Detecting and analysing intraspecific genetic variation with eDNA: From population genetics to species abundance

Environmental DNA: The next chapter

Molecular tools are an indispensable part of ecology and biodiversity sciences and implemented across all biomes. About a decade ago, the use and implementation of environmental DNA (eDNA) to detect biodiversity signals extracted from environmental samples opened new avenues of research. Initial eDNA research focused on understanding population dynamics of target species. Its scope thereafter broadened, uncovering previously unrecorded biodiversity via metabarcoding in both well-studied and understudied ecosystems across all taxonomic groups. The application of eDNA rapidly became an established part of biodiversity research, and a research field by its own. Here, we revisit key expectations made in a land-mark special issue on eDNA in Molecular Ecology in 2012 to frame the development in six key areas: (1) sample collection, (2) primer development, (3) biomonitoring, (4) quantification, (5) behaviour of DNA in the environment and (6) reference database development. We pinpoint the success of eDNA, yet also discuss shortfalls and expectations not met, highlighting areas of research priority and identify the unexpected developments. In parallel, our retrospective couples a screening of the peer-reviewed literature with a survey of eDNA users including academics, end-users and commercial providers, in which we address the priority areas to focus research efforts to advance the field of eDNA. With the rapid and ever-increasing pace of new technical advances, the future of eDNA looks bright, yet successful applications and best practices must become more interdisciplinary to reach its full potential. Our retrospect gives the tools and expectations towards concretely moving the field forward.

A vision for global eDNA-based monitoring in a changing world

Environmental DNA (eDNA) has opened promising avenues for establishing standardized, cost-efficient monitoring of biodiversity. However, comprehensive and systematic implementation is urgently needed to address the current biodiversity crisis. Here, we envision a global eDNA biomonitoring scheme, which could potentially revolutionize the understanding and conservation of life on Earth.

Estimation of spatial demographic maps from polymorphism data using a neural network

A fundamental goal in population genetics is to understand how variation is arrayed over natural landscapes. From first principles we know that common features such as heterogeneous population densities and source sink dynamics of dispersal should shape genetic variation over space, however there are few tools currently available that can deal with these ubiquitous complexities. Geographically referenced single nucleotide polymorphism (SNP) data are increasingly accessible, presenting an opportunity to study genetic variation across geographic space in myriad species. We present a new inference method that uses geo-referenced SNPs and a deep neural network to estimate spatially heterogeneous maps of population density and dispersal rate. Our neural network trains on simulated input and output pairings, where the input consists of genotypes and sampling locations generated from a continuous space population genetic simulator, and the output is a map of the true demographic parameters. We benchmark our tool against existing methods and discuss qualitative differences between the different approaches; in particular, our program is unique because it infers the magnitude of both dispersal and density as well as their variation over the landscape, and it does so using SNP data. Similar methods are constrained to estimating relative migration rates, or require identity by descent blocks as input. We applied our tool to empirical data from North American grey wolves, for which it estimated mostly reasonable demographic parameters, but was affected by incomplete spatial sampling. Genetic based methods like ours complement other, direct methods for estimating past and present demography, and we believe will serve as valuable tools for applications in conservation, ecology, and evolutionary biology. An open source software package implementing our method is available from https://github.com/kr-colab/mapNN.

An integrated method for targeted Oxford Nanopore sequencing and automated bioinformatics for the simultaneous detection of bacteria, fungi, and ARG

AIMS

The use of metagenomics for pathogen identification in clinical practice has been limited. Here we describe a workflow to encourage the clinical utility and potential of NGS for the screening of bacteria, fungi, and antimicrobial resistance genes (ARGs).

METHODS AND RESULTS

The method includes target enrichment, long-read sequencing, and automated bioinformatics. Evaluation of several tools and databases was undertaken across standard organisms (n = 12), clinical isolates (n = 114), and blood samples from patients with suspected bloodstream infections (n = 33). The strategy used could offset the presence of host background DNA, error rates of long-read sequencing, and provide accurate and reproducible detection of pathogens. Eleven targets could be successfully tested in a single assay. Organisms could be confidently identified considering ≥60% of best hits of a BLAST-based threshold of e-value 0.001 and a percent identity of >80%. For ARGs, reads with percent identity of >90% and >60% overlap of the complete gene could be confidently annotated. A kappa of 0.83 was observed compared to standard diagnostic methods. Thus, a workflow for the direct-from-sample, on-site sequencing combined with automated genomics was demonstrated to be reproducible.

CONCLUSION

NGS-based technologies overcome several limitations of current day diagnostics. Highly sensitive and comprehensive methods of pathogen screening are the need of the hour. We developed a framework for reliable, on-site, screening of pathogens.

Nuclear environmental DNA resolves fine-scale population genetic structure in an aquatic habitat

There is considerable potential for nuclear genomic material in environmental DNA (eDNA) to inform us of population genetic structure within aquatic species. We tested if nuclear allelic composition data sourced from eDNA can resolve fine scale spatial genetic structure of the cichlid fish Astatotilapia calliptera in Lake Masoko, Tanzania. In this ∼35 m deep crater lake the species is diverging into two genetically distinguishable ecomorphs, separated by a thermo-oxycline at ∼15 m that divides biologically distinct water masses. We quantified population genetic structure along a depth transect using single nucleotide polymorphisms (SNPs) derived from genome sequencing of 530 individuals. This population genetic structure was reflected in a focal set of SNPs that were also reliably amplified from eDNA - with allele frequencies derived from eDNA reflecting those of fish within each depth zone. Thus, by targeting known genetic variation between populations within aquatic eDNA, we measured genetic structure within the focal species.