ELF4 bridges transcriptional and translational outputs to regulate inflammatory T Cells 3617
Bibliographic record
Abstract
Abstract Description The transcription factor ELF4 generally acts as a brake on the immune system, as patients with Deficiency in ELF4 X-linked (DEX) exhibit symptoms of hyperinflammation. While it has been demonstrated by our lab that global loss of ELF4 results in hyper-cytokine release in macrophages and restrains Th17 differentiation in mice, the breadth and mechanisms of CD4+ T cell regulation by ELF4 remain enigmatic. Here, we show that loss of T cell-intrinsic ELF4 leads to enhanced interferon gamma (IFNγ) production at the protein, but not transcript, level. In line with this, absence of T cell-intrinsic ELF4 leads to decreased expression of several targets of the integrated stress response, demonstrated to be crucial in restraining cytokine production via inhibition of cap-dependent translation during initial activation of naïve T cells. Using stable isotope labeling with amino acids in cell culture (SILAC), we demonstrate ELF4 specifically limits the translation of IFNγ, while upregulating the production of factors known to inhibit cap-dependent translation, Eif4ebp1 and Eif4ebp2. T cell-intrinsic ELF4 deficiency in the context of infection with Lymphocytic Choriomeningitis virus resulted in enhanced IFNγ production by CD4+ T cells during initial infection as well as enhanced multi-cytokine production of memory CD4+ T cells. Thus, we elucidate an essential mechanism for restraining CD4+ T cell inflammatory potential dependent on regulation of the integrated stress response by ELF4. Funding Sources Mathers Foundation, Rainin Foundation, NIAID/NIH Topic Categories Immune Response Regulation: Molecular Mechanisms (IRM)
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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) | 0.003 | 0.001 |
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".