Monkeypox virus (MPXV) has generally circulated in West and Central Africa since its emergence. Recently, sporadic MPXV infections in several nonendemic countries have attracted widespread attention. Here, we conducted a systematic analysis of the recent outbreak of MPXV‐2022, including its genomic annotation and molecular evolution. The phylogenetic analysis indicated that the MPXV‐2022 strains belong to the same lineage of the MPXV strain isolated in 2018. However, compared with the MPXV strain in 2018, in total 46 new consensus mutations were observed in the MPXV‐2022 strains, including 24 nonsynonymous mutations. By assigning mutations to 187 proteins encoded by the MPXV genome, we found that 10 proteins in the MPXV are more prone to mutation, including D2L‐like, OPG023, OPG047, OPG071, OPG105, OPG109, A27L‐like, OPG153, OPG188, and OPG210 proteins. In the MPXV‐2022 strains, four and three nucleotide substitutions are observed in OPG105 and OPG210, respectively. Overall, our studies illustrated the genome evolution of the ongoing MPXV outbreak and pointed out novel mutations as a reference for further studies.
Mutations in SARS-CoV-2 were studied extensively, while only the structure variations on the spike protein were discussed well in previous studies. To study the role of structural variations in virus evolution, we described the distribution of structure variations on the whole genome.
Early identification of adaptive mutations could provide timely help for the control and prevention of the COVID-19 pandemic. The fast accumulation of SARS-CoV-2 sequencing data provides important support, while also raising a great challenge for the recognition of adaptive mutations. Here, we proposed a computational strategy to detect potentially adaptive mutations from their fixed and parallel patterns in the phylogenetic trajectory. We found that the biological meanings of fixed substitution and parallel mutation are highly complementary, and can reasonably be integrated as a fixed and parallel (paraFix) mutation, to identify potentially adaptive mutations. Tracking the dynamic evolution of SARS-CoV-2, 37 sites in spike protein were identified as having experienced paraFix mutations. Interestingly, 70% (26/37) of them have already been experimentally confirmed as adaptive mutations. Moreover, most of the mutations could be inferred as paraFix mutations one month earlier than when they became regionally dominant. Overall, we believe that the concept of paraFix mutations will help researchers to identify potentially adaptive mutations quickly and accurately, which will provide invaluable clues for disease control and prevention.
Background
Identifying polymorphism clades on phylogenetic trees could help detect punctual mutations that are associated with viral functions. With visualization tools coloring the tree, it is easy to visually find clades where most sequences have the same polymorphism state. However, with the fast accumulation of viral sequences, a computational tool to automate this process is urgently needed.
Results
Here, by implementing a branch-and-bound-like search method, we developed an R package named sitePath to identify polymorphism clades automatically. Based on the identified polymorphism clades, fixed and parallel mutations could be inferred. Furthermore, sitePath also integrated visualization tools to generate figures of the calculated results. In an example with the influenza A virus H3N2 dataset, the detected fixed mutations coincide with antigenic shift mutations. The highly specificity and sensitivity of sitePath in finding fixed mutations were achieved for a range of parameters and different phylogenetic tree inference software.
Conclusions
The result suggests that sitePath can identify polymorphism clades per site. The clustering of sequences on a phylogenetic tree can be used to infer fixed and parallel mutations. High-quality figures of the calculated results could also be generated by sitePath.
Synthetic lethality (SL) represents the co‐occurrence of two or more non‐lethal disordered genes that could lead to cell death. SL‐based anticancer therapeutics could specifically kill the cancer cells carrying the targeted mutated gene while leaving normal cells alive. Recent large‐scale computational and experimental screenings provide rich resources of SL information while lacking systematic research on molecular features of SL genes. Combined with comprehensive multi‐omics data analysis and experimental validation of one SL gene pair, Guo et al. portrayed a systematic layout of cancer‐specific SL interactions that could improve understanding of carcinogenesis and potentially assist the subsequent screening of anticancer therapeutic targets.
The emergence and spread of the XBB lineage, a recombinant of SARS-CoV-2 omicron sublineages, has recently raised great concern for viral recombination globally. Since the COVID-19 outbreak, several recombination detection methods have been developed, and some interlineage recombinants have been reported. However, a comprehensive landscape for SARS-CoV-2 recombinants globally and their evolutionary mechanisms is still lacking. Here, we developed a lightweight method called CovRecomb based on lineage-specific feature mutations to detect and dissect interlineage SARS-CoV-2 recombinants quickly and precisely. By assessing over 14.5 million SARS-CoV-2 genomes, 135,567 putative recombinants were identified from 1,451 independent recombination events, 208 of which showed across-country, continental or global transmission. More than half of the manually curated recombinants could be systematically and automatically identified. Recombination breakpoints were distributed throughout the SARS-CoV-2 genome, while hotspots were inferred in six regions, especially in the second halves of the N-terminal domain and receptor-binding domain of spike genome. Epidemiological analyses revealed that recombination events occurred extensively among different SARS-CoV-2 (sub)lineages and were independent of the prevalence frequency of lineages.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.