Conformational Changes of Polyomavirus during Cell Entry

by

Marjan Dolatshahi

Department of Anatomy and Cell Biology

McGill University

Montreal, Quebec, Canada

Submitted in February2008

A thesis submitted to McGill University in partial fulfillment of the role of the requirements of the degree of

Master of Science

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1*1 Canada Acknowledgements

First of all, I would like to thank my supervisor, Dr. Isabelle Rouiller, for all her support, patience, understanding, and generosity during my masters program. She never hesitated to offer any opportunity to improve my scientific knowledge and experience. More importantly, she believed in my potential and amplified my enthusiasm for learning through her persistent encouragement and scientific feedback. Moreover, I am really thankful to her for providing me with financial support during my studies.

I am grateful to my advisory committee members, Dr. Hojatollah Vali and Dr. John Presley who always provided me with constructive criticism and scientific support.

I would also like to thank Dr. Hojatollah Vali for not only being a dedicated teacher, but also a very motivating friend for me. He introduced me to the research field and his intellectual guidance and great advice helped me to maintain the right direction in my research. I can not express my gratitude to him by words.

Special thanks go to Dr. Paulo Amati and Dr. Robert Liddington for providing the viruses.

Many thanks go to Dr. Tim Baker, Dr. Wendy Ochoa and Dr. Norm Olson, Dr. Dorit Hanein and Dr. Robert Liddington for their support in data collection.

Thanks to Dr. Tim Baker, Dr. Xiaodong Yan, Dr. Robert Sinkovits and all the RobEM team for their support in using RobEM.

Many special thanks go to Dr. John Bergeron who provided me with plenty of useful opportunities in his lab when I took my first steps in research.

My sincere thanks go to Ali Fazel for being such a patient teacher and helped me find my way in the beginning of my research experience.

I am really grateful to Dr. Eugene Daniels for the opportunity of working as a teaching assistant in Anatomy labs, which was a highly enriching experience. Also, I would like to thank all the staff and professors in the Anatomy lab especially Dr. Ayman Behiery for his constant encouragement and guidance.

Thanks to the staff of the Departments of Anatomy and Cell Biology who contributed their time and expertise to help me particularly Sandra Botbol, Prabha Ramamurthy, Nancy Nelson, and Abdullah A1 Masud.

Many thanks go to all the staff of the Facility of Electron Microscopy Research, especially Dr. Kelly Sears and Jeannie Mui, who assisted me with electron microscopy experiments.

ii A special thank you goes to Avi Biswas for providing me with a computer program that accelerated my research program.

I am deeply thankful to Martin Fleming for his great immediate help in solving all my computer problems.

I am really thankful to my dear friend Fereshteh Azari for her enjoyable company and genuine friendship through my studies.

I would like to express my gratitude to my caring sister, Masi, and my kind brother in- law, Sasan, who were standing behind me all the way. I do not know how to thank them for their unconditional love and support both emotionally and financially.

Finally I would like to thank my parents who contributed their lives to my success, for flooding my heart with hope and pushing me forward in my weakness with their never- ending love. I would like to extend my thankfulness to my other family members who always cherished me with their care and affection.

iv Abstract

Similar to other non-enveloped viruses, the mechanism of cell entry for polyomaviruses is poorly understood. The polyomavirus capsid is an icosahedron

composed of 72 pentamers of the major capsid protein VP1. There is one copy of minor

capsid proteins, VP2 or VP3, at the center of each pentamer. According to previous

studies, polyomavirus cell entry is a multi-step process which includes: 1) VP1 binding to

sialic acid (SA) on the surface of host cells, 2) interaction of VP1 with a4pl integrin and

3) subsequent cell penetration. Biochemical studies have shown that SA alters polyomavirus protease sensitivity, suggesting a conformational change. The aim of this

study was to determine these conformational changes at the molecular level. Therefore,

we used single particle cryo-electron microscopy to construct 3D maps of wild type (WT)

murine polyomavirus, WT bound to SA, a mutant with a disrupted integrin binding site,

and the mutant bound to SA. Our results reveal that in both WT and mutant viruses, a

significant conformational change happens after binding with SA which is seen as an

additional ring of density inside the virus. Moreover some negative densities are seen in

the difference map of WT and WT bound with SA, which suggests movement of some

viral proteins after binding with SA.

IV Resume

Tout comme les autre virus sans enveloppe, le mecanisme d'entree de polyomavirus dans

la cellule est mal compris. La capside de polyomavirus est un icosaedre compose de 72 pentameres de la proteine principale de la capside VP1. II y a une seul copie de proteines mineures de la capside VP2 ou VP3 au centre de chaque pentamere. D'apres des etudes precedentes, l'entree de polyomavirus dans la cellule est consistee de plusieurs etapes

incluant: 1) l'attachement de VP1 a l'acide sialique (AS) a la surface des cellules hotes,

2) 1'interaction de VP1 avec l'integrine a4(31, et 3) l'entree dans la cellule. Des etudes

biochimiques precedentes ont montrees que AS altere la sensibilite de polyomavirus a la

digestion proteasique suggerant un changement de conformation. L'objectif de cette etude

est de determiner les changements de conformation au niveau moleculaire. Pour ce faire,

nous avons utilise la cryo-microscope electronique et la technique de particule isolee pour

calculer les cartes tridimensionnelles du virus polyomavirus murin sauvage, du virus

sauvage lie a AS, d'un mutant avec une mutation dans la site d'attachement a l'integrine,

et du mutant lie a AS. Nos resultats montrent que le virus sauvage et le mutant subissent

tous les deux un changement de conformation signifiant apres l'attachement de AS. Le

changement est visible comme un anneau de densite a l'interieur du virus. De plus, des

densites negatives sont vues dans la carte de difference entre sauvage et sauvage+AS, ce

qui suggere le mouvement de certaines proteines virales apres la liaison a AS.

v List of Abbreviations

aa: amino acid BKV: BK virus

CC: Correlation Coefficient CCD: Charge Couple Device Cryo-EM: Cryo-electron microscopy

CTF: Contrast Transfer Function D138N: point mutation in amino acid 138 (aspartic acid is replaced by asparagine) DFT: Discrete Fourier Transform EM: Electron microscopy ER: Endoplasmic Reticulum ERAD: Endoplasmic Reticulum Associated Protein Degradation

FFT: Fast Fourier Transform FSC: Fourier Shell Correlation FT: Fourier Transform Gly: Glycine HIV: Human Immunodeficiency Virus JCV: JC virus kbp: Kilobasepair LDV: Leucine, Aspartic acid, Valine LT: Large Tumor

Mutant+ NANA: D138N mutant polyomavirus bound with a 2,3 N-acetyl neuraminic acid

NANA: a 2,3 N-acetyl neuraminic acid

NMR: Nuclear Magnetic Resonance

PARP: Poly ADP-ribose Polymerase SA: Sialic Acid SL: Sialyl Lactose SNR: Signal to Noise Ratio SV40: Simian Vacuolating Virus 40

vi 3D: Three Dimensional 2D: Two Dimensional TMV: Tobacco Mosaic Virus VLP: Virus-Like Particles VP 1: Viral Protein 1 VP2: Viral Protein 2 VP3: Viral Protein 3

VP2/3: The common sequence between VP2 and VP3

WT: Wild Type WT + NANA: Wild Type polyomavirus bound with a 2,3 N-acetyl neuraminic acid

vii Table of Contents

Title page i Acknowledgements ii Abstract iv Resume v List of Abbreviations vi Table of contents viii List of Tables and Figures x

1. Introduction 1 1.1. Overview of general characteristics of polyomaviruses 1 1.2. Polyomavirus structure 2 1.2.1. Virus symmetry and icosahedral symmetry 4 1.2.2. Triangulation numbers 5 1.3. How do viruses enter animal cells? 7 1.4. Life cycle of polyomavirus 9 1.4.1. Attachment and Internalization 9 1.4.2. Transport to the endoplasmic reticulum (ER) 11 1.4.3. Exit from ER 12 1.4.4. Transport to the nucleus and uncoating 13 1.4.5. Replication 14 1.4.6. Assembly/maturation and release 15 1.4.7. Role of VP1, VP2 and VP3 in the virus life cycle 15 1.5. Comparison of murine polyomavirus with SV40 16 1.6. Cryo-EM and single-particle reconstruction 17 1.7. Two important tasks while cryo-EM single particle recostruction 20 1.7.1. CTF estimation 20 1.7.1.1. Defocus Value 20 1.7.1.2. Temperature factor 21 1.7.2. Alignment 21 1.8. Fourier transform and its importance to 3D image reconstruction 22 1.9. Previous 3D EM studies on polyomavirus 22 1.10. Objectives: 23 2.Materials and Methods 34 2.1. Virus purification and sample preparation 34 2.2. Microscope and instrumentation 35 2.3. Image collection 35 2.4. Image analysis 37 2.5. CTF estimation and correction 37 2.5.1. How does ACE calculate CTF parameters? 38 2.6. 3D map reconstruction 39 2.7. Calculation of difference map 41 3.Result s 43 3.1. Electron micrographs of polyomavirus 43

viii 3.2. CTF estimation 43 3.2.1. On carbon 43 3.2.2. On ice 45 3.3. Three dimensional (3D) structure of murine polyomavirus 46 3.3.1. WT 47 3.3.2. Mutant 48 3.3.3. WT+NANA 48 3.3.4. Mutant + NANA 48 3.3.5. Resolution estimation 49 3.4. Subtraction of 3D map reconstructions 49 3.4.1. The conformational change after binding with NANA 49 3.4.2. No conformational difference between the D138N mutant and WT 50 3.4.3. Subtraction of mutant bound with NANA from WT bound with NANA .50 3.5. Controls 50 3.5.1. Rule out the influence of initial model on the final 3D map 50 3.5.2. Check the efficiency of virus-NANA binding 52 3.6. Improving the resolution 53 3.6.1. Setting the calculating parameters in refinement mode 53 3.6.1.1. Inner and outer radii 53 3.6.1.2. Angular space 54 3.6.1.3. Temperature factor 55 3.6.2. Excluding particles with low correlation coefficient (CC) 55 3.6.3. Excluding particles with inaccurate CTF correction 57 4.Discussion and Future Work 83 4.1. Polyomavirus general appearance 83 4.2. Virus-NANA binding leads to a conformational change in the viral capsid 83 4.3. D138N mutation in VP1 LDV does not induce conformational changes in the viral capsid 84 4.4. Observed conformational changes may be attributed to VP1, VP2, VP3, or DNA 85 4.5. Why does the significant conformational change happen at the internal surface of the pentamers instead of at the outer surface of the capsid? 87 4.6. What resolution do we need to interpret our 3D EM map reconstructions? 88 4.7. Resolution limits 90 4.7.1. Virus aggregation 91 4.7.2. CTF estimation 92 4.8. Is the estimated resolution true? 93 4.9. Improving the resolution 93 4.9.1. Setting calculation parameters in the REFINE mode 94 4.9.2. Increasing the number of particles 95 5.References : 96

ix List of Tables

Table 1. Polyomavirus phylogeny reviewed by Zur Hausen, 2008 26 Table 2. Resolution of the 3D map reconstructions changes with different starting models 71

List of Figures

Figure 1. X-ray crystallography maps of the polyomavirus capsid and capsomers 27 Figure 2. Schematic diagram of VP2 and VP3 domains 28 Figure 3. Icosahedral symmetry of the polyomavirus and its triangulation number 29 Figure 4. Polyomavirus violates quasi-equivalence theory of icosahedra 30 Figure 5. Cell entry mechanism of polyomavirus proposed by Caruso et al., 2003a 31 Figure 6. A comparison of the chemical structures of straight- and branched-chain sialylated receptors 32 Figure 7. for the polyomavirus life-cycle proposed by Gilbert and Benjamin, 2004 33 Figure 8. Schematic diagram of our approach to 3D map reconstruction by AUT03DEM 42 Figure 9. Cryo-electron micrograph of murine polyomavirus 59 Figure 10. CTF estimation using ACE software 60 Figure 11. 3D EM map reconstruction of wild type (WT) polyomavirus 61 Figure 12. 3D map reconstruction of mutant Polyomavirus 62 Figure 13. 3map reconstruction of wild type polyomavirus bound with NANA 63 Figure 14. 3D map reconstruction of the mutant polyomavirus bound with NANA 64 Figure 15. Resolution curves 65 Figure 16. The conformational changes in the WT and mutant polyomavirus after binding with NANA 66 Figure 17. No conformational change in D138N mutant polyomavirus 67 Figure 18. Subtraction of wild type bound with NANA (WT+NANA) from mutant bound with NANA (MUT+NANA) reconstructions using RobEM 68 Figure 19. Controls show that the final 3D map is independent of the initial model 69 Figure 20. 3D EM map reconstruction of mutant+NANA polyomaviruses with different starting models 72 Figure 21. Schematic diagram of the approach used to check the efficiency of virus- NANA binding 73 Figure 22. Comparison of 3D map reconstructions after sorting of particles 74 Figure 23. Controls confirm the efficiency of NANA-binding in NANA-bound particles 75 Figure 24. Measuring the inner and outer radii of the area of interest 76 Figure 25. Effect of parameter alteration on resolution 77 Figure 26. Distribution patterns of particle CC and comparison with the Zhang et al., 2008 study 78

x Figure 27. Effect of excluding particles with low CC (correlation coefficient) on resolution 79 Figure 28. Effect of particle CC on resolution, independent of the number of particles... 80 Figure 29. Effect of the accuracy of CTF estimation on resolution 81 Figure 30. Effect of good CTF estimations on resolution, independent of the number of particles 82

XI Chapter 1

1. Introduction

1.1. Overview of general characteristics ofpolyomaviruses

Polyomavirus is the sole genus in the family of Polyomaviridae. Polyomaviruses are small non-enveloped double stranded DNA viruses with icosabedral capsids.

Previously polyomavirus and papoavirus were two genera in the papoaviridae family because of similar capsid structures (icosahedra T=7). However, the two families were too different in terms of genome size, transcription direction, and nucleotide or amino acid sequence to be considered in the same family (Bernard, 2005). Therefore, polyomavirus and papoavirus have been categorized as two different families by the

International Council on Taxonomy of Viruses (De Villiers et al., 2004).

Polyomaviruses are isolated from a variety of species, including humans, monkeys, rodents, and birds (Table 1). They exhibit tissue and species-specific tropism

(Cole, 1996). The name polyoma refers to the ability to produce multiple tumors. They do not produce disease directly but they are potentially oncogenic, meaning they can persist as a latent infection and cause multiple tumors in immunosuppressed hosts of different species (Zur Hausen, 2008). Murine polyomavirus and simian vacuolating virus 40

(SV40), two closely related members of this family, were the first discovered and are the best characterized in terms of structure (reviewed by Cole, 1996).

These viruses have been used extensively as a model for understanding processes such as cell replication, transcriptional and post-transcriptional regulation, and cell cycle control. Ludwig Gross discovered the first polyomavirus accidentally in 1953 while he was studying murine leukemia virus. He observed that inoculation with this virus

1 developed adenocarcinomas of the parotid gland in addition to leukemia. Extracts of the tumor induced the formation of multiple solid tumors in new born mice (Gross, 1953).

SV40, the second member of this family to be isolated, was discovered as a contaminant in the Salk poliovirus vaccines (Sweet and Hilleman, 1960).

During the early 1970s, two new species called JC virus (JCV) and BK virus

(BKV) were isolated from humans. JCV was isolated from the brain tissue of a patient suffering from progressive multifocal leukoencephalopathy (PML) (Padgett et al., 1971), while BKV was isolated from the urine of a kidney transplant patient with advanced renal failure (Gardner el al., 1971). Two new human polyomaviruses KI (Allander et al., 2007) and WU (Gaynor et al., 2007) have recently been isolated from human respiratory secretions. Generally, polyomaviruses are strongly associated with disabling conditions, such as kidney-graft loss and cancer in a broad range of hosts. Thus, uncovering their mechanism of cell entry is of great importance (reviewed by Zur Hausen, 2008).

1.2. Polyomavirus structure

Polyomaviruses are the simplest viruses with a double-stranded DNA genome

(Tooze, 1981) .They are non-enveloped, about 450 A in diameter, with an icosahedral capsid (Figure 1A) (Klug et al., 1965).

The polyomavirus capsid encloses a small viral minichromosome composed of

5.2-kbp circular double-stranded viral DNA and the four host-derived core histones, H2A

, H2B, H3, and H4 (Muller et al., 1978). The viral capsid is composed of three proteins,

VP1, VP2, and VP3 (Rayment et al., 1982). The major viral protein VP1 (45 kDa) forms

2 72 pentameric capsomers (60 hexagonally coordinated plus 12 pentamerically coordinated) associated with a single copy of a minor viral protein, either VP2 (35kDa) or

VP3 (23 kDa), at the center of each pentamer (Figure IB). More succinctly, each virion contains 360 copies of VP1 (i.e. 72 x 5) plus 30-60 copies of minor proteins. While it remains unclear how many copies of each minor protein exist in the viral capsid, the total number of VP2 and VP3 copies is 72 (Stehle et al., 1994).

VP1 has a sialic acid (SA) binding site on the surface that forms the receptor binding site for the virus. VP2 and VP3 have overlapping sequences so that VP3's sequence is entirely contained in C-terminus of VP2, with an additional 115 aa residues forming the VP1 N-terminus (Figure 2; Barouch and Harrison, 1994). VP2/3 is referred when the shared residues are being discussed. VP2 is myristylated at its NH2-terminus

(Streuli and Griffin, 1987); this myristyl group is believed to be important in penetrating the host cell membrane (Sahli et al., 1993), although there is disagreement surrounding its role. Immunofluorescent staining of VP1 demonstrated the ability of myristyl minus mutants to enter and infect cells with the same efficiency as WT polyomavirus (Mannova et al., 2002).

X-ray crystallographic studies of murine polyomavirus (Liddington et al., 1991) and its closely related species SV40 (Stehle et al., 1994) have revealed the virion shell architecture and VP2-VP1 interaction. In both of them, long C-terminal arms of VP1 emanate from each pentamer and insert into subunits of two adjacent pentamers, forming a tied shell. It has been shown by low resolution crystallographic studies (25A) that a part of VP2/3 inserts into the inward-facing cavity along the 5-fold axis of a VP 1 pentamer

(Griffith et al., 1992). In vitro binding assays have confirmed that VP1 interacts tightly

3 with VP2 or VP3 and a C-terminal segment of VP2 and/or VP3 is necessary and sufficient for interaction with VP1 (Barouch and Hasrrison, 1994). A crystal structure of this segment in complex with a VP1 pentamer has been identified at 2.2 A resolution

(Chen et al., 1998). These high resolution X-ray crystallography studies indicate that the

C-terminal segment of VP2 or VP3 associates tightly with VP1 pentamer specifically through hydrophobic interactions. However, the N-terminal regions of internal proteins are highly flexible and sensitive to gentle proteolysis. This structure is compatible with the possible models for exposure of the internal proteins during cell entry (Figure 1A;

Chen etal., 1998).

1.2.1. Virus symmetry and icosahedral symmetry

Viruses are infectious agents whose infectivity is transmitted either by DNA or

RNA. The nucleic acid molecule is usually contained in a protective package (capsid) which serves as to transmit the infectious DNA or RNA molecule. Many viruses have highly symmetrical capsids which are made of multiple identical protein subunits. Their shapes vary from cylindrical with helical symmetry like tobacco mosaic virus (TMV), and spherical with icosahedral symmetry like polyomavirus, poliovirus, rotavirus, adenovirus (Caspar and Klug, 1962). Some other viruses have more complex capsid structures such as human immunodeficiency virus (HIV) whose capsid is conical (Kafaie et al., 2008) with fullerene symmetry (Ganser et al., 1999) whereas some viruses like poxviruses do not have symmetrical capsids (Condit et al., 2006).

4 An icosahedron is an isometric structure with 12 pentagonal vertices and 20 triangular faces. Each icosahedron has a defined set of exact symmetry elements: 6 fivefold axes through the 12 vertices, 10 threefold axes through the 20 triangular faces, and 15 twofold axes through the edges (reviewed by Baker et al., 1999; Figure 3A).

The positions of the symmetry elements are used as landmarks to describe any icosahedral structure. Symmetry elements applied to a subunit not lying on a symmetry axis cause them to be repeated 60 times in the complete structure. Therefore, the complete icosahedral structure can be generated with one asymmetric unit, or l/60th of the structure, and defining the symmetry elements (Caspar and Klug, 1962). Simple icosahedra contain

60 identically interacting subunits. With more than 60 subunits, all subunits cannot have identical environments. This was hypothesized by Caspar and Klug as quasi-equivalence theory holding that large icosahedra are made of different types of subunits forming quasi-equivalent rather than equivalent bondings (Caspar and Klug, 1962). However, some icosahedral viruses such as polyomaviruses and papillomavirues do not strictly obey this theory (see section 1.2.2; Baker et al., 1983; Belnap et al., 1996).

This symmetry aids 3D reconstruction since each particle image potentially contains many different views of the structure; therefore, fewer particles are needed for a

3D reconstruction of symmetric objects than for asymmetric ones. This was shown for the first time by De Rosier and Klug, who were able to create 3D constructions of the bacteriophage T4 tail using only a single two dimensional electron micrograph because of its high rotational symmetry (De Rosier and Klug, 1968).

1.2.2. Triangulation number

5 Triangulation number is an abstract concept that refers to geometric characteristics of an icosahedron and does not necessarily correlate with the structural components of an individual virus. It determines the organization of the geometric figure (Figure 3B).

To make an isometric shell from a flat hexagonal net, there are many possible ways to fold the sheet to form different icosahedrons. The larger icosahedra (with more than 60 subunits) have hexagons between the pentagons and can be formed by replacing the original triangular faces with larger equilateral triangles. The number of triangles replacing the original one is the triangulation number. Mathematically, the triangulation

2 2 number can be calculated by the following relationship, T = h +hk + k , where h and k are positive integers which define the position of the five-fold vertex (capsomers on the surface of viral capsid) on the original hexagonal net (Figure 3B,C). The quasi- equivalence theory stipulates that the triangulation number equals the number of different bonding environments in an icosahedron. It states that there are 60T subunits arranged in

12 pentamers and 10(T-1) hexamers in each icosahedron. For example, bacteriophage

HK97 head is a T=7 icosahedron with seven different environments, 60T or 420 subunits,

12 pentamers and 60 hexamers (Figure 4A). Contrary to this theory, polyomavirus displaying T=7 is composed of 360 subunits (rather than 420) organized as only pentamers (Figure 4B). When the icosahedrons corresponds with quasi-equivalence theory the triangulation number indicates the number of environments; otherwise it remains a descriptive tool (reviewed by Baker et al., 1999)

Polyomavirus has a T=7d icosahedral symmetry. The viral capsid is composed of

72 VP1 pentamers, 12 pentavalent (surrounded by 5 other pentamers) and 60 hexavalent

(surrounded by 6 other pentamers). The d in T=7d refers to handedness, which is dextral

6 or right-handed. Handedness is defined by the arrangement of lattice points between neighboring 5-fold vertices (Figure 3C). Although there is no theoretical limitation against a right or left handedness (Caspar and Klug, 1962), all viruses from a particular species or strain are assumed to have the same hand. The chirality of amino acids leads to a preference of one handedness, where interactions among protein subunits are more energetically favorable (Belnap et al., 1996).

1.3. How do viruses enter animal cells?

Viruses are intracellular parasites that infect all domains of life, including

Eukarya, Bacteria, and Archea. Animal viruses have been extensively studied because of their medical importance (reviewed by Poranen et al., 2002). Lacking a complete set of replication machinery, all viruses rely on the host factors for replication (reviewed by

Smith and Helenius, 2004). Therefore, after reaching the host cell a series of obligatory events happen to deliver the viral genome and accessory proteins to the replication site and establish an effective infection. These stages include cell penetration through cell- surface receptor(s), trafficking to the reproductive site, uncoating, virus replication and maturation, shedding of virions outside the cell, and spreading within the host.

On their way to the replication site, which may be the cytosol or the nucleus, viruses encounter many barriers. In the case of animal viruses, one of the critical barriers is the host plasma membrane. The strategy they use to cross this obstacle depends on both viral factors (e.g. size, constituency, and structure) and cellular conditions (e.g. pH, redox

7 potential, surface molecules; reviewed by Poranen et al., 2002). One decisive factor is the presence of a membrane bilayer, named "envelop", around the virus.

The mechanism of cell entry for enveloped viruses such as retroviruses, paramyxoviruses, or coronaviruses, is well characterized. It involves transcytosis, or direct fusion, such that the viral envelop plays the role of an endocytotic vesicle with the viral capsid as the cargo. This fusion is catalyzed by fusion peptides embedded in the viral envelop (reviewed by Bomsel and Alfsen, 2003). For example, the influenza virus is first taken up by endocytosis into endosomal components, where low pH then triggers the exposure of a hydrophobic peptide. This initiates a fusion reaction between the viral and endosomal membrane that consequently releases the nucleocapsid into the cytosol

(Skehel and Wiley 2000).

In contrast, the entry mechanisms for non-enveloped viruses are poorly understood. Because they lack a lipid bilayer cover, their cell penetration mechanism is fundamentally different from enveloped viruses. They first interact with cellular cues, such as receptors, proteases, and chaperones, or become stimulated by low pH, to induce conformational changes that render the virus hydrophobic. Hydrophobic viruses can then penetrate the membrane by lysing the membrane or by creating pore-like structures in the membrane (reviewed by Tsai, 2007). For example, the adenovirus penton base becomes lytic at low pH, allowing the virus to leave the endosome by rupturing the membrane

(Fitzgerald et al., 1983; Blumenthal et al., 1986). However, the details of these conformational changes and how they facilitate membrane penetration has not yet been clarified.

8 Another critical step in infection is uncoating, whereby the virus sheds its envelop and disassembles the capsid to expose the viral genome. Since some viruses never enter the host cell, they do not undergo uncoating. In this case, their genome is released via a specialized channel located at one of the vertices of the icosahedral virion, with the capsid remaining intact. However, many viruses such as polyomavirus, need to be internalized and completely disassembled (Reviewed by Poranen et al., 2002). It is noteworthy that the location of uncoating varies from virus to virus such as at the plasma membrane, within an intracellular vesicle, in the cytosol, at the nuclear membrane, in the nucleus, or even in a sequential manner during its passage to the replication site (Kasamtsu and

Nakanishi, 1998). Generally, and with few exceptions, DNA viruses go to the nucleus and

RNA viruses replicate in the cytosol (reviewed by Smith and Helenius, 2004).

1.4. Life cycle of polyomavirus

1.4.1. Attachment and Internalization

Data show that the mechanism of cell entry for polyomavirus is most probably a multi-step process involving: (1) VP1 recognition and binding to SA-terminating receptors on the surface of host cell; (2) post-attachment interaction with secondary receptors such as a4pi integrin through a VP1 LDV motif; (3) penetration through the plasma membrane by micropynocytosis (Caruso et al., 2003a; Figure 5). Although efforts to identify a unique receptor for murine polyomavirus (by screening for monoclonal antibodies that protect cells from infection) have been unsuccessful (Bauer et al., 1999), ganglioside GDI a has been implicated as a murine polyomavirus receptor on the surface

9 of host cell plasma membranes (Tsai et al., 2003; Gilbert and Benjamin, 2004; Gilbert et al., 2005).

A rat cell line deficient in ganglioside synthesis was poorly infected by murine polyomavirus, as measured by immunostaining cells with antibodies to the large T antigen, whereas pre-incubation of cells with GDla increased infectability dramatically

(Tsai et al., 2003). Sialic acid (SA) plays an important role in polyomavirus infectivity; polyomavirus ability to cause infection severely decreases when the total amount of available SA residues on host cell membranes are decreased by treatment with neuraminidase or use of N-glycosylation inhibitors (Chen and Benjamin, 1997; Hermann etal., 1997).

Sialic acid (SA) is the generic name for the 9-carbon sugar based on neuraminic acid (NANA). It shows diversity based on the nature of sugar linkages (what sugar and through which carbon they are linked). The two major types with which polyomavirus can interact are straight chain (a 2, 3-linked SA) and branched chain (a 2, 6-linked SA)

(Angata and Varki, 2002; Figure 6). Crystal structures of VP1 complexed with an oligosaccharide fragment receptor have shown that a 2,3-linked SA binds to VP1 pocket

1 and a 2,6-linked SA binds to VP1 pocket 3 (Stehle et al., 1994; Stehle and Harrison,

1996 & 1997). All murine polyomavirus strains can bind a 2,3-linked SA, but only strains containing Gly in position 91 are able to bind branched oligosaccharides carrying a 2,6-linked SA (Fried et al., 1981; Cahan et al., 1983; Chen and Benjamin, 1997). Point mutations in residues of VP1 pocket 1 block cell binding and infectivity (Caruso et al.,

2003b). Preincubation of polyomavirus with NANA (which binds to VP1 pocket 1) increases cell binding, whereas compounds like sialyl lactose (SL), whose sialic acids

10 bind with VP1 pocket 1 and 2, decrease polyomavirus cell binding (Cavaldesi et al.,2004). This implicates the specificity of the SA-VP1 binding site in the process of polyomavirus viral entry.

Following initial SA-mediated cell binding, a4piintegrin acts as a post- attachment receptor for polyomavirus, as indicated by antibodies that partially block internalization and subsequent infection without affecting cell-binding (Caruso et al.,

2003b). Studies have revealed that initial SA binding is a prerequisite for subsequent integrin binding, which suggests a conformational change that makes the VP 1 LDV motif more accessible to an integrin molecule (Caruso et al., 2003b). In fact, this conformation change has been confirmed by biochemical studies that show a transition from a protease- sensitive to a protease-resistant state of VP1 after incubation with N-acetyl neuraminic acid (NANA; Cavaldesi et al., 2004). However, this conformational change has not yet been observed structurally. While SA has an essential role in polyomavirus cell binding

(Caruso et al., 2003b), integrins are not unique receptors for murine polyomavirus. One study found that a mutation in the integrin binding motif (D138N mutation in the VP1-

LDV motif) failed to abrogate infectivity completely, but affected viral infectivity noticeably and modified tissue tropism (Caruso et al., 2007).

1.4.2. Transport to the endoplasmic reticulum (ER)

After cell attachment via GDI a receptor and internalization, polyomavirus traffics to the ER (Gilbert and Benjamin, 2004). It is not well defined how murine polyomavirus transports to the ER. However, the necessity of transport to the ER has been demonstrated

11 by blocking murine polyomavirus infection with brefeldin A, which interferes with ER-

Golgi trafficking (Richards et al., 2002; Gilbert and Benjamin, 2004). Early after incubation, electron microscopy studies have identified virus particles in small, uncoated vesicles, presumably being trafficked to the ER (Griffith et al., 1988). There is disagreement surrounding the exact nature of these vesicles. One group has shown that polyomavirus is endocytosed by non-clathrin non-caveola-derived vesicles in a dynamin- independent manner (Gilbert and Benjamin, 2000; Gilbert et al., 2003), whereas other groups implicated a caveola-based vesicle uptake pathway (Mannova and Forstova, 2003;

Richterova et al., 2001). Similarly, caveola-mediated endocytosis of SV40 into the ER has been visualized by live fluorescence microscopy (Pelksman et al., 2001), whereas another study by the same group has shown that SV40 endocytosis is intact in caveolin-1 knock out or deficient cells (Damm et al., 2005).

There are also conflicting reports surrounding intracellular migration and the role of actin fibers. Some groups have implicated actin fibers in internalization (Krauzewicz et al., 2000; Richterova et al., 2001) while another work suggests that intracellular trafficking of murine polyomavirus is microtubule-dependent (Sanjuan et al., 2003).

Regarding the ubiquity of a 2,3-linked SA residues on the cell surface and the wide range of cell types the virus can infect, it is likely that polyomavirus has more than one single receptor and internalization pathway. The polyomavirus uptake pathway through GDI a receptor is schematically demonstrated in Figure 7.

1.4.3. Exit from ER

12 ERp29, an ER chaperon that belongs to the protein disulfide isomerase (PDI)

family, triggers a kind of conformational change that enables ER membrane binding

(Magnuson et al.,2005) and subsequent transport to the nucleus, the site of replication.

Also, derlin-2, an ER resident protein that removes misfolded proteins from the ER is

essential in polyomavirus escape from the ER and in establishing an effective infection. It

seems that derlin-2 serves as a conducting channel in ER membranes that allows murine polyomavirus to exit (Lilley et al., 2006).

1.4.4. Transport to the nucleus and uncoating

Polyomaviruses enter the nucleus by the nuclear pores. Generally, polyomavirus uncoating occurs inside the nucleus (Kasamatsu and Nakanishi, 1998). There are

paradoxical reports about the role of VP2 myristylated groups in uncoating. While an

early study reported its role during the early steps of infection by investigating the growth

time course of VP2 myristilation defective mutants (Sahli et al., 1993), later studies

showed that it has a role in the late stage of infection, during assembly and reinfection by

viral progeny (Mannova et al., 2002). According to a recent study on SV40, VP2 and

VP3 deletion mutants are defective in nuclear import, suggesting that these proteins are

involved in the process; they reported that transfection of monkey cells with VP1 only

genes lacking the coding region for VP2/3 formed stable particles which were essentially

noninfectious. These mutants were able to enter new host cells but failed to associate with

importins and failed to express large T antigens. This reveals the VP2 and VP3 role in

mediating viral DNA entry into the nucleus of host cell (Nakanishi et al., 2007).

13 Schelhaas et al., 2007 have implicated a stepwise uncoating mechanism of SV40, so that endoplasmic reticulum associated protein degradation (ERAD) detaches 12 pentavalent pentamers of capsid, allowing the virus to exit the ER in a partially disassembled form.

1.4.5. Replication

Polyomavirus replication occurs in the nucleus host cells over two distinct phases, early and late gene expression, which are separated by genome replication (Iacoangeli et al., 1995). The genome is functionally divided into three regions: (1) early region: expressed before genome replication and encodes non-structural proteins, including large tumor (LT), small tumor antigen, and middle tumor antigen, that act in viral DNA replication, transcription, transformation, as well as in cell proliferation; (2) late region: expressed during and after genome replication and encodes VP1, VP2 and VP3; (3) non- coding regulatory region: interspersed between the early and the late regions, and is composed of transcriptional promoters and enhancers as well as being the unique origin of DNA replication.

Because of the relative simplicity of the genome, murine polyomavirus mainly depends on the host cell replication machinery. Protein interactions between T-antigen and DNA polymerase-alpha directly stimulate replication of the virus genome.

Inactivation of tumor suppressor proteins (P53, PI05) bound to T-antigen causes Gl- arrested cells to enter the S phase, promoting DNA replication. Therefore, in addition to increasing transcription, another function of T-antigen is to alter the cellular environment to permit viral genome replication.

14 1.4.6. Assembly/maturation and release

Viral proteins contain 'nuclear localization signals' that cause them to accumulate in the nucleus after being synthesized in the cytoplasm. The nuclear localization signal of

VP3 of SV40 has been shown to interact with the importin a2/p heterodimer, mediating nuclear entry of infecting SV40 (Nakanishi et al., 2002). Due to the simplicity of polyomavirus structure, both assembly and maturation occur in the nucleus simultaneously (Mackay and Consigli, 1976; Tooze, 1981). Viral particles are either exported to the cell surface in cytoplasmic vacuoles or released when the cell lyses. The complete replication cycle takes 48-72h, depending on the infection multiplicity

(Kasamatsu and Nakanishi, 1998). The lytic property of VP2 and VP3 has been implicated in host membrane permeabilization, which facilitates SV40 exit (Daniels et al.,

2006). VP3 can stimulate poly ADP-ribose polymerase (PARP) activity which induces host cell necrosis and subsequent SV40 release (Gordon-Shaag et al., 2003).

In murine polyomavirus VP2 and VP3, despite having their own nuclear localization signals, require co-expression of VP1 for stable nuclear localization

(Forstova et al., 1993; Stamatos et al., 1987). In contrast, VP2 and VP3 of the closely related SV40 can reach the nucleus independent of VP1 (Clever and Kasamatsu, 1991).

This may be due to the lack of a DNA-binding domain in mouse polyomavirus VP2 and

VP3 relative to SV40 VP2 and VP3 (Clever et al., 1993; Chang et al., 1993).

1.4.7. Role of VP1, VP2 and VP3 in the virus life cycle

15 The minor capsid proteins (VP2 and VP3) are not essential for capsid formation, where VP1 alone can self-assemble into capsid-like particles in the presence of Ca2+ ions

(Leavitt et al., 1985; Li et al., 2003). In fact, capsid assembly is an intrinsic property of

VP1 that is sufficient to form capsid like structures in vitro (Salunke et al., 1986), in

Escherichia coli (Ou et al., 1999) , in Saccharomyces cervisiae (Palkova et al., 2000;

Sasnauskas et al., 2002), in insect cells (Chang et al., 1997; Kosukegawa et al., 1996;

Montross et al., 1991; Sandalon et al., 1997), and in mammalian cells (Gharakhanian et al., 2003; Mannova et al., 2002). However, studies have shown that in the absence of either VP2 or VP3, virus particles of reduced infectivity were formed (Mannova et al.,

2002). While their role remains obscure, the fact that minor capsid proteins in murine and other polyomaviruses have important roles in viral infectivity and propagation has also been noted by other groups (Sahli et al., 1993; Nakanishi et al., 2006; Gasparovic et al.,

2006; Gharakhanian et al., 2003). A recent study on closely related viruses, SV40, has

shown that mutants lacking VP2/3 failed to interact with importins and express large T

antigens. Therefore, VP2/3 plays an essential role in nuclear entry (Nakanishi et al.,

2007). However, cells infected with these mutants contained comparable amounts of cell-

associated and internalized viral DNA compared to cells infected with WT virus,

suggesting that VP 1 per se is sufficient for viral entry and viral capsid assembly.

1.5. Comparison of murine polyomavirus with SV40

Similar to other polyomaviruses, both SV40 and murine polyomavirus have

icosahedral capsids (T=7d) composed of VP1, VP2, VP3. The major capsid protein VP1

16 amino acid sequence is 54% homologous in SV40 and murine polyomavirus. One difference in viral capsid proteins is that in polyomavirus, only VP1 has a DNA binding domain (Chang et al., 1993), whereas in SV40, all three structural proteins, VP1 (Soussi,

1986), VP2 and VP3 (Clever et al., 1993) have been shown to exhibit DNA binding activities. Polyomavirus VP2 and VP3 have only 32% amino acid homology with the

SV40 VP2 and VP3 sequence (Tooze, 1981); polyomavirus VP2 and VP3 lack the C- terminal last 28 amino acids of SV40 VP2 and VP3, which have the DNA binding

property (Chang et al., 1993).

The genome structure of the two is very similar, with the exception that the SV40 genome only codes for small and large tumor antigens, but not middle tumor antigen.

SV40 does not bind to sialic acid (SA), but does have similar pockets on its surface and binds to carbohydrate receptor GM1 (Stehle and Harrison, 1996). The general features of the respective life cycles, including internalization to caveosomes, trafficking to the ER,

and replication in the nucleus, are common between SV40 and murine polyomavirus,

although there are some minor differences.

1.6. Cryo-EM and single-particle reconstruction

Cryo-electron microscopy usage in its "single particle" form has increased

dramatically to determine 3D structures of proteins and macromolecular assemblies that are

too large or flexible to be amenable to X-ray crystallography (Frank, 2002).

Cryo-EM is a form of electron microscopy where the sample is preserved in a thin

layer of amorphous ice, showing the biological molecule in its native shape. The

17 specimen is held under cryogenic temperature by rapid plunging of specimen in a bath of

liquid nitrogen surrounded by liquid ethane reservoir. This method of freezing, called

"frozen hydrated", avoids ice crystal formation which damages protein structure. By

maintaining a layer of vitrified water around the specimen, relying on defocus rather than

heavy metal stains to generate contrast, and performing microscopy under low-dose

conditions at near-liquid-nitrogen temperatures, cryo-EM is able to produce data of

unprecedented quality (Adrian et al., 1984).

Single-particle reconstruction is an image analysis method that consists in

calculating the three-dimensional maps of single particles from their multiple identical

copies. Different copies of the particle are considered as its different two-dimensional

(2D) views whose combination produces a final three dimensional (3D) reconstruction of

the particle. This method was first used for negative staining EM studies that were not very successful because of artifacts made by the stain during sample preparation. These

artifacts include distortions of the structure caused by drying, flattening, non-uniform

staining, and radiation damage resulting in a loss of the icosahedral symmetry upon which

the reconstruction method depends. Moreover, many interesting structures, such as

enveloped viruses, become destroyed by interaction with the stain. Even in ideal

conditions, negative staining only reveals the distribution of the heavy metal stain

embedded in the specimen, rather than the density of the specimen itself (reviewed by

Baker et al., 1999). The breakthrough happened after combination of cryo-EM and single

particle method which is called cryo-EM single particle reconstruction, briefly.

Icosahedral viruses were among the first biological specimens to have their 3D molecular

structures solved using single particle cryo-electron microscopy. Due to their large size,

18 high symmetry, and availability in large quantities, viral structures have been frequently studied by this method (reviewed by Baker et al., 1999).

As a quick look at the history of 3D map reconstruction of macromolecules, the bacteriophage T4 tail was the first specimen for which 3D map was derived from EM images using Fourier transform-based techniques (De Rosier and Klug, 1968).

Subsequently, 3D reconstruction of icosahedral viruses was introduced by Crowther and co-workers utilizing negative-stain EM and single particle method (Crowther et al., 1970;

Crowther, 1971). While these works failed to yield high resolution 3D maps, during last two decades cryo-EM single particle reconstruction has yielded high resolution structures near the atomic level (3.8- 4.5 A; reviewed by Zhou, 2008). These resolutions allow researchers to discern protein boundaries, assembly mechanisms of the large virus capsids, and secondary structure elements (alpha helices and beta sheets).

Interestingly, it has been shown by previous works that low-resolution cryo-EM maps can be interpreted by docking to the coordinate atomic structure of the molecule which has been solved by X-ray crystallography or NMR (reviewed by Frank, 2002). A number of softwares such as CoAn (Volkman and Hanein, 1999; 2003) and Situs

(Wriggers and Birmanns, 2001) are designed to fit high-resolution X-ray crystallography maps of the macromolecular assemblies and their components individually into their respective EM maps. These methods allow to model relative domain movements and conformational changes (Volkman and Hanein, 2003).

19 1.7. Two important tasks while cryo-EM single particle reconstruction

1.7.1. CTF estimation

There are some challenging problems in cryo-EM, such as low signal to noise ratio (SNR) in cryo-EM images. Low SNR in cryo-EM is mainly due to low dose electron emission used in this technique to avoid damaging of specimen and the low contrast of biological specimens (Frank, 1996). For example, 99% of the total number of atoms in human body are low molecular weight elements (hydrogen, carbon, nitrogen and oxygen) which only weakly scatter electrons compared to high molecular weight elements (heavy metals used for staining in conventional EM such as lead, uranium, osmium). To increase

SNR, images are taken far from the true focus of the objective lens, imposing a defocus value to the image. Hence, the relationship between the electron image of the specimen and the specimen itself is, unfortunately, not straightforward and is described by the contrast transfer function, or CTF, which arises from characteristics of the particular microscope used, the specimen, and the conditions of imaging ( reviewed by Chiu et al.,

2005).

1.7.1.1. Defocus Value

CTF primarily depends on the defocus value applied to the electron micrograph and should be corrected during 3D map reconstruction. Defocus values dictate the frequency ranges in which phase flipping of the Fourier transform complex values must be made (reviewed by Van Heel et al., 2000; see the following section).

20 1.7.1.2. Temperature factor

Secondly, an experimental B factor (or temperature factor) correction needs to be applied to the Fourier signal amplitude during 3D reconstruction (Saad et al., 2001). The temperature factor, or atomic displacement parameter, in protein crystal structures reflects the fluctuation of an atom about its average position. The distribution of B-factors along a protein sequence is considered as an important indicator of the protein's structure, indicating its flexibility and dynamics (Yuan et al., 2005). The physical origin for the experimental B-factor or temperature factor in electron microscopy is related to the microscope optics and experimental conditions, such as motion of the entire sample, and spherical aberration of the lenses during measurement. While atomic vibrations are also included in this temperature factor, their effect is negligible (Saad et al., 2001).

1.7.2. Alignment

The other important task during 3D map construction is alignment, which involves inferring the orientations of particles from the images. Basically, the icosahedral image reconstruction methods produce a 3D structure by interpreting the separate images of different particles as distinct 2D views of the same structure. Once the cryo-electron micrographs are obtained, the determination of the structure requires three fundamental steps: (1) determining an initial orientation and center for each particle; (2) refining these parameters by comparison of common data among different views; (3) combining the data from a sufficient number of unique views with their relative orientations to produce the final 3D structure (i.e. back projection). Regarding the large number of particles required for achieving high resolution structures and the multiplicity of the steps to

21 calculate particle parameters, the structural biologist is faced with a substantial data management problem. To solve this problem, numerous software packages such as

SPIDER (Frank et al., 1996), EMAN (Ludtke et al., 1999), and FREALIGN (Grigorieff,

2007) have been developed for image processing.

1.8. Fourier transform and its importance to 3D image reconstruction

Jean Baptiste Joseph Fourier defined a mathematical formula by which a function can be analyzed for its frequencies. This function can be a one-dimensional signal like music from a tape recorder or a two dimensional image or a three-dimensional volume or a more multi-dimensional domain. In 1968, DeRosier and Klug described the general methods for reconstruction of 3D structures from electron micrographs using the Fourier transform (FT). The Fourier transformation of an image is best analyzed by looking at its power spectrum, which is the modulus of the complex values of the Fourier transformation, including phase and amplitude. The final density of each point in a 3D map is determined by the summation of Fourier components supplied by all the different images of the object (Moody, 1990). So far, this method has been fundamental for 3D reconstruction of icosahedral viruses whose high symmetry makes common lines in the

Fourier transforms of projected images (Crowther et al., 1970).

1.9. Previous 3D EM studies on polyomavirus

22 Early negative stain electron microscopy studies of SV40 revealed the structure of its icosahedral (T=7d) capsid and subunits (Anderer et al., 1967; Koch et al., 1967;

Mayor et al., 1963). Baker and co-workers (Baker et al., 1983) showed an inconsistency in the quasi-equivalence theory (Caspar and Klug, 1962), where the SV40 capsid is composed of only pentameric subunits. Further studies by the same group using cryo-EM have improved the 3D map of SV40 a great deal, circumventing many artifacts traditionally associated with the negative staining technique (Baker et al., 1983; 1988;

1989).

Later, during efforts to improve cryo-EM techniques, a 3D EM map of mouse polyomavirus was resolved, but at a relatively poor resolution of ~25A (Belnap et al.,

1993). In a cryo-EM study to investigate the preserved features of papillomaviruses, a 3D

EM map of SV40 was resolved to 35A (Belnap et al., 1996). To date, a 3D construction of BK self-assembled VLP (vims-like particle) has been resolved to 20 A, representing the highest resolution map obtained for polyomaviruses (Nilsson et al., 2005). While studies of other icosahedral viruses such as hepatitis B virus (Bottcher et al., 1997), bovine papillomavirus (Trus et al., 1997), herpes virus (Zhou et al, 2000), and rotavirus

(Zhang et al., 2008) have yielded sub-nanometer resolutions (4-9 A), structural studies of polyomaviruses have remained unable to reach these high resolutions.

1.10. Objectives:

Firstly, Cavaldesi and co-workers have shown that preincubation of polyomavirus with NANA increases infectivity. Digestion protection assays with trypsin showed that

23 preincubation of virus-like particles (VLPs; made from VP1 fragment) with NANA resulted in partial protection of VP1 from digestion and a different digestion profile. This effect was specific to a 2,3-linked SA (NANA) which exclusively binds to VP1 pocket

1 while other sugar compounds binding to pocket 2 or pocket 1 and 2 did not present such effect. Therefore, it suggests that binding of VP1 pocket 1 with NANA triggers a conformational change in viral capsid that changes the accessibility of the VP1 cleavage

sites to proteases. To determine whether this conformational change can be detected by other proteinases, similar experiments were performed by proteinase K, which has broader cleavage specificity than trypsin. Similarly, NANA-treated polyomaviruses became protease-resistant relative to untreated ones. To see the effect of VP1 conformational change on minor capsid proteins, the protease digestion assay was done with polyomavirus rather than VLPs. Incubation of viruses with NANA and not with PBS or SL protected VP2 and VP3 against trypsin in a dose dependent manner. In fact, in 32 mM of NANA, some VP2 and VP3 were digested but at 64 and 120 mM, VP2 and VP3 were almost fully protected. This was detected by Western blots using monoclonal antibodies that recognize both VP2 and VP3 proteins (Cavaldesi et al., 2004). To check the sensitivity of polyomavirus to trypsin digestion at the host cell surface, polyomavirus was allowed to bind to 3T6 fibroblat monolayers and trypsin was added to cells for

indicated times. Western blots using anti-VP 1 DE loop antibodies showed that VP1 of

cell-bound polyomaviruses were resistant to trypsin in contrast to that of unbound

polyomaviruses (Cavaldesi et al., 2004). Theses findings along with studies implicating

the essential role of NANA in polyomavirus cell entry and infectivity (Caruso et al.,

2003a, b) suggests that binding of NANA to VP1 pocket 1 of polyomavirus triggers a

24 conformational change in the polyomavirus capsid that is essential for viral cell entry and subsequent infection.

Secondly, it has been shown that VP1 binding to a4(31 integrin through the LDV motif on its DE loop has a role in viral entry at a post-attachment level. Antibodies against a4pi integrin decreased infectivity significantly, but did not affect virus-cell binding (Caruso et al., 2003a). Additionally, D138N point mutation in the LDV motif lowers infectivity and alters tissue specificity (Caruso et al., 2007)

Based on these findings, the main goal of this study was to observe the conformational changes occurring in murine polyomavirus after binding with NANA and after point mutation in the VP1 integrin binding motif. Our study aim was to determine these conformational changes at the molecular levels. Cryo-EM and single particle reconstruction have been extremely used to study structure of many icosahedral viruses and their conformational changes (Jiang and Chiu, 2007). To investigate whether cryo-

EM single particle analysis is an appropriate method for this study, a preliminary 3D map reconstruction including only around 200 polyomavirus particles of each dataset was performed. The result of this trial, showing some conformational changes in viral capsid after binding to NANA, proved that cryo-EM and single particle reconstruction is able to detect polyomavirus conformational changes.

25 Group Group I (ds DNA) Family Polyomaviridae

Genus Polyomavirus

African green monkey polyomavirus Baboon polyomavirus 2 BK polyomavirus Bovine polyomavirus Budgerigar fledgling disease virus Hamster polyomavirus JC polyomavirus Murine polyomavirus Murine pneumotropic polyomavirus Simian virus 12 Species Simian virus 40 Crow polyomavirus Finch polyomavirus Goose hemorrhagic polyomavirus B-lymphotropic polyomavirus Rat polyomavirus Squirrel monkey polyomavirus Chimpanzee polyomavirus Rabbit kidney vacuolating virus KI polyomavirus WU polyomavirus

Table 1. Polyomavirus phylogeny reviewed by Zur Hausen, 2008.

26 B

VP1 Pentamer } (aa 35-317) C-terminal ^ 279 VP2 segment

Figure 1. X-ray crystallography maps of the polyomavirus capsid and capsomers. A) polyomavirus capsid structure map resolved by X-ray crystallography studies (Liddington et al., 1991) at 3.8 A. The capsid is composed of 360 copies of VP1 arranged in 60 hexavalent (color) and 12 pentavalent (white) pentamers. The three kinds of interpentamer clustering are shown in schematic part of the diagram. White (a) and purple (a') and green (a") subunits form a 3-fold interaction. Red (P) and blue (p') subunits form 2-fold interactions. Yellow subunits form another kind of 2-fold interactions. Icosahedral symmetry axes are marked by numbers 5, 3, and 2. B) Structure of the VP1-VP2 complex determined at 2.2 A by X-ray crystallography (one of the pentamers in A). Five monomers of VP1 are shown in contact with a C-terminal segment of VP2 (residues 279-297) (shown as a red ribbon). The remainder of the internal protein (VP2/3) is not observable by X-ray crystallography because of its flexibility (Chen et al., 1998).

27 1 116 319

Figure 2. Schematic diagram of VP2 and VP3 domains. The whole of VP3 is identical to the VP2 C-terminus. VP2 has an additional 115 aa N-terminus that is bound to a myristil group (top). The VP 1-interaction domain of the VP2/3 sequence is shown at the bottom (adapted from Barouch and Harrison, 1994).

28 T=7d

Figure 3. Icosahedral symmetry of the polyomavirus and its triangulation number. A) An icosahedron with symmetry axes and the asymmetric unit used by microscopists. The numbers 2, 3, and 5 indicate the positions of some of the symmetry axes. The white triangle defines the asymmetric unit which is bounded by the lines joining adjacent fivefold and threefold positions. B) an array of hexamers, represented as a flat sheet of hexagons, is the basis for generating icosahedra. The dotted line indicates the triangulation of T=7d (dextro=right handed) and the solid line indicates the T=71 (/aevo=left-handed). C) Cartoons of the polyomavirus capsid demonstrating that values for h and k are defined by the number of hexavalent pentamers that one passes to reach from the center of one pentavalent pentamer to the next pentavalent one (indicated by arrows; Baker et al., 1999; Belnap et al., 1996).

29 Figure 4. Polyomavirus violates quasi-equivalence theory of icosahedra.

A) Schematic capsid structure of bacteriophage HK97, T=7 as a typical icosahedral virus

(correspondent to quasi-equivalence theory of Caspar and Klug, 1962) consisting of 60T=

420 subunits, with 12 pentamers (marked by red dots) and 60 hexamers (adapted from

Twarock and Hendrix, 2006), B) Schematic capsid structure of polyomavirus with T=7 icosahedral symmetry, but inconsistent with quasi-equivalence theory. It consists of 360 subunits and only pentamers, organized as 12 pentavalent (marked by red dots) and 60 hexavalent ones (adapted from Salunk et al., 1986).

30 Sialic acii residues

Cell membrane

SA-containing Integrin 3 receptor

Figure 5. Cell entry mechanism of polyomavirus proposed by Caruso et al., 2003a. The diagram shows the proposed model for the early interaction of murine polyomavirus with host cells. Step 1: polyomavirus interacts with SA-terminating oligosaccharides such as GDI a. Step 2: subsequent recognition of the a4pi integrin. The initial SA binding is a prerequisite for step 2, suggesting that a conformational change happens in the polyomavirus capsid, rendering the LDV motif more accessible to the integrin molecule and bringing it into closer proximity with the integrin. Step 3: penetration into the cells facilitated by step 2. Since both a4 and pi integrin subunits are heavily glycosylated, particularly with terminal SA residues, integrins may themselves play the role of SA-containing receptors for polyomavirus (step la and 2a as alternatives for step 1 and 2).

31 (a)

0 OIO NeuNAc-(a2,3)*Gal-{01,4)-Gic (3'-sialyJ lactose) (b)

OH

OH

A

NeuNAc-(o2f3)-Gal-(p1,3). GlcNAc-(31,3)-Gal-Ol t4)-Glc NeuNAc-(ct2,6)

Figure 6. A comparison of the chemical structures of straight- and branched-chain sialylated receptors, (a) 3'-silayl lactose, a representative of straight-chain receptor fragments whose NANA binds to VP1 pocket 1 (b) the branched-chain oligosaccharide whose NANAs bind to VP1 pocket 1 and 3.

32 Figure7. Model for the polyomavirus life-cycle proposed by Gilbert and Benjamin, 2004. In the GD1 a-mediated uptake pathway, polyomavirus binds to GDI a which is associated with lipid rafts and/or caveolae. Vesicles are endocytosed and routed to caveosomes, which traffic into the ER in a microtubule-dependent manner. From the ER, the virus exits and presumably transits to the nucleus through nuclear pores to replicate its genome.

33 Chapter 2

2. Materials and Methods

2.1. Virus purification and sample preparation

Polyomaviruses used in this study were gifts from Dr. Liddington and Dr. Amati.

They were purified as explained previously (Caruso et al., 2003a). Briefly, polyomavirus strain A2 was propagated at a low multiplicity of infection in 3T6 fibroblasts for 10 days post-infection. The cell lysates were collected by repeated freeze-thaw cycles and centrifuged for 15 min at 8,000g. The resulting supernatant was collected as the viral lysate and purified using CsCl gradients.

D138N mutants were generated by inserting substitutions using the Quick Change site-directed mutagenesis kit, as described in Caruso et al., 2007.

NANA (a 2,3-N-acetyl neuraminic acid, type IV-S min 95%) was purchased from Sigma and was dissolved to 320 mM (10%) in PBS supplemented with 0.1 mM

CaCb, 0.05 mM MgCh. Aliquots were frozen and stored at -20°C. CsCl purified viruses were pre-incubated with 64 mM NANA and stored in PBS (pH 7.5) for lh on ice

(Cavaldesi et al., 2004).

Vitrifying of virus samples was done according to a well-established method

(Adrian et al., 1984). Aliquots (5 |iL) of purified virus samples were applied to Quantifoil

(Spi Supplies, R2/2) glow-discharged EM grids, with the grid secured by a pair of forceps and suspended in a guillotine-like device called cryo-plunger. After blotting, the grid was plunged into a bath of liquid ethane slush maintained near its freezing point by a

34 surrounding reservoir of liquid nitrogen. This method of freezing avoids the formation of ice crystals and leads to formation of amorphous ice.

2.2. Microscope and instrumentation

Frozen hydrated grids were transferred to a FEI Polara transmission electron microscope equipped with a Gatan Ultrascan 4k x 4k charge couple device (CCD) camera at the University of California, San Diego (UCSD), Dr. Tim Baker's lab. The microscope was operated at an accelerating voltage of 200 keV.

2.3. Image collection

Images were collected automatically using the Leginon computer software

(Carragher et al., 2000). We identified suitable areas of vitreous ice at low magnification and allowed the Leginon software to automatically adjust imaging parameters (focus, astigmatism) under low-dose conditions, and to acquire high magnification images. This system was left unattended for over 24 h.

For target selection, a montage of low-magnification images (120x) was acquired to visualize the entire usable area of the grid. Holes covered by a layer of ice of suitable thickness were identified in images acquired at a nominal magnification of 800x. The centers of the holes were refined and targets were selected relative to these holes using images acquired at a magnification of 5,000x. Drift was monitored and the defocus and

35 astigmatism were adjusted using a low dose focus target offset from the targeted hole at a magnification of 50,000x.

Autofocusing was only performed on carbon adjacent to the selected target (2 (im distance). Focusing on adjacent carbon instead of the targeted ice holes is a "low dose emission" technique that avoids exposing the vitrified viruses to high electron doses and its burning effect. Automated focus and astigmatism correction was made using the

"beam tilt-induced image shifts" method (Carragher et al., 2000). This method relies on the linear relation between the amount of "image displacement" resulting from an induced beam tilt and the amount of defocus. Two images were recorded using beam tilts separated by several milliradians, and the displacement between them was measured using cross-correlation techniques. The automated focusing was performed in two steps.

The initial step measured the focus to allow the user to specify the defocus (e.g. -1 p.m). A second measurement was then made and the final desired defocus value was set. If the results of these measurements were anomalous, the automated system would move to an alternative low-dose focus position and try again. If this second position also produced anomalous results, the system would move onto the next hole and notify the operator

(Carragher et al., 2000).

After finding the focus and setting desired defocus value on the carbon support film 2 |a,m from selected target, one image was acquired from the carbon at a magnification of 50,000x. Image shifting was done; then, the electron dose was reduced to low (10-13 e" /A) to acquire an image from the target ice hole at a nominal magnification of 50,000x and a defocus range of 0.8-4 |im with a pixel size of 1.95A.

36 2.4. Image analysis

Image analysis was done using the RobEM and AUTOPP software programs (Yan

et al., 2007a). Images were normalized and bad pixels were removed. Particle selection

was done using a manual boxing protocol included in the RobEM software

(http://bilbo.bio.purdue.edu/~worshop/help_robem/). Boxing was done in a centered

manner to allow for subsequent alignment of the particles. Disrupted, near the edge, or

overlapping particles were not included in the study. The whole algorithm of image

analysis and 3D map reconstruction that we applied in this study is illustrated in Figure 8.

2.5. CTF estimation and correction

During image acquisition, autofocusing was done for every hole in which high-

resolution data was acquired, using a position on the carbon support adjacent to the hole.

The defocus estimates provided by the carbon image are potentially more reliable due to

the higher contrast of these images. Thus, we estimated the CTF of images on adjacent

carbon using RobEM and estimated the CTF on ice using the assumed constant defocus

offset between paired images. By comparative evaluation of defocus values for

counterpart images, we found out that the focus offset applied to ice images is not a

constant value. The reason for this deviation is not clear.

To avoid these deviations, the CTF values for ice images were calculated directly

rather than through the carbon images. However, images on ice were too big to be CTF

corrected by RobEM software. Therefore, we used the Automated Contrast Transfer

Function (CTF) Estimation (ACE) program (Mallick et al., 2005) to estimate the defocus

of images on ice.

37 2.5.1. How does ACE calculate CTF parameters?

A variety of factors modulate the image of the specimen, so they need to be corrected to construct an accurate 3D map of the specimen. Being the major factor among them, the CTF introduces spatial frequency-dependent oscillations into the Fourier space representation of the image. This effect can be easily observed by looking at a micrograph from an amorphous carbon film whose power spectrum exhibits a series of concentric ripples called Thon rings (Thon, 1971).The precise location of zeroes in the CTF is decided by the accelerating voltage, defocus, and spherical aberration of the microscope, while the general shape of the spectrum is determined by the amount of axial astigmatism

in the objective lens of the microscope. If the astigmatism is zero, the Thon rings appear circular; they change to elliptical, parabolic, and hyperbolic patterns when the astigmatism is increased.

Estimating the parameters of the CTF and correcting the images is critical to interpreting any image beyond the resolution corresponding to the first zero of the CTF.

An additional reduction in signal strength, which happens as a function of frequency, is

caused by a variety of factors (finite electron source size, energy spread of the beam, drift,

etc.). These factors, which limit the resolution, can be modeled using an envelop function.

Finally, the image contains background noise that is normally modeled as an additive

linear component.

The ACE program automatically recovers these parameters. In addition, there is a

user selectable option for whether or not to consider astigmatism in the estimation.

Basically, by using elliptical averaging to improve the signal to noise ratio, a 2D power

38 spectrum is reduced to a ID problem. Lower and higher cutoff frequencies are determined

to eliminate areas of the power spectrum that are dominated by a structure factor (Fourier

transform of the projection). After determining and minimizing the noise and envelop

parameters, a CTF ID curve should fit the predicted power spectrum. Once the CTF

parameters are fitted, the corresponding CTF and associated parameters are recorded in the image headers for each particle. ACE also calculates a confidence value from 0.0 to

1.0, where 1.0 is a perfect match; this value serves to evaluate the fit quality.

2.6. 3D map reconstruction

Map reconstruction was computed using the software suite AUT03DEM (Yan et al., 2007b), as illustrated in Figure 8. One of the inputs to the 3D core is an initial model.

AUT03DEM uses a random model calculation with an initial model that is constructed ab initio (Yan et al., 2007a). Therefore, a small subset of particle images, numbering one

or two hundred out of a much larger full data set, is selected and their orientations are

randomly assigned with origins set at the center of the box window. This low resolution

model provides a confidential unbiased model in a short run time.

The initial model, together with the corresponding set of particle images and their

estimated CTF values, are entered in the 3D core computation. The first step is to

determine the orientation parameters, including centers and origins (x,y,z), and the

orientation angles (0, (j), co). This procedure is done in a model-based approach using the

SEARCH and REFINE modes of the program's PFT search (Polar Fourier Transform

search; Baker and Cheng, 1996) and P02R (Parallel Origin and Orientation refinement; Ji

et al., 2006), respectively. SEARCH mode is used to determine the orientation of

39 particles in the early stages of reconstruction, where each particle image is compared against a set of projections of the model evenly covering the asymmetric unit. The orientation of the projection that best matches the images is then assigned. The REFINE mode also compares the particle image against a set of projections, but the orientations are selected from a confined region of orientation space near the most recent estimate for the particle orientation. The origins and orientation parameters resulting from the

SEARCH mode serve as input for the first iteration in the REFINE mode. The latest set of origins and orientations rendered by an iteration of REFINE mode provides the starting point for the next iteration.

To estimate the resolution, the selected particle images are divided into two sets and a map is constructed for each set using the program P3DR (Parallel Three-

Dimensional Reconstruction; Marinescu and Ji, 2003). P3DR interpolates the 2D discrete

Fourier transform (DFT) of each image to 3D DFT by using the particle's origins and orientations, and finally by taking the inverse transform of the 3D DFT. The resulting maps are masked with the program PCUT (Parallel code for Cutting spherical annulus from map) to isolate the ordered portions of the maps and reduce the non-icosahedral noise in the FT of the map. The key parameters provided by the user are inner and outer radii, which set a spherical annulus where the ordered regions are concentrated. Fourier transforms of these masked maps are then calculated and compared by the program PSF

(parallel Structure Factor) to estimate the map resolution. The resolution is defined by the point where the FSC (Fourier shell correlation) curve first drops below a specified threshold, commonly set at 0.5.

40 2.7 Calculation of difference map

3D map reconstructions calculated by AUT03DEM from different datasets were subtracted from each other using RobEM software package, "subtract map" option.

Before subtraction of the two maps from each other the magnification and contrast scale of the two maps are adjusted, the area of interest is framed, and then the difference map is calculated based on subtraction of the electron densities of the 3D EM maps.

41 Normalization and removal of bad RobEM pixels © Boxing out particles ^ CL Micrograph

Get initial estimation of particle centers (x,y,origins) i Determine orientation Impose 532 parameters: origins symmetry PFT (x,y,z) and angles Back project I search (0,4),co) 3D structure make a random initial 3D model Back project / rv^v^cs. 3D structure

Refinement of P02R orientation parameters Final 3D map Cutting ordered reconstruction regions from the map PCUT

Parallel 3D P3DR reconstruction

Estimation of the resolution

Figure 8. Schematic diagram of our approach to 3D map reconstruction by AUT03DEM. This software program contains a package of other software algorithms, including RMC, PFT, P02R, P3DR, PCUT, and PSF. The software used in each step is shown in a small adjacent box.

42 Chapter3

3. Results

3.1. Electron micrographs of polyomavirus

Images were collected by Dr. Isabelle Rouiller at UCSD (University of California,

San Diego, Dr. Tim Baker's lab) from four different virus samples: wild type (WT),

D138N mutant (see Materials and Methods, Chapter 2), wild type bound with sialic acid

(SA) or a 2,3-N-acetyl-neuraminic acid (WT+NANA), and D138N mutant bound with

NANA (mutant+NANA). 470 images from WT, 362 images from mutant, 1,250 images from WT+NANA, and 1,746 images from mutant+NANA were automatically captured by a CCD camera using the computer software Leginon (Carragher et al., 2000).

Figure 9 shows typical cryo-EM images of the four murine polyomavirus datasets.

Murine polyomaviruses in all datasets exhibited similar profiles, having consistent circular diameters of roughly 500A and coarse surface features. Viral particles appear as dark densities over the lighter background. VP1 pentamers can be observed in highly defocused images as mushroom-like protrusions on the surface of viral capsids.

3.2. CTF estimation

3.2.1. On carbon

Due to the higher signal to noise ratio (SNR) on carbon, an attempt was made to profit by estimating the CTF values of cryo-electron micrographs indirectly through the counterpart carbon images. The CTF values of carbon images were calculated using the

43 FFT option of RobEM. The resulting CTF curve estimated by RobEM for a carbon image from the WT dataset is shown in Figure 10B. The next step was to define the defocus offset, or the difference between defocus values of an image on ice with that of its counterpart on carbon. To ensure that this value remained constant for all pairs of images, the following two verification methods were employed:

1. First, the defocus values of carbon images were estimated by RobEM that were

ranging from ~ 0.1 to 6 (im. Images with defocus value of ~l-2 |im were then

selected and their particles were boxed by RobEM using different box sizes and

numbers of boxes to yield the best CTF curve. Afterwards, using the "append"

option, all boxed images were simultaneously opened in one single image. Then

CTF curve for this image was calculated. Since all these images had a fairly equal

defocus value, adding their particles together by this procedure should have

increased the SNR and a better CTF curve should have been resulted. However,

there was no improvement in the estimated CTF curve when compared to the

curves estimated for each image individually.

2. Second, images on ice were identified that consisted of large regions of the carbon

support film at the image margins. Their defocus value was estimated by RobEM.

In comparing the defocus value differences between each pair of counterpart

images, no consistency in the defocus offset was observed.

Based on these trials, it was determined that, for reasons which remain unclear, the defocus offset applied to carbon images was not a constant value for all images.

Therefore, defocus values for images on ice were estimated directly. A comparison of

44 Figures 9B and 9C indicates the differences in SNR and accuracy of the CTF estimation between carbon and ice.

3.2.2. On ice

RobEM was not able to calculate the Fourier transform (FT) of the images on ice

(dimension: 4096x4096) because they were too big. In different trials, we cut each image to some small overlapping boxes of 50, 100, 200, 300, 400 pixels (400 pixels being the largest dimensions that RobEM accepted to calculate FT). Unfortunately, even with box size of 400 pixels, the signal to noise ratio in the image was still too low to estimate the

CTF parameters using RobEM. Thus, ACE was used to estimate the CTF values for images on ice. The ACE (Automated CTF Estimation) software program was used to determine the CTF that has been applied to each acquired micrograph. The graphical user interface of ACE is shown in Figure 10A. In addition to entering defined values, such as the microscope operating voltage, pixel size, etc., parameters such as the number of overlaps and field sizes needed to be identified by trial an error.

To accommodate, smaller images were cropped from each image, from which the power spectrums were then found. These were averaged to improve SNR. "Field size" stands for the width of each small cropped image, while "overlap" refers to the number of smaller images used for averaging. If the signal to noise ratio is very low, the averaging overlap (number of overlaps and field size) were modified to obtain a better power spectrum. Generally, because of the relatively low SNR in vitrified specimens, our

45 micrograph power spectrums were poor compared to images on carbon (Figure 10B,

IOC). Therefore, the level of confidence in the CTF estimations was low (average: 0.4).

For images whose level of CTF estimation confidence was lower than 0.6, different values for "number of overlaps" (2, 4, 8) and "field size" (512, 1024, 2048) were tried to improve the power spectrum and level of confidence. After several such trials for each image, those with poor power spectrums and confidence values less than 0.6 were excluded from the remainder of the study. Our micrographs mostly showed better CTF estimations with a "field size" of 1024 and "number of overlap" of 4, although some images yielded better CTF estimations with other values.

In addition to images whose CTF estimations had low confidence, images with

CTF values lower than 0.8 (itn or greater than 4.0 |im were excluded from the calculation because they were considered as wrong estimation regarding the initial set up of imaging conditions. This resulted in the following exclusions: 9 of 470 images in the WT dataset,

12 of 362 images in the mutant dataset, 137 of 1250 images in the WT+NANA, and 95 of

1746 images in the mutant+NANA dataset.

3.3. Three dimensional (3D) structure of murine polyomavirus

Particle selection and boxing was performed manually as described in chapter 2.

Normalization and removal of bad pixels were also done as mentioned before. Boxed particles, together with the CTF values of each micrograph, were entered into the

AUT03DEM software to compute the 3D EM map reconstructions. Figure 11A shows an isosurface representation of a 3D EM map reconstruction of WT polyomavirus calculated

46 by back-projection of multiple-orientation particle images using Auto3DEM. The threshold used to visualize the isosurface representation was set by eye so that we could observe as much details as possible in the 3D reconstruction.

The resulting 3D map is a T=7 icosahedron with a diameter of -500A. The handedness of this 3DEM map was verified, and corrected to dextral if necessary, with

RobEM. Mushroom-like protrusions on the surface of the reconstruction map are VP1 pentamers, as previously identified (Baker et al., 1988; Stehle et al., 1996). The thickness of the capsid, as estimated in a central section of the map, was ~ 30A (Figure 1 IB). The resulting 3D map is composed of 72 hexavalent and 12 pentavalent pentamers. The triangle depicted over the map on Figure 11A connects three pentavalent pentamers located on its vertices. There are some tiny holes between the VP1 pentamers on the viral capsid surface that are circled in the figure.

3.3.1. WT

2,331 WT viral particles extracted by RobEM were rendered to the software program AUT03DEM to calculate the origin and orientation parameters for each particle by means of random model-based (using de novo model as the initial model) procedures

(Baker and Cheng, 1996). Iterative refinement of the particle parameters was carried out in several cycles, and a final 3D density map was computed by back-projection of multiple-orientation particle images to a resolution limit of 17.56 A at iteration 15; further running for refinement did not improve the resolution. Figure 11 displays an isosurface

47 representation of a WT polyomavirus 3D map reconstruction and a central section as visualized by RobEM.

3.3.2. Mutant

Similarly, 2,834 mutant viral particles were processed by AUT03DEM in a

random model-based method. After 15 refinement iterations, a final 3D density map was computed to a resolution limit of 16.39 A. Again, further running for refinement did not

improve the resolution. Figure 12 displays an isosurface representation of a mutant

polyomavirus map reconstruction and a corresponding central section.

3.3.3. WT+NANA

For WT+NANA particles, 5,832 particles were processed by AUT03DEM in a

random model-based way as explained before. After 15 iterations, the resulting 3D

density map was computed to a resolution limit of 16.88 A. As with the previous two data

sets, further running failed to improve the map resolution. Figure 13 displays the

associated WT+NANA isosurface representation and central section visualized by

RobEM.

3.3.4. Mutant + NANA

Finally, 15,617 mutant bound with NANA particles were processed by random-

model-based AUT03DEM similar to other datasets. After 15 refinement iterations, a final

48 3D density map was computed to a resolution limit of 13.86 A. Figure 14 displays the isosurface representation and central section of the 3D map reconstruction of mutant+NANA polyomavirus, as displayed by RobEM.

3.3.5. Resolution estimation

Figure 15 depicts how the map resolutions were calculated using a Fourier Shell

Correlation (FSC) curve and a cut off value of ~ 0.5 for the 3D map reconstructions discussed above.

3.4. Subtraction of 3D map reconstructions

3.4.1. The conformational change after binding with NANA

In comparing the four different 3D EM map reconstructions, a darker layer was observed below VP1 pentamers inside the shells of specimens bound with NANA

(WT+NANA and mutant+NANA). To document this apparent difference, NANA-bound reconstructions were subtracted from unbound ones by RobEM. The resulting difference maps show that after binding with NANA a significant circular layer of density appears inside the shell, right below pentamers, in both WT and mutant maps (Figure 16).

Moreover, in the difference map of WT and WT+NANA some negative densities are observed at the center of VP1 pentamers and interpentameric spaces. The dark layer

(positive densities) corresponds to viral proteins, while white spots (negative densities) represent an absence of viral proteins, suggesting that in WT polyomavirus some proteins

49 have moved after binding with NANA. In the case of mutant, the appearance of the dark

layer at the internal surface of pentamers in the subtraction map indicates that some viral

proteins that are not observable become apparent after binding with NANA.

3.4.2. No conformational difference between the D138N mutant and WT

Subtraction of the D138N and WT map reconstructions revealed no significant

structural differences (Figure 17), as indicated by a lack of discernable negative or

positive density regions in the calculated difference maps.

3.4.3. Subtraction of mutant bound with NANA from WT bound with NANA

Subtraction of mutant bound with NANA from WT bound with NANA

There were no significant differences in the structures of mutants bound with NANA and

WT bound with NANA (Figure 18), as indicated by a similar lack of significant positive

or negative density regions in the difference map. The shadows seen in this instance

correlate with noise, and do not signify density changes.

3.5. Controls

3.5.1. Rule out the influence of initial model on the final 3D map

One common bias reported in other cryo-EM studies is the bias of the initial

model on the resulting 3D map (Baker and Cheng, 1996). In the above 3D map

50 reconstructions we used random models as initial models which are free of this common bias. However, to assess the potential impact of this effect in our study, 3D map reconstructions were run for each data set (WT, WT+NANA, mutant, mutant +NANA) using different initial models. The best resolved map for each dataset was selected as the initial model for the reconstruction of each of the other datasets, providing three additional reconstructions beyond the random model.

Subsequently, these maps were subtracted from each other. No significant density differences were identified. Figure 19 shows a few important examples of these subtraction maps. Panels a and c display the most important subtraction maps, since the initial models of the subtracted maps have conformational differences (NANA-bound and

NANA-unbound forms). That the difference maps hold no significant positive or negative densities confirm that the 3D reconstructions for each dataset are independent of the initial model.

However, the resolution of 3D map reconstruction using different starting models changed variably in different datasets. In other words, while a specific initial model may have rendered better resolution compared to other initial models in one dataset, it may have had the opposite effect in another dataset (Table 2). In the case of mutant + NANA, taking WT as an initial model improved the resolution to 12.74 A, as compared to 13.86A using the random initial model (Figure 20). In the case of WT viruses, resolution improved to 16.43 A, down from 17.56A in the random model, when WT+NANA was used as a starting model. For the mutant dataset, resolution improved to 16.39A when

WT was used as the initial model (from 16.82A in the random model). Finally, in

51 WT+NANA maps, resolutions did not change significantly with different starting models

(Table 2).

Interestingly, the mutant bound with NANA dataset rendered the best resolutions, with whatever initial model, compared to the others. This could be because of the highest number of particles in this dataset. Further experiments with lower number of particles of this dataset proved this causative relationship as shown in figures 22, 27, 30.

3.5.2. Check the efficiency of virus-NANA binding

Despite analyzing 15,617 particles in the mutant bound with NANA dataset, the final 3D EM map still looks hazy. One question which arises here is whether binding with

NANA has been done completely or whether some of the mutant particles have remained unbound. To investigate, a sorting was performed based on matching with the initial model (Figure 21). The correlation coefficients (CCs) of viral particles from the mutant+NANA dataset obtained from two different reconstruction trials were compared; the first set employed the mutant as initial model, while the second set employed the

Mutant+NANA set as initial model. For each particle, the model that yielded the larger

CC was selected as the matching model, allowing particles to be subdivided into two groups according to their matching model (sort 1 and sort 2).

Surprisingly, a higher number of particles were sorted into the group matching with mutant rather than the mutant+NANA model (9,309 versus 6,308). The 3D EM map reconstructions were done in each group using their own matching models as starting

52 models, i.e. the mutant for sortl and mutant+NANA for sort 2. After several rounds of refinement, maps were eventually produced having resolution limits of 14.35 A and 16.28

A for the mutant-matching (sort 1) and mutant+NANA-matching (sort 2) groups, respectively (Figure 22). Subtraction of these two models yielded no significant differences (Figure 23A). Therefore, mutant-matching group particles show the same conformation as the mutant+NANA matching group.

Furthermore, the subtraction maps of both sortl and sort2 from mutant render the additional dark layer inside the viral capsid, similar to that found in the difference map of mutant and mutant+NANA particles (Figure 23B, C). This confirms that all particles in the mutant+NANA group were efficiently bound with NANA, even though a portion of them expressed higher correlation (bigger CC) with the mutant model.

3.6. Improving the resolution

3.6.1. Setting the calculating parameters in refinement mode

Some of the calculation parameters such as the inner and outer radii, angular space, and temperature factor, can be modified to attain better resolutions. Here, trial and error attempts were made to assess the effects of these parameters on our 3D map reconstruction resolution.

3.6.1.1. Inner and outer radii

53 The focus of our investigation is on the viral capsid and adjacent area. The interior of the virus, which typically contains genomic material, and the region outside of the capsid, add noise to the reconstruction and should not be considered in neither origin nor orientation determination. Therefore, after completing the SEARCH mode, the inner and outer radii of the 3D model were defined from its central section by rotational averaging using SPIDER (Frank et al., 1996; Figure 24). New values (inner radius ~ 195 A, outer radius ~ 253.5 A) were used to replace the default values estimated by AUT03DEM, and were then used for the REFINE mode. Since this modification improved the resolution of

3D map reconstruction in WT dataset (from 17.25 A to 16.82 A), the default values were replaced with the new values to focus the REFINE mode oscillations and to avoid the formation of excessive noise.

3.6.1.2. A ngular space

Since the REFINE mode performs a local search of (9, <|), co) in the space surrounding the current orientation, the angular space which defines the angle between projections in (0,

This intervention leads to higher accuracy in orientation determination, and eventually to better resolution.

54 The effect of angular space was studied using the mutant+NANA dataset. Angular space was reduced from its default value of -0.25 by increments of 0.05 in mutant+NANA dataset. The reductions resulted in incremental improvements in the map resolution until an angular space of 0.15. Further decreases had a negative effect on resolution. The map resolution improved from 12.74 A to 12.09 A. An isosurface representation and central section of the 3D map is shown in Figure 25B.

3.6.1.3. Temperature factor

In the present study, the temperature factor (see section 1.7.1.2) was considered to be zero by default in AUT03DEM. Given that instability of the lenses or specimen may have resulted in model haziness, the temperature factor was increased from its default value, "0", to a maximum of 300 A2 in increments of 100. Although increases in the temperature factor improved the resolution numerically for each increment, the calculated

3D map showed poorer resolution. The resulting reduction in contrast exhibited an increasingly feathery pattern in the map for each increment (Figure 25C). Because of this aggravating trend, values higher than 300 for the temperature factor were not studied.

3.6.2. Excluding particles with low correlation coefficient (CC)

Correlation coefficient (CC) is the linear relationship between the image and the best projection. It has been shown in a recent study that CC is a good indicator of usable particles for 3D reconstruction (Zhang et al., 2008). They computed 3D reconstructions

55 of rotavirus from 18,500 particles collected on two different kinds of grids (lacy carbon grids and c-flat carbon grids). Plotting CC against particle number yielded a clear bimodal distribution (Figure 26a). Selecting only 8,400 particles from the cluster with a higher correlation led to a 3D EM map with a resolution of 5A, whereas 3D reconstruction with the lower cluster resulted in resolution of only 25A. They concluded that CC is a good indicator of usable particles. Based on this report, the effect of CC of particle reconstructions was investigated using the present dataset.

Correlation coefficients for all particles from the WT+NANA dataset (5,832 particles) were plotted against particle number to characterize CC distribution (Figure

26b). Although a bimodal distribution was not observed, the effect of CC on 3D map reconstructions was investigated. Particles having a CC less than 0.25, which was the average CC of particles, were excluded from 3D map reconstruction. The remaining particles that had a CC higher than or equal to 0.25 (3,766 particles) were included in the

3D reconstruction. This intervention, contrary to expectations, did not improve the resolution in comparison to the all-particles-included trial (5,832 particles), either numerically (17.17A versus 16.88 A) or by visualization of the map (Figure 27).

To investigate the effect of CC on resolution, independent of data volume, 3D reconstructions were run in two groups having the same number of particles and different

CC values. Since a 3,766 particle group with CCs higher than 0.25 was already defined, the same number of particles having any CC value was selected as a control group. The control group 3D map reconstruction resulted in a final 3D map reconstruction resolution of 17.65 A. Comparison of this map to that of high-CC group, both numerically (17.65 A

56 versus 17.17 A) and by visualization, indicates that the CC of particles did not have a significant effect on resolution for the polyomaviruses studied (Figure 28).

3.6.3. Excluding particles with inaccurate CTF correction

The above trials included images whose confidence values of their estimated CTF was higher than 0.6. In the mutant+NANA dataset, the existence of excessive noise in the resulting maps, despite having analyzed thousands of particles, created suspicion surrounding the accuracy of the CTF correction. In two separate trials, images were subsequently excluded whose CTF estimation had confidence values of less than 0.9 and

0.95, respectively. 3D map reconstruction was performed using the mutant+NANA map with parameter alteration (shown in figure 25C) as the initial model. The final 3DEM maps in these trials had poorer resolutions (15.35A and 17.76A, respectively) compared to the control trial (13.23 A) that included particles with CTF confidence values greater than 0.6 (Figure 29). The control trial shown in figure 29A presented the best resolved map (by eye visualization) among all the 3D maps we calculated in this study although its resolution number is not the highest one.

Since we expected exclusion of particles with poor CTF estimation to yield better resolutions, we suspected that the decrease in the number of particles resulting from this exclusion is an interfering factor. This highlighted the need to study the effect of CTF estimation on resolution, independent of the number of particles. To do so, two groups with an equivalent number of particles (6,682) were selected, one with CTF confidence

57 values of greater than 0.9 and the other with greater than 0.6. The resulting map resolutions were almost identical numerically (15.42A and 15.35A, respectively). As well, the map appearances looked similar except for the amount of noise (Figure 30).

While there were regions of dark density at the center of pentamer for the group having confidence values > 0.6, this area appears empty in the group having higher confidence values. Interestingly, additional refinement iterations along with adding particles to the

lower confidence group resulted in the removal of these denser regions, which proved to be noise.

58 Figure 9. Cryo-electron micrograph of murine polyomavirus. A) Polyomaviruses frozen in ice over a holey carbon grid are seen as dark densities over the lighter background. Images were captured by CCD camera using the computer software Leginon developed at UCSD. The arrow indicates one partially damaged particle that was not boxed and processed in 3D reconstructions. B) An enlarged view of the polyomavirus virion boxed in A. Since the image in A is highly defocused, it is possible to see the surface details. Pentamers on the surface of murine polyomavirus capsids appear as mushroom-like dark protrusions. Scale bar indicates 1000 A.

59 & acedemo

- Files and diroctories- l«f Input Dir [ f.mrc 1 m atfiles M affiles Directory Output graphs directory

Temporaty files directory

- M icrograph - I 200 Pixel (A/pixel) | Astigm atism [ 2 Sp^br(mm) | D is pI ay f 2 Nom.defocus (um) Medium (o Carbon

Edge Threshold-- - - Power Factor- - Averaging - - Hi-Mag/Hi-d«focu/Lo-ps- Help p^^^ p5.b let | 0.9 | 0.3 I 512 l o |Compress Dynamic Range Carbon Ice Overlap Field size Resample

c

Figure 1(D). CTF estimation using ACE software. A) Graphic user interface (GUI) of the ACE program. B) Power spectrum, estimated by RobEM, of an image on carbon. The concentric ripples, called Thon rings, depicts the CTF. C) Power spectrum, estimated by ACE, of a cryo-electron micrograph, indicating the low SNR on ice. Comparison of B and C indicates the absence of significant Thon rings on ice.

60 Figure 11. 3D EM map reconstruction of wild type (WT) polyomavirus. 3D map reconstruction of WT polyomaviruses (initial model: random, number of particles: 2331) was calculated by AUT03DEM (resolution: 17.56 A). A) Isosurface representation of the WT polyomavirus calculated by back projection of thousands of multiple-orientation particle images. The mushroom-like protrusions are VP1 pentamers that are either pentavalent or hexavalent. The triangle connects three pentavalent pentamers located on the vertices. The circle marks one of the nano-pores on the surface of viral capsid which provides access to viral interior. B) Central section of the 3D map visualized by RobEM. The viral proteins appear as dark densities over the lighter background. One VP1 pentamer is boxed. The viral genome is not observable due to the lack of icosahedral symmetry. Scale bar indicates 100 A.

61 Figure 12. 3D map reconstruction of mutant Polyomavirus. 3D map reconstruction of the mutant polyomaviruses (initial model: random, number of particles: 2641) was calculated by AUT03DEM (resolution: 16.82 A). A) Isosurface representation of the mutant polyomavirus. B) A central slice through the 3D map visualized by RobEM software. Scale bar indicates lOOA.

62 Figure 13. 3D map reconstruction of wild type polyomavirus bound with NANA. 3D map reconstruction of WT+NANA polyomaviruses (initial model: random, number of particles: 5832) was calculated by AUT03DEM (resolution: 16.88 A). A) Isosurface representation of the WT+NANA polyomavirus. B) A slice through the center of the 3D map visualized by RobEM software. Scale bar indicates 100 A.

63 Figure 14. 3D map reconstruction of the mutant polyomavirus bound with NANA. 3D map reconstruction of mutant+NANA polyomaviruses (initial model: random, number of particles: 15,617) was calculated by AUT03DEM (resolution: 13.86 A). A) Isosurface representation of mutant+NANA polyomavirus. B) A slice through the center of the 3D map visualized by RobEM. Scale bar indicates 100 A.

64 WT WT+NANA

0.02 0.04 0.06 0.08 0." 0.02 0.04 0.06 0.08 0.1 1/resolution 1/resolution

Mutant Mutant+NANA

0.75 0.75

o co 0.5 <0 0.5

0.25 0.25

0.02 0 04 0.06 0.08 0.1 0.02 0.04 0.06 0.08 0.1

1/resolution 1/resolution

Figure 15. Resolution curves. Fourier shell correlation (FSC) curves for the respective

datasets plotted against spatial frequency (the inverse of resolution in A). Resolution for

each dataset was determined as the first instance where the curve intersects the FSC

threshold of 0.5 (cut off value).

65 WT+NANA WT difference map

Mutant + NANA mutant difference map

Figure 16. The conformational changes in the WT and mutant polyomaviruses after binding with NANA. A) Subtraction of wild type (WT) from wild type bound with NANA (WT+NANA) reconstruction maps using RobEM. B) Subtraction of mutant from mutant bound with NANA (mutant+NANA) reconstructions using RobEM. Arrows indicate the conformational change after binding with NANA, which appears as /) an additional dark layer at the bottom of pentamers in both WT and mutant (long arrows), and ii) white spots in the center of VP1 pentamers and between VP1 pentamers on the surface of WT viral capsids (short arrows).

66 Mutant WT difference map

Figure 17. No conformational change in D138N mutant polyomavirus. Subtraction of wild type (WT) from mutant reconstructions using RobEM reveals that the difference map contains no significant positive or negative density regions. There is some noise in the difference map. As a result, no conformational change is seen after D138N mutation in the LDV motif of VP 1.

67 mutant + NANA WT+NANA difference map

Figure 18. Subtraction of wild type bound with NANA (WT+NANA) from mutant bound with NANA (MUT+NANA) reconstructions using RobEM. The difference map contains no significant positive or negative density regions. The scattered dark and white dots most likely correlates with noise in the difference map (See discussion section 4.7). As a result, no conformational changes are seen between mutant+NANA and WT+NANA.

68 Figure 19„ Controls show that the final 3D map is independent of the initial model. 3D reconstructions were calculated for each dataset using different initial models. The different maps in each dataset were subtracted from each other by RobEM to assess the effect of the initial model. A) Mutant+NANA (initial model: mutant) subtracted from Mutant+NANA (initial model: Mutant+NANA). B) WT (initial model: WT) subtracted from WT (initial model: WT+NANA). C) WT+NANA (initial model: WT+NANA) subtracted from WT+NANA (initial model: WT). D) Mutant (initial: WT) subtracted from mutant (initial model: mutant).

69 The difference maps (far right) in all these panels show neither positive nor negative density regions, indicating no conformational changes between the subtracted maps. These findings verify that the initial model does not affect the final 3D map conformation.

70 Dataset Starting model Resolution (A)

WT random 17.56

WT mutant 16.65

WT WT+NANA 16.43

WT Mutant+ NANA 16.44

mutant random 16.82

mutant WT 16.87

mutant Mutant +NANA 16.46

mutant WT+NANA 16.83

WT+NANA random 16.88

WT+NANA WT 17.20

WT+NANA mutant 16.96

WT+NANA Mutant+ NANA 16.95

mutant+ NANA random 13.86

mutant+ NANA WT 12.74

mutant+ NANA mutant 13.88

mutant+ NANA WT+NANA 13.96

Table 2. Resolution of the 3D map reconstructions change with different starting models in each dataset. The effect of starting model on resolution alters from one dataset to the next, and is not predictable. For example, while the random model renders the best resolution for the WT+NANA dataset, it renders the worst resolution in the WT dataset. The mutant bound with NANA dataset rendered the best resolutions with whatever initial model, compared to the other datasets. This is due to higher number of particles in this dataset that is tested by other experiments taking smaller numbers of particles of this dataset (e.g. shown in figure 21, 26 and 29).

71 Start: random, res: 13.86 A Start: WT, res: 12.74 A

Figure 20. 3D EM map reconstruction of mutant+NANA polyomaviruses with different starting models. Panels a-d display central map sections of a mutant bound with NANA calculated from the following different starting models: a) random, b) WT, c) mutant, and d) WT+NANA. The respective starting model and resolutions for each 3D map reconstruction are indicated below each image.

72 3D reconstruction with MUT 3D reconstruction with as the initial model MUT+NANA as the initial model

Comparison and subtraction of the two 3D reconstructions

Figure 21. Schematic diagram of the approach used to check the efficiency of virus- NANA binding. Correlation coefficients (CCs) of particles with NANA-bound and NANA-unbound mutants were used to sort them into two groups (sort 1 and sort 2), and to identify the efficiency of virus-NANA binding.

73 Sort 1 particles # 9,309 Sort 2 particles #6,308 Start: mutant, res : 14.35 A Start: mutant+NANA, res : 16.28 A

Figure 22. Comparison of 3D map reconstructions after sorting of particles. A) 3D map of sort 1 particles (9,309 particles from the mutant+NANA dataset that had a bigger CC with mutant) was calculated with AUT03DEM with the mutant as the starting model. B) 3D map of sort 2 particles (6,308 particles from the mutant+NANA dataset that had a bigger CC with mutant+NANA as the starting model) was calculated by AUT03DEM with the mutant+NANA as the initial model. The upper row shows isosurface representations of respective 3D EM maps, while the lower row shows central sections of the respective 3D EM maps, visualized by RobEM. The number of particles (#), starting model (start) and the obtained resolution (res) for respective 3D maps are indicated below each column.

74 Sort2 Sortl Difference map

Sort2 mutant Difference map

Sortl mutant Difference map

Figure 23. Controls confirm the efficiency of NANA-binding in NANA-bound particles. A) Subtraction of the 3D reconstructions of sort 2 and sort 1 by RobEM shows no conformational differences because there are no significant positive or negative density regions in the difference map. B) Subtraction of the 3D reconstructions of sort 2 from that of mutant, with the difference indicated by an arrow. C) Subtraction of the 3D reconstructions of sort 1 from mutant, with the difference indicated by an arrow. These difference maps indicate that both sort 1 and sort 2 are NANA-bound viruses having the same conformational change relative to the mutant.

75 Figure 24. Measuring the inner and outer radii of the area of interest. A) Rotational averaging of the central section of WT polyomavirus by SPIDER. The area between the two white circles correlates with most of the highly ordered density and excludes disordered regions. Then the inner (195 A) and outer radii (253.5 A) of dark density were estimated in SPIDER by measuring the distance of the inner and outer circle from the center of the capsid. The estimated values were used to replace the default ones in the REFINE mode of 3D reconstruction. B) Superimposing the estimated inner and outer radii on the central section of the 3D EM map cut by RobEM truncation option.

76 mutant+NANA, Start:WT mutant+NANA, Start:WT mutant+ NANA, Start:WT dangle: 0.25, tempfac:0 dangle:0.15, tempfac :0 dangle:0.15, tempfac:300 res: 12.74 A res: 12.09 A res:11.68A

Figure 25. Effect of parameter alteration on resolution. The upper row shows isosurface representations of respective 3D EM maps, while the lower row shows central sections of the respective 3D EM maps, calculated by AUT03DEM and visualized using RobEM. A) No parameter alterations have been done. B) Delta angle has been decreased from 0.25 to 0.15. C) Delta angle changed from 0.25 to 0.15, and the temperature factor (tempfac) has been increased from 0 to 300 A2. All trials were done with the mutant+NANA dataset, taking WT as the starting model. Applied values for delta angle (dangle), temperature factor (tempfac), and obtained resolutions (res) for respective maps are indicated at the bottom of each column.

77 a 0.3 c V— Batch #1 Batch #2 o O Lacy carbon grids C-flat grids 0 0 5000 10000 15000 Particle #

0 2000 4000 6000 particle number

Figure 26. Distribution patterns of particle CC and comparison with the Zhang et al., 2008 study, a) Zhang et al, 2008 study: correlation coefficients are plotted as a function of particle and batch number (red, lacy carbon; green, C-flat). There is a bimodal distribution, with a CC of ~ 0.14 separating the two clusters in both batches, b) Scatter pattern of CC in this study: CC of 5,832 particles in the WT+NANA dataset plotted against particle number. The CCs do not show any specific trend and are continuous, with an average ~ 0.25 (CCs calculated from 3D reconstruction of WT+NANA dataset using AUT03DEM; initial model: mutant+NANA).

78 res: 16.88 A, #: 5,832 res:17.17 A, #:3,766 any CC CC> 0.25

Figure 27. Effect of excluding particles with low CC (correlation coefficient) on resolution. 3D reconstruction of WT+NANA particles (initial model: mutant+NANA) in two trials: A) All particles included (any CC). B) Particles having a CC above average (0.25).The resolution (res) and the number of particles (#) are indicated under each column. The upper row shows isosurface representations of respective 3D EM maps, while the lower row shows central sections of the respective 3D EM maps, calculated by AUT03DEM and visualized using RobEM

79 res: 17.17 A, #: 3,766 res: 17.65 A, #: 3,766 COO.25 any CC

Figure 28. Effect of particle CC on resolution, independent of the number of particles. To rule out the interfering effects of the number of particles in the previous experiment, 3D reconstructions of WT+NANA polyomaviruses were done (initial model: mutant+NANA) with an equal number of particles in two trials: A) Including particles with CC>0.25 (3,766), B) Including 3,766 particles (same as A) without regard to their CC. The upper row shows isosurface representations of respective 3D EM maps, while the lower row shows central sections of the respective 3D EM maps, calculated by AUT03DEM and visualized using RobEM. The respective resolution (res) and number of particles (#) are shown at the bottom of each column

80 Confidence value>60% confidence value>90% confidence value>95% #: 15,617, res:13.23 A #:6,684, res: 15.35 A #:2,763, res : 17.76 A

Figure 29. Effect of the accuracy of CTF estimation on resolution. 3D EM maps of mutant+NANA calculated from the same initial model (mutant+NANA with modified parameters), but using different confidence values for the CTF estimations. A) Confidence values <60% excluded. B) Confidence values <90% excluded. C) Confidence values <95% excluded. In each column the top row shows isosurface representation of 3D reconstruction, and the bottom row is a slice through the center of 3D maps calculated by Auto3DEM and visualized by RobEM. The number of particles (#) processed in each step and the resolutions (res) for each map are indicated under each column. The 3D map shown in column C presents the best resolved map (estimated by eye) that we obtained in our study although its resolution number is not the highest.

81 res: 15.35/1,Confidence Value>0.9 res: 15.42 A Confidence Value>0.6 #:6,682 #:6,682

Figure 30. Effect of good CTF estimations on resolution, independent of the number of particles. 3D reconstructions of mutant+NANA particles with the same initial model (mutant+NANA with modified parameters) were calculated by AUT03DEM in two groups different in the confidence value of their CTFs: A) More than 0.9, B) more than 0.6. Since the second group had a higher number of particles, some particles were randomly excluded regardless of their CTF confidence values. The arrow indicates a dark density region at the center of a pentamer that appears to be noise, since it is absent in the group with higher CTF confidence values. The number of particles (#), resolution, and CTF confidence values are indicated below the columns.

82 Chapter 4

4. Discussion and Future Work

4.1. Polyomavirus general appearance

Polyomavirus viral particles in cryo-electron micrographs from all four datasets, including WT, WT+NANA, D138N mutant, D138N mutant+NANA, displayed the same features. All particles appeared circular, with diameters of -500A and with surface protrusions that have previously been identified as VP1 pentamers (Baker et al., 1988;

Griffith et al., 1992). The 3D reconstruction maps indicate a T=7 icosahedron composed of 72 VP1 pentamers, including 60 hexavalent and 12 pentavalent regions. These results correlate well with previous crystallography (Liddington et al., 1991), negative stain

TEM (Salunke et al., 1986) and cryo-EM (Baker et al., 1988; Griffith et al., 1992; Belnap et al., 1993; 1996) studies of the polyomavirus structure. The handedness could not be determined by cryo-EM single particle reconstruction without specimen tilting. However, the dextral handedness of the polyomavirus capsid has been well documented in previous studies (reviewed by Baker et al., 1999).

4.2. Virus-NANA binding leads to a conformational change in the viral capsid

Our results indicate that the murine polyomavirus undergoes a conformational change after binding with NANA, as indicated by the additional dark circular layer inside the viral capsid, below pentamers in both WT and the mutant, as well as by some negative

83 densities at the center of the VP1 pentamers and interpentameric spaces in the case of

WT. These results correlate well with a previous biochemical study showing that VP1 changes from protease-sensitive to protease-resistant status after binding with NANA

(Cavaldesi et al., 2004).

That SA-containing receptors can cause conformational alterations in viral capsids has been reported for other viruses, such as paramyxovirus, by X-ray crystallography studies (Takimoto et al., 2002) and reovirus, by protease digestion assays (Fernandes et al., 1994). However in these viruses SA is not essential for viral entry because of redundancy of molecules. By contrast, SA has been reported as indispensable for polyomavirus cell entry (Caruso et al., 2003b). This fact emphasizes the importance of further investigations to identify the exact nature of the conformational changes induced by binding of SA to VP1.

4.3. D138Nmutation in VP1 LDVdoes not induce conformational changes in the viral capsid

Subtraction of the mutant, holding D138N mutation in the VP1 LDV motif

(integrin-binding motif), from WT displays no significant density changes in the difference map. Thus, no conformational changes exist between WT and the mutant.

According to previous studies, this mutation has been shown to abrogate binding of polyomavirus VP1 to a4pl integrin. On the other hand, it has been shown that polyomavirus VP1 binding to integrin through its LDV motif has a crucial role in viral infectivity (Caruso et al., 2003a, b). One recent in vivo study on the role of integrin in

84 polyomavirus cell entry revealed that D138N mutation in the LDV motif modifies tissue tropism and decreases infectivity (Caruso et al., 2007). In our study, we did not observe any conformational changes due to this mutation. That we observe the same conformational change in D138N mutant and WT polyomavirus after binding with

NANA indicates that this mutation does not disrupt VP 1-NANA binding interaction which leads to conformational changes.

It remains to be determined what conformational changes occur after integrin binding. To address this question we can make 3D reconstructions of WT+NANA polyomaviruses before and after incubation of the viruses with integrin. Subtraction of the two maps may show what conformational changes occur after binding.

4.4. Observed conformational changes may be attributed to VP1, VP2, VP3, or DNA

Conformational changes occurring after polyomavirus-NANA binding are observed as a dark layer of density inside the viral capsid, below the pentamers. The

location of darker regions in the subtraction map suggests that this conformational change

may relate to VP1, VP2, VP3, or DNA. The additional dark layer may be related to VP2

or VP3, as previous X-ray crystallography studies (Chen et al., 1998) have postulated that

the internal viral proteins are exposed during cell entry. According to these studies, the C-

terminal segment of minor proteins and VP1 are tightly associated, and VP1 monomers

are strongly bound to form the viral capsid. However, the N-terminus of VP2 and VP3 is

highly flexible and can have hydrophobic interactions with other proteins. This

85 assumption also correlates with in vitro and in vivo infection assays, which indicate that

VP2/3 knock down mutants are defective in both infectivity and tumorogenicity (Sahli et al., 1993; Mannova et al., 2002). Furthermore, Cavaldesi and coworkers (Cavaldesi et al.,

2004) have shown by protease-sensitivity assays that both VP2 and VP3 change conformationally after binding with NANA. Since VP2 and VP3 are highly flexible and can not be observed by X-ray crystallography or cryo-EM without NANA and we observed additional densities (additional dark layer in the difference map) after binding with NANA, it is possible that these additional densities are contributed at least in part by the stabilization of VP2 and VP3 which are visible after averaging.

Schelhaas et al., 2007, have implied a role for DNA in SV40 capsid stability.

They showed that when empty particles devoid of DNA were subjected to disassembly conditions, the entire capsid dissociated into slowly sedimenting pentamers. However,

WT capsids undergo partial disassembly under the same conditions. This observation

suggests a role for DNA arrangement in the viral capsid's stability. Based on this

observation, it cannot be ruled out that the dark-density layer below the viral pentamers

may be due to DNA arrangements followed by SA binding.

While stabilization of VP2 and VP3 is our favorite interpretation because of the

change in protease sensitivity after NANA binding (Cavaldesi et al., 2004) further studies

should be done. These studies would identify the responsible mechanism for the observed

conformational changes in the EM map, and which viral proteins are participating.

Calculation of 3D EM maps of VP2, VP3 and DNA deletion mutants, both bound and

unbound with NANA, and calculation of their difference maps, would serve to address

this question.

86 4.5. Why does the significant conformational change happen at the internal surface of the pentamers instead of at the outer surface of the capsid?

The presence of significant conformational change as a dark layer on the inner surface of the pentamers, rather than at the outer surface, raises a logical question. How do these changes inside the viral capsid facilitate virus-cell interaction when there is no contact between them?

First of all, the subtraction map of WT and WT+NANA reveals a dark layer of density below the pentamers, and some negative densities (white spots) inside the VP1 pentamers and interpentameric spaces. This observation suggests that following NANA- binding, some proteins from the outer surface may move towards the inside of the capsid, thereby changing the binding surface qualities, potentially exposing a hydrophobic moiety. As suggested before by Sahli et al., 1993, the N-terminus of VP2 contains a myristic acid. Thus, exposure of this moiety resulting from VP1 conformational changes may generate a hydrophobic viral intermediate that could be responsible for membrane binding. This mechanism has been reported for other viruses, such as reovirus, rotavirus and adenovirus (reviewed in Tsai, 2007), in which some viral proteins fold back and expose their hydrophobic moieties to allow them to bind and cross cellular membranes.

Calculation of summation of positive and negative densities in the difference map of

WT+NANA and WT can be done as a future step to test this hypothesis. This can not be done straightforward on our 3D EM map because of low resolutions.

Secondly, there are some nano-pores at the three-fold axis of icosahedral viral capsid in our 3D maps. They are marked with circles on the isosurface representations of

87 our 3D map in Figure 10A. These holes make the inside of the capsid and the additional dark layer accessible for cellular proteins. The interaction of small cellular proteins with the additional dark layer through the nano-pores may play a role in viral internalization.

Baker et al., 1988, have shown by cryo-EM studies the existence of pores of -2.5-3.0 nm in diameter that are surrounded by the three capsomers adjacent to each icosahedron three-fold axis. Such holes may provide access to the virion interior for solvent, ions, and other small molecules. Similarly, micrococcal nuclease has been shown to penetrate virions treated with a low concentration of the reducing agent dithiothreitol (Ng and Bina,

1981).

In a similar way, it is unclear how, in the case of SV40, ERp57 reaches cysteines and disulfide bonds located on the inner surface of the capsid wall. It has been implied that 3-5 nm pores on the capsid wall shown by X-ray crystallography studies, in addition to the flexibility and relatively small size of the oxidoreductase, can account for this event

(Schelhaas et al., 2007).

4.6. What resolution do we need to interpret our 3D EM map reconstructions?

Single particle cryo-electron microscopy is faced with the challenges of analyzing increasingly complex biological systems, many of which render density maps at low to intermediate resolutions of 10-30 A (reviewed by Bajaj, 2007). Atomic models can be generated by combining high-resolution X-ray or NMR maps of individual components in a macromolecular complex with a low-resolution EM density map. Some softwares

88 such as CoAn (Volkman and Hanein, 1999; 2003), and Situs (Wriggers and Birmanns,

2001) are designed to fit modules (domains or substructures) of the assembly individually

into the EM reconstruction. Theses methods allow to model relative domain movements and conformational changes (Volkman and Hanein, 2003).

If X-ray or NMR models of a specific assembly or its structural units are not available, it is possible to build de novo near-atomic models from EM maps (reviewed by

Zhou, 2008). Intermediate-resolution (6-10 A) EM density maps are needed to define the

locations of secondary structural elements which in turn lead to construction of accurate pseudo-atomic models, a-helices and P-sheets constitute the major secondary structural elements in proteins (reviewed by Bajaj, 2007). It has been shown that a-helices, having

an approximately cylindrical shape, can be located in intermediate-resolution (6-10 A) density maps via a five dimensional cross-correlation search called helix hunter (Jiang et

al., 2001). P-sheets, despite being more difficult to be discerned because of variation in the size and shapes, can be detected by two programs called sheet miner (Kong et al.,

2003) and sheet tracer (Kong et al., 2004); they can locate regions belonging to P-sheets

and tracing pseudo-Ca atoms of p-sheets, respectively, in resolutions of ~ 6-10 A.

Recently, SSEhunter has been developed as a tool to detect both a-helices and P-sheets

besides secondary structure topology in intermediate-resolution density maps (Baker et

al., 2007).

Four recent cryo-EM studies (Zhang et al, 2008; Yu et al., 2008; Jiang et al.,

2008; Ludtke et al., 2008) have achieved a resolution around 4 A, allowing Ca model

building and even backbones with side chains for proteins in some segments. At this

resolution, many structural details such as turns and deep grooves of helices, strand

89 separation in (3- sheets, densities for loops and bulky amino acid side chains can be resolved. When X-ray structures were available for the same complexes, an excellent compatibility were seen between the cryo-EM and X-ray structure up to level of amino acid side chains, (reviewed by Zhou, 2008).

As a future step, the atomic structure of polyomavirus solved by x-ray crystallography studies (Chen et al., 1998) at 2.2 A can be docked to our EM density maps of ~ 12-18 A resolutions to interpret the observed conformational changes at the level of protein rigid domain fitting.

4.7. Resolution limits

Ideally, the highest possible resolution one could achieve by cryo-EM is equal to the pixel size of the microscope multiplied by two (Frank, 2006). Thus, for the 1.97A pixel size employed in our study, resolutions around 3.94 A are the best that can be theoretically achieved. Using our data, the best resolution attained was -12 A.

Today's best electron microscopes have resolutions in the range of 1-2 A. This along with dramatic advances in single particle cryo-EM techniques has improved resolutions to near-atomic levels. Recently, four different studies by single particle cryo-

EM reconstructions have yielded resolutions around 4 A; Ludtke et al., 2008, have reported a 3D reconstruction map of GroEL at 4.2 A; Jiang et al., 2008, have reconstructed a 3D EM map of Epsilon 15 bacteriophage at 4.5 A resolution; Yu et al.,

2008, have reported a 3D reconstruction of cytoplasmic polyhedrosis at 3.88 A; and finally, Zhang et al., 2008, have reconstructed a 3D EM map of rotavirus at 3. 8 A.

90 In terms of data volume for icosahedral viruses, taking advantage of their 2, 3, and

5 fold symmetry, the particle numbers decrease by 60 times compared to asymmetric objects such as ribosomes (reviewed by Frank, 2002). Recent studies on icosahedral viruses, such as Zhang et al., 2008, on 8,400 rotavirus particles, and Yu et al., 2008, on

12,814 cytoplasmic polyhedrosis virus particles, have rendered resolutions of ~3.8 A. In considering that we identified 15,617 particles in the mutant bound with NANA dataset, this data volume should be sufficient to achieve resolutions around 5 A. Therefore, the microscope resolution and the data volume do not seem to be the limiting factors in our study. Instead, the practical limitations on resolution have probably resulted from a combination of specimen preparation methods, data collection parameters, and data analysis procedures. In our study, two particular factors were identified as impediments that restricted the resolution as follows.

4.7.1. Virus aggregation

Polyomaviruses tend to aggregate extensively, which led to the formation of virus clumps. This resulted in our cryo-electron micrographs containing either low numbers of viral particles in a scattered pattern or clusters of viruses. The lack of evenly distributed particles on a specimen grid and on cryo-electron micrographs failed to yield sufficiently high signal to noise ratios and accurate CTF estimations. Adding a material like poly-L- lysine to the samples may inhibit virus aggregation and provide more viral particles in each image (personal communication by Dr. Bill Tivol). This would improve the signal to noise ratio, and the resulting final resolution of the map (reviewed by Baker et al., 1999).

91 4.7.2. CTF estim ation

In this study, the average confidence value of defocus values estimated by ACE for the mutant bound with NANA dataset is roughly 0.4, which is relatively low. Other studies such as Stagg et al., 2006, which used ACE for GroEL 3D images, reported average confidence values of -0.95 for their defocus value estimations. Figure 9C shows the power spectrum from one of our images in which the typical Thon rings are not distinguishable. One of the most important reasons for this failure is low signal to noise

ratio. A few modifications can be made to ameliorate this situation. First, film can be used

instead of the CCD camera, since it includes a higher number of particles in each image,

leading to a higher SNR. Second, using lower magnifications would increase the number of particles in each image. Other potential modifications could include adding materials

like carbon support film or TMV (Tobacco Mosaic Virus) to samples.

Our trial to exclude mutant+NANA particles that had poor CTF estimations did not result in better resolutions of the 3D map reconstruction in this dataset. However, more than half of this dataset's particles had CTF estimations whose confidence value was less than 0.9, and the resulting exclusion of half of the particles could be an

interfering factor. Therefore, to verify that the lower resolution we obtained was due to

the decrease in the number of particles, we calculated a new map which include the same

number of particles but randomly chosen. Although higher confidence values in the CTF

estimations did not improve the resolution, it did decrease the amount of noise. This is

consistent with previous studies that indicate the importance of exact CTF estimations

92 and corrections to achieve accurate 3D map reconstructions (reviewed by Jiang and Chiu,

2007)

4.8. Is the estimated resolution true?

The estimation of resolution using a Fourier shell correlation (FSC) is based on a comparison of two reconstructions from randomly selected half-sets of the whole particle dataset. As a result, the resolution is usually underestimated since the total set has better statistics than either half-set (Frank, 2006). Moreover, different areas of the structure have different qualities that are typically due to conformational variability. Therefore, FSC- based resolution estimations with a given threshold are an unreliable criterion forjudging the map (reviewed by Jiang and Chiu, 2007).

4.9. Improving the resolution

As described above, near atomic resolution enables us to trace a-helices, P-sheets, and their structural details such as grooves and strand separation. Therefore, to discover the true nature of the conformational change observed in this study, we need to improve the resolution to at least intermediate resolutions of 6-10 A as discussed above. To achieve this improvement in resolution, we set many alterations in calculation parameters which are discussed below.

93 4.9.1. Setting calculation parameters in the REFINE mode

We attempted to improve the resolution of 3D map reconstructions by trying different values of the following parameters that are specified in the REFINE mode of calculation: inner and outer radii, angular space, temperature factor, and correlation coefficient (CC) of particles.

Consistent with previous studies (Yan et al., 2007b), determining an accurate inner and outer radii for the area of interest, and decreasing the angular space, led to more accurate 3D reconstructions and better resolution estimations.

In contrast, increasing the temperature factor from the default value of ~0 produced inconsistent changes in the resolution. This failure to improve resolution using the temperature factor does not rule out its importance for 3D map reconstruction accuracy. It is important to note that only a few trial and error experiments were performed in this study. Moreover, previous studies have shown the strong relationship between the correction of the temperature factor and resolution (Saad et al., 2001), and have suggested methods for correct estimation. Using the same methods to estimate the exact value of temperature and its correction may lead to higher resolutions in our 3D map reconstruction.

In terms of CC, we did not obtain better resolution by excluding low CC particles, in contrast to the previous work by Zhang et al., 2008. This might be because of the different scatter pattern of particles' CCs found in our studies (Figure 26). Further studies are needed to determine whether or not CC is good indicator of usable particles.

94 4.9.2. Increasing the number of particles

Adding more particles to the current datasets by taking new images may improve the resolution. The number of particles in single particle reconstruction provides more accurate particle averaging, and as a result higher resolution (Stagg et al., 2006). There is no absolute formula to indicate how many particles are needed to obtain a specific resolution. This may differ from case to case, and is currently based on trial and error

(Frank, 2002). However, increasing the raw number of particles can not by itself improve the resolution. Future studies should aim to increase the number of usable particles by optimizing the cryo-specimen preparation and imaging conditions.

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