Tyrosine kinase 2 inhibition improves clinical and molecular hallmarks in subtypes of cutaneous lupus
Bibliographic record
Abstract
BACKGROUND: Cutaneous lupus erythematosus (CLE) is a chronic inflammatory skin disease with various clinical subtypes. Although its pathogenesis is not yet fully understood, T-cell-mediated autoimmunity and elevated levels of type I interferons (IFNs) are two major factors that contribute to the development of cutaneous lesions. Type I IFNs transduce their signal via tyrosine kinase 2 (TYK2). OBJECTIVES: To investigate the impact of TYK2 signalling in preclinical models of CLE. METHODS: CLE skin biopsies were investigated by RNA sequencing (RNAseq) and immunohistochemistry. T cells isolated from CLE skin biopsies (lesional T cells) were restimulated with anti-CD3/anti-CD28 and cytokine release was quantified by enzyme-linked immunosorbent assay and Luminex®. Primary human keratinocytes and three-dimensional skin models were stimulated with IFN-α or lesional T-cell supernatant in the presence or absence of the TYK2 inhibitor deucravacitinib, followed by RNAseq. Skin biopsies from patients with different CLE subtypes were treated ex vivo with deucravacitinib followed by real-time quantitative polymerase chain reaction. RESULTS: Bulk RNAseq revealed a strong correlation between TYK2 and interface dermatitis, a histological hallmark of CLE. Immunohistochemistry confirmed a high abundance of TYK2 in different CLE subtypes. Inhibiting TYK2 reduced inflammation and normalized epidermal impairments in primary human keratinocytes, reconstructed human epidermis and CLE T cells. Ex vivo TYK2 inhibition in CLE skin biopsies reduced IFN response and necroptosis-related gene expression. Finally, four patients with different therapy-refractory subtypes of CLE (acute, subacute, chronic discoid, chilblain CLE) were successfully treated with deucravacitinib. CONCLUSIONS: IFN-α and T-cell-derived cytokines both contribute to skin inflammation in CLE. TYK2 inhibition is a promising approach for different subtypes of CLE as it controls inflammation in various preclinical models and patients with CLE who are refractory to treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| 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".