Characterizing nutritional phenotypes using experimental nutrigenomics: Is there nutrient‐specificity to different types of dietary stress?
Bibliographic record
Abstract
The ability to directly measure and monitor poor nutrition in individual animals and ecological communities is hampered by methodological limitations. In this study, we use nutrigenomics to identify nutritional biomarkers in a freshwater zooplankter, Daphnia pulex, a ubiquitous primary consumer in lakes and a sentinel of environmental change. We grew animals in six ecologically relevant nutritional treatments: nutrient replete, low carbon (food), low phosphorus, low nitrogen, low calcium and high Cyanobacteria. We extracted RNA for transcriptome sequencing to identify genes that were nutrient responsive and capable of predicting nutritional status with a high degree of accuracy. We selected a list of 125 candidate genes, which were subsequently pruned to 13 predictive potential biomarkers. Using a nearest-neighbour classification algorithm, we demonstrate that these potential biomarkers are capable of classifying our samples into the correct nutritional group with 100% accuracy. The functional annotation of the selected biomarkers revealed some specific nutritional pathways and supported our hypothesis that animal responses to poor nutrition are nutrient specific and not simply different presentations of slow growth or energy limitation. This is a key step in uncovering the causes and consequences of nutritional limitation in animal consumers and their responses to small- and large-scale changes in biogeochemical cycles.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".