Estabelecimento de um pipeline para identificação de Mycobacterium leprae em microbiomas através do gene rRNA 16S

• Ano de defesa: 2017
• Autor(a) principal: Felipe Borim Corrêa
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; UFMG
• Palavras-chave em Português: Metagenomics; Leprosy; Bioinformatics; Bioinformática
• Link de acesso: http://hdl.handle.net/1843/BUOS-ARRG4K

Mycobacterium leprae is a pathogenic bacteria and the etiologic agent of leprosy, disease which affects mainly underdeveloped countries. The study of this microorganism is not trivial because it does not grow in traditional culture media; however, culture-independent approaches based on rRNA 16S gene can be used to study microbial communities. It is known that there are biases related to the amplification and taxonomic classification in these approaches; thus, the objective of this work was to define a pipeline which allows the study of M. leprae in microbiomes. The methods were divided into four parts. First was made a pre-selection of primers and its respective simulated amplicons of M. leprae that allowed the taxonomic classification at species level of this organism. The second analysis evaluated the amplification capacity of the selected primers. The third step was the evaluation of the sensitivity of the taxonomic classification of mock communities and, lastly, a logistic regression model was used to identify the most informative hypervariable regions of the 16S rRNA for M. leprae. Only amplicons of primers that covered the regions V1-V2, V2-V3 and V6 of M. leprae could be classified at species level. Though, primers which flank V6 regions generated amplicons that showed higher sensitivity in the classification of M. leprae from the mock communities and allowed the classification of more taxa than the others. Logistic regression corroborated with the results, showing that regions V1, V2 and V6 are the most informative in this organism 16S rRNA. Based on the results, were possible to reach in some recommendations for the classification of M. leprae in microbiomes, being suggested the use of primers for V6 region, Silva reference database and taxonomic classification using 97% similarity OTUs.