RESUMEN
Taxon-specific quantitative PCR (qPCR) assays are commonly used for environmental DNA sampling-based inference of animal presence. These assays require thorough validation to ensure that amplification truly indicates detection of the target taxon, but a thorough validation is difficult when there are potentially many non-target taxa, some of which may have incomplete taxonomies. Here, we use a previously published, quantitative model of cross-amplification risk to describe a framework for assessing qPCR assay specificity when there is missing information and it is not possible to assess assay specificity for each individual non-target confamilial. In this framework, we predict assay specificity against unsampled taxa (non-target taxa without sequence data available) using the sequence information that is available for other confamilials. We demonstrate this framework using four case study assays for: (1) An endemic, freshwater arthropod (meltwater stonefly; Lednia tumana), (2) a globally distributed, marine ascidian (Didemnum perlucidum), (3) a continentally distributed freshwater crustacean (virile crayfish; Faxonius virilis, deanae and nais species complex) and (4) a globally distributed freshwater teleost (common carp; Cyprinus carpio and its close relative C. rubrofuscus). We tested the robustness of our approach to missing information by simulating application of our framework for all possible subsamples of 20-all non-target taxa. Our results suggest that the modelling framework results in estimates which are largely concordant with observed levels of cross-amplification risk using all available sequence data, even when there are high levels of data missingness. We explore potential limitations and extensions of this approach for assessing assay specificity and provide users with an R Markdown template for generating reproducible reports to support their own assay validation efforts.
Asunto(s)
Carpas , ADN Ambiental , Urocordados , Animales , Insectos , Agua DulceRESUMEN
Environmental DNA (eDNA) sampling is a highly sensitive and cost-effective technique for wildlife monitoring, notably through the use of qPCR assays. However, it can be difficult to ensure assay specificity when many closely related species co-occur. In theory, specificity may be assessed in silico by determining whether assay oligonucleotides have enough base-pair mismatches with nontarget sequences to preclude amplification. However, the mismatch qualities required are poorly understood, making in silico assessments difficult and often necessitating extensive in vitro testing-typically the greatest bottleneck in assay development. Increasing the accuracy of in silico assessments would therefore streamline the assay development process. In this study, we paired 10 qPCR assays with 82 synthetic gene fragments for 530 specificity tests using SYBR Green intercalating dye (n = 262) and TaqMan hydrolysis probes (n = 268). Test results were used to train random forest classifiers to predict amplification. The primer-only model (SYBR Green results) and full-assay model (TaqMan probe-based results) were 99.6% and 100% accurate, respectively, in cross-validation. We further assessed model performance using six independent assays not used in model training. In these tests the primer-only model was 92.4% accurate (n = 119) and the full-assay model was 96.5% accurate (n = 144). The high performance achieved by these models makes it possible for eDNA practitioners to more quickly and confidently develop assays specific to the intended target. Practitioners can access the full-assay model online via eDNAssay (https://NationalGenomicsCenter.shinyapps.io/eDNAssay), a user-friendly tool for predicting qPCR cross-amplification.
Asunto(s)
ADN Ambiental , Benzotiazoles , Diaminas , Aprendizaje Automático , Oligonucleótidos , Quinolinas , Reacción en Cadena en Tiempo Real de la Polimerasa/métodos , Sensibilidad y EspecificidadRESUMEN
BACKGROUND: We evaluated whether occupancy modeling, an approach developed for detecting rare wildlife species, could overcome inherent accuracy limitations associated with rapid disease tests to generate fast, accurate, and affordable SARS-CoV-2 prevalence estimates. Occupancy modeling uses repeated sampling to estimate probability of false negative results, like those linked to rapid tests, for generating unbiased prevalence estimates. METHODS: We developed a simulation study to estimate SARS-CoV-2 prevalence using rapid, low-sensitivity, low-cost tests and slower, high-sensitivity, higher cost tests across a range of disease prevalence and sampling strategies. RESULTS: Occupancy modeling overcame the low sensitivity of rapid tests to generate prevalence estimates comparable to more accurate, slower tests. Moreover, minimal repeated sampling was required to offset low test sensitivity at low disease prevalence (0.1%), when rapid testing is most critical for informing disease management. CONCLUSIONS: Occupancy modeling enables the use of rapid tests to provide accurate, affordable, real-time estimates of the prevalence of emerging infectious diseases like SARS-CoV-2.
Asunto(s)
COVID-19 , Tamizaje Masivo/métodos , SARS-CoV-2 , Teorema de Bayes , COVID-19/diagnóstico , COVID-19/epidemiología , Humanos , Modelos Teóricos , Prevalencia , Sensibilidad y EspecificidadRESUMEN
The Sturgeon chub (Macrhybopsis gelida) is a cyprinid fish native to the Missouri and Mississippi River basins of the U.S. Suspected long-term declines in the size of its distribution have prompted a review of its conservation status by the U.S. Fish and Wildlife Service, a process which depends on reliable methods to delineate the distribution and status of extant populations. To facilitate monitoring of Sturgeon chub populations, we developed a quantitative PCR assay to detect Sturgeon chub DNA in environmental samples. The assay consistently detected Sturgeon chub DNA in concentrations as low as 2 copies per reaction, and did not amplify DNA from non-target fish species that are sympatric in the upper Missouri River basin. Field tests of this assay with environmental samples successfully detected Sturgeon chub from sites known to be occupied. This assay offers an extremely sensitive methodology that can be applied to determine the range of Sturgeon chub, regardless of variation in habitat characteristics.