Translational activity is uncoupled from nucleic acid content in bacterial cells of the human gut microbiota
Bibliographic record
Abstract
Abstract Background Changes in bacterial diversity in the human gut microbiome, characterized primarily though DNA sequencing methods, have been associated with many different adverse health conditions. However, these changes do not always reflect changes in bacterial activity, and thus how the gut microbiome is implicated in disease is still not often understood. New methods that link together bacterial function to bacterial identity are needed to further explore the role of the gut microbiome in health and disease. We optimized bioorthogonal non-canonical amino acid tagging (BONCAT) for the gut microbiota and combined it with fluorescently activated cell sorting and sequencing (FACS-Seq) to identify the translationally active members of the community. We then used this novel technique to compare and contrast to other methods of bulk community measurements of activity and viability: physiological staining of relative nucleic acid content and membrane damage. Relative nucleic acid content has previously been linked to metabolic activity, yet remains currently undefined for the human gut microbiota. Results Ten healthy, unrelated individuals were sampled to determine the proportion and diversity of distinct physiological fractions of their gut microbiota. The translationally active bacteria represent about half of the gut microbiota, and are not distinct from the whole community. The high nucleic acid content (HNA) bacteria also represent about half of the gut microbiota, but are distinct from the whole community and correlate with the damaged subset. Perturbing the community with xenobiotics previously shown to alter bacterial activity but not diversity resulted in stronger changes in the distinct physiological fractions than in the whole community. Conclusions BONCAT is a suitable method to probe the translationally active members of the human gut microbiota, and combined with FACS-Seq, allows for their identification. The high nucleic acid content bacteria are not necessarily the protein-producing bacteria in the community, and so further work is needed to understand the relationship between nucleic acid content and bacterial metabolism in the human gut. Taking into account physiologically distinct subsets of the gut microbiota may be more informative than relying on whole community profiling.
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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".