Extensive Screening of Genomic and Metagenomic Data Identifies Novel Components of the Macrolide Resistome
| dc.contributor.author | Lund, David | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för matematiska vetenskaper | sv |
| dc.contributor.examiner | Kristiansson, Erik | |
| dc.contributor.supervisor | Kristiansson, Erik | |
| dc.date.accessioned | 2020-06-11T10:29:15Z | |
| dc.date.available | 2020-06-11T10:29:15Z | |
| dc.date.issued | 2020 | sv |
| dc.date.submitted | 2019 | |
| dc.description.abstract | Antibiotic resistance is growing among pathogenic bacteria all across the world, and has been called one of the most serious threats that humanity is facing. Typically, bacteria are able to develop resistance as a result of acquiring specific antibiotic resistance genes from other bacteria though so called horizontal gene transfer. One commonly used class of antibiotics for which resistance is spreading rapidly is macrolides. While a lot of research has been devoted to studying the genes that confer resistance to these antibiotics, the evolution of these macrolide resistance genes has not been determined. It has been suggested that resistance determinants that eventually find their way into the clinical environment originate from external environments, however the mechanisms behind this flow of resistance is not known. To prevent resistance to macrolide antibiotics from spreading further, it is therefore important to characterize how the resistance genes have evolved. Furthermore, knowledge about which genes are present in what environments will help with anticipating which genes might mobilize into the clinical environment in the future, and facilitate preemptive measures being taken. This project aims to use a bioinformatic approach to characterize novel macrolide resistance genes, applying a computational method called fARGene. To achieve this, profile hidden Markov models have been developed that are able to identify two types of genes that confer resistance to macrolides, mediated by enzymes called Erm 23S rRNA methyltransferases and Mph macrolide phosphotransferases respectively, from biological sequencing data. The models have been used to analyze data representing over 400,000 bacterial genomes, and over 14 terabases of metagenomic data. Hundreds of gene families have been identified from the bacterial genomes, most of which are previously uncharacterized, and these have been analyzed based on their phylogenetic relationships. The results revealed a large variety of uncharacterized macrolide resistance genes that seem to have evolved primarily in bacteria from the phyla Firmicutes and Actinobacteria. In addition, several uncharacterized resistance genes that have potentially been mobilized have been identified from the results. No singular origin was determined for either of the analyzed gene classes, however the previously hypothesized evolutionary relationship between Erm methyltransferases and the housekeeping methyltransferase KsgA is supported by the results. In addition, the results from the analysis of metagenomic data indicate that the studied macrolide resistance genes are likely to mobilize from the human gut, naturally presenting a way through which the genes may enter the clinical environment. | sv |
| dc.identifier.coursecode | MVEX60 | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/300829 | |
| dc.language.iso | eng | sv |
| dc.setspec.uppsok | PhysicsChemistryMaths | |
| dc.subject | Antibiotic resistance, Macrolides, Bioinformatics, fARGene, Metagenomics, Evolution | sv |
| dc.title | Extensive Screening of Genomic and Metagenomic Data Identifies Novel Components of the Macrolide Resistome | sv |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.uppsok | H | |
| local.programme | Biotechnology (MPBIO), MSc |