Using genome sequence data to predict resource competition within the zebrafish gut microbiota Alexandra Weston, University of Oregon Mentor: Zac Stephens Karen Guillemin, PI

You are not just a bunch of Human Cells!  Ecosystem Microbiota  Gut Microbiota: disease states  altered gut microbiota composition Micah Lidberg

Gut Community Assembly  Intestines sterile before birth  What factors affect community assembly? Microbial traits ○ Motility ○ Adhesion Host interactions ○ Host immune response Microbial Competition ○ Locale in gut ○ Resources Micah Lidberg

My Question  Can we use genome data to predict microbial competition within the gut? Resource Competition My specific hypothesis blah blah blah

My strategy for testin gthe hypothesis  Gather predictions from models of other fiolks  Create in vivo conditions to compare in silico anaylisis with in vivo measurrents  Ask whether in silico reflect in vivo  If yes.. If no …

in silico Predictions enzyme reactantsproducts Metabolic Model Sequenced Genome

in silico Predictions Seed Set thiamine-phosphate fructose-1-phosphate Sulfuric acid L-Valine Arsenite 2-Acyl-sn-glycero-3- phosphoglycerol Acetoacetic acid Potassium Glucose non-seed seed

thiamine-phosphate fructose-1-phosphate Sulfuric acid L-Valine Arsenite 2-Acyl-sn-glycero-3- phosphoglycerol Acetoacetic acid Potassium Glucose Imidazole acetaldehyde Glucose Sulfuric acid L-Valine L-myo-Inositol 1- phosphate N-5-phosphoribosyl- anthranilate Ammonium Butyryl-CoA in silico Predictions Program compares seed sets of two microbes thiamine-phosphate fructose-1-phosphate Sulfuric acid L-Valine Arsenite 2-Acyl-sn-glycero-3- phosphoglycerol Acetoacetic acid Potassium Glucose Imidazole acetaldehyde Glucose Sulfuric acid L-Valine L-myo-Inositol 1- phosphate N-5-phosphoribosyl- anthranilate Ammonium Butyryl-CoA  Seed Overlap: Number of compounds that exist in both seed sets  Prediction High seed overlap More competition

The Zebrafish as a Model Organism In Vivo Testing Guillemin lab: collection of commensal zebrafish gut microbes 66 strains 21 genomes germ-free zebrafish

Experiment overview In Vivo Testing Dissect guts and plate out to determine the colonization by each strain Germ-free

Bacterial strains Microbacterium, ZOR0019 Kocuria, ZOR0020 Ensifer, ZNC0028 Bosea, ZNC0032 Bosea, ZNC0037 Chitinibacter, ZOR0017 Variovorax, ZNC0006 Delftia, ZNC0008 Exiquobacterium, ZWU0009 Carnobacterium, ZWU0011 Aeromonas, ZOR0001 Aeromonas, ZOR0002 Pseudomonas, ZWU0006 Vibrio, ZWU0020 Shewanella, ZOR0012 Acinetobacter, ZOR0008 Plesiomonas, ZOR0011 Enterobacter/Lecleria, ZOR0014 Comamonas, ZNC0007 Choosing Competitions Seed Set Analysis Monoassociations 19 Strains 11 Strains Competitions (Seed overlap)

Expected Outcomes Analysis: Competitive Index (CI) 9/1 = 9 (competition) 5/5 = 1 (no competition) High Competitive Exclusion Low Competitive Exclusion

Competitive Index per Competition Results

Two Models of Competitive Exclusion Highly stereotyped Highly Variable Analysis: Competitive Index (CI) CI =(9/1)=9 CI= (1/9) = 0.11 Analysis: Power CI |log(CI)| Allows us to normalize the two different scenarios Power CI=|log(9/1)| = 0.95 Power CI= |log(1/9)| = 0.95 Normalize to monoassociation ability ms1= mean CFU/ gut in mono- colonization for strain 1 ms2= mean CFU/ gut in mono- colonization for strain 1

Power Competitive Index vs. Seed Overlap Results

Conclusion  Is this a good method for predicting in vivo competition? A great deal of fish-to-fish variation Not the best r 2 It’s a start, but it doesn’t tell the whole story of community assembly.

Future Directions  Another possibility: bacteria inhabit discrete locales with different environments

Acknowledgements  Guillemin Lab Karen Guillemin Zac Stephens Jennifer Hampton Annah Rolig Chris Wreden Erika Mittge Rose Sockol  Bohannan Lab Adam Burns Robert Steury  Elhanan Borenstein (UW)  SPUR Peter O’Day  Funding NICHD R25 Summer Research Program (NIH-1R25HD070817) Karen’s NIH grant

Questions?