<i>Campylobacter</i> Metabolomics
Bibliographic record
Abstract
The ultimate goal in metabolomics is to achieve unbiased identification and quantification of all the metabolites in a defined biological system. Much of the work in bacterial metabolomics has involved the study of well-established metabolic pathways such as the tricarboxylic acid cycle, glycolysis, and specific metabolic pathways of microorganisms used in industrial applications. In contrast, the field of Campylobacter metabolomics is very much in its infancy, and considering the lack of information on many of the novel glycoconjugate biosynthesis pathways in Campylobacter, there is much scope to use targeted metabolomics approaches to further define the substrates and genes involved in these metabolic pathways. The main challenges associated with the study of sugar nucleotide metabolites by nuclear magnetic resonance (NMR) have been the instability of the sugar nucleotides and their presence at low concentrations within the bacterial cells. UDP-α-D-QuiNAc4NAc is an important metabolite in the 2,4-diacetamido-bacillosamine biosynthesis pathway, and its accumulation in pseC had not been expected because it has been thought that the inactivation of pseC would lead to an accumulation of a novel precursor directly related to Pse5Ac7Ac biosynthesis. The focused metabolomics studies of flagellin glycosylation in Campylobacter jejuni 81-176 and Campylobacter coli VC167 were extensive and examined unknown gene functions, characterized novel biosynthetic substrates and novel flagellar glycans, and elucidated poorly understood metabolic pathways.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".