Temporal metabolomic fingerprinting identifies adenine as a novel biomarker for early detection of Escherichia coli infection in broiler chickens
Bibliographic record
Abstract
Avian pathogenic Escherichia coli causes septicemia in broiler chickens leading to high mortality and economic losses. Current diagnostic methods, such as serology and culture, cannot detect infections during early asymptomatic stages. Hence, this study focused on identifying novel serum metabolic biomarkers and pathways as an early detection prediction tool. Ross broiler chicks were challenged with E. coli at 3 or 5 d of age, and blood samples collected at 8 and 24 h following infection. Serum samples were analyzed for metabolite alterations using targeted The Metabolomics Innovation Centre (TMIC) mega metabolomics assay. Data was processed through comprehensive statistical analyses, including univariate, multivariate, and meta-analysis approaches. At 8 h post-infection, top metabolites like adenine, N-acetyl-alanine, N-acetyl-soleucine, N-acetyl-valine, and orotic acid related to nucleotide and amino acid metabolisms were downregulated (p = < 0.05). At 24 h, a distinct metabolic shift emerged with hippuric acid increasing, while adenine showed further depletion, accompanied by decreases in N1-acetylspermidine, N-acetylputrescine, and a modest increase in picolinic acid related to nucleotide, polyamine and immune response pathways (p = < 0.05). Correlation metabolite networks show that at 8 h post-infection, broiler chicken showed enhanced metabolic coordination, while at 24 h, disruptions in polyamine, nucleoside, and fatty acid pathways reflected systemic rewiring. The progressive depletion of adenine at both 8 and 24 h post-infection supports it as a novel metabolite signature for E. coli infection.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".