Whole-blood Gene Expression Profiling in Ankylosing Spondylitis Shows Upregulation of Toll-like Receptor 4 and 5
Bibliographic record
Abstract
OBJECTIVE: to identify differentially expressed genes in peripheral blood cells (PBC) of patients with ankylosing spondylitis (AS) relative to healthy controls and controls with systemic inflammation. METHODS: we investigated PBC samples of 16 patients with AS and 14 matched controls, in addition to systemic lupus erythematosus (SLE) and systemic sclerosis (SSc) samples utilizing Illumina Human Ref-8 BeadChips. Candidate genes were confirmed using quantitative PCR. Subsequently, these genes were also validated in a separate sample of 27 patients with AS [before and after anti-tumor necrosis factor (anti-TNF) treatment] and 27 matched controls. RESULTS: we identified 83 differentially expressed transcripts between AS patients and controls. This gene list was filtered through the lists of differentially expressed transcripts in SLE and SSc, which resulted in identification of 52 uniquely dysregulated transcripts in AS. Many of the differentially expressed genes belonged to Toll-like receptor (TLR) and related pathways. TLR4 and TLR5 were the only dysregulated TLR subtypes among AS patients. We confirmed the overexpression of TLR4 and TLR5 in AS patients in comparison to controls (p = 0.012 and p = 0.006, respectively) and SLE (p = 0.002, p = 0.008) using quantitative PCR in the same sample. Similarly, TLR4 (p = 0.007) and TLR5 (p = 0.012) were significantly upregulated among the AS patients before anti-TNF treatment in the confirmatory sample. TLR4 (p = 0.002) and TLR5 (p = 0.025) decreased significantly after anti-TNF treatment. CONCLUSION: PBC gene expression profiling in AS shows an upregulation of TLR4 and TLR5. This supports the importance of TLR subtypes in the pathogenesis of AS that are responsible for the immune response to Gram-negative bacteria.
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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 | 0.001 | 0.001 |
| 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".