Using Next Generation Sequencing to Contextualize Feeding Behavior, Pathogen Carriage, and Geospatial Distribution of the Deer Tick
The black-legged, or deer tick (Ixodes scapularis), an important vector of human and animal diseases in the northeastern United States, is well known as the primary vector of Lyme disease (Borrelia burgdorferi). It also carries emerging pathogens such as Anaplasma phagocytophilum and Babesia microti. My research, which was funded by a Summer Undergraduate Research Fellowship (SURF) from the Hamel Center for Undergraduate Research, focused on understanding feeding behavior/host selection and pathogen carriage in the black-legged tick. My objective was to use genomic approaches to investigate correlations between tick feeding behavior, pathogen presence, and geographic distribution, thereby contributing to a broader understanding of vector ecology and disease risk across the region.
The project was envisioned as a three-part study: (1) identifying tick host species via next generation sequencing; (2) merging these results with location via GIS data visualization; and (3) comparing these findings to pathogens identified via routine quantitative polymerase chain reaction (qPCR). PCR is a molecular laboratory technique that targets and amplifies specific portions of an organism’s genetic material to generate over 20 million copies for analysis. Next generation sequencing is a high-throughput, rigorous method of sequencing genetic material (DNA or RNA) that is highly accurate and increasingly cost effective. When used in combination with other analytic techniques, it represents an important component of the fourth industrial revolution, the confluence of technologies that coalesces and advances digital, physical, chemical, and biological sciences.
Figure 1 (click to enlarge). Reported tickborne diseases from 2019-2022 (CDC, 2025).
Traditional approaches to studying ticks have often emphasized either pathogen detection in isolation or ecological surveys of tick and host distributions. While these studies have provided valuable insights, such as understanding host distribution and pathogen prevalence in certain regions, they frequently rely on direct sampling from vertebrate hosts or focus narrowly on single pathogens. The combination of genomic datasets in this study establishes the groundwork for analyzing how feeding behavior and pathogen carriage intersect at a molecular level.
Application of Metabarcoding with Next Generation Sequencing
The first stage of the project involved DNA extractions from ticks collected across the northeastern United States. Our extraction methodology was honed through consultation with colleagues at UMaine Tick Lab and University of Vermont Entomology Research Laboratory and involves a combination of chemical lysis and physical disruption using magnetic beads and robotic equipment in the UNH Collaborative Core Wet Lab (formerly the UNH COVID lab). We used cytochrome b metabarcoding to confirm tick species while simultaneously identifying vertebrate hosts by targeting DNA in the tick’s blood meal. Metabarcoding is a procedure wherein PCR primers are designed to target a portion of the genome that (1) is conserved across a group of organisms, in this case animals that ticks feed on, and (2) varies across this group of organisms sufficiently to provide species identity and information about inter-individual relatedness.
PCR primers targeting this mitochondrial cytochrome b gene were used to increasingly copy vertebrate DNA that remained in the tick from previous feedings. Once this DNA had been sequenced, the product was then compared to all known and published genetic sequences to determine the identity of the organism to which it belongs. This method enabled detection of blood meal sources across a wide range of animals, including small mammals (rodents), birds, carnivores, hominids, and reptiles, creating the foundation for analyzing potential links between which hosts the ticks fed upon and which pathogens they harbored. Importantly, cytochrome b metabarcoding does not require direct host sampling, thereby broadening the range of species that can be detected and reducing sampling bias.
Pathogen detection was the second major component of the study. I focused on bacterial pathogens detected through 16s rRNA metabarcoding and targeted qPCR assays, which provide both breadth and specificity in characterizing bacterial pathogens. The qPCR analyses were especially useful in identifying the presence of specific tickborne pathogens such as Borrelia burgdorferi, Anaplasma phagocytophilum, and Franciella tularensis. This ensured that high-confidence data was produced for the most epidemiologically important pathogens.
At this point in the study, the dataset consists of host identifications, pathogen detections, and associated location metadata awaiting visualization. For the geographic component of the project, I plan to utilize accessible tools such as Excel MapChart to create simpler visuals of host and pathogen distributions. Although the metadata for geographic location is still in the process of being organized, the framework is in place for mapping once those are finalized.
Preliminary Identification of Prevalent Tick-Borne Pathogens in New England
The host identification data provides insight into the diversity of vertebrates that I. scapularis feeds on, revealing evidence of mammalian blood meals. The pathogen data, meanwhile, allows for assessment of infection prevalence within the tick population and highlight instances of co-occurrence between pathogens. Based on immediate bioinformatic results, some trends are clear.
The overall pattern confirms that deer are a key host for ticks in this region. The most common host identified so far is the white-tailed deer (Odocoileus virginianus), which is expected because adult ticks rely on deer to reproduce and maintain populations. This analysis is not free from interpretation and review, as the genetic sequences amplified are sometimes short enough that the published genes to which they match can include deer species not present in our region (e.g. red brocket deer or reindeer). These results were investigated by accessing specific sequences and comparing them to reference genomes, species distribution maps, and re-analysis of sequences. I will have to reassign these results to white-tailed deer for the final analysis.
When looking at the microbial communities of the ticks, the most common genus detected was Rickettsia. While there are well-known pathogens in this genus, including the agents that cause Rocky Mountain Spotted Fever (R. rickettsii) and murine typhus (R. typhi), not all Rickettsia species cause disease. As a matter of understanding tick ecology and the potential evolution of new pathogens, the widespread presence of these bacteria in ticks is an important acknowledgement. Rocky Mountain Spotted Fever is rare in New England with fewer than ten cases recorded between 1995-2017. Similarly, R. typhi is not established to be spread by ticks, but identification of Rickettsia as a common genus raises importance for monitoring potential future pathogen evolution and vector adaptation.
An additional common pathogen group we detected is the Borrelia genus, including both Borrelia burgdorferi and Borrelia miyamotoi, which cause disease in people, including Lyme disease. This data matches what is already known about Lyme disease being the main tickborne illness in the northeastern United States, and it confirms that the next generation sequencing approach works well. Infections via B. miyamotoi are on the rise, as are more specific identification methods to differentiate disease caused by B. miyamotoi versus B. burgdorferi, so the increased prevalence of this bacterium is also expected. Similarly, another bacterial pathogen identified was Francisella tularensis, the causative agent of tularemia. Although tularemia is not commonly reported in people in New England, this finding highlights a bacterium that may warrant attention in future public health monitoring.
Reflection and Future Plans
Overall, this project has given me a deeper understanding of how deer ticks interact with their environment, hosts, and microbes. It shows that ticks are not just passive carriers of disease, but they are active participants in complex ecological networks. By studying these interactions, we can better understand how diseases like Lyme disease spread, improve public health strategies, and increase awareness of ways to prevent tickborne illnesses. Integrating molecular genetics with ecological context improves our understanding of these dynamics, giving us a more complete view of how feeding behavior, host diversity, and pathogen presence intersect with consideration to the region and environment.
Looking ahead, several tasks remain. Metadata must be carefully organized so that each tick’s host and pathogen information is correctly matched with its location. Once this is complete, I will create maps to visualize patterns of host use and microbial presence across the region. I also need to analyze additional samples to fully understand tick feeding behavior. Organizing data carefully is crucial, because sequencing results are only meaningful when combined with accurate context about location and host.
I have applied the methods from this project to my senior thesis, which focuses on a smaller area at Odiorne Point State Park. There, I am collecting ticks directly in the field and involving the public through citizen science programs in collaboration with the Seacoast Science Center. By combining research and education, I hope to increase awareness about tickborne diseases and help people take steps to prevent exposure while also contributing to scientific knowledge. The data I collect from Odiorne Point State Park will also contribute to this wider tick pathogen and ecology study by Dr. David Needle and the Hubbard Center for Genome Studies. Seeing my research be translated and replicated from the lab to the forest trails, to classrooms, and to community programs has made this project both scientifically exciting and personally meaningful.
This project would not have been possible without the support of many people. I am deeply grateful to my mentor, Dr. David Needle, for his continued support and guidance, as well as the members of the Microbial Ecology and Emerging Diseases laboratory for their support. I am also so grateful for the staff at the Hubbard Center for Genome Studies and the UNH Collaborative Core Wet Lab for their assistance with sample processing and data analysis. Finally, I am grateful to the Hamel Center for Undergraduate Research for supporting this project, and to the late Dr. Dana Hamel for his generous contributions that made this research possible.
References
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Author and Mentor Bios
Originally from Milford, New Hampshire, Adrianna George is an animal science major minoring in biomedical science who will graduate in spring 2026. She has served on the executive board of the UNH Pre-Veterinary Club, held multiple chair positions in the Sigma Alpha Professional Agricultural Sorority, and works as a student technician at the New Hampshire Veterinary Diagnostic Laboratory.
David Needle is a clinical associate professor in the Department of Molecular, Cellular, and Biomedical Sciences at UNH and senior veterinary pathologist at the New Hampshire Veterinary Diagnostic Laboratory. His research is currently comprised of three main foci in the broader context of One Health: (1) emerging disease discovery, (2) wildlife diseases, and (3) comparative microbial genomics.
Copyright © 2025, Adrianna George
