Sequencing by binding rivals SMOR error-corrected sequencing by synthesis technology for accurate detection and quantification of minor (< 0.1%) subpopulation variants.

BACKGROUND: Detecting very minor (< 1%) subpopulations using next-generation sequencing is a critical need for multiple applications, including the detection of drug resistant pathogens and somatic variant detection in oncology. A recently available sequencing approach termed 'sequencing by binding (SBB)' claims to have higher base calling accuracy data "out of the box." This paper evaluates the utility of using SBB for the detection of ultra-rare drug resistant subpopulations in Mycobacterium tuberculosis (Mtb) using a targeted amplicon assay and compares the performance of SBB to single molecule overlapping reads (SMOR) error corrected sequencing by synthesis (SBS) data. RESULTS: SBS displayed an elevated error rate when compared to SMOR error-corrected SBS and SBB techniques. SMOR error-corrected SBS and SBB technologies performed similarly within the linear range studies and error rate studies. CONCLUSIONS: With lower sequencing error rates within SBB sequencing, this technique looks promising for both targeted and unbiased whole genome sequencing, leading to the identification of minor (< 1%) subpopulations without the need for error correction methods.

Pan-Enterovirus Characterization Reveals Cryptic Circulation of Clinically Relevant Subtypes in Arizona Wastewater.

Background: Most seasonally circulating enteroviruses result in asymptomatic or mildly symptomatic infections. In rare cases, however, infection with some subtypes can result in paralysis or death. Of the 300 subtypes known, only poliovirus is reportable, limiting our understanding of the distribution of other enteroviruses that can cause clinical disease. Objective: The overarching objectives of this study were to: 1) describe the distribution of enteroviruses in Arizona during the late summer and fall of 2022, the time of year when they are thought to be most abundant, and 2) demonstrate the utility of viral pan-assay approaches for semi-agnostic discovery that can be followed up by more targeted assays and phylogenomics. Methods: This study utilizes pooled nasal samples collected from school-aged children and long-term care facility residents, and wastewater from multiple locations in Arizona during July-October of 2022. We used PCR to amplify and sequence a region common to all enteroviruses, followed by species-level bioinformatic characterization using the QIIME 2 platform. For Enterovirus-D68 (EV-D68), detection was carried out using RT-qPCR, followed by confirmation using near-complete whole EV-D68 genome sequencing using a newly designed tiled amplicon approach. Results: In the late summer and early fall of 2022, multiple enterovirus species were identified in Arizona wastewater, with Coxsackievirus A6, EV-D68, and Coxsackievirus A19 composing 86% of the characterized reads sequenced. While EV-D68 was not identified in pooled human nasal samples, and the only reported acute flaccid myelitis case in Arizona did not test positive for the virus, an in-depth analysis of EV-D68 in wastewater revealed that the virus was circulating from August through mid-October. A phylogenetic analysis on this relatively limited dataset revealed just a few importations into the state, with a single clade indicating local circulation. Significance: This study further supports the utility of wastewater-based epidemiology to identify potential public health threats. Our further investigations into EV-D68 shows how these data might help inform healthcare diagnoses for children presenting with concerning neurological symptoms.

Understanding the exposure risk of aerosolized Coccidioides in a Valley fever endemic metropolis.

Coccidioides is the fungal causative agent of Valley fever, a primarily pulmonary disease caused by inhalation of fungal arthroconidia, or spores. Although Coccidioides has been an established pathogen for 120 years and is responsible for hundreds of thousands of infections per year, little is known about when and where infectious Coccidioides arthroconidia are present within the ambient air in endemic regions. Long-term air sampling programs provide a means to investigate these characteristics across space and time. Here we present data from > 18 months of collections from 11 air sampling sites across the Phoenix, Arizona, metropolitan area. Overall, prevalence was highly variable across space and time with no obvious spatial or temporal correlations. Several high prevalence periods were identified at select sites, with no obvious spatial or temporal associations. Comparing these data with weather and environmental factor data, wind gusts and temperature were positively associated with Coccidioides detection, while soil moisture was negatively associated with Coccidioides detection. These results provide critical insights into the frequency and distribution of airborne arthroconidia and the associated risk of inhalation and potential disease that is present across space and time in a highly endemic locale.

Variant-specific SARS-CoV-2 shedding rates in wastewater.

Previous studies show that SARS-CoV-2 waste shedding rates vary by community and are influenced by multiple factors; however, differences in shedding rates across multiple variants have yet to be evaluated. The purpose of this work is to build on previous research that evaluated waste shedding rates for early SARS-CoV-2 and the Delta variant, and update population level waste shedding rates for the more-recent Omicron variant in six communities. Mean SARS-CoV-2 waste shedding rates were found to increase with the predominance of the Delta variant and subsequently decrease with Omicron infections. Interestingly, the Delta stage had the highest mean shedding rates and was associated with the most severe disease symptoms reported in other clinical studies, while Omicron, exhibiting reduced symptoms, had the lowest mean shedding rates. Additionally, shedding rates were most consistent across communities during the Omicron stage. This is the first paper to identify waste shedding rates specific to the Omicron variant and fills a knowledge gap critical to disease prevalence modeling.

Estimating disease vector population size from citizen science data.

Citizen science projects have the potential to address hypotheses requiring extremely large datasets that cannot be collected with the financial and labour constraints of most scientific projects. Data collection by the general public could expand the scope of scientific enquiry if these data accurately capture the system under study. However, data collection inconsistencies by the untrained public may result in biased datasets that do not accurately represent the natural world. In this paper, we harness the availability of scientific and public datasets of the Lyme disease tick vector to identify and account for biases in citizen science tick collections. Estimates of tick abundance from the citizen science dataset correspond moderately with estimates from direct surveillance but exhibit consistent biases. These biases can be mitigated by including factors that may impact collector participation or effort in statistical models, which, in turn, result in more accurate estimates of tick population sizes. Accounting for collection biases within large-scale, public participation datasets could update species abundance maps and facilitate using the wealth of citizen science data to answer scientific questions at scales that are not feasible with traditional datasets.

Normalization of SARS-CoV-2 viral load via RT-qPCR provides higher-resolution data for comparison across time and between patients.

The 2020 pandemic has transformed the world and elicited thousands of studies to better understand the SARS-CoV-2 virus. Viral load has been a common measure to monitor treatment therapies and associate viral dynamics with patient outcomes; however, methods associated with viral load have varied across studies. These variations have the potential to sacrifice the accuracy of findings as they often do not account for inter-assay variation or variation across samples. In a retrospective study of nasopharyngeal samples, we found a significant amount of variation within the DNA and RNA targets; for example, across time within a single patient, there was an average of a 32-fold change. Further, we explore the impacts of host normalization on 94 clinical samples using the TGen Quantitative SARS-CoV-2 assay, finding that without host normalization samples with the same viral concentration can have up to 100-fold variation in the viral load.

Citizen Science Provides an Efficient Method for Broad-Scale Tick-Borne Pathogen Surveillance of Ixodes pacificus and Ixodes scapularis across the United States.

Tick-borne diseases have expanded over the last 2 decades as a result of shifts in tick and pathogen distributions. These shifts have significantly increased the need for accurate portrayal of real-time pathogen distributions and prevalence in hopes of stemming increases in human morbidity. Traditionally, pathogen distribution and prevalence have been monitored through case reports or scientific collections of ticks or reservoir hosts, both of which have challenges that impact the extent, availability, and accuracy of these data. Citizen science tick collections and testing campaigns supplement these data and provide timely estimates of pathogen prevalence and distributions to help characterize and understand tick-borne disease threats to communities. We utilized our national citizen science tick collection and testing program to describe the distribution and prevalence of four Ixodes-borne pathogens, Borrelia burgdorferi sensu lato, Borrelia miyamotoi, Anaplasma phagocytophilum, and Babesia microti, across the continental United States. IMPORTANCE In the 21st century, zoonotic pathogens continue to emerge, while previously discovered pathogens continue to have changes within their distribution and prevalence. Monitoring these pathogens is resource intensive, requiring both field and laboratory support; thus, data sets are often limited within their spatial and temporal extents. Citizen science collections provide a method to harness the general public to collect samples, enabling real-time monitoring of pathogen distribution and prevalence.

Examining Prevalence and Diversity of Tick-Borne Pathogens in Questing Ixodes pacificus Ticks in California.

Tick-borne diseases in California include Lyme disease (caused by Borrelia burgdorferi), infections with Borrelia miyamotoi, and human granulocytic anaplasmosis (caused by Anaplasma phagocytophilum). We surveyed multiple sites and habitats (woodland, grassland, and coastal chaparral) in California to describe spatial patterns of tick-borne pathogen prevalence in western black-legged ticks (Ixodes pacificus). We found that several species of Borrelia-B. burgdorferi, Borrelia americana, and Borrelia bissettiae-were observed in habitats, such as coastal chaparral, that do not harbor obvious reservoir host candidates. Describing tick-borne pathogen prevalence is strongly influenced by the scale of surveillance: aggregating data from individual sites to match jurisdictional boundaries (e.g., county or state) can lower the reported infection prevalence. Considering multiple pathogen species in the same habitat allows a more cohesive interpretation of local pathogen occurrence. IMPORTANCE Understanding the local host ecology and prevalence of zoonotic diseases is vital for public health. Using tick-borne diseases in California, we show that there is often a bias to our understanding and that studies tend to focus on particular habitats, e.g., Lyme disease in oak woodlands. Other habitats may harbor a surprising diversity of tick-borne pathogens but have been neglected, e.g., coastal chaparral. Explaining pathogen prevalence requires descriptions of data on a local scale; otherwise, aggregating the data can misrepresent the local dynamics of tick-borne diseases.

Predicting the current and future distribution of the western black-legged tick, Ixodes pacificus, across the Western US using citizen science collections.

In the twenty-first century, ticks and tick-borne diseases have expanded their ranges and impact across the US. With this spread, it has become vital to monitor vector and disease distributions, as these shifts have public health implications. Typically, tick-borne disease surveillance (e.g., Lyme disease) is passive and relies on case reports, while disease risk is calculated using active surveillance, where researchers collect ticks from the environment. Case reports provide the basis for estimating the number of cases; however, they provide minimal information on vector population or pathogen dynamics. Active surveillance monitors ticks and sylvatic pathogens at local scales, but it is resource-intensive. As a result, data are often sparse and aggregated across time and space to increase statistical power to model or identify range changes. Engaging public participation in surveillance efforts allows spatially and temporally diverse samples to be collected with minimal effort. These citizen-driven tick collections have the potential to provide a powerful tool for tracking vector and pathogen changes. We used MaxEnt species distribution models to predict the current and future distribution of Ixodes pacificus across the Western US through the use of a nationwide citizen science tick collection program. Here, we present niche models produced through citizen science tick collections over two years. Despite obvious limitations with citizen science collections, the models are consistent with previously-predicted species ranges in California that utilized more than thirty years of traditional surveillance data. Additionally, citizen science allows for an expanded understanding of I. pacificus distribution in Oregon and Washington. With the potential for rapid environmental changes instigated by a burgeoning human population and rapid climate change, the development of tools, concepts, and methodologies that provide rapid, current, and accurate assessment of important ecological qualities will be invaluable for monitoring and predicting disease across time and space.

Methods for sequencing the pandemic: benefits of rapid or high-throughput processing.

Genomic epidemiology has proven successful for real-time and retrospective monitoring of small and large-scale outbreaks. Here, we report two genomic sequencing and analysis strategies for rapid-turnaround or high-throughput processing of metagenomic samples. The rapid-turnaround method was designed to provide a quick phylogenetic snapshot of samples at the heart of active outbreaks, and has a total turnaround time of <48 hours from raw sample to analyzed data. The high-throughput method was designed for semi-retrospective data analysis, and is both cost effective and highly scalable. Though these methods were developed and utilized for the SARS-CoV-2 pandemic response in Arizona, U.S, and we envision their use for infectious disease epidemiology in the 21 st Century.

COYOTES (CANIS LATRANS) IN ARIZONA, USA, EXHIBIT IMMUNE AND GENETIC EVIDENCE OF RICKETTSIAL INFECTIONS.

Rocky Mountain spotted fever (RMSF), caused by the bacterium Rickettsia rickettsii, was recognized as endemic in Arizona, US after a 2002 outbreak and has since been a public health concern. The brown dog tick (Rhipicephalus sanguineus sensu lato) is the principal vector of this pathogen in Arizona. Domesticated dogs (Canis lupus familiaris) are the tick's main host, so free-roaming dogs in peridomestic areas have been named the primary risk factor for human cases of RMSF. However, the sudden emergence and long-distance dispersal of the pathogen have not been adequately explained, and one possible mechanism could include wildlife. Coyotes (Canis latrans) are wide ranging in Arizona and closely related to dogs, so it is possible that brown dog ticks parasitize coyotes and infect them. Although R. rickettsii is the most severe spotted fever group (SFG) rickettsial pathogen in humans, others occur in Arizona, and antibodies raised against them are cross-reactive, so we more-broadly hypothesized that coyotes in Arizona are exposed to SFG rickettsiae. We collected coyote tissues in spring 2016 and 2017. We tested sera for antibodies to R. rickettsii and found 9% (8/94) of samples were antibody-positive with titers of ≥256. Subsequent quantitative PCR analyses of skin showed evidence for Rickettsia spp. in 2.9% (4/138) of samples. These data suggest that coyotes have a role in the maintenance of SFG rickettsiae in Arizona. Further investigation is warranted to reveal which specific pathogen-vector complexes act on coyotes in the region and whether they represent a risk to human health.

Time of year and outdoor recreation affect human exposure to ticks in California, United States.

Interactions between humans and ticks are often measured indirectly, using surveillance of tick population abundance and pathogen prevalence, or reported human disease data. We used data garnered as part of a free national citizen science research effort to describe actual human exposures to ticks in California. Human-biting ticks (n = 1,905) submitted for identification were predominantly western black-legged ticks (Ixodes pacificus) (68%), American dog ticks (Dermacentor variabilis) (24%), and Pacific Coast ticks (Dermacentor occidentalis) (7%). Tick exposure occurred predominantly during recreational use of the outdoors, rather than exposure near the home environment. Tick submissions peaked in May, but human exposure to ticks occurred throughout the year. Adult I. pacificus were most frequently found on humans during March-May, though previous research demonstrates that questing adults on vegetation are more abundant earlier in the winter.

Citizen science informs human-tick exposure in the Northeastern United States.

BACKGROUND: Tick-borne disease is the result of spillover of pathogens into the human population. Traditionally, literature has focused on characterization of tick-borne disease pathogens and ticks in their sylvatic cycles. A limited amount of research has focused on human-tick exposure in this system, especially in the Northeastern United States. Human-tick interactions are crucial to consider when assessing the risk of tick-borne disease since a tick bite is required for spillover to occur. METHODS: Citizen scientists collected ticks from the Northeastern US through a free nationwide program. Submitted ticks were identified to species, stage, and sex. Blacklegged ticks, Ixodes scapularis, were tested for the presence of Borrelia burgdorferi sensu lato (s.l.) and hard-tick relapsing fever Borrelia. Seasonality of exposure and the citizen science activity during tick exposure was recorded by the citizen scientist. A negative binomial model was fit to predict county level CDC Lyme disease cases in 2016 using citizen science Ixodes scapularis submissions, state, and county population as predictor variables. RESULTS: A total of 3740 submissions, comprising 4261 ticks, were submitted from the Northeastern US and were reported to be parasitizing humans. Of the three species submitted, blacklegged ticks were the most prevalent followed by American dog ticks and lone star ticks. Submissions peaked in May with the majority of exposure occurring during every-day activities. The most common pathogen in blacklegged ticks was B. burgdorferi s.l. followed by hard-tick relapsing fever Borrelia. Negative binomial model performance was best in New England states followed by Middle Atlantic states. CONCLUSIONS: Citizen science provides a low-cost and effective methodology for describing the seasonality and characteristics of human-tick exposure. In the Northeastern US, everyday activities were identified as a major mechanism for tick exposure, supporting the role of peri-domestic exposure in tick-borne disease. Citizen science provides a method for broad pathogen and tick surveillance, which is highly related to human disease, allowing for inferences to be made about the epidemiology of tick-borne disease.

Using citizen science to describe the prevalence and distribution of tick bite and exposure to tick-borne diseases in the United States.

Tick-borne pathogens are increasing their range and incidence in North America as a consequence of numerous factors including improvements in diagnostics and diagnosis, range expansion of primary vectors, changes in human behavior, and an increasing understanding of the diversity of species of pathogens that cause human disease. Public health agencies have access to human incidence data on notifiable diseases e.g., Borrelia burgdorferi, the causative agent of Lyme disease, and often local pathogen prevalence in vector populations. However, data on exposure to vectors and pathogens can be difficult to determine e.g., if disease does not occur. We report on an investigation of exposure to ticks and tick-borne bacteria, conducted at a national scale, using citizen science participation. 16,080 ticks were submitted between January 2016 and August 2017, and screened for B. burgdorferi, B. miyamotoi, Anaplasma phagocytophilum, and Babesia microti. These data corroborate entomologic investigations of tick distributions in North America, but also identify patterns of local disease risk and tick contact with humans throughout the year in numerous species of ticks and associated pathogens.