Host–bacteria interactions: ecological and evolutionary insights from ancient, professional endosymbionts
Bibliographic record
Abstract
Interactions between eukaryotic hosts and their bacterial symbionts drive key ecological and evolutionary processes, from regulating ecosystems to the evolution of complex molecular machines and processes. Over time, endosymbionts generally evolve reduced genomes, and their relationship with their host tends to stabilize. However, host-bacteria relationships may be heavily influenced by environmental changes. Here, we review these effects on one of the most ancient and diverse endosymbiotic groups, formed by-among others-Legionellales, Francisellaceae, and Piscirickettsiaceae. This group is referred to as Deep-branching Intracellular Gammaproteobacteria (DIG), whose last common ancestor presumably emerged about 2 Ga ago. We show that DIGs are globally distributed, but generally at very low abundance, and are mainly identified in aquatic biomes. Most DIGs harbour a type IVB secretion system, critical for host-adaptation, but its structure and composition vary. Finally, we review the different types of microbial interactions that can occur in diverse environments, with direct or indirect effects on DIG populations. The increased use of omics technologies on environmental samples will allow a better understanding of host-bacterial interactions and help unravel the definition of DIGs as a group from an ecological, molecular, and evolutionary perspective.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".