using genome sequence data to predict resource competition within the zebrafish gut microbiota...
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![Page 1: Using genome sequence data to predict resource competition within the zebrafish gut microbiota Alexandra Weston, University of Oregon Mentor: Zac Stephens](https://reader030.vdocument.in/reader030/viewer/2022032804/56649e565503460f94b4d989/html5/thumbnails/1.jpg)
FROM GENOMES TO RESOURCE
COMPETITORSUsing genome sequence data to predict
resource competition within the zebrafish gut microbiota
Alexandra Weston, University of OregonMentor: Zac StephensKaren Guillemin, PI
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You are not just a bunch of Human Cells!
EcosystemMicrobiota
Gut Microbiota: disease states
altered gut microbiota
composition
Micah Lidberg
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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
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My Question
Can we use genome data to predict microbial competition within the gut?Resource Competition
My specific hypothesis blah blah blah
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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 …
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in silico Predictions
enzyme reactants products
Metabolic Model
Sequenced Genome
CTTCCTTTATGGTGAACTTTATCGTGGACGATCTTGAGCAAGCCCTACTTCAAGTCACGCAGGGTGGC
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in silico Predictions
Seed Set
thiamine-phosphatefructose-1-phosphateSulfuric acidL-ValineArsenite2-Acyl-sn-glycero-3-phosphoglycerolAcetoacetic acidPotassiumGlucose
non-seedseed
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thiamine-phosphatefructose-1-phosphateSulfuric acidL-ValineArsenite2-Acyl-sn-glycero-3-phosphoglycerolAcetoacetic acidPotassiumGlucose
Imidazole acetaldehydeGlucoseSulfuric acidL-ValineL-myo-Inositol 1-phosphateN-5-phosphoribosyl-anthranilateAmmoniumButyryl-CoA
in silico Predictions
Program compares seed sets of two microbes
thiamine-phosphatefructose-1-phosphateSulfuric acidL-ValineArsenite2-Acyl-sn-glycero-3-phosphoglycerolAcetoacetic acidPotassiumGlucose
Imidazole acetaldehydeGlucoseSulfuric acidL-ValineL-myo-Inositol 1-phosphateN-5-phosphoribosyl-anthranilateAmmoniumButyryl-CoA
Seed Overlap:Number of
compounds that exist in both seed sets
PredictionHigh seed
overlapMore
competition
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The Zebrafish as a Model Organism
In Vivo Testing
• Guillemin lab: collection of commensal zebrafish gut microbes
• 66 strains• 21 genomes
• germ-free zebrafish
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Experiment overview
In Vivo Testing
Dissect guts and plate out to determine the colonization by each strain
Germ-free
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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)
12 X 1912 X 812 X WU6
12 X11 12 X 14 14 X 19 12 X WU20 12 X 1 12 X 2
135-184 185-230 230-278
11 X 2 14 X 8 14 X 1 14 X 2 1 X 2 WU20 X1
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Expected Outcomes Analysis: Competitive Index (CI)
9/1 = 9(competition)
5/5 = 1(no competition)
High Competitive Exclusion
Low Competitive Exclusion
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Competitive Index per Competition
Results
135-184 185-230 230-278
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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
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Power Competitive Index vs. Seed Overlap
Results
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Conclusion
Is this a good method for predicting in vivo competition?A great deal of fish-to-fish variationNot the best r2
It’s a start, but it doesn’t tell the whole story of community assembly.
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Future Directions
Another possibility: bacteria inhabit discrete locales with different environments
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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
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Questions?