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Viral evolution and pathogenesis. The use of HPC/GRID Technologies to make intelligent biological inferences. Outline. Viral Bioinformatics Resource Center Biodefense/Emerging diseases Poxvirus genomics and evolution Bioinformatics Research Development and use of HPC/GRID technologies

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viral evolution and pathogenesis

Viral evolution and pathogenesis

The use of HPC/GRID Technologies to make intelligent biological inferences

  • Viral Bioinformatics Resource Center
    • Biodefense/Emerging diseases
  • Poxvirus genomics and evolution
    • Bioinformatics Research
    • Development and use of HPC/GRID technologies
  • Monkeypox pathogenesis
    • Real-world case study
the uab mgbf contingent microbial genomics and bioinformatics facility
Graduate Students

Chunlin Wang

Mary Odom


Jim Moon

Don Dempsey


Shankar Changayil

Curtis Hendrickson

Elizaveta Karpova

Technical Writer

Cathy Galloway

UAB CIS Collaborators

Puri Bangalore, CIS

Barrett Bryant, CIS


Najaf Shah

Ritu Arora

Pavithran Sathyanarayana

Catherine Dong

The UAB MGBF Contingent(Microbial Genomics and Bioinformatics Facility)
  • University of Victoria
    • Chris Upton
    • David Esteban
  • St. Louis University
    • Mark Buller
  • Medical College of Wisconsin
    • Paula Traktman
uab grid development
UAB Grid Development
  • Department of Computer and Information Sciences
  • Department of Information Technology, Academic Computing
    • John-Paul Robinson
    • Pravin Joshi
    • Silbia Peechakara
    • Jill Gemmill
viral bioinformatics resource center

Viral Bioinformatics Resource Center

bioinformatics resource centers for biodefense and emerging or re emerging infectious diseases
Bioinformatics Resource Centers for Biodefense and Emerging or Re-Emerging Infectious Diseases
  • Eight centers established by NIH
  • Focus on NIH/CDC Category A-C priority pathogens
  • Each Center maintains data related to a specific set of pathogens
  • Each multi-disciplinary team consists of pathogen domain experts, microbiologists, bioinformaticians and computer scientists.
brcs are designed to support
BRCs are Designed to Support

Basic and applied research on priority pathogens including the development of:

  • Environmental Detectors
  • Diagnostic Reagents
  • Animal Models
  • Vaccines
  • Antimicrobial Compounds


  • Basic Bioinformatics Research
    • Mining the data for meaningful patterns that can then provide inferences on biological function that can be tested in the laboratory
  • To better understand the role individual genes and groups of genes (or other genetic elements) play in poxvirus (especial smallpox ) host range and virulence
  • Try to describe and understand poxvirus diversity via reconstruction of the families evolutionary history
  • Analyze differences in evolutionary patterns of conserved core replicative genes vs. divergent host range/immunomodulatory/virulence factor genes
orthopoxvirus phylogeny
Orthopoxvirus Phylogeny

132 gene tree possible

poxvirus gene prediction
Poxvirus Gene Prediction
  • Little consistency from one genome to another
  • Methods employed
    • Minimum ORF size
    • Similarity with previously described proteins
consistently predict and annotate the gene set for all poxvirus genomes
Consistently predict and annotate the gene set for all Poxvirus genomes
  • Development of a comprehensive gene prediction tool
    • Discovery of new or “missed” genes
    • Removal of “pseudo” genes
  • As an added bonus:
    • Computational annotation of each predicted gene
poxvirus gene prediction and annotation
Poxvirus Gene Prediction and Annotation
  • Chunlin Wang (Graduate Student)
    • Poxvirus Genome Annotation System
vbrc computational tools
VBRC Computational Tools
  • Similarity searching
    • SS-Wrapper
      • HPC – Cluster/Grid
  • Refinement of genome-scale multiple sequence alignments
    • GenAlignRefine
      • HPC Cluster
  • Poxvirus gene prediction
    • Sequence Signals (Promoter prediction, Glimmer)
    • Similarity (BLAST and HMMPFAM)
    • Comparative analyses (Orthologs and Gene synteny)
poxvirus promoter detection
Poxvirus promoter detection
  • Distinct promoters for each phase of gene expression
  • Two conserved regions (core and initiator) separated by variable spacing
  • Sequence conservation generally within each genus.

Early promoter alignment(DNA polymerase)

Late promoter alignment(RAP94)

vacv early promoter dependencies
VACV Early Promoter Dependencies

Base frequencies

Sequence Logo

Base Dependencies

poxvirus promoter prediction
Poxvirus Promoter Prediction
  • Obtain experimentally verified vaccinia virus promoters from the literature
  • Align known promoter sequences to assess sequence conservation
  • Determine statistically significant interactions (dependencies)
  • Build Interpolated Context Models (ICMs) based on VACV early and late promoter sequences
  • Predict the VACV promoters using the ICMs
  • Predict Promoter sequences in other Poxviridae species
  • Evaluate promoter variation for Orthopoxvirus species
high performance computing tools
High Performance Computing Tools
  • Computationally-intensive Bioinformatics analyses
    • Similarity searching
    • Multiple sequence alignment
  • Linux Clusters
  • Grid Computing
ss wrapper
  • QS_search—query splitting approach
    • Accommodate most database searching application effortlessly
  • DS_BLAST—database splitting approach
    • A wrapper application tailored for NCBI BLAST
g blast
  • A native Grid Service Interface for BLAST
  • G-BLAST provides automatic BLAST algorithm selection based on # of queries, length of queries, size of the database used, and machines available
  • BLAST algorithms employed: multi-threaded BLAST, database-splitting BLAST (e.g., mpiBLAST), query-splitting BLAST
gridblast user friendly interface
GridBLAST User-Friendly Interface
  • Access using BlazerID and password
  • Queries and Results easily uploaded & downloaded
  • Web UI can be hosted on your server
  • Web UI can be written in any development language
  • Refinement of multiple whole-genome sequence alignments
  • Supports comparative genomics
    • Identification of genotypic differences
      • Identify changes related to particular phenotypes
        • pathogenic/non-pathogenic strains
    • Evolutionary relationships
    • Annotation of newly sequenced genomes
anchoring extension strategy
“Anchoring-Extension” Strategy

Optimally-aligned Blocks

“Fuzzy” Regions

  • Realign “fuzzy” regions using a genetic algorithm
    • Computationally slow
  • Parallelize process by sending each region to a separate node of the cluster/grid
pgas gene layout panel
PGAS Gene Layout Panel

Open reading frame (no gene prediction)

Predicted gene

Predicted gene with alternate start codon

Gene fragment

orthologous gene transcriptional environment
Orthologous Gene Transcriptional Environment

Predicted coding region

Predicted late promoter

Predicted early promoter

T5NT early transcription terminator

ATG start codon








P. Identical

P. Divergent

ORF (+)

ORF (-)

orthopoxvirus evolution
Orthopoxvirus Evolution

Simple Statement:

  • The evolution of all Orthopoxvirus species reflects:
    • Gene loss
    • Protein sequence variation
    • Variation in gene expression
    • Acquisition of new genes does NOT play a role
future work
Future work
  • Apply the tools and techniques developed for poxviruses to the study of other viral pathogens
    • Identification of significant RNA-virus sequence co-dependencies
    • Identification of amino acid co-dependencies
    • RNA virus evolution
human monkeypox

Human Monkeypox

Bioinformatics, Epidemiology, Evolution, Biology, and Pathogenesis

monkeypox collaborations
Monkeypox Collaborations
  • CDC
    • Inger Damon
    • Joe Esposito
    • Scott Sammons
    • Anna Likos
  • St. Louis University
    • Nanhai Chen
    • Mark Buller
  • University of Victoria
    • Guiyun Li
    • Chris Upton
  • Ft. Detrick
    • Peter Jarhling
  • UAB
    • Elliot Lefkowitz
    • Chunlin Wang
  • And many others…
  • Smallpox-like disease
    • Approximately 10% case fatality rate
  • Rare human-human transmission
    • No more than 2 generations of transmission from an index case
  • Increasing incidence
    • Human encroachment on animal reservoir habitats
u s midwest monkeypox outbreak
U.S. Midwest Monkeypox Outbreak
  • April – June, 2003
  • Imported from West Africa
    • Shipment of infected rodents from Ghana
  • Rodents housed with native prairie dogs
    • Infected prairie dogs transmitted virus to humans
    • Transmission due to respiratory and direct mucocutaneous exposure
  • 72 confirmed or suspected human cases
characteristics of u s monkeypox infection
Characteristics of U.S. Monkeypox Infection
  • No human fatalities
  • No human-human transmission
possible explanations for reduced virulence in the u s monkeypox outbreak
Possible Explanations for Reduced Virulence in the U.S. Monkeypox Outbreak
  • Higher natural resistance of the U.S. population
  • Healthier patient population
  • Better supportive care
  • Viral strain differences with variable pathogenicity
monkeypox cases in africa 1970 1986

West Africa: 6 cases

Origination of rodent shipmentto the US

DRC: 260 cases

Monkeypox Cases in Africa 1970 - 1986

CDC – 2005; Sammons et. al.

variability of monkeypox infections in different regions of africa
Variability of Monkeypox Infections in Different Regions of Africa
  • Prevalence equivalent as determined by antibody titers of the population
  • Central African (Congo basin) Disease
    • >90% of reported cases
    • All reported fatalities
      • 11.5% Case fatality rate
    • Human-human transmission
  • West African
    • No fatalities
    • No human-human transmission
    • Genetically distinct strain(s) of virus
    • Equivalent to what was seen for the 2003 US Midwest outbreak
Aerosol Infection of Cynomolgus Monkeys with West and Congo Basin Isolates Monkeypox virus(Ft. Detrick)
mpxv sequence comparisons

#Gaps / Length Gaps




#substitutions / #identical / %difference

MPXV Sequence Comparisons
mopice structure and function monkeypox inhibitor of complement enzymes
MOPICE structure and function(Monkeypox inhibitor of complement enzymes)

MOPICE: cofactor for complement cleavage by serine protease factor I

  • Genomic sequence differences may be responsible for differences in virulence between Monkeypox strains isolated from geographically-distinct regions
  • Strains with reduced pathogenicity lack the MOPICE gene that codes for a protein with complement inhibitory activity.
future work1
Future work
  • Targeted mutagenesis of the MOPICE gene
    • Effect on pathogenesis
  • Further analysis of newly-sequenced Monkeypox isolates
  • Analysis of the B10R and B14R genes
    • (Among others)