Evaluation of 16S rRNA Hypervariable Regions for Bioweapon Species Detection by Massively Parallel Sequencing

Victor H.G. Dias, Priscila Da S.F.C. Gomes, Allan C. Azevedo-Martins, Bianca C.A. Cabral, August E. Woerner, Bruce Budowle, Rodrigo S. Moura-Neto, Rosane Silva

Research output: Contribution to journalArticlepeer-review

Abstract

Molecular detection and classification of the bacterial groups in a sample are relevant in several areas, including medical research and forensics. Sanger sequencing of the 16S rRNA gene is considered the gold standard for microbial phylogenetic analysis. However, the development of massively parallel sequencing (MPS) offers enhanced sensitivity and specificity for microbiological analyses. In addition, 16S rRNA target amplification followed by MPS facilitates the combined use of multiple markers/regions, better discrimination of sample background, and higher sample throughput. We designed a novel set of 16S rRNA gene primers for detection of bacterial species associated with clinical, bioweapon, and biohazards microorganisms via alignment of 364 sequences representing 19 bacterial species and strains relevant to medical and forensics applications. In silico results indicated that the hypervariable regions (V1V2), (V4V5), and (V6V7V8) support the resolution of a selected group of bacteria. Interspecies and intraspecies comparisons showed 74.23%-85.51% and 94.48%-99.98% sequencing variation among species and strains, respectively. Sequence reads from a simulated scenario of bacterial species mapped to each of the three hypervariable regions of the respective species with different affinities. The minimum limit of detection was achieved using two different MPS platforms. This protocol can be used to detect or monitor as low as 2,000 genome equivalents of bacterial species associated with clinical, bioweapon, and biohazard microorganisms and potentially can distinguish natural outbreaks of pathogenic microorganisms from those occurring by intentional release.

Original languageEnglish
Article number8865520
JournalInternational Journal of Microbiology
Volume2020
DOIs
StatePublished - 2020

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