Metabolomic Differentiation of Nutritional Stress in an Aquatic Invertebrate
Bibliographic record
Abstract
Poor diet quality frequently constrains the growth and reproduction of primary consumers, altering their population dynamics, interactions in food webs, and contributions to ecosystem services such as nutrient cycling. The identification and measurement of an animal's nutritional state are thus central to studying the connections between diet and animal ecology. Here we show how the nutritional state of a freshwater invertebrate, Daphnia magna, can be determined by analyzing its endogenous metabolites using hydrogen nuclear magnetic resonance-based metabolomics. With a multivariate analysis, we observed the differentiation of the metabolite composition of animals grown under control conditions (good food and no environmental stress), raised on different diets (low quantity, nitrogen limited, and phosphorus limited), and exposed to two common environmental stressors (bacterial infection and salt stress). We identified 18 metabolites that were significantly different between control animals and at least one limiting food type or environmental stressor. The unique metabolite responses of animals caused by inadequate nutrition and environmental stress are reflective of dramatic and distinctive effects that each stressor has on animal metabolism. Our results suggest that dietary-specific induced changes in metabolite composition of animal consumers hold considerable promise as indicators of nutritional stress and will be invaluable to future studies of animal nutrition.
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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) | 0.000 | 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".