Genome‐wide analysis of histone <scp>H</scp>3 lysine9 trimethylation by <scp>ChIP</scp>‐seq in peripheral blood mononuclear cells of uremia patients
Bibliographic record
Abstract
Treatment of uremia is now dominated by dialysis, in some cases, patients are treated with dialysis for decades, but overall outcomes are disappointing. A number of studies have confirmed the relevance of several experimental insights to the pathogenesis of uremia, but the specific biomarkers of uremia have not been fully elucidated. Studies of the epigenome have attracted little interest in nephrology, especially in uremia. However, to date, our knowledge about the alterations in histone methylation in uremia is unclear. H3K9me3 variations were analyzed in peripheral blood mononuclear cells from 10 uremia patients and 10 healthy subjects, using chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq). There were 96 genes with significantly different expressions in the uremia patients compared with the normal controls. Forty-two increased and 54 decreased H3K9me3 genes displaying significant differences were found in uremia patients compared with healthy subjects. Five positive genes, ras-related C3 botulinum toxin substrate 3 (RAC3), polycomb group ring finger 2 (PCGF2), myosin heavy chain 3 (MYH3), noggin (NOG), serpin peptidase inhibitor 8 (SERPINB8), were selected and quantified. Our studies indicate that there are significant alterations of H3K9me3 in uremia patients; these significant H3K9me3 candidates may help to explain the immunological disturbance and high cardiovascular complications in uremia patients. Such novel findings show the significance of H3K9me3 as a potential biomarker or promising target for epigenetic-based uremia therapies.
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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".