MicroRNA-mediated Regulation of Mucin-type O-glycosylation Pathway: A Putative Mechanism of Salivary Gland Dysfunction in Sjögren Syndrome
Bibliographic record
Abstract
Objective. To investigate microRNA (miRNA) that is potentially implicated in primary Sjögren syndrome (pSS)–related salivary hypofunction in labial salivary glands and to study miRNA-mediated mechanisms underlying oral dryness and altered rheology, focusing on the mucin O-glycosylation pathway. Methods. We performed miRNA expression profiling in minor salivary gland samples of patients with pSS presenting a different impairment in their unstimulated salivary flow rate. A computational in silico analysis was performed to identify genes and pathways that might be modulated by the deregulated miRNA that we had identified. To confirm in silico analysis, expression levels of genes encoding for glycosyltransferases and glycan-processing enzymes were investigated using Human Glycosylation-RT2 Profiler PCR Array. Results. Among 754 miRNA analyzed, we identified 126 miRNA that were significantly deregulated in pSS compared to controls, with a trend that was inversely proportional with the impairment of salivary flow rates. An in silico approach pinpointed that several upregulated miRNA in patients with pSS target important genes in the mucin O-glycosylation. We confirmed this prediction by quantitative real-time PCR, highlighting the downregulation of some glycosyltransferase and glycosidase genes in pSS samples compared to controls, such as GALNT1, responsible for mucin-7 glycosylation. Conclusion. Collectively, our data suggest that the expression of different predicted miRNA-target genes in the mucin type O-glycan biosynthesis pathway is altered in pSS patients with low salivary flow and that the miRNA expression profile could influence the glycosidase expression levels and consequently the rheology in pSS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".