Intestinal Epithelium-Specific MyD88 Signaling Impacts Host Susceptibility to Infectious Colitis by Promoting Protective Goblet Cell and Antimicrobial Responses
Bibliographic record
Abstract
Intestinal epithelial cells (IECs), including secretory goblet cells, form essential physiochemical barriers that separate luminal bacteria from underlying immune cells in the intestinal mucosa. IECs are common targets for enteric bacterial pathogens, with hosts responding to these microbes through innate toll-like receptors that predominantly signal through the MyD88 adaptor protein. In fact, MyD88 signaling confers protection against several enteric bacterial pathogens, including Salmonella enterica serovar Typhimurium and Citrobacter rodentium. Since IECs are considered innately hyporesponsive, it is unclear whether MyD88 signaling within IECs contributes to this protection. We infected mice lacking MyD88 solely in their IECs (IEC-Myd88(-/-)) with S. Typhimurium. Compared to wild-type (WT) mice, infected IEC-Myd88(-/-) mice suffered accelerated tissue damage, exaggerated barrier disruption, and impaired goblet cell responses (Muc2 and RELMβ). Immunostaining revealed S. Typhimurium penetrated the IECs of IEC-Myd88(-/-) mice, unlike in WT mice, where they were sequestered to the lumen. When isolated crypts were assayed for their antimicrobial actions, crypts from IEC-Myd88(-/-) mice were severely impaired in their antimicrobial activity against S. Typhimurium. We also examined whether MyD88 signaling in IECs impacted host defense against C. rodentium, with IEC-Myd88(-/-) mice again suffering exaggerated tissue damage, impaired goblet cell responses, and reduced antimicrobial activity against C. rodentium. These results demonstrate that MyD88 signaling within IECs plays an important protective role at early stages of infection, influencing host susceptibility to infection by controlling the ability of the pathogen to reach and survive at the intestinal mucosal surface.
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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 | 0.001 |
| 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".