Mapping Gene-Microbe Interactions: Insights from Functional Genomics Co-culture Experiments between <i>Saccharomyces cerevisiae</i> and <i>Pseudomonas</i> spp
Bibliographic record
Abstract
Abstract Microbial interactions contribute to shape ecosystems and their functions. The interplay between microorganisms also shapes the evolutionary trajectory of each species, by imposing metabolic and physiological selective pressures. The mechanisms underlying these interactions are thus of interest to improve our understanding of microbial evolution at the genetic level. Here we applied a functional genomics approach in the model yeast Saccharomyces cerevisiae to identify the fitness determinants of naïve biotic interactions. We used a barcoded prototroph yeast deletion collection to perform pooled fitness competitions in co-culture with seven Pseudomonas spp natural isolates. We found that co-culture had a positive impact on fitness profiles, as in general the deleterious effects of loss of function in our nutrient-poor media were mitigated. In total, 643 genes showed a fitness difference in co-culture, most of which can be explained by a media diversification procured by bacterial metabolism. However, a large fraction (36%) of gene-microbe interactions could not be recaptured in cell-free supernatant experiments, showcasing that feedback mechanisms or physical contacts modulate these interactions. Also, the gene list of some co-cultures was enriched with homologs in other eukaryote species, suggesting a variable degree of specificity underlying the mechanisms of biotic interactions and that these interactions could also exist in other organisms. Our results illustrate how microbial interactions can contribute to shape the interplay between genomes and species interactions, and that S. cerevisiae is a powerful model to study the impact of biotic interactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".