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Nucleic acid sequencing for identification in clinical and environmental microbiology

Conference paper
Authors Edward R.B. Moore
Liselott A Svensson
Christel Unosson
Nahid Karami
Published in Proceedings of the XXX Congresso Chileno de Microbiología. December 4-6, Concepción, Chile
Pages 27
Publication year 2008
Published at Institute of Biomedicine, Department of Infectious Medicine
Pages 27
Language en
Subject categories Biological Systematics


Systematic analyses in the complexity of microbial diversity are increasingly problematic for clinical diagnoses and studies of environmental microbiology. DNA sequence-based analyses and genotyping have enabled the detection and identification of microorganisms in the environment and have been adopted as routine in clinical analyses. Comparative 16S rRNA gene sequence analyses are able to estimate identifications, although it is recognised that such analyses are not able to provide definitive diagnoses at the species level. Among the most difficult problems for clinical cases is the identification of infectious agents within “complexes” of closely related species, often comprising pathogenic and non-pathogenic species or genomovars, with limited differentiating characteristics, e.g., 16S rRNA gene sequence dissimilarities among such organisms is often much less than 1.0%. Genotyping of bacteria enable high-resolution differentiation and identifications, as well as epidemiological monitoring. Furthermore, these approaches provide the means for establishing new criteria for defining bacterial species. For the identification of clinical or environmental isolates, a polyphasic multi-locus sequence analysis (MLSA) can be established, including “first-phase” comparisons of 16S rRNA gene sequences, for identification to the sub-genus level, and subsequent, “secondary-phase” analyses of conserved house-keeping genes, for identification to the species level. An average nucleotide index (ANI), based upon the overall nucleic acid sequence similarities among homologous house-keeping genes, can be applied to establish the expected gene sequence similarity “cut-offs” that differentiate the species of any given taxon. Thus, the key to effective bacterial identification depends upon the selection of conserved genes with levels of resolution able to differentiate the most closely related species.

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