Nutrient Acquisition and Metabolism by Campylobacter jejuni
Bibliographic record
Abstract
The gastrointestinal pathogen Campylobacter jejuni is able to colonize numerous different hosts and compete against the gut microbiota. To do this, it must be able to efficiently acquire sufficient nutrients from its environment to support its survival and rapid growth in the intestine. However, despite almost 50 years of research, many aspects as to how C. jejuni accomplishes this feat remain poorly understood. C. jejuni lacks many of the common metabolic pathways necessary for the use of glucose, galactose, or other carbohydrates upon which most other microbes thrive. It does however make efficient use of citric acid cycle intermediates and various amino acids. C. jejuni readily uses the amino acids aspartate, glutamate, serine, and proline, with certain strains also possessing additional pathways allowing for the use of glutamine and asparagine. More recent work has revealed that some C. jejuni strains can metabolize the sugar l-fucose. This finding has upset years of dogma that C. jejuni is an asaccharolytic organism. C. jejuni also possesses diverse mechanisms for the acquisition of various transition metals that are required for metabolic activities. In particular, iron acquisition is critical for the formation of iron-sulfur complexes. C. jejuni is also unique in possessing both molybdate and tungsten cofactored proteins and thus has an unusual regulatory scheme for these metals. Together these various metabolic and acquisition pathways help C. jejuni to compete and thrive in wide variety of hosts and environments.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".