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      Genomic and Metagenomic Analysis of Diversity-Generating Retroelements Associated with Treponema denticola

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          Abstract

          Diversity-generating retroelements (DGRs) are genetic cassettes that can produce massive protein sequence variation in prokaryotes. Presumably DGRs confer selective advantages to their hosts (bacteria or viruses) by generating variants of target genes—typically resulting in target proteins with altered ligand-binding specificity—through a specialized error-prone reverse transcription process. The only extensively studied DGR system is from the Bordetella phage BPP-1, although DGRs are predicted to exist in other species. Using bioinformatics analysis, we discovered that the DGR system associated with the Treponema denticola species (a human oral-associated periopathogen) is dynamic (with gains/losses of the system found in the isolates) and diverse (with multiple types found in isolated genomes and the human microbiota). The T. denticola DGR is found in only nine of the 17 sequenced T. denticola strains. Analysis of the DGR-associated template regions and reverse transcriptase gene sequences revealed two types of DGR systems in T. denticola: the ATCC35405-type shared by seven isolates including ATCC35405; and the SP32-type shared by two isolates (SP32 and SP33), suggesting multiple DGR acquisitions. We detected additional variants of the T. denticola DGR systems in the human microbiomes, and found that the SP32-type DGR is more abundant than the ATCC35405-type in the healthy human oral microbiome, although the latter is found in more sequenced isolates. This is the first comprehensive study to characterize the DGRs associated with T. denticola in individual genomes as well as human microbiomes, demonstrating the importance of utilizing both individual genomes and metagenomes for characterizing the elements, and for analyzing their diversity and distribution in human populations.

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          Most cited references28

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          Prediction of lipoprotein signal peptides in Gram-negative bacteria.

          A method to predict lipoprotein signal peptides in Gram-negative Eubacteria, LipoP, has been developed. The hidden Markov model (HMM) was able to distinguish between lipoproteins (SPaseII-cleaved proteins), SPaseI-cleaved proteins, cytoplasmic proteins, and transmembrane proteins. This predictor was able to predict 96.8% of the lipoproteins correctly with only 0.3% false positives in a set of SPaseI-cleaved, cytoplasmic, and transmembrane proteins. The results obtained were significantly better than those of previously developed methods. Even though Gram-positive lipoprotein signal peptides differ from Gram-negatives, the HMM was able to identify 92.9% of the lipoproteins included in a Gram-positive test set. A genome search was carried out for 12 Gram-negative genomes and one Gram-positive genome. The results for Escherichia coli K12 were compared with new experimental data, and the predictions by the HMM agree well with the experimentally verified lipoproteins. A neural network-based predictor was developed for comparison, and it gave very similar results. LipoP is available as a Web server at www.cbs.dtu.dk/services/LipoP/.
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            Phase and antigenic variation in bacteria.

            Phase and antigenic variation result in a heterogenic phenotype of a clonal bacterial population, in which individual cells either express the phase-variable protein(s) or not, or express one of multiple antigenic forms of the protein, respectively. This form of regulation has been identified mainly, but by no means exclusively, for a wide variety of surface structures in animal pathogens and is implicated as a virulence strategy. This review provides an overview of the many bacterial proteins and structures that are under the control of phase or antigenic variation. The context is mainly within the role of the proteins and variation for pathogenesis, which reflects the main body of literature. The occurrence of phase variation in expression of genes not readily recognizable as virulence factors is highlighted as well, to illustrate that our current knowledge is incomplete. From recent genome sequence analysis, it has become clear that phase variation may be more widespread than is currently recognized, and a brief discussion is included to show how genome sequence analysis can provide novel information, as well as its limitations. The current state of knowledge of the molecular mechanisms leading to phase variation and antigenic variation are reviewed, and the way in which these mechanisms form part of the general regulatory network of the cell is addressed. Arguments both for and against a role of phase and antigenic variation in immune evasion are presented and put into new perspective by distinguishing between a role in bacterial persistence in a host and a role in facilitating evasion of cross-immunity. Finally, examples are presented to illustrate that phase-variable gene expression should be taken into account in the development of diagnostic assays and in the interpretation of experimental results and epidemiological studies.
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              Reverse transcriptase-mediated tropism switching in Bordetella bacteriophage.

              Host-pathogen interactions are often driven by mechanisms that promote genetic variability. We have identified a group of temperate bacteriophages that generate diversity in a gene, designated mtd (major tropism determinant), which specifies tropism for receptor molecules on host Bordetella species. Tropism switching is the result of a template-dependent, reverse transcriptase-mediated process that introduces nucleotide substitutions at defined locations within mtd. This cassette-based mechanism is capable of providing a vast repertoire of potential ligand-receptor interactions.
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                03 June 2016
                2016
                : 7
                : 852
                Affiliations
                [1] 1School of Informatics and Computing, Indiana University, Bloomington IN, USA
                [2] 2Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh PA, USA
                [3] 3Department of Biology, Indiana University, Bloomington IN, USA
                [4] 4National Center for Genome Analysis Support, Indiana University, Bloomington IN, USA
                Author notes

                Edited by: Jae-Ho Shin, Kyungpook National University, South Korea

                Reviewed by: Philip E. Stewart, Rocky Mountain Laboratories/National Institute of Allergy and Infectious Diseases/National Institutes of Health, USA; Baojun Wu, Wayne State University, USA

                *Correspondence: Yuzhen Ye, yye@ 123456indiana.edu

                This article was submitted to Evolutionary and Genomic Microbiology, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2016.00852
                4891356
                27375574
                0b587f69-5409-473d-af95-70d6189634c1
                Copyright © 2016 Nimkulrat, Lee, Doak and Ye.

                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.

                History
                : 16 March 2016
                : 23 May 2016
                Page count
                Figures: 7, Tables: 3, Equations: 0, References: 42, Pages: 13, Words: 0
                Funding
                Funded by: National Science Foundation 10.13039/100000001
                Award ID: DBI-0845685
                Award ID: DBI-1262588
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                diversity-generating retroelements,treponema denticola,human microbiome,template region,reverse transcriptase

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