Methylomic correlates of autophagy activity in cystic fibrosis
Bibliographic record
Abstract
Autophagy is a highly regulated, biological process that provides energy during periods of stress and starvation. This conserved process also acts as a defense mechanism and clears microbes from the host cell. Autophagy is impaired in Cystic Fibrosis (CF) patients and CF mice, as their cells exhibit low expression levels of essential autophagy molecules. The genetic disorder in CF is due to mutations in the cystic fibrosis transmembrane conductance regulator (cftr) gene that encodes for a chloride channel. CF patients are particularly prone to infection by pathogens that are otherwise cleared by autophagy in healthy immune cells including Burkholderia cenocepacia (B. cenocepacia). The objective of this study is to determine the mechanism underlying weak autophagic activity in CF macrophages and find therapeutic targets to correct it. Using reduced representation bisulfite sequencing (RRBS) to determine DNA methylation profile, we found that the promoter regions of Atg12 in CF macrophages are significantly more methylated than in the wild-type (WT) immune cells, accompanied by low protein expression. The natural product epigallocatechin-3-gallate (EGCG) significantly reduced the methylation of Atg12 promoter improving its expression. Accordingly, EGCG restricted B. cenocepacia replication within CF mice and their derived macrophages by improving autophagy and preventing dissemination. In addition, EGCG improved the function of CFTR protein. Altogether, utilizing RRBS for the first time in the CF field revealed a previously unrecognized mechanism for reduced autophagic activity in CF. Our data also offers a mechanism by which EGCG exerts its positive effects in CF.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".