Identification of Small Molecule Modulators of the RIG-I and MDA5 Pathways

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Citation Ganguli, Sangrag. 2019. Identification of Small Molecule Modulators of the RIG-I and MDA5 Pathways. Master's thesis, Harvard Medical School.

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Identification of small molecule modulators of the RIG-I and MDA5 pathways

Sangrag Ganguli

A Thesis Submitted to the Faculty of

The Harvard Medical School

in Partial Fulfillment of the Requirements

for the Degree of Master of Medical Sciences in Immunology

Harvard University

Boston, Massachusetts.

May, 2019

Thesis Advisor: Dr. Sun Hur Sangrag Ganguli

Identification of small molecule modulators of the RIG-I and MDA5 pathways

Abstract

The vertebrate immune system consists of cytosolic receptors that recognize a wide range of viral nucleic acids during an infection and elicit a robust antiviral response to clear the infection.

Retinoic acid-inducible gene-I (RIG-I) and Melanoma Differentiation-Associated protein 5

(MDA5) are two such pattern recognition receptors (PRRs) that stimulate a type I interferon (IFN) response against a variety of double stranded ribonucleic acid (dsRNA). While the proper functioning of these receptors is crucial for curbing viral egress, mis-regulation of MDA5 or RIG-

I can result in auto-inflammatory and autoimmune conditions such as Systemic Lupus

Erythematosus (SLE), Aicardi-Goutières syndrome (AGS), and Singleton-Merten syndrome

(SMS).

Previous research suggests that the ATPase active site of these receptors acts as an allosteric modulation site, playing a role in altering the confirmation of RIG-I and MDA5 and thereby affecting their signaling activity. In fact, our lab has examined several mutations in the

ATPase site that result in loss of the ATPase activity. While the dsRNA binding capabilities remained intact, these mutants displayed significant changes in signaling activity. Half of these mutant proteins showed a hyper-signaling phenotype, while the other half displayed a complete loss of signaling. This finding has been our rationale for targeting the ATPase site with small molecule intervention. In this study, we have used a biochemical and functional assay to screen for thousands of small molecules to identify potential hits that inhibit the ATP hydrolysis capabilities of RIG-I and/or MDA5. These hits were then further characterized using cell-signaling and cell-free assays to delve into the mechanism of action. More specifically, these assays served

ii to identify whether downstream mediators in the RIG-I and MDA5 pathways were inhibited or activated by the compounds.

Through these assays, we have triaged several candidates that selectively modulate the activity of RIG-I and/or MDA5. The identification and confirmation of such compounds greatly increases our understanding of the biochemistry of antiviral immunity and raises the potential for treatment of a broad range of immune disorders.

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Table of Contents:

Chapter 1: Background

Section 1.1: Introduction

Section 1.2: RIG-I and MDA5

Section 1.3: Immune Disorders

Section 1.4: Cancer Immunotherapy

Section 1.5: Scientific Rationale

Chapter 2: Materials & Methods

Section 2.1: Protein purification

Section 2.2: RNA preparation

Section 2.3: ATP Hydrolysis (ATPase) assay

Section 2.4: Screening

Section 2.5: Cell-based Luciferase assay

Section 2.6: Electrophoretic Mobility Shift Assay (EMSA)

Section 2.7: Cell-free IRF3 dimerization assay

Chapter 3: Results

Chapter 4: Discussion

Chapter 5: Bibliography

Figures

Figure 1.1: Schematic of MDA5 filament formation and signaling. The viral dsRNA receptor,

MDA5, contains different domains such as 2CARD, helicase, and the C-terminal domain (CTD).

The helicase and CTD form a ring-like structure along the dsRNA, which leads to other MDA5 molecules cooperatively stacking along the length of the dsRNA to form a filament.

Consequentially, 2CARD domains undergo proximity-induced oligomerization along these filaments. This then triggers MAVS to undergo monomer-to-filament transition which leads to the downstream IFN signaling. Figure adapted from Del Toro Duany, Wu, & Hur, 2015.

Figure 1.2: Impairing ATPase site has divergent effects on MDA5 signaling. Analysis of different loss-of-function mutations in the ATPase site of MHRD illustrates that impairing this site can either upregulate or downregulate the IFN signaling activity. (A) ATPase assay performed with mutant MHRD to detect levels of inorganic phosphate (Pi) released over the course of 10 minutes. (B) EMSA performed to analyze how these mutant proteins bind RNA. (C) Luciferase assay performed to detect levels of IFNβ released by 293T cells. A western blot was performed to confirm that the differences in IFNβ levels can be attributed solely to the mutations and not the protein levels. (D) Schematic showing the divergent effects on signaling activity caused by loss- of-function mutation in the ATPase site (unpublished data produced by X. Mu).

Optimizing temperature of incubation (C) Optimizing ATP concentration for RHRD (D)

Optimizing ATP concentration for MHRD (E) Testing different concentrations of DMSO for

ATPase assay using RHRD (F) Testing different concentrations of DMSO for ATPase assay using

MHRD (G) Optimizing quenching technique (H) Optimizing the time at which optical density reading would be taken post addition of BioMol Malachite Green

Figure 3.2: Screening results. (A) Example of the layout of a compound library plate. (B)

Heatmap identifying hits among the different experimental wells. While most of the screened small-molecules did not lower the optical density, some of the compounds very potently lowered the OD, implying that they potentially disrupted the ATPase activity of the two proteins. (C)

Scatterplot showing positive control, negative controls, and experimental wells. (D) 3D bar plot of each well, allowing us to identify any edge effects during the screening.

Figure 3.3: Secondary screening results. (A) Dose-dependent ATPase assay with strong hits and MHRD. (B) Dose-dependent ATPase assay with strong hits and RHRD. (C) Dose-dependent

ATPase assay with medium and weak hits and MHRD. (D) Dose-dependent ATPase assay with medium and weak hits and RHRD. (E) Studying the effect of the strong hits on Malachite Green and inorganic phosphate interaction. (F) Studying the effect of the medium and weak hits on

Malachite Green and inorganic phosphate interactions.

Figure 3.4: Aggregation analysis. (A) Effect of Triton X-100 ATPase activity of RHRD using strong hits (B) Effect of Triton X-100 on the ATPase activity using strong hits (C) Effect of Triton

X-100 on the ATPase activity of RHRD using medium and weak hits (D) Effect of Triton X-100 on the ATPase activity of MHRD using medium and weak hits. strong, medium, and weak hits.

Figure 3.5: ATPase assay with varying dosage of cherry-picked compounds. (A) ATPase assay using MHRD and different compounds. (B) ATPase assay with RHRD and different compounds

Figure 3.6: Type I Interferon readout using a HEK 293 T cell-based luciferase assay. (A)

Typical result of a luciferase assay with Empty Vector (EV), MDA5, and RIG-I plasmids. (B)

Dose-response luciferase assay using different compounds. (C) Same as (B). (D) Luciferase assay with Compound 9 using Digitonin, a non-ionic reversible cell membrane permeabilizer.

Figure 3.7: Analysis of protein-RNA interaction using EMSA. (A) shows a schematic of how

EMSA is conducted. Using a gradient polyacrylamide gel, we are able to observe protein-RNA migration for both (B) MHRD and (C) RHRD. Compounds that inhibited this interaction were observed by seeing a less dense band formed by the heavier protein-RNA complexes and a denser band created by the free RNA, which is more mobile in this gel.

Compound 13 using a finer gradient of concentrations. (D) Same as (B) but using Compounds 12,

15, 16, and 17. (E) Same as (B) but using Compounds 20, 21, 23, and 24. (F) Analysis of weak hits (Compounds 71 and 74) using the IRF3 dimerization assay. (G) IRF3 dimerization activity of

MDA5 with 42 bp dsRNA in the presence of Compound 9. (H) IRF3 dimerization activity in the presence of Compound 12 with and without MDA5. (I) IRF3 dimerization activity of RIG-I in the presence of Compounds 8, 15, 23, 12, and 13. (J) Same as (I) but using Compounds 17, 20, 24, 71,

vii and 74. (K) IRF3 dimerization activity of RIG-I in the presence of Compound 9 (unpublished data produced by S. Ahmad).

Figure 3.9: Analysis of Compound 9 using IRF3 dimerization assay. (A) IRF3 dimerization activity of MDA5 with 42 bp dsRNA in the presence and absence of Compound 9. Individual components of signaling complex (MDA5, K63-Ubn, dsRNA, and ATP) were omitted to analyze the mode of action of Compound 9. (B) Analysis of Compound 9’s effect of MDA5 2CARD signaling. (C) The effect of Compound 9 on RIG-I signaling. (D) The effect of Compound 9 on

RIG-I 2CARD signaling (unpublished data produced by S. Ahmad).

Figure 4.1: Issue with receiving consistent optical density readings upon addition of BioMol

Malachite Green. (A) We tried different stabilizers to attempt to diminish any fluctuation in optical density reading. (B) To successfully reduce fluctuations and inconsistencies in optical density readings, a centrifuging step during the screening was necessary.

Tables

Table 1: Summary of the 13 compounds inhibiting MDA5 and/or RIG-I ATPase activity. For impact on protein signaling (based on cell-free IRF3 dimerization assay), strong inhibition is defined as complete disruption at 3 µM of the compounds. Medium inhibition is defined as partial disruption at 3 µM of the compounds. Weak inhibition is defined as partial disruption at 10 µM of the compounds. Finally, no inhibition is defined as little to no disruption at 10 µM of the compound.

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Acknowledgements

Firstly, I would like to thank Dr. Sun Hur and the talented group of postdoctoral fellows, research technicians, and graduate students in her lab. Notably, Sadeem Ahmad and Xin Mu have provided their technical expertise throughout various aspects of this project.

I would also like to thank Dr. Shiv Pillai for his guidance throughout my time at Harvard

Medical School. My research experience occurred concurrently with medical school applications, and he was extremely supportive throughout both endeavors. I am truly grateful for his valuable insights and am appreciative of his efforts to help me succeed both inside and outside the classroom. Additionally, I would like to thank Dr. Scott Lovitch and Dr. Joaquim Havens for their mentorship. I have been fortunate to have nurtured my clinical interests through these physicians.

These mentors have instilled in me the importance of pursuing passion and balance in a career in medicine, and I would be humbled to someday become even half the physicians that they are today.

I must also thank a few other members of Immunology program at Harvard Medical School

- Dr. Michael Carroll, Selina Sarmiento, and Dr. Diane Lam. They have all been committed to continuously adding structure to this graduate program and have been very receptive to student feedbacks.

Finally, I would like to thank my family and my close friends for their unwavering support and interest in my research. I would not be the person I am today without them.

This work was conducted with support from Students in the Master of Medical Sciences in

Immunology program of Harvard Medical School. The content is solely the responsibility of the authors and does not necessarily represent the official views of Harvard University and its affiliated academic health care centers.

Chapter 1 - Background

1.1 Introduction:

Viral infections are one of the most common causes of infectious diseases acquired within contained environments. Viruses are obligate intracellular parasites that rely on the hosts’ biological machinery to replicate and produce more viral progeny. Remarkably, viruses can infect eukaryotes, bacteria, and archaea. Upon infecting cells, viruses typically cause cell death or cell damage by turning off the cell’s macromolecular synthesis, inhibiting antiviral defense mechanisms, diverting the cell’s energy, integrating into the host genome, or inducing mutations in the host genome (Barron, Fons, & Albrecht, 1996). Successful defense against viral infections relies on a rapid immune response. The vertebrate immune system consists of two main components: the innate and adaptive immune systems. While the innate immune system elicits a non-specific, immediate response against foreign pathogens, the adaptive immune system involves precise and long-term defense against pathogens. While most viral infections can be controlled by the different components of the innate immune system, when the infection evades innate immunity, adaptive immune system is mobilized.

The innate immune system contains pattern recognition receptors (PRRs) that recognize conserved molecular features on microorganisms called pathogen-associated molecular patterns

(PAMPs) (Kumar, Kawai, & Akira, 2011). PRRs include Toll-like receptors, RIG-I-like receptors,

NOD-like receptors, and C-type lectin receptors, all of which induce intracellular signaling cascades which then lead to the transcriptional expression of inflammatory molecules responsible for the eradication of pathogens (Takeuchi & Akira, 2010). Interestingly, a lot of these receptors can induce the formation of complex, multiprotein intracellular structures, which then result in the production of pro-inflammatory cytokines and other inflammatory molecules. In recent years, these multiprotein components have garnered more attention because of their structural and

functional complexity.

One of the most widely studied PRRs include Toll-like receptors (TLRs). These receptors can either be present on cell surface or be expressed within endocytic compartments, and they play a role in detecting PAMPs derived from bacteria, fungi, and protozoa (Kumar, Kawai, & Akira,

2011; Kawai & Akira, 2010). Some common ligands of TLRs include peptidoglycan, lipoteichoic acid, and triacyl lipopeptides (Kumar, Kawai, & Akira, 2011). NOD-like receptors are present in the cytoplasm and play a role in the detection of crystals, muramyl dipeptides, extracellular ATP, and other microbial molecules (Kumar, Kawai, & Akira, 2011). Finally, C-type lectins are secreted or transmembrane proteins that have diverse antimicrobial roles. For example, they can facilitate phagocytic uptake of pathogens, activate the complement pathway, and even induce pro- inflammatory cytokines that are normally associated with adaptive immunity (Brown, Willment,

& Whitehead, 2018).

RIG-I-like receptors (RLRs) are a special class of PRRs that play a critical role in the detection of cytoplasmic viral RNA during a viral infection. More specifically, these are DExD/H box RNA helicases that recognize certain regions of viral RNA, and upon detection, they activate downstream Type I interferon (IFN) signaling in order to eradicate the infection (Loo & Gale,

2011). There are three known RLRs: Retinoic acid-inducible gene I (RIG-I), Melanoma

Differentiation-Associated protein 5 (MDA5), and Laboratory of Genetics and Physiology 2

(LGP2). These receptors are broadly expressed in various cells types such as epithelial cells, myeloid cells, and cells of the central nervous system (Loo & Gale, 2011). Upon recognizing and binding to viral nucleic acid, these receptors induce a signaling cascade which ultimately leads to the transcription of pro-inflammatory cytokines and Type I IFN (Takeuchi & Akira, 2008).

Type I IFNs are a class of pleiotropic molecules that exert an antiviral response during early phases of an infection. Type I IFNs comprise of IFNa, IFNb, and several other molecules,

2 all of which signal through the IFNAR receptor pair (Teijaro, 2016). The binding of Type I IFNs to the IFNAR receptor induces the activation of JAK1/TYK2. This results in the phosphorylation of several Stat-proteins (signal transducers and activators of transcription) such as STAT1 and

STAT2. Activation of such proteins and interferon regulatory factor 9 (IRF9) leads to the induction of various interferon stimulatory genes (ISGs), which restrict viral replication by targeting different steps in a virus life cycle (Platanias & Fish, 1999; Schoggins & Rice, 2011; Teijaro,

2016).

1.2 RIG-I and MDA5:

Retinoic acid-inducible gene I (RIG-I) and Melanoma Differentiation-Associated protein

5 (MDA5) are two RLRs that recognize viral double-stranded RNA (dsRNA) in the cytoplasm.

MDA5 recognizes long RNAs from viruses containing dsRNA or dsRNA intermediates of single- stranded RNA (ssRNA) viruses such as coxsackievirus B3, vesicular stomatitis virus (VSV) or

Encephalomyocarditis virus (EMCV) (Kato, Takahasi, & Fujita, 2011; Triantafilou et al., 2012).

In contrast, RIG-I recognizes dsRNA with a 5’-triphosphate (5’ppp) and the blunt ends of viral panhandle structures such as hairpins. These structures are found in a variety of negative strand viruses and certain double-stranded and positive strand RNA viruses. For example, influenza viruses require these panhandle structures during RNA transcription initiation (Schlee et al., 2009).

RIG-I and MDA5 have similar structures with two N-terminal caspase activation and recruitment domain (CARDs), one DExD/H box RNA helicase, and a C-terminal domain (CTD).

The helicase and CTD of MDA5 bind to dsRNA and form filaments that extend the length of the

RNA. The MDA5 filament formation begins in the interior of the dsRNA and extends non- preferentially to the termini of the dsRNA. Current evidence suggests that filament formation then brings nearby MDA5 molecules into proximity to promote their oligomerization (A. Peisley et al.,

2012; B. Wu et al., 2013). Consequentially, the CARD domains interact with mitochondrial

3 antiviral-signaling protein (MAVS), a signaling adaptor protein on mitochondria, which then activates the downstream signaling pathway. This interaction requires the oligomerization of

MDA5 and RIG-I through polyubiquitin and RNA-dependent mechanisms (X. Mu, Ahmad, &

Hur, 2016; B. Wu & Hur, 2015). After RIG-I or MDA5 interacts with MAVS, MAVS then polymerizes and recruits signaling molecules such as TRAF2, 3, 5, and 6, which then lead to the activation of transcription factors such as IRF3/7 and NF-kB (X. Mu et al., 2016; Saha et al., 2006).

These transcription factors then translocate into the nucleus where they induce the expression of type I IFN and other inflammatory molecules (Reikine, Nguyen, & Modis, 2014).

Figure 1.1: Schematic of MDA5 filament formation and signaling. The viral dsRNA receptor, MDA5, contains different domains such as 2CARD, helicase, and the C-terminal domain (CTD). The helicase and CTD form a ring-like structure along the dsRNA, which leads to other MDA5 molecules cooperatively stacking along the length of the dsRNA to form a filament. Consequentially, 2CARD domains undergo proximity-induced oligomerization along these filaments. This then triggers MAVS to undergo monomer-to-filament transition which leads to the downstream IFN signaling. Figure adapted from Del Toro Duany, Wu, & Hur, 2015.

The oligomerization of RIG-I and MDA5 is also dependent on unanchored lysine 63 (K63) linked polyubiquitin chains (Jiang et al., 2012; Reikine et al., 2014). More specifically, the CARD domains of the two receptors bind K63 polyubiquitin chains to stimulate IRF3 dimerization (Jiang

4 et al., 2012). In fact, MDA5’s and RIG-I’s ability to activate IRF3 and induce Type I IFN was impaired when the binding of their conserved residues to ubiquitin chains was disrupted (Jiang et al., 2012). However, the process of how polyubiquitination leads to the activation of these receptors is still being investigated.

1.3 Immune Disorders

Although the proper functioning of these proteins is necessary for effective immunity against viral infection, mis-regulation of the signaling activity of MDA5 and/or RIG-I can often result in autoimmune and auto-inflammatory conditions. More specifically, genome-wide association studies have shown that MDA5 is involved in the pathogenesis of Type I Diabetes,

Systemic Lupus Erythematosus (SLE), Graves’ disease, and rheumatoid arthritis. Furthermore, the established role of interferon (IFN) in these disorders and the importance of MDA5 in type I IFN signaling activity implies a possible hyper-activation of these receptors that contributes to the pathogenesis of these autoimmune diseases (Cen et al., 2013; Del Toro Duany et al., 2015;

Robinson et al., 2011; Smyth et al., 2006).

The causal role of MDA5 in autoimmune and autoinflammatory diseases has been demonstrated in studies involving mouse models of monogenic inflammatory disease Aicardi-

Goutières Syndrome (AGS). AGS is an inflammatory condition characterized by mutations in any of the genes encoding the DNA 3’ repair exonuclease 1 (TREX1), the three non-allelic subunits of the ribonuclease H2 (RNase H2) endonuclease complex, adenosine deaminase acting on RNA

(ADAR), the deoxynucleoside triphosphate triphosphorylase SAMHD1, or the double-stranded

RNA (dsRNA) cytosolic sensor IFN-induced helicase C domain-containing protein 1 (IFIH1 or

MDA5) (Crow & Manel, 2015). Many of the clinical phenotypes of AGS overlap with those of

SLE. Briefly, AGS particularly affects the brain as seen by the increased number of white blood cells in the cerebrospinal fluid (Crow & Manel, 2015). Additionally, other features consistent with

5 this condition include skin lesions commonly known as chilblains and raised intraocular pressure

(Crow & Manel, 2015). Clinical case studies of this condition have also elucidated other signs and symptoms such as hearing loss, basal ganglia calcification, seizures, dystonia, hypotonia, and microcephaly (Ramantani et al., 2010). Whole-exome sequencing of patients with this condition has revealed gain of function mutant IFIH1 (Rice et al., 2014). More specifically, these mutant

MDA5 proteins bind RNA more avidly and tightly, resulting in increased interferon signaling

(Rice et al., 2014). The findings of Rice et al., 2014 implies that subtle changes in binding of protein and RNA can have drastic biological effects. Furthermore, the novel findings regarding how these mutant MDA5 function has therapeutic implications for autoimmune and autoinflammatory conditions.

These antiviral helicase pathways can also be dysregulated in SLE patients (Oliveira,

Sinicato, Postal, Appenzeller, & Niewold, 2014). Multiple genetic factors are thought to be involved in this condition that affects around a million people in the United States (Genetics Home

Reference, NIH, 2019). These antiviral signaling pathways seem to be affected as polymorphisms in the IFIH1 gene and mitochondrial antiviral signaling protein (MAVS) have been implicated with the pathogenesis of this condition (Oliveira et al., 2014). Serum and DNA samples from SLE patients have shown that gain-of-function polymorphisms in IFIH1 is associated with presence of autoantibodies, increased IFN-induced gene expression in circulating blood cells, and increased sensitivity to Type I IFN (Oliveira et al., 2014; Robinson et al., 2011).

1.4 Cancer Immunotherapy

Emerging evidence indicates that controlled activation of RIG-I and MDA5 can provide beneficial results in various cancer therapies (Elion & Cook, 2018; Li, Qu, Chen, Wu, & Shi, 2017;

Y. Wu, Wu, Wu, Wang, & Liu, 2017). More specifically, activation of RIG-I and MDA5 using

RNA-mimetics can induce expression of pro-apoptotic proteins to activate the mitochondrial

6 apoptosis pathway in melanoma cells (Besch et al., 2009). Interestingly, cancer therapies such as ionizing radiation and chemotherapy can activate RIG-I though the means of small endogenous non-coding dsRNA (Ranoa et al., 2016).

Furthermore, stimulating the RIG-I/MDA5 pathway with a small molecule agonist would lead to greater interferon release, and this has implications in the inhibition of tumor cells and would likely promote immune response towards the tumor (Swann et al., 2007). In fact, a recent study aimed to assess the effect of stimulating MDA5 using the synthetic ligand polyinosinic: polycytidylic acid (poly(I:C)) on pancreatic cancer cells has demonstrated some interesting results.

Activating MDA5 can lead to a Type I IFN-mediated reduction in tumor size and reduction of T cell suppressive capacity of myeloid-derived suppressor cells (MDSC) (Metzger et al., 2018).

These studies indicate that further analysis of the MDA5 and RIG-I pathway could yield promising results in regard to cancer-specific cytotoxicity and cancer immunotherapy.

1.5 Scientific Rationale

Since MDA5 and RIG-I sense viral dsRNA, a proposed inhibitor may be a molecule that disrupts RNA binding, whereas, a proposed activator may be a viral dsRNA mimic. This, however, poses a significant challenge, because impairing RNA binding would likely require multiple mutation, since RNA binding by the two receptors are mediated by large protein surfaces (B. Wu et al., 2013). This implies that the signaling capabilities of these proteins require not only protein-

RNA interactions, but also protein-protein interactions, which are difficult targets for small molecule intervention.

For this project, we propose that the ATPase site of RIG-I/MDA5 can act as an allosteric site that can modulate the overall protein conformation. More specifically, mutations that impair the ATPase site of these proteins can confer conformational changes that can either activate or inactivate the signaling activity of the protein. Our lab has analyzed over 20 such mutations in the

ATPase site, all of which impair the ATP hydrolyzing abilities of the proteins (Figure 1.2A).

Furthermore, these mutant proteins showed intact RNA binding and filament formation, implying that the conformational changes due to the mutations were subtle (Figure 1.2B). Despite this subtle conformational change, all of the mutants showed significant changes in signaling activity. While some of these mutants displayed a loss of Type I IFN signaling, other mutants showed a hyperactive phenotype, releasing high levels of Type I IFN (Figure 1.2C). Figure 1.2D refers to a schematic that summarizes the divergent effects on MDA5 and RIG-I signaling that is caused by loss-of-function mutations in the ATPase site of these enzymes. Although the cause of this divergent effect is unknown, these results suggest a relationship between the ATPase site and the overall conformation and signaling activity of the proteins. We hypothesize that a small molecule that targets the ATPase site of these proteins could potentially act as an inhibitor or an activator of

RIG-I/MDA5 signaling. Initially, ATPase assay can be used to screen for compounds that inhibit the ATP hydrolyzing capabilities of these proteins. Subsequently, cell-free and cell-based assays can be performed to discern inhibitors of MDA5/RIG-I from activators. Helicases have previously been studied as targets of drug-induced inhibition, and the possibility of successfully inhibiting these enzymes through various mechanisms supports the possibility of identifying inhibitors of

MDA5/RIG-I, members of the helicase family (Shadrick et al., 2012).

We plan on screening each small molecule compound independently against MDA5 and

RIG-I. Identifying mono-specific and bi-specific inhibitors/activators would increase the chance of successful drug discovery. Given that MDA5 and RIG-I share the same downstream pathway, it is reasonable to believe that inhibiting or activating both receptors at once would be potentially more effective than modulating either one of the receptors. However, the implications of this in

9 the context of disease is not yet well characterized. Further analysis regard potential cellular and host toxicity of mono-specific and bi-specific modulators is required.

Chapter 2: Materials and Method

2.1 Protein Purification

Protein purification of the G495R variant of MHRD (MHRD refers to purified helicase and

C-terminal domains of MDA5) and WT RHRD (RHRD refers to purified helicase and C-terminal domains of RIG-I) was performed as described in a previously published paper (Alys Peisley et al., 2011). Constructs of G495R MHRD and WT RHRD were recombinantly expressed from pET50b plasmid (Novagen) in BL21 (Novagen) strain of Escherichia coli. Cells were lysed using a high-pressure homogenizer Emulsiflex. After lysis, proteins were purified through a sequential combination of Ni-nitrilotriacetate affinity chromatography (Qiagen), heparin affinity chromatography, and size exclusion chromatography. All MDA5 constructs contained an N- terminal NusA fusion construct, which was cleaved by HRV3C proteolysis.

2.2 RNA preparation

112 bp RNA (4AB) and 512 bp RNA (7AB) were prepared by in vitro transcription using the bacteriophage T7 RNA polymerase and native gel electrophoresis. PCR reactions were used to prepare templates for in vitro transcription, which was performed in a reaction containing 250 mM HEPES at pH 7.5, 30 mM MgCl2, 2 mM Spermidine, 40 mM DTT, 0.1 mg/ml BSA, 5 mM

NTP, 0.5 mg/ml T7 polymerase, and 0.25 mg/ml DNA template. This reaction was incubated for

4 hours at 37oC, and the DNA template was digested using DNase I. The transcript was then purified using phenol: chloroform extraction, ethanol precipitation and using the QIAquick PCR purification kit (Qiagen). These purified transcripts were then analyzed using TBE 6% polyacrylamide gel. The RNAs were stained with SybrGold (Thermofisher), and the fluorescent

10 gel images were generated using FLA9000 gel scanner (GE). (Xin Mu, Greenwald, Ahmad, &

Hur, 2018).

2.3 ATP Hydrolysis (ATPase) assay

ATPase assay was performed as described in Mu et al., 2018. Briefly, the hydrolysis activity of the ATPase domains of the proteins (MHRD and RHRD) was measured using Green

Reagent (Enzo Life Sciences). MHRD, RHRD, ATP, and RNAs were all prepared in RNA binding buffer (20 mM HEPES at pH 7.5, 100 mM NaCl, 1.5 mM MgCl2, 2 mM DTT). The reaction was initiated when a mixture of RNA and ATP was added to pre-incubated protein in 37OC. 10 µl aliquots were withdrawn before and 15 minutes after RNA and ATP addition and were quenched using 100 mM EDTA on ice. The Green Reagent (90 µl) was added to the quenched reaction at a ratio of 9:1. Finally, OD650 was measured using a Synergy2 plate reader (BioTek).

2.4 Screening

23,021 small molecules were screened from different libraries available at the Institute of

Chemistry and Cell Biology (ICCB) – Longwood Screening Facility. 640 of the molecules were from the Biomol 4 – FDA approved library, which contains well-characterized bioactives that have undergone bioavailability and safety assessments. 248 of the molecules were from the eMolecules library, which also contained bioactives and FDA-approved clinical compounds from various sources including DrugBank, ChEMBL, and ClinicalTrials. 1,902 of the small molecules were part of the Selleck Bioactives Compound library, which contained most of Selleck’s inhibitors, some

FDA-approved compounds, chemotherapeutic drugs, active pharmaceutical agents, and some natural products. 250 of the small molecules were from the ChemBridge Focused Kinase-Based

Core, which is a library comprised of compounds that are knowns to interact with the ATP ligand site of kinases. 187 of the compounds were from the Gray Kinase Inhibitor Focused library, which contained known and suspected ATP-site kinase inhibitors that target both active and inactive

11 kinase conformations. The remainder of the small molecules were part of a commercial library known as ChemDiv6, which contains compounds that provide drug-like qualities and chemical diversity. More information regarding the compound libraries are available at the ICCB-

Longwood website (https://iccb.med.harvard.edu/compound-libraries).

The ATPase assay (described above) was used to screen for small molecule inhibitors of the ATPase site of either MDA5 and/or RIG-I. Proteins, RNAs, and ATP were prepared in RNA

Binding Buffer (20 mM HEPES at pH 7.5, 100 mM NaCl, 1.5 mM MgCl2, 2 mM DTT ). At the screening facility, Thermo Multidrop Combi was used to dispense either RHRD or MHRD into separate low-volume 384-well plates (Corning). Between each dispensing step of different reagents, the 8-channel cassette of the Combi was washed with water and ethanol. After centrifuging these assay plates, the plates were installed on a pin transfer robot (Seiko or Epson) to transfer 100 nL from compound storage plates into assay plates using stainless steel pin arrays.

To prevent compound carry over, after every library transfer, each pin array was washed with PBS,

100% methanol flow-through wash, 20% methanol sonication, and a 10-second high-pressure air dry. After the compound transfer, the assay plates were left at room temperature for 30-40 minutes to allow the compounds to dissolve properly. The Combi was then used to add a premade mixture of ATP and RNA into the assay to initiate the reaction. The assay contents were then mixed using a microplate shaker (Scientific Industries) and a centrifuge (1000 rpm for 1 minute) and stored in

37OC for 1 hour. In order to quench the reaction, ethylenediaminetetraacetic acid (EDTA) was dispensed using the Combi. After shaking and centrifuging the plates, the Agilent VPrep was used to transfer 4 µL of the assay contents on to new low-volume 384-well plates (Corning). To these new assay plates, Biomol Green (Enzo) was added while maintaining a 9:1 BioMol Green to reaction ratio. After leaving these plates at room temperature for 12 minutes, absorbance (OD620) was measured using the EnVision plate reader (Perkin Elmer) at the screening facility.

After the initial screen, compounds that classified as a hit were cherry-picked and a secondary screen was performed using the Hewlett Packard D300e digital, non-contact dispenser.

This machine was used to perform titration using the cherry-picked compounds in order to generate

EC-50 values.

2.5 Cell-based Luciferase Assay

293T cells were seeded in 48-well plates in Dulbecco’s modified Eagle medium (DMEM)

(Cellgro) with 10% Fetal Bovine Serum (FBS). At ~80-90% confluency (~20 hours post seeding), cells were transfected with pFLAG-CMV4 plasmids encoding MDA5 (10 ng), RIG-I (10 ng), the interferon-promoter driven firefly luciferase reporter plasmid (100 ng), and a constitutively expressed Renilla luciferase reporter plasmid (10 ng). To increase transfection efficiency,

Lipofectamine2000 was used. Six hours after the initial transfection, the medium was changed to

DMEM + 1% FBS. To test the effect of the compounds on the 293T cells, the compounds were added to this medium. Cells were lysed 24 hours after this stimulation, and IFN-β promoter activity was measured using the Dual Luciferase Reporter assay (Promega) and a Synergy2 plate reader

(BioTek). Firefly activity was then normalized against Renilla activity (B. Wu et al., 2013).

2.6 Electrophoretic Mobility Shift Assay (EMSA)

EMSA was conducted as described in an earlier literature (Xu et al., 2015). RHRD and

MHRD were prepared in RNA Binding Buffer (20 mM HEPES at pH 7.5, 100 mM NaCl, 1.5 mM

MgCl2, 2 mM DTT). EMSA was performed by incubating the proteins, RNA, and the small- molecule compounds in 37OC for 15 minutes. These complexes were resolved by 4 to 12% Bis-

Tris NativePAGE gels. Gels were then stained with SYBR gold for 5 minutes (Life Technologies).

Fluorescent gel images were then acquired and analyzed using the Gel Doc XR imaging system.

2.6 Cell-free IRF3 Dimerization Assay

Cell-free IRF3 dimerization assay was performed as previously described in Ahmad, et al.

2018. HEK293T cells were homogenized in hypotonic buffer (10 mM Tris pH 7.5, 10 mM KCl,

0.5 mM EGTA, 1.5 mM MgCl2, 1 mM sodium orthovanadate, 1X mammalian ProteaseArrest

GBiosciences) and centrifuged for 5 minutes at 1000 g to sequester the nuclei in the pellet. The cytosolic and mitochondrial fractions, present in the supernatant, were used for in vitro IRF3 dimerization assay. The IRF3 activation stimulation mix was made by mixing 10-12.5 ng/ml

MDA5, 3.1 ng/ml K63-Ubn in the presence of absence of varying amounts of RNA. This mixture was then pre-incubated for 30 minutes at 4OC in a buffer containing 20 mM HEPES at pH 7.4, 4

35 mM MgCl2, and 2 mM ATP. Radioactively labelled S-IRF3 was prepared via in vitro translation using T7 coupled Reticulocyte Lysate System (Promega) as instructed in the manufacturer’s protocol. The IRF3 activation reaction was then performed by adding 1.5 mL of the stimulation mixture to 15 mL reaction mixture containing 10 mg/ml of the supernatant containing cytosolic and mitochondrial fractions, 0.5 mL 35S-IRF3 (in a buffer containing 20 mM HEPES 7.4, 4 mM

O MgCl2, and 2 mM ATP) and incubated for an hour at 30 C. The samples were then centrifuged for 5 minutes at 18,000 g and the supernatant for analyzed in 9% native Tris-glycine gels. Gel images were subsequently analyzed by autoradiography and phosphorimaging (FLA900, Fuji) for visualization of dimerized and monomeric IRF3 (Ahmad et al., 2018).

Chapter 3: Results

3.1 ATPase assay optimization

In order to utilize the ATPase assay to screen for thousands of small molecules in a high- throughput fashion, the biochemical assay was optimized to be compatible with the configuration of the various robotic tools at the Institute of Chemistry and Cell Biology (ICCB) – Longwood

Screening Facility. In brief, the ATPase assay requires a regent called Malachite Green to detect

14 the level of inorganic phosphate released during ATP hydrolysis. This reagent forms a dark green color once the inorganic phosphate is liberated by the ATPase, and this results in a colorimetric product proportional to the enzyme activity (Baykov, Evtushenko, & Avaeva, 1988).

To perform the assay, we used purified protein, in vitro transcribed RNA, and ATP. We purified the helicase and C-terminal domains of the two receptors. For the remainder of this thesis, these purified MDA5 and RIG-I domains will be referred to as MHRD and RHRD, respectively.

MHRD binds 512 bp RNA, I will refer to as 7AB, and RHRD binds 112 bp, which I will refer to as 4AB.

In a typical assay, we observed that, for both MHRD and RHRD, the level of inorganic phosphate released rose sharply during the first few minutes of the assay, after which it began to plateau (Figure 3.1A). Additionally, we noticed that MHRD usually released a greater amount of inorganic phosphate when compared to that released by RHRD (Figure 3.1A). As expected, we observed that both proteins released greater levels of inorganic phosphate when the assay is conducted at 37oC as opposed to in room temperature (Figure 3.1B). In order to use the reagents of the assay more economically, we performed titrations of the different reagents. For example, we decided that 125 nM of MHRD and 85 nM of RHRD yields a robust level of inorganic phosphate release for us to use this assay for screening. The assay usually required 2 mM of ATP per reaction; however, after testing various concentration of ATP, we observed that 1.5 mM and 2 mM of ATP yielded essentially identical levels of inorganic phosphate released for both RHRD

(Figure 3.1C) and MHRD (Figure 3.1D). These were some ways in which we were able to conserve the amounts of reagents throughout the screening process.

At the screening facility, the compounds of each library plates were dissolved in 100% dimethyl sulfoxide (DMSO). Since we used 100 nL of these compounds for our assay containing

10 µl of the reaction, the final concentration of DMSO per reaction was roughly 1%. Prior to

15 screening, we were interested in whether DMSO negatively affects the assay. We determined that the ATPase assay is able to tolerate small volumes of DMSO, and this was true for both RHRD

(Figure 3.1E) and MHRD (Figure 3.1F). However, higher concentrations of DMSO (such as

20%) negatively impacted the level of inorganic phosphate released (Figure 3.1E, F). These findings gave us the confidence that 1% DMSO at the screening facility would not interfere with the overall efficacy of the ATPase assay.

We were also interested in quenching the assay more effectively. Typically, in the screening facility, 2 µl of EDTA was added to the 10 µl reaction. Following this step, Malachite green was added at a 9:1 ratio to the reaction. We tested two different conditions. The first condition was the traditional, aforementioned method of quenching. In the second condition, we made a pre-mixed mixture of EDTA and Malachite Green and added this to quench the reaction.

We found that the levels of inorganic phosphate detected using both methods were similar; however, in using the traditional method, we observed higher levels of inorganic phosphate released for both RHRD and MHRD (Figure 3.1G).

We next sought to determine the best time to take the optical density reading. Upon adding malachite green to the reaction, the optical density (OD) rose slowly up to a certain amount of time after which the reading began to fluctuate. We decided to take the reading right before the point of fluctuation at 12 minutes after the introduction of Malachite Green to our reaction (Figure 3.1H).

We also debated whether to take an end-point read or to take multiple OD readings. We observed that take three time point readings (at 8, 10, and 12 minutes) essentially showed similar readings, making the end-point read a more efficient method of screening.

Collectively, these optimization data allowed us to determine the most suitable conditions for screening. It was crucial to be able to get a robust release of inorganic phosphate in order to successfully discern the hits from the compounds that do not inhibit the ATPase site of the proteins.

Furthermore, this phase of the study made this assay high throughput and allowed us to efficiently screen for thousands of small molecule compounds.

Figure 3.1: Optimization of the ATPase assay. Various aspects of the ATPase assay were optimized prior to implementing this functional assay to screen small molecules. (A) A typical result from an ATPase assay with inorganic phosphate (Pi) as the readout measured over time (B) Optimizing temperature of incubation (C) Optimizing ATP concentration for RHRD (D) Optimizing ATP concentration for MHRD (E) Testing different concentrations of DMSO for ATPase assay using RHRD (F) Testing different concentrations of DMSO for ATPase assay using MHRD (G) Optimizing quenching technique (H) Optimizing the time at which optical density reading would be taken post addition of BioMol Malachite Green

3.2 Screening strategy and analysis

Compound screening was conducted at the ICCB- Longwood screening facility. Every compound was tested in replicates for each MHRD and RHRD. Compound library plates were stored in -20oC in desiccated storage containers. Prior to the robotic pin transfer of 100 nL compounds into our assay, the library plates were left in room temperature for about half an hour and centrifuged to ensure proper compound transfer. In a typical library plate, most of the wells contain different compounds, and the last two columns were left empty (Figure 3.2A). We used

Column 23 for our negative control, which consists of all components of the reaction with the exception of any small-molecule compounds. We used Column 24 as our positive control, which is defined as the control that will mimic a hit. For this control, we add RNA Binding Buffer instead of the proteins.

After the initial screen, our data was visualized in various ways. We obtained a heat map that allowed us to correlate different colors with the optical density (Figure 3.2B). More specifically, we typically noticed that only a few compounds mimicked our positive control

(shown in red); whereas, most screened compounds did not lower the optical density significantly.

This implies that most screened compounds do not successfully inhibit the ATPase site of either

MHRD or RHRD. Interestingly, some compounds displayed intermediate effects on lowering the optical density; however, these compounds were not selected for further analysis because of their weak inhibition of ATPase activity.

Finally, additional means of visualizing the data involved generating scatterplots through which we confirmed that majority of the experimental wells lined up along with the negative controls, and the hits displayed similar OD as the positive controls (Figure 3.2C). Three- dimensional heat maps were also generated to further confirm the absence of edge effects during the screening (Figure 3.2D). Using the optical density information for all the compounds, we calculated the z-score for each screened compound. This statistical computation allowed us to determine how many standard deviations away each compound lied relative to the average optical density of the plate. The benefit of using this method of analysis was that by comparing each compound’s effect on the optical density to the plate average rather than average of the negative control, we essentially included another internal control. Additionally, in order to be classified as a hit, a compound would have to significantly lower the optical density reading on both biological replicate plates.

Another level of quality assessment that we used for this screening was a Z-factor measurement. This number reflects the dynamic range and the variation in the data associated with the optical density measurements (Zhang, Chung, & Oldenburg, 1999). The formula for this scoring method is described below:

3𝑆𝐷 𝑜𝑓 𝑠𝑎𝑚𝑝𝑙𝑒 + 3𝑆𝐷 𝑜𝑓𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑍 = 1 − |𝑚𝑒𝑎𝑛 𝑜𝑓 𝑠𝑎𝑚𝑝𝑙𝑒 − 𝑚𝑒𝑎𝑛 𝑜𝑓 𝑐𝑜𝑛𝑡𝑟𝑜𝑙|

A Z-factor value of 1 implied no variation of controls, which would make the assay an ideal assay. Contrarily, a Z-factor value of 0 implied no distinguishable separation between the controls and the experimental values, making the screening essentially impossible. The screening facility required most biochemical assays to have a Z-factor score of at least 0.5 or above to proceed with the screening.

Figure 3.2: Screening results. (A) Example of the layout of a compound library plate. (B) Heatmap identifying hits among the different experimental wells. While most of the screened small-molecules did not lower the optical density, some of the compounds very potently lowered the OD, implying that they potentially disrupted the ATPase activity of the two proteins. (C) Scatterplot showing positive control, negative controls, and experimental wells. (D) 3D bar plot of each well, allowing us to identify any edge effects during the screening.

3.3 Technical reproducibility

ICCB allowed screener to cherry-pick up to 0.3% of screened compounds; therefore, we chose to select all our strong hits (numbered 1-25) and most of our medium and weak hits

(numbered 26-74). Hits were stratified based on the level of lowering of optical density. If a certain compound lowered the optical density greater than 5 standard deviations below the plate average, the compound was classified as a strong hit. Likewise, if a compound lowered the optical density anywhere between 4-5 standard deviations below the plate average, the compound was classified as a medium hit. Lastly, if a compound lowered the optical density anywhere between 3-4 standard deviations below the plate average, then the compound was classified as a weak hit. A minimum

20 of 3 standard deviations below the plate average was required for a compound to be classified as a hit.

For the secondary screen, we tested all the strong hits and found that most of the inhibition was reproducible for both MHRD (Figure 3.3A) and RHRD (Figure 3.3B), and a group of selected compounds are shown in Figure 3.3. While hoping to recapitulate the inhibitory effects observed in the primary screen, we noticed that certain compounds, such as Compound 13, continued to demonstrate strongly inhibitory effects on both proteins. However, other compounds, such as

Compound 14, were not able to inhibit either of the proteins during the secondary screen.

Additionally, we tested all the medium and weak hits and observed that the reproducibility was much lower for both MHRD (Figure 3.3C) and RHRD (Figure 3.3D). We suspect that a lot of the medium and weak hits induce conformational changes that may not be stable enough to produce reproducible inhibition of ATPase activity. As a result, we triaged down the number of hits based on compounds that show inhibition during this dose-response secondary screen.

During the secondary screen, we also sought to determine whether ours hits were specific to the MDA5 and RIG-I pathway and not lowering the optical density by disrupting the interaction between inorganic phosphate and Malachite Green. To test this, we conducted the screen with a pre-determined concentration (150 µM) of inorganic phosphate and found that none of our strong

(Figure 3.3E) or weak (Figure 3.3F) hits affected the interaction between inorganic phosphate and Malachite Green.

Figure 3.3: Secondary screening results. (A) Dose-dependent ATPase assay with strong hits and MHRD. (B) Dose-dependent ATPase assay with strong hits and RHRD. (C) Dose-dependent ATPase assay with medium and weak hits and MHRD. (D) Dose-dependent ATPase assay with medium and weak hits and RHRD. (E) Studying the effect of the strong hits on Malachite Green and inorganic phosphate interaction. (F) Studying the effect of the medium and weak hits on Malachite Green and inorganic phosphate interactions.

3.4 Aggregation analysis

Organic molecules can form colloidal aggregates in aqueous solution, and this process can artifactually and non-specifically inhibit protein activity (Irwin et al., 2015). Such aggregates could result in numerous false positives in our assay. To mitigate this, we used a non-ionic detergent called Triton X-100, which can disrupt aggregates (Feng & Shoichet, 2006; Irwin et al., 2015).

The hypothesis was that if a certain compound lost its inhibitory effect in the presence of this detergent, then the inhibition was likely due to aggregation rather than true biological inhibition of RHRD or MHRD. We discovered that nearly half of our strong hits for both RHRD (Figure

3.4A) and for MHRD (Figure 3.4B) lost their inhibitory effect on the proteins in the presence of this detergent, indicating that the compounds likely formed aggregates with our proteins. We also found the inhibition of most of the medium and weak hits were not persistent in the presence of

23 the detergent. This was true for both RHRD (Figure 3.4C) and MHRD (Figure 3.4D). Prior to initiating this aggregation analysis, we utilized an aggregation advisor application designed by the

Shoichet Laboratory at UCSF (http://advisor.bkslab.org/). Here, we inputted the structure of our compounds, and the application yielded a LogP value and information regarding whether the compound was previously reported to be an aggregator. LogP was defined as the partition coefficient of a solute between octanol and water, and generally, known aggregators have a LogP of greater than 3 (Irwin et al., 2015). In the context of our screened compound, we found the LogP values to be arbitrary, since they were not accurate predictors of compounds that aggregated.

Numerous compounds with high LogP values displayed inhibitory effects even in the present of

Triton X-100, while many compounds with low LogP values turned out to be aggregators. For example, certain compounds shown in Figure 3.4A and Figure 3.4B (red box) were predicted by the Aggregate Advisor application to not be an aggregator. However, we observed that for compounds such as Compound 1, 8, 10, 19, 22, and 23 this prediction was inaccurate, as introduction of the detergent eradicated any inhibitory affect previously observed during the primary screen. This further confirms the notion that this application needs further modification in order to provide more accurate predictions of which compounds would likely aggregate.

Figure 3.4: Aggregation analysis. (A) Effect of Triton X-100 ATPase activity of RHRD using strong hits (B) Effect of Triton X-100 on the ATPase activity using strong hits (C) Effect of Triton X-100 on the ATPase activity of RHRD using medium and weak hits (D) Effect of Triton X-100 on the ATPase activity of MHRD using medium and weak hits. strong, medium, and weak hits.

3.5 Determination of half-maximal effective concentration (EC50)

After identifying the compounds that were not false positive hits, we ordered the compounds and performed an ATPase assay with varying dosage. This allowed us to stratify our hits based on different levels of potency. Using the results from this experiment, we were able to estimate EC50 values for the compounds i.e. the concentration at which a specific compound inhibits the protein activity to half the maximal level of activity (marked by a dashed, red line in

Figure 3.6). In general, we noticed that the compounds are more potent inhibitors of the ATPase site of MHRD (Figure 3.6A) compared to RHRD (Figure 3.6B). A more comprehensive list of

EC50 values is available in Table 1.

Figure 3.5: ATPase assay with varying dosage of cherry-picked compounds. (A) ATPase assay using MHRD and different compounds. (B) ATPase assay with RHRD and different compounds

Compound EC50 EC50 Impact on Impact on Impact on Impact on MDA5 RIG-I MDA5:512bp RIG- MDA5 RIG-I (approx.) (approx.) dsRNA I:112bp signaling signaling binding dsRNA (IRF3 (IRF3 (approx.) binding dimerization) dimerization) (approx.) 8 3 µM 20 µM < 10 µM 20 µM Strong Strong inhibition inhibition 9 35 µM 90-100 40 µM No Strong No inhibition µM inhibition activation 12 12 µM 20 µM 40 µM 20 µM Medium Weak inhibition inhibition 13 10 µM 15 µM 10 µM 20 µM Strong Strong inhibition inhibition 15 15 µM 40 µM 40 µM 40 µM No inhibition No inhibition 16 40 µM 50 µM 40 µM No Medium Weak inhibition inhibition inhibition 17 15 µM 15 µM 20µM 20 µM Strong Strong inhibition inhibition 20 35 µM 80 µM 40 µM > 40 µM Medium No inhibition inhibition 21 30 µM 30 µM No inhibition No Strong Medium inhibition inhibition inhibition 23 15 µM 15 µM 20 µM 20 µM No inhibition No inhibition 24 15 µM 30 µM 20 µM Inconclusive Weak No inhibition data inhibition 71 15 µM 25 µM 40 µM Inconclusive Medium Weak data inhibition inhibition 74 35 µM 40 µM 40 µM Inconclusive Weak Weak data inhibition inhibition

3.6 Cell based Luciferase assay

To determine whether a given small molecule is an agonist or antagonist, we examined its effect on the transcriptional responses by determining the levels of interferon-beta (IFNb) released by Human Embryonic Kidney (HEK) 293 T cells. The IFN transcriptional activity was measured using an IFN-promoter driven luciferase assay. The assay relies on two reporter genes – Renilla and firefly luciferase (McNabb, Reed, & Marciniak, 2005). The gene encoding Renilla luciferase is fused to a constitutive promoter, providing internal control; whereas, the gene encoding firefly luciferase is fussed to the test promoter, which is the IFNb promoter in our experiment (McNabb,

Reed, & Marciniak, 2005). The luciferase assay is a highly robust assay that is performed by sequentially measuring the firefly and Renilla luciferase activities of the same sample, with the results being expressed as a ratio of the two activities (F/R) (McNabb, Reed, & Marciniak, 2005).

We first performed the assay using MDA5 and RIG-I plasmids and their respective stimulants. Adding the stimulant RNA yielded robust IFNb release, the levels of which were much higher than the one observed when using empty vector (EV) transfection and stimulant-less transfection (Figure 3.6A). Next, we performed the luciferase assay using the compounds. We focused on using a gain-of-function mutant of MDA5 (495MDA5) in order to better identify compounds that can inhibit IFN release. We observed that some of the compounds were not successful in clearly inhibiting or upregulating IFN released (Compounds 8, 9, 12, and 71) (Figure

3.6B, C). For compounds such as Compounds 8 and 12, these findings were surprising, as the IRF3 dimerization assay indicated them to be inhibitors of MDA5 signaling activity.

We also tested the effect of Compound 9 in the presence of Digitonin. Digitonin is a steroidal saponin that can induce reversible permeabilization of the plasma membrane of numerous types of cells (Miyamoto et al., 2008). We performed the luciferase assay in the presence of a buffer containing Digitonin. While the 293T cells appeared attached and healthy post-transfection,

28 our measurement of IFN released were not conclusive (Figure 3.6D). For both WT MDA5 and

495MDA5, we noticed a slight decrease in the levels of IFN released as the concentration of

Compound 9 was increased; however, this was in contrast to our findings from the IRF3 dimerization assay, which indicated that Compound 9 is likely an activator the MDA5 signaling pathway.

Cellular toxicity (because of DMSO) appeared to be a common issue while conducting this assay. Furthermore, certain compounds showed no activity in either upregulating or downregulating IFN transcriptional activity, making the luciferase assay an unreliable assay to identify activators and inhibitors of the MDA5 and RIG-I signaling pathway. We therefore elected to conduct more biochemical assay to observe protein-RNA interaction and study downstream mediators in the signaling pathway.

Figure 3.6: Type I Interferon readout using a HEK 293 T cell-based luciferase assay. (A) Typical result of a luciferase assay with Empty Vector (EV), MDA5, and RIG-I plasmids. (B) Dose-response luciferase assay using different compounds. (C) Same as (B). (D) Luciferase assay with Compound 9 using Digitonin, a non-ionic reversible cell membrane permeabilizer.

3.7 Analysis of dsRNA binding using Electrophoretic Mobility Shift Assay (EMSA)

Electrophoretic Mobility Shift Assay (EMSA) is a rapid and sensitive method used to detect protein complexes with nucleic acids (Hellman & Fried, 2007). Briefly, the results were interpreted based on the observation that the electrophoretic mobility of protein-RNA complexes was less than that of RNA alone (Hellman & Fried, 2007).

We tested the triaged compounds in the presence of MHRD and 7AB (Figure 3.7B) and

RHRD and 4AB (Figure 3.7C) to see if any of the hits interrupted the interaction between protein and nucleic acids. For our control, we used the respective proteins and nucleic acid in the absence of any compound interference (labelled by Ø). Furthermore, we tested out difference micromolar concentrations to determine the potency of inhibition, if any inhibition is observed. We observed that some compounds such as Compounds 8 and 17 (17 not shown) strongly inhibited the interaction between MHRD and 7AB; whereas other compounds such as Compound 15 seemed to have no inhibitory effect on this interaction (Figure 3.7B). When tested using RHRD, we observed that Compounds 13 and 17 (not shown) strongly inhibited protein-RNA interaction; whereas

Compounds 20 and 21 seemed to have no such inhibitory effect (not shown). This assay was conducted with all of the 13 compounds, and some representative images are provided in Figure

3.7. Certain compounds such as Compound 24 and 71 showed interesting phenomenon where both inhibitory and activating effects were observed in varying concentrations. This could have been either due to contamination or because of a dual effect of these compounds on the proteins.

Figure 3.7: Analysis of protein-RNA interaction using EMSA. (A) shows a schematic of how EMSA is conducted. Using a gradient polyacrylamide gel, we are able to observe protein-RNA migration for both (B) MHRD and (C) RHRD. Compounds that inhibited this interaction were observed by seeing a less dense band formed by the heavier protein-RNA complexes and a denser band created by the free RNA, which is more mobile in this gel.

3.8 Identifying inhibitors and activators using the cell-free IRF3 dimerization assay

We used a cell-free IRF3 dimerization assay to study the effect of the 13 compounds on the MDA5/RIG-I signaling activity (Figure 3.8A). Since most of the hits showed stronger ATPase inhibitory activity against MDA5, we opted to focus on this protein for majority of this assay.

Consistent with previous studies, under MDA5-stimulatory conditions, which include MDA5 complexed with 512 bp dsRNA (7AB), K63-Ubn, and ATP, we noticed robust IRF3 dimerization, indicated strong MDA5 signaling (Figure 3.8A) (Ahmad et al., 2018). The presence of Compound

8 and Compound 13 strongly inhibited IRF3 dimerization even as concentrations as low as 1 µM

(Figure 3.8B). This was an interesting finding, because this concentration was much lower than the EC50 calculated for these compounds using ATPase assay (Table 1). Compound 9, on the other hand, did not show any inhibitory effect on IRF3 dimerization (Figure 3.8B). We chose to further delve into the some of the compounds that had an inhibitory effect on IRF3 dimerization. By using a finer stratification of compound concentrations, we were able to see that Compound 13, for example, begins to inhibit IRF3 dimerization around 0.25 µM, and this effect was dependent on the concentration of the compound (Figure 3.8C). Several other compounds also demonstrated similar or weaker inhibitory activities against MDA5 as compared to Compounds 8 and 13 (Figure

3.8D, E). Interestingly, some of the cherry-picked weak hits from the primary screening

(Compounds 71 and Compounds 74) also showed inhibitory effects on IRF3 dimerization (Figure

3.8F).

Since Compound 9 did not initially show any inhibitory effect on IRF3 dimerization, we asked if this compound is perhaps an activator of the MDA5 signaling system. Using short, 42 bp

32 dsRNA was especially important in this case, because the basal MDA5 signaling activity would be limited, thus allowing us to more efficiently identify activators. We observed that in the presence of 10 µM Compound 9, MDA5 signaling was significantly increased (Figure 3.8G). This compound was studied more in depth in the later phases of this experiment.

We also observed some interesting effects with certain compounds. For example, up to 3

µM of Compound 12 was sufficient in upregulating IRF3 dimerization for MDA5; however, upon increasing the concentration beyond that point, we noticed an inhibition of IRF3 dimerization

(Figure 3.8H).

We also tested the compounds against RIG-I. Again, we noticed that Compounds 8 and 13 strongly inhibited IRF3 dimerization, thereby impairing RIG-I signaling (Figure 3.8I). Other compounds had similar or weaker inhibition of IRF3; while certain compounds (such as

Compounds 15, 20, and 24) did not seem to effect IRF3 dimerization (Figure 3.8I, J). We were also interested in determining if Compound 9 was a bi-specific activator for both MDA5 and RIG-

I; however, we noticed that Compound 9 does not play a role in either activating or inhibiting IRF3 dimerization in the RIG-I signaling pathway (Figure 3.8K).

Figure 3.8: Cell-free IRF3 dimerization assay with screened hits. (A) Schematic of the IRF3 dimerization assay. (B) IRF3 dimerization of MDA5 with 512 bp dsRNA was measured in the presence of an increasing concentration of Compounds 8, 13, and 9. (C) Further analysis of Compound 13 using a finer gradient of concentrations. (D) Same as (B) but using Compounds 12, 15, 16, and 17. (E) Same as (B) but using Compounds 20, 21, 23, and 24. (F) Analysis of weak hits (Compounds 71 and 74) using the IRF3 dimerization assay. (G) IRF3 dimerization activity of MDA5 with 42 bp dsRNA in the presence of Compound 9. (H) IRF3 dimerization activity in the presence of Compound 12 with and without MDA5. (I) IRF3 dimerization activity of RIG-I in the presence of Compounds 8, 15, 23, 12, and 13. (J) Same as (I) but using Compounds 17, 20, 24, 71, and 74. (K) IRF3 dimerization activity of RIG-I in the presence of Compound 9 (unpublished data produced by S. Ahmad).

3.9 Further analysis of Compound 9

We next focused on the mechanism of action of Compound 9 and its ability to activate

MDA5. In order to examine how Compound 9 upregulates MDA5 signaling, we performed the

IRF3 dimerization assay in the presence or absence of the key components required for MDA5 signaling. These different components include MDA5, K63-Ubn, dsRNA, and ATP. We observed that in the absence of MDA5 or K63-Ubn, no IRF3 dimerization was observed regardless of whether Compound 9 was present or not (Figure 3.9A). We also observed that Compound 9 activates MDA5 signaling even in the absence of dsRNA and/or ATP (Figure 3.9A). In fact,

Compound 9 stimulatory effect on MDA5 signaling seemed to be enhanced in the absence of ATP, which may indicate that Compound 9 could compete with ATP for the same binding pocket

(Figure 3.9A). We also performed the IRF3 dimerization assay using isolated MDA5 2CARD domain and found that Compound 9 did not demonstrate any stimulatory activity in this condition

(Figure 3.9B).

Lastly, Compound 9 did not show any stimulatory activity against RIG-I or the isolated

RIG-I 2CARD domain (Figure 3.9C, D).

Chapter 4: Discussion

This project validates that the ATPase assay can be used to screen for either inhibitors or activators of the RIG-I and/or MDA5 pathways. Since this screening project has not yet been published, we can only provide a limited amount of information regarding the compounds. We noticed that a large number of our strong hits contained long, aliphatic chains and aromatic groups.

Among the hits were inhibitors of several known enzymes such as certain dehydrogenases,

Cytochrome P40, tyrosine and phosphoinositide kinases, topoisomerase, cyclooxygenase, and acyl coenzyme A-cholesterol acyltransferase. Interestingly, some of the compounds were anti-infective

36 agents and antiseptics. Other hits were agonists of certain known receptors, hormone analogs, BCL inhibitors, and chemicals with a variety of biological functions.

During the screening process, we encountered certain challenges which impeded our progress. Some of these issues could be attributed to the robots at the screening facility. Firstly, the Combi robot required priming in the beginning of each dispensing step. Additionally, the manifold needed to have a certain amount of reagent (protein, RNA, ATP) throughout the dispensing process. These accounted for some dead volume (~5% of the reagent) that required us to prepare excess amounts of reagents per screening. Furthermore, although the Combi was impressive in its ability to dispense as low as 1 µl of a reagent to the assay plates, it was also prone to clogging, which often lead to reagents not being dispensed in certain rows. To minimize this problem, we ensured to use a sequential wash step with water and ethanol to remove and clogged contaminants. Occasionally, we had to use detergents washes to thoroughly remove clogged components.

We were also concerned about evaporation of our 10µl reaction during the incubation in

37OC. However, upon measuring the level of evaporation after the incubation time, we noticed that the effect of evaporation was negligible. Furthermore, the wells of the 384-well Corning 3540 plates allowed for a very small surface area, thereby minimizing the possibility of evaporation to adversely affect our assay.

While optimizing the ATPase assay to be able to screen for thousands of small molecules, we noticed instability in the optical density reading after adding BioMol Malachite Green.

Specifically, a constant concentration of phosphate yielded fluctuating levels of optical density readings. To amend this part of the assay, we tested out two different stabilizers suggested in

Baykov et al., 1988 (Citric acid and Tween-20). Upon using the recommended concentrations of these stabilizers, we observed that these chemicals actually killed the assay entirely (Figure 4.1A).

Surprisingly, we observed that centrifuging our assay plates after adding BioMol Malachite Green actually removed any fluctuations and resulted in stable optical density readings (Figure 4.1B).

Figure 4.1: Issue with receiving consistent optical density readings upon addition of BioMol Malachite Green. (A) We tried different stabilizers to attempt to diminish any fluctuation in optical density reading. (B) To successfully reduce fluctuations and inconsistencies in optical density readings, a centrifuging step during the screening was necessary.

While conducting the luciferase assay, we faced common issues with DNA transfection such as reduced transfection efficiency and issues with cell viability. The majority of our compounds were stored in 100% DMSO; however, among these compounds, there were some that were stored at low concentrations (because of the availability of these compounds). When performing luciferase assay with these compounds, we inevitably introduced high concentrations of DMSO to the 293T cells. As a result, when attempting to study the effect of these compounds

38 of IFN transcriptional activity, we observed that at the end of the transfection, the 293T cells were detached or dead. This is likely due to the toxic effect of DMSO on the viability of 293 T cells.

We were also surprised by Compound 9’s inability to upregulate IFN expression given that it strongly activated IRF3 dimerization. Even after attempting to use Digitonin to have Compound 9 permeate the cell membrane, our results remained inconclusive. Given the cells looked healthy after treatment with digitonin, it is unlikely that the reversible permeability step was invasive and fatal to these cells; however, it is possible that the permeabilization was not thorough enough for

Compound 9 to actually enter the cell. Further level of confirmation is needed to ensure that

Compound 9, and essentially all the other tested compounds, are in fact entering the cell. When measuring IFN transcription levels, we observed that certain compound resulted in a dose- dependent increase in IFN release but only up to a certain concentration, after which increasing the compound concentration had an inhibitory effect on the level of IFN released. This phenomenon could suggest that the compound may have two binding sites with varying affinities.

In this scenario, the binding site with a greater affinity for the compound would likely be responsible for the phenotype observed in the lower concentration range; whereas, at higher concentrations, the binding site with the lower affinity would override the effect of the higher affinity binding site.

When performing EMSA, most compounds showed a clearly interpretable result – where the interaction between protein and RNA was either interrupted or not. However, certain compounds (such as Compounds 24 and 71) showed pattern with RHRD:dsRNA that were difficult to interpret. Specifically, at the 20 µM concentration of the compound, there were absolutely no protein-RNA complexes; however, at the 40 µM concentration, a strong band of protein-RNA complex reappeared. This could be in part due to experimental error or contamination, or these compounds might have both inhibitory and activating properties at varying concentrations.

In order to further characterize these hits, more analysis of downstream molecules could be performed. After viral infection, MAVS, one of the downstream mediators, is known to form polymers that eventually activate the downstream transcription factors NF-kB and IRF3 (Cai et al., 2014). Our lab has developed a technique to study this RLR-catalyzed filament assembly of

MAVS (Wu, Huoh, & Hur, 2016). Using purified CARD of MAVS and a preformed filament seed, we can monitor the monomer-to-filament transition of MAVS upon stimulation (Wu, Huoh, &

Hur, 2016). To this environment, we can introduce our screened hits to determine whether they upregulate or downregulate this transition. Lastly, our lab is well-trained in electron microscopy, and this technique could serve as an excellent tool to observe how these compounds are affecting

RNA-protein interaction structurally.

The results from this project suggest that Compound 9 is an MDA5-specific signaling activator. Although Compound 9 did not upregulate IFN transcription in 293 T cells, this was likely because of limited cell permeability of this compound. Continued investigation of

Compound 9 could yield interesting results in the future, and this could have implications in developing a therapeutic activator. Investigation of the remainder of compounds in Table 1 would also facilitate in the development of therapeutic inhibitors or activators.

The interaction between protein and ligand can also be studied using thermal shift assays

(Huynh and Partch 2015). In a protein thermal shift assay, the MDA5/RIG-I proteins would be heated prior to plotting their respective denaturing curves. In a presence of a compound that binds to the protein, the protein would become more stable, thereby melting at a higher temperature, which would be detected via this assay. Our lab has recently started implementing this technique to study the compounds in order to analyze how the compounds interact with these proteins. Other techniques can also be used to study the thermodynamic parameters of this interaction. For

40 example, isothermal titration calorimetry is commonly used to study how small molecules interact with larger macromolecules in a solution.

Lastly, we need to conduct structure-activity relationship (SAR) analysis to further understand how these compounds have their biological effects. Additionally, these compounds might need to be optimized to ensure efficacy and eliminate associated toxicity.

After characterizing these hits, in vivo experiments should be performed to study how these compounds function in a more dynamic environment. These compounds could be tested in a mouse model of autoimmunity to rule out any potential toxicity.

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