Chaperoning viral protein evolution

Chaperoning viral protein evolution

by

Angela Marie Phillips

B.S. in Chemistry (2012)

University of Florida, Gainesville, FL

Submitted to the Department of Chemistry

in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy in Biological Chemistry

at the

Massachusetts Institute of Technology

June 2018

@

2018 Massachusetts Institute of Technology

All rights reserved

Signature of Author:

Signature redacted

Department

d

Chemistry

MY

,20

Certified by:

Signature redacted

Matthew D. Shoulders

Whitehead Career Development Associate Professor

Thesis supervisor

Accepted by:

Signature redacted

Robert W. Field

Chairman, Department Committee on Graduate Students

This doctoral thesis has been examined by a committee of the Department of Chemistry as

follows:

Signature redacted

\K)

John M. Essigmann

William R. (1956) & Be

sy

P. Leitch Professor in Residence

Professor of Chemistry, Toxicology, and Biological Engineering

Director, MIT Center for Environmental Health Sciences (CEHS)

Department of Chemistry, and Department of Biological Engineering, MIT

Thesis committee chair

_00 lo-1 0_

Signature redacted

Matthew D. Shoulders

Whitehead Career Development Associate Professor

Associate Member, Broad Institute at Harvard and MIT

Investigator, Center for Skeletal Research at Massachusetts General Hospital

Member, MIT Center for Environmental Health Sciences

Department of Chemistry, MIT

Thesis supervisor

Signature redacted

0'

Leonid A. Mirny

Professor of Medical Engineering and Science, and Physics

Associate Member, Dana-Farber Cancer Institute

Associate Member, Broad Institute at Harvard and MIT

Institute of Medical Engineering and Science, and Department of Physics, MIT

Thesis committee member

Chaperoning viral protein evolution

by

Angela Marie Phillips

Submitted to the Department of Chemistry on May 7, 2018 in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Biological Chemistry

ABSTRACT

Preventing viral pandemics and developing effective antiviral therapeutics demands understanding the molecular mechanisms that both potentiate and constrain viral evolution. The rapid evolution of viruses is mediated in part by their high mutation rates, enabling resistance to antiviral drugs, seasonal vaccines, and innate and adaptive immune responses. Fortunately for us, the same mutations responsible for resistance are often biophysically deleterious to viral proteins. Thus, viral evolution is inherently constrained by the proper folding of viral proteins into functional, stable conformations. In cells, protein folding and homeostasis are assisted by complex networks of chaperones and quality control machinery. Though the evolutionary implications of most chaperones and quality control factors remain unexplored, the HSP90 chaperone can buffer and potentiate the phenotypic effects of mutations in endogenous client proteins in bacteria, fungi, plants, and other eukaryotic organisms. Viruses acquire mutations at a rate several orders of magnitude above that of the aforementioned organisms, yet they do not encode any machinery to assist destabilized protein variants to their folded, functional conformations. However, viral proteins are known to interact with host chaperones and quality control machinery. My graduate work has focused on determining whether and how host proteostasis machinery modulates viral protein evolution. First, I employed a serial passaging approach to evolve influenza in host cells with remodeled proteostasis capacities, revealing that cytosolic host proteostasis capacity is indeed a critical determinant of influenza evolutionary trajectories. This work motivated systematic quantification of influenza protein mutational tolerance upon perturbation of host proteostasis, for which I applied deep mutational scanning

to comprehensively profile the mutational tolerance of influenza nucleoprotein and

hemagglutinin in modulated cytosolic and endoplasmic reticulum (ER) folding environments, respectively. The nucleoprotein work provides the first experimental evidence that host chaperones can enhance the accessibility of biophysically deleterious, adaptive viral protein variants. The hemagglutinin work establishes evolutionary implications for the ER proteostasis machinery, and demonstrates that ER proteostasis mechanisms enhance mutational tolerance across the entire HA protein. Overall, it is clear that host chaperones and quality control

Chapter Abstracts

Chapter 1. Protein homeostasis and evolution at the host-pathogen interface

The evolution of host and viral proteins is mediated by missense mutations that can endow new function, which are often biophysically deleterious. Thus, evolution is necessarily constrained by protein stability and folding. In host cells, protein-folding challenges are addressed by proteostasis networks composed of chaperones and quality control factors that work in concert to shepherd nascent proteins to folded, functional conformations. Work focused primarily on the HSP90 chaperone has suggested a critical role for chaperones in modulating the evolution of their endogenous clients, in part by buffering biophysically deleterious effects of non-synonymous mutations. Viral genomes, which acquire mutations at a rate several orders of magnitude above that of both prokaryotes and eukaryotes, typically do not encode endogenous chaperones or other co-factors to assist protein folding. However, viral proteins do engage host chaperones, and host chaperone inhibitors have been shown to limit the viability of certain RNA viruses. While there is substantial evidence that viral protein evolution is constrained by stability, the possibility that host chaperones can shape the evolution of viral pathogens had not been studied before this thesis. Here, we review progress towards understanding the impact of endogenous proteostasis machinery on client protein evolution, and furthermore, what consequences this machinery has on the evolution of invading pathogens.

Chapter 2. Host proteostasis modulates influenza evolution

Predicting and constraining RNA virus evolution require understanding the molecular factors that define the mutational landscape accessible to these pathogens. RNA viruses typically have high mutation rates, resulting in frequent production of protein variants with compromised biophysical properties. Their evolution is necessarily constrained by the consequent challenge

to protein folding and function. We hypothesize that host proteostasis mechanisms may be significant determinants of the fitness of viral protein variants, serving as a critical force shaping viral evolution. Here, we test this hypothesis by propagating influenza in host cells displaying chemically-controlled, divergent proteostasis environments. We find that both the nature of selection on the influenza genome and the accessibility of specific mutational trajectories are significantly impacted by host proteostasis. These findings provide new insights into features of host-pathogen interactions that shape viral evolution, and into the potential design of host proteostasis-targeted antiviral therapeutics that are refractory to resistance.

Chapter 3. Host chaperones buffer the fitness of destabilized adaptive influenza variants The threat of viral pandemics demands a comprehensive understanding of evolution at the host-pathogen interface. Here, we systematically show that the accessibility of adaptive mutations in influenza nucleoprotein at fever-like temperatures is mediated by host chaperones. Particularly noteworthy, we observe that the Pro283 nucleoprotein variant, which is conserved across human influenza strains, confers resistance to the MxA antiviral-restriction factor, and played a key role in the adaptation to humans of the 1918 pandemic influenza strain, is rendered unfit by host chaperone depletion. This fitness loss is linked to biophysical defects that chaperones cannot address when heat shock factor-I is inhibited. Thus, host chaperones can uncouple biophysically deleterious consequences of mutations from the benefits of immune escape. In summary, host chaperones play a central role in shaping influenza adaptation, with implications for the evolution of other viruses, for viral host-switching, and for the design of

Chapter 4. ER proteostasis and temperature modulate the mutational tolerance of influenza hemagglutinin

Influenza virus hemagglutinin (HA) evolves rapidly to escape antibodies, but also performs an essential role in viral replication. Therefore, the virus's rapid evolution is contingent on HA possessing sufficient mutational tolerance to acquire antibody resistance while continuing to properly fold and assemble in the ER of host cells. Here, we investigate the mutational tolerance of HA in host cells with modulated ER protein homeostasis (proteostasis) machinery, at normal body temperature and at a biophysically challenging, fever-like temperature. We find that upregulation of ER proteostasis machinery generally enhances HA mutational tolerance, and that increased temperature generally reduces HA mutational tolerance. Intriguingly, variants that are most temperature-sensitive, which are likely also biophysically problematic, benefit most from upregulation of proteostasis machinery. Overall, this work demonstrates that host ER proteostasis mechanisms and temperature modulate HA mutational tolerance, and reports the first evidence of evolutionary implications for the ER proteostasis machinery.

Chapter 5. Conclusions and future directions

The findings reported in this thesis are briefly summarized, and possible future directions for investigating the evolutionary implications of maintaining proteostasis are discussed.

Some days the positive path is harder to find and we have to be relentless in its pursuit. But a better outlook is always there and worth chasing. On the other side are potential-and

possibility.

Acknowledgements

The work presented in this thesis would not have been remotely possible without many people. First and foremost, I need to thank my advisor, Prof. Matthew Shoulders. Throughout my Ph.D., Matt has led by example, pushing me to become a more creative, efficient, and cognizant scientist. I think Matt would agree that most of the techniques and expertise required for my

Ph.D. research were not in our lab's wheelhouse five years ago. Daily, Matt made sure I had everything I needed to move forward, whether that meant obtaining materials from another lab, taking a course, or even being co-advised. His support was essential for my progress, and ultimately enabled us to answer fundamental questions about protein evolution. Beyond the daily experimental grind, I am so thankful that Matt involved me in his grant writing, which was one of my favorite parts of graduate school, constantly provided me with opportunities to present my work, and allowed me to mentor Luna, Anna, and Apolonia. Matt also encouraged me to pursue my interests outside the lab-such as advocacy trips to D.C. and running the

12 2nd Boston Marathon-for which I am greatly appreciative. Finally, I need to thank Matt for

sharing his perspective on pursuing an academic career and for providing critical feedback on my fellowship and post-doctoral application packages. I know that I'll continue to look to Matt for advice throughout my career, and hope that I can become such an effective scientist and mentor.

Next, I need to thank some of my other mentors who have supported my scientific career. Thanks to Prof. Leonid Mirny, for welcoming me into his lab, for providing essential feedback on my research, and for supporting my post-doctoral search. Thanks to Prof. John Essigmann, for preparing me for my qualifiying exam, for inspiring me to be a better teacher, and for always making me step back and think more broadly. I must also thank Leonid and John for taking the time to be on my thesis committee-they both have provided meaningful feedback throughout my Ph.D., and for that I am very thankful. I'd also like to thank my undergraduate advisors-Profs. Laura Bohn and William Ja-who continued to support me throughout graduate school, writing many letters for graduate programs and fellowship applications, and offering their advice whenever I needed it.

Throughout graduate school I have had the privilege to participate in many productive collaborations. I'd like to thank Jesse Bloom and the Bloom Lab, especially Orr Ashenberg and Mike Doud, for welcoming me into their lab, for helping me implement an approach that transformed my Ph.D. research, and for providing feedback and reagents faster than I could ask for them. I am also indebted to Vincent Butty and Stuart Levine at the MIT BioMicro Center, for helping us design our sequencing projects, analyzing massive amounts of sequencing data to meet grant deadlines, and troubleshooting sequencing anomalies. I am also grateful for our collaboration with Yu-Shan Lin's Lab at Tufts, especially Sean McHugh and Jiayuan Miao, who did the molecular dynamics simulations in Chapters 2 and 3. Special thanks to Yu-Shan for her tireless efforts to compile a pipeline for the data analysis presented in Chapter 4-her energy is inspiring. I also need to thank Alex Shalek and Jon Trombetta for introducing us to library prep and sequencing, and for their patience and connections to the necessary resources.

welcoming me into the viral evolution project and helping me hit the ground running; Luna, for the ingenuity and honest skepticism she brought to our project; Anna, for her energy and motivation in pushing our project forward; Louis and Chris R., for chasing me down Beacon street into my last mile of the Boston Marathon; and my neighbor of four years-Chet, for his enthusiastic criticism and active listening skills.

Thanks to my colleagues on the Science Policy Initiative Exec. team-for teaching me so much over the past two years, for the time they invest in SPI, and for the support from the MIT administration and MIT D.C. office that make our work possible.

Thanks to my fellow WIC Admin members for making WIC the professional development organization it is today, and for the relationships WIC fosters between women in the Chemistry Department.

Next, I need to thank my friends, for making graduate school a great time. Alex, Wade, and Justin, thanks for dragging me out for 9pm champagne, for weekends of board games, biking, and road trips, and for not leaving Boston (at least for now). Drennan Lab, thanks for adopting me, for margs at Border and pitchers at Newtown, and for greatly improving my interview talk. Cullen, Sydney, and Jenny, thanks for sending a care package when I got my first F, and for always providing sound advice. Joanna, Chloe, Kiplyn, and Reagan, thanks for your support and confidence, and for your inspiring strength and impact.

Thanks to my family, not just for supporting me remotely, but for encouraging me throughout my education and for having unyielding confidence in my abilities. Thanks to my Mom, for instilling a fierce determination in me that has powered me through the many long days that make up a Ph.D. Thanks to my Dad, for his ruthless commitment to routine that has ingrained the time management skills essential for my productivity. Thanks to Julie, for her energy and selflessness that has kept us so close despite being so far apart. Thanks to Hailey, for her inspirational strength and grit. Thanks to Joe, for those one-liners that always come at the right time. Thanks to Matt, for always having a solution when something was broken. Special thanks to Mom, Dad, and Julie, for the daily phone calls on my walk home and for visiting Boston so many times (Hailey and Joe, my post-doc at Harvard buys you more time). Thanks to my Aunt Jenny and Uncle Rich, for providing a relaxing getaway from MIT and the city.

Last but certainly not least, I need to thank Kenny Kang, for supporting me everyday, scientifically and personally. Over the past five years, Kenny has listened to dozens of practice talks, helped me work through numerous confusing results, and has never hesitated to give his honest advice. Though he rarely cared to rant about his day, he always let me unload my daily stress on him. All of the success I've had has been supported by his love, confidence, and selflessness. I must also thank Kenny's family for being so welcoming, for entertaining several ambitious vacation itineraries, and for old vine zinfandels.

I'd finally like to thank Matt, Madeline, Cullen, Andrew, Chris R., Kenny, Luna, and Chet, for feedback on the chapters in this thesis, and the National Science Foundation for funding the last three years of my Ph.D.

Table of Contents Abstract ₃ Chapter Abstracts ₄ Acknowledgements ₉ Table of Contents ₁₁ List of Tables ₁₄ List of Figures ₁₅ List of Abbreviations 18

Chapter 1: Protein homeostasis and evolution at the host-pathogen interface

1.1 Summary ₂₇

1.2 The host-pathogen interface 28

1.3 Protein evolution at the host-pathogen interface 29

1.4 Host proteostasis mechanisms assist host protein folding 32

1.5 _{Host proteostasis both constrains and potentiates host protein evolution 35}

1.6 Viruses engage host proteostasis factors 38

1.7 _{Host proteostasis mechanisms potentiate and constrain viral protein evolution 40}

1.8 Summary and future directions 42

1.9 References 45

Chapter 2: Host proteostasis mechanisms modulate influenza

2.1 Author Contributions 51

2.2 Introduction ₅₂

2.3 Results ₅₄

Small molecule-based strategies create three distinctive host proteostasis 54 environments for influenza evolution experiments

Serial passaging to emulate influenza evolution 61

Nature of selection pressure differs in modified proteostasis environments 63

Influenza protein mutational landscapes are modulated by host proteostasis 71

qPCR 81

RNA-Seq 82

RNA-Seq analysis 82

Cellular thermal shift assay (CETSA) 83

Serial passaging and hemagglutination-based titering 84

Infectious viral titering via tissue culture infectious dose (TCID50) assay 85

Reverse genetics 85

Deep sequencing 85

Sequencing data analysis 86

Statistics 87

Molecular dynamics simulations 88

2.6 Acknowledgements 91

2.7 References 92

Chapter 3: Host chaperones can buffer the fitness of destabilized adaptive influenza variants

3.1 Author Contributions 98

3.2 Introduction 99

3.3 Results 101

Deep mutational scanning of nucleoprotein in a biophysically challenging 101

environment

Elucidating the cause of compromised fitness of WT residues in a biophysically 113

challenging environment

Balancing protein biophysical properties with immune escape 117

3.4 Discussion 119

3.5 Materials and Methods 122

Plasmids 122

Antibodies 122

Cell lines 122

Cell line characterization 123

Deep mutational scanning 125

Pairwise viral competitions 127

Molecular dynamics simulations 128

Biophysical characterization 130

Data availability 131

Statistics 131

3.6 Acknowledgements 134

Chapter 4: ER proteostasis and temperature modulate influenza hemagglutinin mutational tolerance

4.1 Author Contributions 139

4.2 Introduction 140

4.3 Results 143

Modulating ER proteostasis during influenza infection 143

Evaluating the impact of ER proteostasis mechanisms on a known trafficking- 150

defective HA variant

Deep mutational scanning of HA in modulated ER proteostasis environments 152

4.4 Discussion 167

4.5 Materials and Methods 169

Plasmids 169

Cell culture 169

Influenza virus 169

Compounds and antibodies 169

qPCR 170

RNA-seq 170

Deep mutational scanning 172

Deep mutational scanning data analysis 173

Infectious viral titering via tissue culture infectious dose (TCID50) assay 173

Reverse genetics viral competitions 174

Pairwise competition sequencing data analysis 175

Flow cytometry 175

Statistics 176

4.6 Acknowledgements 179

4.7 References 180

Chapter 5: Conclusions and future directions

5.1 Conclusions 184

5.2 Future directions 187

5.3 References 191

List of Tables

Chapter 2: Host proteostasis mechanisms modulate influenza evolution

Table 2. 3. 1 List of ten highest % frequency non-synonymous mutations for each proteostasis en vironm ent and p assage...68 Table 2. 3. 2 List of ten highest % frequency synonymous mutations for each proteostasis en vironm ent and p assage...69 Table 2. 3. 3 Molecular Dynamics Simulations: Energy Contributions...74 Table 2. 5. 1 Primer sequences for qPCR and PA sequencing ... 81

Chapter 3: Host chaperones can buffer the fitness of destabilized adaptive influenza variants

Table 3. 5. 1 List of primers for qPCR and sequencing library preparation. ... 133

Chapter 4: ER proteostasis and temperature modulate influenza hemagglutinin mutational tolerance

List of Figures

Chapter 1: Protein homeostasis and evolution at the host-pathogen interface

Figure 1. 3. 1 Biophysical boundary model of protein evolution ... ... ... 30

Figure 1. 4. 1 Protein folding energy landscape... ... 32

Figure 1. 4. 2 Eukaryotic chaperone-assisted protein folding... ... 33

Figure 1. 5. 1 Genetically diverse populations respond to inhibition of chaperone buffers (A) and

potentiators (B) ... . _ .... 35

Figure 1. 5. 2 Chaperones can shift the boundary of accessible protein stabilities. ... ... 37

Figure 1. 6. 1 Influenza proteins interact extensively with host proteostasis factors... 38 Figure 1. 7. 1 Model for impact of chaperones on viral protein evolution.. ... ... 41 Chapter 2: Host proteostasis mechanisms modulate influenza evolution

Figure 2. 3. 1 Chemical biology methods to modify the host cell's proteostasis environment ... 56

Figure 2. 3. 2 Validation of chemical biology tools used to perturb proteostasis ... 57

Figure 2. 3. 3 Heat shock protein transcript expression during influenza infection in modulated

p ro te o sta sis e n viro n m e n ts ... ... ... ... ... 5 8

Figure 2. 3. 4 Transcriptomic analysis of perturbed host cell proteostasis environments ... 60 Figure 2. 3. 5 Serial passaging of Influenza A /Wuhan/95 H3N2 ... ... 61

Figure 2. 3. 6 Multiplicity of infection and hemagglutination titers during serial passaging ... 62

Figure 2. 3. 7 Site frequency spectra show frequency distribution of non-synonymous (A) and synonymous (B) mutations in a given folding environment at a particular passage...66 Figure 2. 3. 8 Trajectories for non-synonymous (A) and synonymous (B) mutations that increase in frequency during serial passaging ... ... 6 7

Figure 2. 3. 9 Analysis of non-synonymous mutations observed in distinctive proteostasis environments. Aligned variants were observed in any of three biological replicates ... 72

Figure 2. 3. 10 HA and PA display divergent mutational trajectories in HSF1-activated versus basal versus H sp90-inhibited environm ents ... 76

Chapter 3: Host chaperones can buffer the fitness of destabilized adaptive influenza variants

Figure 3. 3. 1 Transcriptional profiles of modulated host environments... ... 102 Figure 3. 3. 2 HSFIi is effective during influenza infection and does not significantly perturb influenza propagation or host cell metabolic activity ... 103

Figure 3. 3. 3 Deep mutational scanning reveals positively selected sites upon chaperone

depletion at a restrictive tem perature ... ... 104 Figure 3. 3. 4 Pairwise competition recapitulates deep mutational scanning batch competition Figure 3. 3. 5 Full sequence logo plot for nucleoprotein HSF1-inhibited environment at 39 'C

relative to a basal environment at 37 'C . . 107

Figure 3. 3. 6 Full sequence logo plot for nucleoprotein: HSF1-inhibited environment at 39 'C

relative to a basal environment at 39 C .... _. 108

Figure 3. 3. 7 Full sequence logo plot for nucleoprotein: 39 'C relative to 37 'C in a basal

e n viro n m e n t ... ... ... .. 10 9

Figure 3. 3. 8 Full sequence logo plot for nucleoprotein: HSF1-inhibited environment at 37 'C

relative to a basal environment at 37 'C ... 110

Figure 3. 3. 9 Full sequence logo plot for nucleoprotein: Hsp90-inhibited environment at 39 'C

relative to a basal environment at 39 'C . ... 111

Figure 3. 3. 10 Pairwise competitions recapitulate deep mutational scanning batch

com p etition ... ... 1...112

Figure 3. 3. 11 Pro283 disrupts nucleoprotein a-helical content and is destabilized relative to variants at site 283

... 1 15 Figure 3. 3. 12 Purification and thermal denaturation of recombinant nucleoprotein variants ...116

Figure 3. 3. 13 Host chaperones modulate immune escape of Pro283 nucleoprotein...117 Figure 3. 3. 14 Host chaperones mediate the accessibility of biophysically destabilized adaptive m u ta tio n s ... ... ... 1 1 8

Chapter 4: ER proteostasis and temperature modulate influenza hemagglutinin

mutational tolerance

Figure 4. 2. 1 The unfolded protein response is a stress-responsive integrated signaling

Figure 4. 3. 1 Induction of XBP1s and A TF6f/XBPIs is selective and does not cause global

stress...

Figure 4. 3. 2 Full transcriptome analysis confirmed selectivity of XBP1s and A TF6f/XBP1s

induction ... ... 147

Figure 4. 3. 3 Methods to induce XBPIs and A TF6f/XBPIs are functional during influenza

infection ... .. ... ... 149

Figure 4. 3. 4 YI 74H-HA viral growth and surface expression are affected by XBPIs

induction...

...

Figure 4. 3. 5 Deep mutational scanning of HA in modulated ER proteostasis environments ...153

Figure 4. 3. 6 ER proteostasis mechanisms and temperature impact HA global mutational

to le ra n c e ... ... . .. ... .. ... . . ... ... 15 5

Figure 4. 3. 7 HA variants are depleted upon increased temperature and enriched upon XBPIs

and A TF6f/XBPIs induction. . ... ... .. 156

Figure 4. 3. 8 Divergent fitness effects revealed by DMS were validated by pairwise

competitions... . ₁₅₈

Figure 4. 3. 9 Selection on HA imposed by XBPIs induction is opposite that of increased

temperature.. . . 159

Figure 4. 3. 10 Mutational tolerance at sites across HA is impacted by XBPIs induction and

increased temperature ... . 161

Figure 4. 3. 11 Differential selection on WSN HA in a basal environment at 39 'C relative to 37

'C was quantified by DMS... - . . 162

Figure 4. 3. 12 Differential selection on WSN HA upon XBPIs induction at 37 'C was quantified

by D M S ... ... ... 163

Figure 4. 3. 13 Differential selection on WSN HA upon XBPIs induction at 39 'C was quantified

by D M S .. ... ... 16 4

Figure 4. 3. 14 Differential selection on WSN HA upon A TF6f/XBPls induction at 37 'C was

quantified by DMS . .. . 165

Figure 4. 3. 15 Differential selection on WSN HA upon A TF6f/XBPls induction at 39 'C was

List of Abbreviations 0C AGfolding

A

A

A280

adp

ATF4

ATF6

ATF6f

ATP

BH

BSA

C

CD

cHSF1

CHOP

cDNA

CDS

Degrees Celsius

Energy of folding

Angstrom

Alanine

Absorbance at 280 nm

Actin

Adjusted p-value

Amidohydrolase domain containing 2

ADP-ribosylation factor-like 4C

Activating transcription factor 4

Activating transcription factor 6

Transcriptionally-active activating transcription factor 6

Adenosine triphosphate

BLC2 associated anathogene 2

BLC2 associated anathogene 3

BLC2 associated anathogene 5

Benjamin-Hochberg-adjusted p-value

Basic helix-loop-helix family member Al 5

Binding immunoglobulin protein

Bovine serum albumin

Cysteine

Calreticulin

Calcium/calmodulin dependent protein kinase I

Calnexin

C-C motif chemokine receptor 7

Circular dichroism

ChaC glutathione specific gamma-glutamylcyclotransferase 1

Constituitive heat shock factor 1

CCAAT-enhancer-binding protein homologous protein

Complementary deoxynucleic acid

CETSA _{Cellular thermal shift assay}

cHSF1 Constitutive heat shock factor 1

CLDNI _{Claudin 1}

CLU Clusterin

CMV Cytomegalovirus

COPI Coat protein 11

COX3 Cytochrome C oxidase subunit 3

CRELD2 _{Cysteine rich with EGF (epidermal like growth factor) like domains 1} CYPIBI Cytochrome P450 family 1 subfamily B member 1

D Aspartate

DAPI 4',6-diamidino-2-phenylindole

DDIT4 DNA damage inducible transcript 4

DERL3 _{Degradation in endoplasmic reticulum protein 3}

DHFR Diydrofolate reductase

diffsel Differential selection

DMEM Dulbecco's modified eagle medium

DMS Deep mutational scanning

DMSO Dimethyl sulfoxide

DNA Deoxynucleic acid

DNAJA 1 DnaJ heat shock protein family (Hsp40) member Al

DNA JA2 _{DnaJ heat shock protein family (Hsp40) member A2}

DNAJA3 _{DnaJ heat shock protein family (Hsp40) member A3}

DNAJBI _{DnaJ heat shock protein family (Hsp40) member B1}

DNAJB2 DnaJ heat shock protein family (Hsp40) member B2

DNAJB6 DnaJ heat shock protein family (Hsp40) member B6

DNA JB8 DnaJ heat shock protein family (Hsp40) member B8

DNAJB9 _{DnaJ heat shock protein family (Hsp40) member B9}

DNAJB 11 DnaJ heat shock protein family (Hsp40) member B1 1

EDEMI EDTA EEF1A EELE elF2a EIF2AK3 EIF5 EINT ER ERAD Erg3 ERQ1B EVDW f F FAM19A4 FBS FBXW1O FGF21 Fl6v3 F/CD FKBP14 FKBP4 FPLC fwd G GADD34 GAPDH GBSA GEO GJA I GRP94 GSG GSOL-POLGB

ER degradation enhancing alpha-mannosidase like protein 1 Ethylenediaminetetraacetic acid

Eukaryotic translation elongation factor 1 alpha 1 Coulombic energy

Eukaryotic translation initiation factor 2A

Eukaryotic translation initiation factor 2 alpha kinase 3 Eukaryotic translation initiation factor-5

Internal energy

Endoplasmic reticulum

Endoplasmic reticulum-associated degradation S. cerevisiae C-5 sterol desaturase

Endoplasmic reticulum oxidoreductase 1 beta Van der Waals energy

Mutation frequency Phenylalanine

Family with sequence similarity 19 member A4 Fetal bovine serum

F-box and WD repeat domain-containing 10 Fibroblast growth factor 21

F domain broadly neutralizing HA antibody

FIC domain containing (Huntingtin-interacting protein) FK506 binding protein 14

FK506 binding protein 4

Fast protein liquid chromatography Forward

Glycine

Growth arrest and DNA damage-inducible protein Glyceraldehyde-3-phosphate dehydrogenase Generalized Born and surface area

Gene Expression Omnibus Gap junction protein alpha 1 Glucose-regulated protein 94 kDa Glycine-serine-glycine

GSOL,NP

H

h

H1

HA

HEK

HIV

H1N1

H3N2

hpi

HSF1

HSP

HSP40

HSP70

HSP90

Nonpolar solvation free energy Histidine

Hour(s)

Hemagglutinin subtype 1 influenza Hemagglutinin

Human embryonic kidney

Homocysteine inducible ER protein with ubiquitin like domain 1

High-temperature-induced Dauer formation domain containing Human immunodeficiency virus

Hemagglutinin subtype 1, neuraminidase subtype 1 influenza Hemagglutinin subtype 3, neuraminidase subtype 2 influenza Hours post-infection

Heat shock factor 1 Heat shock protein Heat shock Heat shock Heat shock Heat shock Heat shock Heat shock Heat shock Heat shock Heat shock Heat shock Heat shock Heat shock Heat shock Heat shock protein protein protein protein protein protein protein protein protein protein protein protein protein protein 40 70 90 90 90 kDa kDa kDa

alpha family class A member 1 alpha family class A member 2 family A (Hsp70) member 2

family

family

family

family

family

Heat shock protein family

(Hsp70) member 4 (Hsp70) member 4 like (Hsp70) member 5 (Hsp70) member 6 (Hsp70) member 1A (Hsp70) member 1 B (Hsp70) member 1 like (Hsp70) member 8 (Hsp70) member 9

IFIT1 IgG IPTG IRE1 IRES ISG15 ISG20 K kcal/mol KDEL KDELR3 KRT27 LB M M MANIBI MD MDCK MDCK-SIAT1 MIA mL MMP13 MOI mRNA MUC20 mut MWCO MxA MX1 MX2 N NA NEP NP

Interferon induced protein with tetratricopeptide repeats 1 Immunoglobulin G

Isopropyl P-D-1-thiogalactopyranoside

Inositol-requiring enzyme 1 Internal ribosomal entry site

Interferon stimulated exonuclease gene 15 Interferon stimulated exonuclease gene 20 Lysine

Kilocalories per mole

Lysine-aspartate-glutamate-leucine

KDEL endoplasmic reticulum protein retention receptor 3 Keratin 27

Lysogeny broth Matrix

Methionine

Mannosidase alpha class 1B member 1 Molecular dynamics

Madin Darby canine kidney cells

Madin Darby canine kidney cells expressing a-2,6-sialic acid receptors Melanoma inhibitory activity

Milliliters

Matrix metallopeptidase 13 Multiplicity of infection Messenger ribonucleic acid Mucin 20

Mutant

Molecular weight cut off Myxovirus resistance protein 1 MX dynamin like GTPase 1 MX dynamin like GTPase 2 Asparagine

Neuraminidase Nuclear export protein Nucleoprotein

NQOI

NAD(P)H quinone dehydrogenase 1

ns

Not significant

NS1

Non-structural protein 1

OAS2

2'-5'-Oligoadenylate synthetase 2

OD

Optical density

P

Proline

PA

Polymerase acidic unit

PARP3

Poly(ADP-ribose) polymerase family member 3

PBS

Phosphate buffered saline

PBSA

Phosphate buffered saline with BSA

PB1

Polymerase basic unit 1

PB2

Polymerase basic unit 2

PCDH7

Protocadherin 7

PCR

Polymerase chain reaction

PDB

Protein database

PDI

Protein disulfide isomerase

PDIA1

Protein disulfide isomerase family A member 1

PD/A3

Protein disulfide isomerase family A member 3

PD/A6

Protein disulfide isomerase family A member 6

PDL

Poly-D-lysine

pDZ

Post synaptic density protein domain

PERK

Protein kinase RNA-like endoplasmic reticulum kinase

pH

Potential of hydrogen

PIN4

Peptidylprolyl cis/trans isomerase

PLPP5

Phospholipid Phosphatase 5

pme

Posterior mean estimates

PME

Particle mesh Ewald

PR8

Puerto Rico 8

RBC

rev

RI

RIPA

RNA

RNase H

RNA-seq

RPKM

RSA

S

Sip

S2P

S. cerevisiae

SDF2L I

SDS

SDS-PAGE

SE

SEC24D

SEC6IAl

SEM

SERPI

SerpinH1

SFS

SIAT1

SLC

SNP

SPINK5

SPP1

SS

ssRNA

Pearson correlation coefficient Red blood cell

RNA-binding motif protein 3 Reverse

Recombinant inbred

Radioimmunoprecipitation assay buffer Ribonucleic acid

Ribonuclease H

Ribonucleic acid sequencing Reads per kilobase million Ribosomal protein L36a

Ribosomal protein lateral stalk subunit P2 Relative surface accessibility

Serine

Site 1 protease Site 2 protease

Saccharomyces cerevisiae

Stromal cell derived factor 2 Like 1 Sodium dodecyl sulfate

Sodium dodecyl sulfate polyacrylamide gel electrophoresis Site entropy

SEC24 homolog D, COPlI coat complex component Sec6l translocon alpha 1 subunit

Standard error of the mean

Stress associated endoplasmic reticulum protein 1 Serpin family H member 1

Site frequency spectrum

a-2,6-Linked sialic acid receptor-expressing Solute carrier family member

Single nucleotide polymorphism

Serine peptidase inhibitor, kazal type 5 Secreted phosphoprotein 1

Secondary structure

STC Staniocalcin 1

STR _{Short tandem repeat}

syn Synonymous

T Threonine

TCID50 Tissue culture infectious dose 50

Tm Apparent melting temperature

ts Temperature sensitive

TMP Trimethoprim

TPCK L-1-tosylamide-2-phenylethyl chloromethyl ketone

TRIB3 Tribbles Pseudokinase 3

TRPA1 Transient receptor potential ankyrin 1

UCSC University of California, Santa Cruz

UGGT1 _{UDP-glucose glycoprotein glucosyltransferase 1}

UPR Unfolded protein response

W Tryptophan

WSN Influenza virus A/WSN/1 933

WT Wild-type

XBP1 X-box like protein 1

XBP1s Spliced X-box like protein 1

Y Tyrosine

Chapter 1: Protein homeostasis and evolution

at the host-pathogen interface

1.1

Summary

The evolution of host and viral proteins is mediated by missense mutations that can endow new function, which are often biophysically deleterious. Thus, evolution is necessarily constrained by protein stability and folding. In host cells, protein-folding challenges are addressed by proteostasis networks composed of chaperones and quality control factors that work in concert to shepherd nascent proteins to folded, functional conformations 1-3 Work focused primarily on the HSP90 chaperone has suggested a critical role for chaperones in modulating the evolution of their endogenous clients 4-1, in part by buffering biophysically deleterious effects of non-synonymous mutations. Viral genomes, which acquire mutations at a rate several orders of magnitude above that of both prokaryotes and eukaryotes, typically do not encode endogenous chaperones or other co-factors to assist protein folding. Instead, viral proteins engage host chaperones to assist the folding of their proteins 14-18, and host chaperone inhibitors have been

shown to limit the viability of certain RNA viruses 1923. While there is substantial evidence that viral protein evolution is constrained by stability, the possibility that host chaperones can shape the evolution of viral pathogens had not been studied before this thesis 24-27. Here, we review

progress towards understanding the impact of endogenous proteostasis machinery on client protein evolution, and furthermore, what consequences this machinery has on the evolution of invading pathogens.

1.2 The host-pathogen interface

Hosts have evolved diverse signaling pathways to sense and respond to viral infections. For instance, the innate immune system fights viral infection by killing infected cells, degrading viral genomic material, and inhibiting viral replication and translation 2-30. In response, viruses have

evolved mechanisms to evade these host systems, including mutating epitope regions to evade host recognition and activating inhibitors of the innate immune response 29.30 If the host and

virus both survive initial infection, the virus may be cleared by the adaptive immune response, which elicits virus-specific neutralizing antibodies. Alternatively, the viral infection may persist if the virus acquires mutations that enable it to escape neutralization 31. Thus, the host and virus

are continuously co-evolving. The host must eliminate the invading pathogen while maintaining organismal viability. In contrast, viruses are minimalist pathogens that rely on their host's viability to propagate. Therefore, viruses must strike a delicate balance of hijacking host machinery and evading the host immune response without inducing widespread cytotoxicity.

1.3 Protein evolution at the host-pathogen interface

The evolution of host and viral proteins is in part mediated by missense mutations that can enhance protein fitness. For example, amino acid substitutions can enable viral escape from neutralizing antibodies, or increase the antiviral activity of host restriction factors. Often, these missense mutations diminish protein thermodynamic and kinetic stability and, hence, activity and organismal fitness.

Protein evolvability is therefore constrained by stability. Most globular proteins are marginally stable, with a AGfolding of approximately -10 kcal/mol 32,33. The high dimensionality of

protein sequence space clusters viable sequences in this marginally stable regime. Very few sequences have higher stability, while numerous sequences have lower stability 34. It follows that amino acid substitutions are often destabilizing 35. Moreover, random walk lattice models, in concert with biochemical reconstitution, have demonstrated that stable proteins tend to be more evolvable, presumably because they can accommodate more mutations and still maintain their functional folds 36,37. Experimentally, a combination of biochemical and biophysical tools has 37-40

identified protein stability as a molecular signature of protein fitness and protein function

-Phylogenetic analyses validated by ancestral protein reconstruction have established that the thermodynamic stability of viral and host protein variants corresponds to their inferred fitness based on evolutionary sequence data 3738,40

Moreover, protein folding has been implicated as the mediator between protein stability and protein evolvability. Proteins must fold in a reasonable amount of time into a functional form that is stable to side-reactions like aggregation and proteolysis 34. Random walk lattice models of protein evolution have revealed that protein evolutionary trajectories are confined to neutral

can be phenotypically silent at other positions if they do not impact protein folding or function

.

Theoretical work has attributed the covariation between sequence evolution, codon usage, and

mRNA levels across many organisms to selection against misfolded proteins

Experimental

work on adenylate kinase demonstrated that protein fitness is not solely dependent on enzyme

activity, but also on resistance to denaturation and aggregation

Additionally, divergent RNase

H sequences have conserved folding pathways that are more kinetically stable than their most

recent common ancestor

These studies, among many others, support a model in which the

evolutionary accessibility of protein variants depends not only on inherent thermodynamic

stability, but also on kinetic stability, defined by folding and misfolding rates (Figure 1. 3. 1).

This biophysical constraint has significant implications for viruses, which acquire

mutations at rates several orders of magnitude above that of eukaryotes

Such high mutation

rates enable viruses to rapidly explore amino acid space as they adapt to new environmental

pressures,

but are often

detrimental to

viral

fast

folding rate

_{propagation. Viral genomes are small, largely protein}

slow

coding, and frequently contain overlapping reading

fast

%

eframes.

_{Thus, the deleterious effects of a single}

misfolding

rate

mutation are often amplified in a viral genome

slow

These deleterious effects can be relieved, however, by

stability

_{stabilizing compensatory mutations}

₅

_{or by assistance}

stable

Figure 1. 3. 1 Biophysical boundary from more fit viral genomes 49,51.

model of protein evolution.

Because protein evolution is constrained by

The accessibility of protein variants is

dictated by thermodynamic and kinetic

stability, destabilizing adaptive variants often must be

(folding

versus

misfolding

rate)

stability. Protein variants inside the

preceded or accompanied by permissive mutations

biophysical boundary are accessible,

and those outside the boundary, are

that are thermodynamically or kinetically stabilizing

.

inaccessible. Adapted from Powers et

a/.3

Frequently, these stabilizing mutations are neutral in

the absence of the destabilizing adaptive mutation, as selection demands proteins meet the

stability threshold, but does not favor increased stability above that threshold. Thus, permissive stabilizing mutations occur stochastically, making specific protein evolutionary trajectories unpredictable 5.53. _{Still, numerous cases of host and viral proteins exhibiting this idiosyncratic}

epistasis, when the effects of one mutation are influenced by another, have experimentally demonstrated that protein thermodynamic stability and the kinetic stability of folding intermediates are critical constraints of protein evolution 38,40,50,54 This is particularly evident for

influenza proteins. For example, influenza nucleoprotein was not able to tolerate destabilizing immune escape mutations until stabilizing permissive mutations occurred 4 , and influenza neuraminidase was unable to tolerate a drug resistance mutation unless it was accompanied by a mutation that rescues neuraminidase surface expression 39. Thus, epistasis is pervasive in protein evolution, and is often explained by protein stability or folding and assembly kinetics.

Another important consideration in examining the constraints on protein evolution is the dynamic nature of the fitness landscape, which is especially relevant at the continuously changing host-pathogen interface. Protein variants that are fit in one environment may not be fit in another environment, a concept known as pleiotropy. For example, mutations in S. cerevisiae HSP90 that are beneficial in a high salinity environment are costly in a low salinity environment

.Similarly, many antiviral resistance mutations compromise fitness in the absence of the drug, but enhance fitness in the presence of the drug 56. Different environments, especially variance in temperature, can elicit pleiotropic effects on protein stability. Variants marginally stable at a permissive temperature may not be accessible at elevated temperatures 5, and this pleiotropy

can therefore significantly impact evolutionary trajectories, especially in fluctuating

1.4 Host proteostasis mechanisms assist host protein folding

While proteins fold quickly in aqueous solution, the crowded cellular milieu enhances protein propensity towards aggregation. In cells, these protein aggregates are often toxic, and protein quality control and maintenance of proteome homeostasis (proteostasis) are critical for cellular health and organismal fitness. Thus, organisms are equipped with proteostasis networks, which consist of hundreds of proteins, including protein-folding chaperones and quality control machineries like the ubiquitin-proteasome and autophagy systems 2. Together, these folding

quality control factors work in concert to shepherd nascent proteins to folded, functional conformations while minimizing the

accumulation of misfolded and/or

aggregated proteins ". On a

protein-chaperones _{folding landscape, the native state is a} chaperones

local minimum that can often only be

chaperones

reached by overcoming kinetic

oligomers barriers 2 (Figure 1. 4. 1). Meanwhile,

foldina protein aggregates fall into a global

misfolded intermediate

state minimum that is typically more

~

amorphous

aggregates _{kinetically accessible. Such}

a

native state _{landscape demands that proteins are}

fibrils guided to their native state instead of Figure 1. 4. 1 Protein folding energy landscape. partially folded states or amorphous The protein folding energy landscape is rugged,

where the native state is at a local minimum, but aggregates. terminally misfolded amorphous aggregates and fibrils

are lower in energy. Chaperones promote protein folding by blocking off-target pathways and assisting proteins over kinetic folding barriers. Adapted from Hartl et al.2

Proteino

fanin

ribsm

nascent chaperones

begins co-translationally, polypet ide

chain

when chaperones bind to r y HSP7O/4O

terminally

the nascent chain and misfolded

prevent misfolding by

proteasome +co-chaperones

avoiding non-native _{+ATP I_}

contacts (Figure 1. 4. 2) 58. autophagjio Te or I+ATP

Unfolded polypeptides are native state HSP90

system

recognized by exposed chaperonins /+ATP

+ AT P hydrophobic regions, and

bind chaperones such as native state

eukaryotic HSP70/HSP40 Figure 1. 4. 2 Eukaryotic chaperone-assisted protein folding.

for one ATP hydrolysis Representative chaperone pathways that help nascent

polypeptide chains to attain their folded native state and direct cycle, after which they are terminally misfolded proteins for degradation.

released into bulk solution to either continue down a productive protein-folding pathway or

rebind HSP70 for another ATP hydrolysis cycle 59-61. Downstream of the HSP70/HSP40

chaperones, the chaperonins, such as HSP60/110 in eukaryotes and GroEL/ES in bacteria, can enclose select client proteins in a 'cage' that allows proteins to explore a perhaps more limited conformational space, thereby minimizing the entropic costs of folding and preventing aggregation. The HSP90 chaperone system also operates downstream of HSP70/HSP40, by an ATP-driven mechanism that is regulated by several co-chaperones. Generally, chaperones facilitate protein folding by both independent and dependent mechanisms.

ATP-native state or decrease the concentration of unbound non-ATP-native substrates to avoid aggregation 63. Together, these protein-folding macromolecular machines assist protein folding

and prevent misfolding (Figure 1. 4. 1). When proteins are terminally misfolded or form aggregates, the proteostasis network directs them to degradation by the ubiquitin-proteasome or autophagy pathways (Figure 1. 4. 2).

Furthermore, different sub-cellular compartments have distinct but integrated

proteostasis networks. For example, the cytosolic HSP70 and HSP90 systems discussed above have endoplasmic reticulum (ER)-localized counterparts to assist membrane and secretory proteins as they are folded in the secretory pathway 64. These ER protein folding mechanisms

are particularly important at the host-pathogen interface, as they assist the folding of cell surface viral receptors and secreted antibodies. ER-resident proteins often involve complex folding pathways, as they can require glycosylation, formation of disulfide bonds, among other post-translational modifications. Thus, the ER proteostasis machinery contains additional components, such as the protein disulfide isomerase chaperone family, which shuffles disulfide bonds on client proteins, and calnexin and calreticulin, which assist glycoprotein folding and release from the ER 64,65. If ER proteins are terminally misfolded, they can be retro-translocated

to the cytosol, where they are degraded by the ubiquitin-proteasome or autophagy pathways 65. Proper function of cellular proteostasis networks is essential for organismal health. The ability of these networks to fold proteins that would otherwise form aggregates has prompted the hypothesis that protein-folding machinery can impact protein fitness, and thus, protein evolution.

1.5

_{Host proteostasis both constrains and potentiates host protein evolution}

Chaperones are upregulated by cellular stress and can buffer the effects of changing

environmental conditions, such as temperature fluctuation, oxidative stress, or invading

pathogens

They also buffer the phenotypic effects of genetic change (Figure 1. 5.

_1A),

which has been revealed by inhibition of HSP90 in plants, fungi, and Drosophila

.

For

example, in Arabidopsis thaliana, HSP90 buffers the phenotypic expression of genetic variation,

morphogenic variation in response to environmental changes, and developmental stability

against stochastic processes

Similarly, in Drosophila, HSP90 buffers standing morphogenic

variation that is revealed upon disruption of HSP90 activity

In addition to buffering the

phenotypic effects of standing genetic variation (Figure 1. 5.

1A),

HSP90 has also been

observed to potentiate, or enable, the phenotypic effects of new mutations in budding yeast

(Figure 1. 5.

1B)

Although the molecular origins of these results have typically not been

characterized, in principle they may be caused by HSP90 directly engaging an evolving client

protein (termed a primary effect) or mediated indirectly as HSP90 influences the folding of other

endogenous clients that themselves engage relevant evolving proteins (termed a secondary

effect). HSP90-dependent azole resistance in Candida albicans, for instance, is mediated not by

A _B

Chaperones as buffers: Chaperones as potentiators:

genetically diverse population genetically diverse population

Chaperone _Chaperone

inhibition _inhibition

HSP90 directly engaging azole-resistant Erg3 variants, but instead by HSP90-mediated

activation of calcineurin, an HSP90 client that controls responses to various environmental

stimuli 4.

Efforts to look beyond HSP90 and understand how other components of the metazoan

proteostasis machinery modulate endogenous protein evolution (e.g., HSP40/70 chaperones or

protein misfolding stress responses like the heat shock response) have been slowed by a lack

of chemical biology tools to perturb the activities of these systems

Recently, work on heat

shock factor 1 (HSF1), the master transcriptional regulator of the heat shock protein chaperones

and cytosolic quality control machinery

demonstrated that HSF1 potentiates phenotypic

variation by mechanisms distinct from HSP90

Moreover, computational modeling suggests

69,70

that other chaperones, such as the HSP40 family, may have roles in evolution

. In bacteria,

Tawfik and coworkers have shown that the GroEL/ES chaperonin system can alter the

accessibility of endogenous client protein variants. Specifically, GroEL/ES overexpression

potentiates GroEL/ES-dependent enzyme variants containing mutations in the protein core that

were predicted to be more destabilizing than those in GroEL/ES-independent variants

In

another study, the same group found that GroEL/ES-dependent variant expression correlated

with stabilization of folding intermediates, rather than in vitro protein stability

Together, these

studies implicate a role for chaperones in shifting the accessible stability range for evolving host

A Basal chaperone _{B Enhanced chaperone C Reduced chaperone}

levels levels levels

folding rate fast

slow

fast

* _*0

misfolding 0 * *

slo

--unstable _{protein inside protein oustide}

stability expanded boundary restricted boundary stable can now function _{can no longer function}

Figure 1. 5. 2 Chaperones can shift the boundary of accessible protein stabilities. (A) Protein biophysical boundary model of evolution (see Figure 1. 3. 1). (B) Enhancing chaperone levels can expand the boundary to allow access to additional protein variants. (C) Reducing chaperone levels can contract the boundary and make currently accessible variants inaccessible. In A-C, variants inside the boundary are accessible in the given chaperone environment; variants outside the boundary (in red) are inaccessible. Axes are identical in A-C. Adapted from Powers et al.3

1.6

_{Viruses engage host proteostasis factors}

Identifying the constraints operating on viral protein evolution and adaptation is essential for

preventing and treating viral pandemics. Similar to host proteins, the distribution of mutational

fitness effects for viral proteins can be largely accounted for by considering protein-folding

biophysics

Chaperones and other proteostasis mechanisms are therefore theoretically

well-positioned to address the biophysical challenges created by high mutation rates in viruses.

Though there are rare examples of virus-encoded chaperones

75,

most RNA viruses do

not encode autonomous chaperones or other co-factors to assist protein folding. Instead,

viruses upregulate and engage host chaperones

(Figure 1. 6. 1), and host chaperone

inhibitors can limit the viability of certain RNA viruses

For example, HSP90 assists viral

polymerase assembly, and HSP90-inhibition can severely reduce replication of influenza

hepatitis B

and ebola virus

among others. Similarly, HSP70 assists the folding and

assembly of viral proteins

across several viral families

Beyond promoting folding and

M1

CALR CANX

assembly, the heat shock

SP90

1

proteins can also regulate

SPA1B

HSPA

SPA8

PDIA6

HSBH1 PDIA

antiviral responses indirectly

_NAJA

NNAJ'

through their interactions with PB NAJB NAJB

NAJC7- A

viral

proteins

Viral

NAJB1

surface proteins also engage

components of the host's ER

_{Figure 1. 6. 1 Influenza proteins interact extensively with}

host proteostasis factors.

protein folding machinery. The Characterized interactions between influenza

_proteins

folding

of

influenza

(colored along perimeter) and human proteostasis factors

(gray ovals)1 4 18 66

hemagglutinin (HA) is perhaps