Presentation by: Kyle Borge, David Byon, & Jim Hall Herpesviral Protein Networks and Their Interaction with the Human Proteome Jim reconstruction of a Herpes Virus capsid Presentation by: Kyle Borge, David Byon, & Jim Hall

Introduction to the Herpesvirus Large double-stranded DNA genomes Eight different strains Causes diseases ranging from cold sores to shingles Vaccine available for Varicella-Zoster Virus (VZV) Little known about protein interactions Jim Little is known about herpesviral protein interactions Herpesviruses are divided into three subfamilies; α - alphaherpesvirus (three viruses including VZV), β - betaherpesvirus (two viruses) & γ - gammaherpesvirus (two viruses; KSHV and Epstein-Barr Virus(EBV))

Types of Herpesviruses Investigated Kaposi’s Sarcoma-associated Herpesvirus (KSHV) In the gamma (γ) herpes virus phylogenetic class Causes cancerous tumors Mostly associated with HIV patients Sequenced in 1996 Genome is roughly 165 kbs 89 open reading frames (ORFs) 113 ORFs used in experiment (included 15 cytoplasmic and 5 external domains derived from transmembrane proteins) Varicella-Zoster Virus (VZV) , in the alpha (α) herpesvirus phylogenetic class Causes chicken pox in children and shingles in adults Sequenced in 1986 Genome is roughly 125 kbs 69 open reading frames (ORFs) 96 ORFs used in experiment (Included 13 cytoplasmic and 10 external domains derived from transmembrane proteins) Jim

Methods of Investigating Protein Protein Interactions (PPI) Many Methods The Y2H technique is one of the top techniques for detecting protein-protein interactions This article used Y2H to investigate protein-protein interactions Kyle

Y2H Advantages http://www.dnatube.com/video/993/Plasmid-Cloning Relatively simple (automated) Quick Inexpensive Only need the sequenced genome (or sequence of interest) Scalable, its possible to screen for interactions among many proteins creating a more high-throughput screen (ex. viral genome) Protein/polypeptides can be from various sources; eukaryotes, prokaryotes, viruses and even artificial sequences…allows the comparison of interactomes w/in and between different species…in this paper, eukaryote (human) interactome vs. viral interactome Kyle

Y2H Limitations http://www.dnatube.com/video/993/Plasmid-Cloning The Y2H system cant analyze some classes of proteins Transmembrane proteins, specifically their hydrophobic regions which may prevent the protein from reaching the nucleus Transcriptional activators; may activate transcription w/out any interaction False-negatives Y2H screen fails to detect a protein-protein interactions False-positives Y2H screen produces a positive result (characterized by reporter gene activity) where no protein-protein interaction took place Ex. bait proteins activate, transcribing the reporter gene, w/out the binding of the AD (bait proteins act as transcriptional activators)

Yeast’s GAL4 transcriptional activator GAL4 transcriptional activator which splits into two separate fragments; a binding domain (BD) and an activating domain (AD) Kyle

Y2H Method ORFs selected from published sequences Amplified by nested PCR Made primer sets of ends of ORFs Y2H bait and prey vectors Vectors transformed into Y187 and AH109 haploid yeast cells creating pools; a bait pool and a prey pool Bait and prey mated in quadruplicates Positive diploid yeasts are selected - Kyle - Viral genes selected from published sequences - pDEST-GADT7 and pDEST-GBKT7 into bait and prey vectors -

Open Reading Frames (ORFs) Every ORFs of both KSHV & VZV were cloned & ligated into both a bait and prey GAL4 vector Bait protein of interest the protein is fused to the yeast Gal4 DNA-binding domain (DBD) Prey a protein/ORF fused to the Gal4 transcriptional activation domain (AD) interacting protein Physical interaction between the bait and prey brings the DNA-BD and an AD of Gal4 together, thus re-creating a transcriptionally active Gal4 hybrid Gal4 activity can be assayed by the expression of reporter genes and selectable markers Kyle

(1-2) ORFs cloned into vectors via Nested PCR KSHV 113 full-length and partial ORFs including 15 cytoplasmic and 5 external domains derived from transmembrane proteins VZV 96 full-length and partial ORFs including 13 cytoplasmic and 10 external domains derived from transmembrane proteins - Kyle - Turns blue to indicate interaction between viral proteins

Yeast-Two-Hybrid Bait pool: Prey pool: (target) Each individual ORF sequence is cloned into the ‘prey’ vector (down stream of the GAL4 AD gene) and is essentially fused to the GAL4 AD gene Ampr for selection Hemagglutinin Bait pool: Each individual ORF sequence is also cloned into the ‘bait’ vector (down stream of the GAL4 DBD gene) and is essentially fused to the GAL4 DBD gene vector conveys Kanr for selection Amp & Kan genes of these plasmids ‘give/promote’ Ampr & Kanr resistance, individually…and later on, they will permit selection for diploid cells (from the two haploids mating) Both the KSHV & VZV genomes had been previously sequenced which enabled researchers to identify the ORFs as they have a couple different characteristic sequences organisms with dsDNA…obtained from other cells ORFeomes for both viruses were amplified via nested PCR (giving better assurance for ORF sequence specificity) PCR product were cloned by BS clonase into the gent

Yeast-Two-Hybrid Background …in a diploid cell. Amp & Kan genes of these plasmids ‘give/promote’ Ampr & Kanr resistance, individually…and later on, they will permit selection for diploid cells (from the two haploids mating) Both the KSHV & VZV genomes had been previously sequenced which enabled researchers to identify the ORFs as they have a couple different characteristic sequences organisms with dsDNA…obtained from other cells ORFeomes for both viruses were amplified via nested PCR (giving better assurance for ORF sequence specificity) PCR product were cloned by BS clonase into the gent

Viral Protein Interactions in KSHV 12,000 Viral Protein Interactions tested Identified 123 nonredundant interacting protein pairs 118/123 were novel 7/123 were previously reported Screen captures 5/7 (71%) of previously reported interactions 50% of Y2H interactions confirmed by coimmunoprecipitation (CoIP) DAVID Of the 123 unique interactions detected in KSHV, 4.1% (5/123) were previously reported (additional 2 were known by personal communication) and 95.9% (118/123) novel, of which 50% (59/118) could be confirmed by CoIP. 71.4% (5/7) of the previously known KSHV interactions were captured by the Y2H screen.

Viral Protein Interactions in KSHV David Fig. S1 The red boxes indicate the mirror axis – same protein tested for interaction The yellow boxes are self-activating baits The black boxes are positive for interaction between the two proteins The number of interactions (black boxes) are added up on the axis of the red box Two clusters of protein interactions can be noticed here and here. Take note there are very few clusterings.

Previously Reported Protein Interactions of KSHV David Table S2 Previously reported data The columns from left to right depict the two proteins tested for interaction, the protein type, where it was referenced, and the results on Y2H and CoIP.

Coimmunoprecipitation JIM As good a method Y2H is in detecting protein-protein interactions, it still has limitation, and due to these limitations (false-negatives, false-positives, ect.) involved with Y2H assay, protein interactions detected by the Y2H analysis need to be varified via other qualitative experiments, such as CoIP; and in fact, of all the KSHV protein-protein interactions detected by Y2H (as well as by orthologous proteins in HSV-1, VZV, CMV, and EBV), 48% were verified by a beta-galactosidase assay adn 48% were verified by a CoIP. And the same was done for VZV protein-interactions, predicted KSHV-human protein interactions, ect. CoIP was determined by... - bait are myc tagged, prey are ha tagged - potential interacters are first precipitated with one antibody, then immuno-labeled with the other. - only co-precipitaed proteins will bind the immuno label.

Verification of Predicted Interactions in other Herpesvirus Species Jim - since only 50% or Y2H interactions could be confirmed by co-immunoprecipitation, the researchers looked to other viruses with orthologs that could be confirmed. n.d. means “not done” Table S3

Correlation Between Viral Protein Interaction and Expression Profile Figure S3: Jim – table on the left was created from someone else’s work looking at expression profile for 81 ORF’s, yellow show that they have similar expression profile, where red ones are more distinct. The small circles in this graph show interacting protein pairs. From this data expression profile correlation was calculated. In the graph on the right, this compares the correlation between AEC and clustering coefficient. Size of circle represents number of interacting partners, color represents functional class, If interactions are static such as in a large complex proteins are more likely to interact with each other (hence higher C), and to be expressed at the same time as their interacting partners, hence higher AEC. On the other hand if the interactions take place at a different time/place, both C and AEC should be lower. “Party vs. Date” hubs. If i Fig. S3 Average expression correlation [AEC]was calculated For random pairs of ORFs: 0.804 For interacting pairs of ORFs: 0.839 Correlation between AEC and clustering coefficient Used to propose static or dynamic interaction for viral hubs

Protein Interaction Networks David Since the researchers now had protein interaction data from KSHV and VZV and also their interactions with orthologous proteins in other herpesviruses, they further investigated herpesvirus protein interactions by constructing networks of the viral proteins and then predicted their interactions with the human protein network. The figure shown here could represent a typical protein network. The circles represent nodes, the lines edges,

Network Terminology Node – represents a protein Edge – represents interaction between two nodes Average (node) degree – the average number of neighbors or connections that any given node has Power coefficient (g) – derived from an approximate power law degree distribution plotted on a bilogarithmic scale and fitted by linear regression P value - (significance under linear regression) as fitted by a power-law degree distribution (‘‘scale-free’’ property) Characteristic path length – the distance between two nodes Diameter (d) - describes the interconnectedness of a network; defined as the average length of the shortest paths between any two nodes in the network Clustering coefficient – A value given to depict the number of fold enrichment over comparable random networks (‘‘small-world’’ property) Small world property/network – Any network that has characteristics of a relatively short path and dense cluster (high cluster coefficient) -So the power coefficient is used in this paper to compare the herpesviral protein networks to both yeast and human protein networks. The power coefficient is derived from plot of the log for the node degree vs. the log of the probability or frequency of node degree. The plot depicts a noticeable difference between the virus and human/yeast protein interactive network. It is more likely for the viruses to have nodes with higher degree than humans and yeasts. -The diameter characterizes the ability of two nodes to communicate with each other: the smaller d is, the shorter is the expected path between them Networks with a very large number of nodes can have quite a small diameter

Topology of KSHV and VZV Interaction Network KSHV protein interaction network David VZV protein interaction network

Comparison of Protein Interaction Networks David Table 1 This table shows the comparison of network parameters of eight different organisms and viruses using a power law distribution. Node indicates protein; edges are number of interaction between nodes; average degree is the average number of interactions per node; the power coefficient and P value determines the relative stability or resistance of a particular organism or virus under a linear regression; characteristic path length is the relative distance between nodes; the diameter is the maximum distance between any two nodes; the clustering coefficient is a value depicting the

Power Law Distribution Comparison http://www.dnatube.com/video/993/Plasmid-Cloning David - Figure 1B This figure is a comparison of the approximate power law distribution of two herpesviruses, yeast and human protein interaction networks. The x-axis is the log of k which is the node degrees. The y-axis is the relative frequency (i.e. probability). A linear regression was fit to the datasets. Can everyone see the difference between the viral and yeast/human datasets? (They say yeah I do see a difference.) This shows that organization of the interaction networks of viruses are significantly different than from yeast and human.

Removal of Nodes in KSHV Network David Fig. 1c&d These figures represents the removal of nodes and its overall effect on path length and network size of yeast and KSHV. The figure on the right has an x-axis with percentage values of hubs removed. Hubs are nodes with many interacting partners. The y-axis represents network path length. As the percentage of nodes removed increases there is a significant difference between the trail of KSHV and yeast. There is a noticeable fluctuation in path length of the yeast network. In the graph to the right a very similar picture unfolds. The x-axis still remains the same as the left graph as its plotted against a relative network size. The yeast show steep decline in its network size as more hubs are removed relative to KSHV. Both graphs depict the yeast network to become more unstable as more hubs are removed. This suggest KSHV to be more resilient than their yeast counterpart. Path length – the distance between two nodes This path length represented here is the average path length between any two nodes. Removing highly connected nodes from yeast when compared with KSHV greatly reduces network size since they are small Figure 1C. Depicts as the percentage of hubs removed increases yeasts have

Protein Interaction KSHV & Sequence Conservation to EBV Jim Fig. s7b

Correlation Between Functional and Phylogenetic Herpesviral Classes Fig S8a - Jim KHSV ORF’s were partitioned into five functional classes. Then they were further partitioned into whether they were KSHV specific or had orthologs n

Viral protein interactions between functional classes http://www.dnatube.com/video/993/Plasmid-Cloning S8B - Kyle Size = the number of proteins within the functional class Interactions with = the number and functional class of the interactors Interestingly enough, more then 70% of all protein protein interactions occurred between viral proteins belonging to different functional classes…for instance, as can be seen from the graph, the number of proteins involved in replication was approximated to equal a size of 10. However, the proteins that interact to influence replication are very diverse (are proteins that represent many different functional classes); In general, interacting proteins are not more likely to belong to the same functional class than random pairs of proteins, suggesting that the majority of viral proteins either have multiple, previously unknown functions, or are assigned incorrectly. significantly increased numbers of intra-class interactions were only identified between proteins belonging to the gene regulation and unknown functional classes (statistics see Table S7)…. the most significant correlation between, proteins that belong to the same functional group interacting (PPI) to and ‘effecting’ that specific function was seen in regulation and unknown… For instance, gene regulation. The size (# proteins within functional group) is almost similar to the # gene regulation proteins that actually interact to influence/effect gene regulation (or a gene regulation event) ?Intra-class interactions - protein of certain functional group interact to influence that actual function? Whereas interactions between proteins involved in host interaction were most highly suppressed (as expected) interactions between proteins involved in both host interaction and replication were significantly increased Interactions between proteins with unknown function are significantly overrepresented (among each cellular function), implying that they are within the same functional group (other than host interaction) and parts of identical complexes or processes

Viral Protein Interactions Between Phylogenetic Classes S8c - kyle FYI…The β and αγ subclasses, which contain only 1 member each, do not provide additional information and are thus not shown Herpesviruses are divided into three subfamilies; α - alphaherpesvirus (three viruses including VZV), β - betaherpesvirus (two viruses) & γ - gammaherpesvirus (two viruses; KSHV and Epstein-Barr Virus(EBV)) Phylogenetic classes were partitioned depending on whether they are specific for KSHV OR possess orthologs in the γ (γ, alphaherpesvirus subfamilies), β and γ (βγ, alphaherpesvirus & betaherpesvirus subfamilies) or α, β and γ (αβγ, alphaherpesvirus, betaherpesvirus & gammaherpesvirus subfamilies) subfamilies

View of the Human-Herpesviral Networks Varicella-Zoster Virus Kaposi Sarcoma-associated Herpesvirus David Figure S10 The viral proteins are depicted by red nodes and the cellular interacting human proteins or target proteins are depicted by the blue nodes. Predicted viral interactions are shown with red edges (or lines connecting the nodes), cellular interactions are blue edges and viral-human node interactions with green nodes. Both VZV and KSHV appear to show very similar predicted network patterns which adds some fidelity to their approach.

Power Coefficient of KSHV-Human Network David Figure S9 This is a power graph depicting viral-host network level vs. the power coefficient. The red line is the power coefficient for KSHV protein network and the blue line is the power coefficient of the predicted human protein network. The green line starts at level 1 which is where the viral protein interacts with the target human protein. It steadily increases until level 6 where the power coefficient is very close to the protein coefficient of the human network. So why does

Interplay between KSHV and Human Network David Figure 2A. This figure represents the global topology of the integrated KSHV network into the interacting human network. Red nodes and edges depict viral proteins and interactions them; blue nodes and lines represent level 1 or 2 interacting cellular proteins and interactions between them; gray nodes are cellular proteins greater than level 2 (nodes that are indirectly affected by viral nodes). There are 10,636 edges and 3,169 nodes which complements

Viral Host Network / Random Network Comparison Jim Figure 2C: Random Combined Viral /Host network was compared to 1000 random networks, that were generated by rewiring fixed virus interactors to swapped cellular proteins with the same degree as actual target. Random networks are blue circles, KSHV/Human network is a red triangle. What is noted is that only 65 random networks have a higher Power coefficient

Conclusions Virus and host interactomes possess distinct network topologies Integration of viral and host protein network may lead to better understanding of viral pathogenicity Future interactome data from other viruses may improve understanding of functions of viral proteins and their phylogeny Understanding networks may help to develop future therapies David