DSBA

The use of bioinformatics tools in identifying molecular profiles of bacteria enables a precise and efficient approach to disease diagnosis. Furthermore, it fosters a deeper understanding of bacterial genetic diversity and facilitates well-informed clinical decision-making. In the field of animal health, researchers focus on studying bacteria of the genus Brucella, which cause a disease known as brucellosis. This disease, also called Malta fever or undulant fever, affects a wide range of mammals, exhibiting zoonotic and cosmopolitan characteristics and posing a significant risk to public health with substantial economic losses. Brucellosis can cause various symptoms, ranging from cold-like signs to complications in the nervous system, musculoskeletal system, and heart. In canines (affected by B. canis), nonspecific signs are observed, like those in humans, but reproductive failures and joint issues related to this bacterium are commonly diagnosed. Due to the diversity of clinical signs, diagnosing brucellosis in humans and animals presents a significant challenge, with underdiagnosis contributing to the spread of infection. Despite this, few genomic studies with different strains of B. canis have been developed so far. In this regard, there is a demand for more information, such as virulence factors, antimicrobial resistance genes, and the evolutionary profile of the pathogen, which can greatly contribute to decision-making in government responses to public health, as well as in storing and comparing data about this agent.

In the experimental front of this project, team members recently sequenced 20 B. canis genomes using two sequencing technologies (for obtaining short reads and long reads), which will contribute to the data used in solving this biological problem, along with 60 public genomes of B. canis and 160 public genomes of B. suis. This data will be analyzed by the computational tools developed in this proposal to identify species-specific genetic variations to serve as diagnostic markers for brucellosis. Interpretable machine learning algorithms will be employed to create a genotypic profile of virulent strains and differentiate them between species based on their phenotypic differences and antimicrobial susceptibility profiles.