Autor(es): Pelerito, Ana ; Nunes, Alexandra ; Grilo, Teresa ; Isidro, Joana ; Silva, Catarina ; Ferreira, Ana Cristina ; Valdezate, Sylvia ; Núncio, Maria Sofia ; Georgi, Enrico ; Gomes, João Paulo
Data: 2021
Identificador Persistente: http://hdl.handle.net/10362/129671
Origem: Repositório Institucional da UNL
Assunto(s): Brucellaspp; genotyping; MLVA; Python script; whole-genome sequencing; zoonosis; Microbiology; Microbiology (medical)
Funding Information: Funding: This work is a result of the GenomePT project (POCI-01- 0145-FEDER-022184), supported by the COMPETE 2020 – Operational Program for Competitiveness and Internationalisation (POCI), Lisboa Portugal Regional Operational Program (Lisboa2020), Algarve Portugal Regional Operational Program (CRESC Algarve2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by the Fundação para a Ciência e a Tecnologia (FCT). The studies have arisen from the Project QUANDHIP (Chafea Grant Agreement no. 2010 21 02), which has been funded by the European Commission in the framework of the Health Program.
Brucellosis is an important zoonosis that is emerging in some regions of the world, gaining increased relevance with the inclusion of the causing agent Brucella spp. in the class B bioterrorism group. Until now, multi-locus VNTR Analysis (MLVA) based on 16 loci has been considered as the gold standard for Brucella typing. However, this methodology is laborious, and, with the rampant release of Brucella genomes, the transition from the traditional MLVA to whole genome sequencing (WGS)-based typing is on course. Nevertheless, in order to avoid a disruptive transition with the loss of massive genetic data obtained throughout the last decade and considering that the transition timings will vary considerably among different countries, it is important to determine WGS-based MLVA alleles of the nowadays sequenced genomes. On this regard, we aimed to evaluate the performance of a Python script that had been previously developed for the rapid in silico extraction of the MLVA alleles, by comparing it to the PCR-based MLVA procedure over 83 strains from different Brucella species. The WGS-based MLVA approach detected 95.3% of all possible 1,328 hits (83 strains×16 loci) and showed an agreement rate with the PCR-based MLVA procedure of 96.4% for MLVA-16. According to our dataset, we suggest the use of a minimal depth of coverage of ~50x and a maximum number of ~200 contigs as guiding “boundaries” for the future application of the script. In conclusion, the evaluated script seems to be a very useful and robust tool for the in silico determination of MLVA profiles of Brucella strains, allowing retrospective and prospective molecular epidemiological studies, which are important for maintaining an active epidemiological surveillance of brucellosis.
