Rapidly evolving orphan immunity genes protect human gut bacteria from intoxication by the type VI secretion system
Bibliographic record
Abstract
Bacteria encode diverse mechanisms for mediating interbacterial antagonism through the exchange of toxic effector proteins. Although the structure, function, and regulation of these pathways has been well established for many organisms, an understanding of their ecological and evolutionary dynamics lags behind. Type VI secretion systems (T6SS) deliver effectors between competing Gram-negative bacteria, including among mammalian gut Bacteroidales, resulting in the evolution of elaborate defense mechanisms that protect against T6SS attack. One such mechanism is the recombinase-associated acquired interbacterial defence (rAID) system, which harbors arrays of orphan immunity genes that diverge in sequence from T6SS-associated cognate immunity genes. It is not known if such sequence divergence impacts rAID orphan immunity function, or how rAID distribution across microbiomes relates to the T6SS. Here, we show that divergent rAID orphan immunity factors that possess SUKH domains allow bacteria to survive intoxication by cognate effectors. Such protection is due to high affinity protein-protein interactions between orphan immunity and effector that are comparable to that of cognate effector-immunity. Unlike other examples of T6SS effector-immunity interactions, we find that the binding interface is comprised of electrostatic interactions with a high degree of redundancy underlying its protective capacity. Finally, we quantify orphan immunity and effector gene abundance and dynamics across human gut metagenomes, revealing patterns of co-occurrence indicative of positive selection. Population genetic analyses of longitudinal data suggests that orphan immunity genes accumulate non-synonymous mutations that lie at the predicted effector-immunity interface. Together, our findings establish rAID orphan immunity genes as important bacterial fitness determinants in the human gut.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".