Literature DB >> 16564151

Raman microspectroscopy as an identification tool within the phylogenetically homogeneous 'Bacillus subtilis' group.

Didier Hutsebaut1, Joachim Vandroemme, Jeroen Heyrman, Peter Dawyndt, Peter Vandenabeele, Luc Moens, Paul de Vos.   

Abstract

Vibrational methods have multiple advantages compared to more classic, chemotaxonomic and even molecular microbial tools for the identification of bacteria. Nevertheless, their definite breakthrough in diagnostic microbiology laboratories is determined by their identification potential. This paper reports on the profound evaluation of Raman spectroscopy to identify closely related species by means of 68 Bacillus strains that are assigned or closely related to the phylogenetically homogeneous 'Bacillus subtilis'-group (sensu stricto). These strains were chosen to represent biological variation within the selected species and to create a realistic view on the possibilities of this technique The evaluation resulted in 49/54 correct identifications at the species level for intern and 15/19 for extern testing. The correct identification of strains, which were not represented in the training set, supports the potential as an identification tool within the 'B. subtilis group'. Considering the vague borderline between the species studied, Raman spectroscopy can be regarded here as a promising application for identifications at the species level.

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Year:  2006        PMID: 16564151     DOI: 10.1016/j.syapm.2006.02.001

Source DB:  PubMed          Journal:  Syst Appl Microbiol        ISSN: 0723-2020            Impact factor:   4.022


  9 in total

1.  Biochemical characterization of pathogenic bacterial species using Raman spectroscopy and discrimination model based on selected spectral features.

Authors:  Fernanda SantAna de Siqueira E Oliveira; Adriano Moraes da Silva; Marcos Tadeu Tavares Pacheco; Hector Enrique Giana; Landulfo Silveira
Journal:  Lasers Med Sci       Date:  2020-06-05       Impact factor: 3.161

2.  Surface enhanced Raman spectroscopy (SERS) for the discrimination of Arthrobacter strains based on variations in cell surface composition.

Authors:  Kate E Stephen; Darren Homrighausen; Glen DePalma; Cindy H Nakatsu; Joseph Irudayaraj
Journal:  Analyst       Date:  2012-07-30       Impact factor: 4.616

3.  From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification.

Authors:  Bram Slabbinck; Willem Waegeman; Peter Dawyndt; Paul De Vos; Bernard De Baets
Journal:  BMC Bioinformatics       Date:  2010-01-30       Impact factor: 3.169

4.  In-vitro antibacterial, antifungal, antioxidant and functional properties of Bacillus amyloliquefaciens.

Authors:  Shine Kadaikunnan; Thankappan Rejiniemon; Jamal M Khaled; Naiyf S Alharbi; Ramzi Mothana
Journal:  Ann Clin Microbiol Antimicrob       Date:  2015-02-22       Impact factor: 3.944

5.  Antifungal Activity of Isolated Bacillus amyloliquefaciens SYBC H47 for the Biocontrol of Peach Gummosis.

Authors:  Xunhang Li; Yanzhou Zhang; Zhiwen Wei; Zhengbing Guan; Yujie Cai; Xiangru Liao
Journal:  PLoS One       Date:  2016-09-01       Impact factor: 3.240

6.  Detectability of biosignatures in a low-biomass simulation of martian sediments.

Authors:  Adam H Stevens; Alison McDonald; Coen de Koning; Andreas Riedo; Louisa J Preston; Pascale Ehrenfreund; Peter Wurz; Charles S Cockell
Journal:  Sci Rep       Date:  2019-07-04       Impact factor: 4.379

7.  Sensitive and specific discrimination of pathogenic and nonpathogenic Escherichia coli using Raman spectroscopy-a comparison of two multivariate analysis techniques.

Authors:  Khozima Hamasha; Qassem I Mohaidat; Russell A Putnam; Ryan C Woodman; Sunil Palchaudhuri; Steven J Rehse
Journal:  Biomed Opt Express       Date:  2013-03-01       Impact factor: 3.732

8.  Control efficacy of an endophytic Bacillus amyloliquefaciens strain BZ6-1 against peanut bacterial Wilt, Ralstonia solanacearum.

Authors:  Xiaobing Wang; Guobin Liang
Journal:  Biomed Res Int       Date:  2014-01-12       Impact factor: 3.411

9.  Translational utility of a hierarchical classification strategy in biomolecular data analytics.

Authors:  Dieter Galea; Paolo Inglese; Lidia Cammack; Nicole Strittmatter; Monica Rebec; Reza Mirnezami; Ivan Laponogov; James Kinross; Jeremy Nicholson; Zoltan Takats; Kirill A Veselkov
Journal:  Sci Rep       Date:  2017-11-03       Impact factor: 4.379

  9 in total

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