Recombinant Echovirus E32 VP1 Protein [His]

Recent studies reveal that even the smallest genomes such as viruses evolve through complex and stochastic processes, and the assumption of independent alleles is not valid in most applications. Advances in sequencing technologies produce multiple time-point whole-genome data, which enable potential interactions between these alleles to be investigated empirically. To investigate these interactions, we represent alleles as distributed vectors that encode for relationships with other alleles in the course of evolution and apply artificial neural networks to time-sampled whole-genome datasets for feature learning. We build this platform using methods and algorithms derived from natural language processing (NLP), and we denote it as the nucleotide skip-gram neural network. We learn distributed vectors of alleles using the changes in allele frequency of echovirus 11 in the presence or absence of the disinfectant (ClO2) from the experimental evolution data. Results from the training using a new open-source software TensorFlow show that the learned distributed vectors can be clustered using principal component analysis and hierarchical clustering to reveal a list of non-synonymous mutations that arise on the structural protein VP1 in connection to the candidate mutation for ClO2 adaptation. Furthermore, this method can account for recombination rates by setting the extent of interactions as a biological hyper-parameter, and the results show that the most realistic scenario of mid-range interactions across the genome is most consistent with the previous studies.

Background: Diarrhea is one of the most significant diseases in children, causing morbidity and mortality worldwide. Diarrhea is caused by viruses, bacteria, and parasites. There are several viruses that can cause diarrhea, including some groups of enteroviruses that have a significant role in acute diarrhea in children. Objectives: This study was conducted to evaluate the presence of enteroviruses in the stool of children with diarrhea. Methods: We collected 85 stool samples including 50 (58.82%) from males and 35 (41.17%) from females with acute diarrhea. All the stool samples proved negative for bacterial and parasitic pathogens. The RNA was extracted from the stool samples and cDNA was prepared. The semi-nested PCR was carried out for the detection of the 5'-UTR region of enteroviruses. To determine the enterovirus serotypes, the sequences of semi-nested PCR product was performed using conserved primers for the 5'-UTR region. Results: Overall, 21/85 (24.7%) patients including 12/50 (24%) males and 9/35 (25.71%) females showed positive results for enteroviruses (P = 0.3). Based on the results of sequencing, one of the isolated serotypes was identified as coxsackievirus A6 and the other isolated serotype was echovirus 9. Conclusions: Overall, 21/85 (24.7%) children with acute diarrhea were infected with enteroviruses. The distribution of enteroviruses was not significantly different between male and female patients. The results of sequencing indicated that one of the isolated serotypes was coxsackievirus A6 and the other isolated serotype was echovirus 9. The remaining 64/85(75.29%) isolates were negative for enteroviruses. The role of other viral gastroenteritis agents including rotaviruses, noroviruses, calicivirus, astrovirus, and adenoviruses was not explored that needs further investigation.