Hybrid Genomic Analysis of Salmonella enterica Serovar Enteritidis SE3 Isolated from Polluted Soil in Brazil.
Danitza Xiomara Romero-CalleFrancisnei Pedrosa-SilvaLuiz Marcelo Ribeiro ToméThiago J SousaLeila Thaise Santana de Oliveira SantosVasco Ariston de Carvalho AzevedoBertram BrenigRaquel Guimarães BenevidesThiago Motta VenancioCraig BillingtonAristóteles Goes-NetoPublished in: Microorganisms (2022)
In Brazil, Salmonella enterica serovar Enteritidis is a significant health threat. Salmonella enterica serovar Enteritidis SE3 was isolated from soil at the Subaé River in Santo Amaro, Brazil, a region contaminated with heavy metals and organic waste. Illumina HiSeq and Oxford Nanopore Technologies MinION sequencing were used for de novo hybrid assembly of the Salmonella SE3 genome. This approach yielded 10 contigs with 99.98% identity with S. enterica serovar Enteritidis OLF-SE2-98984-6. Twelve Salmonella pathogenic islands, multiple virulence genes, multiple antimicrobial gene resistance genes, seven phage defense systems, seven prophages and a heavy metal resistance gene were encoded in the genome. Pangenome analysis of the S. enterica clade, including Salmonella SE3, revealed an open pangenome, with a core genome of 2137 genes. Our study showed the effectiveness of a hybrid sequence assembly approach for environmental Salmonella genome analysis using HiSeq and MinION data. This approach enabled the identification of key resistance and virulence genes, and these data are important to inform the control of Salmonella and heavy metal pollution in the Santo Amaro region of Brazil.
Keyphrases
- heavy metals
- listeria monocytogenes
- genome wide
- genome wide identification
- escherichia coli
- risk assessment
- health risk assessment
- dna methylation
- bioinformatics analysis
- health risk
- staphylococcus aureus
- copy number
- sewage sludge
- pseudomonas aeruginosa
- genome wide analysis
- human health
- electronic health record
- randomized controlled trial
- antimicrobial resistance
- systematic review
- transcription factor
- healthcare
- single cell
- biofilm formation
- big data
- gene expression
- health information
- cystic fibrosis
- deep learning
- water quality
- particulate matter