Role of mucus-bacteria interactions in Enterotoxigenic Escherichia coli (ETEC) H10407 virulence and interplay with human microbiome
Bibliographic record
Abstract
The intestinal mucus layer has a dual role in human health constituting a well-known microbial niche that supports gut microbiota maintenance but also acting as a physical barrier against enteric pathogens. Enterotoxigenic Escherichia coli (ETEC), the major agent responsible for traveler's diarrhea, is able to bind and degrade intestinal mucins, representing an important but understudied virulent trait of the pathogen. Using a set of complementary in vitro approaches simulating the human digestive environment, this study aimed to describe how the mucus microenvironment could shape different aspects of the human ETEC strain H10407 pathophysiology, namely its survival, adhesion, virulence gene expression, interleukin-8 induction and interactions with human fecal microbiota. Using the TNO gastrointestinal model (TIM-1) simulating the physicochemical conditions of the human upper gastrointestinal (GI) tract, we reported that mucus secretion and physical surface sustained ETEC survival, probably by helping it to face GI stresses. When integrating the host part in Caco2/HT29-MTX co-culture model, we demonstrated that mucus secreting-cells favored ETEC adhesion and virulence gene expression, but did not impede ETEC Interleukin-8 (IL-8) induction. Furthermore, we proved that mucosal surface did not favor ETEC colonization in a complex gut microbial background simulated in batch fecal experiments. However, the mucus-specific microbiota was widely modified upon the ETEC challenge suggesting its role in the pathogen infectious cycle. Using multi-targeted in vitro approaches, this study supports the major role played by mucus in ETEC pathophysiology, opening avenues in the design of new treatment strategies.
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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) | 0.000 | 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 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".