Evolutionary perspectives and adaptation dynamics of human seasonal influenza viruses from 2009 to 2019: An insight from codon usage

Insight into the codon usage patterns and adaptation of Tembusu Virus

Since its emergence in 2010, Tembusu virus (TMUV) has rapidly spread across poultry populations in Asia, leading to substantial economic losses in these areas. Here, we investigate the codon usage patterns (CUPs) underlying TMUV's adaptation and evolutionary dynamics within host environments. Phylogenetic and compositional analyses consistently classify TMUV into four evolutionary lineages-Clusters 1, 2, 3, and ancestral TMUV-with Cluster 2 emerging as the dominant lineage. Codon adaptation index (CAI) analysis reveals that this lineage of TMUV show best adapted to the CUPs of duck than other lineages, underscoring the role of natural selection in shaping viral evolution, a finding in line with evidence that CUPs in the TMUV genome is predominantly shaped by natural selection. Furthermore, TMUV exhibits markedly higher adaptation to the CUPs of poultry hosts (duck, goose, and chicken) compared to potential host humans or vector mosquito. Thus, species-specific adaptability to the host environment may be a reason account for the distinct infectivity and clinic outcome of TMUV acted on hosts. Analysis of dinucleotide distribution reveals significant suppression of CpG and UpA dinucleotides in the TMUV genome, reflecting adaptive pressures to evade vertebrate immune responses. During transmission, TMUV shows increasing alignment with host CUPs and a continuous reduction in CpG dinucleotides, potentially enhancing its fitness within host microenvironments. This work advances our understanding of the basic biology underlying TMUV epidemiology, pathogenicity, and species-specific adaptation.

Base composition, adaptation, and evolution of goose astroviruses: codon-based investigation

Goose astroviruses (GoAstVs) are causative agents that account for fatal infection of goslings characterized by visceral urate deposition, resulting in severe economic losses in major goose-producing regions in China since 2017. In this study, we sought to unravel the intrinsic properties associated with adaptation and evolution in the host environment of GoAstVs. Consistent results from phylogenetic analysis and correspondence analysis performed on the codon usage patterns (CUPs) reveal 2 clusters of GoAstVs, namely, GoAstV-1 and GoAstV-2. However, multiple similar compositional characteristics were found, despite the high divergence between GoAstV-1 and GoAstV-2. Studies on the base composition of GoAstVs reveal an A/U bias, indicating a compositional constraint, while natural selection prevailed in determining the CUPs in the virus genome based on our neutrality plot analysis, reflecting high adaptive pressure to fit the host environment. Codon adaptation index (CAI) analysis revealed a higher degree of fitness to the CUPs of the corresponding host for GoAstVs than avian influenza virus and betacoronaviruses, which may be a favorable factor contributing to the high pathogenicity and wide distribution of GoAstVs in goslings. In addition, GoAstVs were less adapted to ducks and chickens, with significantly lower CAI values than to geese, which may be a reason for the different prevalence of GoAstVs among these species. Extensive investigations on dinucleotide distribution revealed a significant suppression of the CpG and UpA motifs in the virus genome, which may facilitate adaptation to the host's innate immune system by evading surveillance. In addition, our study reported the trends of increasing fitness to the host's microenvironment for GoAstVs through increasing adaptation to host CUPs and ongoing reduction of CpG motifs in the virus genome. The present analysis deepens our understanding of the basic biology, pathogenesis, adaptation and evolutionary pattern of GoAstVs, and contributes to the development of novel antiviral strategies.

Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace

In recent years, there has been a growing amount of discussion on the use of big data to prevent and treat pandemics. The current research aimed to use CiteSpace (CS) visual analysis to uncover research and development trends, to help academics decide on future research and to create a framework for enterprises and organizations in order to plan for the growth of big data-based epidemic control. First, a total of 202 original papers were retrieved from Web of Science (WOS) using a complete list and analyzed using CS scientometric software. The CS parameters included the date range (from 2011 to 2022, a 1-year slice for co-authorship as well as for the co-accordance assessment), visualization (to show the fully integrated networks), specific selection criteria (the top 20 percent), node form (author, institution, region, reference cited, referred author, journal, and keywords), and pruning (pathfinder, slicing network). Lastly, the correlation of data was explored and the findings of the visualization analysis of big data pandemic control research were presented. According to the findings, "COVID-19 infection" was the hottest cluster with 31 references in 2020, while "Internet of things (IoT) platform and unified health algorithm" was the emerging research topic with 15 citations. "Influenza, internet, China, human mobility, and province" were the emerging keywords in the year 2021-2022 with strength of 1.61 to 1.2. The Chinese Academy of Sciences was the top institution, which collaborated with 15 other organizations. Qadri and Wilson were the top authors in this field. The Lancet journal accepted the most papers in this field, while the United States, China, and Europe accounted for the bulk of articles in this research. The research showed how big data may help us to better understand and control pandemics.