presentation by: kyle borge, david byon, & jim hall

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

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Presentation by: Kyle Borge, David Byon, & Jim Hall . Herpesviral Protein Networks and Their Interaction with the Human Proteome. reconstruction of a Herpes Virus capsid. Presentation by: Kyle Borge, David Byon, & Jim Hall . Introduction to the Herpesvirus. Large double-stranded DNA genomes - PowerPoint PPT Presentation

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Page 1: Presentation by: Kyle Borge, David Byon, & Jim Hall

Presentation by:Kyle Borge, David Byon, & Jim Hall

Herpesviral Protein Networks and Their Interaction with the Human Proteome

reconstruction of a Herpes Virus capsid

Presentation by:Kyle Borge, David Byon, & Jim Hall

Page 2: 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

Page 3: Presentation by: Kyle Borge, David Byon, & Jim Hall

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)

Page 4: Presentation by: Kyle Borge, David Byon, & Jim Hall

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

Page 5: Presentation by: Kyle Borge, David Byon, & Jim Hall

• http://www.dnatube.com/video/993/Plasmid-Cloning

Y2H Advantages

• 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

Page 6: Presentation by: Kyle Borge, David Byon, & Jim Hall

• http://www.dnatube.com/video/993/Plasmid-Cloning

Y2H Limitations• 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 placeEx. bait proteins activate, transcribing the reporter gene, w/out the

binding of the AD (bait proteins act as transcriptional activators)

Page 7: Presentation by: Kyle Borge, David Byon, & Jim Hall

Yeast’s GAL4 transcriptional activator

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

Page 8: Presentation by: Kyle Borge, David Byon, & Jim Hall

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

Page 9: Presentation by: Kyle Borge, David Byon, & Jim Hall

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

Page 10: Presentation by: Kyle Borge, David Byon, & Jim Hall

(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

Page 11: Presentation by: Kyle Borge, David Byon, & Jim Hall

Yeast-Two-Hybrid

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

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

Page 12: Presentation by: Kyle Borge, David Byon, & Jim Hall

Yeast-Two-Hybrid Background

…in a diploid cell.

Page 13: Presentation by: Kyle Borge, David Byon, & Jim Hall

•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)

Viral Protein Interactions in KSHV

Page 14: Presentation by: Kyle Borge, David Byon, & Jim Hall

Viral Protein Interactions in KSHV

Page 15: Presentation by: Kyle Borge, David Byon, & Jim Hall

Previously Reported Protein Interactions of KSHV

Page 16: Presentation by: Kyle Borge, David Byon, & Jim Hall

Coimmunoprecipitation

Page 17: Presentation by: Kyle Borge, David Byon, & Jim Hall

Verification of Predicted Interactions in other Herpesvirus Species

Page 18: Presentation by: Kyle Borge, David Byon, & Jim Hall

Correlation Between Viral Protein Interaction and Expression Profile

•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

Page 19: Presentation by: Kyle Borge, David Byon, & Jim Hall

Protein Interaction Networks

Page 20: Presentation by: Kyle Borge, David Byon, & Jim Hall

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)

Page 21: Presentation by: Kyle Borge, David Byon, & Jim Hall

Topology of KSHV and VZV Interaction Network

KSHV protein interaction network

VZV protein interaction network

Page 22: Presentation by: Kyle Borge, David Byon, & Jim Hall

Comparison of Protein Interaction Networks

Page 23: Presentation by: Kyle Borge, David Byon, & Jim Hall

• http://www.dnatube.com/video/993/Plasmid-Cloning

Power Law Distribution Comparison

Page 24: Presentation by: Kyle Borge, David Byon, & Jim Hall

Removal of Nodes in KSHV Network

Page 25: Presentation by: Kyle Borge, David Byon, & Jim Hall

Protein Interaction KSHV & Sequence Conservation to EBV

Page 26: Presentation by: Kyle Borge, David Byon, & Jim Hall

Correlation Between Functional and Phylogenetic Herpesviral Classes

Page 27: Presentation by: Kyle Borge, David Byon, & Jim Hall

• http://www.dnatube.com/video/993/Plasmid-Cloning

Viral protein interactions between functional classes

Page 28: Presentation by: Kyle Borge, David Byon, & Jim Hall

Viral Protein Interactions Between Phylogenetic Classes

Page 29: Presentation by: Kyle Borge, David Byon, & Jim Hall

View of the Human-Herpesviral Networks

Varicella-Zoster Virus Kaposi Sarcoma-associated Herpesvirus

Page 30: Presentation by: Kyle Borge, David Byon, & Jim Hall

Power Coefficient of KSHV-Human Network

Page 31: Presentation by: Kyle Borge, David Byon, & Jim Hall

Interplay between KSHV and Human Network

Page 32: Presentation by: Kyle Borge, David Byon, & Jim Hall

Viral Host Network / Random Network Comparison

Page 33: Presentation by: Kyle Borge, David Byon, & Jim Hall

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