Babam1 Regulation of Nlrp3 Inflammasome in Thp- 1 Monocyte-Like Cell Lines

BABAM1 REGULATION OF NLRP3 INFLAMMASOME IN THP- 1 MONOCYTE-LIKE CELL LINES

Bachelor Degree Project in Bioscience 30 ECTS Spring term 2020

Supervisors: Mikael Ejdebäck [email protected]

Examiner: Maria Algerin [email protected]

Abstract The nucleotide-binding oligomerization domain–like receptor family pyrin domain containing 3 (NLRP3) inflammasome is a group of multi-protein complex that mediates immune responses through the production of biologically active IL-1β and IL-18. Dysregulation of NLRP3 inflammasome has been linked to various diseases associated with infection, inflammation, and cancer. However, the molecular mechanisms of ligand binding that result in the formation of the NLRP3 inflammasome are not fully understood. Potential therapeutics for NLRP3 inflammasomes related diseases are relatively nonspecific, have low efficacy, and may cause unexpected side effects. Therefore, this study aimed to understand if BABAM1 can serve as a potential target for treating NLRP3 inflammasome related diseases. This was done by attempting to knock out BABAM1 gene in THP-1 monocyte-like cell line using the CRISPR-Cas9 gene-editing technology. The cDNA prepared from THP-1 cells in which an attempt was made to knock out BABAM1 gene were amplified using qPCR. The result showed no biologically relevant difference in BABAM1 gene expression level between the target and control THP-1 cells. Additionally, this study found that the expression of the reference gene, ACTB, was not stable as the cycle threshold values for untransfected cells were lower when compared to cells transfected the plasmid DNA. In conclusion, successful attempts were made in this study to understand the role of BABAM1 in regulating the activation of NLRP3 inflammasome and that further research are needed if BABAM1 is to be considered as a potential target for treating NLRP3 inflammasome related diseases.

List of abbreviations ACTB Actin Beta ALRs Absent-in-melanoma (AIM)-Like Receptors ASC Adaptor associated Speck-like protein BABAM1 BRISC and BRCA1–A complex member 1 CARD Caspase Activation and Recruitment Domain CMV Cytomegalovirus CRISPR Clustered Regularly Interspaced Short Palindromic Repeats crRNA CRISPR RNAs CTLs C-Type Lectins DAMPs Damaged-Associated Molecular Patterns DBS Double-Stranded Break FACS Fluorescence-Activated Cell Sorting GFP Green Fluorescent Protein gRNA guide-RNA HDR Homology Directed Repair IL-18 Interleukin-18 IL-1b Interleukin-1b KO Knockout LPS Lipopolysaccharide LRRs Leucine-Rich Repeats MAPK Mitogen-Activated Protein Kinase MSU Monosodium Urate NACHT NAIP (neuronal apoptosis inhibitory protein), CIITA (MHC class II transcription activator), HET-E (incompatibility locus protein from Podospora anserina) and TP1 (telomerase-associated protein) NF-kB Nuclear Factor-kb NHEJ Non-Homologous End Joining NLRP3 NLR Family Pyrin Domain Containing 3 NLRs Nucleotide-binding Leucine-rich Repeat-containing receptors PAM Protospacer Adjacent Motif PAMPs Pathogen-Associated Molecular Patterns PRRs Pattern Recognition Receptors PTM Post-Translational Modification PYD Pyrin Domain qPCR Quantitative Polymerase Chain Reaction RLRs Retinoic acid-inducible gene-I (RIG-I)-Like Receptors ROS Reactive Oxygen Species TALEN Transcription Activator-Like Effector Nucleases TLRs Toll-Like Receptors tracrRNA trans-activating RNA ZFNs Zinc-Finger Nucleases

Table of Contents 1. Introduction ...... 1 Immune system ...... 1 NLRP3 Inflammasome ...... 2 Priming of NLRP3 inflammasome ...... 2 Activation of NLRP3 inflammasome ...... 2 NLRP3 Domains ...... 3 NLRP3 Dysregulation ...... 4 CRISPR-Cas9 ...... 4 BABAM1 ...... 6 Research Question ...... 6 Research Aim ...... 6 2. Materials and Methods ...... 7 Oligos Design ...... 7 Construction of CRISPR-Cas9 construct ...... 7 Cell culture and Transfection ...... 8 RT-qPCR ...... 9 qPCR Data Analysis ...... 9 Results ...... 10 CRISPR-Cas9 construct ...... 10 RT-qPCR ...... 10 qPCR Data Analysis ...... 11 3. Discussion ...... 14 4. Conclusion ...... 20 5. Ethics and impact of this study on the society ...... 21 6. Acknowledgements ...... 21 7. References ...... 21 8. Appendices ...... 29 Appendix 1: Transfection Plate Layout ...... 29 Appendix 2: Sanger Sequencing Chromatogram Data ...... 29 Appendix 3: qPCR Data ...... 30

1. Introduction

Immune system The internal environment of the human body is a warm, moist, and good source of nutrients for many disease-causing pathogens (Alberts et al., 2015). Through many years of adaptation, the human body has developed a defence system, known as the immune system, to help protect itself for invading pathogens (Campbell et al., 2018). The defence mechanism of the immune system is divided into two immune responses: innate immunity and adaptive immunity (Tomar & De, 2014). Innate immunity is the body´s first line of defence, which consists of barrier defences such as the skin and mucous membranes, and internal defences such as phagocytic cells, natural killer cells, antimicrobial proteins and inflammatory responses (Campbell et al., 2018; Tomar & De, 2014). The adaptive immunity is a specific and slower immune response that is activated after innate immune responses. It consists of humoral response, an antibodies-mediated defend against infection in body fluid, and a cell-mediated response, a cytotoxic cell defence against infection in body cells (Campbell et al., 2018; Tomar & De, 2014).

The immune system ability to recognize and discriminate between self and non-self molecules relies on pattern recognition receptors (PRRs) that are present on sensor cells (Murphy & Weaver, 2017). These PRRs do recognize pathogen-associated molecular patterns (PAMPs), which are simple and regular molecules of many microorganisms, as well as damaged-associated molecular patterns (DAMPs), which are released upon damage to the cell (Murphy & Weaver, 2017; Thi Tran & Kitami, 2019). Currently, there are five identified classes of the PRRs families: Toll-like receptors (TLRs), nucleotide-binding leucine-rich repeat-containing receptors (NLRs), retinoic acid-inducible gene-I (RIG-I)-like receptors (RLRs), C-type lectins (CTLs), and absent-in- melanoma (AIM)-like receptors (ALRs) (Motta, Soares, Sun & Philpott, 2015). Two of these PRRs classes, TLRs and CTLs are membrane-bound receptors, while the remaining three, NLRs, RLRs, and ALRs, are intracellular receptors (Takeuchi & Akira, 2010). Recognition of PAMPs and DAMPs molecules by the different classes of PRRs families can induce a wide range of responses, resulting in the activation of downstream signalling pathways such as nuclear factor-kb (NF-kB), mitogen- activated protein kinase (MAPK) and type I interferon pathways (Chen & Nuñez, 2010). Activation of these downstream pathways can subsequently result in the production of inflammatory cytokines, antimicrobial peptides, chemotactic factors, and the antiviral cytokines (Chen & Nuñez, 2010; Murphy & Weaver, 2017).

NLRs and ALRs are distinct from the other classes of PRRs in terms of their structural domain and function (Cridland et al., 2012; Lu & Wu, 2014). ALRs consist of a C-terminal HIN-200 domain, which can directly bind to dsDNA, and an N-terminal pyrin domain (PYD), which can bind to the pyrin domain of other molecules (Man & Kanneganti, 2015). NLRs are well-studied proteins that respond to a wide variety of ligands from microbial pathogen, toxin, and other host-derived ligands such as ATPs, uric acids, and damaged cells (Kim, Shin & Nahm, 2016; Motta et al., 2015; Shaw, Reimer, Kim & Nuñez, 2008). NLRs proteins have a common domain organization consisting of a central nucleotide-binding and oligomerization domain known as NACHT (NAIP (neuronal apoptosis inhibitory protein), CIITA (MHC class II transcription activator), HET-E (incompatibility locus protein from Podospora anserina) and TP1 (telomerase-associated protein)), which is usually flanked by C-terminal leucine-rich repeats (LRRs) and N-terminal caspase activation and recruitment domain (CARD) or pyrin (PYD) domains (Damiano, Oliveira, Welsh & Reed, 2004; Schroder & Tschopp, 2010). The NACHT domain is important in forming oligomeric structures. The LRR is essential for ligand binding and autoregulation, and the CARD or PYD domain mediates protein-protein interactions and downstream signal transduction (Murphy & Weaver, 2017; Schroder & Tschopp, 2010). Currently, there are about 22 genes that encoded for NLRs in the human genome (Harton, Linhoff, Zhang & Ting, 2002). Upon activation of NLRs and ALRs, inflammasomes complexes within the cells are assembled, resulting in the

1 production of pro-inflammatory cytokines and a type of inflammatory cell death known as pyroptosis (Davis, Wen & Ting, 2011; Man & Kanneganti, 2016).

NLRP3 Inflammasome Inflammasome is a pro-inflammatory intracellular protein complex that initiates the activation of pro-inflammatory cytokines, interleukin-1b (IL-1β) and interleukin-18 (IL-18), upon recognition of PAMPs and DAMPs signals (Murphy & Weaver, 2017). Out of the 22 genes that encode the NLRs in the human genome, only few: NLRP1, NLRP3 and NLRC4 have been well established to assemble into their respect inflammasomes. Others, such as NLRP6 and NLRP12, are being considered to potentially form inflammasome complexes (Sharma & Kanneganti, 2016). Of all the NLR family, the nucleotide-binding oligomerization domain–like receptor family pyrin domain containing 3 (NLRP3) inflammasome is the best study, partly due to its involvement in several human diseases and is the main focus of this current study (Kawashima et al., 2017; Kesavardhana & Kanneganti, 2017).

NLRP3 inflammasome is a group of multi-protein complex that mediates immune responses by recruiting pro-caspase-1 through the adaptor associated speck-like protein (ASC). Pro-caspase-1 then undergoes self-cleavage into active caspase-1 and proceeds to cleave the immature form of pro-inflammatory cytokines IL-1β and IL-18 into their mature and biologically active IL-1β and IL-18 form (Martinon, Burns & Tschopp, 2002; Murphy & Weaver, 2017). The release of the biologically active IL-1β and IL-18 results in the recruitment of innate immune cells and increases the cytolytic activity of natural killer cells and T cells at the site of infection (Dinarello, 2009). NLRP3 inflammasome consists of the NLRP3 scaffold, the ASC adaptor, and caspase-1, and is activated by a wide range of molecules including pathogens such as Candida albicans, Saccharomyces cerevisiae, Staphylococcus aureus, influenza virus, adenovirus (Gross et al., 2009; Kanneganti et al., 2006; Mariathasan et al., 2006; Muruve et al., 2008), endogenous DAMPs such as extracellular ATP, glucose, uric acid, monosodium urate (MSU) crystals (Martinon, Pétrilli, Mayor, Tardivel & Tschopp, 2006; Rock, Latz, Ontiveros & Kono, 2010; Zhou, Tardivel, Thorens, Choi & Tschopp, 2009), and environmental activator such as silica, alum, and asbestos (Dostert et al., 2008; Li, Willingham, Ting & Re, 2008). However, the molecular mechanisms of ligand binding that result in the formation of the NLRP3 inflammasome are not fully understood.

Priming of NLRP3 inflammasome There are two steps that are involved in the activation of NLRP3 inflammasomes in macrophages: priming and activation (Figure 1) (Yang, Wang, Kouadir, Song & Shi, 2019). One function of the priming step is to upregulate the gene expression of NLRP3 and pro-IL-1β. The upregulations of these genes are induced through the recognition of PAMPs by PRRs, which then activate the NF- kB pathway resulting in pro-inflammatory gene expression (Bauernfeind et al., 2009; Swanson, Deng & Ting, 2019). Another function of the priming stage is the post-translational modification (PTM) of NLRP3, such as phosphorylation and ubiquitination, to keep it in its inactive form in the cytoplasm (Swanson et al., 2019).

Activation of NLRP3 inflammasome The activation stage of NLRP3 is triggered by cellular stress caused by PAMPs and DAMPs, which promotes the assembly of NLRP3 inflammasomes, resulting in caspase-1 mediated secretion of mature IL-1β and IL-18 (Figure 1) (Bauernfeind et al., 2009). Examples of cellular stresses that are proposed to activate NLRP3 are ion fluxes, reactive oxygen species (ROS) production, lysosomal damage, or formation of large-pore in the cell membrane (Franchi, Muñoz-Planillo & Núñez, 2012). With regard to ion fluxes, loss of intracellular potassium ion (K+) caused by infection or release of ATP into extracellular fluid has been reported to activated NLRP3 (Pétrilli et al., 2007). The production of ROS, triggered by asbestos particles through activation of NADPH oxidase, has been reported to activate NLRP3 (Dostert et al., 2008). However, another study found no evidence that the signalling cascade of Txnip, a ROS-sensitive molecule, led to the activation of

NLRP3 (Masters et al., 2010). Several bacteria pathogens can also activate NLRP3 inflammasome by secreting toxins that form pores in the cell membrane (Franchi et al., 2012). Additionally, phagocytosis of crystals can induce lysosomal damage, which can also activate NLRP3 inflammasome (Hornung et al., 2008). Overall, the actual mechanisms of NLRP3 inflammasome activation remain unclear and are still under investigation.

NLRP3 Domains NLRP3 consists of three domains, the PYD domain, the NACHT domain, and the LRR domain (Murphy & Weaver, 2017). Upon stimulation of danger signals by the LRR domain, several NLRP3 monomers oligomerize through homotypic interaction between their NACHT domains (Swanson et al., 2019). This oligomerization results in the recruitment of ASC, an adaptor protein that consists of an amino-terminal pyrin domain, and a carboxy-terminal CARD domain (Murphy & Weaver, 2017). The PYD domain of NLRP3 then binds with the PYD domain of ASC. This NLRP3- ASC complex recruits procaspase-1, an aspartic acid-specific cysteine protease that consists of an amino-acid CARD domain, a central large catalytic domain (p20), and a carboxy-terminal small catalytic domain (p10) (Elliott, Rouge, Wiesmann & Scheer, 2008; Swanson et al., 2019). Upon recruitment of procaspase-1, the CARD domain of ASC binds with the CARD domain of procaspase- 1, resulting in self-cleavage of procaspase-1 to form an active caspase-1 (Murphy & Weaver, 2017). Active caspase-1 can then carry out the proteolytic processing of pro-inflammatory cytokines (IL-1β and IL-18), which is ATP dependent, to form mature IL-1β and IL-18. These mature IL-1β and IL-18 are then secreted out of the cell to trigger pro-inflammatory and antimicrobial responses (Franchi, Warner, Viani & Nuñez, 2009). Additionally, active caspase-1 can also induce a specialized form of cell death called pyroptosis (Franchi et al, 2009).

Figure 1. Priming and activation stages of NLRP3 inflammasome. Dotted arrows indicate several signalling pathways. The priming stage to the left is induced by the recognition of a PAMP such as lipopolysaccharide (LPS), which is recognized by (1) Toll-like receptor 4-MD2 complex (TLR4-MD2). Activation of this complex results in the activation of several signalling pathways (2) that then activate the transcription factor NF-kB. Activated NF-kB enters the nucleus (3) and induces the transcription of (4) NLRP3, Pro IL-1β and Pro IL-18, that are then (5) translated to proteins. During the activation stage to the right, inactive NLRP3 is (6) activated by a wide variety of PAMPs and DAMPs stimulus provided by extracellular ATP, RNA viruses, K+ efflux, lysosomal damage, large pore-forming toxins, uric acid, mitochondrial dysfunction and reactive oxygen species (ROS). These activation signals result in (7) the oligomerization of NLRP3 that recruits ASC and Pro-caspase 1 to form the NLRP3 inflammasome complex.

This complex then leads to the production of (8) active caspase-1 that cleaves (9) Pro IL-1β and Pro IL-18 into their mature and biologically active forms (IL-1β and IL-18). Active IL-1β and IL-18 are (10) released out of the cell and trigger inflammatory responses. Figure 1 was designed using Biorender online application (https://app.biorender.com/), and inspired by Swanson, Deng & Ting (2019).

NLRP3 Dysregulation Though NLRP3 inflammasome plays an important role in defending the body against invading pathogens, its dysregulation has been linked to various diseases associated with infection, inflammation, and cancer (Kim et al., 2016). Gout is an inflammation of the joints caused by precipitation of monosodium urate (MSU) and bursal tissues of individuals with hyperuricemia (Kingsbury, Conaghan & McDermott, 2011). Neutrophil influx into the synovium and joint fluid is considered as a pathological hallmark in gout attacks (Landis & Haskard, 2001). A study by Martinon et al. (2006) found that MSU crystals can activate NLRP3 inflammasomes resulting in the release of pro-inflammatory cytokines that can recruit neutrophil and promotes its influx into the joint fluid. NLRP3 inflammasome has also been shown to influence the pathogenesis of different tumour growth and metastasis, which are induced by the increased level of IL-1β (Guo, Fu, Zhang, Liu & Li, 2016). NLRP3 has been demonstrated to promote tumour growth and proliferation in other types of cancer such as lung cancer (Goldberg et al., 1997), breast cancer (Kolb, Liu, Janowski, Sutterwala & Zhang, 2014), melanoma (Drexler et al., 2012), and head and neck cancer (Bae et al., 2017). On the contrary, NLRP3 inflammasome was shown to suppress colorectal cancer metastatic growth in the liver (Dupaul-Chicoine et al., 2015). This indicates that more studies are needed to better understand the role of NLRP3 inflammasomes in tumour progression. Other metabolic products triggered by NLRP3 inflammasome and their associated diseases include accumulation of amyloid-β (Aβ) associated with Alzheimer’s disease (Halle et al., 2008), cholesterol crystals associated with atherosclerosis (Duewell et al., 2010), drusen accumulation associated with age-related macular degeneration (AMD) (Doyle et al., 2012), and islet amyloid polypeptide (IAPP) association with type II diabetes (Masters et al., 2010).

The involvement of NLRP3 inflammasome in a wide variety of diseases invokes how important it is to discover effective therapeutics or inhibitors that selectively inhibit its pathway. Currently, clinical treatments of NLRP3-related diseases such as rilonacept, canakinumab, and anakinra target the active IL-1β cytokine through an IL-1β blocking agent (Yang et al., 2019). Targeting just IL-1β is not the best therapeutic for NLRP3-related diseases as other pro-inflammatory cytokines such as HMGBI and IL-18 can result in the pathogenesis of these diseases (Lu et al., 2012; Nowarski et al., 2015). Moreover, other small-molecular inhibitors such as MCC950 (Coll et al., 2015), dimethyl sulfoxide (DMSO) (Ahn, Kim, Jeung & Lee, 2014), β-hydroxybutyrate (BHB) (Youm et al., 2015), Bay 11-7089 (Juliana et al., 2010), and type I interferon (Inoue et al., 2012) have been shown as potential therapeutics for NLRP3 inflammasomes related diseases. Disadvantages associated with most of these small-molecular inhibitors are that they are relatively nonspecific, have low efficacy, and may cause unexpected side effects (Yang et al., 2019). A better therapeutic for treating NLRP3 inflammasome related diseases should be specific, cost- effective and less invasive. This can be achieved by directly targeting NLRP3 or other proteins that are directly associated with NLRP3 (Yang et al., 2019). Such targets can be achieved through a gene-editing technology known as CRISPR-Cas9.

CRISPR-Cas9 The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas system was firstly discovered in archaea and bacteria, where it helped the cells to defend themselves against invading bacteriophages (Ishino, Shinagawa, Makino, Amemura & Nakata, 1987). Unlike the zinc- finger nucleases (ZFNs) and the transcription activator-like effector nucleases (TALEN) that require series of engineered proteins to manipulate an organism´s genome, the CRISPR-Cas system is based on small RNAs that guide the nuclease to the complementary sequence of the target gene (Gaj, Gersbach & Barbas, 2013). Of the three known types of CRISPR-Cas system, type II has been widely used in genetic application as it requires a single CRISPR associated protein,

Cas9, to function properly (Deltcheva et al., 2011). The activity of the CRISPR Cas9 system is controlled by the CRISPR locus in Streptococcus pyogenes, which consists of several repeats separated by unique spacer sequences (Wiedenheft, Sternberg & Doudna, 2012). These repeats and unique spacer sequences are processed into individual CRISPR RNAs (crRNA) following transcription, and are adjacent to the genes encoding the CRISPR-Cas9 protein and the trans- activating RNA (tracrRNA) (Wiedenheft et al., 2012). The unique spacer sequences were introduced to the bacteria from past phage infection. Upon transcription of the CRISPR locus induced by bacteriophages, the repeat sequences of the long crRNA strand hybridize with the tracrRNA to form a double-stranded RNA complex. This complex is then cleaved by RNase III to form smaller targeting crRNA complexes. These complexes can then bind to Cas9 proteins, which consist of two endonuclease domains (HNH and RuvC), to form a CRISPR-Cas9 complex (Deltcheva et al., 2011). The CRISPR-Cas9 complex will then be directed to the target sequence of the viral DNA which will be cleaved by the endonuclease domains of Cas9 (Deltcheva et al., 2011; Jinek et al., 2012; Sampson & Weiss, 2013). To prevent the CRISPR-Cas9 complex from cleaving the bacteria own genome, the endonuclease activity of Cas9 is only induced if the target sequence is adjacent to a Protospacer Adjacent Motif (PAM), which is the case in many bacteriophages (Jinek et al., 2012).

The CRISPR-Cas9 complex has been simplified to edit the genome of complex organisms by constructing a single RNA followed by a hairpin structure that guides Cas9 to the target sequence of interest, which are together referred to as the guide-RNA (gRNA) (Figure 2) (Jinek et al., 2012). The hairpin structure is meant to mimic the hybridization of crRNA and tracrRNA needed to recruit Cas9. Additionally, the PAM sequence, which is usually 5’-NGG-3’ in the case of Cas9, must be adjacent to the target sequence for a double stranded break (DSB) to occur (Jinek et al., 2012). Introduction of DSB in the cell´s genome can result to the inactivation of a particular gene when the cell tries to repair itself through random insertion or deletion of nucleotides (Jiang & Doudna, 2017). This repair mechanism is known as non-homologous end joining (NHEJ) (Jiang & Doudna, 2017). Another DNA repair mechanism that is more specific and does not rely on random insertion or deletion of nucleotides is the homology directed repair (HDR). HDR requires a donor template flanked by homology arms that is used to repair the site at the DSB was introduced (Jiang & Doudna, 2017). The CRISPR-Cas9 genome editing system was used in a research study by Kawashima et al. (2017) to delete the ARIH2 gene, which was concluded to induce NLRP3 ubiquitination and act as a negative regulator in NLRP3 inflammasome assembly and activation.

Figure 2. Simplified CRISPR-Cas9 complex. Cas9 protein forms a complex with the gRNA containing a sequence that is complementary to the sequence of the target gene within the genomic DNA. The target

5 sequence is adjacent to the PAM sequence, which is required for a DSB to occur. Upon recognition of the target sequence by the CRISPR-Cas9 complex, the two distinct endonuclease domains of Cas9 (HNH and RuvC) induce a DSB in the genomic DNA. Induction of DSB is subsequently repaired by either HRD, where a donor template is provided, or by NHEJ, where the cell tries to repair the break through insertion of deletion of nucleotides. Both repair mechanisms can result to the knockout of a particular gene of interest. Figure 2 was designed using Biorender online application (https://app.biorender.com/), and inspired by Jiang & Doudna (2017).

BABAM1 BRISC and BRCA1–A complex member 1 (BABAM1), also referred to as MERIT40 or NBA1, is a protein-encoding gene found on chromosome 19 in the human genome. It consists of nine exons and is a member of two complexes: BRCA1 complex within the nucleus and BRISC complex within the cytoplasm (Hu et al., 2011; Shao et al., 2009). Within the nucleus, BABAM1 plays a vital role in recruiting the BRCA1 complex to the site of DNA damage, irradiation resistance and regulates the cell cycle (Wang, Hurov, Hofmann & Elledge, 2009). Within the cytoplasm, BABAM1 helps in maintaining the integrity of the BRISC complex through its interaction with the BRISC and BRCA1– A complex member 2 (BABAM2), also referred to as BRE (Hu et al., 2011). The BRISC complex consists of ABRO1, BABAM1, BABAM2, and BRCC36 (Hu et al., 2011). BRCC36 is a deubiquitinating enzyme that selective cleaves Lys-63 (K63) linked polyubiquitin (Cooper et al., 2009). However, the underlining molecular mechanisms of BRCC36 deubiquitylation in vivo are still unknown (Ren et al., 2019). In resting macrophages, NLRP3 is reported to be poly-ubiquitinated with Lys-48 (K48) and Lys-63 (K63), and that deubiquitylation of NLRP3 is required for the activation of NLRP3 inflammasome (Juliana et al., 2012; Py, Kim, Vakifahmetoglu-Norberg & Yuan, 2013; Ren et al., 2019). Within the BRISC complex, ABRO1 and BRCC36 have been reported to promote NLRP3 inflammasome activation through the regulation of NLRP3 deubiquitylation (Py et al., 2013; Ren et al., 2019). BABAM1 deficiency has been reported to lead to a decrease level of BABAM2 and BRCC36 (Feng, Huang & Chen, 2009; Wang et al., 2009). Although BABAM1 plays an important role in maintaining the integrity of the BRISC complex, there were no studies to my knowledge that describe its function as a regulator of NLRP3 inflammasome at the time of writing this report. For this reason, it will be interesting to know if BABAM1 plays an essential role in regulating the activation of NLRP3 inflammasome. This led to the research question and aim of this study seen below:

Research Question Can directly targeting BABAM1 regulate the activation of NLRP3 inflammasomes to provide insight into therapeutic strategies for treating NLRP3 inflammasome related diseases?

Research Aim The aim of this research study was:

1) to construct a CRISPR knockout of BABAM1 gene in THP-1 monocyte like cell line 2) to investigate the effect of the CRISPR knockout in THP-1 monocyte like cell line to understand its role in the activation of NLRP3 inflammasome.

This aim will be achieved by firstly designing oligonucleotides that will be ligated in a linear plasmid DNA containing the Cas9 and tracrRNA protein sequences. The recombinant plasmid DNA will then be transfected into THP-1 monocyte-like cell lines to induce a DSB upon recognize the target sequence. Induction of a DSB is enough to disrupt the function of BABAM1 gene. BABAM1 gene expression in knockout and control cells will be measured using qPCR and analysed using the delta-delta Ct method of relative quantification.

2. Materials and Methods After formulating the aim and research question of this project, an overall experimental workflow seen in Figure 3 was created. An advantage associated with creating the overall workflow was to make sure that there was a general understanding of what needed to be done to meet the aim and answer the research question of this study.

Figure 3. Overall experimental workflow. The experimental workflow is divided into two sections: construction of recombinant DNA and lipid-based transection. The arrows indicate the direction in which the laboratory work was conducted. The plus sign indicates the combination of the recombinant plasmid DNA and the transfection reagent to form a liposome that was used to deliver the recombinant DNA into the target THP-1 cells.

Oligos Design The target sequence seen in Figure 4 was selected from exon two of BABAM1 gene based on an on-target score of 70.8% and an off-target score of 87.6%. The instructions given in the Guide-it™ CRISPR/Cas9 Systems User Manual (Clontech) for designing oligos were used as a guide to design the forward and reverse oligos for the target sequence, as seen in Figure 4. The designed oligos were ligated into the pre-linearized pGuide-it-ZsGreen1 vector (Clontech) to become part of the sgRNA that was expressed from the human U6 promoter. The oligos were ordered from Invitrogen.

Figure 4. BABAM1 target sequence and the sequences of the forward and reverse oligos. The above target sequence consists of 20 nucleotide bases that will be recognised by the CRISPR-Cas9 complex. Highlighted in blue is the PAM sequence that is required for Cas9 protein to introduce a double-stranded break upon recognition of the target sequence. Highlighted in red are the additional overhang sequences that are required for cloning.

Construction of CRISPR-Cas9 construct Both the forward and reverse oligos were resuspended in nuclease-free water to give a working concentration of 100 µM. The Guide-it™ CRISPR/Cas9 System (Green) (Clontech) was used to anneal both oligos according to the manufacturer´s instruction. Two ligation reactions were set

7 up to clone the target annealed oligos and the Guide-it control annealed oligos (came with the Guide-it™ CRISPR/Cas9 System) into the linear pGuide-it Vector (also came with the Guide-it™ CRISPR/Cas9 System) according to the manufacturer´s instructions. All components were the same in both ligation reactions except for the annealed oligos that were used. The reaction mixtures were incubated at room temperature for 10 min.

The entire 10 µl ligation reactions containing the different CRISPR-Cas9 constructs were used to transform separate tubes of Stella Competent Cells (came with the Guide-it™ CRISPR/Cas9 System). All tubes were allowed to stand on ice for 30 min, and then heat-shocked at 42 °C for 45 s. The tubes were transferred immediately on ice and incubated for 2 min. 1 ml of SOC medium was added to each tube and incubated at 37 °C for 1 h in a 300-rpm shaking incubator. Three different volumes (25 µl, 100 µl, and 250 µl) were spread on pre-warmed LB agar plates with ampicillin (100 µg/ml), and 100 µl was plated on LB agar plates without ampicillin. All plates were incubated overnight at 37 °C. Four different colonies were picked from the overnight LB agar plates and inoculated in separate falcon tubes containing 5 ml of LB-medium with ampicillin (100 µg/ml). All tubes were incubated overnight at 37 °C in a 300-rpm shaking incubator.

Plasmid DNA was extracted from the overnight cultures using the QIAprep Spin Miniprep Kit (Qiagen) according to the manufacturer´s instructions. All 5 ml of overnight culture was used during plasmid extraction, including all recommended steps. Extracted plasmid DNA was eluted using 50 µl of nuclease-free water, and the concentration and purities were measured using the Qubit 3.0 Fluorometer (ThermoFisher Scientific) and NanoDrop 1000 spectrophotometer (ThermoFisher Scientific). Three samples from the extracted plasmid DNA were prepared for sequencing. Two of the samples contained the target plasmid DNA, and the third sample contained the Guide-it control plasmid DNA. 250 ng of plasmid DNA in 7 µl of nuclease-free water was prepared from all three samples and sent to Karolinska Institute for sequencing. The Guide-it sequencing primer 1 (came with the Guide-it™ CRISPR/Cas9 System) with sequence 5´– AATGGACTATCATATGCTTACCGT–3´ was provided for sequencing. The sequencing results were analysed using FinchTV version 1.4.0 chromatogram viewer by Geospiza, Inc.

Cell culture and Transfection THP-1-ASC-GFP cells (Invivogen) were cultured in Roswell Park Memorial Institute (RPMI-1640) medium with 2 mM L-glutamine (Sigma), 10% heat-inactivated Fetal Bovine Serum (FBS) (Sigma), 10mM HEPES (Sigma), 1mM sodium pyruvate (Sigma), 0.45% glucose (Sigma), and 100 U/ml pen- strep. Cells were cultured in a T25 flask (NuncTM) at 37 °C, 5% CO2. Cultured cells were passaged every two to three days. During cell passaging, cells were counted and centrifuged at 157 g for 5 min. The cell pellet was then resuspended in a fresh culture medium and kept between 5 x 105 and 1.5 x 106 cells/ml. The ViaFectTM Transfection Reagent was used to transfect THP-1 cells according to the manufacturer’s instructions. Proliferating THP-1 cells were centrifuged at 200 x g and resuspended in a fresh culture medium. 0.5 µg of plasmid DNA was mixed with 2 µl of ViaFectTM Transfection Reagent in a serum-free medium to give a 50 µl total volume of transfection complex (serum-free medium + plasmid DNA + Transfection Reagent). The transfection complex was incubated at room temperature for 15 min and added to each well containing 200 000 THP-1 cells. For untransfected cells, the transfection complex without plasmid DNA was used. A simplified figure of all transfected wells can be seen in Appendix 1, Figure 1. All transfected wells were mixed by gently shaking the entire plate for 30 seconds. The plate was incubated at 37 °C, 5% CO2 for 24 h.

Transfected THP-1 cells were differentiated into macrophage-like cells using 10 ng/ml of PMA (Phorbol Myristate Acetate, Invivogen) for 4 h. After differentiation, old culture medium was removed and replaced with fresh medium containing serum. The cells were allowed to rest for 18 h at 37 °C, 5% CO2. After resting, some of the wells were primed with 100 ng/ml of LPS (LPS-B5 Ultrapure, Invivogen) for 4 h, and some wells were activated with 10 mM of ATP (Sigma) for 30 min. The different wells that were primed and activated can be seen in Appendix 1, Figure 1.

RT-qPCR Total RNA extraction and purification were performed using the Total RNA Purification Kit (Norgen) according to the manufacturer´s instruction for cells growing in a monolayer. Cells were gently washed with 500 µl of ice-cold PBS before being lysed with Buffer RL. Total RNA was extracted from nine of the 18 transfected wells (Appendix 1, Figure 1) for cDNA synthesis. The concentration and purity of all RNA samples were measured using the Qubit 3.0 Fluorometer (ThermoFisher Scientific) and NanoDrop 1000 spectrophotometer (ThermoFisher Scientific). The High-Capacity RNA-to-cDNATM Kit (Thermo Fisher Scientific) was used for cDNA synthesis according to the manufacturer´s instruction. RNA samples were diluted to a final concentration of 8 ng/µl to ensure that the amount of RNA used during cDNA synthesis was the same for all samples. The following thermal cycling conditions were used during cDNA synthesis: step one for 60 min and 37 °C, step two for 5 min at 95 °C, and step three on hold at 4 °C.

Quantitative real-time PCR analysis was performed using the SYBR® Select Master Mix (Applied Biosystems®) according to the manufacturer’s instructions. The sequences of BABAM1 primer pair (FP 5`–CCACAATGGCACTGAGGAGAAG–3´, RP 5`–GCCACCTCATACTTGTAGCTGG–3´), and ACTB primer pair (FP 5´–CACCATTGGCAATGAGCGGTTC–3´, RP 5´– AGGTCTTTGCGGATGTCCACGT–3´) were taken from Origen Inc. website: https://www.origin.com/ (CAT#: HP226345 and CAT#: HP204660), and ordered from Invitrogen. 150 nM of each primer and 11.4 ng of cDNA sample were used in a 20 µl total reaction volume for each well. The qPCR reaction was prepared in a 96-well plate and ran on a 7300 Real- Time qPCR System (ThermoFisher Scientific). The thermal cycling conditions used were: an initial activation step for 2 min at 50 °C, a denaturation step for 10 min at 95 °C, followed by 40 cycles of 15 s at 95 °C, and extended for 1 min at 60 °C. qPCR Data Analysis The ΔΔCt method of relative quantification was used to measure BABAM1 gene expression level for the different treated cells. During the ΔΔCt analysis, the mean Ct values of all replicates were firstly calculated. The precision of the different Ct values for each replicate was accessed by calculating the difference between the largest and the smallest Ct values. Upon a difference of 0.5, the Ct value further away from the two Ct values within a sample replicate was removed (Kennedy & Oswald, 2011).

The Delta Ct (ΔCt) for each sample was then calculated by subtracting the average Ct value of ACTB from the average Ct value of BABAM1 as seen in the equation below:

ΔCt = average Ct value 퐵퐴퐵퐴푀1 − average Ct value 퐴퐶푇퐵 Equation (1)

The standard deviation (SD) of the ΔCt value was calculated from the SD of the Ct for both the BABAM1 and ACTB according to the equation below:

1 푆퐷 = (푆 2 + 푆 2)^ Equation (2) 1 2 2 where S1 is the SD of the Ct values of BABAM1, and S2 is the SD of the Ct values of ACTB.

Next, the ΔΔCt values were then calculated by subtracting the ΔCt value of the calibrator sample from the ΔCt value of the test sample as seen in the equation below:

ΔΔCt = ΔCt test sample − ΔCt calibrator sample Equation (3)

The choice of the calibrator was dependent on the comparison needed to be made. The SD of the ΔΔCt was the same as the SD of the corresponding ΔCt value. The ΔΔCt was used to calculate the fold change (FC) in gene expression according to the equation below:

FC = 2−ΔΔCt Equation (4)

The FC value was then used to calculate the Log2 fold change (Applied Biosystems, 2008; Livak & Schmittgen, 2001; Schmittgen & Livak, 2008).

Statistical analysis was not used when analysing the qPCR data for this experiment. It was because several biological replicates are required to draw a meaningful and reliable conclusion from qPCR data, which was not the case for this experiment. However, a FC of at least two was selected as the threshold for the difference in BABAM1 gene expression level to be considered biologically relevant. This is because the FC difference of two is a commonly used threshold for gene expression study (Kennedy & Oswald, 2011).

Results

CRISPR-Cas9 construct Stella Competent Cells that were transformed with the recombinant plasmid DNA containing the CRISPR-Cas9 constructs, and plated on LB agar plates with ampicillin were observed to see if transformation was successful. Bacteria colonies were seen on all plates containing cells that were transformed with either the target plasmid DNA or the Guide-it control plasmid DNA, and no colony was seen on the negative control plate, which was plated with untransformed cells. This indicates that transformation was successful in this study.

After successful transformation, plasmid DNA was extracted from the four different colonies that were picked and cultured from the plates containing cells transformed with the target plasmid DNA, and the single colony that was cultured from the plate containing cells transformed with the Guide-it control plasmid DNA. The concentration and purities of the extracted plasmid DNA are seen in Table 1.

Table 1. Concentration and purities of extracted plasmid DNA Purities Sample Colony Concentration (ng/µl) A260/280 A260/230 1 994 2.09 1.86 Transformed with the 2 1100 2.08 1.86 target plasmid DNA 3 1240 2.13 1.88 4 1060 2.08 1.87 Transformed with the Guide-it control 1 322 1.89 2.51 plasmid DNA

To ensure that the extracted plasmid DNA seen in Table 1 were the desired plasmids of interest, they were sequenced using the Sanger sequencing method. The results show that the two samples containing the target plasmid DNA and the one sample containing the Guide-it control plasmid DNA that were sent for sequencing had the correct target sequences when analysed using FinchTV chromatogram viewer (Appendix 2, Figure 1 and 2).

RT-qPCR RNA samples were extracted from transfected and untransfected cells that were treated in different ways. The result in Table 2 shows the concentration and purities of the total RNA extracted from all nine samples. Group A corresponds to cells that were transfected with the target plasmid DNA. Group B corresponds to untransfected cells, and group C corresponds to cells that were transfected with the Guide-it control plasmid DNA. As seen in Table 2, (PA) represents samples that were primed with LPS and activated with ATP, and (P) represents samples that were primed with LPS.

Table 2. Concentration and purities of total RNA extraction. PA = Primed with LPS and Activated with ATP, and P = Primed with LPS Purity

Group Sample Concentration (ng/µl) A260/280 A260/230 A 1 (PA) 13.7 2.16 1.16 2 (P) 10.2 1.68 2.09 3 14.8 2.10 0.74 B 4 (PA) 37.4 1.99 3.42 5 (P) 52.2 2.07 1.25 6 59.4 2.10 3.29 C 7 (PA) 8.36 2.15 0.70 8 (P) 8.86 2.25 0.66 9 12.7 2.02 0.94

qPCR Data Analysis The extracted RNA was converted to cDNA and expression level of BABAM1 gene and ACTB control gene were determined by qPCR. The results of relative changes in BABAM1 gene expression level between different experimental groups are shown in Figures 5 to 11 below, using the ΔΔCt method for relative quantification. The raw qPCR used to calculate relative changes in BABAM1 gene expression level is seen in Appendix 3, Figure 1.

The comparison in Figure 5 was performed to see if the CRISPR-Cas9 construct successfully disrupted the function of BABAM1 gene in cells transfected with the target plasmid DNA. The result shows no biologically relevant difference in BABAM1 gene expression level between the two treatment groups.

Figure 5. BABAM1 gene expression level in transfected cells. (A) Fold change in BABAM1 gene expression level between cells transfected with the target plasmid DNA and cells transfected with the Guide- it control plasmid DNA, plotted on a linear scale. TPA means transfected, primed and activated, indicating that the cells were firstly transfected with plasmid DNA, primed with LPS and activated with ATP. (B) Log2 fold change of the different treatment groups shown on a logarithm scale. Error bars represent the ±S.D.

The comparison seen in Figure 6 was performed to see if priming with LPS had an effect on cells transfected with the target plasmid DNA. This comparison made it easy to understand if priming the cells did influence the expression of BABAM1 gene. The result shows that priming did not have an effect on BABAM1 gene expression level between the different treatment groups.

Figure 6. Effect of priming on BABAM1 gene expression in transfected cells. (A) Fold change in BABAM1 gene expression level between cells transfected with the target plasmid DNA. TP means transfected and primed, indicating that the cells were firstly transfected with the target plasmid DNA and then primed with LPS. T means that the cells were transfected with the target plasmid DNA but not primed with LPS. (B) Log2 fold change of the different treatment groups shown on a logarithm scale. Error bars represent the ±2S.D.

Knowing that priming did not affect the expression of BABAM1 in target cells (Figure 6), it was essential to know if activating the cell with ATP did influence the expression of BABAM1 in this study. This was done through the comparison seen in Figure 7, which shows that activation did not have any biologically relevant effect on BABAM1 gene expression level between the different treatment groups.

Figure 7. Effect of activation on BABAM1 gene expression in transfected cells. (A) Fold change in BABAM1 gene expression level between cells transfected with the target plasmid DNA. TPA and TP mean that the cells were firstly transfected with the target plasmid DNA, primed with LPS and either activated with ATP or not. (B) Log2 fold change of the different treatment groups on a log scale. Error bars represent the ±2S.D.

The result in Figure 8 was performed to see if transfecting the cells with plasmid DNA had an effect on the expression of BABAM1 gene. This comparison was important to understand if the use of the Viafect transfection reagent plus plasmid DNA affected the viability of the THP-1 cells, which could influence the expression of BABAM1 gene in the different samples. The result shows that transfection with plasmid DNA did have any biologically relevant effect on BABAM1 gene expression level between the different treatment groups.

Figure 8. Effect of transfecting cells with plasmid DNA on BABAM1 gene expression level. (A) Fold change in BABAM1 gene expression level between cells transfected with the Guide-it control plasmid DNA and untransfected cells. T means that the cells were transfected. (B) Log2 fold change of the different treatment groups. Error bars represent the ±2S.D.

Similar result seen in Figure 8 was observed when cells transfected with that target plasmid DNA were compared to those that were untransfected, as seen in Figure 9. The result shows that transfecting cells with plasmid DNA did have a biologically relevant effect on the expression of BABAM1 gene between the different treatment groups.

Figure 9. Effect of transfecting cells with plasmid DNA on BABAM1 gene expression level. (A) Fold change in BABAM1 gene expression level between cells transfected with the target plasmid DNA and untransfected cells. T means that the cells were transfected. (B) Log2 fold change of the different treatment groups. Error bars represent the ±2S.D.

Due to the biologically relevant differences in BABAM1 gene expression level between transfected and untransfected cells seen in Figures 8 and 9, it was vital to know if priming and activation affected BABAM1 gene expression level in untransfected cells. By comparing untransfected cells to each other, the effect that adding plasmid had on the expression of BABAM1 gene can be ruled out. These comparisons are seen in Figures 10 and 11. The results in both figures show that priming and activation did not have any biologically relevant effect on the expression level of BABAM1 gene in the different treatment groups.

Figure 10. Effect of priming on BABAM1 gene expression level in untransfected cells. (A) Fold change in BABAM1 gene expression level between untransfected cells that were primed with LPS and those that were not. P means that the cells with primed with LPS. (B) Log2 fold change of the different treatment groups. Error bars represent the ±2S.D.

Figure 11. Effect of activation on BABAM1 gene expression level in untransfected cells. (A) Fold change in gene expression level between untransfected cells. PA means that the cells were primed with LPS and activated with ATP, and P means that the cells were primed LPS. (B) Log2 fold change of the different treatment groups. Error bars represent the ±2S.D.

3. Discussion The traditional gene knockout (KO) method was used in an attempt to disrupt the function of BABAM1 gene to understand its role in the activation of NLRP3 inflammasome. An advantage of traditional gene KO is that permanent changes are made to the genome of the target organisms, which was required in this study. An attempt to knock out BABAM1 gene in this study was made using CRISPR-Cas9 gene-editing technology, which is the most well-characterized and widely used genome editing system currently available (Jiang & Doudna, 2017). The CRISPR-Cas9 gene- editing technology was chosen to disrupt the function of BABAM1 gene in this study because of its ease of use, high specificity, and low risk of off-target effect when compared to other gene-editing methods such as zinc-finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALEN) (Gaj et al., 2013). A disadvantage associated with using the CRISPR-Cas9 system is the size of the Cas9 protein required to induce a double-stranded break (DSB) upon recognition of the target sequence. The cDNA that encodes S. pyogenes Cas9 (SpCas9) is approximately 4.2 kb in size (Gupta & Musunuru, 2014), which is about half of the size of the CRISPR construct (8.303 kb) used to transfect THP-1 cells in this study. This made it challenging to deliver the CRISPR-Cas9 system into the cells of the target organism. However, several research studies have successfully used the

CRISPR-Cas9 system to edit the genome of THP-1 cells (Chen et al., 2018; Goetze, Kim, Schinazi & Kim, 2017; Kawashima et al., 2017).

To knockout BABAM1 gene in THP-1 cells in this study, the target sequence seen in Figure 4 was identified using benchling.com, a cloud-based informatics platform for life sciences. This online platform was used to ensure that an appropriate target sequence for BABAM1 gene was selected, which was essential to maximise the potential of efficient cleavage upon recognition by the CRISPR-Cas9 system. An advantage of using benchling.com was that the different identified potential target sequences for BABAM1 were presented with their respective on and off-target scores, which was useful when selecting the best possible target sequence. The higher the on and off-target scores, the better the target sequence. Additionally, the target sequence was selected from an early exon, exon two, of BABAM1 gene to ensure a complete disruption of its function upon gene knockout. Targeting a later exon in BABAM1 gene would have resulted in truncated protein with some possible functional activities upon translation, which could have influenced the outcome of this study (Housden et al., 2016).

The quantity and quality of the extracted plasmid DNA (Table 1) could be vital to the outcome of various downstream applications in this study. Since the target and the Guide-it control plasmid DNA were used to transfect THP-1 cells, it was essential to make sure that both plasmids were free of impurities. A study by De La Vega, Ter Braak, Azzoni, Monteiro, and Prazeres, (2013) showed that impurities in extracted DNA could lower transfection efficiency in mammalian cells. As seen in Table 1, the concentration of all plasmid DNA was high enough to be used throughout this study. To access the purities of all DNA samples seen in Table 1, the absorbance ratio of 260/280 (A260/280) and 260/230 (A260/230) nm were used (Desjardins & Conklin, 2010). According to Desjardins and Conklin (2010), an A260/280 value of ~1.8 and ~2.0 are generally accepted as “pure” DNA and RNA respectively. The ViaFectTM Transfection Reagent user manual recommended an A260/280 between 1.7–1.9 for pure DNA to be used during transfection. Some of the A260/280 values of the DNA samples seen in Table 1 are slightly above the recommended range for pure DNA. This slight increase in these values does not mean that the samples were contaminated or impure to be used in this study. It could have been caused by a change in pH and ionic strength of the buffer used to prepare the samples for spectrophotometric analysis. A study by Wilfinger, Mackey, and Chomczynski (1997) demonstrated that both the pH and the presence of salt in the solution used to measure the purity of nucleic acid could significantly affect the A260/280 ratio. Desjardins and Conklin (2010) reported that acidic solutions would under-represent the A260/280 ratio by 0.2–0.3, while a basic solution would over-represent the ratio by 0.2–0.3. The A260/230 ratio is normally used as a secondary measure for nucleic acid purity with a recommended range of 1.8–2.2 (Desjardins & Conklin, 2010). All of the A260/230 values in Table 1 are within this range except one, which could possibly be due to a slight change in the pH of the solution used during purity analysis.

The ViaFectTM Transfection Reagent, which is a lipid-based transfection method was used to transfect the extracted target and Guide-it control plasmid (seen in Table 1) into THP-1 cells. The theory behind this method is that when cationic lipids are mixed with nucleic acid, they form a positively charged complex (lipoplex) that can be used to deliver the negatively charged nucleic acid molecule into the cell (Felgner et al., 1987; Lai & van Zanten, 2002). The advantages associated with using lipid-based transfection method are that it is cost-effective, relatively easy to use, and plenty of commercially available products. Additionally, this transfection method does not require the use of viruses/viral particles or expensive instruments (electroporators), which are necessary for other conventional transfection methods (Kim & Eberwine, 2010). A significant disadvantage associated with the lipid-based transfection method is the very low transfection efficiency when transfecting primary cell lines, when compared to another method such as a viral- mediated transfection method (Kim & Eberwine, 2010). Lipoplexes formation has been reported to be unstable in solution and thus result to a decrease in transfection efficiency (Lai & van Zanten, 2002; Zelphati et al., 1998). However, the University of Skövde did not have permission to use a

15 viral-mediated transfection method at the time of conducting this study and an electroporator was not available. The ViaFectTM Transfection Reagent was chosen over other commercially available transfection kits as it was used to successfully transfect proliferating THP-1 cells in a research study by Wiśnik, Płoszaj, and Robaszkiewicz (2017). THP-1 cell lines are known to be notoriously hard to transfect but was chosen for this study as it is a relevant cell type that is widely used to study immune responses (Maeß et al., 2014).

Throughout this study, it is difficult to tell if transfection was successful in THP-1 cells since the transfection efficiency was not calculated, and a selection of THP-1 cells containing the target plasmid DNA was not generated. Both the target and Guide-it control plasmids used to transfect THP-1 cells in this study had a nucleotide sequence that encodes a green fluorescent protein (GFP), which was under the control of the minimal cytomegalovirus (CMV) promoter two. The GFP protein could have been used as a reporter to measure transfection efficiency by taking images of transfected cells using fluorescence microscopy and calculating the percentage of successfully transfected THP-1 cells. Alternatively, a more sensitive and accurate method of calculating transfection efficiency would have been flow cytometry analysis, which enables the measurement of GFP fluorescence in a large population of cells (Soboleski, Oaks & Halford, 2005). A specialized type of flow cytometry, known as fluorescence-activated cell sorting (FACS) could have been used to select THP-1 cells containing the target plasmid, provided that transfection was successful. FACS method can separate a population of cell into subgroups based on fluorescent marker (Liao, Makris & Luo, 2016). Sorting transfected THP-1 cells through the tradition drug selection method was not an option in this study because an appropriate drug selection marker was not included on the plasmid DNA used during transfection. Calculating transfection efficiency or selecting THP-1 cells that were successfully transfected with the target plasmid were vital to the outcome of this study. However, neither of these processes were performed due to financial limitation and time restraints.

Assuming that THP-1 cells were successfully transfected in this study, it is impossible to say if BABAM1 gene was successfully knocked out or not. This is because no analysis was performed to see if the CRISPR-Cas9 system was successful in introducing a DSB at the target sequence, and if this DSB was repaired by the cell’s DNA repair mechanisms. What could have been done to confirm that the CRISPR-Cas9 system worked successfully in the target cells, provided that transfection worked, was to sequence the DNA region around the target sequence of BABAM1 gene, where the DSB was expected to be introduced. The sequencing results could have then been compared to the correct DNA sequence of BABAM1 gene to know what kind of mutation has been introduced. However, all THP-1 cells, if successfully transfected with the target plasmid DNA would not have had the same mutation as the induction of DSB was expected to result to an insertion or deletion of nucleotide bases when the cells try to repair the break. Insertion or deletion of nucleotide bases could have resulted in a frameshift mutation in some cells as long as the number of bases being added or deleted is not a multiple of three (Roth, 1974). This would have resulted in a shift of the three bases reading frame of BABAM1 mRNA during translation, which usually introduces premature stop codon, resulting in the production of non-functional or partly functional BABAM1 proteins. Another type of mutation that could occur in some THP-1 cells is a non-sense mutation, where insertion or deletion of nucleotide bases during DNA repair results to the RNA sequence encoding a stop codon. This could have also resulted to a non-functional or partly functional BABAM1 protein. cDNA samples used for qPCR analysis in this study were prepared from the different RNA samples seen in Table 2. The A260/280 ratios of all RNA samples except one (Table 2) are very close to the generally accepted value of ~2.0 for “pure” RNA, indicating that these samples were free from impurities (Desjardins & Conklin, 2010). The small shift of the A260/280 ratio (Table 2) from ~2.0 could possibly be caused by a change in the pH or ionic strength of the buffer used to elute the RNA samples before spectrophotometric analysis (Wilfinger et al., 1997). The A260/230 ratios (Table 2) range from 0.66 – 3.42, with some samples being lower or higher than the recommended

16 range of 1.8–2.2 for pure RNA (Desjardins & Conklin, 2010). This means that these samples were contaminated with commonly used reagents in nucleic acid purification such as phenol, carbohydrates, guanidine or glycogen, which absorb light at a wavelength of 230 nm (Matlock, 2015). These contaminations can potentially affect the efficiency of qPCR amplification (Carvalhais, Delgado-Rastrollo, Melo & Cerca, 2013). In spite of the difference of A260/230 ratio for some RNA samples (Table 2) from the recommended range of pure RNA, they were still used for cDNA synthesis in this study. Krsek and Wellington (1999) reported that the success of PCR amplification was more influenced by the A260/280 than the A260/230 ratio. This was also confirmed in a study by Ning et al. (2009). Additionally, other studies have found no significant correlation between the A260/230 ratio and qPCR amplification efficiency (Cicinnati et al., 2008; Kuang, Yan, Genders, Granata & Bishop, 2018).

The cDNA samples generated in this study were run in triplicate during qPCR amplification of BABAM1 and the reference gene, ACTB, which is the gene that encodes the beta-actin protein. An advantage of running each sample in triplicate was to control for the validity of the measured Ct values, which could be influenced by the amount of cDNA sample added to each qPCR well (Taylor et al., 2019). However, running these samples in triplicate did not control for anything else apart from calculating average Ct values. In other words, they could not be used to perform any statistical analysis as they would have only accounted for technical variation (Kennedy & Oswald, 2011). A better approach would have been to run biological replicates, which was not possible within the time-frame of this study.

Relative quantification was used to measure the expression level of BABAM in the different treated groups in this study. This is because relative quantification enables the determination of relative changes in gene expression between different experimental groups (Livak & Schmittgen, 2001). Relative quantification requires the use of a stable reference gene, which serves as an internal control to normalise the expression level of the gene of interest, thereby reducing variation in the quality and quantity of the RNA used during qPCR amplification (Fleige et al., 2006; Livak & Schmittgen, 2001). ACTB was used as the reference gene in this study because it was concluded to be one of the two most stable reference genes from a list of 21 preselected potential reference genes during mRNA expression analysis of THP-1 monocyte differentiated into macrophages (Maeß, Sendelbach & Lorkowski, 2010).

The ΔΔCt method of relative quantification was used to analyse the qPCR data generated in this study. This was because the expression efficiencies of BABAM1 and ACTB were assumed to be the same in all samples (Livak & Schmittgen, 2001). Additionally, the ΔΔCt method provides the possibility to present data as “fold change (FC)” in gene expression (Schmittgen & Livak, 2008). A drawback associated with the ΔΔCt method is that a validation curve is required to accurately determine the amplification efficiencies of the target and reference genes, which should be approximately equal for the ΔΔCt calculation to be valid (Livak & Schmittgen, 2001). A study by Ramakers, Ruijter, Deprez and Moorman (2003) reported that a variation of 0.04 in expression efficiencies resulted to a 4-fold error in fold-difference, indicating that the ΔΔCt method is very sensitive to variation in amplification efficiency. However, it was impossible to determine the amplification efficiencies of BABAM1 and ACTB in this study due to time constraint. Because of this, the same amount of RNA was used for cDNA synthesis to normalize the qPCR amplification of both BABAM1 and ACTB.

Before performing qPCR data analysis, the data was first observed for quality control using the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines (Bustin et al., 2009). At first, the Ct values for all the non-template controls (NTCs) were observed to make sure that there was no amplification of unintended products such as primer dimers. The dissociation curves of all samples were also observed to ensure that just one PCR product was being amplified (Appendix 3, Figure 2). The amplification of non-specific products could have influenced the outcome of this study by affecting the Ct values generated by the qPCR instrument. A study by Ruiz-Villalba, van Pelt-Verkuil, Gunst, Ruijter and van den Hoff (2017)

17 reported that the Ct values of reactions with non-specific products were systematically higher than those of the correct product for the same gene.

Fold change (FC) was used to show relative changes in BABAM1 gene expression level for the different treatment groups in this study (Figure 5 to 11). FC is the ratio in gene expression normalized to an internal reference gene and relative to the untreated control sample (Livak & Schmittgen, 2001). By definition, the FC in gene expression relative to the control sample should be equal to one. This is because the ΔΔCt value used to calculate this FC of the control sample is equal to zero, as 20 = 1 (Livak & Schmittgen, 2001). A problem associated with using FC values to show the difference in gene expression on a linear scale (Figure 5 to 11) is that up-regulated genes are perceived to be of high importance than down-regulated genes. This is because up-regulated gene will take the scale value from one to positive infinity whereas down-regulated genes will take the scale value between one and zero, making the scale highly asymmetric (Causton, Quackenbush & Brazma, 2009; Knudsen, 2002). To resolve this problem, the FC values were Log- transformed (Log2 base 10) to make the scale symmetric, where positive values indicated up- regulated genes and negative values indicate down-regulated genes (Figure 5 to 11) (Kennedy & Oswald, 2011). A FC difference of at least two was selected as the threshold for the difference in BABAM1 gene expression to be considered biologically relevant. This is because the FC difference of two is a commonly used threshold for gene expression study (Kennedy & Oswald, 2011). As an example, if a fold change value is two for a particular treatment group, it means that there is a two-fold increase in gene expression in the treatment group relative to the control group. In other words, the gene is two-fold upregulated in the treatment group compared to the control group. Additionally, if the fold change value is less than one, for example, 0.5 for a treatment group, it means that there is a two-fold decrease in gene expression in the treatment group relative to the control group. In other words, the gene is two-fold downregulated in the treatment group compared to the control group. Therefore, for a downregulated gene to be considered biologically relevant, the fold change value should be less than or equal to 0.5.

The comparison seen in Figure 5A was done to see if there was a biologically relevant difference in BABAM1 gene expression level between cells that were transfected with the target plasmid DNA and those transfected with the Guide-it control plasmid DNA. The result shows a FC of 0.9 for cells transfected with the target plasmid DNA, indicating that there was a 0.9-fold decrease in BABAM1 gene expression level relative to cells transfected with the control plasmid DNA (Figure 5A). However, this decrease is not biologically relevant as a FC of 0.9, which indicate downregulated gene is greater than 0.5. The Log2FC result seen in Figure 5B shows this decrease in BABAM1 gene expression as down-regulated gene relative to the control sample.

Though the decrease in BABAM1 gene expression level for cells transfected with the target plasmid compared to control was not biologically relevant (Figure 5), it was important to know if this decrease was caused by just priming the cells with LPS or priming and activating them with ATP. To understand this, the comparisons seen in Figure 6 and Figure 7 were made. Figure 6A shows virtually the same FC and Log2FC values for transfected cells that were primed and unprimed. This indicates that priming did not have an effect on the expression level of BABAM1 gene in this study. On the contrary, the result in Figure 7 shows the FC of 0.88 and a Log2FC of - 0.18 for transfected cells that were primed and activated. This indicates that the decrease in BABAM1 gene expression level seen in Figure 5 for the primed and activated cells transfected with the target plasmid DNA could have been caused by activating the cells with ATP. A hypothesis is that activating transfected cells with ATP did triggered cell death in this study. Several studies have reported that extracellular ATP can mediate cell death through the P2X7 receptor via necrosis, which is the process of cell death caused by noxious stimuli (Blanchard et al., 1995; Falzoni et al., 1995; Murphy & Weaver, 2017; Zambon et al., 1994). P2X7 has been reported to be expressed in various types of immune cells, which was the type of cell used in this study (Bours, Swennen, Di Virgilio, Cronstein & Dagnelie, 2006).

Additionally, it was important to know if transfecting the cells with plasmid DNA had an effect on BABAM1 expression level in this study. To understand this, cells that were transfected with the control plasmid DNA were compared to untransfected cell, as seen in Figure 8. The result in Figure 8A shows a FC of 2.7 indicating that cells transfected with the control plasmid had a 2.7-fold higher gene expression of BABAM1 relative to the untransfected cells. This difference in BABAM1 gene expression is biologically relevant as it is higher than two, which is the defined threshold for BABAM1 gene expression to be considered biologically relevant. The result in Figure 8B further highlights this biological relevance in BABAM1 gene expression with a Log2FC of 1.5 indicating that cell transfected with the Guide-it control plasmid DNA were up-regulated when compared to untransfected cells. However, this was not the anticipated result as BABAM1 gene expression level was expected to be relatively equal for both treatment groups seen in Figure 8. Similar result is also seen in Figure 9, where cells that were transfected with the target plasmid DNA were compared to those that were transfected but with no plasmid DNA. The result shows a FC of 3.58 indicating that cells transfected with the target plasmid DNA had a 3.58-fold higher gene expression of BABAM1 relative to the untransfected cells (Figure 9).

A possible explanation for the biological relevant differences in BABAM1 gene expression level seen in Figures 8 and 9 was due to the instability of ACTB used to normalize the expression of BABAM1 gene in the different treatment groups. This is supported by the raw qPCR data generated in this study, which is seen in Appendix 3, Figure 1. If the expression of ACTB was stable in this study, the generated Ct values of ACTB in all treatment groups should have been approximately equal. As seen in Appendix 3, Figure 1, this was not the case. The Ct values for ACTB in all untransfected samples are lower than the Ct values of ACTB in samples transfected with either the target or the Guide-it control plasmid DNA (Appendix 3, Figure 1). This indicates that ACTB was more highly expressed in the untransfected cells than in the transfected cells. This finding is contradictory to that of Maeß et al. (2010), who concluded that ACTB was one of the two most stable reference genes from a list of 21 preselected reference gene during mRNA expression analysis of THP-1 monocyte. However, a study by Warrington, Nair, Mahadevappa, and Tsyganskaya, (2000) found that ACTB level in standard gene expression analysis could vary by 7- fold. Additionally, the Ct values of BABAM1 gene expression varies a bit between transfected cells (cells transfected with the target plasmid DNA and the Guide-it control plasmid DNA) and untransfected cells but the variation is generally not that much, as seen in Appendix 3, Figure 1.

ACTB is a ubiquitously expressed gene that encodes an isoform of actin, which is an essential component of the cytoskeleton, and plays an important role in cell migration, cell division, and regulation of gene expression (Bunnell, Burbach, Shimizu & Ervasti, 2011). It is possible that transfecting the cells with plasmid DNA in this study impaired cell growth, migration, or gene expression and therefore caused the decrease in ACTB gene expression in transfected cells (Appendix 3, Figure 1). A study by Joseph, Srivastava, and Pfister (2014) concluded that a decrease of ACTB expression in normal corneal stomach clearly disrupts the function and structure of the cytoskeleton and cell motility. Another study Bunnell et al. (2011) reported that the lethality in ACTB knockout mice was likely due to the defects in cell growth and migration, which were severely impaired in β-actin–knockout primary mouse embryonic fibroblasts (MEFs). The instability of ACTB in this study could have been avoided by firstly validating the expression of multiple reference genes for the experimental conditions specific to this study (Bustin, 2002). Alternatively, several reference genes could have been used in this study to obtain the most reliable result for differences in BABAM1 gene expression in treated and untreated cells (Vandesompele et al., 2002). However, it was impossible to validate or use multiple reference genes in this study due to financial limitations and time constraints. ACTB was the best possible reference gene that could have been chosen as it was reported to be a stable endogenous reference gene in macrophages (Bao et al., 2019; Maeß et al., 2010; Roy, McElhaney & Verschoor, 2020). The instability of ACTB in this study only had an effect on comparison made with untransfected cells. All other comparisons between cells transfected with the target plasmid DNA and those

19 transfected with the Guide-it control plasmid DNA are valid as ACTB gene expression was stable in these cells (Appendix 3, Figure 1).

Additionally, it was important to understand if priming and activation affected the expression of BABAM1 gene in untransfected cells. As seen in Figure 10A, untransfected cells that were primed with LPS show a FC of 0.8, indicating that there was a 0.8-fold decrease in BABAM1 expression in these cells relative to those that were not primed. However, this decrease (Figure 10) is not biologically relevant and could have been caused by apoptosis upon stimulating the cells with LPS. A supporting study by Xaus et al. (2000) reported that LPS induces apoptosis in macrophages through the production of nitric oxide and secretion of tumour necrosis factor (TNF). Another study by Suzuki et al. (2004) also reported that LPS induces apoptosis of human macrophages. The result in Figure 11 shows a FC of 0.9 and a Log2FC of -0.15 for untransfected cells that were primed and activated compared to those that were primed but not activated. This indicates that the decrease in BABAM1 gene expression for untransfected cells is not biologically relevant, and could have been caused by ATP induced apoptosis that was triggered when untransfected cells were activated with ATP (Figure 11). A supporting study by Bulanova et al. (2005) provided experimental evidence that extracellular ATP induces the P2X7 apoptosis in macrophages.

Throughout this study, the expression of BABAM1 gene in the different treatment groups was analysed at the transcriptional level using RT-qPCR. The problem associated with this is that the region of BABAM1 RNA that was reverse transcribed to cDNA, and amplified during qPCR analysis was the same for both BABAM1 wild type cells and cells in which attempts were made to knock out BABAM1 gene. This is because the induction of a DSB at the target site of BABAM1 gene, provided that transfection and the CRISPR-Cas9 system worked successfully, and repaired by the cell`s DNA repair mechanism will shift the entire downstream frame following the target site. Thus, no difference will be seen at the RNA level during qPCR analysis. However, this could have been resolved by analysing the expression of BABAM1 gene at the translation level, i.e., by measuring the protein encoded by BABAM1 gene in the different treatment groups. Assuming that the CRISPR-Cas9 system was successfully transfected in the target cell and induced a DSB at the target site, the cells would have tried to repair the break through the error-prone non-homologous end joining (NHEJ), resulting to the insertion or deletion of nucleotide bases. This would have resulted in the production of non-functional or partly functional BABAM1 protein, which could have produced a noticeable difference in BABAM1 expression level in the different treatment groups when analysed at the translation level.

Lastly, this study planned to measure the production of IL-1β in the different treatment groups by ELISA. This was not done due to the lack of biologically relevant difference in BABAM1 gene expression level in the different treatment groups that was not caused by the instability of ACTB during qPCR data analysis. However, this is something that can be done in the future.

4. Conclusion Throughout the experimental work of this study, successful attempts were made to answer the research question and meet the aim and objectives of this project. A CRISPR-Cas9 construct for the target gene was successfully created along with a control construct. The construct was transfected into THP-1 cells, which were primed and activated. Quality controls of the qPCR data generated in this study show that there was no amplification of unwanted products during qPCR amplification. This means that the qPCR data could be used to analyse the expression of BABAM1 in the different treatment groups to understand if it regulates the activation of NLRP3 inflammasomes. Analysis of the qPCR data in this study found no biologically relevant difference in BABAM1 gene expression level between cells in which attempts were made to knockout BABAM1 gene and the control cells (which were cells transfected with the Guide-it control plasmid DNA). Additionally, the expression of the reference gene, ACTB, was unstable in transfected and untransfected cells. This is because the Ct values of ACTB were lower in untransfected cells compared to transfected cells. However, the instability of ACTB did not have an effect on the

20 comparisons made between cells that were transfected with the target and the control plasmid DNA. Based on these findings, it is difficult to conclude if BABAM1 can be considered as a potential target for treating NLRP3 inflammasome related diseases. Further research needs to be done to come up with a meaningful conclusion.

Recommendation for future research investigating BABAM1 regulation of NLRP3 inflammasome activation are: 1) to use a more efficient transfection method, 2) generate a stable transfection of individual cell clones containing the transfected plasmid DNA, 3) insure that both transfection and CRISPR-Cas9 gene knockout have worked before analysing BABAM1 gene expression in the different treatment groups, and 4) analyse BABAM1 gene expression at the translational level, i.e., to measure the protein encoded by BABAM1 gene in the different treatment groups rather than analysing BABAM1 gene expression level at the transcriptional level, which was the major limitation of this study. Additionally, Future research could investigate if BABAM2, which is another member of the BRISC complex, can regulate the activation of NLRP3 inflammasome.

5. Ethics and impact of this study on the society There is no major concern regarding ethical consideration in this study, as no human or animal subject was used. However, a human cell line, THP-1 cells, was used during transfection. The permission for the biosafety level 2 associated with using this cell line was held by the University of Skövde, where this experiment was conducted. The THP-1 cell lines do not cause any environmental or human health problems but were handled with care in a protective environment. Furthermore, some chemicals used during the laboratory work were reported to cause both direct and indirect harm to human health and the environment. The Safety Data Sheets (SDS) for these chemicals reveal that they can form highly reactive compounds when combined with bleach, making them dangerous to both human and aquatic life. To limit the potential harm and risks of these chemicals, they were carefully handled by working in a sterile fume hood and following the safety instructions provided in the SDS. This current study impacts the society in that it further invokes the importance of discovering specific, cost-effective, and less invasive therapeutics or inhibitors that selectively inhibit NLRP3 inflammasome since BABAM1 was inconclusive to be considered as a potential target for treating NLRP3 inflammasome related diseases. Additionally, this study is the first to investigate BABAM1 regulation of NLRP3 inflammasome activation in THP-1 monocyte-like cell lines and could serve as a reference for future studies on the same topic.

6. Acknowledgements I would like to express my thanks and gratitude to my mother, Augusta Gbenga, who has strongly supported me through my academic career. I want to thank my supervisors, Mikael Ejdebäck and Matthew Herring, for their extraordinary support throughout this project. Additionally, I would like to thank my examiner, Maria Algerin, for the constructive feedback that I received when writing this report. Finally, I am grateful to Marianne Hauge, who has supported me for the past three years, and Ing-Marie Åström Swahn, who has been my source of inspiration since I moved to Sweden.

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8. Appendices

Appendix 1: Transfection Plate Layout

Figure 1. Transfection plate layout. The 24-well plate is divided into three groups (A, B, and C), and each group contains six wells. Group A was transfected using BABAM1 target plasmid DNA, group B was transfected without any plasmid DNA, and group C was transfection using the Guide-it control plasmid DNA. Group D is marked with a X, indicating that it was not used during transfection. For the different letters in the different wells, T = Transfected, P = Primed, A = activated, and N = untransfected. The combination of letters such as TPA = Transfected + Prime + Activated, and the patter is the same for all other combination of letters. Wells containing RNA indicate that total RNA was extracted from the cells in these wells. The remaining wells were used to collect media for ELISA analysis. The figure was designed using Biorender online application (https://app.biorender.com/).

Appendix 2: Sanger Sequencing Chromatogram Data The chromatogram data in Figure 1 below shows the sequencing result of only one of the two target plasmid DNA that were sent for sequencing.

Figure 1. Sanger sequencing result of the target plasmid DNA. The figure shows the chromatogram data of the target plasmid DNA that was sequenced using the Guide-it sequencing primer. Highlighted in blue is the target sequence of the forward oligo, which is identical to the target sequence of BABAM1. The different peak colours correspond to the different bases that were detected during sequencing analysis.

Figure 2. Sanger sequencing result of the Guide-it control plasmid DNA. The figure shows the chromatogram data of the control plasmid that was sequenced using the Guide-it sequencing primer. Highlighted in blue is the target sequence of the forward oligo, which is identical to the control target sequence provided in the Guide-itTM CRISPR/Cas9 Systems User Manual. The different peak colours correspond to the different oligonucleotides that were detected during sequencing analysis.

Appendix 3: qPCR Data

Figure 1. Raw qPCR data. The green rectangle represents cells that were transfected with the target plasmid DNA. For samples with the number “1” followed by (TPA) or (TP) or (T) within the green rectangle, the BABAM1 primer pair was used to amplified the BABAM1 genes, while the ACTB primer pair was used to amplify the ACTB gene for samples with the number “1” followed by -ACTB. The same concept applies to both the red and blue rectangle with the difference being that the red rectangle represents untransfected cells (indicated with the number “2” under the “sample” column), and the blue rectangle represents cells that were transfected with the Guide-it control plasmid DNA (indicated by the number “3” under the “sample” column). The combination of letters within parentheses represent different treatment conditions where T = transfection, P = prime, A = activation, and N = untransfected. The combination such as “(TPA)”

30 means that the cells were firstly transfected, then primed and activated, while “(NPA)” means that the cells were untransfected, then primed and activated. The same concept applies for all other combinations of letters in parentheses. LPS and ATP were used for priming and activation. All samples were run in triplicate, and the ΔCt were calculated from the generated Ct values for each triplicate. Ct values shown in red were excluded during ΔCt calculation.

Figure 2. Dissociation curves of all qPCR samples. The figure shows the dissociation curves of all samples amplified with either the primer pair for BABAM1 (A) or the primer pair for ACTB (B). All the dissociation curves for the different primer pair pairs are very closed to each other indicating that the same qPCR amplicon was produced.