Towards a better understanding of the bacterial pan-genome

Authors

  • Dawid Gmiter Jan Kochanowski University of Kielce, Faculty of Natural Science, Institute of Biology, Division of Microbiology and Parasitology, Uniwersytecka 7, 25-460 Kielce, Poland https://orcid.org/0000-0001-8663-129X
  • Sylwia Nawrot Jan Kochanowski University of Kielce, Faculty of Natural Science, Institute of Biology, Division of Microbiology and Parasitology, Uniwersytecka 7, 25-460 Kielce, Poland
  • Ilona Pacak Jan Kochanowski University of Kielce, Faculty of Natural Science, Institute of Biology, Division of Microbiology and Parasitology, Uniwersytecka 7, 25-460 Kielce, Poland
  • Katarzyna Zegadło Jan Kochanowski University of Kielce, Faculty of Natural Science, Institute of Biology, Division of Microbiology and Parasitology, Uniwersytecka 7, 25-460 Kielce, Poland
  • Wiesław Kaca Jan Kochanowski University of Kielce, Faculty of Natural Science, Institute of Biology, Division of Microbiology and Parasitology, Uniwersytecka 7, 25-460 Kielce, Poland https://orcid.org/0000-0002-8734-7191

DOI:

https://doi.org/10.18778/1730-2366.16.19

Keywords:

pan-genome, bacterial pan-genome, genome comparison, Roary workflow

Abstract

The bacterial pan-genome is a relatively new concept that refers to the number of genes observed in a given set of bacterial genome sequences, either at the intra- or inter-species level. Determining the pan-genome of a given species of bacteria using a large number of strains allows one to compare multiple genes and to determine evolutionary links between isolates. This information can help to determine population structure, diversity in terms of prevalence in a given environment and pathogenicity of microorganisms. Within this review, we explain the most important issues related to pan-genome studies. We also include a brief description of some selected bacterial pan-genomes. Finally, we propose an easy-toperform workflow to study bacterial pan-genomes that will facilitate nonexperts in a pan-genome-based investigation.

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References

Abudahab, K., Prada, J.M., Yang, Z., Bentley, S.D., Croucher, N.J., Corander, J., Aanensen, D.M. 2019. PANINI: pangenome neighbour identification for bacterial populations. Microbial Genomics, 5(4): e000220.
Google Scholar DOI: https://doi.org/10.1099/mgen.0.000220

Argemi, X., Matelska, D., Ginalski, K., Riegel, P., Hansmann, Y., Bloom, J., Pestel-Caron, M., Dahyot, S., Lebeurre, J., Prévost, G. 2018. Comparative genomic analysis of Staphylococcus lugdunensis shows a closed pan-genome and multiple barriers to horizontal gene transfer. BMC Genomics, 19(1): 1–16.
Google Scholar DOI: https://doi.org/10.1186/s12864-018-4978-1

Bazinet, A.L. 2017. Pan-genome and phylogeny of Bacillus cereus sensu lato. BMC Evolutionary Biology, 17(1): 1–16.
Google Scholar DOI: https://doi.org/10.1186/s12862-017-1020-1

Brynildsrud, O., Bohlin, J., Scheffer, L., Eldholm, V. 2016. Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary. Genome Biology, 17(1): 1–9.
Google Scholar DOI: https://doi.org/10.1186/s13059-016-1108-8

Caierão, J., Paiva, J.A.C.D., Sampaio, J.L.M., da Silva, M.G., Santos, D.R. de S., Coelho, F.S., Fonseca, L. de S., Duarte, R.S., Armstrong, D.T., Regua-Mangia, A.H. 2016. Multilocus enzyme electrophoresis analysis of rapidly-growing mycobacteria: An alternative tool for identification and typing. International Journal of Infectious Diseases, 42: 11–16.
Google Scholar DOI: https://doi.org/10.1016/j.ijid.2015.11.010

Costa, S.S., Guimarães, L.C., Silva, A., Soares, S.C., Baraúna, R.A. 2020. First steps in the analysis of prokaryotic pan-genomes. Bioinformatics and Biology Insights, 14: 1–9.
Google Scholar DOI: https://doi.org/10.1177/1177932220938064

Decano, A.G., Downing, T. 2019. An Escherichia coli ST131 pangenome atlas reveals population structure and evolution across 4,071 isolates. Scientific Reports, 9(1): 1–13.
Google Scholar DOI: https://doi.org/10.1038/s41598-019-54004-5

Espadinha, D., Sobral, R.G., Mendes, C.I., Méric, G., Sheppard, S.K., Carriço, J.A., Lencastre, H. de, Miragaia, M. 2019. Distinct phenotypic and genomic signatures underlie contrasting pathogenic potential of Staphylococcus epidermidis clonal lineages. Frontiers in Microbiology, 10: 1971.
Google Scholar DOI: https://doi.org/10.3389/fmicb.2019.01971

Freschi, L., Vincent, A.T., Jeukens, J., Emond-Rheault, J.G., Kukavica-Ibrulj, I., Dupont, M.J., Charette, S.J., Boyle, B., Levesque, R.C. 2019. The Pseudomonas aeruginosa Pan-genome provides new insights on its population structure, horizontal gene transfer, and pathogenicity. Genome Biology and Evolution, 11(1): 109–120.
Google Scholar DOI: https://doi.org/10.1093/gbe/evy259

Gordienko, E.N., Kazanov, M.D., Gelfand, M.S. 2013. Evolution of pan-genomes of Escherichia coli, Shigella spp., and Salmonella enterica. Journal of Bacteriology, 195(12): 2786–2792.
Google Scholar DOI: https://doi.org/10.1128/JB.02285-12

Grüning, B., Dale, R., Sjödin, A., Chapman, B.A., Rowe, J., Tomkins-Tinch, C.H., Köster, J., The Bioconda Team. 2018. Bioconda : sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15: 475–476.
Google Scholar DOI: https://doi.org/10.1038/s41592-018-0046-7

Guimarães, L.C., Benevides De Jesus, L., Vinícius, M., Viana, C., Silva, A., Thiago, R., Ramos, J., De, S., Soares, C., Azevedo, V. 2015. Inside the pan-genome – methods and software overview. Current Genomics, 16: 245–252.
Google Scholar DOI: https://doi.org/10.2174/1389202916666150423002311

Guindon, S., Dufayard, J., Lefort, V., Anisimova, M., Hordijk, W., Gascuel, O. 2010. New algorithms and methods to estimate maximum-likelihood phylogenies assessing the performance of PhyML 3.0. Systematic Biology, 59(3): 307–321.
Google Scholar DOI: https://doi.org/10.1093/sysbio/syq010

Guo, Y., Song, G., Sun, M., Wang, J., Wang, Y. 2020. Prevalence and therapies of antibiotic-resistance in Staphylococcus aureus. Frontiers in Cellular and Infection Microbiology, 10: 107.
Google Scholar DOI: https://doi.org/10.3389/fcimb.2020.00107

Hadfield, J., Croucher, N.J., Goater, R.J., Abudahab, K., Aanensen, D.M., Harris, S.R. 2018. Phandango: an interactive viewer for bacterial population genomics. Bioinformatics, 34(2): 292–293.
Google Scholar DOI: https://doi.org/10.1093/bioinformatics/btx610

Jamrozy, D.M., Harris, S.R., Mohamed, N., Peacock, S.J., Tan, C.Y., Parkhill, J., Anderson, A.S., Holden, M.T.G. 2016. Pangenomic perspective on the evolution of the Staphylococcus aureus USA300 epidemic. Microbial Genomics, 2(5): e000058.
Google Scholar DOI: https://doi.org/10.1099/mgen.0.000058

Jin, Y., Zhou, J., Zhou, J., Hu, M., Zhang, Q., Kong, N., Ren, H., Liang, L., Yue, J. 2020. Genome-based classification of Burkholderia cepacia complex provides new insight into its taxonomic status. Biology Direct, 15(1): 1–14.
Google Scholar DOI: https://doi.org/10.1186/s13062-020-0258-5

John, J., George, S., Nori, S.R.C., Nelson-Sathi, S., Pisani, D. 2019. Phylogenomic analysis reveals the evolutionary route of resistant genes in Staphylococcus aureus. Genome Biology and Evolution, 11(10): 2917–2926.
Google Scholar DOI: https://doi.org/10.1093/gbe/evz213

Lee, A.H.Y., Flibotte, S., Sinha, S., Paiero, A., Ehrlich, R.L., Balashov, S., Ehrlich, G.D., Zlosnik, J.E.A., Mell, J.C., Nislow, C. 2017. Phenotypic diversity and genotypic flexibility of Burkholderia cenocepacia during long-term chronic infection of cystic fibrosis lungs. Genome Research, 27(4): 650–662.
Google Scholar DOI: https://doi.org/10.1101/gr.213363.116

Lloyd, J.P.B. 2018. Ubuntu on Windows for computational biology. protocols.Io. Available from: https://www.protocols.io/view/ubuntu-on-windows-for-computational-biology-sfuebnw (accessed 28.06.2021).
Google Scholar

Mahenthiralingam, E., Baldwin, A., Dowson, C.G. 2008. Burkholderia cepacia complex bacteria: Opportunistic pathogens with important natural biology. Journal of Applied Microbiology, 104(6): 1539–1551.
Google Scholar DOI: https://doi.org/10.1111/j.1365-2672.2007.03706.x

Méric, G., Miragaia, M., De Been, M., Yahara, K., Pascoe, B., Mageiros, L., Mikhail, J., Harris, L. G., Wilkinson, T.S., Rolo, J., Lamble, S., Bray, J.E., Jolley, K.A., Hanage, W.P., Bowden, R., Maiden, M.C.J., Mack, D., De Lencastre, H., Feil, E.J., Corander J., Sheppard, S.K. 2015. Ecological overlap and horizontal gene transfer in Staphylococcus aureus and Staphylococcus epidermidis. Genome Biology and Evolution, 7(5): 1313–1328.
Google Scholar DOI: https://doi.org/10.1093/gbe/evv066

Mira, A., Martín-Cuadrado, A.B., D’Auria, G., Rodríguez-Valera, F. 2010. The bacterial pangenome: A new paradigm in microbiology. International Microbiology, 13(2): 45–57.
Google Scholar

Möller, S., Krabbenhöft, H.N., Tille, A., Paleino, D., Williams, A., Wolstencroft, K., Goble, C., Holland, R., Belhachemi, D., Plessy, C. 2010. Community-driven computational biology with Debian Linux. BMC Bioinformatics, 11(SUPPL. 12): S5.
Google Scholar DOI: https://doi.org/10.1186/1471-2105-11-S12-S5

Mosquera-Rendón, J., Rada-Bravo, A.M., Cárdenas-Brito, S., Corredor, M., Restrepo-Pineda, E., Benítez-Páez, A. 2016. Pangenome-wide and molecular evolution analyses of the Pseudomonas aeruginosa species. BMC Genomics, 17(1): 1–15.
Google Scholar DOI: https://doi.org/10.1186/s12864-016-2364-4

Page, A.J., Cummins, C.A., Hunt, M., Wong, V.K., Reuter, S., Holden, M.T.G., Fookes, M., Falush, D., Keane, J.A., Parkhill, J. 2015. Roary: rapid large-scale prokaryote pangenome analysis. Bioinformatics, 31(22): 3691–3693.
Google Scholar DOI: https://doi.org/10.1093/bioinformatics/btv421

Rambaut A. 2013. FigTree. Available from: http://tree.bio.ed.ac.uk/software/figtree/ (accessed 28.06.2021).
Google Scholar

Rouli, L., Merhej, V., Fournier, P.E., Raoult, D. 2015. The bacterial pangenome as a new tool for analysing pathogenic bacteria. New Microbes and New Infections, 7: 72–85.
Google Scholar DOI: https://doi.org/10.1016/j.nmni.2015.06.005

Seemann, T. 2014. Prokka: Rapid prokaryotic genome annotation. Bioinformatics, 30(14): 2068–2069.
Google Scholar DOI: https://doi.org/10.1093/bioinformatics/btu153

Sitto, F., Battistuzzi, F.U. 2020. Estimating pangenomes with Roary. Molecular Biology and Evolution, 37(3): 933–939.
Google Scholar DOI: https://doi.org/10.1093/molbev/msz284

Thorpe, H.A., Bayliss, S.C., Sheppard, S.K., Feil, E.J. 2018. Piggy: A rapid, large-scale pangenome analysis tool for intergenic regions in bacteria. GigaScience, 7(4): 1–11.
Google Scholar DOI: https://doi.org/10.1093/gigascience/giy015

Touchon, M., Perrin, A., De Sousa, J.A.M., Vangchhia, B., Burn, S., O’Brien, C.L., Denamur, E., Gordon, D., Rocha, E.P.C. 2020. Phylogenetic background and habitat drive the genetic diversification of Escherichia coli. PLoS Genetics, 16(6): e1008866.
Google Scholar DOI: https://doi.org/10.1371/journal.pgen.1008866

Whelan, F.J., Rusilowicz, M., McInerney, J.O. 2020. Coinfinder: Detecting significant associations and dissociations in pangenomes. Microbial Genomics, 6(3): 1–7.
Google Scholar DOI: https://doi.org/10.1099/mgen.0.000338

Zhou, J., Ren, H., Hu, M., Zhou, J., Li, B., Kong, N., Zhang, Q., Jin, Y., Liang, L., Yue, J. 2020. Characterization of Burkholderia cepacia complex core genome and the underlying recombination and positive selection. Frontiers in Genetics, 11: 1–15.
Google Scholar DOI: https://doi.org/10.3389/fgene.2020.00506

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Published

2021-09-29

How to Cite

Gmiter, D., Nawrot, S., Pacak, I., Zegadło, K., & Kaca, W. (2021). Towards a better understanding of the bacterial pan-genome. Acta Universitatis Lodziensis. Folia Biologica Et Oecologica, 17, 84–96. https://doi.org/10.18778/1730-2366.16.19