Competitive binding of transcription factors underlies flexibility of T peripheral helper cells and T follicular helper cells in SLE
Bibliographic record
Abstract
OBJECTIVE: Peripheral helper T (Tph) cells interact with B cells and promote immune responses at sites of ectopic lymphoid structures (ELSs). To assess the characteristics of Tph cells, we investigated the phenotype of T helper (Th) cells in patients with SLE and the underlying competitive binding mechanisms using cytokine-mediated signal transducer and activator of transcription (STAT) factors. METHODS: Peripheral blood mononuclear cells from SLE patients and healthy controls were analysed for phenotypic identification. Serum cytokine levels were detected using Luminex assays. In vitro culture was performed to assess cytokine-induced conversion of phenotypes and transcriptional regulation using flow cytometry and PCR. Chromatin immunoprecipitation was used to evaluate STAT binding and histone modifications. RESULTS: CXCR5-PD-1+Tph-like cells were increased in SLE patients and showed strong association with disease activity and renal involvement. Serum IFN-α levels were increased and associated with Tph frequency. IFN-α promoted the differentiation of IL-10-producing CXCR5-PD-1+Tph-like cells, increased the responsiveness of IL-2 and induced the conversion of Tfh-like cells to Tph-like cells. STAT5 gained a competitive advantage and bound to the BCL6 locus at the expense of STAT1, accompanied by suppression of H3K4me3. Finally, anti-IFNAR1 decreased the differentiation of Tph-like cells, thereby suppressing the generation of CD38highCD27highplasmablasts. CONCLUSION: Tph cells might be crucial makers to effectively reflect disease activity level in SLE patients. The finding that synergy of IFN-α and IL-2 increases Tph cells through competitive transcriptional regulation could be one of the mechanisms responsible for pathological formation of ELSs and helpful for selection of individualized therapeutic approaches for SLE.
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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) | 0.002 | 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".