Molecular Surveillance Reveals Diverse Tick-Borne Pathogens in Northern California
Year: 2026
Type: article
Abstract: Ticks are vectors of bacterial, viral, and protozoan pathogens and represent a growing public health concern in the United States and worldwide. In California, vector surveillance has largely focused on Ixodes pacificus, the major vector of the spirochete, Borrelia burgdorferi. However, the presence of multiple medically important Dermacentor species may harbor additional, understudied pathogens. To address this gap, broad pathogen screening was conducted on ticks collected from Siskiyou, Humboldt, Trinity, and Mendocino counties in Northern California, using multiplex quantitative PCR targeting 15 known tick-borne pathogens in the USA. A total of 376 adult ticks were collected in total, comprising I. pacificus, D. similis, D. andersoni, and D. occidentalis, from vegetation and animals between 2023 and 2024. Five established bacterial pathogens, Anaplasma phagocytophilum, B. burgdorferi, B. miyamotoi, Rickettsia monacensis, and R. rhipicephali were detected. Additionally, a genetically divergent Rickettsia-like sequence was detected in one I. pacificus sample that did not match any previously described species in phylogenetic analyses. Furthermore, a short DNA fragment highly similar to the Colorado tick-fever-virus VP12 gene was detected in D. similis. These findings suggest pathogen diversity across multiple tick species in Northern California, including evidence of co-infections, and the detection of potentially novel or poorly characterized microorganisms.
Source: Pathogens
Institution: University of Nevada, Reno
Language: English
Subfield: Parasitology
Field: Immunology and Microbiology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,654
Awards: G1196656923, New molecular targets for pest management in the cuticle of the Lyme disease vector, Ixodes scapularis, Development of transgenic protocols for the Lyme disease vector, Ixodes scapularis, for tick and tick-borne disease control, RII Track-2 FEC: Leveraging Big Data to Improve Prediction of Tick-Borne Disease Patterns and Dynamics, G6508865893, T32 Predoctoral UNR Interdisciplinary Basic Biomedical Sciences Training Program
OpenAlex ID: W7203635437
