Viral evolution and pathogenesis

1. Viral evolution and pathogenesis The use of HPC/GRID Technologies to make intelligent biological inferences

2. Outline • 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

3. Graduate Students Chunlin Wang Mary Odom Programmers Jim Moon Don Dempsey Fellows Shankar Changayil Curtis Hendrickson Elizaveta Karpova Technical Writer Cathy Galloway UAB CIS Collaborators Puri Bangalore, CIS Barrett Bryant, CIS Students Najaf Shah Ritu Arora Pavithran Sathyanarayana Catherine Dong The UAB MGBF Contingent(Microbial Genomics and Bioinformatics Facility)

4. Collaborators • University of Victoria • Chris Upton • David Esteban • St. Louis University • Mark Buller • Medical College of Wisconsin • Paula Traktman

5. UAB Grid Development • Department of Computer and Information Sciences • Department of Information Technology, Academic Computing • John-Paul Robinson • Pravin Joshi • Silbia Peechakara • Jill Gemmill

6. Viral Bioinformatics Resource Center www.biovirus.org www.poxvirus.org

7. 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.

8. 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 and… • Basic Bioinformatics Research • Mining the data for meaningful patterns that can then provide inferences on biological function that can be tested in the laboratory

10. Genomic and Evolutionary Analysis of Poxviruses

11. Objectives • 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

13. Orthopoxvirus Phylogeny 132 gene tree possible

15. Genomic and Evolutionary Analysis of Poxviruses We have a problem…

16. Gene Synteny MPXV vs. VARV

22. Poxvirus Gene Prediction • Little consistency from one genome to another • Methods employed • Minimum ORF size • Similarity with previously described proteins

23. 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

24. Poxvirus Gene Prediction and Annotation • Chunlin Wang (Graduate Student) • Poxvirus Genome Annotation System

25. VBRC Computational Tools • Similarity searching • SS-Wrapper • NCBI BLAST, WU BLAST, FASTA, PC_SCAN, HMMPFAM… • 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)

26. 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.

27. Early promoter alignment(DNA polymerase) Late promoter alignment(RAP94)

28. VACV Early Promoter Dependencies Base frequencies Sequence Logo Base Dependencies

29. 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

31. High Performance Computing Tools • Computationally-intensive Bioinformatics analyses • Similarity searching • Multiple sequence alignment • Linux Clusters • Grid Computing

32. SS-Wrapper • QS_search—query splitting approach • Accommodate most database searching application effortlessly • All variants of NCBI BLAST, WU BLAST, FASTA, PC_SCAN, HMMPFAM… • DS_BLAST—database splitting approach • A wrapper application tailored for NCBI BLAST

33. The performance of QS-search for BLAST

34. The performance of QS-search for HMMPFAM

35. 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

36. 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

37. GenAlignRefine • 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

38. “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

39. The Poxvirus Genome Annotation System (PGAS) Pipeline

40. PGAS Gene Layout Panel Open reading frame (no gene prediction) Predicted gene Predicted gene with alternate start codon Gene fragment

41. PGAS Genome Comparison Panel

42. Orthologous Gene Transcriptional Environment Predicted coding region Predicted late promoter Predicted early promoter T5NT early transcription terminator ATG start codon

43. Predicted Gene Map CMLV-M96

44. Re-prediction of Orthopoxvirus Genes

45. Early Intermediate Late E/I E/L N.D. P. Identical P. Divergent ORF (+) ORF (-)

46. Phylogeny of Poxvirus Gene gain and loss events

47. 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

48. 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

49. Human Monkeypox Bioinformatics, Epidemiology, Evolution, Biology, and Pathogenesis

50. 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…