Comparação entre os programas MARVEL, VirFinder e VirSorter quanto a identificação de bacteriófagos a partir de dados metagenômicos

TCC(graduação) - Universidade Federal de Santa Catarina. Centro de Ciências Biológicas. Biologia.Os bacteriófagos, comumente chamados de fagos, são vírus intracelulares obrigatórios capazes de infectar arqueas e bactérias. A interação entra bacteriófago e bactérias podem levar a morte bacteriana e com isso impactar na comunidade microbiana interferindo nos ciclos ambientais e nos setores industriais nos quais estes microrganismos então envolvidos. Esse trabalho teve como objetivo analisar a variação entre os resultados de predição de bacteriófagos ambientais utilizando diferentes programas de predição viral. Para tal, utilizamos sequências de 927 genomas completos, obtidos do NCBI/GenBank, os quais foram utilizados para uma simulação de montagem de sequenciamento metagenômico através do programa ART, compondo um grupo controle. Para testes em amostras reais, utilizamos dados brutos de amostras metagenomas ambientais de Ganzi (China), Mar Mediterrâneo (Europa) e Santa Mônica (EUA) depositados no do NCBI/SRA. Os dados brutos passaram por uma etapa de controle de qualidade e montagem metagenômica pelos programas Trimmomatic e metaSPAdes, respectivamente. Predições de bacteriófagos para as montagens foram realizadas pelos programas VirFinder, VirSorter e MARVEL. Dos 927 bacteriófagos utilizados na montagem da amostra controle, o VirFinder encontrou 633 fagos (68%) em menos de uma hora de processamento, o ViSorter identificou 530 fagos (57%) em cerca de dez horas de análise e o MARVEL retornou 432 fagos (47%) em nove horas de processamento. Para as amostras ambientais, os programas foram capazes de identificar bacteriófagos apenas na amostra do Mar Mediterrâneo. O VirFinder identificou oito fagos, sendo sete destes da família Siphoviridae classificados como bacteriófagos não cultivados do Mar Mediterrâneo e um referente à família Microviridae. O VirSorter identificou apenas um bacteriófago da família Microviridae, enquanto o MARVEL identificou sete bacteriófagos, todos da família Siphoviridae classificados como não cultivados do Mediterrâneo. Os programas VirFinder e MARVEL identificaram quatro bacteriófagos exclusivos, não identificados por nenhum outro programa. Considerando o tempo de desempenho e a diversidade de fagos identificados, o programa VirFinder obteve o melhor resultado para as amostras analisadas. Nota-se que apenas a amostra do Mar Mediterrâneo faz parte de um estudo sobre o viroma, enquanto as demais são amostras de metagenoma global, o que demonstra que as etapas anteriores ao sequenciamento como construção de biblioteca e preparação de amostras, são essenciais para uma melhor identificação de bacteriófagos em amostras ambientais. Apesar de o VirFinder ter apresentado melhor desempenho, não podemos descartar o uso dos demais programas, em razão da identificação de fagos exclusivos por estes programas, sendo assim os programas estes podem ser utilizados de forma complementar para uma melhor interpretação da diversidade da amostra, além disso o preparo da amostra se demonstrou fundamental para capacidade de identificação de bacteriófagos ambientais.Bacteriophages, commonly known as phages, are mandatory intracellular viruses able to infect archaea and bacteria. Phage-host interaction can impact different areas, such as pharmaceutical sectors, the food industry and biogeochemical cycles. This study aimed to analyse viral prediction results from three different softwares and their accuracy for environmental bacteriophage prediction. For the control sample, a total of 927 complete bacteriophage genomes obtained from NCBI/GenBank were concatenated and used to simulate raw data through the programa ART. Raw data for environmental samples were obtained from NCBI/SRA and corresponded to samples from Ganzi (China), Mediterranean Sea and Santa Monica (USA). Raw data were preprocessed and assembled with Trimmomatic and metaSPAdes, respectively. Bacteriophage prediction was performed by VirFinder, VirSorter and MARVEL. VirFinder identified 633 phages (68%) in less than one hour of processing, VirSorter identified 530 (57%) in around ten hours of analyse and MARVEL identified 432 (47%) in nine hours of processing from the 927 bacteriophages used in the control sample. The three prediction programas were able to identify bacteriophages only in the Mediterranean Sea sample. VirFinder identified eight phages, seven of which belonged to the Siphoviridae family and were classified as Uncultured Mediterranean Sea phages, and one of the Microviridae family. VirSorter identified only one bacteriophage of the Microviridae family. MARVEL identified seven bacteriophages, all from the Siphoviridae family and classified as Uncultured Mediterranean Sea phages. Both VirFinder and MARVEL identified four exclusive bacteriophages, which were not found by VirSorter. VirFinder presented the best performance regarding processing time and organism identification in all analysed samples. The sample of Mediterranean Sea is part of a virome study, while the others samples are from global metagenome, this shows that steps pre processing are essential for a more accurate identification. Although VirFinder had shown best performance, we cannot discard the use of other softwares, because there are exclusive phage identification with this programas, therefore the softwares can be used in complementary ways for better interpretations related to sample diversity

New antibiotic resistance genes and their diversity

Antibiotic resistance is increasing worldwide and is considered a severe threat to public health. Often, antibiotic resistance is caused by antibiotic resistance genes, of which many are hypothesized to have been transferred into human pathogens from environmental bacteria. It is, therefore, of great importance to explore bacterial communities to identify new antibiotic resistance genes before they reach clinical settings. The six papers presented in this thesis aim to identify new antibiotic resistance genes in large genomic and metagenomic datasets and to place them in an evolutionary context. In Paper I, a new method for the identification and reconstruction of new antibiotic resistance genes directly from fragmented metagenomic data was developed and was shown to outperform other methods significantly. In Papers II and III, novel genes of the clinically important class metallo-β-lactamases were identified. By analyzing metagenomes and bacterial genomes, 96 novel putative metallo-β-lactamase genes were predicted. In Paper IV, the diversity and phylogeny of the metallo-β-lactamases were further investigated. The results showed that the genes mainly clustered based on the taxonomy of the host species and that many of the mobile metallo-β-lactamases potentially were mobilized from species of the phylum Proteobacteria. In Paper V, the aim was to identify new genes providing resistance to the antibiotic class tetracyclines. A total of 195 gene families were predicted, of which 164 were new putative tetracycline resistance genes. Finally, in Paper VI, we searched for and predicted 20 novel putative quinolone resistance (qnr) genes from a large amount of metagenomic data. Throughout the thesis, a total of 54 novel genes have been functionally verified in Escherichia coli, of which 37 expressed the predicted phenotype. The results of this thesis provide deeper insights into the diversity and evolutionary history of three major classes of antibiotic resistance genes. It also provides new methodologies for efficient and reliable identification of new resistance genes in genomic and metagenomic data