Editorial: Post-transcriptional regulation of viral protein expression and function
Bibliographic record
Abstract
Editorial on the research topic Post-transcriptional regulation of viral protein expression and functionPost-transcriptional regulation includes both RNA and protein modifications, leading to the modulation of protein expression levels or protein functions.These modifications may virtually regulate every cellular process, including DNA repair, transcription, cell cycle, apoptosis, environmental stress response and immune response.The use of systems biology has discovered the extraordinary complexity and the cross-talk between different posttranscriptional modification networks (1).As obligatory intracellular pathogens, posttranscriptional modification networks are common targets for viruses, not only affecting viral protein expression or function, but also providing a fine-tuning for the viral regulation of host cell biology (2).In addition, post-transcriptional modifications during viral infections have attracted increasing interest as a potential target for the development of novel antiviral strategies.In this Research Topic, four groups of authors provide new insights into different aspects of virus-host interactions involving post-transcriptional regulation.RNA modification, or epitranscriptiomics, is one of the mechanisms for posttranscriptional regulation that is growing in interest, aided by the novel omics technologies facilitating the study of these modifications.Many chemical RNA modifications have been identified to date, playing a relevant role in multiple cellular functions and pathologic processes (3).Some of these modifications, such as N6-methyladenosine (m 6 A) are more abundantly described than others, and play different roles during viral infections (4, 5).Two of the contributions in this Research Topic expose how viruses modify cellular RNAs for their own benefit.In Cristinelli et al., the RNA methylation in human immunodeficiency virus (HIV) infected cells is analyzed.The described modifications would lead to the identification of novel virus-host interactions, and these RNA modification pathways may be shared by other viruses.In Tallo-Parra et al., tRNA modification is proposed as a novel mechanism shared by RNA viruses to modulate protein expression in their own benefit.Altogether, these works highlight that epitranscriptomic analyses during virus infections may potentially uncover novel targets of therapeutic interventions.Eukaryotic cell RNA is associated with proteins, forming ribonucleoprotein complexes (RNPs), including stress granules (SGs) and processing bodies (PBs).RNA-protein Frontiers in Virology frontiersin.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.029 | 0.019 |
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".