Borrelia miyamotoi in Human-Biting Ticks, United States, 2013-2019.

During 2013-2019, Borrelia miyamotoi infection was detected in 19 US states. Infection rate was 0.5%-3.2%; of B. miyamotoi-positive ticks, 59.09% had concurrent infections. B. miyamotoi is homogeneous with 1 genotype from Ixodes scapularis ticks in northeastern and midwestern states and 1 from I. pacificus in western states.

Spatially distinct and metabolically active membrane domain in mycobacteria.

Protected from host immune attack and antibiotic penetration by their unique cell envelope, mycobacterial pathogens cause devastating human diseases such as tuberculosis. Seamless coordination of cell growth with cell envelope elongation at the pole maintains this barrier. Unraveling this spatiotemporal regulation is a potential strategy for controlling mycobacterial infections. Our biochemical analysis previously revealed two functionally distinct membrane fractions in Mycobacterium smegmatis cell lysates: plasma membrane tightly associated with the cell wall (PM-CW) and a distinct fraction of pure membrane free of cell wall components (PMf). To provide further insight into the functions of these membrane fractions, we took the approach of comparative proteomics and identified more than 300 proteins specifically associated with the PMf, including essential enzymes involved in cell envelope synthesis such as a mannosyltransferase, Ppm1, and a galactosyltransferase, GlfT2. Furthermore, comparative lipidomics revealed the distinct lipid composition of the PMf, with specific association of key cell envelope biosynthetic precursors. Live-imaging fluorescence microscopy visualized the PMf as patches of membrane spatially distinct from the PM-CW and notably enriched in the pole of the growing cells. Taken together, our study provides the basis for assigning the PMf as a spatiotemporally distinct and metabolically active membrane domain involved in cell envelope biogenesis.

A Computer Vision-Based Approach for Tick Identification Using Deep Learning Models.

A wide range of pathogens, such as bacteria, viruses, and parasites can be transmitted by ticks and can cause diseases, such as Lyme disease, anaplasmosis, or Rocky Mountain spotted fever. Landscape and climate changes are driving the geographic range expansion of important tick species. The morphological identification of ticks is critical for the assessment of disease risk; however, this process is time-consuming, costly, and requires qualified taxonomic specialists. To address this issue, we constructed a tick identification tool that can differentiate the most encountered human-biting ticks, Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis, by implementing artificial intelligence methods with deep learning algorithms. Many convolutional neural network (CNN) models (such as VGG, ResNet, or Inception) have been used for image recognition purposes but it is still a very limited application in the use of tick identification. Here, we describe the modified CNN-based models which were trained using a large-scale molecularly verified dataset to identify tick species. The best CNN model achieved a 99.5% accuracy on the test set. These results demonstrate that a computer vision system is a potential alternative tool to help in prescreening ticks for identification, an earlier diagnosis of disease risk, and, as such, could be a valuable resource for health professionals.

A Borrelia burgdorferi outer surface protein C (OspC) genotyping method using Luminex technology.

Borrelia burgdorferi is an important tickborne human pathogen comprising several strains based on nucleotide sequence of the outer surface protein C (ospC) gene. Detection and characterization of different ospC genotypes is vital for research on B. burgdorferi and the risk it poses to humans. Here we present a novel, multiplex assay based on Luminex xMAP technology for the detection of B. burgdorferi ospC genotypes. The assay has five major steps: amplification of the ospC gene, hydrolyzation of surplus primers and nucleotides, incorporation of biotinylated nucleotides into the template DNA, hybridization to Luminex microspheres, and detection of fluorescent signals corresponding to each ospC genotype. We validated the protocol by comparing results obtained from our method against results from an established ospC genotyping method. This protocol can be used for the characterization of ospC genotypes in B. burgdorferi infected ticks, reservoir hosts, and/or clinical samples.

Development of a Taqman Real-Time PCR for the Identification of Haemaphysalis longicornis (Acari: Ixodidae).

Haemaphysalis longicornis Neumann, a vector of various pathogens with medical and veterinary importance, is a recent invasive species in the United States. Like many tick species, discerning H. longicornis from congeners can be a challenge. To overcome the difficulty of morphological identification, a Taqman quantitative real-time PCR based on the internal transcribed spacer gene (ITS2) was developed for quick and accurate identification of H. longicornis with a detection limit of as low as 19.8 copies. We also applied the assay to 76,004 archived ticks and found 37 ticks were H. longicornis. One H. longicornis was submitted from Warren, Somerset County, New Jersey in June 2015, 2 yr earlier than the initial report from the United States. None of these 37 H. longicornis was positive for Anaplasma phagocytophilum, Borrelia burgdorferi sensu lato, B. miyamotoi, B. mayonii, Babesia microti, or Ehrlichia muris-like agent.

Crowdsourced Photographs as an Effective Method for Large-Scale Passive Tick Surveillance.

As tick vector ranges expand and the number of tickborne disease cases rise, physicians, veterinarians, and the public are faced with diagnostic, treatment, and prevention challenges. Traditional methods of active surveillance (e.g., flagging) can be time-consuming, spatially limited, and costly, while passive surveillance can broadly monitor tick distributions and infection rates. However, laboratory testing can require service fees in addition to mailing and processing time, which can put a tick-bite victim outside the window of potential prophylactic options or under unnecessary antibiotic administration. We performed a retrospective analysis of a national photograph-based crowdsourced tick surveillance system to determine the accuracy of identifying ticks by photograph when compared to those same ticks identified by microscopy and molecular methods at a tick testing laboratory. Ticks identified by photograph were correct to species with an overall accuracy of 96.7% (CI: 0.9522, 0.9781; P < 0.001), while identification accuracy for Ixodes scapularis Say (Ixodida: Ixodidae), Amblyomma americanum Linnaeus (Ixodida: Ixodidae), and Dermacentor variabilis Say (Ixodida: Ixodidae), three ticks of medical importance, was 98.2% (Cohen's kappa [κ] = 0.9575; 95% CI: 0.9698, 0.9897), 98.8% (κ = 0.9466, 95% CI: 0.9776, 0.9941), and 98.8% (κ = 0.9515, 95% CI: 0.9776, 0.9941), respectively. Fitted generalized linear models revealed that tick species and stage were the most significant predictive factors that contributed to correct photograph-based tick identifications. Neither engorgement, season, nor location of submission affected identification ability. These results provide strong support for the utility of photograph-based tick surveillance as a tool for risk assessment and monitoring among commonly encountered ticks of medical concern.