PEA-m6A: an ensemble learning framework for accurately predicting N6-methyladenosine modifications in plants.

N 6-methyladenosine (m6A), which is the mostly prevalent modification in eukaryotic mRNAs, is involved in gene expression regulation and many RNA metabolism processes. Accurate prediction of m6A modification is important for understanding its molecular mechanisms in different biological contexts. However, most existing models have limited range of application and are species-centric. Here we present PEA-m6A, a unified, modularized and parameterized framework that can streamline m6A-Seq data analysis for predicting m6A-modified regions in plant genomes. The PEA-m6A framework builds ensemble learning-based m6A prediction models with statistic-based and deep learning-driven features, achieving superior performance with an improvement of 6.7% to 23.3% in the area under precision-recall curve compared with state-of-the-art regional-scale m6A predictor WeakRM in 12 plant species. Especially, PEA-m6A is capable of leveraging knowledge from pretrained models via transfer learning, representing an innovation in that it can improve prediction accuracy of m6A modifications under small-sample training tasks. PEA-m6A also has a strong capability for generalization, making it suitable for application in within- and cross-species m6A prediction. Overall, this study presents a promising m6A prediction tool, PEA-m6A, with outstanding performance in terms of its accuracy, flexibility, transferability, and generalization ability. PEA-m6A has been packaged using Galaxy and Docker technologies for ease of use and is publicly available at https://github.com/cma2015/PEA-m6A.

RSVdb: a comprehensive database of transcriptome RNA structure.

RNA fulfills a crucial regulatory role in cells by folding into a complex RNA structure. To date, a chemical compound, dimethyl sulfate (DMS), has been developed to probe the RNA structure at the transcriptome level effectively. We proposed a database, RSVdb (https://taolab.nwafu.edu.cn/rsvdb/), for the browsing and visualization of transcriptome RNA structures. RSVdb, including 626 225 RNAs with validated DMS reactivity from 178 samples in eight species, supports four main functions: information retrieval, research overview, structure prediction and resource download. Users can search for species, studies, transcripts and genes of interest; browse the quality control of sequencing data and statistical charts of RNA structure information; preview and perform online prediction of RNA structures in silico and under DMS restraint of different experimental treatments and download RNA structure data for species and studies. Together, RSVdb provides a reference for RNA structure and will support future research on the function of RNA structure at the transcriptome level.

Genome-wide characterization, evolution, structure, and expression analysis of the F-box genes in Caenorhabditis.

BACKGROUND: F-box proteins represent a diverse class of adaptor proteins of the ubiquitin-proteasome system (UPS) that play critical roles in the cell cycle, signal transduction, and immune response by removing or modifying cellular regulators. Among closely related organisms of the Caenorhabditis genus, remarkable divergence in F-box gene copy numbers was caused by sizeable species-specific expansion and contraction. Although F-box gene number expansion plays a vital role in shaping genomic diversity, little is known about molecular evolutionary mechanisms responsible for substantial differences in gene number of F-box genes and their functional diversification in Caenorhabditis. Here, we performed a comprehensive evolution and underlying mechanism analysis of F-box genes in five species of Caenorhabditis genus, including C. brenneri, C. briggsae, C. elegans, C. japonica, and C. remanei. RESULTS: Herein, we identified and characterized 594, 192, 377, 39, 1426 F-box homologs encoding putative F-box proteins in the genome of C. brenneri, C. briggsae, C. elegans, C. japonica, and C. remanei, respectively. Our work suggested that extensive species-specific tandem duplication followed by a small amount of gene loss was the primary mechanism responsible for F-box gene number divergence in Caenorhabditis genus. After F-box gene duplication events occurred, multiple mechanisms have contributed to gene structure divergence, including exon/intron gain/loss, exonization/pseudoexonization, exon/intron boundaries alteration, exon splits, and intron elongation by tandem repeats. Based on high-throughput RNA sequencing data analysis, we proposed that F-box gene functions have diversified by sub-functionalization through highly divergent stage-specific expression patterns in Caenorhabditis species. CONCLUSIONS: Massive species-specific tandem duplications and occasional gene loss drove the rapid evolution of the F-box gene family in Caenorhabditis, leading to complex gene structural variation and diversified functions affecting growth and development within and among Caenorhabditis species. In summary, our findings outline the evolution of F-box genes in the Caenorhabditis genome and lay the foundation for future functional studies.

Genomic insight into the origins and evolution of symbiosis genes in Phaseolus vulgaris microsymbionts.

BACKGROUND: Phaseolus vulgaris (common bean) microsymbionts belonging to the bacterial genera Rhizobium, Bradyrhizobium, and Ensifer (Sinorhizobium) have been isolated across the globe. Individual symbiosis genes (e.g., nodC) of these rhizobia can be different within each genus and among distinct genera. Little information is available about the symbiotic structure of indigenous Rhizobium strains nodulating introduced bean plants or the emergence of a symbiotic ability to associate with bean plants in Bradyrhizobium and Ensifer strains. Here, we sequenced the genomes of 29 representative bean microsymbionts (21 Rhizobium, four Ensifer, and four Bradyrhizobium) and compared them with closely related reference strains to estimate the origins of symbiosis genes among these Chinese bean microsymbionts. RESULTS: Comparative genomics demonstrated horizontal gene transfer exclusively at the plasmid level, leading to expanded diversity of bean-nodulating Rhizobium strains. Analysis of vertically transferred genes uncovered 191 (out of the 2654) single-copy core genes with phylogenies strictly consistent with the taxonomic status of bacterial species, but none were found on symbiosis plasmids. A common symbiotic region was wholly conserved within the Rhizobium genus yet different from those of the other two genera. A single strain of Ensifer and two Bradyrhizobium strains shared similar gene content with soybean microsymbionts in both chromosomes and symbiotic regions. CONCLUSIONS: The 19 native bean Rhizobium microsymbionts were assigned to four defined species and six putative novel species. The symbiosis genes of R. phaseoli, R. sophoriradicis, and R. esperanzae strains that originated from Mexican bean-nodulating strains were possibly introduced alongside bean seeds. R. anhuiense strains displayed distinct host ranges, indicating transition into bean microsymbionts. Among the six putative novel species exclusive to China, horizontal transfer of symbiosis genes suggested symbiosis with other indigenous legumes and loss of originally symbiotic regions or non-symbionts before the introduction of common bean into China. Genome data for Ensifer and Bradyrhizobium strains indicated symbiotic compatibility between microsymbionts of common bean and other hosts such as soybean.

Exploring the evolutionary dynamics of Rhizobium plasmids through bipartite network analysis.

The genus Rhizobium usually has a multipartite genome architecture with a chromosome and several plasmids, making these bacteria a perfect candidate for plasmid biology studies. As there are no universally shared genes among typical plasmids, network analyses can complement traditional phylogenetics in a broad-scale study of plasmid evolution. Here, we present an exhaustive analysis of 216 plasmids from 49 complete genomes of Rhizobium by constructing a bipartite network that consists of two classes of nodes, the plasmids and homologous protein families that connect them. Dissection of the network using a hierarchical clustering strategy reveals extensive variety, with 34 homologous plasmid clusters. Four large clusters including one cluster of symbiotic plasmids and two clusters of chromids carrying some truly essential genes are widely distributed among Rhizobium. In contrast, the other clusters are quite small and rare. Symbiotic clusters and rare accessory clusters are exogenetic and do not appear to have co-evolved with the common accessory clusters; the latter ones have a large coding potential and functional complementarity for different lifestyles in Rhizobium. The bipartite network also provides preliminary evidence of Rhizobium plasmid variation and formation including genetic exchange, plasmid fusion and fission, exogenetic plasmid transfer, host plant selection, and environmental adaptation.

CellSim: a novel software to calculate cell similarity and identify their co-regulation networks.

BACKGROUND: Cell direct reprogramming technology has been rapidly developed with its low risk of tumor risk and avoidance of ethical issues caused by stem cells, but it is still limited to specific cell types. Direct reprogramming from an original cell to target cell type needs the cell similarity and cell specific regulatory network. The position and function of cells in vivo, can provide some hints about the cell similarity. However, it still needs further clarification based on molecular level studies. RESULT: CellSim is therefore developed to offer a solution for cell similarity calculation and a tool of bioinformatics for researchers. CellSim is a novel tool for the similarity calculation of different cells based on cell ontology and molecular networks in over 2000 different human cell types and presents sharing regulation networks of part cells. CellSim can also calculate cell types by entering a list of genes, including more than 250 human normal tissue specific cell types and 130 cancer cell types. The results are shown in both tables and spider charts which can be preserved easily and freely. CONCLUSION: CellSim aims to provide a computational strategy for cell similarity and the identification of distinct cell types. Stable CellSim releases (Windows, Linux, and Mac OS/X) are available at: www.cellsim.nwsuaflmz.com , and source code is available at: https://github.com/lileijie1992/CellSim/ .

Dynamic and modular gene regulatory networks drive the development of gametogenesis.

Gametogenesis is a complex process, which includes mitosis and meiosis and results in the production of ovum and sperm. The development of gametogenesis is dynamic and needs many different genes to work synergistically, but it is lack of global perspective research about this process. In this study, we detected the dynamic process of gametogenesis from the perspective of systems biology based on protein-protein interaction networks (PPINs) and functional analysis. Results showed that gametogenesis genes have strong synergistic effects in PPINs within and between different phases during the development. Addition to the synergistic effects on molecular networks, gametogenesis genes showed functional consistency within and between different phases, which provides the further evidence about the dynamic process during the development of gametogenesis. At last, we detected and provided the core molecular modules of different phases about gametogenesis. The gametogenesis genes and related modules can be obtained from our Web site Gametogenesis Molecule Online (GMO, http://gametsonline.nwsuaflmz.com/index.php), which is freely accessible. GMO may be helpful for the reference and application of these genes and modules in the future identification of key genes about gametogenesis. Summary, this work provided a computational perspective and frame to the analysis of the gametogenesis dynamics and modularity in both human and mouse.

Deciphering the rules of mRNA structure differentiation in Saccharomyces cerevisiae in vivo and in vitro with deep neural networks.

The structure of mRNA in vivo is unwound to some extent in response to multiple factors involved in the translation process, resulting in significant differences from the structure of the same mRNA in vitro. In this study, we have proposed a novel application of deep neural networks, named DeepDRU, to predict the degree of mRNA structure unwinding in vivo by fitting five quantifiable features that may affect mRNA folding: ribosome density (RD), minimum folding free energy (MFE), GC content, translation initiation ribosome density (INI) and mRNA structure position (POS). mRNA structures with adjustment of the simulated structural features were designed and then fed into the trained DeepDRU model. We found unique effect regions of these five features on mRNA structure in vivo. Strikingly, INI is the most critical factor affecting the structure of mRNA in vivo, and structural sequence features, including MFE and GC content, have relatively smaller effects. DeepDRU provides a new paradigm for predicting the unwinding capability of mRNA structure in vivo. This improved knowledge about the mechanisms of factors influencing the structural capability of mRNA to unwind will facilitate the design and functional analysis of mRNA structure in vivo.

miRNA editing landscape reveals miR-34c regulated spermatogenesis through structure and target change in pig and mouse.

Spermatogenesis has a close relationship with male infertility. MicroRNAs (miRNAs) play crucial roles in their regulation of target genes during spermatogenesis. A huge dataset of high-throughput sequencing all over the world provides the basis to dig the cryptic molecular mechanism. But how to take advantage of the big data and unearth the miRNA regulation is still a challenging problem. Here we integrated transcriptome of spermatogenesis and found miRNA regulate spermatogenesis through miRNA editing. We then compared different species and found that the distributions of miRNA editing site number and editing types among different cell types during spermatogenesis are conservative. Interesting, we further found that nearly half of the editing events occurred in the seed region in both mouse and pig. Finally, we foundmiR-34c, which is edited frequently at all stages during spermatogenesis, regulates its target genes through the RNA structure changing and shows dysfunction when it is edited. Summary, we depicted the overall profile of miRNA editing during spermatogenesis in mouse and pig and reveal miR-34c may play its roles through miRNA editing.

Codon usage bias in 5' terminal coding sequences reveals distinct enrichment of gene functions.

Codon bias at the 5' terminal of coding sequence (CDS) is known to be distinct from the rest of the CDS. A number of events occur in this short region to regulate early translation elongation and co-translational translocation. In the genes encoding secretory proteins, there is a special signal sequence which has a higher occurrence of rare codons. In this study, we analyzed codon bias of secretory genes in several eukaryotes. The results showed that secretory genes in the species except mammals had a higher occurrence of rare codons in the 5' terminal of CDS, and the bias was greater than the same region of non-secretory genes. GO analysis revealed that secretory genes containing rare codon clusters in different regions were responsible for various roles in gene functions. Moreover, codon bias in the region encoding the hydrophobic region of protein is similar in secretory and non-secretory genes, indicating that codon bias in secretory genes was partly influenced by amino acid bias. Rare codon clusters are found more frequently in specific regions, and continuous rare codons are not favoured probably because they will increase the probability of ribosome collision and drop-off. Based on ribosome profiling data, there is no significant difference in the average translation efficiencies between rare and optimal codons. Higher ribosomal density in the 5' terminal may result from ribosome pausing which could be involved in different translation events. These findings collectively provided rich information on codon bias in secretory genes, which may shed light on the co-effect of codon bias, mRNA structure and tRNA abundance in translational regulations.

Deciphering the rules by which dynamics of mRNA secondary structure affect translation efficiency in Saccharomyces cerevisiae.

Messenger RNA (mRNA) secondary structure decreases the elongation rate, as ribosomes must unwind every structure they encounter during translation. Therefore, the strength of mRNA secondary structure is assumed to be reduced in highly translated mRNAs. However, previous studies in vitro reported a positive correlation between mRNA folding strength and protein abundance. The counterintuitive finding suggests that mRNA secondary structure affects translation efficiency in an undetermined manner. Here, we analyzed the folding behavior of mRNA during translation and its effect on translation efficiency. We simulated translation process based on a novel computational model, taking into account the interactions among ribosomes, codon usage and mRNA secondary structures. We showed that mRNA secondary structure shortens ribosomal distance through the dynamics of folding strength. Notably, when adjacent ribosomes are close, mRNA secondary structures between them disappear, and codon usage determines the elongation rate. More importantly, our results showed that the combined effect of mRNA secondary structure and codon usage in highly translated mRNAs causes a short ribosomal distance in structural regions, which in turn eliminates the structures during translation, leading to a high elongation rate. Together, these findings reveal how the dynamics of mRNA secondary structure coupling with codon usage affect translation efficiency.

Genome-wide identification and evolution of HECT genes in soybean.

Proteins containing domains homologous to the E6-associated protein (E6-AP) carboxyl terminus (HECT) are an important class of E3 ubiquitin ligases involved in the ubiquitin proteasome pathway. HECT-type E3s play crucial roles in plant growth and development. However, current understanding of plant HECT genes and their evolution is very limited. In this study, we performed a genome-wide analysis of the HECT domain-containing genes in soybean. Using high-quality genome sequences, we identified 19 soybean HECT genes. The predicted HECT genes were distributed unevenly across 15 of 20 chromosomes. Nineteen of these genes were inferred to be segmentally duplicated gene pairs, suggesting that in soybean, segmental duplications have made a significant contribution to the expansion of the HECT gene family. Phylogenetic analysis showed that these HECT genes can be divided into seven groups, among which gene structure and domain architecture was relatively well-conserved. The Ka/Ks ratios show that after the duplication events, duplicated HECT genes underwent purifying selection. Moreover, expression analysis reveals that 15 of the HECT genes in soybean are differentially expressed in 14 tissues, and are often highly expressed in the flowers and roots. In summary, this work provides useful information on which further functional studies of soybean HECT genes can be based.

Neighbor preferences of amino acids and context-dependent effects of amino acid substitutions in human, mouse, and dog.

Amino acids show apparent propensities toward their neighbors. In addition to preferences of amino acids for their neighborhood context, amino acid substitutions are also considered to be context-dependent. However, context-dependence patterns of amino acid substitutions still remain poorly understood. Using relative entropy, we investigated the neighbor preferences of 20 amino acids and the context-dependent effects of amino acid substitutions with protein sequences in human, mouse, and dog. For 20 amino acids, the highest relative entropy was mostly observed at the nearest adjacent site of either N- or C-terminus except C and G. C showed the highest relative entropy at the third flanking site and periodic pattern was detected at G flanking sites. Furthermore, neighbor preference patterns of amino acids varied greatly in different secondary structures. We then comprehensively investigated the context-dependent effects of amino acid substitutions. Our results showed that nearly half of 380 substitution types were evidently context dependent, and the context-dependent patterns relied on protein secondary structures. Among 20 amino acids, P elicited the greatest effect on amino acid substitutions. The underlying mechanisms of context-dependent effects of amino acid substitutions were possibly mutation bias at a DNA level and natural selection. Our findings may improve secondary structure prediction algorithms and protein design; moreover, this study provided useful information to develop empirical models of protein evolution that consider dependence between residues.

A new computational strategy for predicting essential genes.

BACKGROUND: Determination of the minimum gene set for cellular life is one of the central goals in biology. Genome-wide essential gene identification has progressed rapidly in certain bacterial species; however, it remains difficult to achieve in most eukaryotic species. Several computational models have recently been developed to integrate gene features and used as alternatives to transfer gene essentiality annotations between organisms. RESULTS: We first collected features that were widely used by previous predictive models and assessed the relationships between gene features and gene essentiality using a stepwise regression model. We found two issues that could significantly reduce model accuracy: (i) the effect of multicollinearity among gene features and (ii) the diverse and even contrasting correlations between gene features and gene essentiality existing within and among different species. To address these issues, we developed a novel model called feature-based weighted Naïve Bayes model (FWM), which is based on Naïve Bayes classifiers, logistic regression, and genetic algorithm. The proposed model assesses features and filters out the effects of multicollinearity and diversity. The performance of FWM was compared with other popular models, such as support vector machine, Naïve Bayes model, and logistic regression model, by applying FWM to reciprocally predict essential genes among and within 21 species. Our results showed that FWM significantly improves the accuracy and robustness of essential gene prediction. CONCLUSIONS: FWM can remarkably improve the accuracy of essential gene prediction and may be used as an alternative method for other classification work. This method can contribute substantially to the knowledge of the minimum gene sets required for living organisms and the discovery of new drug targets.

Large-Scale Detection of Telomeric Motif Sequences in Genomic Data Using TelFinder.

Telomeres are regions of tandem repeated sequences at the ends of linear chromosomes that protect against DNA damage and chromosome fusion. Telomeres are associated with senescence and cancers and have attracted the attention of an increasing number of researchers. However, few telomeric motif sequences are known. Given the mounting interest in telomeres, an efficient computational tool for the de novo detection of the telomeric motif sequence of new species is needed since experimental-based methods are costly in terms of time and effort. Here, we report the development of TelFinder, an easy-to-use and freely available tool for the de novo detection of telomeric motif sequences from genomic data. The vast quantity of readily available genomic data makes it possible to apply this tool to any species of interest, which will undoubtedly inspire studies requiring telomeric repeat information and improve the utilization of these genomic data sets. We have tested TelFinder on telomeric sequences available in the Telomerase Database, and the detection accuracy reaches 90%. In addition, variation analyses in telomere sequences can be performed by TelFinder for the first time. The telomere variation preference of different chromosomes and even at the ends of the chromosome can provide clues regarding the underlying mechanisms of telomeres. Overall, these results shed new light on the divergent evolution of telomeres. IMPORTANCE Telomeres are reported to be highly correlated with the cell cycle and aging. As a result, research on telomere composition and evolution has become more and more urgent. However, using experimental methods to detect telomeric motif sequences is slow and costly. To combat this challenge, we developed TelFinder, a computational tool for the de novo detection of the telomere composition only using genomic data. In this study, we showed that a lot of complicated telomeric motifs could be identified by TelFinder only using genomic data. In addition, TelFinder can be used to check variation analyses in telomere sequences, which could lead to a deeper understanding of telomere sequences.

Natural selection pressure exerted on "Silent" mutations during the evolution of SARS-CoV-2: Evidence from codon usage and RNA structure.

From the first emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) till now, multiple mutations that caused synonymous and nonsynonymous substitutions have accumulated. Among them, synonymous substitutions were regarded as "silent" mutations that received less attention than nonsynonymous substitutions that cause amino acid variations. However, the importance of synonymous substitutions can not be neglected. This research focuses on synonymous substitutions on SARS-CoV-2 and proves that synonymous substitutions were under purifying selection in its evolution. The evidence of purifying selection is provided by comparing the mutation number per site in coding and non-coding regions. We then study the two forces of purifying selection: synonymous codon usage and RNA secondary structure. Results show that the codon usage optimization leads to an adapted codon usage towards humans. Furthermore, our results show that the maintenance of RNA secondary structure causes the purifying of synonymous substitutions in the structural region. These results explain the selection pressure on synonymous substitutions during the evolution of SARS-CoV-2.

Metagenomics insights into responses of rhizobacteria and their alleviation role in licorice allelopathy.

BACKGROUND: Allelopathy is closely associated with rhizosphere biological processes, and rhizosphere microbial communities are essential for plant development. However, our understanding of rhizobacterial communities under influence of allelochemicals in licorice remains limited. In the present study, the responses and effects of rhizobacterial communities on licorice allelopathy were investigated using a combination of multi-omics sequencing and pot experiments, under allelochemical addition and rhizobacterial inoculation treatments. RESULTS: Here, we demonstrated that exogenous glycyrrhizin inhibits licorice development, and reshapes and enriches specific rhizobacteria and corresponding functions related to glycyrrhizin degradation. Moreover, the Novosphingobium genus accounted for a relatively high proportion of the enriched taxa and appeared in metagenomic assembly genomes. We further characterized the different capacities of single and synthetic inoculants to degrade glycyrrhizin and elucidated their distinct potency for alleviating licorice allelopathy. Notably, the single replenished N (Novosphingobium resinovorum) inoculant had the greatest allelopathy alleviation effects in licorice seedlings. CONCLUSIONS: Altogether, the findings highlight that exogenous glycyrrhizin simulates the allelopathic autotoxicity effects of licorice, and indigenous single rhizobacteria had greater effects than synthetic inoculants in protecting licorice growth from allelopathy. The results of the present study enhance our understanding of rhizobacterial community dynamics during licorice allelopathy, with potential implications for resolving continuous cropping obstacle in medicinal plant agriculture using rhizobacterial biofertilizers. Video Abstract.

Conservation and evolution in and among SRF- and MEF2-type MADS domains and their binding sites.

Serum response factor (SRF) and myocyte enhancer factor 2 (MEF2) represent two types of members of the MCM1, AGAMOUS, DEFICIENS, and SRF (MADS)-box transcription factor family present in animals and fungi. Each type has distinct biological functions, which are reflected by the distinct specificities of the proteins bound to their cognate DNA-binding sites and activated by their respective cofactors. However, little is known about the evolution of MADS domains and their DNA-binding sites. Here, we report on the conservation and evolution of the two types of MADS domains with their cognate DNA-binding sites by using phylogenetic analyses. First, there are great similarities between the two types of proteins with amino acid positions highly conserved, which are critical for binding to the DNA sequence and for the maintenance of the 3D structure. Second, in contrast to MEF2-type MADS domains, distinct conserved residues are present at some positions in SRF-type MADS domains, determining specificity and the configuration of the MADS domain bound to DNA sequences. Furthermore, the ancestor sequence of SRF- and MEF2-type MADS domains is more similar to MEF2-type MADS domains than to SRF-type MADS domains. In the case of DNA-binding sites, the MEF2 site has a T-rich core in one DNA sequence and an A-rich core in the reverse sequence as compared with the SRF site, no matter whether where either A or T is present in the two complementary sequences. In addition, comparing SRF sites in the human and the mouse genomes reveals that the evolution rate of CArG-boxes is faster in mouse than in human. Moreover, interestingly, a CArG-like sequence, which is probably functionless, could potentially mutate to a functional CArG-box that can be bound by SRF and vice versa. Together, these results significantly improve our knowledge on the conservation and evolution of the MADS domains and their binding sites to date and provide new insights to investigate the MADS family, which is not only on evolution of MADS factors but also on evolution of their binding sites and even on coevolution of MADS factors with their binding sites.

A detailed comparative analysis of codon usage bias in Alongshan virus.

Alongshan virus (ALSV) is an emerging tick-borne pathogen that infects humans, causing febrile disease. ALSV uses Ixodes Persulcatus ticks to infect humans with a wide range of signs, from asymptomatic to encephalitis-like syndrome. There is an increasing public health concern about the ALSV infection. To get insight into the impacts of viral relations with their hosts on viral ability, survival, and evasion from hosts immune systems remain unknown. The codon usage is a driving force in viral genome evolution; therefore, we enrolled 41 ALSV strains in codon usage analysis to elucidate the molecular evolutionary dynamics of ALSV. The results indicate that the overall codon usage among ALSV isolates is relatively similar and slightly biased. Base compositions for the cds were in order of G >A >C >U and in the third position of codons G3 >A3 >C3 >T3. The RSCU values revealed that the more frequently used codons were mostly GC ended. Different codon preferences in ALSV genes in relation to codon usage of H. sapiens and Ixodes Persulcatus genes were found. Neutrality plot was determined to reveal the superiority of natural selection over directional mutation pressure in causing CUB based on GC12 versus GC3 contents. The results of these studies suggest that the emergence of ALSV in China, Russia and Finland may also be reflected in ALSV codon usage. Altogether, the presence of both mutation pressure and natural selection effect in shaping the codon usage patterns of ALSV.

Edging on Mutational Bias, Induced Natural Selection From Host and Natural Reservoirs Predominates Codon Usage Evolution in Hantaan Virus.

The molecular evolutionary dynamics that shape hantaviruses' evolution are poorly understood even now, besides the contribution of virus-host interaction to their evolution remains an open question. Our study aimed to investigate these two aspects in Hantaan virus (HTNV)-the prototype of hantaviruses and an emerging zoonotic pathogen that infects humans, causing hemorrhagic fever with renal syndrome (HFRS): endemic in Far East Russia, China, and South Korea-via a comprehensive, phylogenetic-dependent codon usage analysis. We found that host- and natural reservoir-induced natural selection is the primary determinant of its biased codon choices, exceeding the mutational bias effect. The phylogenetic analysis of HTNV strains resulted in three distinct clades: South Korean, Russian, and Chinese. An effective number of codon (ENC) analysis showed a slightly biased codon usage in HTNV genomes. Nucleotide composition and RSCU analyses revealed a significant bias toward A/U nucleotides and A/U-ended codons, indicating the potential influence of mutational bias on the codon usage patterns of HTNV. Via ENC-plot, Parity Rule 2 (PR2), and neutrality plot analyses, we would conclude the presence of both mutation pressure and natural selection effect in shaping the codon usage patterns of HTNV; however, natural selection is the dominant factor influencing its codon usage bias. Codon adaptation index (CAI), Relative codon deoptimization index (RCDI), and Similarity Index (SiD) analyses uncovered the intense selection pressure from the host (Human) and natural reservoirs (Striped field mouse and Chinese white-bellied rat) in shaping HTNV biased codon choices. Our study clearly revealed the evolutionary processes in HTNV and the role of virus-host interaction in its evolution. Moreover, it opens the door for a more comprehensive codon usage analysis for all hantaviruses species to determine their molecular evolutionary dynamics and adaptability to several hosts and environments. We believe that our research will help in a better and deep understanding of HTNV evolution that will serve its future basic research and aid live attenuated vaccines design.