Well-fed or nearly dead? Using quantitative PCR to detect dietary stress in Daphnia pulex
Bibliographic record
Abstract
Nutrition is at the center of interactions between organisms and their environment. During this time of unprecedented anthropogenic environmental change, biological indicators of nutritional changes and their effects on ecosystems are crucial if we are to understand and mitigate these changes. In this study, we developed a qPCR assay to examine nutritional responsiveness of ten genes identified as potential indicators of nutritional state in the freshwater zooplankton Daphnia pulex. We grew animals in six ecologically relevant treatments: nutrient replete, low carbon (food), low phosphorus, low nitrogen, low calcium, and high Cyanobacteria. We measured the growth rate and elemental composition of these animals and extracted their RNA. We then selected ten nutrient sensitive genes, two per limiting nutrient, and two reference genes from an RNA sequencing dataset of Daphnia pulex grown under the same six nutritional treatments. We then designed and validated qPCR primers for the ten indicator and two reference genes. We ran qPCR using these primers on cDNA from our experimental animals and found that the differential expression patterns of these genes could discriminate between our six nutritional states with high levels of accuracy. These results represent a compelling proof of concept for the use of gene-based nutritional biomarkers, paving the way for the use of genetically based nutritional state biomarkers in the study of ecology
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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".