ISOTHERMAL RECOMBINANT POLYMERASE AMPLIFICATION AND CRIPSR(CAS12A) ASSAY DETECTION OF RENIBACTERIUM SALMONINARUM AS AN EXAMPLE FOR WILDLIFE PATHOGEN DETECTION IN ENVIRONMENTAL DNA SAMPLES.

Improving rapid detection methods for pathogens is important for research as we collectively aim to improve the health of ecosystems globally. In the northern hemisphere, the success of salmon (Oncorhynchus spp.) populations is vitally important to the larger marine, aquatic, and terrestrial ecosystems they inhabit. This has led to managers cultivating salmon in hatcheries and aquaculture to bolster their populations, but young salmon face many challenges, including diseases such as bacterial kidney disease (BKD). Early detection of the BKD causative agent, Renibacterium salmoninarum, is useful for managers to avoid outbreaks in hatcheries and aquaculture stocks to enable rapid treatment with targeted antibiotics. Isothermal amplification and CRIPSR-Cas12a systems may enable sensitive, relatively rapid, detection of target DNA molecules from environmental samples compared to quantitative PCR (qPCR) and culture methods. We used these technologies to develop a sensitive and specific rapid assay to detect R. salmoninarum from water samples using isothermal recombinase polymerase amplification (RPA) and an AsCas12a RNA-guided nuclease detection. The assay was specific to R. salmoninarum (0/10 co-occurring or closely related bacteria detected) and sensitive to 0.0128 pg/µL of DNA (approximately 20-40 copies/µL) within 10 min of Cas activity. This assay successfully detected R. salmoninarum environmental DNA in 14/20 water samples from hatcheries with known quantification for the pathogen via previous qPCR (70% of qPCR-positive samples). The RPA-CRISPR/AsCas12a assay had a limit of detection (LOD) of >10 copies/µL in the hatchery water samples and stochastic detection below 10 copies/µL, similar to but slightly higher than the qPCR assay. This LOD enables 37 C isothermal detection, potentially in the field, of biologically relevant levels of R. salmoninarum in water. Further research is needed to develop easy-to-use, cost-effective, sensitive RPA/CRISPR-AsCas12a assays for rapidly detecting low concentrations of wildlife pathogens in environmental samples.

Signal and noise in metabarcoding data.

Metabarcoding is a powerful molecular tool for simultaneously surveying hundreds to thousands of species from a single sample, underpinning microbiome and environmental DNA (eDNA) methods. Deriving quantitative estimates of underlying biological communities from metabarcoding is critical for enhancing the utility of such approaches for health and conservation. Recent work has demonstrated that correcting for amplification biases in genetic metabarcoding data can yield quantitative estimates of template DNA concentrations. However, a major source of uncertainty in metabarcoding data stems from non-detections across technical PCR replicates where one replicate fails to detect a species observed in other replicates. Such non-detections are a special case of variability among technical replicates in metabarcoding data. While many sampling and amplification processes underlie observed variation in metabarcoding data, understanding the causes of non-detections is an important step in distinguishing signal from noise in metabarcoding studies. Here, we use both simulated and empirical data to 1) suggest how non-detections may arise in metabarcoding data, 2) outline steps to recognize uninformative data in practice, and 3) identify the conditions under which amplicon sequence data can reliably detect underlying biological signals. We show with both simulations and empirical data that, for a given species, the rate of non-detections among technical replicates is a function of both the template DNA concentration and species-specific amplification efficiency. Consequently, we conclude metabarcoding datasets are strongly affected by (1) deterministic amplification biases during PCR and (2) stochastic sampling of amplicons during sequencing-both of which we can model-but also by (3) stochastic sampling of rare molecules prior to PCR, which remains a frontier for quantitative metabarcoding. Our results highlight the importance of estimating species-specific amplification efficiencies and critically evaluating patterns of non-detection in metabarcoding datasets to better distinguish environmental signal from the noise inherent in molecular detections of rare targets.

Toward quantitative metabarcoding.

Amplicon-sequence data from environmental DNA (eDNA) and microbiome studies provide important information for ecology, conservation, management, and health. At present, amplicon-sequencing studies-known also as metabarcoding studies, in which the primary data consist of targeted, amplified fragments of DNA sequenced from many taxa in a mixture-struggle to link genetic observations to the underlying biology in a quantitative way, but many applications require quantitative information about the taxa or systems under scrutiny. As metabarcoding studies proliferate in ecology, it becomes more important to develop ways to make them quantitative to ensure that their conclusions are adequately supported. Here we link previously disparate sets of techniques for making such data quantitative, showing that the underlying polymerase chain reaction mechanism explains the observed patterns of amplicon data in a general way. By modeling the process through which amplicon-sequence data arise, rather than transforming the data post hoc, we show how to estimate the starting DNA proportions from a mixture of many taxa. We illustrate how to calibrate the model using mock communities and apply the approach to simulated data and a series of empirical examples. Our approach opens the door to improve the use of metabarcoding data in a wide range of applications in ecology, public health, and related fields.