRAXAS2 and NLRP3, as can be seen in Figure 9 (Ren et al., 2019).

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Figure 9. Interaction network of human ABRAXAS2 (FAM175B, red circle) obtained from the STRING database.

BRCC3 BRCC3 (UniProt entry identifier: P46736) is also known as CXorf53, C6.1A and BRCC36. The BRCC3 gene is located on chromosome position Xq28. BRCC3 is involved in many of processes described earlier, e.g. ubiquitination and DSBs. There have been several reports of effects of BRCC3 on the NLRP3 inflammasome pathways (Cheng et al., 2020; Chi et al., 2017; Hu, Wu, Wang, Yang & Dong, 2019; Py, Kim, Vakifahmetoglu-Norberg, & Yuan, 2013; Rao et al., 2019; Ren et al, 2019). BRCC3 has protein-protein interactions with the other BRISC complex members Figure 11. There have been physical protein-protein interactions found with BABAM1 (Huttlin et al., 2015; Shao et al., 2009), BABAM2 (BRE in Figure 10) (Hu et al., 2011) and ABRAXAS2 (Huttlin et al., 2015; Zheng et al., 2013). A physical protein-protein interaction as demonstrated by affinity capture-western is also found between BRCC3 and NLRP3 (Ren et al., 2019). A vitamin D receptor can hinder BRCC3-mediated deubiquitination of itself and thus inhibit NLRP3 activation (Rao et al., 2019). It has also been proposed that BRCC3 promotes deubiquitination of NLRP3 (Py, Kim, Vakifahmetoglu-Norberg, & Yuan, 2013). Thus, BRCC3 can regulate NLRP3 inflammasome activity. Recently, Chen and colleagues (2020) propose that CDK5 regulates BRCC3 in NLRP3 inflammasome-mediated Parkinson’s disease.

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Figure 10. Interaction network of human BRCC3 (red circle) obtained from the STRING database.

BABAM1 BABAM1 (UniProt entry identifier: Q9NWV8) is also known as C19orf62, FLJ201571, HSPC142, NBA1 and MERIT40. The BABAM1 protein is crucial for protein interactions of BRCA-1-Rap80, double stranded break targeting, DNA repair and BRCA1-Rap80 stability (Shao et al., 2009). Furthermore, BABAM1 is involved in response to ionizing radiation and deubiquitination. In Figure 11 the protein-protein interaction (PPI) network obtained from STRING of human BABAM1 can be seen. BABAM1 has protein-protein interactions with the other BRISC complex members. There have been physical protein-protein interactions found with BABAM2 (BRE in Figure 10) (Wan et al., 2015; Wang, Hurov, Hofmann & Elledge, 2009), ABRAXAS2 (FAM175B in Figure 10) (Huttlin et al., 2015; Zheng et al., 2013) and BRCC3 (Huttlin et al., 2015; Shao et al., 2009). There are yet no reports of effects of BABAM1 on the NLRP3 inflammasome pathways. Thus, there are no interactions visible with NLRP3 inflammasome key components (NLRP3, PYCARD and caspase-1).

Figure 11. Interaction network of human BABAM1 (red circle) obtained from the STRING database. Page: 19

BABAM2 BABAM2 (UniProt entry identifier: Q9NXR7) is also known as BRE, BRCC45 and BRCC4. The BABAM2 gene is located on chromosome position 2p32.2. The BABAM2 protein is involved in inhibition of apoptosis (Li et al., 2004) and DNA repair and DUBs as part of BRCA1 (Shao et al., 2009). Additionally, BABAM2 is essential for the integrity of the BRISC complex (Hu et al., 2011). In Figure 12 the interaction network from STRING of human BABAM2 can be seen. As can be seen in Figure 12, BABAM2 has protein-protein interactions with the other BRISC complex members. There have been physical protein-protein interactions found with BABAM1 (Wan et al., 2015; Wang, Hurov, Hofmann & Elledge, 2009), ABRAXAS2 (FAM175B in Figure 11) (Zheng et al., 2013) and BRCC3 (Hu et al., 2011). There have been no reports of effects of BABAM2 on the NLRP3 inflammasome pathways. Thus, there are no interactions visible with NLRP3 inflammasome key components.

Figure 12. Interaction network of human BABAM2 (BRE, red circle) obtained from the STRING database. Table 14. General information of ABRAXAS2, BRCC3, BABAM1 and BABAM2 and their effects on NLRP3 inflammasome key components.

Other names Chromosome location NLRP3 inflammasome effects ABRAXAS2 KIAA0157 10q.26.13 Essential for activation FAM175B of NLRP3, PYCARD ABRO1 recruitment and inflammasome construction (Ren et al., 2019) BRCC3 CXorf53 Xq28 BRCC3 promotes C6.1A deubiquitination of BRCC36 NLRP3 (Py, Kim, Vakifahmetoglu- Norberg, & Yuan, 2013). CDK5 regulates BRCC3 in NLRP3 inflammasome- mediated Parkinson’s disease (Cheng et al., 2020).

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BABAM1 C19orf62 19p13.11 N/a FLJ201571 HSPC142 NBA1 MERIT40 BABAM2 BRE 2p32.2 N/a BRCC45 BRCC4

Table 15. Composition and interactions in BRISC complex.

BABAM1 BABAM2 ABRAXAS2 BRCC3 BABAM1 - Wan et al. (2015); Huttlin et al. Huttlin et al. Wang, Hurov, (2015); Zheng et (2015); Shao et al. Hofmann & al. (2013) (2009) Elledge (2009) BABAM2 - - Zheng et al. Hu et al. (2011) (2013) ABRAXAS2 - - - Huttlin et al. (2015); Zheng et al. (2013) BRCC3 - - - -

Discussion The main aim of this study is to increase knowledge about the protein-protein interactions with and within the NLRP3 inflammasome. A literature study was performed of the NLRP3 inflammasome and its regulation. Data from STRING was used to determine the type of protein- protein interactions with and within the NLRP3 inflammasome. STRING was chosen to generate all of the PPI networks. STRING uses both multiple curated databases (e.g. Reactome, KEGG and Biocyc) and computational methods to generate the networks. STRING is also easy to use. The interactions shown in the networks are a helpful tool to expand the knowledge about cellular mechanisms. Each interaction between two proteins can also be further analyzed within STRING to determine what kind of interaction, either functional or physical, it is. The interaction between two proteins is visualized by a line, which has a specific color. The colors indicate the type of evidence and can be split up in three main groups. The first group is the know interactions, which contains interactions from curated databases (light blue) and experimentally determined interactions (purple). The second group is predicted interactions, which contains gene neighborhood evidence (green), gene fusion evidence (red), and gene occurrence evidence (dark blue). The third group is other types of evidence, which contains textmining (where two proteins are mentioned together in the text) (yellow), co-expression (black), and protein homology (purple). Based on the different types of evidence, a score is computed and the different protein-protein interactions are ranked (Von Mering, 2005). Furthermore, STRING also links to the PubMed articles that have been found that experimentally have determined the physical interactions. STRING has been found to be one of the best in overall performance in disease genes (Huang et al., 2018). However, not all the data is always up-to-date in the STRING database. Thus, some interactions may not show up in the STRING database but in other bioinformatical tools or databases. Furthermore, proteins and their interactions are dynamic and experiments can only give a picture of static interaction between two proteins. A protein is dynamic and can go through conformational changes depending on its environment. The physical interactions is determined Page: 21 experimentally and verified interactions are used in the STRING database to generate the PPI network. This can lead to false-negatives because not all interactions can be experimentally determined and be mapped in the network. There are between 130 000 to 650 000 interactions in the human interactome (Strumpf et al., 2008; Venkatesan, 2009) and only a small part of these are experimentally determined (Furlong, 2013). Thus, STRING uses prediction models for interactions other than experimentally determined, especially in bigger networks. These predictions can lead to false-positives. In this study, a brief literature overview of the NLRP3 inflammasome and its regulation was assembled. Using the STRING database, human, mouse and macaque NLRP3, PYCARD and caspase-1 orthologs were used for searches. The types of protein-protein interactions were determined by being either indirect and functional or direct and physical (Table 3-11). All of the interactions were further supported by either research literature (for physical interactions) or by the KEGG database (for functional interactions, i.e. proteins that belong to the same pathway). The PPI networks were also compared between the different animal orthologs (Figure 6-8). Interestingly, members of a multiprotein complex called the BRISC complex were found in the extended PPI network of human NLRP3 (Figure 3). Further literature studies show that two members of the BRISC complex have effects on the NLRP3 inflammasome (Table 14). Marrah (2020) performed CRISPR/Cas9-mediated gene knockout of the BABAM1 gene and transfected monocyte-like cells. Preliminary results show that no difference was found in gene expression levels of BABAM1 between the BABAM1 knockout cells and control. Data from mass spectrometry of stimulated and non-stimulated THP-1 cells was analyzed to see if these experimental data could further support this theoretical data found in this study, i.e. if the proteins from the mass spectrometry data could be found in the PPI networks or research literature. The mass spectrometry data gave no results that were expected. PYCARD was found in nine out of twelve samples. A repeat of the experiment may be needed in order for a proper analysis of the differences in protein content between non-stimulated and stimulated THP-1 cells. This whole study was limited in scope, both for the literature overview and the bioinformatical analysis. However, this is to be expected because of time constraints. There are studies that have been done that are similar to this study but not completely. For example, there have been several literature reviews performed about the NLRP3 inflammasome and its regulation (e.g. Lu et al., 2019; Swanson, Deng & Ting, 2019). However, these literature review articles and other ones have not incorporated bioinformatical research, like protein- protein interactions as found in bioinformatical databases. Furthermore, the data collected for this study originated from previous studies and data published, but the data was all collected in this study to give both a literary and bioinformatical overview of the NLRP3 inflammasome, its protein interactions and regulation. The combination of literature review and bioinformatics in regards to the NLRP3 inflammasome is thus a new type of study that has not been performed up to now.

Verification of results and broader perspective Possible verification of the data could be to compare the results of this study with other bioinformatical tools that use different databases. Generally, the results should be the same, because the protein-protein interactions that were showed, were found in scientific literature. However, some changes could be present in the interactions that have not been showed in scientific articles, i.e. the predicted protein-protein interactions. Depending on the computational algorithm there might also be some changes between each bioinformatical tool in these types of interactions. The predicted protein-protein interactions could be verified in a laboratory settings to see if they are really present or not. Page: 22

The data of this study could be interesting for the research society because it offers an overview and also some recommendations for further research, not only in laboratory settings but also bioinformatical. Bioinformatical research in this study, for instance, has led to a discovery of the BRISC complex and its members of which not much is known yet in relation to the NLRP3 inflammasome. Researchers can benefit from the information gathered from PPI networks, because it helps expanding the understanding of the NLRP3 inflammasome pathways and proteins associated with NLRP3-related diseases (Sevimoglu & Arga, 2014).

Ethical considerations and impact on society There are no direct ethical considerations because there were no humans or animals used in this study. However, further research in bioinformatics can create unrealistic and wrong predictions and thus wrong conclusions. This is because databases are regularly updated. Furthermore, bioinformatical research can be used to identify groups in data and can be used for further, incorrect categorization and judgements of people. In addition, biological data that is collected could also be linked to individuals and thus violate privacy rights. However, this is not applicable to this project because no datasets were used. It is also important to consider that every researcher has a certain bias. As a researcher, one should be aware of these biases and minimize them as much as possible, even though it might be difficult to eradicate bias completely.

The importance of theoretical research and impact on society Theoretical research can help the scientific community by making theories understood more without the need of experimental laboratory work. Theoretical research can be, for example, literature studies and bioinformatical studies. This study uses both forms of theoretical approaches. Bioinformatical tools are useful in finding new, predicted protein-protein interactions, which are based on information available in the database (Bayat, 2002). This can be interactions that have not been found before and could be of interest. Experimental work in the laboratory can be limited, especially in protein-protein interaction studies. Proteins are not static so there is always an interaction that would not be shown in the results but is still there (Philips, 2009). Proteins are dynamic and their form is dependent on the environment. Another positive aspect of theoretical studies is the decrease of overall animal experiments that are needed. There are the ‘Three R’s’ as described by Russel and Burch (1959) about the use of animals in research and other areas. One of the Three R’s is Replacement, which is the replacement of animals with other techniques, e.g. computational methods like bioinformatics. It has even been suggested that in silico models could be used to test drugs instead of trials with humans or animals (Pappalardo, Russo, Tshinanu, & Viceconti, 2018). Development of effective drugs has positive effect on both human health and the economic impact on society.

Conclusion and future perspectives In conclusion, the main aim of this study is to increase knowledge of the protein-protein interactions with and within the NLRP3 inflammasome. Thus, this study will give further insight into NLRP3 inflammasome pathways and can aid in the discovery of novel treatment targets for different NLRP3-associated diseases in the future. The PPI networks some proteins that overlapped between human, mouse and macaque orthologs of NLRP3 inflammasome key components and are thus central to the NLRP3 inflammasome. There were also some species- specific proteins found in the PPI networks. STRING showed good results when generating PPI networks and determining the types of interactions. However, STRING is not complete because interactions are missing. Further literature studies were performed on the BRISC complex and its members. Finally, mass spectrometry data of stimulated and non-stimulated THP-1 cells were analyzed.

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It would be of interest to research the effects of the BRISC complex and its members. Especially research of BABAM1 and BABAM2 because there is no research on those proteins whatsoever. Protein-protein interactions are essential for all biological processes and thus are interesting research topics. The pathways of different diseases can then be understood more and this information can be used for finding new treatment targets.

Acknowledgements I would like to thank Mikael Ejdebäck for his continuous support and guidance throughout this study. I would also like to thank Matthew Herring for his help and providing MS data to me. Additionally, I would like to thank my examiner Erik Gustafsson for his feedback on my scientific writing. Finally yet importantly, I would also like to thank my fiancée, my father and mother, and other family members and friends for their love and support while working on this study.

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Appendix 1 – Schematic NLRP3 inflammasome construction reaction

L R R R

N A C P H Y T Q D

P C C Y A A D R R D D

P20

P10

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Appendix 2 – MS data analysis

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