Understanding metabolic processes shaping adaptation of E. coli to the gut
Bibliographic record
Abstract
Many microbes colonise the gut establishing interactions with their host and their nutritional environment. Studying genetics and metabolism brought about the drive and potential to engineer communities to promote health and improve industrial processes. However, structuring artificial communities in predictable ways is underdeveloped. We studied Escherichia coli ’s genetic targets and physiological mechanisms during gut colonisation and adaptation and how metabolic environment/microbiota complexity shape these processes. We introduced a tractable E. coli K-12 in mice Germ-free or with polymicrobial communities. Whole Genome Sequencing identified potential adaptive targets. Here, we established phenotypic assays as well characterising effects of key mutations and metabolomics was performed with 1H-NMR of intestinal contents. Genes for sugar alcohol metabolism (gat) was the only target common to both mouse models, evidencing specificity. Facing complex microbiota E. coli targeted use of sugar alcohols (srlR, kdgR) and anaerobic respiration (dcuB, focA) [1] whereas alone, we observed instead mutations pointing to increased ability for amino acid use (lrp, dtpB, alaA). Mutations selected correlated dinamically with metabolomics: our results fit the model whereby other microbiota members scavenge oxygen and breakdown complex sugars, limiting E. coli to anaerobically respire simple by-product carbon sources. In the opposing scenario (functional absence) improved amino acid use are favoured colonisation factors. Through experimental evolution we gained insight on shaping E. coli ’s metabolic traits through genetic engineering to colonise specific host environments. This work also highlights the versatility of E. coli as potential biotic sensor. [1] Barroso-Batista, J. et al . The first steps of adaptation of Escherichia coli to the gut are dominated by soft sweeps, 2014.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".