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Proteome-wide prediction of bacterial carbohydrate-binding proteins as a tool for understanding commensal and pathogen colonisation of the vaginal microbiome

François Bonnardel, Stuart M. Haslam, Anne Dell, Ten Feizi, Yan Liu, Virginia Tajadura-Ortega, Yukie Akune, Lynne Sykes, Phillip R. Bennett, View ORCID ProfileDavid A. MacIntyre, View ORCID ProfileFrédérique Lisacek, View ORCID ProfileAnne Imberty
doi: https://doi.org/10.1101/2020.09.10.291781
François Bonnardel
1University Grenoble Alpes, CNRS, CERMAV, Grenoble, France
2Swiss Institute of Bioinformatics, Geneva, Switzerland
3Computer Science Department, UniGe, Geneva, Switzerland
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Stuart M. Haslam
4Department of Life Sciences, Imperial College London, London, UK
5March of Dimes European Prematurity Research Centre, Imperial College London, UK
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Anne Dell
4Department of Life Sciences, Imperial College London, London, UK
5March of Dimes European Prematurity Research Centre, Imperial College London, UK
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Ten Feizi
5March of Dimes European Prematurity Research Centre, Imperial College London, UK
6Glycosciences Laboratory, Department of Metabolism Digestion and Reproduction, Imperial College London, London, UK
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Yan Liu
5March of Dimes European Prematurity Research Centre, Imperial College London, UK
6Glycosciences Laboratory, Department of Metabolism Digestion and Reproduction, Imperial College London, London, UK
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Virginia Tajadura-Ortega
5March of Dimes European Prematurity Research Centre, Imperial College London, UK
6Glycosciences Laboratory, Department of Metabolism Digestion and Reproduction, Imperial College London, London, UK
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Yukie Akune
6Glycosciences Laboratory, Department of Metabolism Digestion and Reproduction, Imperial College London, London, UK
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Lynne Sykes
5March of Dimes European Prematurity Research Centre, Imperial College London, UK
7Imperial College Parturition Research Group, Division of the Institute of Reproductive and Developmental Biology, Department of Metabolism Digestion and Reproduction, Imperial College London, London, UK
8Queen Charlotte’s Hospital, Imperial College Healthcare NHS Trust, London, UK
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Phillip R. Bennett
5March of Dimes European Prematurity Research Centre, Imperial College London, UK
7Imperial College Parturition Research Group, Division of the Institute of Reproductive and Developmental Biology, Department of Metabolism Digestion and Reproduction, Imperial College London, London, UK
8Queen Charlotte’s Hospital, Imperial College Healthcare NHS Trust, London, UK
9Tommy’s National Centre for Miscarriage Research, Imperial College London, London, UK
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David A. MacIntyre
5March of Dimes European Prematurity Research Centre, Imperial College London, UK
7Imperial College Parturition Research Group, Division of the Institute of Reproductive and Developmental Biology, Department of Metabolism Digestion and Reproduction, Imperial College London, London, UK
9Tommy’s National Centre for Miscarriage Research, Imperial College London, London, UK
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  • ORCID record for David A. MacIntyre
  • For correspondence: d.macintyre@imperial.ac.uk frederique.lisacek@sib.swiss anne.imberty@cermav.cnrs.fr
Frédérique Lisacek
2Swiss Institute of Bioinformatics, Geneva, Switzerland
3Computer Science Department, UniGe, Geneva, Switzerland
10Section of Biology, UniGe, Geneva, Switzerland
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  • ORCID record for Frédérique Lisacek
  • For correspondence: d.macintyre@imperial.ac.uk frederique.lisacek@sib.swiss anne.imberty@cermav.cnrs.fr
Anne Imberty
1University Grenoble Alpes, CNRS, CERMAV, Grenoble, France
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  • ORCID record for Anne Imberty
  • For correspondence: d.macintyre@imperial.ac.uk frederique.lisacek@sib.swiss anne.imberty@cermav.cnrs.fr
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Abstract

Bacteria use protein receptors called lectins to anchor to specific host surface sugars. The role of lectins in the vaginal microbiome, and their involvement in reproductive tract pathophysiology is poorly defined. Here we establish a classification system based on taxonomy and protein 3D structure to identify 109 lectin classes. Hidden Markov Model (HMM) profiles for each class were used to search bacterial genomes, resulting in the prediction of >100 000 bacterial lectins available at unilectin.eu/bacteria. Genome screening of 90 isolates from 21 vaginal bacterial species showed that potential pathogens produce a larger variety of lectins than commensals indicating increased glycan-binding potential. Both the number of predicted bacterial lectins, and their specificities for carbohydrates correlated with pathogenicity. This study provides new insights into potential mechanisms of commensal and pathogen colonisation of the reproductive tract that underpin health and disease states.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

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  • Clarification in the discussion

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 18, 2020.
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Proteome-wide prediction of bacterial carbohydrate-binding proteins as a tool for understanding commensal and pathogen colonisation of the vaginal microbiome
François Bonnardel, Stuart M. Haslam, Anne Dell, Ten Feizi, Yan Liu, Virginia Tajadura-Ortega, Yukie Akune, Lynne Sykes, Phillip R. Bennett, David A. MacIntyre, Frédérique Lisacek, Anne Imberty
bioRxiv 2020.09.10.291781; doi: https://doi.org/10.1101/2020.09.10.291781
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Proteome-wide prediction of bacterial carbohydrate-binding proteins as a tool for understanding commensal and pathogen colonisation of the vaginal microbiome
François Bonnardel, Stuart M. Haslam, Anne Dell, Ten Feizi, Yan Liu, Virginia Tajadura-Ortega, Yukie Akune, Lynne Sykes, Phillip R. Bennett, David A. MacIntyre, Frédérique Lisacek, Anne Imberty
bioRxiv 2020.09.10.291781; doi: https://doi.org/10.1101/2020.09.10.291781

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