Proteomics‐inferred genome typing (PIGT) demonstrates inter‐population recombination as a strategy for environmental adaptation
Bibliographic record
Abstract
Analyses of ecological and evolutionary processes that shape microbial consortia are facilitated by comprehensive studies of ecosystems with low species richness. In the current study we evaluated the role of recombination in altering the fitness of chemoautotrophic bacteria in their natural environment. Proteomics-inferred genome typing (PIGT) was used to genotype the dominant Leptospirillum group II populations in 27 biofilms sampled from six locations in the Richmond Mine acid mine drainage system (Iron Mountain, CA) over a 4-year period. We observed six distinct genotypes that are recombinants comprised of segments from two 'parental' genotypes. Community genomic analyses revealed additional low abundance recombinant variants. The dominance of some genotypes despite a larger available genome pool, and patterns of spatiotemporal distribution within the ecosystem, indicate selection for distinct recombinants. Genes involved in motility, signal transduction and transport were over-represented in the tens to hundreds of kilobase recombinant blocks, whereas core metabolic functions were significantly under-represented. Our findings demonstrate the power of PIGT and reveal that recombination is a mechanism for fine-scale adaptation in this system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".