1. Ben Dan Deepak Esha Kelly Pramod Raghav Smruthy Vartika Will

2.  An aquatic bacterium  First isolated from sewage in Navarra, Spain in 1982  Gram negative  Non-spore forming rods  Motile by means of single polar flagellum

3. 1.Sixteen strains clustered with V. navarrensis type strain LMG15976 16S rRNA, pyrH, recA and rpoA Four formed a distinct cluster V. vulnificus  Closest relative to both lineages of V. navarrensis “Is it a different species or biotype?” 2.V. navarrensis strains isolated from various sources. nav_2423 (VN1) : Blood nav_2462 (VN2) : Surface Wound nav_2541 (VN3) : Sewage nav_2756 (VN4) : Water “Is Vibrio navarrensis pathogenic?”

4. Concatenated pryH,recA,rpoA; 16S was not used Neighbor-joining method, Kimura2P, pairwise deletion and 1000 interior branch tests. 1443 nt total pyrH (321nt), recA(606 nt), and rpoA(516 nt) Vibrio navarrensis Vibrio vulnificus L1 L2 2421-86 08-2466 1397-6T Vibrio navarrensis LMG 15976T 2544-86 2232 or 2541-90 2422-86 0053-83 2578-87 08-2461 08-2462 AM 37820 08-2467 2462-79 AM 36848 2543-80 2481-86 1048-83 2756-81 2538-88 2423-01 Vibrio vulnificus LMG 13545T Vibrio vulnificus CMCP6 Vibrio vulnificus YJ016 75 98 55 76 48 66 99 55 38 48 60 99 31 30 48 99 54 44 99 0.01 Red and blue indicate an available genome sequence. Red indicates it was isolated in blood; blue indicates it was isolated in an environmental setting (water or sewage)

6. Strategy for Defining/Distinguishing Species ANI (average nucleotide identity) Robustly assessing phylogenetic relationships between strains – Supertree approach – Supermatrix approach If there is interest: – Genes under positive selection (DN/DS) – Rates of Divergence

7. Old School Method for Defining Species DNA / DNA Hybridization – Tedious, hard to have good reproducibility – Coherent group of strains sharing > 70% DDH considered a species Still need to have a phenotype associated with the group

8. Genomics Approach to DDH Developed by Dr. Konstantinidis – (Konstantinidis and Tiedje et al. IJSEM, 2005) We’re employing a modified version of his script for whole genome ANI comparisons Original Script: – Takes two genomes as input – Parses genomes into 1kb fragments, and uses blastn to find reciprocal orthologs – Takes average nucleotide idenity (ANI) for all reciprocal orthologs for each pair of draft genomes Coherent groups sharing – >95%  Same Species – <95% to sister group/subgroup  Candidate New Species

9. Whole Genome Tree A.First required identification of all orthologous proteins common to all strains (should we exclude VN2?)  Perl script: uses reciprocal blastp, keeps top hit, >70% length of reference genes, >40% ID Outputs a file that can be used for interograting presence/absense of metabolic/virulence genes later on  OrthoMCL Genome scale algorithm for grouping orthologous protein sequences

10. B.Align all orthologous genes  Clustal  Muscle C.Supertree approach  Generally considered more robust and allows further investigation of HGT  Make separate tree for each gene, find consensus tree D.Supermatrix approach  Concatenate all alignments  Generate tree

11. Tree Building Approaches Neighbor-joining – Fast, decently robust when bootstrapping Utilizing complex substitution models – Maximum Likelihood – Bayesian Analysis – Computationally demanding, thought to do better with missing data, generally work better for divergent organisms. To publish we’ll probably need to generate one of these trees to confirm NJ topology

12. Tree Building Software MEGA – Easy to use GUI – Not very customizable, but very quick PHYLIP – Command-line based – Very customizable PAUP – Command-line – Customizable Mr Bayes – Uses MCMC to generate bayesian trees – Has >11,000 citations…

13. Draft Genome Gene Predictions Translated Genes ANI Dendrogram Identifying Core Genome OrthoMCL Custom Script Multiple Alignment Super Tree Super Matrix Super Matrix Consensus Tree Strategy for Defining Species New Species?? PHYLIP MEGA PAUP Mr Bayes Clustal Ω MUSCLE

14. COGS – how it works, rationale behind it Clusters of Orthologous Groups (COG): For a hierarchical functional classification, proteins that fell into OrthoMCL groups will be used to search against the COG’s database COG’s Uses: A comparison of the Core proteome to the Accessory proteome. A comparison of the Orthologous groups of V. narvarrensis to the 5 Vibrio pathogenic strains Core = proteins found in all genomes Accessory = those encoded by two or more but not all 11

16. Pathogenecity Challenges: 1.Well known databases and tools are lacking a complete list of virulence factors. 2.Non-human pathogenic Vibrios are sometimes pathogenic in their marine hosts. As a result, some non-human pathogenic Vibrios share virulence factors with the human pathogenic Vibrios. 3.The plasticity of Vibrio genome: Many virulence factors are present in mobile elements and they can be shared through Horizontal Gene Transfer (HGT). Hence, its difficult to draw a line between pathogenic (to humans) and non-pathogenic Vibrios.

17. Types of Infection 1.Gastroenteritis 2.Septicemia 3.Wound Infection

18. Association of Vibrio species with different clinical symptoms * Less common presentation, ** common presentation, (*) rare presentation Vibrio sp.Wound InfectionGastroenteritisSepticemia Vibrio cholerae O1 ** Vibrio cholerae non O1 **** Vibrio parahemolyticus ***(*) Vibrio vulnificus *** Vibrio mimicus (*)* Vibrio alginolyticus *** Vibrio fluvialis (*)**(*) Photobacterium damsela ** Grimontia hollisae (*)**(*) Vibrio furnissi ** Alivibrio fischeri Vibrio splendidus Vibrio harveyi Vibrio anguillarum PathogenicPotentially PathogenicNon-Pathogenic (Daniels et al., 2000)

19. Genomic Islands Discrete DNA segments differing between closely related bacterial strains  Usually some past or present mobility is attributed. Why of our interest??  Virulence factors are often associated with GEIs!! Features of GEIs:  GEIs are relatively large segments of DNA, usually between 10 and 200 kb detected by comparisons among closely related strains.  GEIs may be recognized by nucleotide statistics that usually differ from the rest of the chromosome, such as 1.GC content 2.Cumulative GC skew 3.Codon usage  GEIs are often inserted at tRNA genes.

20. GEIs are often flanked by 16– 20bp perfect or almost perfect direct repeats (DR) GEIs often harbor functional or cryptic genes encoding integrases or factors related to plasmid conjugation systems or phages involved in GEI transfer. GEIs often carry insertion elements or transposons GEIs often carry genes offering a selective advantage for host bacteria. According to their gene content, GEIs are often described as pathogenicity, symbiosis, metabolic, fitness or resistance islands. General features of GEIs (Mario Juhas et al., 2009)

21. Integration, development and excision of GEIs. (Mario Juhas et al., 2009)

22. Variable types of GEIs (Mario Juhas et al., 2009)

23. Virulence Factors in Vibrio Virulence factors

24. Original strategy (proposed by Lee Katz) Possible ways – Discover homologous genes to other Vibrio virulence factors (esp. V. vulnificus ) – Uncover genes that appear in closely-related pathogenic species but do not appear in closely-related non-pathogenic species V. navarrensis V. non- pathogenic V. pathogenic Sweet spot

25. Distribution of virulence-associated orthologous groups across eleven Vibrionaceae genomes (Lilburn et al., 2010)

26. NMPDR + VFDB PAI DB Literature Survey MvirDB Virulence-Related Protein Collection

27. Strategy to Determine Pathogenecity Checking for Presence/Absence of – Toxins – Adherence factors Type IV pilus system – Secretion systems – Siderophores

28. Annotated Dataset Existence of Toxins Machinery for Incorporation (Pili/Adherence Factors) Machinery for Incorporation (Pili/Adherence Factors) PresenceAbsence Machinery for Incorporation (Pili/Adherence Factors) Machinery for Incorporation (Pili/Adherence Factors) Potentially Pathogenic Unlikely Pathogenic Yes No Correlation with Pathway (KEGG) Pathogenic or Putatively Pathogenic Pathogenic or Putatively Pathogenic Connecting the dots Strategy for Pathogenicity

29. Road ahead Environment v/s clinical strains comparison All v/s All within our nine strains Core genes v/s best genes trees (Morrison et al., 2012)