Location: Veterinary Pest Genetics Research Unit
Project Number: 3094-10400-004-000-D
Project Type: In-House Appropriated
Start Date: May 8, 2025
End Date: May 7, 2030
Objective:
Objective 1: Integrate molecular, genetic, and AI/machine learning methods to identify regulatory and genetic mechanisms to control cattle fever ticks and New World screwworm.
Sub-objective 1: Develop a machine learning and artificial intelligence software for the advancement of identifying candidate regulatory enhancers in the southern cattle tick and New World screwworm.
Objective 2: Develop and improve genetic monitoring techniques to monitor and reduce cattle fever tick, Asian longhorned tick, and New World screwworm population expansion due to extreme climate conditions.
Sub-objective 2A: Improve the current NWS population genetic database by replacing geographic colony strain samples with wild-caught reference samples.
Sub-objective 2.B: Integrate genetic data from multiple SCT populations collected from unique geographical and environmental niches to determine the variables that influence tick range and suitability for introduction.
Sub-objective 2.C: Identify genetic markers to elucidate potential pathways of introduction and local adaptation of Asian longhorned tick within the US.
Objective 3: Develop antigen protein presenting vaccines to mitigate stable flies, horn flies, cattle fever ticks, and emerging invasive/exotic ticks.
Sub-objective 3.A: Develop molecular and computational approaches towards the advancement of vaccines to mitigate horn flies, cattle fever ticks, and emerging invasive/exotic ticks.
Sub-objective 3.B: Produce and/or synthesize and purify candidate recombinant protein, peptides, and nucleic acids identified in silico.
Subobjective 3.C. Characterize tick salivary cholinesterases and define their role in impacting the immune response at the tick-host interface.
Objective 4: Perform de novo genome sequencing to improve genetic resources to identify and mitigate introductions of emerging invasive/exotic tick species.
Sub-objective 4.A: Investigate the genetic diversity and population structure of a recently invasive population of tropical bont tick.
Approach:
Our research aims to enhance genomic resources through advanced applications of artificial intelligence and machine learning, genomics, and therapeutic development. By leveraging cutting-edge computational tools and population genetics analyses, we will fill existing gaps in understanding the genetic makeup of ticks and vector pests. The comprehensive investigation spans multiple objectives: We will develop advanced computational tools to understand vector pest biology (Objective 1) to enhance genomic resources available in vector arthropod pests. We will update current genetic databases and monitoring techniques which fall short in real-time assessments (Objective 2). Vaccine target identification and development utilizing computational models to predict antigenic targets, facilitating novel vaccine design (Objective 3). Finally, emerging tick species threaten ecosystems, livestock, and public health. We will employ analytics and modeling for early detection and prevention of invasive tick populations (Objective 4). Anticipated impacts include precise pest management strategies, improved livestock health, and enhanced agricultural productivity. Stakeholders, including national and state livestock producers, wildlife commissions, and academic institutions, stand to benefit from our research outcomes. By addressing knowledge gaps and applying innovative technologies, our project strives to revolutionize vector pest management for a sustainable and resilient future.