The complexity of co-limitation: nutrigenomics reveal non-additive interactions of calcium and phosphorus on gene expression in <i>Daphnia pulex</i>
Bibliographic record
Abstract
Many lakes across Canada and northern Europe have experienced declines in ambient phosphorus (P) and calcium (Ca) supply for over 20 years. While these declines might create or exacerbate nutrient limitation in aquatic food webs, our ability to detect and quantify different types of nutrient stress on zooplankton remains rudimentary. Here, we used growth bioassay experiments and whole transcriptome RNAseq, collectively nutrigenomics, to examine the nutritional phenotypes produced by low supplies of P and Ca separately and together in the freshwater zooplankter Daphnia pulex . We found that daphniids in all three nutrient-deficient categories grew slower and differed in their elemental composition. Our RNAseq results show distinct responses in singly limited treatments (Ca or P) and largely a mix of these responses in animals under low Ca and P conditions. Deeper investigation of effect magnitude and gene functional annotations reveals this patchwork of responses to cumulatively represent a co-limited nutritional phenotype. Linear discriminant analysis identified a significant separation between nutritional treatments based upon gene expression patterns with the expression patterns of just five genes needed to predict animal nutritional status with 99% accuracy. These data reveal how nutritional phenotypes are altered by individual and co-limitation of two highly important nutritional elements (Ca and P) and provide evidence that aquatic consumers can respond to limitation by more than one nutrient at a time by differentially altering their metabolism. This use of nutrigenomics demonstrates its potential to address many of the inherent complexities in studying interactions between multiple nutritional stressors in ecology and beyond.
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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) | 0.001 | 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".