Small RNA profiling reveals antisense transcription throughout the KSHV genome and novel small RNAs
Bibliographic record
Abstract
Kaposi's sarcoma-associated herpesvirus (KSHV) is a human tumor virus that encodes 12 precursor microRNAs (pre-miRNAs) that give rise to 17 different known approximately 22-nucleotide (nt) effector miRNAs. Like all herpesviruses, KSHV has two modes of infection: (1) a latent mode whereby only a subset of viral genes are expressed and (2) a lytic mode during which the full remaining viral genes are expressed. To date, KSHV miRNAs have been mostly identified via analysis of cells that are undergoing latent infection. Here, we developed a method to profile small RNAs ( approximately 18-75 nt) from populations of cells undergoing predominantly lytic infection. Using two different next-generation sequencing platforms, we cloned and sequenced both pre-miRNAs and derivative miRNAs. Our analysis shows that the vast majority of viral and host 5p miRNAs are co-terminal with the 5' end of the cloned pre-miRNAs, consistent with both being defined by microprocessor cleavage. We report the complete repertoire (25 total) of 5p and 3p derivative miRNAs from all 12 previously described KSHV pre-miRNAs. Two KSHV pre-miRNAs, pre-miR-K12-8 and pre-miR-K12-12, encode abundant derivative miRNAs from the previously unreported strands of the pre-miRNA. We identify several novel small RNAs of low abundance, including viral miRNA-offset-RNAs (moRNAs), and antisense viral miRNAs (miRNA-AS) that are encoded antisense to previously reported KSHV pre-miRNAs. Finally, we observe widespread antisense transcription relative to known coding sequences during lytic replication. Despite the enormous potential to form double-stranded RNA in KSHV-infected cells, we observe no evidence for the existence of abundant viral-derived small interfering RNAs (siRNAs).
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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".