Global Population Dynamics and Evolutionary Selection in Classical Swine Fever Virus Complete Genomes: Insights from Bayesian Coalescent Analysis

Global Population Dynamics and Evolutionary Selection in Classical Swine Fever Virus Complete Genomes: Insights from Bayesian Coalescent Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Global Population Dynamics and Evolutionary Selection in Classical Swine Fever Virus Complete Genomes: Insights from Bayesian Coalescent Analysis Roopa Mahadevaswamy, Vijay Muruganantham, Varsha Ramesh, Shijili Mambully, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6089266/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Apr, 2025 Read the published version in Virus Genes → Version 1 posted 7 You are reading this latest preprint version Abstract Classical swine fever virus (CSFV) is a pathogen that affects pigs and wild boars. This contagious RNA virus is a high threat to swine industries throughout the world because it has high mortality and morbidity rates, leading to economic losses. Although previous studies primarily focused on isolated regions or specific genotypes, our study leverages a global dataset of 220 CSFV whole-genome sequences retrieved from the NCBI repository along with2 CSFV complete genome sequence from our laboratory (Accession number: MH734359.1 and OR4282229.1) and carefully curated to 66 sequences. The refined dataset is subjected to Bayesian analysis along with selection pressure analysis. The outcome of this experiment, the mean substitution rate was estimated at 2.06 x 10 − 3 substitutions/site/year with the Highest Posterior Density (HPD) (95% HPD 6.8012 x 10 − 4 − 3.3044 x 10 − 3 ), and the estimated average time to the most recent common ancestor (tMRCA) for the analyzed dataset was the year 1877 (95% HPD 1833.8181–1932.3176). Among the curated data set, 2 CSFV complete genome sequences (Accession number: MH734359.1 and OR428229.1) from our laboratory showed of Chinese origin. Additionally, pervasive and episodic selection pressure revealed that both had ongoing diversifying natural positive selection, which could lead to increased genetic diversity and possibly emergence of the new lineage. This potential information could be used for future evaluation of strategies to control emerging new genotypes of CSFV with high mortality and morbidity. CSFV Bayesian analysis tMRCA BEAST Selection pressure Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Classical swine fever (CSF) is a highly contagious haemorrhagic viral disease caused by Pestivirus suis [Classical Swine Fever Virus (CSFV)] and affects domestic pigs and wild boars. The global swine sector suffers large financial losses because of CSF's high mortality and morbidity rates, severe disruption of pork production, and trade restrictions implemented to contain outbreaks. The disease manifests in acute, subacute, or chronic forms in nature and is determined by the virulence of the infecting strain, where symptoms are marked by conjunctivitis, hyperthermia, anorexia, ataxia, depression, vomiting, diarrhoea, enteritis, purple skin discoloration, and skin haemorrhages, acute infection often resulting in death within 1–2 weeks. A virulent form of CSF causes 100% mortality and morbidity. Pig comes into contact with infected ones or contact with contaminated objects/surfaces can spread the virus, replicating in the tonsils and lymph nodes, and spreading through blood to organs like the spleen, bone marrow, and visceral lymph nodes. CSF is endemic in many parts of the world, including Asia, Central and South America, and parts of Europe and Africa. Regions like North America, Australia, and New Zealand are free from the disease due to stringent control measures and disease surveillance [ 1 ]. Nevertheless, CSF is still omnipresent in most parts of the world since the end of the 20th century. Further, historical outbreaks in Europe during the 1990s and recent events in Korea, Colombia, Russia, Brazil, and Japan have highlighted the enduring global impact of this disease [ 2 ]. In an event 225 years ago, the Tunisian sheep virus from the host sheep is transmitted to a new host pig, causing the emergence of CSFV [ 3 ]. The CSFV is a small, +ssRNA virus belonging to the genus Pestivirus within the family Flaviviridae. Its genome size is approximately about 12.3kb, and it comprises a single open reading frame (ORF) composed of 3898 amino acids flanked by 5’UTR and 3’UTR. The ORF encodes for four structural (C, E rns , E1, E2) and eight non-structural proteins (Npro, p7, NS2, NS3, NS4A, NS4B, NS5A, and NS5B), many of which play critical roles in viral replication and immune evasion [ 4 , 5 , 6 ]. One of the key features of CSFV is its genetic diversity, which is divided into three primary genotypes (1, 2, and 3) and several sub-genotypes (1.1, 1.2, 1.3; 2.1, 2.2, 2.3; 3.1, 3.2, 3.3, 3.4) [ 7 ]. From the virus history seen, genotype 1 has been responsible for most outbreaks, but in recent years, genotype 2, particularly the sub-genotype 2.1, has become more common in Europe and Asia [ 4 , 8 ]. Although, Rios et al [ 9 ] have proposed new genotypes and sub-genotypes: genotype 1 (1.1–1.7), genotype 2 (2.1–2.7), genotype 3, genotype 4, and genotype 5 based on the genetic diversity of 517 complete E2 gene. Recently, in Tamilnadu, India, evolutionary study conducted by Parthiban et al [ 10 ], demonstrated the emergence of distinct CSFV sub-genotype 1.7. The E2 gene is the best Phylogenetic marker for all Pestiviruses , including CSFV [ 11 , 3 ]. This type of genotypic variation is significant because the different genotypes and sub-genotypes may differ regarding their virulence and vaccine response; hence, continuous monitoring and study of the virus's evolution is necessary. Unlike the earlier studies, which have mainly relied on isolated areas or specific genotypes of CSFV, we leverage a global dataset of CSFV complete genome sequences. In addition, through the use of advanced Bayesian Evolutionary Analysis by Sampling Trees (BEAST), this study provides a global context perspective, which bridges the gap between regional and international research, enabling a comprehensive evolutionary analysis of CSFV. To delineate the evolutionary pathway of virus and population dynamics, we used Bayesian coalescent methods while reconstructing the global phylogenetic and phylogeographic history of CSFV. By integrating 2 complete genome sequences of CSFV from ICAR-NIVEDI, Bengaluru, India into the analysis, we aim to place Indian CSFV strains within a broader global context, providing valuable insights into the evolutionary trajectory of this economically significant virus. Materials and methods Sequence data repository All the available complete genome sequences of CSFV representing diverse geographic were retrieved from GenBank database ( http://www.ncbi.nlm.nih.gov/ ) to conduct a comprehensive analysis of CSFV evolution on a global scale by time-calibrated phylogenomic approach, which determines rates of nucleotide substitution per site, per year and the time to the Most Recent Common Ancestors (tMRCA) of complete genome. Sequence data curation Sequence Retrieval and Metadata Filtering To ensure the high-quality representation of CSFV diversity across time and geography, the sequences underwent advanced processing, making them precisely ready for further evaluation. Only those sequences that had the proper data, including year of collection, geographic origin, and genotype name, were considered, whereas the poor-quality sequences and those with incomplete metadata were excluded at this stage to maintain the integrity and reliability of the dataset [ 12 ]. Redundancy and Bias Reduction To reduce potential biases and minimize redundancy in the dataset, sequences with nucleotide similarities exceeding 99% within the same year and region were systematically removed [ 13 , 14 ]. Recombination Detection Recognizing that recombination can bring forward complexities in the viral evolutionary studies and can potentially invalidate the results of coalescent analyses, sequences are subjected to recombination detection using RDP4 software employing multiple detection methods such as Rescan/Bootscan [ 16 ], GENECOV, MaxChi, RDP, 3Seq [ 16 ], Siscan and the Chimaera [ 17 , 18 , 19 , 20 ]. The highest acceptable p-value threshold was set at 0.05, incorporating Bonferroni correction for multiple comparisons to eliminate any contradictory or erroneous signals from the data. All potential recombinants were removed from the dataset for further analysis. Inclusion of ICAR-NIVEDI Sequences To complete the curated dataset, two sequences from ICAR-National Institute of Veterinary Epidemiology and Disease Informatics (ICAR-NIVEDI), Bengaluru, India (GenBank accession number: MH734359.1 and OR428229.1) were also included to evaluate their evolutionary relationships within the global phylogeny. Sequence Alignment and Model Selection The sequences were aligned with the Multiple Sequence Comparison by Log- Expectation (MUSCLE) in MEGA 11 [ 21 ]. To estimate the best-fit model, the software jModelTest 2.1.7v20141120 was employed, utilizing the Akaike information criteria (AIC) and Bayesian information criteria (BIC) [ 20 ]. Evolutionary rate, time-scaled phylogenetic analysis and population dynamics To elucidate the evolutionary population dynamics and divergence of the CSFV genome, a comprehensive Bayesian phylogenetic framework was employed using BEAST v1.10.4 with the support of the BEAGLE library. The Markov Chain Monte Carlo (MCMC) approach, which is accessible within BEAST, was used to approximate the nucleotide substitution rate as well as the time to the most recent common ancestor (tMRCA). In this analysis, the GTR + G + I model was selected as the best-fit substitution model, while the relaxed uncorrelated lognormal distribution (UCLD) molecular clock model was chosen for its ability to accommodate rate heterogeneity across lineages. The Bayesian Skyline Plot (BSP) prior was chosen to estimate changes in effective population size over time, as it accommodates fluctuating population dynamics [ 22 , 23 , 24 ]. The Sampling times corresponding to the isolation years and geographic location as a trait were integrated through the BEAUti interface alongside the above parameters to construct the input .XML file. An MCMC chain length of 1 billion with 10% burn-in was incorporated into BEAUti interference, employed to make an effective sample size (ESS) for parameter estimates > 100. This .XML file was then executed in BEAST, generating the output files, including the .log file and .tree file. The resulting convergence was analyzed by using the generated .log file in Tracer v1.7.2 software, which contains the mean, median, and 95% Highest Posterior Density (HPD) intervals with substitution rates derived from the mean rate/UCLD mean and divergence time (tMRCA) determined from the tree height. The .tree file was processed using TreeAnnotator v1.10.4 to generate the Maximum Clade Credibility (MCC) tree, which was subsequently visualized using FigTree v1.4.4 for an intuitive graphical representation of the phylogenetic tree. Using the Bayesian Skyline Plot (BSP) model, this comprehensive dataset was inferred for virus demographic history to ascertain changes in genetic divergence throughout a worldwide temporal range from 1945 to 2022. Selection Pressure The Datamonkey Adaptive Evolution (DAE) ( www.datamonkey.org ) server was employed to investigate the modes of natural selection, including diversifying (positive) and purifying (negative) selection [ 25 , 26 ]. To enhance the detection of relevant site-specific selection pressure acting on the coding sequence of CSFV of the selected dataset, a combination of different codon-based maximum likelihood and Bayesian framework methods [ 27 ] like Fixed Effects Likelihood (FEL) and Mixed Effects Model of Evolution (MEME) were used. The selection pressure is primarily determined by considering the ratios of non-synonymous (dN) to synonymous (dS) substitution rates on a per-site basis for given coding alignment and corresponding phylogeny [ 28 ]. The FEL and MEME were employed for the evaluation of the dN, dS, and dN/dS (ω) rate per site of coding alignment sequences, while FEL was utilized to detect sites under pervasive diversifying or purifying selection, MEME was specifically used to identify sites subject to both pervasive and episodic diversifying selection [ 29 ]. The ω ratio is used to infer the type of selection, where ω equals 1 for neutral selection, less than 1 for negative selection, and greater than 1 for positive selection. FEL and MEME were applied with a p-value threshold of 0.1 to ensure robust detection of selection sites, as this strategy expects that the determination of positive selection for each site is consistent along the whole phylogeny. Results Sequence data repository and refinement In this study, a total of 220 complete genome sequences of CSFV were initially retrieved from the GenBank database, representing diverse geographic regions across the globe, including 22 countries. This dataset was refined by excluding poor-quality sequences and those with incomplete metadata. Recombination analysis was carried out using RDP4 software, and sequences identified as recombinants were removed. Additionally, sequences with more than 99% nucleotide similarity within the same year and region were excluded to avoid redundancy and sampling bias. Following these refinement steps, the final dataset comprised 66 CSFV complete genome sequences (Table 1) throughout a worldwide temporal range from 1945 to 2022, representing genotypes 1, 2, and 3, were selected for further analysis. This curated dataset provided a robust foundation for downstream phylogenomic and evolutionary analyses. Sequence Alignment and Model Selection Using the MUSCLE algorithm in MEGA11 software, 66 non-redundant sequences of the complete genome of CSFV were subjected to multiple sequence alignment. Using the program jModelTest 2.1.7v20141120, the General Time Reversible (GTR) model with a gamma distribution (G) and invariant sites (I) was selected as the best-fit nucleotide substitution model based on the lowest AIC and BIC scores. The use of both AIC and BIC statistical criteria ensures strong model selection; however, AIC is better suited for choosing models that maximize predictive accuracy and frequently favour more complex models, while BIC is better suited for choosing simpler models because it penalizes complexity more severely [30]. These results were consistent across both criteria, confirming the appropriateness of the GTR+G+I model for subsequent phylogenetic analysis. Evolutionary Rate and Time-Scaled Phylogenetic Analysis Investigating the evolutionary patterns of CSFV by analyzing evolutionary rates provides important insights into nucleotide substitution patterns, revealing the timescales and mechanisms driving genetic diversification. We used the Bayesian-based coalescent methodology to analyze the curated 66 complete genome sequences of CSFV strains from around the world that are available at NCBI [31]. In the BEAUti tool from BEAST v1.10.4 software, the sequence data, GTR+G+I best-fit nucleotide substitution model, with the uncorrelated lognormal distribution (UCLD) molecular clock model and the Bayesian Skyline Plot (BSP) prior and Markov Chain Monte Carlo (MCMC) chain lengths for 1 billion iterations were configured and saved in XML format. The XML file is executed in the BEAST tool with a burn-in of 10%, achieving effective sample sizes (ESS) >100 for all parameters. The resulting output comprised log files and tree files. The log files were examined in Tracer v1.7.2 to determine the nucleotide substitution rates/site/year and estimate the divergence time parameters’ time to the most recent common ancestor (tMRCA). Datasets for the demographic model were selected according to the 95% Highest Posterior Density (HPD) intervals. Subsequently, the MCC phylogenetic tree is visualized for its divergence using FigTree v1.4.4. (Fig. 1). Substitution Rate, tMRCA, and Population Dynamics Next, the nucleotide substitution rate per site per year was calculated based on the posterior estimates obtained from the BEAST analysis. The mean substitution rate was found to be 2.06 x 10 -3 substitutions per site per year with a 95% HPD interval of [6.8012 x 10 -4 , 3.3044 x 10 -3 ], reflecting the mutation dynamics of CSFV strains globally. These values align with previously reported rates for RNA viruses, emphasizing the evolutionary stability of CSFV. The mean estimated time to the most recent common ancestor (tMRCA) for the analyzed dataset was the year 1877, with a 95% HPD interval [1833.8181, 1932.3176] (Table 2). Also, our laboratory sequences (MH734359.1 and OR428229.1) showed Chinese origin in this phylogenetic analysis. These results highlight the divergence timeline of CSFV, suggesting that the virus likely emerged during the mid-19th century. The Bayesian skyline plot for CSFV shows a gradual increase in effective population size from the 1600s to the mid-20th century (~1945), reflecting historical spread. Post-1945, with some fluctuations, the population stabilizes, with a slight decline around the 1990s, likely reflecting the impact of global eradication and control efforts (Fig. 2). Phylogeographic observation showed transmission on both the neighbouring route and the long-distance route (Fig. 3). Inference of Selection Pressure The codon-based methods embedded in the Datamonkey Adaptive Evolution (DAE) server, including FEL, were utilized to evaluate selection pressure on the coding sequences of CSFV from the selected dataset. The results provided insights into the evolutionary forces acting on the selected dataset of the CSF virus. The FEL identified 2534 sites under pervasive purifying selection (ω 1) at p ≤ 0.1, representing long-term adaptive evolution. The MEME method revealed 61 sites undergoing episodic diversifying selection (p ≤ 0.1), indicating that certain codon positions experience positive selection in a subset of lineages (Table 3). These pervasive and episodic events may reflect adaptive changes in response to host immune pressures or environmental conditions (Fig. 4). Discussion In this study, 66 complete genomes of CSFV were used to perform a phylogenetic analysis, and it allowed us to trace the evolution and positive selection of the virus throughout the world. Among the 66 sequences analyzed, two of them were from ICAR-NIVEDI, our phylogenetic analysis indicating their Chinese origin. Using the Bayesian analysis approach for estimating coefficients, the Uncorrelated Lognormal Relaxed Clock (UCLD) model is selected for the estimation of the most recent common ancestor (tMRCA) as well as the substitution rate. Our estimation of tMRCA was found to have emerged around 1877 (95% HPD, 1833.8181, 1932.3176) with 2.06 x 10 − 3 (95% HPD, 6.8012 x 10 − 4 , 3.3044 x 10 − 3 ) substitution/site/year. This gives an idea of the origin of the common ancestor at a significant period, possibly the late 19th century, when global trade was popular, and environmental changes may have led to the dissemination and diversification of the species. These are the evidence that CSFV used to be globally spread and also observation of distinct phylogenetic clades. Presently, CSFV is not globally endemic because popular countries such as the US, Canada, Europe, Australia, and New Zealand have eradicated CSFV through stringent biosecurity, intensive control, and surveillance programs. In Korea, CSFV E2 evolution is studied and found out 2.2 x 10 − 3 substitutions/site/year with tMRCA of 1761 [ 32 ]. In Italy, over 6 years CSFV E2 evolution was studied and found to be approximately 2.7 x 10 − 3 substitutions/site/year [ 33 ], which is almost similar to our substitution rate. Kwon et al [ 34 ] inferred for CSFV, relaxed uncorrelated exponential clock and expansion growth population is the best fit and also estimated 1.03 x 10 − 3 substitutions/site/year with tMRCA of 2770.2 years ago for 37 CSFV sequences. Later it found to be several biases in this result reported by Rios et al [ 3 ]. Another study Garrido Haro et al [ 35 ], suggests CSFV originated in the mid-1500s and all three genotypes have different tMRCA respectively, genotype 1 (tMRCA; 1720), genotype 2 (tMRCA: 1760), and genotype 3 (tMRCA: 1640). Although Rios et al [ 3 ] suggested that, genotype 1 (tMRCA; 1869), genotype 2 (tMRCA: 1907), and genotype 3 (tMRCA: 1955) and also proved the emergence of CSFV genotype 1 is first and followed by genotype 2 and genotype 3. We also examined the population dynamics of CSFV over a period, which reveals key insights into its evolution. The Bayesian skyline plot showed a gradual increase from the 17th century to the 19th century due to anthropogenic factors such as animal movement, increased trade, and ecological changes, and a slight decrease in the 20th century which is due to the global eradication efforts. In Korea, the population size of CSFV fell from 1984 to 2000 [ 32 ]. The increase in population in the middle of the 1990s and remained constant till the present is due to the introduction of vaccines to prevent global CSF outbreaks [ 34 ]. Rios [ 3 ] reported genotype 1 had a steady genetic diversity over the years, while genotype 2 experienced a sharp decline in the 1990s, and genotype 3 showed a fluctuating pattern dynamic unique to this genotype. In addition to that, we also analyzed the positive selection of the coding region of all 66 sequences, which gave an idea of viral evolution by the FEL method, and it identified 2,534 sites under pervasive purifying selection. The previous studies on selection pressure focused on the complete E2 genome of CSFV, and few sites were on positive selection. Among 37 sequences, 62.1% were conserved across the viral population, the higher similarity was specific to NS3, NS4A, and NS4B regions and the E2 gene is the most variable one [ 34 ]. Rios et al [ 3 ] used site mode and branch-site model from the CODEML program of PAML software and inferred evolution among sub-genotypes 1.4, 2.2, and 2.3 were on positive selection, and their antigenic and structural domains primarily demonstrated positive selection. In Brazil, sub-genotype 1.5 had two sites that were found to be under positive pressure [ 36 ]. In our study, the phylogeographic analysis further supports the idea that CSFV evolution is due to host diversity and geographic separation. The observation on phylogeographic structure showed some transmission routes occurred between neighbouring regions while others occurred long-distance transmission. These long-distant events are likely to be linked to human-mediated activities. Other studies show the continuous presence of viruses and constant evolution in particular geographic locations. For instance, in Ceará (CE) state in Brazil, localized outbreaks lead to several viral diversity, which is slightly similar to neighbouring states [ 36 ]. Between 2018 and 2020, Japan had an outbreak of CSFV in different regions; initial virus introduction was detected approximately 146 days after the first case, later speeded away from Gifu to Okinawa [ 37 ]. Conclusion This study provides valuable and detailed knowledge on the global analysis of CSFV evolutionary history, population dynamics, and selection pressures. The FEL findings strongly suggest the conservation of CSFV proteins, with few sites leading to long-term adaptive evolution. In the case of antigenic drift and adaption, the immune evasion mechanism happens due to adaptive evolution. Further investigation needs to be done on structural mapping, and experimental validation could provide insights into the functional properties of these adaptive evolutions. These findings will help to understand the evolution of CSFV and the importance of global surveillance. Insights of this study will play a crucial role in the development of more effective control strategies ensuring long-term protection of swine industries throughout the world from this highly virulent and rapidly evolving virus characterized by high mortality and high morbidity rates. Declarations Acknowledgment The authors express their sincere gratitude to the Director of ICAR-NIVEDI, Bengaluru, for their support and invaluable guidance throughout the study. Statement of author contributions R.M.: conceptualized the study, designed the methodology, led the analysis, and drafted the original manuscript. V.M.: contributed to data curation and assisted with manuscript review and editing. V.R. and S.M.: provided support with the data analysis. J.H. and S.N.: provided the technical support. S.P. and K.P.S.: supervised and oversaw the study. B.G.: provided overall direction and oversight the project. Funding The study was funded by DAHD, Govt of India under livestock health and disease control scheme (No. K-11053(5313)/21/2019-LH (E-14082)) Data availability The data generated and analyzed during this study are available from the corresponding author upon reasonable request. Conflict of Interest Authors declare that they have no conflict of interest. Ethical approval Not applicable. Informed consent Not applicable, as this study does not involve any human and animal participation. References Moennig V, Floegel-Niesmann G, Greiser-Wilke I (2003) Clinical signs and epidemiology of classical swine fever: A review of new knowledge. Vet J 165(1):11–20. https://doi.org/10.1016/S1090-0233(02)00128-3 OIE (World Organisation for Animal Health) (2019) World Animal Health Information System (WAHIS). Classical Swine Fever. 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Vet Microbiol 225:79–88.https://doi.org/10.1016/j.vetmic.2018.09.020 Lowings P, Ibata G, Needham J, Paton D (1996) Classical swine fever virus diversity and evolution. J Gen Virol 77(6):1311–1321. https://doi.org/10.1099/0022-1317-77-6-1311 Kwon T, Yoon SH, Kim KW, Caetano-Anolles K, Cho S, Kim H (2015) Time-calibrated phylogenomics of the classical swine fever viruses: genome-wide Bayesian coalescent approach. PLoS One 10(3):e0121578. https://doi.org/10.1371/journal.pone.0121578 Garrido Haro AD, Barrera Valle M, Acosta A, Flores FJ (2018) Phylodynamics of classical swine fever virus with emphasis on Ecuadorian strains. Transbound Emerg Dis 65(3):782–790. https://doi.org/10.1111/tbed.12803 Júnior AAF, Laguardia-Nascimento M, Barbosa AAS, da Silva Gonçalves VL, Freitas TRP, Júnior AVR, Camargos MF (2022) Phylodynamics of classical swine fever virus in Brazil. Braz J Microbiol 53(2):1065–1075. https://doi.org/10.1007/s42770-022-00724-2 Sawai K, Nishi T, Fukai K, Kato T, Hayama Y, Yamamoto T (2022) Phylogenetic and phylodynamic analysis of a classical swine fever virus outbreak in Japan (2018–2020). Transbound Emerg Dis 69(3):1529–1538. https://doi.org/10.1111/tbed.14117 Tables Table 1 Details of complete genome sequences of CSFV included in this study Sl.No. Accession number Genotype Collection Year Collection Place HQ148062.1 2.3 2007 Bulgaria HQ148061.1 2.3 2002 Croatia OR997840.1 1.4 1958 Cuba KX576461.1 1.4 2010 Cuba HM237795.1 1.1 2010 Czech Republic KF977607.1 1.1 2013 Denmark X87939.1 1.1 1968 France X96550.1 1.1 1978 France J04358.2 2.3 1980 France EU490425.1 1.1c 2008 France LT158410.1 2.3 2007 France LT593759.1 2.1b 2015 Germany LT593760.1 2.3 2007 Germany GU233733.1 2.3 2009 Germany GQ902941.1 2.1 1997 Germany OR428229.1 1.1 2014 India MH734359.1 1.1 2007 India EU857642.1 2.1 2008 India MK405703.1 2.2 2012 India AF091661.1 1.1 1945 Italy EU789580.1 1.1 1980 Japan HQ148063.1 2.1 2009 Lithuania LC086647 2.1b 2014 Mongolia M31768.1 1.2 1990 Netherlands KJ619377.1 2.2 1977 Netherlands KY849594.1 2.3 2006 Serbia AF099102.3 1.2 1998 Russia AY646427 3.4 1994 Taiwan AY554397 2.1 1996 Taiwan AY568569 2.1 2001 Taiwan AF326963.1 1.1a 1965 Switzerland NC_038912.1 1.1c 1995 Switzerland AY259122.1 1.1a 2003 Switzerland FJ265020.1 2.3 2001 Spain MH979232.1 2.2 2014 Vietnam LC374604 1.1 1991 Vietnam AY578688.1 1.2 2001 USA AY578687.1 1.2 2001 USA KC149990.1 2 2011 South Korea KF669877.1 3.2 1998 South Korea KY290453.1 2.1 2016 South Korea MN558862.1 1 1987 South Korea MN558863.1 1 2002 South Korea MN399383.1 1.1c 2019 South Korea MK093249.1 1.1c 2018 South Korea MK093246.1 1.1c 2018 South Korea MN558879.1 1 2017 South Korea MN558875.1 1 2016 South Korea MN558869.1 1 2004 South Korea MN558867.1 1 2016 South Korea AY775178.2 1.1 2004 China MF150642.1 2.1d 2015 China KU504339.1 2.1g 2011 China KP343640.1 2.1 2011 China HQ380231.1 1.1 2009 China HM175885.1 1.1 2008 China GQ923951.1 2.1 2009 China OQ883956.1 1.1 2022 China OR459954.1 1.1 2018 China MW853925.1 2.1b 2017 China MW853924.1 2.1c 2017 China MF149061.1 2.1b 2014 China KY132096.1 2.1i 2011 China AY382481.1 1.1a 2003 China AF531433.1 1.1a 2002 China AF092448.2 1.1 1945 China Table 2 Substitution rate/site/year and tMRCA of CSFV CSFV genome Host Substitution rate/site/year tMRCA Sus scrofa Mean 95% HPD interval Mean 95% HPD interval 2.06 x 10 -3 [6.8012 x 10 -4 (Lower), 3.3044 x 10 -3 (Upper)] 1877 [1833.8181(Lower), 1932.3176(Upper)]. Table 3 Selection pressure analysis: Identified sites under Pervasive and Episodic selection using FEL and MEME Method Sites FEL only (Pervasive) 385, 2981 MEME only (Episodic) 11, 223, 289, 326, 374, 409, 416, 423, 476, 477, 480, 484, 500, 503, 581, 600, 640, 651, 667, 678, 690, 701, 720, 761, 774, 855, 871, 929, 962, 1131, 1395, 1587, 1709, 1710, 2004, 2070, 2243, 2483, 2539, 2549, 2617, 2638, 2793, 2988, 3006, 3211, 3275, 3278, 3390, 3532, 3565, 3782, 3830 Both (Pervasive and Episodic) 258, 723, 2353, 2705, 2789, 2982, 2898, 3852 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 Apr, 2025 Read the published version in Virus Genes → Version 1 posted Editorial decision: Revision requested 05 Mar, 2025 Reviews received at journal 05 Mar, 2025 Reviewers agreed at journal 04 Mar, 2025 Reviewers invited by journal 03 Mar, 2025 Editor assigned by journal 24 Feb, 2025 Submission checks completed at journal 24 Feb, 2025 First submitted to journal 23 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6089266","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":420283292,"identity":"bf0d40ff-035c-46da-bec5-01ce2d1ee24e","order_by":0,"name":"Roopa Mahadevaswamy","email":"","orcid":"","institution":"ICAR-National Institute of Veterinary Epidemiology and Disease Informatics","correspondingAuthor":false,"prefix":"","firstName":"Roopa","middleName":"","lastName":"Mahadevaswamy","suffix":""},{"id":420283294,"identity":"32517ae5-bfbb-44f9-a5c1-e5ac6437cff7","order_by":1,"name":"Vijay Muruganantham","email":"","orcid":"","institution":"ICAR-National Institute of Veterinary Epidemiology and Disease Informatics","correspondingAuthor":false,"prefix":"","firstName":"Vijay","middleName":"","lastName":"Muruganantham","suffix":""},{"id":420283297,"identity":"182131ed-5982-4ff5-8e82-97a88ef294ac","order_by":2,"name":"Varsha Ramesh","email":"","orcid":"","institution":"ICAR-National Institute of Veterinary Epidemiology and Disease Informatics","correspondingAuthor":false,"prefix":"","firstName":"Varsha","middleName":"","lastName":"Ramesh","suffix":""},{"id":420283300,"identity":"e50b6cce-e09c-4aa2-981d-02a69c1af859","order_by":3,"name":"Shijili Mambully","email":"","orcid":"","institution":"ICAR-National Institute of Veterinary Epidemiology and Disease Informatics","correspondingAuthor":false,"prefix":"","firstName":"Shijili","middleName":"","lastName":"Mambully","suffix":""},{"id":420283301,"identity":"6c2dad7a-0e97-427d-aef7-14dbef52090e","order_by":4,"name":"Kuralayanapalya Puttahonnappa Suresh","email":"","orcid":"","institution":"ICAR-National Institute of Veterinary Epidemiology and Disease Informatics","correspondingAuthor":false,"prefix":"","firstName":"Kuralayanapalya","middleName":"Puttahonnappa","lastName":"Suresh","suffix":""},{"id":420283302,"identity":"27afa406-04a8-4f44-9cd7-dae69dcb1fb7","order_by":5,"name":"Jagadish Hiremath","email":"","orcid":"","institution":"ICAR-National Institute of Veterinary Epidemiology and Disease Informatics","correspondingAuthor":false,"prefix":"","firstName":"Jagadish","middleName":"","lastName":"Hiremath","suffix":""},{"id":420283303,"identity":"47c42d07-8c8c-4944-a66f-6793bd853e05","order_by":6,"name":"Shivasharanappa Nayakvadi","email":"","orcid":"","institution":"ICAR-National Institute of Veterinary Epidemiology and Disease Informatics","correspondingAuthor":false,"prefix":"","firstName":"Shivasharanappa","middleName":"","lastName":"Nayakvadi","suffix":""},{"id":420283305,"identity":"54e56941-2197-423f-9acf-1f62cc5b3e38","order_by":7,"name":"Baldev Gulati","email":"","orcid":"","institution":"ICAR-National Institute of Veterinary Epidemiology and Disease Informatics","correspondingAuthor":false,"prefix":"","firstName":"Baldev","middleName":"","lastName":"Gulati","suffix":""},{"id":420283307,"identity":"dcdb9fc8-29cb-4c36-ae64-1a88aac20e01","order_by":8,"name":"Sharanagouda Patil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACCSjNxsDY/OADiMFOvBbmNsMZIAYzsVoYGNgbpHlANCEt8rObn334uaMuj0+6scHY5tc2eT5mBsYPH3NwazG4c8x4Zu+Zw8VsMgcbHuf23TZsY2Zglpy5DY8WiQRjBt62A4ltEokNxrk9txmBWtiYefFokZ+R/pnxb1sdWIu0Zc9te4JaGG7kGDPztjFDtDD8uJ1IUIvBjZxiZtk2oF8kEtsMextuJ7cxMzbj9QvQYZsZ37bV5QEZjx/8+HPbdn5788EPH/E5DAoSwCRjG5hsIKweroXhD1GKR8EoGAWjYIQBAPfsT4S1AEz2AAAAAElFTkSuQmCC","orcid":"","institution":"ICAR-National Institute of Veterinary Epidemiology and Disease Informatics","correspondingAuthor":true,"prefix":"","firstName":"Sharanagouda","middleName":"","lastName":"Patil","suffix":""}],"badges":[],"createdAt":"2025-02-23 09:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6089266/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6089266/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11262-025-02154-2","type":"published","date":"2025-04-08T16:05:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":77194603,"identity":"e71bf833-40d8-4fd1-8371-02bf7a4e8cd9","added_by":"auto","created_at":"2025-02-26 06:02:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":376450,"visible":true,"origin":"","legend":"\u003cp\u003eThe Bayesian maximum clade credibility phylogenetic tree of CSFV complete genome obtained from BEAST software with branches colour-coded according to geographic origin. The legend indicates the countries corresponding to each colour\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6089266/v1/40c28bf903a80bf0ee678889.png"},{"id":77194599,"identity":"9f2323b7-9bdc-4224-bfa5-ae2dcea6a75a","added_by":"auto","created_at":"2025-02-26 06:02:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":42246,"visible":true,"origin":"","legend":"\u003cp\u003eThe Bayesian skyline plot represents the effective population size (y-axis, on a log scale) over time (x-axis)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6089266/v1/726efa2d36d555dcdb8842aa.png"},{"id":77195583,"identity":"8039ec72-938a-43fb-9475-264f7cd9ea81","added_by":"auto","created_at":"2025-02-26 06:18:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":233467,"visible":true,"origin":"","legend":"\u003cp\u003eMap representing a phylogeographic analysis of CSFV, showcasing the global spread and movement patterns of the virus across the World\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6089266/v1/2679a9a7f5f538788ebc42dd.png"},{"id":77195581,"identity":"be0eb0d0-90d2-445b-bc5f-5a17eece75d4","added_by":"auto","created_at":"2025-02-26 06:18:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":114718,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of diversifying selection sites in the complete genome of Classical Swine Fever Virus (CSFV) from global isolates. (a) The FEL method estimates site-specific dN/dS ratios, displaying 10 codons under pervasive diversifying selection (β/α \u0026gt; 1). The red-shaded regions highlight significant sites. (b) The MEME analysis detects episodic diversifying selection, showing 61 codons where selection occurs in specific lineages. Red dots represent codons with statistically significant evidence of diversifying selection\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6089266/v1/dae72c69c8854ee9dd67abc3.png"},{"id":80558944,"identity":"62cb4939-0d59-4b09-a7c4-8b19e3944a25","added_by":"auto","created_at":"2025-04-14 16:17:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1744887,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6089266/v1/9d9ece33-6404-4526-bf52-03f6934dde38.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global Population Dynamics and Evolutionary Selection in Classical Swine Fever Virus Complete Genomes: Insights from Bayesian Coalescent Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClassical swine fever (CSF) is a highly contagious haemorrhagic viral disease caused by \u003cem\u003ePestivirus suis\u003c/em\u003e [Classical Swine Fever Virus (CSFV)] and affects domestic pigs and wild boars. The global swine sector suffers large financial losses because of CSF's high mortality and morbidity rates, severe disruption of pork production, and trade restrictions implemented to contain outbreaks. The disease manifests in acute, subacute, or chronic forms in nature and is determined by the virulence of the infecting strain, where symptoms are marked by conjunctivitis, hyperthermia, anorexia, ataxia, depression, vomiting, diarrhoea, enteritis, purple skin discoloration, and skin haemorrhages, acute infection often resulting in death within 1\u0026ndash;2 weeks. A virulent form of CSF causes 100% mortality and morbidity. Pig comes into contact with infected ones or contact with contaminated objects/surfaces can spread the virus, replicating in the tonsils and lymph nodes, and spreading through blood to organs like the spleen, bone marrow, and visceral lymph nodes. CSF is endemic in many parts of the world, including Asia, Central and South America, and parts of Europe and Africa. Regions like North America, Australia, and New Zealand are free from the disease due to stringent control measures and disease surveillance [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Nevertheless, CSF is still omnipresent in most parts of the world since the end of the 20th century. Further, historical outbreaks in Europe during the 1990s and recent events in Korea, Colombia, Russia, Brazil, and Japan have highlighted the enduring global impact of this disease [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In an event 225 years ago, the Tunisian sheep virus from the host sheep is transmitted to a new host pig, causing the emergence of CSFV [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe CSFV is a small, +ssRNA virus belonging to the genus \u003cem\u003ePestivirus\u003c/em\u003e within the family Flaviviridae. Its genome size is approximately about 12.3kb, and it comprises a single open reading frame (ORF) composed of 3898 amino acids flanked by 5\u0026rsquo;UTR and 3\u0026rsquo;UTR. The ORF encodes for four structural (C, E\u003csup\u003erns\u003c/sup\u003e, E1, E2) and eight non-structural proteins (Npro, p7, NS2, NS3, NS4A, NS4B, NS5A, and NS5B), many of which play critical roles in viral replication and immune evasion [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. One of the key features of CSFV is its genetic diversity, which is divided into three primary genotypes (1, 2, and 3) and several sub-genotypes (1.1, 1.2, 1.3; 2.1, 2.2, 2.3; 3.1, 3.2, 3.3, 3.4) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. From the virus history seen, genotype 1 has been responsible for most outbreaks, but in recent years, genotype 2, particularly the sub-genotype 2.1, has become more common in Europe and Asia [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although, Rios et al [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] have proposed new genotypes and sub-genotypes: genotype 1 (1.1\u0026ndash;1.7), genotype 2 (2.1\u0026ndash;2.7), genotype 3, genotype 4, and genotype 5 based on the genetic diversity of 517 complete E2 gene. Recently, in Tamilnadu, India, evolutionary study conducted by Parthiban et al [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], demonstrated the emergence of distinct CSFV sub-genotype 1.7. The E2 gene is the best Phylogenetic marker for all \u003cem\u003ePestiviruses\u003c/em\u003e, including CSFV [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This type of genotypic variation is significant because the different genotypes and sub-genotypes may differ regarding their virulence and vaccine response; hence, continuous monitoring and study of the virus's evolution is necessary.\u003c/p\u003e \u003cp\u003eUnlike the earlier studies, which have mainly relied on isolated areas or specific genotypes of CSFV, we leverage a global dataset of CSFV complete genome sequences. In addition, through the use of advanced Bayesian Evolutionary Analysis by Sampling Trees (BEAST), this study provides a global context perspective, which bridges the gap between regional and international research, enabling a comprehensive evolutionary analysis of CSFV. To delineate the evolutionary pathway of virus and population dynamics, we used Bayesian coalescent methods while reconstructing the global phylogenetic and phylogeographic history of CSFV. By integrating 2 complete genome sequences of CSFV from ICAR-NIVEDI, Bengaluru, India into the analysis, we aim to place Indian CSFV strains within a broader global context, providing valuable insights into the evolutionary trajectory of this economically significant virus.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSequence data repository\u003c/h2\u003e \u003cp\u003eAll the available complete genome sequences of CSFV representing diverse geographic were retrieved from GenBank database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to conduct a comprehensive analysis of CSFV evolution on a global scale by time-calibrated phylogenomic approach, which determines rates of nucleotide substitution per site, per year and the time to the Most Recent Common Ancestors (tMRCA) of complete genome.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSequence data curation\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSequence Retrieval and Metadata Filtering\u003c/h2\u003e \u003cp\u003eTo ensure the high-quality representation of CSFV diversity across time and geography, the sequences underwent advanced processing, making them precisely ready for further evaluation. Only those sequences that had the proper data, including year of collection, geographic origin, and genotype name, were considered, whereas the poor-quality sequences and those with incomplete metadata were excluded at this stage to maintain the integrity and reliability of the dataset [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRedundancy and Bias Reduction\u003c/h3\u003e\n\u003cp\u003eTo reduce potential biases and minimize redundancy in the dataset, sequences with nucleotide similarities exceeding 99% within the same year and region were systematically removed [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eRecombination Detection\u003c/h3\u003e\n\u003cp\u003eRecognizing that recombination can bring forward complexities in the viral evolutionary studies and can potentially invalidate the results of coalescent analyses, sequences are subjected to recombination detection using RDP4 software employing multiple detection methods such as Rescan/Bootscan [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], GENECOV, MaxChi, RDP, 3Seq [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], Siscan and the Chimaera [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The highest acceptable p-value threshold was set at 0.05, incorporating Bonferroni correction for multiple comparisons to eliminate any contradictory or erroneous signals from the data. All potential recombinants were removed from the dataset for further analysis.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInclusion of ICAR-NIVEDI Sequences\u003c/h2\u003e \u003cp\u003eTo complete the curated dataset, two sequences from ICAR-National Institute of Veterinary Epidemiology and Disease Informatics (ICAR-NIVEDI), Bengaluru, India (GenBank accession number: MH734359.1 and OR428229.1) were also included to evaluate their evolutionary relationships within the global phylogeny.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSequence Alignment and Model Selection\u003c/h3\u003e\n\u003cp\u003eThe sequences were aligned with the Multiple Sequence Comparison by Log- Expectation (MUSCLE) in MEGA 11 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. To estimate the best-fit model, the software jModelTest 2.1.7v20141120 was employed, utilizing the Akaike information criteria (AIC) and Bayesian information criteria (BIC) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eEvolutionary rate, time-scaled phylogenetic analysis and population dynamics\u003c/h3\u003e\n\u003cp\u003eTo elucidate the evolutionary population dynamics and divergence of the CSFV genome, a comprehensive Bayesian phylogenetic framework was employed using BEAST v1.10.4 with the support of the BEAGLE library. The Markov Chain Monte Carlo (MCMC) approach, which is accessible within BEAST, was used to approximate the nucleotide substitution rate as well as the time to the most recent common ancestor (tMRCA). In this analysis, the GTR\u0026thinsp;+\u0026thinsp;G\u0026thinsp;+\u0026thinsp;I model was selected as the best-fit substitution model, while the relaxed uncorrelated lognormal distribution (UCLD) molecular clock model was chosen for its ability to accommodate rate heterogeneity across lineages. The Bayesian Skyline Plot (BSP) prior was chosen to estimate changes in effective population size over time, as it accommodates fluctuating population dynamics [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The Sampling times corresponding to the isolation years and geographic location as a trait were integrated through the BEAUti interface alongside the above parameters to construct the input .XML file. An MCMC chain length of 1\u0026nbsp;billion with 10% burn-in was incorporated into BEAUti interference, employed to make an effective sample size (ESS) for parameter estimates\u0026thinsp;\u0026gt;\u0026thinsp;100. This .XML file was then executed in BEAST, generating the output files, including the .log file and .tree file. The resulting convergence was analyzed by using the generated .log file in Tracer v1.7.2 software, which contains the mean, median, and 95% Highest Posterior Density (HPD) intervals with substitution rates derived from the mean rate/UCLD mean and divergence time (tMRCA) determined from the tree height. The .tree file was processed using TreeAnnotator v1.10.4 to generate the Maximum Clade Credibility (MCC) tree, which was subsequently visualized using FigTree v1.4.4 for an intuitive graphical representation of the phylogenetic tree. Using the Bayesian Skyline Plot (BSP) model, this comprehensive dataset was inferred for virus demographic history to ascertain changes in genetic divergence throughout a worldwide temporal range from 1945 to 2022.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSelection Pressure\u003c/h2\u003e \u003cp\u003eThe Datamonkey Adaptive Evolution (DAE) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.datamonkey.org\u003c/span\u003e\u003cspan address=\"http://www.datamonkey.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) server was employed to investigate the modes of natural selection, including diversifying (positive) and purifying (negative) selection [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo enhance the detection of relevant site-specific selection pressure acting on the coding sequence of CSFV of the selected dataset, a combination of different codon-based maximum likelihood and Bayesian framework methods [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] like Fixed Effects Likelihood (FEL) and Mixed Effects Model of Evolution (MEME) were used. The selection pressure is primarily determined by considering the ratios of non-synonymous (dN) to synonymous (dS) substitution rates on a per-site basis for given coding alignment and corresponding phylogeny [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The FEL and MEME were employed for the evaluation of the dN, dS, and dN/dS (ω) rate per site of coding alignment sequences, while FEL was utilized to detect sites under pervasive diversifying or purifying selection, MEME was specifically used to identify sites subject to both pervasive and episodic diversifying selection [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The ω ratio is used to infer the type of selection, where ω equals 1 for neutral selection, less than 1 for negative selection, and greater than 1 for positive selection. FEL and MEME were applied with a p-value threshold of 0.1 to ensure robust detection of selection sites, as this strategy expects that the determination of positive selection for each site is consistent along the whole phylogeny.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSequence data repository and refinement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, a total of 220 complete genome sequences of CSFV were initially retrieved from the GenBank database, representing diverse geographic regions across the globe, including 22 countries. This dataset was refined by excluding poor-quality sequences and those with incomplete metadata. Recombination analysis was carried out using RDP4 software, and sequences identified as recombinants were removed. Additionally, sequences with more than 99% nucleotide similarity within the same year and region were excluded to avoid redundancy and sampling bias.\u003c/p\u003e\n\u003cp\u003eFollowing these refinement steps, the final dataset comprised 66 CSFV complete genome sequences (Table 1) throughout a worldwide temporal range from 1945 to 2022, representing genotypes 1, 2, and 3, were selected for further analysis. This curated dataset provided a robust foundation for downstream phylogenomic and evolutionary analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequence Alignment and Model Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the MUSCLE algorithm in MEGA11 software, 66 non-redundant sequences of the complete genome of CSFV were subjected to multiple sequence alignment. Using the program jModelTest 2.1.7v20141120, the General Time Reversible (GTR) model with a gamma distribution (G) and invariant sites (I) was selected as the best-fit nucleotide substitution model based on the lowest AIC and BIC scores. The use of both AIC and BIC statistical criteria ensures strong model selection; however, AIC is better suited for choosing models that maximize predictive accuracy and frequently favour more complex models, while BIC is better suited for choosing simpler models because it penalizes complexity more severely [30]. These results were consistent across both criteria, confirming the appropriateness of the GTR+G+I model for subsequent phylogenetic analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvolutionary Rate and Time-Scaled Phylogenetic Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInvestigating the evolutionary patterns of CSFV by analyzing evolutionary rates provides important insights into nucleotide substitution patterns, revealing the timescales and mechanisms driving genetic diversification. We used the Bayesian-based coalescent methodology to analyze the curated 66 complete genome sequences of CSFV strains from around the world that are available at NCBI [31]. In the BEAUti tool from BEAST v1.10.4 software, the sequence data, GTR+G+I best-fit nucleotide substitution model, with the uncorrelated lognormal distribution (UCLD) molecular clock model and the Bayesian Skyline Plot (BSP) prior and Markov Chain Monte Carlo (MCMC) chain lengths for 1 billion iterations were configured and saved in XML format. The XML file is executed in the BEAST tool with a burn-in of 10%, achieving effective sample sizes (ESS) \u0026gt;100 for all parameters. The resulting output comprised log files and tree files. The log files were examined in Tracer v1.7.2 to determine the nucleotide substitution rates/site/year and estimate the divergence time parameters\u0026rsquo; time to the most recent common ancestor (tMRCA). Datasets for the demographic model were selected according to the 95% Highest Posterior Density (HPD) intervals. Subsequently, the MCC phylogenetic tree is visualized for its divergence using FigTree v1.4.4. (Fig. 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubstitution\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Rate, tMRCA, and Population Dynamics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, the nucleotide substitution rate per site per year was calculated based on the posterior estimates obtained from the BEAST analysis. The mean substitution rate was found to be 2.06 x 10\u003csup\u003e-3\u003c/sup\u003e substitutions per site per year with a 95% HPD interval of [6.8012 x 10\u003csup\u003e-4\u003c/sup\u003e, 3.3044 x 10\u003csup\u003e-3\u003c/sup\u003e], reflecting the mutation dynamics of CSFV strains globally. These values align with previously reported rates for RNA viruses, emphasizing the evolutionary stability of CSFV. The mean estimated time to the most recent common ancestor (tMRCA) for the analyzed dataset was the year 1877, with a 95% HPD interval [1833.8181, 1932.3176] (Table 2). Also, our laboratory sequences (MH734359.1 and OR428229.1) showed Chinese origin in this phylogenetic analysis. These results highlight the divergence timeline of CSFV, suggesting that the virus likely emerged during the mid-19th century. The Bayesian skyline plot for CSFV shows a gradual increase in effective population size from the 1600s to the mid-20th century (~1945), reflecting historical spread. Post-1945, with some fluctuations, the population stabilizes, with a slight decline around the 1990s, likely reflecting the impact of global eradication and control efforts (Fig. 2). Phylogeographic observation showed transmission on both the neighbouring route and the long-distance route (Fig. 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInference of Selection Pressure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe codon-based methods embedded in the Datamonkey Adaptive Evolution (DAE) server, including FEL, were utilized to evaluate selection pressure on the coding sequences of CSFV from the selected dataset. The results provided insights into the evolutionary forces acting on the selected dataset of the CSF virus. The FEL identified 2534 sites under pervasive purifying selection (\u0026omega; \u0026lt;1) and 10 sites under diversifying positive selection (\u0026omega; \u0026gt; 1) at p \u0026le; 0.1, representing long-term adaptive evolution. The MEME method revealed 61 sites undergoing episodic diversifying selection (p \u0026le; 0.1), indicating that certain codon positions experience positive selection in a subset of lineages (Table 3). These pervasive and episodic events may reflect adaptive changes in response to host immune pressures or environmental conditions (Fig. 4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, 66 complete genomes of CSFV were used to perform a phylogenetic analysis, and it allowed us to trace the evolution and positive selection of the virus throughout the world. Among the 66 sequences analyzed, two of them were from ICAR-NIVEDI, our phylogenetic analysis indicating their Chinese origin. Using the Bayesian analysis approach for estimating coefficients, the Uncorrelated Lognormal Relaxed Clock (UCLD) model is selected for the estimation of the most recent common ancestor (tMRCA) as well as the substitution rate. Our estimation of tMRCA was found to have emerged around 1877 (95% HPD, 1833.8181, 1932.3176) with 2.06 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e (95% HPD, 6.8012 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, 3.3044 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) substitution/site/year. This gives an idea of the origin of the common ancestor at a significant period, possibly the late 19th century, when global trade was popular, and environmental changes may have led to the dissemination and diversification of the species. These are the evidence that CSFV used to be globally spread and also observation of distinct phylogenetic clades. Presently, CSFV is not globally endemic because popular countries such as the US, Canada, Europe, Australia, and New Zealand have eradicated CSFV through stringent biosecurity, intensive control, and surveillance programs. In Korea, CSFV E2 evolution is studied and found out 2.2 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e substitutions/site/year with tMRCA of 1761 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In Italy, over 6 years CSFV E2 evolution was studied and found to be approximately 2.7 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e substitutions/site/year [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], which is almost similar to our substitution rate. Kwon et al [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] inferred for CSFV, relaxed uncorrelated exponential clock and expansion growth population is the best fit and also estimated 1.03 x 10\u0026thinsp;\u0026minus;\u0026thinsp;3 substitutions/site/year with tMRCA of 2770.2 years ago for 37 CSFV sequences. Later it found to be several biases in this result reported by Rios et al [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Another study Garrido Haro et al [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], suggests CSFV originated in the mid-1500s and all three genotypes have different tMRCA respectively, genotype 1 (tMRCA; 1720), genotype 2 (tMRCA: 1760), and genotype 3 (tMRCA: 1640). Although Rios et al [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] suggested that, genotype 1 (tMRCA; 1869), genotype 2 (tMRCA: 1907), and genotype 3 (tMRCA: 1955) and also proved the emergence of CSFV genotype 1 is first and followed by genotype 2 and genotype 3.\u003c/p\u003e \u003cp\u003eWe also examined the population dynamics of CSFV over a period, which reveals key insights into its evolution. The Bayesian skyline plot showed a gradual increase from the 17th century to the 19th century due to anthropogenic factors such as animal movement, increased trade, and ecological changes, and a slight decrease in the 20th century which is due to the global eradication efforts. In Korea, the population size of CSFV fell from 1984 to 2000 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The increase in population in the middle of the 1990s and remained constant till the present is due to the introduction of vaccines to prevent global CSF outbreaks [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Rios [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] reported genotype 1 had a steady genetic diversity over the years, while genotype 2 experienced a sharp decline in the 1990s, and genotype 3 showed a fluctuating pattern dynamic unique to this genotype.\u003c/p\u003e \u003cp\u003eIn addition to that, we also analyzed the positive selection of the coding region of all 66 sequences, which gave an idea of viral evolution by the FEL method, and it identified 2,534 sites under pervasive purifying selection. The previous studies on selection pressure focused on the complete E2 genome of CSFV, and few sites were on positive selection. Among 37 sequences, 62.1% were conserved across the viral population, the higher similarity was specific to NS3, NS4A, and NS4B regions and the E2 gene is the most variable one [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Rios et al [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] used site mode and branch-site model from the CODEML program of PAML software and inferred evolution among sub-genotypes 1.4, 2.2, and 2.3 were on positive selection, and their antigenic and structural domains primarily demonstrated positive selection. In Brazil, sub-genotype 1.5 had two sites that were found to be under positive pressure [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, the phylogeographic analysis further supports the idea that CSFV evolution is due to host diversity and geographic separation. The observation on phylogeographic structure showed some transmission routes occurred between neighbouring regions while others occurred long-distance transmission. These long-distant events are likely to be linked to human-mediated activities. Other studies show the continuous presence of viruses and constant evolution in particular geographic locations. For instance, in Cear\u0026aacute; (CE) state in Brazil, localized outbreaks lead to several viral diversity, which is slightly similar to neighbouring states [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Between 2018 and 2020, Japan had an outbreak of CSFV in different regions; initial virus introduction was detected approximately 146 days after the first case, later speeded away from Gifu to Okinawa [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides valuable and detailed knowledge on the global analysis of CSFV evolutionary history, population dynamics, and selection pressures. The FEL findings strongly suggest the conservation of CSFV proteins, with few sites leading to long-term adaptive evolution. In the case of antigenic drift and adaption, the immune evasion mechanism happens due to adaptive evolution. Further investigation needs to be done on structural mapping, and experimental validation could provide insights into the functional properties of these adaptive evolutions. These findings will help to understand the evolution of CSFV and the importance of global surveillance. Insights of this study will play a crucial role in the development of more effective control strategies ensuring long-term protection of swine industries throughout the world from this highly virulent and rapidly evolving virus characterized by high mortality and high morbidity rates.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u0026nbsp;\u003c/strong\u003eThe authors express their sincere gratitude to the Director of ICAR-NIVEDI, Bengaluru, for their support and invaluable guidance throughout the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of author contributions\u0026nbsp;\u003c/strong\u003eR.M.: conceptualized the study, designed the methodology, led the analysis, and drafted the original manuscript. V.M.: contributed to data curation and assisted with manuscript review and editing. V.R. and S.M.: \u003csup\u003e\u0026nbsp;\u003c/sup\u003eprovided support with the data analysis. J.H. and\u0026nbsp;S.N.: provided the technical support. S.P. and\u0026nbsp;K.P.S.: supervised and oversaw the study. B.G.: provided overall direction and oversight the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThe study was funded by DAHD, Govt of India under livestock health and disease control scheme (No. K-11053(5313)/21/2019-LH (E-14082))\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eThe data generated and analyzed during this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u0026nbsp;\u003c/strong\u003eAuthors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u0026nbsp;\u003c/strong\u003eNot applicable, as this study does not involve any human and animal participation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMoennig V, Floegel-Niesmann G, Greiser-Wilke I (2003) Clinical signs and epidemiology of classical swine fever: A review of new knowledge. \u003cem\u003eVet J\u003c/em\u003e 165(1):11\u0026ndash;20. https://doi.org/10.1016/S1090-0233(02)00128-3\u003c/li\u003e\n\u003cli\u003eOIE (World Organisation for Animal Health) (2019) World Animal Health Information System (WAHIS). 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https://doi.org/10.1093/bioinformatics/16.6.562\u003c/li\u003e\n\u003cli\u003eDarriba D, Taboada GL, Doallo R, Posada D (2012) jModelTest 2: More models, new heuristics, and parallel computing. \u003cem\u003eNat Methods\u003c/em\u003e 9(8):772. https://doi.org/10.1038/nmeth.2109\u003c/li\u003e\n\u003cli\u003eKumar S, Stecher G, Li M, Knyaz C, Tamura K (2018) MEGA X: Molecular evolutionary genetics analysis across computing platforms. \u003cem\u003eMol Biol Evol\u003c/em\u003e 35(6):1547\u0026ndash;1549. https://doi.org/10.1093/molbev/msy096\u003c/li\u003e\n\u003cli\u003eDrummond AJ, Rambaut A, Shapiro B, Pybus OG (2005) Bayesian coalescent inference of past population dynamics from molecular sequences. \u003cem\u003eMol Biol Evol\u003c/em\u003e 22(6):1185\u0026ndash;1192. https://doi.org/10.1093/molbev/msi103\u003c/li\u003e\n\u003cli\u003eDrummond AJ, Suchard MA, Xie D, Rambaut A (2012) Bayesian phylogenetics with BEAUti and the BEAST 1.7. \u003cem\u003eMol Biol Evol\u003c/em\u003e 29(8):1969\u0026ndash;1973. https://doi.org/10.1093/molbev/mss075\u003c/li\u003e\n\u003cli\u003eBaele G, Lemey P, Bedford T, Rambaut A, Suchard MA, Alekseyenko AV (2012) Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty. \u003cem\u003eMol Biol Evol\u003c/em\u003e 29(9):2157\u0026ndash;2167. https://doi.org/10.1093/molbev/mss084\u003c/li\u003e\n\u003cli\u003eRani S, Ramesh V, Beelagi MS, Raaga R, Puttahonnappa Suresh KP, Barman NN, Pati SS (2024) Genetic symphony: Investigating codon usage bias and evolutionary dynamics in West Nile virus across diverse geographical regions. \u003cem\u003eExplor Anim Med Res\u003c/em\u003e 14(2):187\u0026ndash;200. https://doi.org/10.52635/eamr/14.2.187-200\u003c/li\u003e\n\u003cli\u003eWeaver S, Shank SD, Spielman SJ, Li M, Muse SV, Kosakovsky Pond SL (2018) Datamonkey 2.0: A modern web application for characterizing selective and other evolutionary processes. \u003cem\u003eMol Biol Evol\u003c/em\u003e 35(3):773\u0026ndash;777. https://doi.org/10.1093/molbev/msx335\u003c/li\u003e\n\u003cli\u003eKosakovsky PS, Frost SD (2005) Not so different after all: A comparison of methods for detecting amino acid sites under selection. \u003cem\u003eMol Biol Evol\u003c/em\u003e 22(5):1208\u0026ndash;1222. https://doi.org/10.1093/molbev/msi105\u003c/li\u003e\n\u003cli\u003eCarney J, Daly JM, Nisalak A, Solomon T (2012) Recombination and positive selection identified in complete genome sequences of Japanese encephalitis virus. \u003cem\u003eArch Virol\u003c/em\u003e 157(1):75\u0026ndash;83. https://doi.org/10.1007/s00705-011-1114-5\u003c/li\u003e\n\u003cli\u003eSpielman SJ, Weaver S, Shank SD, Magalis BR, Li M, Kosakovsky Pond SL (2019) Evolution of viral genomes: Interplay between selection, recombination, and other forces. \u003cem\u003eMethods Mol Biol\u003c/em\u003e 1910:427\u0026ndash;468. https://doi.org/10.1007/978-1-4939-9074-0_14\u003c/li\u003e\n\u003cli\u003ePosada D, Buckley TR (2004) Model selection and model averaging in phylogenetics: Advantages of Akaike information criterion and Bayesian approaches over likelihood ratio tests. \u003cem\u003eSyst Biol\u003c/em\u003e 53(5):793\u0026ndash;808. https://doi.org/10.1080/10635150490522304\u003c/li\u003e\n\u003cli\u003eDrummond AJ, Rambaut A (2007) BEAST: Bayesian evolutionary analysis by sampling trees. \u003cem\u003eBMC Evol Biol\u003c/em\u003e 7:1\u0026ndash;8. https://doi.org/10.1186/1471-2148-7-214\u003c/li\u003e\n\u003cli\u003eAn DJ, Lim SI, Choe S, Kim KS, Cha RM, Cho IS, Park BK (2018) Evolutionary dynamics of classical swine fever virus in South Korea: 1987\u0026ndash;2017. \u003cem\u003eVet Microbiol\u003c/em\u003e 225:79\u0026ndash;88.https://doi.org/10.1016/j.vetmic.2018.09.020\u003c/li\u003e\n\u003cli\u003eLowings P, Ibata G, Needham J, Paton D (1996) Classical swine fever virus diversity and evolution. \u003cem\u003eJ Gen Virol\u003c/em\u003e 77(6):1311\u0026ndash;1321. 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https://doi.org/10.1007/s42770-022-00724-2\u003c/li\u003e\n\u003cli\u003eSawai K, Nishi T, Fukai K, Kato T, Hayama Y, Yamamoto T (2022) Phylogenetic and phylodynamic analysis of a classical swine fever virus outbreak in Japan (2018\u0026ndash;2020). \u003cem\u003eTransbound Emerg Dis\u003c/em\u003e 69(3):1529\u0026ndash;1538. https://doi.org/10.1111/tbed.14117\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e Details of complete genome sequences of CSFV included in this study\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSl.No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccession number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCollection Year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCollection Place\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eHQ148062.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eBulgaria\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"2\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eHQ148061.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eCroatia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"3\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eOR997840.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e1958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eCuba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"4\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eKX576461.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eCuba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"5\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eHM237795.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eCzech Republic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"6\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eKF977607.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eDenmark\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"7\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eX87939.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e1968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"8\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eX96550.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e1978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"9\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eJ04358.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e1980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"10\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eEU490425.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"11\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eLT158410.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"12\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eLT593759.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"13\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eLT593760.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"14\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eGU233733.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"15\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eGQ902941.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e1997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"16\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eOR428229.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"17\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n 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12.3377%;\"\u003e\n \u003col start=\"19\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eMK405703.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"20\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eAF091661.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e1945\u003c/p\u003e\n \u003c/td\u003e\n 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17.5325%;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eLithuania\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"23\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eLC086647\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eMongolia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"24\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eM31768.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eNetherlands\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"25\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eKJ619377.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e1977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eNetherlands\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"26\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eKY849594.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eSerbia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"27\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eAF099102.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n 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\u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eAY554397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e1996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eTaiwan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"30\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eAY568569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eTaiwan\u003c/p\u003e\n \u003c/td\u003e\n 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25.8117%;\"\u003e\n \u003cp\u003e1995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eSwitzerland\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"33\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eAY259122.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.1a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eSwitzerland\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"34\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n 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12.3377%;\"\u003e\n \u003col start=\"36\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eLC374604\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e1991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eVietnam\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"37\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eAY578688.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"38\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eAY578687.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"39\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eKC149990.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"40\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eKF669877.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e1998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"41\"\u003e\n 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\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"48\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eMN558875.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"49\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eMN558869.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"50\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eMN558867.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"51\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eAY775178.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"52\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eMF150642.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.1d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"53\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eKU504339.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.1g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"54\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eKP343640.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"55\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eHQ380231.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"56\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eHM175885.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"57\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eGQ923951.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"58\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eOQ883956.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"59\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eOR459954.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"60\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eMW853925.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"61\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eMW853924.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"62\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eMF149061.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"63\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eKY132096.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e2.1i\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"64\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eAY382481.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.1a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"65\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eAF531433.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.1a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.3377%;\"\u003e\n \u003col start=\"66\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003eAF092448.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5325%;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.8117%;\"\u003e\n \u003cp\u003e1945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.7857%;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e Substitution rate/site/year and tMRCA of CSFV\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCSFV genome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eSubstitution rate/site/year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003etMRCA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eSus scrofa\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95% HPD interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95% HPD interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2.06 x 10\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[6.8012 x 10\u003csup\u003e-4\u003c/sup\u003e(Lower), 3.3044 x 10\u003csup\u003e-3\u003c/sup\u003e(Upper)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[1833.8181(Lower), 1932.3176(Upper)].\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Selection pressure analysis: Identified sites under Pervasive and Episodic selection using FEL and MEME\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eSites\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eFEL only (Pervasive)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e385, 2981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eMEME only (Episodic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e11, 223, 289, 326, 374, 409, 416, 423, 476, 477, 480, 484, 500, 503, 581, 600, 640, 651, 667, 678, 690, 701, 720, 761, 774, 855, 871, 929, 962, 1131, 1395, 1587, 1709, 1710, 2004, 2070, 2243, 2483, 2539, 2549, 2617, 2638, 2793, 2988, 3006, 3211, 3275, 3278, 3390, 3532, 3565, 3782, 3830\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eBoth (Pervasive and Episodic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e258, 723, 2353, 2705, 2789, 2982, 2898, 3852\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"virus-genes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"viru","sideBox":"Learn more about [Virus Genes](http://link.springer.com/journal/11262)","snPcode":"11262","submissionUrl":"https://submission.nature.com/new-submission/11262/3","title":"Virus Genes","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"CSFV, Bayesian analysis, tMRCA, BEAST, Selection pressure","lastPublishedDoi":"10.21203/rs.3.rs-6089266/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6089266/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClassical swine fever virus (CSFV) is a pathogen that affects pigs and wild boars. This contagious RNA virus is a high threat to swine industries throughout the world because it has high mortality and morbidity rates, leading to economic losses. Although previous studies primarily focused on isolated regions or specific genotypes, our study leverages a global dataset of 220 CSFV whole-genome sequences retrieved from the NCBI repository along with2 CSFV complete genome sequence from our laboratory (Accession number: MH734359.1 and OR4282229.1) and carefully curated to 66 sequences. The refined dataset is subjected to Bayesian analysis along with selection pressure analysis. The outcome of this experiment, the mean substitution rate was estimated at 2.06 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e substitutions/site/year with the Highest Posterior Density (HPD) (95% HPD 6.8012 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e \u0026minus;\u0026thinsp;3.3044 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), and the estimated average time to the most recent common ancestor (tMRCA) for the analyzed dataset was the year 1877 (95% HPD 1833.8181\u0026ndash;1932.3176). Among the curated data set, 2 CSFV complete genome sequences (Accession number: MH734359.1 and OR428229.1) from our laboratory showed of Chinese origin. Additionally, pervasive and episodic selection pressure revealed that both had ongoing diversifying natural positive selection, which could lead to increased genetic diversity and possibly emergence of the new lineage. This potential information could be used for future evaluation of strategies to control emerging new genotypes of CSFV with high mortality and morbidity.\u003c/p\u003e","manuscriptTitle":"Global Population Dynamics and Evolutionary Selection in Classical Swine Fever Virus Complete Genomes: Insights from Bayesian Coalescent Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-26 06:02:01","doi":"10.21203/rs.3.rs-6089266/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-05T16:09:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-05T15:40:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73332239989119009408195826719232984139","date":"2025-03-04T14:50:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-03T16:08:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-24T16:38:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-24T16:36:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Virus Genes","date":"2025-02-23T09:28:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"virus-genes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"viru","sideBox":"Learn more about [Virus Genes](http://link.springer.com/journal/11262)","snPcode":"11262","submissionUrl":"https://submission.nature.com/new-submission/11262/3","title":"Virus Genes","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2ec9b400-3e94-4ea1-8fa6-e71e7f7b4877","owner":[],"postedDate":"February 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-14T16:13:10+00:00","versionOfRecord":{"articleIdentity":"rs-6089266","link":"https://doi.org/10.1007/s11262-025-02154-2","journal":{"identity":"virus-genes","isVorOnly":false,"title":"Virus Genes"},"publishedOn":"2025-04-08 16:05:37","publishedOnDateReadable":"April 8th, 2025"},"versionCreatedAt":"2025-02-26 06:02:01","video":"","vorDoi":"10.1007/s11262-025-02154-2","vorDoiUrl":"https://doi.org/10.1007/s11262-025-02154-2","workflowStages":[]},"version":"v1","identity":"rs-6089266","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6089266","identity":"rs-6089266","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}