Introgression impacts the evolution of bacteria, but species borders are rarely fuzzy
Bibliographic record
Abstract
Most bacteria engage in gene flow through homologous recombination, and this mechanism may play a crucial role in maintaining species cohesiveness, much like sexual reproduction does in eukaryotes. However, introgression has been reported in bacteria and is associated with fuzzy species borders in some lineages, but its prevalence and impact on the delimitation of bacterial species have not been systematically characterized. Here, we use the term “introgression” to describe gene flow between the genomic backbone of distinct species (i.e., their core genomes)—an analogy to the classical usage in sexual organisms, but distinct in mechanism. We quantified the patterns of introgression across 50 major bacterial lineages. Our results reveal that bacteria present various levels of introgression, with an average of 2% of introgressed core genes and up to 14% in Escherichia–Shigella. Furthermore, our results show that some species are more prone to introgression than others within the same genus, and introgression is most frequent between highly related species. We found evidence that the various levels of introgression across lineages are likely associated with sequence relatedness, but the impact of ecology on this process was less clear. Introgression can occasionally lead to fuzzy species borders, although many of these cases are likely instances of ongoing speciation. Overall, our results indicate that introgression has substantially shaped the evolution and the diversification of bacteria, but this process does not substantially blur species borders. It is commonly thought that bacterial species borders tend to be fuzzy, due to frequent exchange of DNA. Here, Diop et al. quantify the patterns of gene flow between core genomes across 50 major bacterial lineages, showing that defining species using a framework inspired by the Biological Species Concept allows to identify clear species borders in most lineages.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".