Pilot Lipidomics Study of Copepods: Investigation of Potential Lipid-Based Biomarkers for the Early Detection and Quantification of the Biological Effects of Climate Change on the Oceanic Food Chain
Bibliographic record
Abstract
Maintenance of the health of our oceans is critical for the survival of the oceanic food chain upon which humanity is dependent. Zooplanktonic copepods are among the most numerous multicellular organisms on earth. As the base of the primary consumer food web, they constitute a major biomass in oceans, being an important food source for fish and functioning in the carbon cycle. The potential impact of climate change on copepod populations is an area of intense study. Omics technologies offer the potential to detect early metabolic alterations induced by the stresses of climate change. One such omics approach is lipidomics, which can accurately quantify changes in lipid pools serving structural, signal transduction, and energy roles. We utilized high-resolution mass spectrometry (≤2 ppm mass error) to characterize the lipidome of three different species of copepods in an effort to identify lipid-based biomarkers of copepod health and viability which are more sensitive than observational tools. With the establishment of such a lipid database, we will have an analytical platform useful for prospectively monitoring the lipidome of copepods in a planned long-term five-year ecological study of climate change on this oceanic sentinel species. The copepods examined in this pilot study included a North Atlantic species (Calanus finmarchicus) and two species from the Gulf of Mexico, one a filter feeder (Acartia tonsa) and one a hunter (Labidocerca aestiva). Our findings clearly indicate that the lipidomes of copepod species can vary greatly, supporting the need to obtain a broad snapshot of each unique lipidome in a long-term multigeneration prospective study of climate change. This is critical, since there may well be species-specific responses to the stressors of climate change and co-stressors such as pollution. While lipid nomenclature and biochemistry are extremely complex, it is not essential for all readers interested in climate change to understand all of the various lipid classes presented in this study. The clear message from this research is that we can monitor key copepod lipid families with high accuracy, and therefore potentially monitor lipid families that respond to environmental perturbations evoked by climate change.
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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 | 0.001 | 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".