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A phylogenomic and molecular signature based approach for characterization of the phylum Spirochaetes and its major clades: proposal for a taxonomic revision of the phylum

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      The Spirochaetes species cause many important diseases including syphilis and Lyme disease. Except for their containing a distinctive endoflagella, no other molecular or biochemical characteristics are presently known that are specific for either all Spirochaetes or its different families. We report detailed comparative and phylogenomic analyses of protein sequences from Spirochaetes genomes to understand their evolutionary relationships and to identify molecular signatures for this group. These studies have identified 38 conserved signature indels (CSIs) that are specific for either all members of the phylum Spirochaetes or its different main clades. Of these CSIs, a 3 aa insert in the FlgC protein is uniquely shared by all sequenced Spirochaetes providing a molecular marker for this phylum. Seven, six, and five CSIs in different proteins are specific for members of the families Spirochaetaceae, Brachyspiraceae, and Leptospiraceae, respectively. Of the 19 other identified CSIs, 3 are uniquely shared by members of the genera Sphaerochaeta, Spirochaeta, and Treponema, whereas 16 others are specific for the genus Borrelia. A monophyletic grouping of the genera Sphaerochaeta, Spirochaeta, and Treponema distinct from the genus Borrelia is also strongly supported by phylogenetic trees based upon concatenated sequences of 22 conserved proteins. The molecular markers described here provide novel and more definitive means for identification and demarcation of different main groups of Spirochaetes. To accommodate the extensive genetic diversity of the Spirochaetes as revealed by different CSIs and phylogenetic analyses, it is proposed that the four families of this phylum should be elevated to the order level taxonomic ranks (viz. Spirochaetales, Brevinematales ord. nov., Brachyspiriales ord. nov., and Leptospiriales ord. nov.). It is further proposed that the genera Borrelia and Cristispira be transferred to a new family Borreliaceae fam. nov. within the order Spirochaetales.

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      Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

      The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.
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        MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods.

        Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from
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          The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

          SILVA (from Latin silva, forest, is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.

            Author and article information

            Department of Biochemistry and Biomedical Sciences, McMaster University Hamilton, ON, Canada
            Author notes

            Edited by: Hiromi Nishida, Toyama Prefectural University, Japan

            Reviewed by: Viktoria Shcherbakova, Institute of Biochemistry and Physiology of Microorganisms, Russian Academy of Sciences, Russia; David L. Bernick, University of California, Santa Cruz, USA

            *Correspondence: Radhey S. Gupta, Department of Biochemistry and Biomedical Sciences, McMaster University, 1280 Main Street West, Hamilton, ON L8N 3Z5, Canada e-mail: gupta@

            This article was submitted to Frontiers in Evolutionary and Genomic Microbiology, a specialty of Frontiers in Microbiology.

            Front Microbiol
            Front Microbiol
            Front. Microbiol.
            Frontiers in Microbiology
            Frontiers Media S.A.
            04 July 2013
            30 July 2013
            : 4
            Copyright © 2013 Gupta, Mahmood and Adeolu.

            This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

            Figures: 8, Tables: 5, Equations: 0, References: 81, Pages: 18, Words: 10228
            Original Research Article


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