Lateral gene transfer of an ABC transporter complex between major constituents of the human gut microbiome
Bibliographic record
Abstract
BACKGROUND: Several links have been established between the human gut microbiome and conditions such as obesity and inflammatory bowel syndrome. This highlights the importance of understanding what properties of the gut microbiome can affect the health of the human host. Studies have been undertaken to determine the species composition of this microbiome and infer functional profiles associated with such host properties. However, lateral gene transfer (LGT) between community members may result in misleading taxonomic attributions for the recipient organisms, thus making species-function links difficult to establish. RESULTS: We identified a peptides/nickel transport complex whose components differed in abundance based upon levels of host obesity, and assigned the encoded proteins to members of the microbial community. Each protein was assigned to several distinct taxonomic groups, with moderate levels of agreement observed among different proteins in the complex. Phylogenetic trees of these proteins produced clusters that differed greatly from taxonomic attributions and indicated that habitat-directed LGT of this complex is likely to have occurred, though not always between the same partners. CONCLUSIONS: These findings demonstrate that certain membrane transport systems may be an important factor within an obese-associated gut microbiome and that such complexes may be acquired several times by different strains of the same species. Additionally, an example of individual proteins from different organisms being transferred into one operon was observed, potentially demonstrating a functional complex despite the donors of the subunits being taxonomically disparate. Our results also highlight the potential impact of habitat-directed LGT on the resident microbiota.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".