Enhancer-mediated enrichment of interacting JMJD3–DDX21 to ENPP2 locus prevents R-loop formation and promotes transcription
Bibliographic record
Abstract
ENPP2, which encodes for the enzyme autotaxin (ATX), is overexpressed during chronic inflammatory diseases and various cancers. However, the molecular mechanism involved in the ENPP2 transcription remains elusive. Here, in HEK 293T cells, we demonstrated that lipopolysaccharide (LPS) increased the transcription process at ENPP2 locus through a NF-кB pathway and a reduction of H3K27me3 level, a histone repressive mark, by the demethylase UTX. Simultaneously, the H3K27me3 demethylase JMJD3/KDM6B was recruited to the transcription start site (TSS), within the gene body and controlled the expression of ENPP2 in a non-enzymatic manner. Mass spectrometry data revealed a novel interaction for JMJD3 with DDX21, a RNA helicase that unwinds R-loops created by nascent transcript and DNA template. Upon LPS treatment, JMJD3 is necessary for DDX21 recruitment at ENPP2 locus allowing the resolution of aberrant R-loops. CRISPR-Cas9-mediated deletion of a distant-acting enhancer decreased the expression of ENPP2 and lowered the recruitment of JMJD3-DDX21 complex at TSS and its progression through the gene body. Taken together, these findings revealed that enhancer-mediated enrichment of novel JMJD3-DDX21 interaction at ENPP2 locus is necessary for nascent transcript synthesis via the resolution of aberrant R-loops formation in response to inflammatory stimulus.
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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) | 0.002 | 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".