Comparative profiling of epigenetic modifications among individuals living in different high and low air pollution zones: A pilot study from India
Bibliographic record
Abstract
Epigenetic modifications act as an important bridge to regulate the complex network of gene-environment interactions. As these mechanisms determine the gene-expression patterns via regulating the transcriptomic machinery, environmental stress induced epigenetic modifications may interrupt distinct cellular functions resulting into generation of diseased phenotypes. In the present study, we used a multi-city approach to compare the epigenomic signatures of individuals living in two tiers of Indian cities categorized as low-risk and high-risk air pollution zones. The high-risk group reported marked changes in the expression levels of epigenetic modifiers (DNMT1, DNMT3a, EZH2, EHMT2 and HAT), that maintains the levels of specific epigenetic marks essential for appropriate gene functioning. These results also coincided with the observed alterations in the levels of DNA methylation (LINE-1 and % 5mC), and histone modifications (H3 and H4), among the high-risk group. In addition, higher degree of changes reported in the expression profile of a selected miRNA panel in the high-risk group indicated the probability of deregulated transcriptional machinery. This was further confirmed by the analysis of a target gene panel involved in various signalling pathways, which revealed differential expression of the gene transcripts regulating cell cycle, inflammation, cell survival, apoptosis and cell adhesion. Together, our results provide first insights of epigenetic modifications among individuals living in different high and low levels of air pollution zones of India. However, further steps to develop a point-of-care epigenomic assay for human bio-monitoring may be immensely beneficial to reduce the health burden of air pollution especially in low and-middle-income countries.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".