Research Article A new Informatics Framework for Evaluating the Codon Usage Metrics, Evolutionary Models and Phylogeographic reconstruction of Tomato yellow leaf curl virus (TYLCV) in different regions of Asian countries

Tomato yellow leaf curl virus (TYLCV) is a major devastating viral disease, majorly affecting the tomato production globally. The disease is majorly transmitted by the Whitefly. The Begomovirus (TYLCV) having a six major protein coding genes, among them the C1/AC1 is evidently associated with viral replication. Owing to immense role of C1/AC1 gene, the present study is an initial effort to elucidate the factors shaping the codon usage bias and evolutionary pattern of TYLCV-C1/AC1 gene in five major Asian countries. Based on publically available nucleotide sequence data the Codon usage pattern, Evolutionary and Phylogeographic reconstruction was carried out. The study revealed the presence of significant variation between the codon bias indices in all the selected regions. Implying that the codon usage pattern indices (eNC, CAI, RCDI, GRAVY, Aromo) are seriously affected by selection and mutational pressure, taking a supremacy in shaping the codon usage bias of viral gene. Further, the tMRCA age was 1853, 1939, 1855, 1944, 1828 for China, India, Iran, Oman and South Korea, respectively for TYLCV-C1/AC1 gene. The integrated analysis of Codon usage bias, Evolutionary rate and Phylogeography analysis in viruses signifies the positive role of selection and mutational pressure among the selected regions for TYLCV (C1/AC1) gene

The first study on analysis of the codon usage bias and evolutionary analysis of the glycoprotein envelope E2 gene of seven Pestiviruses

Background and Aim: Pestivirus, a genus of the Flaviviridae family, comprises viruses that affect bovines, sheep, and pigs. Symptoms, including hemorrhagic syndromes, abortion, respiratory complications, and deadly mucosal diseases, are produced in infected animals, which cause huge economic losses to the farmers. Bovine viral diarrhea virus-1, bovine viral diarrhea virus-2, classical swine fever virus, border disease virus, Bungowannah, Hobi-like, and atypical porcine pestivirus belonging to the Pestivirus genus were selected for the study. This study aimed to estimate the codon usage bias and the rate of evolution using the glycoprotein E2 gene. Furthermore, codon usage bias analysis was performed using publicly available nucleotide sequences of the E2 gene of all seven Pestiviruses. These nucleotide sequences might elucidate the disease epidemiology and facilitate the development of designing better vaccines. Materials and Methods: Coding sequences of the E2 gene of Pestiviruses A (n = 89), B (n = 60), C (n = 75), D (n = 10), F (n = 07), H (n = 52), and K (n = 85) were included in this study. They were analyzed using different methods to estimate the codon usage bias and evolution. In addition, the maximum likelihood and Bayesian methodologies were employed to analyze a molecular dataset of seven Pestiviruses using a complete E2 gene region. Results: The combined analysis of codon usage bias and evolutionary rate analysis revealed that the Pestiviruses A, B, C, D, F, H, and K have a codon usage bias in which mutation and natural selection have played vital roles. Furthermore, while the effective number of codons values revealed a moderate bias, neutrality plots indicated the natural selection in A, B, F, and H Pestiviruses and mutational pressure in C, D, and K Pestiviruses. The correspondence analysis revealed that axis-1 significantly contributes to the synonymous codon usage pattern. In this study, the evolutionary rate of Pestiviruses B, H, and K was very high. The most recent common ancestors of all Pestivirus lineages are 1997, 1975, 1946, 1990, 2004, 1990, and 1990 for Pestiviruses A, B, C, D, F, H, and K, respectively. This study confirms that both mutational pressure and natural selection have played a significant role in codon usage bias and evolutionary studies. Conclusion: This study provides insight into the codon usage bias and evolutionary lineages of pestiviruses. It is arguably the first report of such kind. The information provided by the study can be further used to elucidate the respective host adaptation strategies of the viruses. In turn, this information helps study the epidemiology and control methods of pestiviruses

Analysis of codon usage bias of classical swine fever virus

Background and Aim: Classical swine fever (CSF), caused by CSF virus (CSFV), is a highly contagious disease in pigs causing 100% mortality in susceptible adult pigs and piglets. High mortality rate in pigs causes huge economic loss to pig farmers. CSFV has a positive-sense RNA genome of 12.3 kb in length flanked by untranslated regions at 5' and 3' end. The genome codes for a large polyprotein of 3900 amino acids coding for 11 viral proteins. The 1300 codons in the polyprotein are coded by different combinations of three nucleotides which help the infectious agent to evolve itself and adapt to the host environment. This study performed and employed various methods/techniques to estimate the changes occurring in the process of CSFV evolution by analyzing the codon usage pattern. Materials and Methods: The evolution of viruses is widely studied by analyzing their nucleotides and coding regions/ codons using various methods. A total of 115 complete coding regions of CSFVs including one complete genome from our laboratory (MH734359) were included in this study and analysis was carried out using various methods in estimating codon usage bias and evolution. This study elaborates on the factors that influence the codon usage pattern. Results: The effective number of codons (ENC) and relative synonymous codon usage showed the presence of codon usage bias. The mononucleotide (A) has a higher frequency compared to the other mononucleotides (G, C, and T). The dinucleotides CG and CC are underrepresented and overrepresented. The codons CGT was underrepresented and AGG was overrepresented. The codon adaptation index value of 0.71 was obtained indicating that there is a similarity in the codon usage bias. The principal component analysis, ENC-plot, Neutrality plot, and Parity Rule 2 plot produced in this article indicate that the CSFV is influenced by the codon usage bias. The mutational pressure and natural selection are the important factors that influence the codon usage bias. Conclusion: The study provides useful information on the codon usage analysis of CSFV and may be utilized to understand the host adaptation to virus environment and its evolution. Further, such findings help in new gene discovery, design of primers/probes, design of transgenes, determination of the origin of species, prediction of gene expression level, and gene function of CSFV. To the best of our knowledge, this is the first study on codon usage bias involving such a large number of complete CSFVs including one sequence of CSFV from India

A New Methodology to Comprehend the Effect of El Niño and La Niña Oscillation in Early Warning of Anthrax Epidemic Among Livestock

Anthrax is a highly fatal zoonotic disease that affects all species of livestock. The study aims to develop an early warning of epidemiological anthrax using machine learning (ML) models and to study the effect of El Niño and La Niña oscillation, as well as the climate–disease relationship concerning the spatial occurrence and outbreaks in Karnataka. The disease incidence data are divided based on El Niño and La Niña events from 2004–2019 and subjected to climate-disease modeling to understand the disease pattern over the years. Machine learning models were implemented using R statistical software version 3.1.3 with Livestock density, soil profile, and meteorological and remote sensing variables as risk factors associated with anthrax incidence. Model evaluation is performed using statistical indices, viz., Cohen’s kappa, receiver operating characteristic (ROC) curve, true skill statistics (TSS), etc. Models with good predictive power were combined to develop an average prediction model. The predicted results were mapped onto the Risk maps, and the Basic reproduction numbers (R0) for the districts that are significantly clustered were calculated. Early warning or risk prediction developed with a layer of R0 superimposed on a risk map helps in the preparedness for the disease occurrence, and precautionary measures before the spread of the disease