Adipose Tissue Transcriptome Is Related to Pollutant Exposure in Polar Bear Mother–Cub Pairs from Svalbard, Norway
Bibliographic record
Abstract
) are highly contaminated with persistent organic pollutants (POPs). Females transfer POPs to their offspring through gestation and lactation; therefore, young cubs present higher POPs concentrations than their mothers. Recent studies suggest that POPs affect the lipid metabolism in female polar bears; however, the mechanisms and impact on their offspring remain unknown. Here, we hypothesized that exposure to POPs differentially alters genome-wide gene transcription in the adipose tissue from mother polar bears and their cubs, highlighting molecular differences in response between adults and young. Adipose tissue biopsies were collected from 13 adult female polar bears and their twin cubs in Svalbard, Norway, in April 2011, 2012, and 2013. Total RNA extracted from biopsies was subjected to next-generation RNA sequencing. Plasma concentrations of summed polychlorinated biphenyls, organochlorine pesticides, and polybrominated diphenyl ethers in mothers ranged from 897 to 13620 ng/g wet weight and were associated with altered adipose tissue gene expression in both mothers and cubs. In mothers, 2502 and 2586 genes in total were positively and negatively, respectively, correlated to POP exposure, whereas in cubs, 2585 positively and 1690 negatively genes. Between mothers and cubs, 743 positively and negatively genes overlapped between mothers and cubs suggesting partially shared molecular responses to ΣPOPs. ΣPOP-associated genes were involved in numerous metabolic pathways in mothers and cubs, indicating that POP exposure alters the energy metabolism, which, in turn, may be linked to metabolic dysfunction.
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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".