Enterohemorrhagic <i>Escherichia coli</i> responds to gut microbiota metabolites by altering metabolism and activating stress responses
Bibliographic record
Abstract
Enterohemorrhagic Escherichia coli (EHEC) is a major cause of severe bloody diarrhea, with potentially lethal complications, such as hemolytic uremic syndrome. In humans, EHEC colonizes the colon, which is also home to a diverse community of trillions of microbes known as the gut microbiota. Although these microbes and the metabolites that they produce represent an important component of EHEC’s ecological niche, little is known about how EHEC senses and responds to the presence of gut microbiota metabolites. In this study, we used a combined RNA-Seq and Tn-Seq approach to characterize EHEC’s response to metabolites from an in vitro culture of 33 human gut microbiota isolates (MET-1), previously demonstrated to effectively resolve recurrent Clostridioides difficile infection in human patients. Collectively, the results revealed that EHEC adjusts to growth in the presence of microbiota metabolites in two major ways: by altering its metabolism and by activating stress responses. Metabolic adaptations to the presence of microbiota metabolites included increased expression of systems for maintaining redox balance and decreased expression of biotin biosynthesis genes, reflecting the high levels of biotin released by the microbiota into the culture medium. In addition, numerous genes related to envelope and oxidative stress responses (including cpxP, spy, soxS, yhcN, and bhsA) were upregulated during EHEC growth in a medium containing microbiota metabolites. Together, these results provide insight into the molecular mechanisms by which pathogens adapt to the presence of competing microbes in the host environment, which ultimately may enable the development of therapies to enhance colonization resistance and prevent infection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".