Editorial: Current progress in genomic and genetic research on human viral diseases
Bibliographic record
Abstract
understanding of these genetic and genomic aspects, this research topic aimed to advance our knowledge and contribute to the development of effective strategies against human viral diseases.We are delighted to have received many excellent submissions, nine of which have been successfully published in this collection. This editorial piece may serve as a brief overview of these articles, highlighting their substantial contributions to the research topic. This compilation includes three original research articles addressing the pressing issues of COVID-19 and SARS-CoV-2. Čiučiulkaitė, et al. focused on the effect of the human GNB3 c.825C>T polymorphism on immune responses upon COVID-19 mRNA vaccination. This was one of the few studies at the time to document the significant decline in antibody titers and T-cell responses from months 1 to 6 after the second dose of vaccination. It also was one of the few studies to examine the kinetics of T-cell responses against SARS-CoV-2 after mRNA vaccination. While the GNB3 c.825C>T polymorphism had no significant effect on the humoral immune response, individuals with the CC genotype at this locus exhibited significantly enhanced T-cell responses after mRNA-1273 vaccination, suggesting improved protection against COVID-19. Möhlendick, et al. from the same research group explored the potential impact of the GNB3 c.825C>T polymorphism on the clinical outcome of COVID-19. This study, which included 1570 individuals, examined the potential associations between demographic factors, pre-existing conditions, laboratory parameters, GNB3 rs5443 genotype, and COVID-19 clinical outcome. Notably, in addition to identifying associating factors against fatal COVID-19 outcomes among other examined parameters, they reported that the GNB3 rs5443 TT genotype was significantly associated with a 40% reduced risk of COVID-19 fatality, suggesting its potential as a prognostic biomarker. While the findings of Möhlendick et al. on GNB3 genotypes may seem to contradict the observations of Čiučiulkaitė et al., it is important to note that the former focused on clinical outcomes in unvaccinated individuals, while the latter focuses on immune responses to COVID-19 vaccination. These divergent findings underscore the complexity of the interaction between SARS-CoV-2 and the host genetics and immune responses, and further research is needed to reconcile and fully understand these relationships.Another COVID-19-related study was conducted by Li et al., who investigated into the SARS-CoV-2 pathogenesis from a viral genetics perspective. Among many SARS-CoV-2 variants of concern (VOC), the Delta variant emerged from India in 2020 and swept the world in mid-2021 with a high transmission rate and high mortality. This study investigated the evolution of the Delta variant by analyzing mutational patterns in viral genomes originating from India at three different time periods surrounding the emergence of Delta VOC in the country. The study revealed a progressive increase in viral mutations, with viral genomes exhibiting the highest diversity following the establishment of Delta VOC. Notably, the pre-Delta phase showed a higher number of negatively selected sites, which may have protected critical gene regions during evolution. This study suggests ongoing viral adaptation and evolution and highlights the dynamic genetic landscape of SARS-CoV-2.Not surprisingly, HIV-1 is another virus heavily studied in this collection of publications. Yang et al.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".