In silico interrogation of the miRNAome of infected hematopoietic cells to predict processes important for human cytomegalovirus latent infection
Bibliographic record
Abstract
Human cytomegalovirus (HCMV) latency in CD34+ progenitor cells is the outcome of a complex and continued interaction of virus and host that is initiated during very early stages of infection and reflects pro- and anti-viral activity. We hypothesized that a key event during early infection could involve changes to host miRNAs, allowing for rapid modulation of the host proteome. Here, we identify 72 significantly upregulated miRNAs and three that were downregulated by 6hpi of infection of CD34+ cells which were then subject to multiple in silico analyses to identify potential genes and pathways important for viral infection. The analyses focused on the upregulated miRNAs and were used to predict potential gene hubs or common mRNA targets of multiple miRNAs. Constitutive deletion of one target, the transcriptional regulator JDP2, resulted in a defect in latent infection of myeloid cells; interestingly, transient knockdown in differentiated dendritic cells resulted in increased viral lytic IE gene expression, arguing for subtle differences in the role of JDP2 during latency establishment and reactivation of HCMV. Finally, in silico predictions identified clusters of genes with related functions (such as calcium signaling, ubiquitination, and chromatin modification), suggesting potential importance in latency and reactivation. Consistent with this hypothesis, we demonstrate that viral IE gene expression is sensitive to calcium channel inhibition in reactivating dendritic cells. In conclusion, we demonstrate HCMV alters the miRNAome rapidly upon infection and that in silico interrogation of these changes reveals new insight into mechanisms controlling viral gene expression during HCMV latency and, intriguingly, reactivation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".