Hcmv-miR-UL22A-5p: A Biomarker in Transplantation With Broad Impact on Host Gene Expression and Potential Immunological Implications
Bibliographic record
Abstract
Cytomegalovirus (CMV) encodes multiple microRNAs. While these have been partially characterized in vitro, their relevance to clinical CMV infection has not been evaluated. We analyzed samples from a cohort of solid organ transplant patients with CMV disease (n = 245) for viral microRNA expression. Several CMV microRNAs were readily detectable in patients with CMV disease in variable relative abundance. Expression level generally correlated with DNA viral load and the absence of viral microRNA was associated with faster viral clearance. Detection of hcmv-miR-UL22A-5p at baseline independently predicted the recurrence of CMV viremia upon discontinuation of antiviral therapy (OR 3.024, 95% CI: 1.35-6.8; p = 0.007). A combination of direct mRNA targeting by the microRNA and indirect modulation of gene expression involving isoforms of the transcriptional regulator C-MYC may be responsible for the broad effects seen in the association of gene transcripts with the RNA-induced silencing complex and in global protein expression upon hcmv-miR-UL22A-5p transfection. This novel study of in vivo viral microRNA expression profiles provides unique insight into the complexity of clinical CMV infection following transplantation. We provide evidence that viral microRNAs may have complex effects on gene expression and be associated with specific virologic and clinical outcomes, and thus could be further evaluated as biomarkers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".