Whole blood microRNA expression pattern differentiates patients with rheumatoid arthritis, their seropositive first-degree relatives, and healthy unrelated control subjects
Bibliographic record
Abstract
BACKGROUND: Epigenetic mechanisms can integrate gene-environment interactions that mediate disease transition from preclinical to clinically overt rheumatoid arthritis (RA). To better understand their role, we evaluated microRNA (miRNA, miR) expression profile in indigenous North American patients with RA who were positive for anticitrullinated protein antibodies; their autoantibody-positive, asymptomatic first-degree relatives (FDRs); and disease-free healthy control subjects (HCs). METHODS: Total RNA was isolated from whole blood samples obtained from HC (n = 12), patients with RA (n = 18), and FDRs (n = 12). Expression of 35 selected relevant miRNAs, as well as associated downstream messenger RNA (mRNA) targets of miR-103a-3p, was determined by qRT-PCR. RESULTS: Whole blood expression profiling identified significantly differential miRNA expression in patients with RA (13 miRNAs) and FDRs (10 miRNAs) compared with HCs. Among these, expression of miR-103a-3p, miR-155, miR-146a-5p, and miR-26b-3p was significantly upregulated, whereas miR-346 was significantly downregulated, in both study groups. Expression of miR-103a-3p was consistently elevated in FDRs at two time points 1 year apart. We also confirmed increased miR-103a-3p expression in peripheral blood mononuclear cells from patients with RA compared with HCs. Predicted target analyses of differentially expressed miRNAs in patients with RA and FDRs showed overlapping biological networks. Consistent with these curated networks, mRNA expression of DICER1, AGO1, CREB1, DAPK1, and TP53 was downregulated significantly with miR-103a-3p expression in FDRs. CONCLUSIONS: We highlight systematically altered circulating miRNA expression in at-risk FDRs prior to RA onset, a profile they shared with patients with RA. Prominently consistent miR-103a-3p expression indicates its utility as a prognostic biomarker for preclinical RA while highlighting biological pathways important for transition to clinically detectable disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".