The Transcriptome of Paired Major and Minor Salivary Gland Tissue in Patients With Primary Sjögren’s Syndrome
Bibliographic record
Abstract
Background While all salivary glands (SGs) can be involved in primary Sjögren’s syndrome (pSS), their respective role in pathogenesis remains unclear. Our objective was to assess immunopathway activation in paired parotid and labial gland tissue from biopsy-positive and biopsy-negative pSS and non-SS sicca patients. Methods Paraffin-embedded, paired parotid and labial salivary gland tissue and peripheral blood mononuclear cells were obtained from 39 pSS and 20 non-SS sicca patients. RNA was extracted, complementary DNA libraries were prepared and sequenced. For analysis of differentially expressed genes (DEGs), patients were subdivided based on fulfillment of ACR-EULAR criteria and histopathology. Results With principal component analysis, only biopsy-positive pSS could be separated from non-SS sicca patients based on SG gene expression. When comparing the transcriptome of biopsy-positive pSS and biopsy-negative non-SS sicca patients, 1235 and 624 DEGs (FDR<0.05, log2FC<-1 or >1) were identified for parotid and labial glands, respectively. The number of DEGs between biopsy-negative pSS and non-SS sicca patients was scarce. Overall, transcript expression levels correlated strongly between parotid and labial glands (R 2 = 0.86, p-value<0.0001). Gene signatures present in both glands of biopsy-positive pSS patients included IFN-α signaling, IL-12/IL-18 signaling, CD3/CD28 T-cell activation, CD40 signaling in B-cells, DN2 B-cells, and FcRL4+ B-cells. Signature scores varied considerably amongst pSS patients. Conclusion Transcriptomes of paired major and minor SGs in pSS were overall comparable, although significant inter-individual heterogeneity in immunopathway activation existed. The SG transcriptome of biopsy-negative pSS was indistinguishable from non-SS sicca patients. Different patterns of SG immunopathway activation in pSS argue for personalized treatment approaches.
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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".