DNA metabarcoding of unfractionated water samples relates phyto-, zoo- and bacterioplankton dynamics and reveals a single-taxon bacterial bloom

Study of the Bacterial, Fungal, and Archaeal Communities Structures near the Bulgarian Antarctic Research Base "St. Kliment Ohridski" on Livingston Island, Antarctica

As belonging to one of the most isolated continents on our planet, the microbial composition of different environments in Antarctica could hold a plethora of undiscovered species with the potential for biotechnological applications. This manuscript delineates our discoveries after an expedition to the Bulgarian Antarctic Base "St. Kliment Ohridski" situated on Livingston Island, Antarctica. Amplicon-based metagenomics targeting the 16S rRNA genes and ITS2 region were employed to assess the metagenomes of the bacterial, fungal, and archaeal communities across diverse sites within and proximal to the research station. The predominant bacterial assemblages identified included Oxyphotobacteria, Bacteroidia, Gammaprotobacteria, and Alphaprotobacteria. A substantial proportion of cyanobacteria reads were attributed to a singular uncultured taxon within the family Leptolyngbyaceae. The bacterial profile of a lagoon near the base exhibited indications of penguin activity, characterized by a higher abundance of Clostridia, similar to lithotelm samples from Hannah Pt. Although most fungal reads in the samples could not be identified at the species level, noteworthy genera, namely Betamyces and Tetracladium, were identified. Archaeal abundance was negligible, with prevalent groups including Woesearchaeales, Nitrosarchaeum, Candidatus Nitrosopumilus, and Marine Group II.

Environmental DNA metabarcoding serves as a promising method for aquatic species monitoring and management: A review focused on its workflow, applications, challenges and prospects

Marine and freshwater biodiversity is under threat from both natural and manmade causes. Biological monitoring is currently a top priority for biodiversity protection. Given present limitations, traditional biological monitoring methods may not achieve the proposed monitoring aims. Environmental DNA metabarcoding technology reflects species information by capturing and extracting DNA from environmental samples, using molecular biology techniques to sequence and analyze the DNA, and comparing the obtained information with existing reference libraries to obtain species identification. However, its practical application has highlighted several limitations. This paper summarizes the main steps in the environmental application of eDNA metabarcoding technology in aquatic ecosystems, including the discovery of unknown species, the detection of invasive species, and evaluations of biodiversity. At present, with the rapid development of big data and artificial intelligence, certain advanced technologies and devices can be combined with environmental DNA metabarcoding technology to promote further development of aquatic species monitoring and management.

Large-scale sampling of the freshwater microbiome suggests pollution-driven ecosystem changes

Freshwater microbes play a crucial role in the global carbon cycle. Anthropogenic stressors that lead to changes in these microbial communities are likely to have profound consequences for freshwater ecosystems. Using field data from the coordinated sampling of 617 lakes, ponds, rivers, and streams by citizen scientists, we observed linkages between microbial community composition, light and chemical pollution, and greenhouse gas concentration. All sampled water bodies were net emitters of CO₂, with higher concentrations in running waters, and increasing concentrations at higher latitudes. Light pollution occurred at 75% of sites, was higher in urban areas and along rivers, and had a measurable effect on the microbial alpha diversity. Genetic elements suggestive of chemical stress and antimicrobial resistances (IntI1, blaOX₅₈) were found in 85% of sites, and were also more prevalent in urban streams and rivers. Light pollution and CO₂ were significantly related to microbial community composition, with CO₂ inversely related to microbial phototrophy. Results of synchronous nationwide sampling indicate that pollution-driven alterations to the freshwater microbiome lead to changes in CO₂ production in natural waters and highlight the vulnerability of running waters to anthropogenic stressors.

Monitoring harmful microalgal species and their appearance in Tokyo Bay, Japan, using metabarcoding

During the recent decade, high-throughput sequencing (HTS) techniques, in particular, DNA metabarcoding, have facilitated increased detection of biodiversity, including harmful algal bloom (HAB) species. In this study, the presence of HAB species and their appearance patterns were investigated by employing molecular and light microscopy-based monitoring in Tokyo Bay, Japan. The potential co-appearance patterns between the HAB species, as well as with other eukaryotes and prokaryotes were investigated using correlation and association rule-based time-series analysis. In total, 40 unique HAB species were detected, including 12 toxin-producing HAB species previously not reported from the area. More than half of the HAB species were present throughout the sampling season (summer to autumn) and no structuring or succession patterns associated with the environmental conditions could be detected. Statistically significant (p < 0.05, rS ranging from −0.88 to 0.90) associations were found amongst the HAB species and other eukaryotic and prokaryotic species, including genera containing growth-limiting bacteria. However, significant correlations between species differed amongst the years, indicating that variability in environmental conditions between the years may have a stronger influence on the microalgal community structure and interspecies interactions than the variability during the sampling season. The association rule-based time-series analysis allowed the detection of a previously reported negative relationship between Synechococcus sp. and Skeletonema sp. in nature. Overall, the results support the applicability of metabarcoding and HTS-based microalgae monitoring, as it facilitates more precise species identification compared to light microscopy, as well as provides input for investigating potential interactions amongst different species/groups through simultaneous detection of multiple species/genera.

Assessment of Biological Contribution to Natural Recovery of Anthropized Freshwater Sediments From Argentina: Autochthonous Microbiome Structure and Functional Prediction

Monitored natural recovery (MNR) is an in situ technique of conventional remediation for the treatment of contaminated sediments that relies on natural processes to reduce the bioavailability or toxicity of contaminants. Metabarcoding and bioinformatics approaches to infer functional prediction were applied in bottom sediments of a tributary drainage channel of Río de La Plata estuary, in order to assess the biological contribution to MNR. Hydrocarbon concentration in water samples and surface sediments was below the detection limit. Surface sediments were represented with high available phosphorous, alkaline pH, and the bacterial classes Anaerolineae, Planctomycetia, and Deltaproteobacteria. The functional prediction in surface sediments showed an increase of metabolic activity, carbon fixation, methanogenesis, and synergistic relationships between Archaeas, Syntrophobacterales, and Desulfobacterales. The prediction in non-surface sediments suggested the capacity to respond to different kinds of environmental stresses (oxidative, osmotic, heat, acid pH, and heavy metals), predicted mostly in Lactobacillales order, and the capacity of Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, and Actinomyces classes to degrade xenobiotic compounds. Canonical correspondence analysis (CCA) suggests that depth, phosphate content, redox potential, and pH were the variables that structured the bacterial community and not the hydrocarbons. The characterization of sediments by metabarcoding and functional prediction approaches, allowed to assess how the microbial activity would contribute to the recovery of the site.

The Microbiome Stress Project: Toward a Global Meta-Analysis of Environmental Stressors and Their Effects on Microbial Communities

Microbial community structure is highly sensitive to natural (e.g., drought, temperature, fire) and anthropogenic (e.g., heavy metal exposure, land-use change) stressors. However, despite an immense amount of data generated, systematic, cross-environment analyses of microbiome responses to multiple disturbances are lacking. Here, we present the Microbiome Stress Project, an open-access database of environmental and host-associated 16S rRNA amplicon sequencing studies collected to facilitate cross-study analyses of microbiome responses to stressors. This database will comprise published and unpublished datasets re-processed from the raw sequences into exact sequence variants using our standardized computational pipeline. Our database will provide insight into general response patterns of microbiome diversity, structure, and stability to environmental stressors. It will also enable the identification of cross-study associations between single or multiple stressors and specific microbial clades. Here, we present a proof-of-concept meta-analysis of 606 microbiomes (from nine studies) to assess microbial community responses to: (1) one stressor in one environment: soil warming across a variety of soil types, (2) a range of stressors in one environment: soil microbiome responses to a comprehensive set of stressors (incl. temperature, diesel, antibiotics, land use change, drought, and heavy metals), (3) one stressor across a range of environments: copper exposure effects on soil, sediment, activated-sludge reactors, and gut environments, and (4) the general trends of microbiome stressor responses. Overall, we found that stressor exposure significantly decreases microbiome alpha diversity and increases beta diversity (community dispersion) across a range of environments and stressor types. We observed a hump-shaped relationship between microbial community resistance to stressors (i.e., the average pairwise similarity score between the control and stressed communities) and alpha diversity. We used Phylofactor to identify microbial clades and individual taxa as potential bioindicators of copper contamination across different environments. Using standardized computational and statistical methods, the Microbiome Stress Project will leverage thousands of existing datasets to build a general framework for how microbial communities respond to environmental stress.

Comparison of environmental DNA and bulk-sample metabarcoding using highly degenerate cytochrome c oxidase I primers

Freshwater biodiversity provides important ecosystem services and is at the core of water quality monitoring worldwide. To assess freshwater biodiversity, genetic methods such as metabarcoding are increasingly used as they are faster and allow better taxonomic resolution than manual identification methods. Either sampled organisms are used directly for "bulk metabarcoding," or water is filtered and the extracted environmental DNA serves as a proxy for biodiversity via "eDNA metabarcoding." Despite the advantages of both methods, questions remain regarding their comparability and applicability for routine biomonitoring and stressor impact assessment. Therefore, we compared metabarcoding results from bulk and eDNA samples taken from 19 streams spanning a wide gradient of farming intensities in New Zealand. We performed PCR with highly degenerate cytochrome c oxidase I primers and sequenced libraries on an Illumina MiSeq. The inferred community composition differed strongly between the two methods. More taxa were captured by eDNA than bulk-sample metabarcoding (5,819 vs. 1,483), but more of the commonly used invertebrate bioindicator taxa (mayflies, stoneflies and caddisflies) were found in bulk (47) than eDNA samples (37). Catchment-wide and local land use impacts on communities were detected better by eDNA metabarcoding, especially for non-metazoan taxa. Our findings imply that bulk-sample metabarcoding resembles classical freshwater biomonitoring approaches better, as more indicator macroinvertebrate taxa are captured. However, eDNA metabarcoding might be better suited to infer the impact of stressors on stream ecosystems at larger scales, as many new and potentially more informative taxa are registered. We therefore suggest exploring both methods in future assessments of stream biodiversity.