<i>Malat1</i> regulates female Th2 cell cytokine expression through controlling early differentiation and response to IL-2
Bibliographic record
Abstract
Identifying cell intrinsic regulators of immune sexual dimorphism is critical for treatment of several immunopathologies. We show that Malat1 is required for appropriate cytokine expression in female but not male T helper 2 (Th2) cells. Malat1 deficiency impairs in vitro Th2 differentiation of naïve CD4+ T cells from female mice, characterized by transcriptome-wide effects and suppression of cytokine expression, particularly interleukin (IL)-10. Upon IL-10 receptor (IL10R) blockade a pronounced effect is also seen on IL-4 and IL-13. Mechanistically, naïve CD4+ T cells from Malat1-/- female mice demonstrate altered early activation kinetics and impaired early differentiation gene expression, including upregulation of an interferon-stimulated gene (ISG) module. This is followed by suppression of IL2Rα and IL2Rγ expression and IL-2-mediated differentiation. Mimicking the effect of Malat1 loss by maintaining early ISG expression in WT cells with interferon β treatment partially phenocopies the effects of Malat1 deficiency. A subset of the effects of Malat1 loss in female cells is also observed in male cells. However, this does not affect endpoint Th2 differentiation. Male CD4+ T cells demonstrate stronger early activation, higher ISG expression during early differentiation, maintenance of IL2Rα expression independently of Malat1, and lower sensitivity to exogenous IL-2 during late differentiation compared with female cells. In vivo, female, but not male, Malat1-/- mice demonstrate altered Th2 cytokine expression characterized by a reduction in IL-10+ Th2 cells in both lung and spleen following priming and challenge with Schistosoma mansoni eggs, a model of lung type 2 inflammation. Overall, these findings reveal Malat1 as a novel determinant of immune sexual dimorphism.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".