Gene Expression Pathways across Multiple Tissues in Antineutrophil Cytoplasmic Antibody-associated Vasculitis Reveal Core Pathways of Disease Pathology
Bibliographic record
Abstract
Objective. To identify commonalities in gene expression data across all antineutrophil cytoplasmic antibody (ANCA)–associated vasculitis (AAV) tissues thus far characterized. Methods. Gene expression data were collected from the 3 AAV tissues thus far characterized (orbit, peripheral leukocytes, and sinus brushings). These data were analyzed to identify commonly expressed genes and disease pathways. The pathways data were adjusted for multiple comparisons using a combined local false discovery rate, which estimates the probability of a false discovery of a given pathway in all 3 tissues analyzed. Results. Only 4 genes were upregulated in all 3 tissues —IL1RN,TLR2, SLC11A1, andMMP9. After multiple comparison adjustments, the network pathway analysis revealed 28 pathways associated with all 3 tissues. The most strongly associated pathway for all 3 tissues was the neutrophil degranulation pathway [multidimensional local false discovery (md-locfdr) = 1.05 × 10−12], followed by the osteoclast differentiation (md-locfdr = 3.8 × 10−05), cell surface interactions at the vascular wall (md-locfdr = 4.2 × 10−04), signaling by interleukins (md-locfdr = 6.1 × 10−04), and phagosome (md-locfdr = 0.003) pathways. There were no downregulated genes or pathways common to all 3 tissues. Conclusion. This analysis identified individual genes and pathways of disease common to all AAV tissues thus far characterized. The use of a network pathway analysis allowed us to identify pathologic mechanisms that were not readily apparent in the commonly expressed genes alone. Many of these pathways are consistent with current theories about infectious drivers and the crossroads of innate and adaptive immune mechanisms. In addition, this analysis highlights novel pathways, such as vessel wall interactions and platelet activation, which require further investigation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".