Dataset Information

High-throughput environmental DNA analysis informs a biological assessment of an urban stream.

ABSTRACT: There is growing interest in the use of DNA barcoding and metabarcoding approaches to aid biological assessments and monitoring of waterbodies. While biodiversity measured by morphology and by DNA often has been found correlated, few studies have compared DNA data to established measures of impairment such as multimetric pollution tolerance indices used by many bioassessment programs. We incorporated environmental DNA (eDNA) metabarcoding of seston into a rigorous watershed-scale biological assessment of an urban stream to examine the extent to which eDNA richness and diversity patterns were correlated with multimetric indices and ecological impairment status designations. We also evaluated different filtering approaches and taxonomic classifications to identify best practices for environmental assessments. Seston eDNA revealed a wide diversity of eukaryotic taxa but was dominated by diatoms (36%). Differentiation among sites in alpha and beta diversity was greater when operational taxonomic units (OTUs) were classified taxonomically, but coarse resolution taxonomy (kingdom) was more informative than finer resolution taxonomy (family, genus). Correlations of DNA richness and diversity with multimetric indices for fish and macroinvertebrates were generally weak, possibly because Metazoa were not highly represented in our DNA dataset. Nonetheless, sites could be differentiated based on ecological impairment status, with more impaired sites having lower eDNA diversity as measured by the Shannon index, but higher taxonomic richness. Significant environmental drivers of community structure, as inferred from constrained ordination analyses, differed among kingdoms within the eDNA dataset, as well as from fish and macrobenthos, suggesting that eDNA provides novel environmental information. These results suggest that even a simple seston eDNA filtering protocol can provide biodiversity information of value to stream bioassessment programs. The approach bears further investigation as a potentially useful rapid assessment protocol to supplement more intensive field sampling efforts.

SUBMITTER: Bagley M

PROVIDER: S-EPMC6605098 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundHigh-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus.ResultsFour complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFα, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFα-induced perturbation for each network model when compared against NF-κB nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and inflammatory signaling were meaningfully determined.ConclusionsThe NPA scoring method leverages high-throughput measurements and a priori literature-derived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments.

Project description:Use of environmental DNA (eDNA) to assess distributions of aquatic and semi-aquatic macroorganisms is promising, but sampling schemes may need to be tailored to specific objectives. Given the potentially high variance in aquatic eDNA among replicate grab samples, compositing smaller water volumes collected over a period of time may be more effective for some applications. In this study, we compared eDNA profiles from composite water samples aggregated over three hours with grab water samples. Both sampling patterns were performed with identical autosamplers paired at two different sites in a headwater stream environment, augmented with exogenous fish eDNA from an upstream rearing facility. Samples were filtered through 0.8 μm cellulose nitrate filters and DNA was extracted with a cetyl trimethylammonium bromide procedure. Eukaryotic and bacterial community profiles were derived by amplicon sequencing of 12S ribosomal, 16S ribosomal, and cytochrome oxidase I loci. Operational taxa were assigned to genus with a lowest common ancestor approach for eukaryotes and to family with the RDP Classifier software for prokaryotes. Eukaryotic community profiles were more consistent with composite sampling than grab sampling. Downstream, rarefaction curves suggested faster taxon accumulation for composite samples, and estimated richness was higher for composite samples as a set than for grab samples. Upstream, composite sampling produced lower estimated richness than grab samples, but with overlapping standard errors. Furthermore, a bimodal pattern of richness as a function of sequence counts suggested the impact of clumped particles on upstream samples. Bacterial profiles were insensitive to sample method, consistent with the more even dispersion expected for bacteria compared with eukaryotic eDNA. Overall, samples composited over 3 h performed equal to or better than triplicate grab sampling for quantitative community metrics, despite the higher total sequencing effort provided to grab replicates. On the other hand, taxon-specific detection rates did not differ appreciably and the two methods gave similar estimates of the ratio of the common fish genera Salmo and Coregonus at each site. Unexpectedly, Salmo eDNA dropped out substantially faster than Coregonus eDNA between the two sites regardless of sampling method, suggesting that differential settling affects the estimation of relative abundance. We identified bacterial patterns that were associated with eukaryotic diversity, suggesting potential roles as biomarkers of sample representativeness.

Project description:Molecular typing methods are used to characterize the relatedness between bacterial isolates involved in infections. These approaches rely mostly on discrete loci or whole-genome sequencing (WGS) analyses of pure cultures. On the other hand, their application to environmental DNA profiling to evaluate epidemiological relatedness among patients and environments has received less attention. We developed a specific, high-throughput short sequence typing (HiSST) method for the opportunistic human pathogen Serratia marcescens. Genes displaying the highest polymorphism were retrieved from the core genome of 60 S. marcescens strains. Bioinformatics analyses showed that use of only three loci (within bssA, gabR, and dhaM) distinguished strains with a high level of efficiency. This HiSST scheme was applied to an epidemiological survey of S. marcescens in a neonatal intensive care unit (NICU). In a first case study, a strain responsible for an outbreak in the NICU was found in a sink drain of this unit, by using HiSST scheme and confirmed by WGS. The HiSST scheme was also applied to environmental DNA extracted from sink-environment samples. Diversity of S. marcescens was modest, with 11, 6, and 4 different sequence types (ST) of gabR, bssA, and dhaM loci among 19 sink drains, respectively. Epidemiological relationships among sinks were inferred on the basis of pairwise comparisons of ST profiles. Further research aimed at relating ST distribution patterns to environmental features encompassing sink location, utilization, and microbial diversity is needed to improve the surveillance and management of opportunistic pathogens. IMPORTANCE Serratia marcescens is an important opportunistic human pathogen, often multidrug resistant and involved in outbreaks of nosocomial infections in neonatal intensive care units. Here, we propose a quick and user-friendly method to select the best typing scheme for nosocomial outbreaks in relating environmental and clinical sources. This method, named high-throughput short sequence typing (HiSST), allows to distinguish strains and to explore the diversity profile of nonculturable S. marcescens. The application of HiSST profile analysis for environmental DNA offers new possibilities to track opportunistic pathogens, identify their origin, and relate their distribution pattern with environmental features encompassing sink location, utilization, and microbial diversity. Adaptation of the method to other opportunistic pathogens is expected to improve knowledge regarding their ecology, which is of significant interest for epidemiological risk assessment and elaborate outbreak mitigation strategies.

Project description:In this study, I use microbiome datasets from global soil samples and diverse hosts to learn whether soil microbial taxa are found in host microbiomes, and whether these observations fit the narrative that environmental interaction influences human microbiomes. A major motivation for conducting host-associated microbiome research is to contribute towards understanding how the environment may influence host physiology. The microbial molecular network is considered a key vector by which environmental traits may be transmitted to the host. Research on human evolution seeks evidence that can inform about the living experiences of human ancestors. This objective is substantially enhanced by recent work on ancient biomolecules from preserved microbial tissues, such as dental calculus, faecal sediments and whole coprolites. A challenge yet is to distinguish authentic biomolecules from environmental contaminants deposited contemporaneously, primarily from soil. However, we do not have sound expectations about the soil microbial elements arriving to host-associated microbiomes in a modern context. One assumption in human microbiome research is that proximity to the natural environment should affect biodiversity or impart genetic elements. I present evidence supporting the assumption that environmental soil taxa are found among host-associated gut taxa, which can recapitulate the surrounding host habitat ecotype. Soil taxa found in gut microbiomes relate to a set of universal 'core' taxa for all soil ecotypes, demonstrating that widespread host organisms may experience a consistent pattern of external environmental cues, perhaps critical for development. Observed differentiation of soil feature diversity, abundance and composition among human communities, great apes and invertebrate hosts also indicates that lifestyle patterns are inferable from an environmental signal that is retrievable from gut microbiome amplicon data. This article is part of the theme issue 'Insights into health and disease from ancient biomolecules'.

Dataset Information

High-throughput environmental DNA analysis informs a biological assessment of an urban stream.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets