Community genomics of the Chagas Disease vector, Triatoma dimidiata: uncovering genetic variation and gut microbial fauna of a deadly kissing bug
Conference: Entomological Society of America Annual Meeting 2014, , , 2014
Abstract: Chagas Disease is a human illness endemic to Latin America and caused by the parasite Trypanosoma cruzi. Multiple insects from the Triatominae sub-family transmit the disease, including Triatoma dimidiata, the predominant vector in Central America. We used Restriction-site Associated DNA Sequencing (RAD-Seq) to analyze the DNA from thirty-two T. dimidiata samples collected across Central America. DNA was obtained from the lower abdomen of the insects to uncover genetic variation of the vector and the parasite, identify vertebrate blood meals, and assess the microbial diversity present in the insects’ hindgut. Raw sequence reads were mapped to the vector and the T. cruzi genome to obtain single nucleotide polymorphisms (SNPs). We identified 5631 SNPs from the vector, with significant spatial clustering at the regional scale as well as more locally among departments and villages within Guatemala. A total of 719 T. cruzi SNPS were found in the six specimens known to be infected with the parasite. Population genetic structure of the parasite resembled closely that found from the vector. BLAST searches identified human, rat and chicken as the most frequent blood meal sources. An estimate of 18 percent of reads not attributed to either the vector or parasite corresponded to bacterial fauna, while more than 50 percent were viral, predominantly Invertebrate iridescent virus type 6 (IIV-6). The use of RAD-seq allowed us to simultaneously retrieve high-resolution information at different geographical scales and across multiple taxa. This tool will enable future assessment of host-parasite genetics and detailed characterization of gut composition.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).