MicroRNAs (miRNAs) are among the most important regulatory elements of gene expression in animals and plants. However, their origin and evolutionary dynamics have not been studied systematically. In this paper, we identified putative miRNA genes in 11 plant species using the bioinformatic technique and examined their evolutionary changes. Our homology search indicated that no miRNA gene is currently shared between green algae and land plants. The number of miRNA genes has increased substantially in the land plant lineage, but after the divergence of eudicots and monocots, the number has changed in a lineage-specific manner. We found that miRNA genes have originated mainly by duplication of preexisting miRNA genes or protein-coding genes. Transposable elements also seem to have contributed to the generation of species-specific miRNA genes. The relative importance of these mechanisms in plants is quite different from that in Drosophila species, where the formation of hairpin structures in the genomes seems to be a major source of miRNA genes. This difference in the origin of miRNA genes between plants and Drosophila may be explained by the difference in the binding to target mRNAs between plants and animals. We also found that young miRNA genes are less conserved than old genes in plants as well as in Drosophila species. Yet, nearly half of the gene families in the ancestor of flowering plants have been lost in at least one species examined. This indicates that the repertoires of miRNA genes have changed more dynamically than previously thought during plant evolution.

MicroRNAs (miRs) regulate gene expression at the posttranscriptional level. To obtain some insights into the origins and evolutionary patterns of miR genes, we have identified miR genes in the genomes of 12 Drosophila species by bioinformatics approaches and examined their evolutionary changes. The results showed that the extant and ancestral Drosophila species had more than 100 miR genes and frequent gains and losses of miR genes have occurred during evolution. Although many miR genes appear to have originated from random hairpin structures in intronic or intergenic regions, duplication of miR genes has also contributed to the generation of new miR genes. Estimating the rate of nucleotide substitution of miR genes, we have found that newly arisen miR genes have a substitution rate similar to that of synonymous nucleotide sites in protein-coding genes and evolve almost neutrally. This suggests that most new miR genes have not acquired any important function and would become inactive. By contrast, old miR genes show a substitution rate much lower than the synonymous rate. Moreover, paired and unpaired nucleotide sites of miR genes tend to remain unchanged during evolution. Therefore, once miR genes acquired their functions, they appear to have evolved very slowly, maintaining essentially the same structures for a long time.

Background Among the most consequential unknowns of the devastating COVID-19 pandemic are the durability of immunity and time to likely reinfection. There are limited direct data on SARS-CoV-2 long-term immune responses and reinfection. The aim of this study is to use data on the durability of immunity among evolutionarily close coronavirus relatives of SARS-CoV-2 to estimate times to reinfection by a comparative evolutionary analysis of related viruses SARS-CoV, MERS-CoV, human coronavirus (HCoV)-229E, HCoV-OC43, and HCoV-NL63. Methods We conducted phylogenetic analyses of the S , M, and ORF1b genes to reconstruct a maximum-likelihood molecular phylogeny of human-infecting coronaviruses. This phylogeny enabled comparative analyses of peak-normalised nucleocapsid protein, spike protein, and whole-virus lysate IgG antibody optical density levels, in conjunction with reinfection data on endemic human-infecting coronaviruses. We performed ancestral and descendent states analyses to estimate the expected declines in antibody levels over time, the probabilities of reinfection based on antibody level, and the anticipated times to reinfection after recovery under conditions of endemic transmission for SARS-CoV-2, as well as the other human-infecting coronaviruses. Findings We obtained antibody optical density data for six human-infecting coronaviruses, extending from 128 days to 28 years after infection between 1984 and 2020. These data provided a means to estimate profiles of the typical antibody decline and probabilities of reinfection over time under endemic conditions. Reinfection by SARS-CoV-2 under endemic conditions would likely occur between 3 months and 5·1 years after peak antibody response, with a median of 16 months. This protection is less than half the duration revealed for the endemic coronaviruses circulating among humans (5–95% quantiles 15 months to 10 years for HCoV-OC43, 31 months to 12 years for HCoV-NL63, and 16 months to 12 years for HCoV-229E). For SARS-CoV, the 5–95% quantiles were 4 months to 6 years, whereas the 95% quantiles for MERS-CoV were inconsistent by dataset. Interpretation The timeframe for reinfection is fundamental to numerous aspects of public health decision making. As the COVID-19 pandemic continues, reinfection is likely to become increasingly common. Maintaining public health measures that curb transmission—including among individuals who were previously infected with SARS-CoV-2—coupled with persistent efforts to accelerate vaccination worldwide is critical to the prevention of COVID-19 morbidity and mortality. Funding US National Science Foundation.

Global sequencing of hundreds of thousands of genomes of Severe acute respiratory syndrome coronavirus 2, SARS-CoV-2, has continued to reveal new genetic variants that are the key to unraveling its early evolutionary history and tracking its global spread over time. Here, we present the heretofore cryptic mutational history and spatiotemporal dynamics of SARS-CoV-2 from an analysis of thousands of high-quality genomes. We report the likely most recent common ancestor of SARS-CoV-2, reconstructed through a novel application and advancement of computational methods initially developed to infer the mutational history of tumor cells in a patient. This progenitor genome differs from genomes of the first coronaviruses sampled in China by three variants, implying that none of the earliest patients represent the index case or gave rise to all the human infections. However, multiple coronavirus infections in China and the USA harbored the progenitor genetic fingerprint in January 2020 and later, suggesting that the progenitor was spreading worldwide months before and after the first reported cases of COVID-19 in China. Mutations of the progenitor and its offshoots have produced many dominant coronavirus strains, which have spread episodically over time. Fingerprinting based on common mutations reveals that the same coronavirus lineage has dominated North America for most of the pandemic in 2020. There have been multiple replacements of predominant coronavirus strains in Europe and Asia and the continued presence of multiple high-frequency strains in Asia and North America. We have developed a continually updating dashboard of global evolution and spatiotemporal trends of SARS-CoV-2 spread (http://sars2evo.datamonkey.org/).

Supplementary data are available at Bioinformatics online.

Severe acute respiratory syndrome coronavirus 2, SARS-CoV-2, was quickly identified as the cause of COVID-19 disease soon after its earliest reports. The knowledge of the contemporary evolution of SARS-CoV-2 is urgently needed not only for a retrospective on how, when, and why COVID-19 has emerged and spread, but also for creating remedies through efforts of science, technology, medicine, and public policy. Global sequencing of thousands of genomes has revealed many common genetic variants, which are the key to unraveling the early evolutionary history of SARS-CoV-2 and tracking its global spread over time. However, our knowledge of fundamental events in the evolution and spread of this coronavirus remains grossly incomplete and highly uncertain. Here, we present the heretofore cryptic mutational history, phylogeny, and dynamics of SARS-CoV-2 from an analysis of tens of thousands of high-quality genomes. The reconstructed mutational progression is highly concordant with the timing of coronavirus sampling dates. It predicts the progenitor genome whose earliest offspring without any non-synonymous mutations were still spreading worldwide months after the report of COVID-19. Over time, mutations gave rise to seven major lineages that spread episodically, some of which arose in Europe and North America after the genesis of the ancestral lineages in China. Mutational barcoding establishes that North American coronaviruses harbor very different genome signatures than coronaviruses prevalent in Europe and Asia that have converged over time. These spatiotemporal patterns continue to evolve as the pandemic progresses and can be viewed live online.

Background/Aims: Aortic stiffness, determined by pulse wave velocity (PWV), is an independent marker of cardiovascular risk. PWV is mainly influenced by age-associated alterations in arterial wall structure and blood pressure. The present study was conducted to assess the impact of menopause on the brachial-ankle PWV (baPWV) in healthy women. Methods: Fifty premenopausal women aged 22–54 years and 40 postmenopausal women aged 40–73 years were recruited for this study. Subjects with hypertension, diabetes, and hyperlipidemia were strictly excluded. The results of baPWV were analyzed chronologically by 10- or 5-year age intervals. Results: There was no significant difference in baPWV between premenopausal and postmenopausal women in their 40s and 50s. The baPWV of postmenopausal women aged over 60 years was significantly higher than that of postmenopausal women in their 50s. To clarify the age-dependent elevation in baPWV in detail, women their 50s and 60s were divided into subgroups by 5-year age intervals. There was no significant difference in baPWV among the 50–54-, 55–59- and 60–64-year subgroups. baPWV significantly increased in the 65–69- year subgroup (p< 0.05). There was a significant relationship between baPWV and age in premenopausal (r = 0.452, p = 0.001) and postmenopausal (r = 0.581, p < 0.0001) women. The slope of the regression line for baPWV plotted against age was steeper in postmenopausal than in premenopausal women. Conclusions: This study produces suggestive evidence that menopause amplifies the age-dependent increase in arterial stiffness.

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