Sialic acid plays a pivotal role in licensing <i>Citrobacter rodentium’s</i> transition from the intestinal lumen to a mucosal adherent niche
Bibliographic record
Abstract
Enteric bacterial pathogens pose significant threats to human health; however, the mechanisms by which they infect the mammalian gut in the face of daunting host defenses and an established microbiota remain poorly defined. For the attaching and effacing (A/E) bacterial family member and murine pathogen Citrobacter rodentium , its virulence strategy likely involves metabolic adaptation to the host’s intestinal luminal environment, as a necessary precursor to reach and infect the mucosal surface. Suspecting this adaptation involved the intestinal mucus layer, we found that C. rodentium was able to catabolize sialic acid, a monosaccharide derived from mucins, and utilize it as its sole carbon source for growth. Moreover, C. rodentium also sensed and displayed chemotactic activity toward sialic acid. These activities were abolished when the nanT gene, encoding a sialic acid transporter, was deleted (Δ nanT ). Correspondingly, the Δ nanT C. rodentium strain was significantly impaired in its ability to colonize the murine intestine. Intriguingly, sialic acid was also found to induce the secretion of two autotransporter proteins, Pic and EspC, which possess mucinolytic and host-adherent properties. As a result, sialic acid enhanced the ability of C. rodentium to degrade intestinal mucus (through Pic), as well as to adhere to intestinal epithelial cells (through EspC). We thus demonstrate that sialic acid, a monosaccharide constituent of the intestinal mucus layer, functions as an important nutrient and a key signal for an A/E bacterial pathogen to escape the colonic lumen and directly infect its host’s intestinal mucosa.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".