Tet2 restrains inflammatory gene expression in macrophages
Bibliographic record
Abstract
Tet methylcytosine dioxygenase 2 (TET2) is one of the earliest and most frequently mutated genes in clonal hematopoiesis of indeterminate potential (CHIP) and myeloid cancers, including myelodysplastic syndromes (MDS) and chronic myelomonocytic leukemia (CMML). TET2 catalyzes the oxidation of 5-methylcytosine to 5-hydroxymethylcytosine, leading to DNA demethylation, and also affects transcription by recruiting histone modifiers. Inactivating TET2 mutations cause epigenetic dysregulation, clonal hematopoietic stem cell (HSC) dominance, and monocytic lineage skewing. Here, we found that Tet2 was the most highly expressed Tet enzyme in murine macrophage (MΦ) differentiation. Tet2 transcription was further induced by lipopolysaccharide (LPS), but not interleukin (IL)-4, stimulation, potentially in a nuclear factor κβ-dependent manner. Tet2 loss did not affect early LPS gene responses in vitro, but increased Il-1b, Il-6, and Arginase 1 (Arg1) mRNA expression at later stages of stimulation in bone-marrow-derived MΦs (BMMΦs). Tet2-deficient peritoneal MΦs, however, demonstrated profound, constitutive expression of LPS-induced genes associated with an inflammatory state in vivo. In contrast, Tet2 deficiency did not affect alternative MΦ gene expression significantly in response to IL-4. These results suggested impaired resolution of inflammation in the absence of Tet2 both in vitro and in vivo. For the first time, we also detected TET2 mutations in BMMΦs from MDS and CMML patients and assayed their effects on LPS responses, including their potential influence on human IL-6 expression. Our results show that Tet2 restrains inflammation in murine MΦs and mice, raising the possibility that loss of TET2 function in MΦs may alter the immune environment in the large elderly population with TET2-mutant CHIP and in TET2-mutant myeloid cancer patients.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".