Alu repeats as transcriptional regulatory platforms in macrophage responses to<i>M. tuberculosis</i>infection
Bibliographic record
Abstract
To understand the epigenetic regulation of transcriptional response of macrophages during early-stage M. tuberculosis (Mtb) infection, we performed ChIPseq analysis of H3K4 monomethylation (H3K4me1), a marker of poised or active enhancers. De novo H3K4me1 peaks in infected cells were associated with genes implicated in host defenses and apoptosis. Our analysis revealed that 40% of de novo regions contained human/primate-specific Alu transposable elements, enriched in the AluJ and S subtypes. These contained several transcription factor binding sites, including those for members of the MEF2 and ATF families, and LXR and RAR nuclear receptors, all of which have been implicated in macrophage differentiation, survival, and responses to stress and infection. Combining bioinformatics, molecular genetics, and biochemical approaches, we linked genes adjacent to H3K4me1-associated Alu repeats to macrophage metabolic responses against Mtb infection. In particular, we show that LXRα signaling, which reduced Mtb viability 18-fold by altering cholesterol metabolism and enhancing macrophage apoptosis, can be initiated at response elements present in Alu repeats. These studies decipher the mechanism of early macrophage transcriptional responses to Mtb, highlighting the role of Alu element transposition in shaping human transcription programs during innate immunity.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".