A genomic portrait of the genetic architecture and regulatory impact of microRNA expression in response to infection
Bibliographic record
Abstract
MicroRNAs (miRNAs) are critical regulators of gene expression, and their role in a wide variety of biological processes, including host antimicrobial defense, is increasingly well described. Consistent with their diverse functional effects, miRNA expression is highly context dependent and shows marked changes upon cellular activation. However, the genetic control of miRNA expression in response to external stimuli and the impact of such perturbations on miRNA-mediated regulatory networks at the population level remain to be determined. Here we assessed changes in miRNA expression upon Mycobacterium tuberculosis infection and mapped expression quantitative trait loci (eQTL) in dendritic cells from a panel of healthy individuals. Genome-wide expression profiling revealed that ∼40% of miRNAs are differentially expressed upon infection. We find that the expression of 3% of miRNAs is controlled by proximate genetic factors, which are enriched in a promoter-specific histone modification associated with active transcription. Notably, we identify two infection-specific response eQTLs, for miR-326 and miR-1260, providing an initial assessment of the impact of genotype-environment interactions on miRNA molecular phenotypes. Furthermore, we show that infection coincides with a marked remodeling of the genome-wide relationships between miRNA and mRNA expression levels. This observation, supplemented by experimental data using the model of miR-29a, sheds light on the role of a set of miRNAs in cellular responses to infection. Collectively, this study increases our understanding of the genetic architecture of miRNA expression in response to infection, and highlights the wide-reaching impact of altering miRNA expression on the transcriptional landscape of a cell.
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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".