Active Transport of Phosphorylated Carbohydrates Promotes Intestinal Colonization and Transmission of a Bacterial Pathogen
Bibliographic record
Abstract
Efficient acquisition of extracellular nutrients is essential for bacterial pathogenesis, however the identities and mechanisms for transport of many of these substrates remain unclear. Here, we investigate the predicted iron-binding transporter AfuABC and its role in bacterial pathogenesis in vivo. By crystallographic, biophysical and in vivo approaches, we show that AfuABC is in fact a cyclic hexose/heptose-phosphate transporter with high selectivity and specificity for a set of ubiquitous metabolites (glucose-6-phosphate, fructose-6-phosphate and sedoheptulose-7-phosphate). AfuABC is conserved across a wide range of bacterial genera, including the enteric pathogens EHEC O157:H7 and its murine-specific relative Citrobacter rodentium, where it lies adjacent to genes implicated in sugar sensing and acquisition. C. rodentium ΔafuA was significantly impaired in an in vivo murine competitive assay as well as its ability to transmit infection from an afflicted to a naïve murine host. Sugar-phosphates were present in normal and infected intestinal mucus and stool samples, indicating that these metabolites are available within the intestinal lumen for enteric bacteria to import during infection. Our study shows that AfuABC-dependent uptake of sugar-phosphates plays a critical role during enteric bacterial infection and uncovers previously unrecognized roles for these metabolites as important contributors to successful pathogenesis.
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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) | 0.001 | 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".