Abstract
Metagenomic sequencing is a powerful tool that is widely used in laboratories worldwide for taxonomic characterization of microorganisms in clinical and environmental samples. In this study, we utilized metagenomics to investigate comprehensively the microbial diversity in fecal samples of children over a four-year period. Our methods were carefully designed to ensure accurate and reliable results. Validated and analyzed were metagenomic data obtained from sequencing 27 fecal samples from children under 10 years old with gastroenteritis over a four-year period (2012-2016). The fecal specimens were collected from patients who received care at public health facilities in the northern region of Brazil. Sequencing libraries were prepared from cDNA and sequenced on the Illumina HiSeq. Kraken-2 was utilized to classify bacterial taxonomy based on the 16S rRNA gene, using the Silva rRNA database. Additionally, the Diamond program was used for mapping to the non-redundant protein database (NR database). Phylogenomic analyses were conducted using Geneious R10 and MEGA X software, and Bayesian estimation of phylogeny was performed using the MrBayes program. The results indicate significant heterogeneity among norovirus strains, with evidence of recombination and point mutations. This study presents the first complete genome of parechovirus 8 in the region. Additionally, it describes the bacterial populations and bacteriophages present in feces, with a high abundance of Firmicutes and Proteobacteria, including an increased proportion of the Enterobacteriaceae family. The presented data demonstrate the genetic diversity of microbial populations and provide a comprehensive report on viral molecular characterization. These findings are relevant for genomic studies in gastrointestinal infections. The metagenomic approach is a powerful tool for investigating microbial diversity in children with gastroenteritis. However, further studies are imperative to conduct genomic analysis of identified bacterial strains and thoroughly analyze antimicrobial resistance genes.
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