Whole Blood Targeted Bisulfite Sequencing and Differential Methylation in the <i>C6ORF10</i> Gene of Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
Objective. Polymorphisms in human major histocompatibility complex (MHC) are the strongest genetic associations with rheumatoid arthritis (RA). Epigenome-wide methylation studies suggest DNA methylation changes within MHC may contribute to disease susceptibility. We profiled MHC-specific methylated CpG (5′–C–phosphate–G–3′) in autoantibody-positive patients with RA and matched unaffected anticitrullinated protein antibodies–negative first-degree relatives (ACPA−/FDR) from an indigenous North American (INA) population that is known to have prevalent RA. Methods. DNA was isolated from whole blood and targeted bisulfite sequencing was used to profile methylated CpG in patients with RA and ACPA−/FDR. Differentially methylated CpG loci (DML) were mapped and gene annotated. Ingenuity pathway analysis (IPA) was used for curating biomolecular networks of mapped genes. Transcript abundance was determined by quantitative (q)PCR. Results. We identified 74 uniquely methylated CpG sites within the MHC region that were differentially methylated in patients with RA (p < 0.05), compared to ACPA−/FDR. Of these, 32 DML were located on 22 genes. IPA showed these genes are involved in regulating the nuclear factor–κB complex and processes involved in antigen presentation, and immune cell crosstalk in autoimmunity. Pearson correlation analysis demonstrated a negative association between differentially methylated CpG in the C6ORF10 gene and risk factors associated with RA. Analysis by qPCR confirmed differential abundance of C6ORF10, TNXB, and HCG18 mRNA in patients with RA compared to ACPA−/FDR. Conclusion. Our results confirm the presence of differential methylation at specific gene loci within the MHC region of INA patients with RA. These epigenetic signatures may precede disease onset, or alternatively, may be a result of developing RA.
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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 | 0.000 |
| 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".