Abstract
The identification in bacteria, of the set of genes and amino acid positions showing evidence for positive selection, can give insight, among others, on which genes and amino acid positions are responsible for modulating the host immune response. However, such analyses are time consuming, and the frequency of genes showing evidence for positively selected amino acid sites (PSS) can be low. Therefore, the quick identification of the set of genes that likely show PSS can lead to great savings in both computational and research time. Here, we present GenomeFastScreen, a Compi-based pipeline distributed as a Docker image, that automates the process of identifying genes that likely show PSS, starting from a set of FASTA files, one per genome, containing all coding sequences. GenomeFastScreen automatically removes problematic sequences such as those showing ambiguous positions and identifies orthologous gene sets. It is also possible to identify the orthologous genes in an external reference species, a requirement for comparisons across species, or to conduct gene ontology enrichment analyses when there is no data for the species being analysed. An example of what can be achieved when using the GenomeFastScreen pipeline is given for Mycobacterium leprae that causes leprosy. In this species, after detailed analyses, PSS were found at 31 genes, including nine genes likely relevant in the context of leprosy. The orthologs of those genes in M. tuberculosum (the species used as external reference) are Rv3632 (a protein membrane gene), Rv0177 (a mce1 gene), PPE68 (a cell envelope protein), RpfB (a resuscitation-promoting factor), RecG (that provides protection against mitomycin C), lipQ and lipU (lipases) and Rv3220c and tesB1 (esterases). Therefore, the study of these genes may reveal interesting hints on the modulation of the different M. leprae phenotypes.
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References
Yang, Z.: PAML: a program package for phylogenetic analysis by maximum likelihood. Bioinformatics 13, 555–556 (1997). https://doi.org/10.1093/bioinformatics/13.5.555
Murrell, B., Moola, S., Mabona, A., Weighill, T., Sheward, D., Kosakovsky Pond, S.L., Scheffler, K.: FUBAR: a Fast, Unconstrained Bayesian AppRoximation for Inferring Selection. Mol. Biol. Evol. 30, 1196–1205 (2013). https://doi.org/10.1093/molbev/mst030
Wilson, D.J., McVean, G.: Estimating diversifying selection and functional constraint in the presence of recombination. Genetics 172, 1411–1425 (2006). https://doi.org/10.1534/genetics.105.044917
López-Fernández, H., Duque, P., Vázquez, N., Fdez-Riverola, F., Reboiro-Jato, M., Vieira, C.P., Vieira, J.: Inferring positive selection in large viral datasets. In: Fdez-Riverola, F., Rocha, M., Mohamad, M.S., Zaki, N., Castellanos-Garzón, J.A. (eds.) 13th International Conference on Practical Applications of Computational Biology and Bioinformatics, pp. 61–69. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-23873-5_8
Osório, N.S., Rodrigues, F., Gagneux, S., Pedrosa, J., Pinto-Carbó, M., Castro, A.G., Young, D., Comas, I., Saraiva, M.: Evidence for diversifying selection in a set of Mycobacterium tuberculosis genes in response to antibiotic- and nonantibiotic-related pressure. Mol. Biol. Evol. 30, 1326–1336 (2013). https://doi.org/10.1093/molbev/mst038
Chavarro-Portillo, B., Soto, C.Y., Guerrero, M.I.: Mycobacterium leprae’s evolution and environmental adaptation. Acta Trop. 197, 105041 (2019). https://doi.org/10.1016/j.actatropica.2019.105041
Shen, W., Le, S., Li, Y., Hu, F.: SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLoS ONE 11, e0163962 (2016). https://doi.org/10.1371/journal.pone.0163962
López-Fernández, H., Duque, P., Henriques, S., Vázquez, N., Fdez-Riverola, F., Vieira, C.P., Reboiro-Jato, M., Vieira, J.: Bioinformatics protocols for quickly obtaining large-scale data sets for phylogenetic inferences. Interdiscip. Sci. Comput. Life Sci. 11, 1–9 (2019). https://doi.org/10.1007/s12539-018-0312-5
Reboiro-Jato, D., Reboiro-Jato, M., Fdez-Riverola, F., Vieira, C.P., Fonseca, N.A., Vieira, J.: ADOPS–Automatic Detection Of Positively Selected Sites. J Integr Bioinform. 9, 200 (2012). https://doi.org/10.2390/biecoll-jib-2012-200
Vázquez, N., Vieira, C.P., Amorim, B.S.R., Torres, A., López-Fernández, H., Fdez-Riverola, F., Sousa, J.L.R., Reboiro-Jato, M., Vieira, J.: Large scale analyses and visualization of adaptive amino acid changes projects. Interdiscip. Sci. Comput. Life Sci. 10, 24–32 (2018). https://doi.org/10.1007/s12539-018-0282-7
Edgar, R.C.: MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004). https://doi.org/10.1093/nar/gkh340
Ronquist, F., Teslenko, M., van der Mark, P., Ayres, D.L., Darling, A., Höhna, S., Larget, B., Liu, L., Suchard, M.A., Huelsenbeck, J.P.: MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012). https://doi.org/10.1093/sysbio/sys029
Mi, H., Huang, X., Muruganujan, A., Tang, H., Mills, C., Kang, D., Thomas, P.D.: PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements. Nucleic Acids Res. 45, D183–D189 (2017). https://doi.org/10.1093/nar/gkw1138
Casali, N., White, A.M., Riley, L.W.: Regulation of the Mycobacterium tuberculosis mce1 operon. J. Bacteriol. 188, 441–449 (2006). https://doi.org/10.1128/JB.188.2.441-449.2006
Shimono, N., Morici, L., Casali, N., Cantrell, S., Sidders, B., Ehrt, S., Riley, L.W.: Hypervirulent mutant of Mycobacterium tuberculosis resulting from disruption of the mce1 operon. Proc. Natl. Acad. Sci. 100, 15918–15923 (2003). https://doi.org/10.1073/pnas.2433882100
Demangel, C., Brodin, P., Cockle, P.J., Brosch, R., Majlessi, L., Leclerc, C., Cole, S.T.: Cell envelope protein PPE68 contributes to Mycobacterium tuberculosis RD1 immunogenicity independently of a 10-kilodalton culture filtrate protein and ESAT-6. Infect. Immun. 72, 2170–2176 (2004). https://doi.org/10.1128/IAI.72.4.2170-2176.2004
Squeglia, F., Romano, M., Ruggiero, A., Vitagliano, L., De Simone, A., Berisio, R.: Carbohydrate recognition by RpfB from Mycobacterium tuberculosis unveiled by crystallographic and molecular dynamics analyses. Biophys. J. 104, 2530–2539 (2013). https://doi.org/10.1016/j.bpj.2013.04.040
Thakur, R.S., Basavaraju, S., Somyajit, K., Jain, A., Subramanya, S., Muniyappa, K., Nagaraju, G.: Evidence for the role of Mycobacterium tuberculosis RecG helicase in DNA repair and recombination. FEBS J. 280, 1841–1860 (2013). https://doi.org/10.1111/febs.12208
Li, C., Li, Q., Zhang, Y., Gong, Z., Ren, S., Li, P., Xie, J.: Characterization and function of Mycobacterium tuberculosis H37Rv Lipase Rv1076 (LipU). Microbiol. Res. 196, 7–16 (2017). https://doi.org/10.1016/j.micres.2016.12.005
Acknowledgments
The SING group thanks the CITI (Centro de Investigación, Transferencia e Innovación) from the University of Vigo for hosting its IT infrastructure. This work was partially supported by the Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding ED431C2018/55-GRC Competitive Reference Group.
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López-Fernández, H., Vieira, C.P., Fdez-Riverola, F., Reboiro-Jato, M., Vieira, J. (2021). Inferences on Mycobacterium Leprae Host Immune Response Escape and Antibiotic Resistance Using Genomic Data and GenomeFastScreen. In: Panuccio, G., Rocha, M., Fdez-Riverola, F., Mohamad, M., Casado-Vara, R. (eds) Practical Applications of Computational Biology & Bioinformatics, 14th International Conference (PACBB 2020). PACBB 2020. Advances in Intelligent Systems and Computing, vol 1240. Springer, Cham. https://doi.org/10.1007/978-3-030-54568-0_5
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