A networks-based approach to spatial-genomic associations of Brucella spp. in southern Kazakhstan
Topics:
Keywords: Brucellosis, Brucella bacteria, genotyping, disease ecology, network modeling, medical geography
Abstract Type: Poster Abstract
Authors:
Lylybell Y. Zhou, University of Florida
Sheldon G. Waugh, University of Florida
Igor Sytnik, National Reference Veterinary Center
Talgat Karibayev, National Reference Veterinary Center
Alim Aikimbayev, Research Institute for Biological Special Problems
Mukhit Ornybayev, Scientific Practical Center for Sanitary Epidemiological Expertise and Monitoring
Nurgisa Rametov, Scientific Practical Center for Sanitary Epidemiological Expertise and Monitoring
Sue Hagius, Louisiana State University
Philip Elzer, Louisiana State University
Ted L. Hadfield, University of Florida
Abstract
Brucellosis is one of the most prevalent bacterial zoonoses worldwide. Kazakhstan, located in central Asia, is considered to have among the world’s highest incidence rates of brucellosis, posing a serious economic and public health concern to the country. We build upon previous work describing the correlation between genetic similarity and geographic distances between Brucella spp. isolates by implementing a networks-based approach to study spatial-genomic associations of human and animal isolates of Brucella spp. in southern Kazakhstan. Using a database of 487 B. melitensis isolates collected from animals (domestic livestock) and humans during two two-year survey periods 2007-2008 and 2012-2013, separated by five years. Sampling was conducted independently between human health surveillance and animal surveillance and patterns analyzed separating by groups and sampling phases. Here we constructed individual networks with nodes representing sample collection sites (villages where humans or animals were tested) with connections between sites reflecting a 15-marker based multi-locus variable number tandem repeat analysis (MLVA-15) defined genotype being shared between sites. Each of the 487 samples was assigned a genotype. We calculated average degree to describe the change in genetic diversity over time and applied the Louvain algorithm to explore the community structure among networks. Our network analysis showed an increase in shared genotypes and spatial distribution of isolates over time, as well as identifying patterns of highly localized transmission of genotypes isolated only from humans. The results from our work demonstrate the potential utility of network analysis in studying spatial-genomic patterns of pathogens such as Brucella spp.
A networks-based approach to spatial-genomic associations of Brucella spp. in southern Kazakhstan
Category
Poster Abstract
