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Inferences onMycobacterium Leprae Host Immune Response Escape and Antibiotic Resistance Using Genomic Data and GenomeFastScreen

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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 forMycobacterium 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 inM. tuberculosum (the species used as external reference) areRv3632 (a protein membrane gene),Rv0177 (amce1 gene),PPE68 (a cell envelope protein),RpfB (a resuscitation-promoting factor),RecG (that provides protection against mitomycin C),lipQ andlipU (lipases) andRv3220c andtesB1 (esterases). Therefore, the study of these genes may reveal interesting hints on the modulation of the differentM. leprae phenotypes.

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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.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Vigo, ESEI, Campus As Lagoas, 32004, Ourense, Spain

    Hugo López-Fernández, Florentino Fdez-Riverola & Miguel Reboiro-Jato

  2. The Biomedical Research Centre (CINBIO), Campus Universitario Lagoas-Marcosende, 36310, Vigo, Spain

    Hugo López-Fernández, Florentino Fdez-Riverola & Miguel Reboiro-Jato

  3. SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain

    Hugo López-Fernández, Florentino Fdez-Riverola & Miguel Reboiro-Jato

  4. Instituto de Investigação e Inovação em Saúde (I3S), Universidade do Porto, Rua Alfredo Allen, 208, 4200-135, Porto, Portugal

    Cristina P. Vieira & Jorge Vieira

  5. Instituto de Biologia Molecular e Celular (IBMC), Rua Alfredo Allen, 208, 4200-135, Porto, Portugal

    Cristina P. Vieira & Jorge Vieira

Authors
  1. Hugo López-Fernández

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  2. Cristina P. Vieira

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  3. Florentino Fdez-Riverola

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  4. Miguel Reboiro-Jato

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  5. Jorge Vieira

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Corresponding author

Correspondence toJorge Vieira.

Editor information

Editors and Affiliations

  1. Enhanced Regenerative Medicine, Istituto Italiano di Tecnologia, Genoa, Genova, Italy

    Gabriella Panuccio

  2. Department de Informática, Universidade do Minho, Braga, Portugal

    Miguel Rocha

  3. Computer Science Department, University of Vigo, Vigo, Spain

    Florentino Fdez-Riverola

  4. Institute for Artificial Intelligence and Big Data (AIBIG), Universiti Malaysia Kelantan, Kampus Kota, Kota Bharu, Malaysia

    Mohd Saberi Mohamad

  5. Biotechnology, Intelligent Systems and Educational Technology (BISITE) Research Group, University of Salamanca, Salamanca, Salamanca, Spain

    Roberto Casado-Vara

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López-Fernández, H., Vieira, C.P., Fdez-Riverola, F., Reboiro-Jato, M., Vieira, J. (2021). Inferences onMycobacterium 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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