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Start Over Descriptor "phage-bacteria arms race" Topic biology.organism_classification
2 results on '"phage-bacteria arms race"'

1. Expanding Diversity of Firmicutes Single-Strand Annealing Proteins: A Putative Role of Bacteriophage-Host Arms Race

Author

Elena Bidnenko, Agnieszka K. Szczepankowska, Eric Prestel, Kamil Steczkiewicz, MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and This work was in part supported by the joint research project between Institut National de la Recherche Agronomique (INRA), France and the Ministry of Science and Higher Education (MSHE), Poland (number P260/N/INRA/2008/0).

Subjects
Microbiology (medical), In silico, [SDV]Life Sciences [q-bio], phage recombinase, Context (language use), Computational biology, Biology, single strand annealing proteins (SSAP), Microbiology, DNA sequencing, Firmicutes bacteriophages, Bacteriophage, 03 medical and health sciences, CLANS, Recombinase, CRISPR, Gene, 030304 developmental biology, abortive intection, 0303 health sciences, 030306 microbiology, biology.organism_classification, QR1-502, CRISPR/cas, Horizontal gene transfer, Sak3/DUF1071, phage-bacteria arms race
Abstract

Bacteriophage-encoded single strand annealing proteins (SSAPs) are recombinases which can substitute the classical, bacterial RecA and manage the DNA metabolism at different steps of phage propagation. SSAPs have been shown to efficiently promote recombination between short and rather divergent DNA sequences and were exploited for in vivo genetic engineering mainly in Gram-negative bacteria. In opposition to the conserved and almost universal bacterial RecA protein, SSAPs display great sequence diversity. The importance for SSAPs in phage biology and phage-bacteria evolution is underlined by their role as key players in events of horizontal gene transfer (HGT). All of the above provoke a constant interest for the identification and study of new phage recombinase proteins in vivo, in vitro as well as in silico. Despite this, a huge body of putative ssap genes escapes conventional classification, as they are not properly annotated. In this work, we performed a wide-scale identification, classification and analysis of SSAPs encoded by the Firmicutes bacteria and their phages. By using sequence similarity network and gene context analyses, we created a new high quality dataset of phage-related SSAPs, substantially increasing the number of annotated SSAPs. We classified the identified SSAPs into seven distinct families, namely RecA, Gp2.5, RecT/Redβ, Erf, Rad52/22, Sak3, and Sak4, organized into three superfamilies. Analysis of the relationships between the revealed protein clusters led us to recognize Sak3-like proteins as a new distinct SSAP family. Our analysis showed an irregular phylogenetic distribution of ssap genes among different bacterial phyla and specific phages, which can be explained by the high rates of ssap HGT. We propose that the evolution of phage recombinases could be tightly linked to the dissemination of bacterial phage-resistance mechanisms (e.g., abortive infection and CRISPR/Cas systems) targeting ssap genes and be a part of the constant phage-bacteria arms race.

Published
2021
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2. Condition-dependent transcriptome reveals high-level regulatory architecture in Bacillus subtilis

Author

Nicolas, P., Mader, U., Dervyn, Etienne, Rochat, T., Leduc, A., Pigeonneau, N., Marchadier, E., Hoebeke, M., Aymerich, S., Becher, D., Bisicchia, P., Botella, E., Delumeau, O., Doherty, G., Denham, E., Fogg, M., Fromion, V., Goelzer, A., Hansen, A., Hartig, E., Harwood, C., Homuth, G., Jarmer, H., Jules, M., Klipp, E., Le Chat, L., Lecointe, F., Lewis, P., Liebermeister, W., March, A., Mars, R., Nannapaneni, P., Noone, D., Pohl, S., Rinn, B., Rugheimer, F., Sappa, P., Samson, F., Schaffer, M., Schwikowski, B., Steil, L., Stulke, J., Wiegert, T., Devine, K., Wilkinson, A., Maarten van Dijl, J., Hecker, M., VOLKER, U., Bessieres, P., Noirot, P., Steczkiewicz, Kamil, Prestel, Eric, Bidnenko, Elena, Szczepankowska, Agnieszka, Unité Mathématique, Informatique et Génome (MIG), Institut National de la Recherche Agronomique (INRA), Institut für Mikrobiologie - Institute for Microbiology, Universität Greifswald - University of Greifswald, Interfaculty Institute for Genetics and Functional Genomics, MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Smurfit Institute of Genetics, Trinity College Dublin, School of Environmental and Life Sciences, Newcastle University [Newcastle], Department of Medical Microbiology, University of Groningen [Groningen], York Structural Biology Laboratory, Department of Chemistry, University of York [York, UK], Institute of Microbiology, Technische Universität Braunschweig [Braunschweig], Centre for Bacterial Cell Biology, Institute, of Cell and Molecular Biosciences, Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark [Lyngby] (DTU), Theoretical Biophysics [Berlin], Humboldt Universität zu Berlin, Center for Information Sciences and Databases - Department of Biosystems Science and Engineering, Biologie systémique - Systems Biology, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Department of General Microbiology Georg-Augus, Georg-August-Universität Göttingen, FN Biotechnologie, Laurea University of Applied Sciences, Unité Mathématique Informatique et Génome (MIG), Technische Universität Braunschweig = Technical University of Braunschweig [Braunschweig], Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Humboldt University Of Berlin, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Georg-August-University = Georg-August-Universität Göttingen, Humboldt-Universität zu Berlin, Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], Georg-August-University [Göttingen], Unité du méningocoque, Centre Collaborateur OMS, Institut de Médecine Tropicale du Service de Santé des Armées-Institut de Recherches Biomédicales des Armées, Institute of Geological Sciences [Bern], University of Bern, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ALK Abelló, Partenaires INRAE, Laboratoire de Spectroscopie Biomédicale, Institut de Physique, Université de Liège, AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Faculteit Medische Wetenschappen/UMCG, Microbes in Health and Disease (MHD), and Translational Immunology Groningen (TRIGR)

Subjects
[SDV]Life Sciences [q-bio], antisense, phage recombinase, Bacillus subtilis, [SDV.BC]Life Sciences [q-bio]/Cellular Biology, Biology, single strand annealing proteins (SSAP), Firmicutes bacteriophages, Transcriptome, 03 medical and health sciences, chemistry.chemical_compound, RNA-POLYMERASE, Sigma factor, Transcription (biology), RHO, RNA polymerase, CLANS, bacteria, Gene, 030304 developmental biology, abortive intection, Regulation of gene expression, Genetics, bacterie, 0303 health sciences, Multidisciplinary, IDENTIFICATION, SEQUENCES, LANDSCAPE, 030306 microbiology, Promoter, BIOLOGIE, DNA, BIOLOGIE MOLECULAIRE, biology.organism_classification, GENE, GENOME, CRISPR/cas, chemistry, ESCHERICHIA-COLI, facteur sigma, sigma factor, transcription, Sak3/DUF1071, phage-bacteria arms race
Abstract

Outside In Acquisition and analysis of large data sets promises to move us toward a greater understanding of the mechanisms by which biological systems are dynamically regulated to respond to external cues. Now, two papers explore the responses of a bacterium to changing nutritional conditions (see the Perspective by Chalancon et al. ). Nicolas et al. (p. 1103 ) measured transcriptional regulation for more than 100 different conditions. Greater amounts of antisense RNA were generated than expected and appeared to be produced by alternative RNA polymerase targeting subunits called sigma factors. One transition, from malate to glucose as the primary nutrient, was studied in more detail by Buescher et al. (p. 1099 ) who monitored RNA abundance, promoter activity in live cells, protein abundance, and absolute concentrations of intracellular and extracellular metabolites. In this case, the bacteria responded rapidly and largely without transcriptional changes to life on malate, but only slowly adapted to use glucose, a shift that required changes in nearly half the transcription network. These data offer an initial understanding of why certain regulatory strategies may be favored during evolution of dynamic control systems.

Published
2012
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