Consolidation of a genomic epidemiological surveillance network for tuberculosis (REVIGET) in northern and northeastern Brazil: a study protocol.

Globally, tuberculosis (TB) remains a top cause of death from infectious diseases, with an estimated 1.5 million deaths annually. Given its substantial social and economic burden, TB is a priority in the United Nations 2030 Agenda for Sustainable Development. The WHO's End TB Strategy emphasizes research, innovation, and the rapid implementation of new technologies, such as whole-genome sequencing (WGS), which are pivotal for precision health approaches and drug-resistant TB (DR-TB) surveillance. This study aims to strengthen genomic TB surveillance in the North and Northeast Brazilian regions by applying WGS to study DR-TB cases, training professionals in genomics and bioinformatics, and deploying a national surveillance platform (GEMIBRA). This is an observational, cross-sectional, prospective, quantitative, and qualitative study to be conducted in the states of Pará, Amazonas, Ceará, and Bahia. A total of 500 Mycobacterium tuberculosis complex (MTBC) isolates from DR-TB cases will undergo whole-genome sequencing (WGS), representing 87% of the expected DR-TB cases, including non-tuberculous mycobacteria (NTM) isolates among the DR samples. Data will be analyzed for genotype-phenotype correlations, mutation patterns, and associations with clinical and epidemiological characteristics. Capacity-building activities, including theoretical and hands-on bioinformatics training, will be carried out. The GEMIBRA platform will support data visualization, spatial distribution, and clinical decision-making. The project will generate evidence to validate the contribution of WGS integration in Brazil's TB surveillance system, support precision treatment approaches, and inform public health interventions. It will also contribute to workforce development and the introduction of decentralized WGS-based diagnostics in resource-limited regions. The project adopts a translational research model and a networked, decentralized approach, facilitating the prompt integration of the knowledge generated into public health practice. Ultimately, this work will contribute to combating TB transmission by identifying transmission dynamics, emerging resistant strains, and informing the National Plan to End TB as a public health problem.