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Polyphasic phenotypic and genotypic analyses for diagnosing closely related clinically-relevant bacteria

Conference paper
Authors Edward R.B. Moore
Liselott A Svensson
Christel Unosson
Enevold Falsen
Sashka A Mihaylova
Marcel Erhard
Wibke Kallow
Published in Proceedings of the XXIX Congreso Chileno de Microbología, December 3-5, Viña del Mar, Chile
Pages 44-45
Publication year 2007
Published at Institute of Biomedicine, Department of Infectious Medicine
Pages 44-45
Language en
Subject categories Microbiology in the medical area


The majority of known pathogenic bacteria have been recognised for more than a century. However, reliable identification of specific pathogens within the increasing complexity of bacterial diversity is becoming more problematic for clinical diagnoses. DNA sequencing and genotyping of bacteria has helped enable recognition of the extensive diversity of microorganisms in the environment and the same approaches are more recently being adopted in clinical microbiology. Genotypic methods are especially suited to the analyses of the “difficult-to-cultivate” organisms, as well as bacteria that pose significant health risks during cultivation. Traditional phenotyping, as well as genotyping by 16S rRNA gene sequence analysis, in many cases are able to provide only good estimations of identifications, although they may be able to provide information on what an isolate is not. In some cases of clinical diagnoses, such information may be useful and adequate. However, such analyses are often not able to provide definitive identifications. Among the most difficult problem for diagnoses in clinical cases, is the identification of organisms within “complexes” of closely related species, e.g., the Burkholderia cepacia “complex”, the Mycobacterium tuberculosis “complex”, the Streptococcus mitis “complex”, etc. These, so called “complexes” are comprised of closely related pathogenic and non-pathogenic species or genomovars, with limited phenotypic and genotypic differentiating characteristics, e.g., the 16S rRNA gene sequence differences among such organisms is less than 1.0%. Because these species “complexes” are comprised of organisms with different pathogenic and virulence potential and different treatment regimen, it is essential to be able to provide reliable identifications to the clinicians. Genotyping of bacteria provides the means for detailed, high-resolution differentiation and identification of organisms, as well as epidemiological monitoring. For identification of clinical isolates, a “multi-locus sequence analysis (MLSA)”, is used, including “first-phasic” comparisons of partial 16S rRNA gene sequences, for identification to the sub-genus level, and subsequent analyses of one or more “house-keeping” genes, for identification to the species level. However, not all house-keeping genes are equally useful for all taxa. Thus, the key to effective identifications, with respect to the speed and cost of analyses, and the resolution and reproducibility of identifications, depends upon the selection of the most applicable house-keeping genes, i.e., those with adequately high levels of resolution among the most closely related species. Protocols and results for species-resolving MLSA, using “house-keeping” genes enable effective differentiation of clinically-relevant closely related species of respective genera of Burkholderia, Mycobacterium, Streptococcus and Staphylococcus, as well as Pseudomonas and Stenotrophomonas. Furthermore, Matrix-Assisted Laser Desorption Ionization Time-of-Flight mass-spectrometry (MALDI-TOF MS), in combination with Spectral Archive And Microbial Identification System (SARAMIS) software analyses, were able to further define species-level differentiation among the most closely-related members of species “complexes”. MALDI-TOF MS, generating cell protein profiles, may exhibit even higher levels of resolution than the most discriminating genotypic analyses for species, as well as strain, identifications. These phenotypic data, as well as traditional phenotypic data, and genotypic MLSA data are correlated in the polyphasic assessment and definition of selected species “complexes”.

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