Assessing marine ecosystem health using multi-omic analysis of blue mussel liquid biopsies: A case study within a national marine park
Bibliographic record
Abstract
Marine ecosystems are under escalating threats from myriad environmental stressors, necessitating a deeper understanding of their impact on biodiversity and the health of sentinel organisms. In this study, we carried out a spatiotemporal multi-omic analysis of liquid biopsies collected from mussels (Mytilus spp.) in marine ecosystems of a national park. We delved into the epigenomic, transcriptomic, glycomic, proteomic, and microbiomic profiles to unravel the intricate interplay between ecosystem biodiversity and mussels' biological response to their environments. Our analysis revealed temporal fluctuations in the alpha diversity of the circulating microbiome associated with human activities. Analysis of the hemolymphatic circulating cell-free DNA (ccfDNA) provided information on the biodiversity and the presence of potential pathogens. Epigenomic analysis revealed widespread hypomethylation sites within the mitochondrial (mtDNA). Comparative transcriptomic and glycomic analyses highlighted differences in metabolic pathways and genes associated with immune and wound healing functions. This study demonstrates the potential of multi-omic analysis of liquid biopsy in sentinel to provide a holistic view of human activities' environmental impacts on marine coastal ecosystems. Overall, this approach has the potential to enhance the effectiveness and efficiency of various conservation efforts, leading to more informed decision-making and better outcomes for biodiversity and ecosystem conservation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".