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Smucker NJ, Pilgrim EM, Nietch CT, Gains-Germain L, Carpenter C, Darling JA, Yuan LL, Mitchell RM, Pollard AI. Using DNA metabarcoding to characterize national scale diatom-environment relationships and to develop indicators in streams and rivers of the United States. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 939:173502. [PMID: 38815829 DOI: 10.1016/j.scitotenv.2024.173502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 05/16/2024] [Accepted: 05/23/2024] [Indexed: 06/01/2024]
Abstract
Recent advancements in DNA techniques, metabarcoding, and bioinformatics could help expand the use of benthic diatoms in monitoring and assessment programs by providing relatively quick and increasingly cost-effective ways to quantify diatom diversity in environmental samples. However, such applications of DNA-based approaches are relatively new, and in the United States, unknowns regarding their applications at large scales exist because only a few small-scale studies have been done. Here, we present results from the first nationwide survey to use DNA metabarcoding (rbcL) of benthic diatoms, which were collected from 1788 streams and rivers across nine ecoregions spanning the conterminous USA. At the national scale, we found that diatom assemblage structure (1) was strongly associated with total phosphorus and total nitrogen concentrations, conductivity, and pH and (2) had clear patterns that corresponded with differences in these variables among the nine ecoregions. These four variables were strong predictors of diatom assemblage structure in ecoregion-specific analyses, but our results also showed that diatom-environment relationships, the importance of environmental variables, and the ranges of these variables within which assemblage changes occurred differed among ecoregions. To further examine how assemblage data could be used for biomonitoring purposes, we used indicator species analysis to identify ecoregion-specific taxa that decreased or increased along each environmental gradient, and we used their relative abundances of gene reads in samples as metrics. These metrics were strongly correlated with their corresponding variable of interest (e.g., low phosphorus diatoms with total phosphorus concentrations), and generalized additive models showed how their relationships compared among ecoregions. These large-scale national patterns and nine sets of ecoregional results demonstrated that diatom DNA metabarcoding is a robust approach that could be useful to monitoring and assessment programs spanning the variety of conditions that exist throughout the conterminous United States.
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Affiliation(s)
- Nathan J Smucker
- United States Environmental Protection Agency, Office of Research and Development, Cincinnati, OH 45268, USA.
| | - Erik M Pilgrim
- United States Environmental Protection Agency, Office of Research and Development, Cincinnati, OH 45268, USA
| | - Christopher T Nietch
- United States Environmental Protection Agency, Office of Research and Development, Cincinnati, OH 45268, USA
| | | | | | - John A Darling
- United States Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC 27703, USA
| | - Lester L Yuan
- United States Environmental Protection Agency, Office of Water, Washington, D.C. 20004, USA
| | - Richard M Mitchell
- United States Environmental Protection Agency, Office of Wetlands, Oceans, and Watersheds, Washington, D.C. 20004, USA
| | - Amina I Pollard
- United States Environmental Protection Agency, Office of Water, Washington, D.C. 20004, USA
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2
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Múrria C, Wangensteen OS, Somma S, Väisänen L, Fortuño P, Arnedo MA, Prat N. Taxonomic accuracy and complementarity between bulk and eDNA metabarcoding provides an alternative to morphology for biological assessment of freshwater macroinvertebrates. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 935:173243. [PMID: 38761946 DOI: 10.1016/j.scitotenv.2024.173243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 04/04/2024] [Accepted: 05/12/2024] [Indexed: 05/20/2024]
Abstract
Determining biological status of freshwater ecosystems is critical for ensuring ecosystem health and maintaining associated services to such ecosystems. Freshwater macroinvertebrates respond predictably to environmental disturbances and are widely used in biomonitoring programs. However, many freshwater species are difficult to capture and sort from debris or substrate and morphological identification is challenging, especially larval stages, damaged specimens, or hyperdiverse groups such as Diptera. The advent of high throughput sequencing technologies has enhanced DNA barcoding tools to automatise species identification for whole communities, as metabarcoding is increasingly used to monitor biodiversity. However, recent comparisons have revealed little congruence between morphological and molecular-based identifications. Using broad range universal primers for DNA barcode marker cox1, we compare community composition captured between morphological and molecular-based approaches from different sources - tissue-based (bulk benthic and bulk drift samples) and environmental DNA (eDNA, filtered water) metabarcoding - for samples collected along a gradient of anthropogenic disturbances. For comparability, metabarcoding taxonomic assignments were filtered by taxa included in the standardised national biological metric IBMWP. At the family level, bulk benthic metabarcoding showed the highest congruence with morphology, and the most abundant taxa were captured by all techniques. Richness captured by morphology and bulk benthic metabarcoding decreased along the gradient, whereas richness recorded by eDNA remained constant and increased downstream when sequencing bulk drift. Estimates of biological metrics were higher using molecular than morphological identification. At species level, diversity captured by bulk benthic samples were higher than the other techniques. Importantly, bulk benthic and eDNA metabarcoding captured different and complementary portions of the community - benthic versus water column, respectively - and their combined use is recommended. While bulk benthic metabarcoding can likely replace morphology using similar benthic biological indices, water eDNA will require new metrics because this technique sequences a different portion of the community.
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Affiliation(s)
- Cesc Múrria
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain; Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Catalonia, Spain; Grup de Recerca Zoological Systematics & Evolution (ZooSysEvo), Universitat de Barcelona, Barcelona, Catalonia, Spain.
| | - Owen S Wangensteen
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain; Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Catalonia, Spain; Norwegian College of Fishery Science, UiT - The Arctic University of Norway, Tromsø, Norway
| | - Simona Somma
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Leif Väisänen
- Stream Ecology Research Group, Department of Ecology and Genetics, University of Oulu, Finland
| | - Pau Fortuño
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain; Grup de Recerca Freshwater Ecology, Hydrology and Management (FEHM), Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Miquel A Arnedo
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain; Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Catalonia, Spain; Grup de Recerca Zoological Systematics & Evolution (ZooSysEvo), Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Narcís Prat
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain; Grup de Recerca Freshwater Ecology, Hydrology and Management (FEHM), Universitat de Barcelona, Barcelona, Catalonia, Spain
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3
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Pawlowski J, Cermakova K, Cordier T, Frontalini F, Apothéloz-Perret-Gentil L, Merzi T. Assessing the potential of nematode metabarcoding for benthic monitoring of offshore oil platforms. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 933:173092. [PMID: 38729369 DOI: 10.1016/j.scitotenv.2024.173092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/11/2024] [Accepted: 05/07/2024] [Indexed: 05/12/2024]
Abstract
Environmental DNA metabarcoding is gaining momentum as a time and cost-effective tool for biomonitoring and environmental impact assessment. Yet, its use as a replacement for the conventional marine benthic monitoring based on morphological analysis of macrofauna is still challenging. Here we propose to study the meiofauna, which is much better represented in sediment DNA samples. We focus on nematodes, which are the most numerous and diverse group of meiofauna. Our aim is to assess the potential of nematode metabarcoding to monitor impacts associated with offshore oil platform activities. To achieve this goal, we used nematode-optimized marker (18S V1V2-Nema) and universal eukaryotic marker (18S V9) region to analyse 252 sediment DNA samples collected near three offshore oil platforms in the North Sea. For both markers, we analysed changes in alpha and beta diversity in relation to distance from the platforms and environmental variables. We also defined three impact classes based on selected environmental variables that are associated with oil extraction activities and used random forest classifiers to compare the predictive performance of both datasets. Our results show that alpha- and beta-diversity of nematodes varies with the increasing distance from the platforms. The variables directly related to platform activity, such as Ba and THC, strongly influence the nematode community. The nematode metabarcoding data provide more robust predictive models than eukaryotic data. Furthermore, the nematode community appears more stable in time and space, as illustrated by the overlap of nematode datasets obtained from the same platform three years apart. A significative negative correlation between distance and Shannon diversity also advocates for higher performance of the V1V2-Nema over the V9. Overall, these results suggest that the sensitivity of nematodes is higher compared to the eukaryotic community. Hence, nematode metabarcoding has the potential to become an effective tool for benthic monitoring in marine environment.
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Affiliation(s)
- J Pawlowski
- Institute of Oceanology, Polish Academy of Sciences, Sopot, Poland; ID-Gene ecodiagnostics, Plan-les-Ouates, Switzerland.
| | - K Cermakova
- ID-Gene ecodiagnostics, Plan-les-Ouates, Switzerland
| | - T Cordier
- NORCE Climate and Environment, NORCE Norwegian Research Centre AS, Norway
| | - F Frontalini
- Department of Pure and Applied Sciences, University of Urbino "Carlo Bo", Urbino, Italy
| | | | - T Merzi
- TotalEnergies OneTech, Centre Scientifique et Technique Jean Feger, Pau, France
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4
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Turrini P, Chebbi A, Riggio FP, Visca P. The geomicrobiology of limestone, sulfuric acid speleogenetic, and volcanic caves: basic concepts and future perspectives. Front Microbiol 2024; 15:1370520. [PMID: 38572233 PMCID: PMC10987966 DOI: 10.3389/fmicb.2024.1370520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 03/07/2024] [Indexed: 04/05/2024] Open
Abstract
Caves are ubiquitous subterranean voids, accounting for a still largely unexplored surface of the Earth underground. Due to the absence of sunlight and physical segregation, caves are naturally colonized by microorganisms that have developed distinctive capabilities to thrive under extreme conditions of darkness and oligotrophy. Here, the microbiomes colonizing three frequently studied cave types, i.e., limestone, sulfuric acid speleogenetic (SAS), and lava tubes among volcanic caves, have comparatively been reviewed. Geological configurations, nutrient availability, and energy flows in caves are key ecological drivers shaping cave microbiomes through photic, twilight, transient, and deep cave zones. Chemoheterotrophic microbial communities, whose sustenance depends on nutrients supplied from outside, are prevalent in limestone and volcanic caves, while elevated inorganic chemical energy is available in SAS caves, enabling primary production through chemolithoautotrophy. The 16S rRNA-based metataxonomic profiles of cave microbiomes were retrieved from previous studies employing the Illumina platform for sequencing the prokaryotic V3-V4 hypervariable region to compare the microbial community structures from different cave systems and environmental samples. Limestone caves and lava tubes are colonized by largely overlapping bacterial phyla, with the prevalence of Pseudomonadota and Actinomycetota, whereas the co-dominance of Pseudomonadota and Campylobacterota members characterizes SAS caves. Most of the metataxonomic profiling data have so far been collected from the twilight and transient zones, while deep cave zones remain elusive, deserving further exploration. Integrative approaches for future geomicrobiology studies are suggested to gain comprehensive insights into the different cave types and zones. This review also poses novel research questions for unveiling the metabolic and genomic capabilities of cave microorganisms, paving the way for their potential biotechnological applications.
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Affiliation(s)
- Paolo Turrini
- Department of Science, Roma Tre University, Rome, Italy
| | - Alif Chebbi
- Department of Science, Roma Tre University, Rome, Italy
| | | | - Paolo Visca
- Department of Science, Roma Tre University, Rome, Italy
- National Biodiversity Future Center, Palermo, Italy
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5
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Colas S, Le Faucheur S. How do biomarkers dance? Specific moves of defense and damage biomarkers for biological interpretation of dose-response model trends. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133180. [PMID: 38104522 DOI: 10.1016/j.jhazmat.2023.133180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 11/13/2023] [Accepted: 12/03/2023] [Indexed: 12/19/2023]
Abstract
Omics studies are currently increasingly used in ecotoxicology to highlight the induction of known or novel biomarkers when organisms are exposed to contaminants. Although it is virtually impossible to identify all biomarkers from all organisms, biomarkers can be grouped as defense or damage biomarkers, exhibiting a limited number of response trends. Our working hypothesis is that defense and damage biomarkers follow different dose-response patterns. A meta-analysis of 156 articles and 2595 observations of dose-response curves of defense and damage biomarkers was carried out in order to characterize the response trends of these biological parameters in a large panel of living organisms (18 phyla) exposed to inorganic or organic contaminants (176 in total). Using multinomial logistic regression models, defense biomarkers were found to describe biphasic responses (bell- and U-shaped) to a greater extent (2.5 times) than damage biomarkers. In contrast, damage biomarkers varied mainly monotonically (decreasing or increasing), representing 85% of the observations. Neither the nature of the contaminant nor the type of organisms belonging to 4 kingdoms, influence these specific responses. This result suggests that cellular defense and damage mechanisms are not specific to stressors and are conserved throughout life. Trend analysis of dose-response models as a biological interpretation of biomarkers could thus be a valuable way to exploit large omics datasets.
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Affiliation(s)
- Simon Colas
- Universite de Pau et des Pays de l'Adour, E2S-UPPA, CNRS, IPREM, Pau, France.
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6
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Múrria C, Maceda-Veiga A, Barata C, Gomà J, Faria M, Antich A, Arnedo MA, Bonada N, Prat N. From biomarkers to community composition: Negative effects of UV/chlorine-treated reclaimed urban wastewater on freshwater biota. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169561. [PMID: 38142994 DOI: 10.1016/j.scitotenv.2023.169561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/25/2023] [Accepted: 12/19/2023] [Indexed: 12/26/2023]
Abstract
The use of urban wastewater reclaimed water has recently increased across the globe to restore stream environmental flows and mitigate the effects of water scarcity. Reclaimed water is disinfected using different treatments, but their effects into the receiving rivers are little studied. Physiological bioassays and biomarkers can detect sub-lethal effects on target species, but do not provide information on changes in community structure. In contrast, official monitoring programs use community structure information but often at coarse taxonomic resolution level that may fail to detect species level impacts. Here, we combined commonly used biomonitoring approaches from organism physiology to community species composition to scan a broad range of effects of disinfection of reclaimed water by UV-light only and both UV/chlorine on the biota. We (1) performed bioassays in one laboratory species (water flea Daphnia magna) and measured biomarkers in two wild species (caddisfly Hydropsyche exocellata and the barbel Luciobarbus graellsii), (2) calculated standard indices of biotic quality (IBQ) for diatoms, benthic macroinvertebrates, and fishes, and (3) analysed community species composition of eukaryotes determined by Cytochrome Oxidase C subunit I (cox1) metabarcoding. Only the UV/chlorine treatment caused significant changes in feeding rates of D. magna and reduced antioxidant defenses, increased anaerobic metabolism and altered the levels of lipid peroxidiation in H. exocellata. However, inputs of reclaimed water were significantly associated with a greater proportion of circulating neutrophils and LG-PAS cells in L. graellsii. Despite IBQ did not discriminate between the two water treatments, metabarcoding data detected community composition changes upon exposure to UV/chlorine reclaimed water. Overall, despite the effects of UV/chlorine-treated water were transient, our study suggests that UV-light treated is less harmful for freshwater biota than UV/chlorine-treated reclaimed water, but those effects depend of the organizational level.
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Affiliation(s)
- Cesc Múrria
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain; Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Catalonia, Spain; Grup de Recerca Zoological Systematics & Evolution (ZooSysEvo), Universitat de Barcelona, Barcelona, Catalonia, Spain.
| | - Alberto Maceda-Veiga
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain; Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Catalonia, Spain; Grup de Recerca FORESTREAM, Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Carlos Barata
- Institute for Environmental Assessment and Water Research (IDAEA-CSIC), Jordi Girona 18, 08034 Barcelona, Catalonia, Spain
| | - Joan Gomà
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain; Grup de Recerca Freshwater Ecology, Hydrology and Management (FEHM), Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Melissa Faria
- Institute for Environmental Assessment and Water Research (IDAEA-CSIC), Jordi Girona 18, 08034 Barcelona, Catalonia, Spain
| | - Adrià Antich
- Department of Marine Ecology, Centre for Advanced Studies of Blanes (CEAB-CSIC), Blanes (Girona), Catalonia, Spain
| | - Miquel A Arnedo
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain; Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Catalonia, Spain; Grup de Recerca Zoological Systematics & Evolution (ZooSysEvo), Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Núria Bonada
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain; Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Catalonia, Spain; Grup de Recerca Freshwater Ecology, Hydrology and Management (FEHM), Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Narcís Prat
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain; Grup de Recerca Freshwater Ecology, Hydrology and Management (FEHM), Universitat de Barcelona, Barcelona, Catalonia, Spain
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7
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Schaerer L, Ghannam R, Olson A, Van Camp A, Techtmann S. Persistence of location-specific microbial signatures on boats during voyages. MARINE POLLUTION BULLETIN 2024; 199:115884. [PMID: 38118397 DOI: 10.1016/j.marpolbul.2023.115884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 11/21/2023] [Accepted: 12/01/2023] [Indexed: 12/22/2023]
Abstract
Objects collect microorganisms from their surroundings and develop a microbial "fingerprint" that may be useful for determining an object's past location (provenance). It may be possible to use ubiquitous microorganisms for forensics or as environmental sensors. Here, we use microbial communities in the Chesapeake Bay region to demonstrate the use of natural microorganisms as biological sensors to determine the past location of boats. The microbiomes of two boats and of the open water were sampled as these vessels traveled from the Port of Baltimore to the Port of Norfolk, and back to Baltimore. 16S rRNA sequencing was performed to identify microorganisms. Differential abundance and machine learning analyses were utilized to identify microbial signatures and predicted probabilities which were used to determine the vessel's previous location. The work presented here provides a better understanding of how microbes in aquatic systems can be leveraged as utility for object biosensors.
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Affiliation(s)
- Laura Schaerer
- Department of Biological Sciences, Michigan Technological University, Houghton, MI 49931, USA
| | - Ryan Ghannam
- Department of Biological Sciences, Michigan Technological University, Houghton, MI 49931, USA
| | - Allison Olson
- Department of Biological Sciences, Michigan Technological University, Houghton, MI 49931, USA
| | - Annika Van Camp
- Department of Biological Sciences, Michigan Technological University, Houghton, MI 49931, USA
| | - Stephen Techtmann
- Department of Biological Sciences, Michigan Technological University, Houghton, MI 49931, USA.
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8
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Arepalli PG, Khetavath JN. An IoT framework for quality analysis of aquatic water data using time-series convolutional neural network. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:125275-125294. [PMID: 37284950 DOI: 10.1007/s11356-023-27922-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 05/22/2023] [Indexed: 06/08/2023]
Abstract
Water quality monitoring and analysis in fish farms are of paramount importance for the aquaculture sector; however, traditional methods can pose difficulties. To address this challenge, this study proposes an IoT-based deep learning model using a time-series convolution neural network (TMS-CNN) for monitoring and analyzing water quality in fish farms. The proposed TMS-CNN model can handle spatial-temporal data effectively by considering temporal and spatial dependencies between data points, which allows it to capture patterns and trends that would not be possible with traditional models. The model calculates the water quality index (WQI) using correlation analysis and assigns class labels to the data based on the WQI. Then, the TMS-CNN model analyzed the time-series data. It produces high accuracy of 96.2% in analysis of water quality parameters for fish growth and mortality conditions. The proposed model accuracy is higher than the current best model MANN, which has only had an accuracy of 91%.
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Affiliation(s)
- Peda Gopi Arepalli
- Department of Computer Science & Engineering, National Institute of Technology Raipur, Raipur, India
| | - Jairam Naik Khetavath
- Department of Computer Science & Engineering, National Institute of Technology Raipur, Raipur, India.
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9
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Hu H, Wei XY, Liu L, Wang YB, Jia HJ, Bu LK, Pei DS. Supervised machine learning improves general applicability of eDNA metabarcoding for reservoir health monitoring. WATER RESEARCH 2023; 246:120686. [PMID: 37812979 DOI: 10.1016/j.watres.2023.120686] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 09/25/2023] [Accepted: 09/29/2023] [Indexed: 10/11/2023]
Abstract
Effective and standardized monitoring methodologies are vital for successful reservoir restoration and management. Environmental DNA (eDNA) metabarcoding sequencing offers a promising alternative for biomonitoring and can overcome many limitations of traditional morphological bioassessment. Recent attempts have even shown that supervised machine learning (SML) can directly infer biotic indices (BI) from eDNA metabarcoding data, bypassing the cumbersome calculation process of BI regardless of the taxonomic assignment of eDNA sequences. However, questions surrounding the general applicability of this taxonomy-free approach to monitoring reservoir health remain unclear, including model stability, feature selection, algorithm choice, and multi-season biomonitoring. Here, we firstly developed a novel biological integrity index (Me-IBI) that integrates multitrophic interactions and environmental information, based on taxonomy-assigned eDNA metabarcoding data. The Me-IBI can better distinguish the actual health status of the Three Gorges Reservoir (TGR) than physicochemical assessments and have a clear response to human activity. Then, taking this reliable Me-IBI as a supervised label, we compared the impact of selecting different numbers of features and SML algorithms on the stability and predictive performance of the model for predicting ecological conditions in multiple seasons using taxonomy-free eDNA metabarcoding data. We discovered that even with a small number of features, different SML algorithms can establish a stable model and obtain excellent predictive performance. Finally, we proposed a four-step strategy for standardized routine biomonitoring using SML tools. Our study firstly explores the general applicability problem of the taxonomy-free eDNA-SML approach and establishes a solid foundation for the large-scale and standardized biomonitoring application.
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Affiliation(s)
- Huan Hu
- Chongqing Jiaotong University, Chongqing, 400074, China; Chongqing Institute of Green and Intelligent Technology, Chongqing School of University of Chinese Academy of Sciences, Chinese Academy of Sciences, Chongqing, 400714, China
| | - Xing-Yi Wei
- Chongqing Jiaotong University, Chongqing, 400074, China; Chongqing Institute of Green and Intelligent Technology, Chongqing School of University of Chinese Academy of Sciences, Chinese Academy of Sciences, Chongqing, 400714, China
| | - Li Liu
- Chongqing Institute of Green and Intelligent Technology, Chongqing School of University of Chinese Academy of Sciences, Chinese Academy of Sciences, Chongqing, 400714, China; Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Yuan-Bo Wang
- Chongqing Jiaotong University, Chongqing, 400074, China; Chongqing Institute of Green and Intelligent Technology, Chongqing School of University of Chinese Academy of Sciences, Chinese Academy of Sciences, Chongqing, 400714, China
| | - Huang-Jie Jia
- Chongqing Institute of Green and Intelligent Technology, Chongqing School of University of Chinese Academy of Sciences, Chinese Academy of Sciences, Chongqing, 400714, China
| | - Ling-Kang Bu
- Chongqing Institute of Green and Intelligent Technology, Chongqing School of University of Chinese Academy of Sciences, Chinese Academy of Sciences, Chongqing, 400714, China
| | - De-Sheng Pei
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China.
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10
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Cambon MC, Trillat M, Lesur-Kupin I, Burlett R, Chancerel E, Guichoux E, Piouceau L, Castagneyrol B, Le Provost G, Robin S, Ritter Y, Van Halder I, Delzon S, Bohan DA, Vacher C. Microbial biomarkers of tree water status for next-generation biomonitoring of forest ecosystems. Mol Ecol 2023; 32:5944-5958. [PMID: 37815414 DOI: 10.1111/mec.17149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 09/14/2023] [Accepted: 09/19/2023] [Indexed: 10/11/2023]
Abstract
Next-generation biomonitoring proposes to combine machine-learning algorithms with environmental DNA data to automate the monitoring of the Earth's major ecosystems. In the present study, we searched for molecular biomarkers of tree water status to develop next-generation biomonitoring of forest ecosystems. Because phyllosphere microbial communities respond to both tree physiology and climate change, we investigated whether environmental DNA data from tree phyllosphere could be used as molecular biomarkers of tree water status in forest ecosystems. Using an amplicon sequencing approach, we analysed phyllosphere microbial communities of four tree species (Quercus ilex, Quercus robur, Pinus pinaster and Betula pendula) in a forest experiment composed of irrigated and non-irrigated plots. We used these microbial community data to train a machine-learning algorithm (Random Forest) to classify irrigated and non-irrigated trees. The Random Forest algorithm detected tree water status from phyllosphere microbial community composition with more than 90% accuracy for oak species, and more than 75% for pine and birch. Phyllosphere fungal communities were more informative than phyllosphere bacterial communities in all tree species. Seven fungal amplicon sequence variants were identified as candidates for the development of molecular biomarkers of water status in oak trees. Altogether, our results show that microbial community data from tree phyllosphere provides information on tree water status in forest ecosystems and could be included in next-generation biomonitoring programmes that would use in situ, real-time sequencing of environmental DNA to help monitor the health of European temperate forest ecosystems.
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Affiliation(s)
- Marine C Cambon
- INRAE, University of Bordeaux, BIOGECO, Pessac, France
- School of Natural Sciences, Bangor University, Bangor, UK
| | | | - Isabelle Lesur-Kupin
- INRAE, University of Bordeaux, BIOGECO, Pessac, France
- HelixVenture, Mérignac, France
| | - Régis Burlett
- INRAE, University of Bordeaux, BIOGECO, Pessac, France
| | | | | | | | | | | | | | - Yves Ritter
- INRAE, University of Bordeaux, BIOGECO, Pessac, France
| | | | | | - David A Bohan
- Agroécologie, INRAE, Université Bourgogne Franche-Comté, Dijon, France
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11
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Zhang M, Zou Y, Xiao S, Hou J. 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 POLLUTION BULLETIN 2023; 194:115430. [PMID: 37647798 DOI: 10.1016/j.marpolbul.2023.115430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 08/10/2023] [Accepted: 08/15/2023] [Indexed: 09/01/2023]
Abstract
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.
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Affiliation(s)
- Miaolian Zhang
- MOE Key Laboratory of Resources and Environmental Systems Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
| | - Yingtong Zou
- State Key Joint Laboratory of Environment Simulation and Pollution Control, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shan Xiao
- MOE Key Laboratory of Resources and Environmental Systems Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
| | - Jing Hou
- MOE Key Laboratory of Resources and Environmental Systems Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China.
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12
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Keck F, Brantschen J, Altermatt F. A combination of machine-learning and eDNA reveals the genetic signature of environmental change at the landscape levels. Mol Ecol 2023; 32:4791-4800. [PMID: 37436405 DOI: 10.1111/mec.17073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/23/2023] [Accepted: 07/03/2023] [Indexed: 07/13/2023]
Abstract
The current advances of environmental DNA (eDNA) bring profound changes to ecological monitoring and provide unique insights on the biological diversity of ecosystems. The very nature of eDNA data is challenging yet also revolutionizing how biological monitoring information is analysed. In particular, new metrics and approaches should take full advantage of the extent and detail of molecular data produced by genetic methods. In this perspective, machine learning algorithms are particularly promising as they can capture complex relationships between the multiple environmental pressures and the diversity of biological communities. We investigated the potential of a new generation of biomonitoring tools that implement machine-learning techniques to fully exploit eDNA datasets. We trained a machine learning model to discriminate between reference and impacted communities of freshwater macroinvertebrates and assessed its performances using a large eDNA dataset collected at 64 standard federal monitoring sites across Switzerland. We show that a model trained on eDNA is significantly better than a naive model and performs similarly to a model trained on traditional data. Our proof-of-concept shows that such a combination of eDNA and machine learning approaches has the potential to complement or even replace traditional environmental monitoring, and could be scaled along temporal or spatial dimensions.
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Affiliation(s)
- François Keck
- Department of Aquatic Ecology, Eawag, Swiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland
- Department of Evolutionary Biology and Environmental Studies, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Jeanine Brantschen
- Department of Aquatic Ecology, Eawag, Swiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland
- Department of Evolutionary Biology and Environmental Studies, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Florian Altermatt
- Department of Aquatic Ecology, Eawag, Swiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland
- Department of Evolutionary Biology and Environmental Studies, Faculty of Science, University of Zurich, Zurich, Switzerland
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13
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Fontaine L, Pin L, Savio D, Friberg N, Kirschner AKT, Farnleitner AH, Eiler A. Bacterial bioindicators enable biological status classification along the continental Danube river. Commun Biol 2023; 6:862. [PMID: 37596339 PMCID: PMC10439154 DOI: 10.1038/s42003-023-05237-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 08/10/2023] [Indexed: 08/20/2023] Open
Abstract
Despite the importance of bacteria in aquatic ecosystems and their predictable diversity patterns across space and time, biomonitoring tools for status assessment relying on these organisms are widely lacking. This is partly due to insufficient data and models to identify reliable microbial predictors. Here, we show metabarcoding in combination with multivariate statistics and machine learning allows to identify bacterial bioindicators for existing biological status classification systems. Bacterial beta-diversity dynamics follow environmental gradients and the observed associations highlight potential bioindicators for ecological outcomes. Spatio-temporal links spanning the microbial communities along the river allow accurate prediction of downstream biological status from upstream information. Network analysis on amplicon sequence veariants identify as good indicators genera Fluviicola, Acinetobacter, Flavobacterium, and Rhodoluna, and reveal informational redundancy among taxa, which coincides with taxonomic relatedness. The redundancy among bacterial bioindicators reveals mutually exclusive taxa, which allow accurate biological status modeling using as few as 2-3 amplicon sequence variants. As such our models show that using a few bacterial amplicon sequence variants from globally distributed genera allows for biological status assessment along river systems.
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Affiliation(s)
- Laurent Fontaine
- Section for Aquatic Biology and Toxicology, Centre for Biogeochemistry in the Anthropocene, Department of Biosciences, University of Oslo, Blindernv. 31, 0371, Oslo, Norway
| | - Lorenzo Pin
- Section for Aquatic Biology and Toxicology, Centre for Biogeochemistry in the Anthropocene, Department of Biosciences, University of Oslo, Blindernv. 31, 0371, Oslo, Norway
- Norsk Institutt for Vannforskning (NIVA) Gaustadalléen 21, 0349, Oslo, Norway
| | - Domenico Savio
- Division Water Quality and Health, Department Pharmacology, Physiology and Microbiology, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
- Interuniversity Cooperation Centre for Water and Health, Vienna, Austria
- Research Group for Microbiology and Molecular Diagnostics 166/5/3, Institute of Chemical, Environmental and Bioscience Engineering, TU Wien, Vienna, Austria
| | - Nikolai Friberg
- Norsk Institutt for Vannforskning (NIVA) Gaustadalléen 21, 0349, Oslo, Norway
- Freshwater Biological Section, University of Copenhagen, Universitetsparken 4, Third Floor, 2100, Copenhagen, Denmark
- School of Geography, University of Leeds, Leeds, LS2 9JT, UK
| | - Alexander K T Kirschner
- Division Water Quality and Health, Department Pharmacology, Physiology and Microbiology, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
- Interuniversity Cooperation Centre for Water and Health, Vienna, Austria
- Medical University Vienna, Institute for Hygiene and Applied Immunology, Water Microbiology, Kinderspitalgasse 15, 1090, Vienna, Austria
| | - Andreas H Farnleitner
- Division Water Quality and Health, Department Pharmacology, Physiology and Microbiology, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
- Interuniversity Cooperation Centre for Water and Health, Vienna, Austria
- Research Group for Microbiology and Molecular Diagnostics 166/5/3, Institute of Chemical, Environmental and Bioscience Engineering, TU Wien, Vienna, Austria
| | - Alexander Eiler
- Section for Aquatic Biology and Toxicology, Centre for Biogeochemistry in the Anthropocene, Department of Biosciences, University of Oslo, Blindernv. 31, 0371, Oslo, Norway.
- eDNA Solutions AB, Kärrbogata 22, 44196, Alingsås, Sweden.
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14
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Machuca-Sepúlveda J, Miranda J, Lefin N, Pedroso A, Beltrán JF, Farias JG. Current Status of Omics in Biological Quality Elements for Freshwater Biomonitoring. BIOLOGY 2023; 12:923. [PMID: 37508354 PMCID: PMC10376755 DOI: 10.3390/biology12070923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/01/2023] [Accepted: 06/02/2023] [Indexed: 07/30/2023]
Abstract
Freshwater ecosystems have been experiencing various forms of threats, mainly since the last century. The severity of this adverse scenario presents unprecedented challenges to human health, water supply, agriculture, forestry, ecological systems, and biodiversity, among other areas. Despite the progress made in various biomonitoring techniques tailored to specific countries and biotic communities, significant constraints exist, particularly in assessing and quantifying biodiversity and its interplay with detrimental factors. Incorporating modern techniques into biomonitoring methodologies presents a challenging topic with multiple perspectives and assertions. This review aims to present a comprehensive overview of the contemporary advancements in freshwater biomonitoring, specifically by utilizing omics methodologies such as genomics, metagenomics, transcriptomics, proteomics, metabolomics, and multi-omics. The present study aims to elucidate the rationale behind the imperative need for modernization in this field. This will be achieved by presenting case studies, examining the diverse range of organisms that have been studied, and evaluating the potential benefits and drawbacks associated with the utilization of these methodologies. The utilization of advanced high-throughput bioinformatics techniques represents a sophisticated approach that necessitates a significant departure from the conventional practices of contemporary freshwater biomonitoring. The significant contributions of omics techniques in the context of biological quality elements (BQEs) and their interpretations in ecological problems are crucial for biomonitoring programs. Such contributions are primarily attributed to the previously overlooked identification of interactions between different levels of biological organization and their responses, isolated and combined, to specific critical conditions.
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Affiliation(s)
- Jorge Machuca-Sepúlveda
- Doctoral Program on Natural Resources Sciences, Universidad de La Frontera, Avenida Francisco Salazar, 01145, P.O. Box 54-D, Temuco 4780000, Chile
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Temuco 4811230, Chile
| | - Javiera Miranda
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Temuco 4811230, Chile
| | - Nicolás Lefin
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Temuco 4811230, Chile
| | - Alejandro Pedroso
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Temuco 4811230, Chile
| | - Jorge F Beltrán
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Temuco 4811230, Chile
| | - Jorge G Farias
- Department of Chemical Engineering, Faculty of Engineering and Science, Universidad de La Frontera, Temuco 4811230, Chile
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15
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Leontidou K, Rubel V, Stoeck T. Comparing quantile regression spline analyses and supervised machine learning for environmental quality assessment at coastal marine aquaculture installations. PeerJ 2023; 11:e15425. [PMID: 37334127 PMCID: PMC10274583 DOI: 10.7717/peerj.15425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 04/25/2023] [Indexed: 06/20/2023] Open
Abstract
Organic enrichment associated with marine finfish aquaculture is a local stressor of marine coastal ecosystems. To maintain ecosystem services, the implementation of biomonitoring programs focusing on benthic diversity is required. Traditionally, impact-indices are determined by extracting and identifying benthic macroinvertebrates from samples. However, this is a time-consuming and expensive method with low upscaling potential. A more rapid, inexpensive, and robust method to infer the environmental quality of marine environments is eDNA metabarcoding of bacterial communities. To infer the environmental quality of coastal habitats from metabarcoding data, two taxonomy-free approaches have been successfully applied for different geographical regions and monitoring goals, namely quantile regression splines (QRS) and supervised machine learning (SML). However, their comparative performance remains untested for monitoring the impact of organic enrichment introduced by aquaculture on marine coastal environments. We compared the performance of QRS and SML using bacterial metabarcoding data to infer the environmental quality of 230 aquaculture samples collected from seven farms in Norway and seven farms in Scotland along an organic enrichment gradient. As a measure of environmental quality, we used the Infaunal Quality Index (IQI) calculated from benthic macrofauna data (reference index). The QRS analysis plotted the abundance of amplicon sequence variants (ASVs) as a function to the IQI from which the ASVs with a defined abundance peak were assigned to eco-groups and a molecular IQI was subsequently calculated. In contrast, the SML approach built a random forest model to directly predict the macrofauna-based IQI. Our results show that both QRS and SML perform well in inferring the environmental quality with 89% and 90% accuracy, respectively. For both geographic regions, there was high correspondence between the reference IQI and both the inferred molecular IQIs (p < 0.001), with the SML model showing a higher coefficient of determination compared to QRS. Among the 20 most important ASVs identified by the SML approach, 15 were congruent with the good quality spline ASV indicators identified via QRS for both Norwegian and Scottish salmon farms. More research on the response of the ASVs to organic enrichment and the co-influence of other environmental parameters is necessary to eventually select the most powerful stressor-specific indicators. Even though both approaches are promising to infer environmental quality based on metabarcoding data, SML showed to be more powerful in handling the natural variability. For the improvement of the SML model, addition of new samples is still required, as background noise introduced by high spatio-temporal variability can be reduced. Overall, we recommend the development of a powerful SML approach that will be onwards applied for monitoring the impact of aquaculture on marine ecosystems based on eDNA metabarcoding data.
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16
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Hendricks A, Mackie CM, Luy E, Sonnichsen C, Smith J, Grundke I, Tavasoli M, Furlong A, Beiko RG, LaRoche J, Sieben V. Compact and automated eDNA sampler for in situ monitoring of marine environments. Sci Rep 2023; 13:5210. [PMID: 36997631 PMCID: PMC10063616 DOI: 10.1038/s41598-023-32310-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 03/25/2023] [Indexed: 04/01/2023] Open
Abstract
Using environmental DNA (eDNA) to monitor biodiversity in aquatic environments is becoming an efficient and cost-effective alternative to other methods such as visual and acoustic identification. Until recently, eDNA sampling was accomplished primarily through manual sampling methods; however, with technological advances, automated samplers are being developed to make sampling easier and more accessible. This paper describes a new eDNA sampler capable of self-cleaning and multi-sample capture and preservation, all within a single unit capable of being deployed by a single person. The first in-field test of this sampler took place in the Bedford Basin, Nova Scotia, Canada alongside parallel samples taken using the typical Niskin bottle collection and post-collection filtration method. Both methods were able to capture the same aquatic microbial community and counts of representative DNA sequences were well correlated between methods with R[Formula: see text] values ranging from 0.71-0.93. The two collection methods returned the same top 10 families in near identical relative abundance, demonstrating that the sampler was able to capture the same community composition of common microbes as the Niskin. The presented eDNA sampler provides a robust alternative to manual sampling methods, is amenable to autonomous vehicle payload constraints, and will facilitate persistent monitoring of remote and inaccessible sites.
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Affiliation(s)
- Andre Hendricks
- Department of Electrical and Computer Engineering, Dalhousie University, Halifax, NS, Canada
| | | | - Edward Luy
- Dartmouth Ocean Technologies Inc, Dartmouth, NS, Canada
| | - Colin Sonnichsen
- Department of Electrical and Computer Engineering, Dalhousie University, Halifax, NS, Canada
- Dartmouth Ocean Technologies Inc, Dartmouth, NS, Canada
| | - James Smith
- Dartmouth Ocean Technologies Inc, Dartmouth, NS, Canada
| | - Iain Grundke
- Dartmouth Ocean Technologies Inc, Dartmouth, NS, Canada
| | - Mahtab Tavasoli
- Department of Pharmacology, Dalhousie University, Halifax, NS, Canada
| | | | - Robert G Beiko
- Dartmouth Ocean Technologies Inc, Dartmouth, NS, Canada
- Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada
| | - Julie LaRoche
- Dartmouth Ocean Technologies Inc, Dartmouth, NS, Canada
- Department of Biology, Dalhousie University, Halifax, NS, Canada
| | - Vincent Sieben
- Department of Electrical and Computer Engineering, Dalhousie University, Halifax, NS, Canada.
- Dartmouth Ocean Technologies Inc, Dartmouth, NS, Canada.
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17
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Scott-Fordsmand JJ, Amorim MJB. Using Machine Learning to make nanomaterials sustainable. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 859:160303. [PMID: 36410486 DOI: 10.1016/j.scitotenv.2022.160303] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/06/2022] [Accepted: 11/15/2022] [Indexed: 06/16/2023]
Abstract
Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.
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Affiliation(s)
| | - Mónica J B Amorim
- Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal.
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18
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Garate L, Alonso‐Sáez L, Revilla M, Logares R, Lanzén A. Shared and contrasting associations in the dynamic nano- and picoplankton communities of two close but contrasting sites from the Bay of Biscay. Environ Microbiol 2022; 24:6052-6070. [PMID: 36054533 PMCID: PMC10087561 DOI: 10.1111/1462-2920.16153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 07/30/2022] [Indexed: 01/12/2023]
Abstract
Pico- and nanoplankton are key players in the marine ecosystems due to their implication in the biogeochemical cycles, nutrient recycling and the pelagic food webs. However, the specific dynamics and niches of most bacterial, archaeal and eukaryotic plankton remain unknown, as well as the interactions between them. Better characterization of these is critical for understanding and predicting ecosystem functioning under anthropogenic pressures. We used environmental DNA metabarcoding across a 6-year time series to explore the structure and seasonality of pico- and nanoplankton communities in two sites of the Bay of Biscay, one coastal and one offshore, and construct association networks to reveal potential keystone and connector taxa. Temporal trends in alpha diversity were similar between the two sites, and concurrent communities more similar than within the same site at different times. However, we found differences between the network topologies of the two sites, with both shared and site-specific keystones and connectors. For example, Micromonas, with lower abundance in the offshore site is a keystone here, indicating a stronger effect of associations such as resource competition. This study provides an example of how time series and association network analysis can reveal how similar communities may function differently despite being geographically close.
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Affiliation(s)
- Leire Garate
- AZTI, Marine ResearchBasque Research and Technology Alliance (BRTA)PasaiaSpain
| | - Laura Alonso‐Sáez
- AZTI, Marine ResearchBasque Research and Technology Alliance (BRTA)PasaiaSpain
| | - Marta Revilla
- AZTI, Marine ResearchBasque Research and Technology Alliance (BRTA)PasaiaSpain
| | - Ramiro Logares
- Institute of Marine Sciences (ICM)CSICBarcelonaCataloniaSpain
| | - Anders Lanzén
- AZTI, Marine ResearchBasque Research and Technology Alliance (BRTA)PasaiaSpain
- IKERBASQUEBasque Foundation for ScienceBilbaoBizkaiaSpain
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19
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Oladi M, Leontidou K, Stoeck T, Shokri MR. Environmental DNA-based profiling of benthic bacterial and eukaryote communities along a crude oil spill gradient in a coral reef in the Persian Gulf. MARINE POLLUTION BULLETIN 2022; 184:114143. [PMID: 36182786 DOI: 10.1016/j.marpolbul.2022.114143] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 09/09/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
Coral reef ecosystems in the Persian Gulf are frequently exposed to crude oil spills. We investigated benthic bacterial and eukaryote community structures at such coral reef sites subjected to different degrees of polycyclic aromatic hydrocarbon (PAH) pollution using environmental DNA (eDNA) metabarcoding. Both bacterial and eukaryote communities responded with pronounced shifts to crude oil pollution and distinguished control sites, moderately and heavily impacted sites with significant confidentiality. The observed community patterns were predominantly driven by Alphaproteobacteria and metazoans. Among these, we identified individual genera that were previously linked to oil spill stress, but also taxa, for which a link to hydrocarbon still remains to be established. Considering the lack of an early-warning system for the environmental status of coral reef ecosystems exposed to frequent crude-oil spills, our results encourage further research towards the development of an eDNA-based biomonitoring tool that exploits benthic bacterial and eukaryote communities as bioindicators.
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Affiliation(s)
- Mahshid Oladi
- Technische Universität Kaiserslautern, Ecology Group, Kaiserslautern, Germany; Department of Animal Sciences and Marine Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, G.C., Evin, Tehran, Iran
| | - Kleopatra Leontidou
- Technische Universität Kaiserslautern, Ecology Group, Kaiserslautern, Germany
| | - Thorsten Stoeck
- Technische Universität Kaiserslautern, Ecology Group, Kaiserslautern, Germany
| | - Mohammad Reza Shokri
- Department of Animal Sciences and Marine Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, G.C., Evin, Tehran, Iran.
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20
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Bhattacharya C, Tierney BT, Ryon KA, Bhattacharyya M, Hastings JJA, Basu S, Bhattacharya B, Bagchi D, Mukherjee S, Wang L, Henaff EM, Mason CE. Supervised Machine Learning Enables Geospatial Microbial Provenance. Genes (Basel) 2022; 13:1914. [PMID: 36292799 PMCID: PMC9601318 DOI: 10.3390/genes13101914] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/14/2022] [Accepted: 10/18/2022] [Indexed: 11/04/2022] Open
Abstract
The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applications within forensic science and public health. To determine the regional specificity for environmental metagenomes, we examined 4305 shotgun-sequenced samples from the MetaSUB Consortium dataset-the most extensive public collection of urban microbiomes, spanning 60 different cities, 30 countries, and 6 continents. We were able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental accuracy ranging from 85-89% to 90-94%, respectively. Thereafter, we used these results to develop Cassandra, a random-forest-based classifier that identifies bioindicator species to aid in fingerprinting and can infer higher-order microbial interactions at each site. We further tested the Cassandra algorithm on the Tara Oceans dataset, the largest collection of marine-based microbial genomes, where it classified the oceanic sample locations with 83% accuracy. These results and code show the utility of SML methods and Cassandra to identify bioindicator species across both oceanic and urban environments, which can help guide ongoing efforts in biotracing, environmental monitoring, and microbial forensics (MF).
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Affiliation(s)
- Chandrima Bhattacharya
- Tri-Institutional Computational Biology & Medicine Program, Weill Cornell Medicine, New York, NY 10065, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10065, USA
- Integrated Design and Media, Center for Urban Science and Progress, NYU Tandon School of Engineering, Brooklyn, New York, NY 11201, USA
| | - Braden T. Tierney
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10065, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
| | - Krista A. Ryon
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10065, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
| | - Malay Bhattacharyya
- Center for Artificial Intelligence and Machine Learning, Indian Statistical Institute, Kolkata 700108, India
- Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108, India
| | - Jaden J. A. Hastings
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10065, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
| | - Srijani Basu
- Department of Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Bodhisatwa Bhattacharya
- Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, India
| | - Debneel Bagchi
- Department of Metallurgy & Materials Engineering, Indian Institute of Engineering Science & Technology, Shibpur, Howrah 711103, India
| | - Somsubhro Mukherjee
- Department of Biological Sciences, National University of Singapore, Singapore 117558, Singapore
| | - Lu Wang
- Department of Biological Sciences, National University of Singapore, Singapore 117558, Singapore
| | - Elizabeth M. Henaff
- Integrated Design and Media, Center for Urban Science and Progress, NYU Tandon School of Engineering, Brooklyn, New York, NY 11201, USA
| | - Christopher E. Mason
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10065, USA
- Integrated Design and Media, Center for Urban Science and Progress, NYU Tandon School of Engineering, Brooklyn, New York, NY 11201, USA
- WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY 10065, USA
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21
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Hempel CA, Wright N, Harvie J, Hleap JS, Adamowicz S, Steinke D. Metagenomics versus total RNA sequencing: most accurate data-processing tools, microbial identification accuracy and perspectives for ecological assessments. Nucleic Acids Res 2022; 50:9279-9293. [PMID: 35979944 PMCID: PMC9458450 DOI: 10.1093/nar/gkac689] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/05/2022] [Accepted: 07/29/2022] [Indexed: 12/24/2022] Open
Abstract
Metagenomics and total RNA sequencing (total RNA-Seq) have the potential to improve the taxonomic identification of diverse microbial communities, which could allow for the incorporation of microbes into routine ecological assessments. However, these target-PCR-free techniques require more testing and optimization. In this study, we processed metagenomics and total RNA-Seq data from a commercially available microbial mock community using 672 data-processing workflows, identified the most accurate data-processing tools, and compared their microbial identification accuracy at equal and increasing sequencing depths. The accuracy of data-processing tools substantially varied among replicates. Total RNA-Seq was more accurate than metagenomics at equal sequencing depths and even at sequencing depths almost one order of magnitude lower than those of metagenomics. We show that while data-processing tools require further exploration, total RNA-Seq might be a favorable alternative to metagenomics for target-PCR-free taxonomic identifications of microbial communities and might enable a substantial reduction in sequencing costs while maintaining accuracy. This could be particularly an advantage for routine ecological assessments, which require cost-effective yet accurate methods, and might allow for the incorporation of microbes into ecological assessments.
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Affiliation(s)
- Christopher A Hempel
- To whom correspondence should be addressed. Tel: +1 519 824 4120; Fax: +1 519 824 5703;
| | - Natalie Wright
- Department of Integrative Biology, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Julia Harvie
- Department of Integrative Biology, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Jose S Hleap
- SHARCNET, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Sarah J Adamowicz
- Department of Integrative Biology, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Dirk Steinke
- Department of Integrative Biology, University of Guelph, Guelph, ON N1G 2W1, Canada,Centre for Biodiversity Genomics, University of Guelph, Guelph, ON N1G 2W1, Canada
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22
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Pilgrim EM, Smucker NJ, Wu H, Martinson J, Nietch CT, Molina M, Darling JA, Johnson BR. Developing Indicators of Nutrient Pollution in Streams Using 16S rRNA Gene Metabarcoding of Periphyton-Associated Bacteria. WATER 2022; 14:1-24. [PMID: 36213613 PMCID: PMC9534034 DOI: 10.3390/w14152361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Indicators based on nutrient-biota relationships in streams can inform water quality restoration and protection programs. Bacterial assemblages could be particularly useful indicators of nutrient effects because they are species-rich, important contributors to ecosystem processes in streams, and responsive to rapidly changing conditions. Here, we sampled 25 streams weekly (12-14 times each) and used 16S rRNA gene metabarcoding of periphyton-associated bacteria to quantify the effects of total phosphorus (TP) and total nitrogen (TN). Threshold indicator taxa analysis identified assemblage-level changes and amplicon sequence variants (ASVs) that increased or decreased with increasing TP and TN concentrations (i.e., low P, high P, low N, and high N ASVs). Boosted regression trees confirmed that relative abundances of gene sequence reads for these four indicator groups were associated with nutrient concentrations. Gradient forest analysis complemented these results by using multiple predictors and random forest models for each ASV to identify portions of TP and TN gradients at which the greatest changes in assemblage structure occurred. Synthesized statistical results showed bacterial assemblage structure began changing at 24 μg TP/L with the greatest changes occurring from 110 to 195 μg/L. Changes in the bacterial assemblages associated with TN gradually occurred from 275 to 855 μg/L. Taxonomic and phylogenetic analyses showed that low nutrient ASVs were commonly Firmicutes, Verrucomicrobiota, Flavobacteriales, and Caulobacterales, Pseudomonadales, and Rhodobacterales of Proteobacteria, whereas other groups, such as Chitinophagales of Bacteroidota, and Burkholderiales, Rhizobiales, Sphingomonadales, and Steroidobacterales of Proteobacteria comprised the high nutrient ASVs. Overall, the responses of bacterial ASV indicators in this study highlight the utility of metabarcoding periphyton-associated bacteria for quantifying biotic responses to nutrient inputs in streams.
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Affiliation(s)
- Erik M. Pilgrim
- United States Environmental Protection Agency, Office of Research and Development, Cincinnati, OH 45268, USA
| | - Nathan J. Smucker
- United States Environmental Protection Agency, Office of Research and Development, Cincinnati, OH 45268, USA
| | - Huiyun Wu
- School of Public Health & Tropical Medicine, Tulane University, New Orleans, LA 70112, USA
| | - John Martinson
- United States Environmental Protection Agency, Office of Research and Development, Cincinnati, OH 45268, USA
| | - Christopher T. Nietch
- United States Environmental Protection Agency, Office of Research and Development, Cincinnati, OH 45268, USA
| | - Marirosa Molina
- United States Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC 27711, USA
| | - John A. Darling
- United States Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC 27711, USA
| | - Brent R. Johnson
- United States Environmental Protection Agency, Office of Research and Development, Cincinnati, OH 45268, USA
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23
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Kanjer L, Filek K, Mucko M, Majewska R, Gračan R, Trotta A, Panagopoulou A, Corrente M, Di Bello A, Bosak S. Surface microbiota of Mediterranean loggerhead sea turtles unraveled by 16S and 18S amplicon sequencing. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.907368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The loggerhead sea turtle is considered a keystone species with a major ecological role in Mediterranean marine environment. As is the case with other wild reptiles, their outer microbiome is rarely studied. Although there are several studies on sea turtle’s macro-epibionts and endo-microbiota, there has been little research on epibiotic microbiota associated with turtle skin and carapace. Therefore we aimed to provide the identification of combined epibiotic eukaryotic, bacterial and archaeal microbiota on Mediterranean loggerhead sea turtles. In this study, we sampled skins and carapaces of 26 loggerheads from the Mediterranean Sea during 2018 and 2019. To investigate the overall microbial diversity and composition, amplicon sequencing of 16S and 18S rRNA genes was performed. We found that the Mediterranean loggerhead sea turtle epibiotic microbiota is a reservoir of a vast variety of microbial species. Microbial communities mostly varied by different locations and seas, while within bacterial communities’ significant difference was observed between sampled body sites (carapace vs. skin). In terms of relative abundance, Proteobacteria and Bacteroidota were the most represented phyla within prokaryotes, while Alveolata and Stramenopiles thrived among eukaryotes. This study, besides providing a first survey of microbial eukaryotes on loggerheads via metabarcoding, identifies fine differences within both bacterial and eukaryotic microbial communities that seem to reflect the host anatomy and habitat. Multi-domain epi-microbiome surveys provide additional layers of information that are complementary with previous morphological studies and enable better understanding of the biology and ecology of these vulnerable marine reptiles.
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Smucker NJ, Pilgrim EM, Wu H, Nietch CT, Darling JA, Molina M, Johnson BR, Yuan LL. Characterizing temporal variability in streams supports nutrient indicator development using diatom and bacterial DNA metabarcoding. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 831:154960. [PMID: 35378187 PMCID: PMC9169572 DOI: 10.1016/j.scitotenv.2022.154960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/25/2022] [Accepted: 03/28/2022] [Indexed: 05/26/2023]
Abstract
Interest in developing periphytic diatom and bacterial indicators of nutrient effects continues to grow in support of the assessment and management of stream ecosystems and their watersheds. However, temporal variability could confound relationships between indicators and nutrients, subsequently affecting assessment outcomes. To document how temporal variability affects measures of diatom and bacterial assemblages obtained from DNA metabarcoding, we conducted weekly periphyton and nutrient sampling from July to October 2016 in 25 streams in a 1293 km2 mixed land use watershed. Measures of both diatom and bacterial assemblages were strongly associated with the percent agriculture in upstream watersheds and total phosphorus (TP) and total nitrogen (TN) concentrations. Temporal variability in TP and TN concentrations increased with greater amounts of agriculture in watersheds, but overall diatom and bacterial assemblage variability within sites-measured as mean distance among samples to corresponding site centroids in ordination space-remained consistent. This consistency was due in part to offsets between decreasing variability in relative abundances of taxa typical of low nutrient conditions and increasing variability in those typical of high nutrient conditions as mean concentrations of TP and TN increased within sites. Weekly low and high nutrient diatom and bacterial metrics were more strongly correlated with site mean nutrient concentrations over the sampling period than with same day measurements and more strongly correlated with TP than with TN. Correlations with TP concentrations were consistently strong throughout the study except briefly following two major precipitation events. Following these events, biotic relationships with TP reestablished within one to three weeks. Collectively, these results can strengthen interpretations of survey results and inform monitoring strategies and decision making. These findings have direct applications for improving the use of diatoms and bacteria, and the use of DNA metabarcoding, in monitoring programs and stream site assessments.
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Affiliation(s)
- Nathan J Smucker
- United States Environmental Protection Agency, Office of Research and Development, Cincinnati, OH 45268, USA.
| | - Erik M Pilgrim
- United States Environmental Protection Agency, Office of Research and Development, Cincinnati, OH 45268, USA
| | - Huiyun Wu
- Oak Ridge Institute for Science and Education, P.O. Box 117, Oak Ridge, Tennessee 37831 USA c/o United States Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC 27711, USA
| | - Christopher T Nietch
- United States Environmental Protection Agency, Office of Research and Development, Cincinnati, OH 45268, USA
| | - John A Darling
- United States Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC 27711, USA
| | - Marirosa Molina
- United States Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC 27711, USA
| | - Brent R Johnson
- United States Environmental Protection Agency, Office of Research and Development, Cincinnati, OH 45268, USA
| | - Lester L Yuan
- United States Environmental Protection Agency, Office of Water, Washington, DC 20460, USA
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Mostafa-Hedeab G, Allayeh AK, Elhady HA, Eledrdery AY, Mraheil MA, Mostafa A. Viral Eco-Genomic Tools: Development and Implementation for Aquatic Biomonitoring. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19137707. [PMID: 35805367 PMCID: PMC9265447 DOI: 10.3390/ijerph19137707] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 06/21/2022] [Accepted: 06/21/2022] [Indexed: 12/17/2022]
Abstract
Enteric viruses (EVs) occurrence within aquatic environments varies and leads to significant risk on public health of humans, animals, and diversity of aquatic taxa. Early and efficacious recognition of cultivable and fastidious EVs in aquatic systems are important to ensure the sanitary level of aquatic water and implement required treatment strategies. Herein, we provided a comprehensive overview of the conventional and up-to-date eco-genomic tools for aquatic biomonitoring of EVs, aiming to develop better water pollution monitoring tools. In combination with bioinformatics techniques, genetic tools including cloning sequencing analysis, DNA microarray, next-generation sequencing (NGS), and metagenomic sequencing technologies are implemented to make informed decisions about the global burden of waterborne EVs-associated diseases. The data presented in this review are helpful to recommend that: (1) Each viral pollution detection method has its own merits and demerits; therefore, it would be advantageous for viral pollution evaluation to be integrated as a complementary platform. (2) The total viral genome pool extracted from aquatic environmental samples is a real reflection of pollution status of the aquatic eco-systems; therefore, it is recommended to conduct regular sampling through the year to establish an updated monitoring system for EVs, and quantify viral peak concentrations, viral typing, and genotyping. (3) Despite that conventional detection methods are cheaper, it is highly recommended to implement molecular-based technologies to complement aquatic ecosystems biomonitoring due to numerous advantages including high-throughput capability. (4) Continuous implementation of the eco-genetic detection tools for monitoring the EVs in aquatic ecosystems is recommended.
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Affiliation(s)
- Gomaa Mostafa-Hedeab
- Pharmacology Department and Health Research Unit, Medical College, Jouf University, Skaka 11564, Saudi Arabia
- Correspondence: (G.M.-H.); (M.A.M.); (A.M.)
| | - Abdou Kamal Allayeh
- Water Pollution Department, Virology Laboratory, National Research Centre, Dokki, Giza 12622, Egypt;
| | | | - Abozer Y. Eledrdery
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Sakaka 11564, Saudi Arabia;
| | - Mobarak Abu Mraheil
- German Center for Infection Research (DZIF), Institute of Medical Microbiology, Justus-Liebig University, 35392 Giessen, Germany
- Correspondence: (G.M.-H.); (M.A.M.); (A.M.)
| | - Ahmed Mostafa
- Center of Scientific Excellence for Influenza Viruses, National Research Centre, Giza 12622, Egypt
- Correspondence: (G.M.-H.); (M.A.M.); (A.M.)
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26
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Flück B, Mathon L, Manel S, Valentini A, Dejean T, Albouy C, Mouillot D, Thuiller W, Murienne J, Brosse S, Pellissier L. Applying convolutional neural networks to speed up environmental DNA annotation in a highly diverse ecosystem. Sci Rep 2022; 12:10247. [PMID: 35715444 PMCID: PMC9205931 DOI: 10.1038/s41598-022-13412-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 05/24/2022] [Indexed: 01/04/2023] Open
Abstract
High-throughput DNA sequencing is becoming an increasingly important tool to monitor and better understand biodiversity responses to environmental changes in a standardized and reproducible way. Environmental DNA (eDNA) from organisms can be captured in ecosystem samples and sequenced using metabarcoding, but processing large volumes of eDNA data and annotating sequences to recognized taxa remains computationally expensive. Speed and accuracy are two major bottlenecks in this critical step. Here, we evaluated the ability of convolutional neural networks (CNNs) to process short eDNA sequences and associate them with taxonomic labels. Using a unique eDNA data set collected in highly diverse Tropical South America, we compared the speed and accuracy of CNNs with that of a well-known bioinformatic pipeline (OBITools) in processing a small region (60 bp) of the 12S ribosomal DNA targeting freshwater fishes. We found that the taxonomic labels from the CNNs were comparable to those from OBITools, with high correlation levels for the composition of the regional fish fauna. The CNNs enabled the processing of raw fastq files at a rate of approximately 1 million sequences per minute, which was about 150 times faster than with OBITools. Given the good performance of CNNs in the highly diverse ecosystem considered here, the development of more elaborate CNNs promises fast deployment for future biodiversity inventories using eDNA.
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Affiliation(s)
- Benjamin Flück
- Department of Environmental System Science, ETH Zürich, 8092, Zurich, Switzerland. .,Swiss Federal Research Institute WSL, 8903, Birmensdorf, Switzerland.
| | - Laëtitia Mathon
- CEFE, Univ. Montpellier, CNRS, EPHE-PSL University, IRD, Montpellier, France
| | - Stéphanie Manel
- CEFE, Univ. Montpellier, CNRS, EPHE-PSL University, IRD, Montpellier, France
| | | | | | - Camille Albouy
- DECOD (Ecosystem Dynamics and Sustainability), IFREMER, INRAE, Institut Agro - Agrocampus Ouest, Rue de l'Ile d'Yeu, BP21105, 44311, Nantes Cedex 3, France
| | - David Mouillot
- MARBEC, Univ. Montpellier,CNRS, IRD, Ifremer, Montpellier, France.,Institut Universitaire de France, IUF, 75231, Paris, France
| | - Wilfried Thuiller
- CNRS, LECA, Laboratoire d'Écologie Alpine, Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, 38000, Grenoble, France
| | - Jérôme Murienne
- Laboratoire Evolution et Diversité Biologique (UMR5174), CNRS, IRD, Université Paul Sabatier, Toulouse, France
| | - Sébastien Brosse
- Laboratoire Evolution et Diversité Biologique (UMR5174), CNRS, IRD, Université Paul Sabatier, Toulouse, France
| | - Loïc Pellissier
- Department of Environmental System Science, ETH Zürich, 8092, Zurich, Switzerland. .,Swiss Federal Research Institute WSL, 8903, Birmensdorf, Switzerland.
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27
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Le JT, Levin LA, Lejzerowicz F, Cordier T, Gooday AJ, Pawlowski J. Scientific and budgetary trade-offs between morphological and molecular methods for deep-sea biodiversity assessment. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2022; 18:655-663. [PMID: 34019727 DOI: 10.1002/ieam.4466] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 02/22/2021] [Accepted: 05/14/2021] [Indexed: 06/12/2023]
Abstract
Deep-sea biodiversity, a source of critical ecological functions and ecosystem services, is increasingly subject to the threat of disturbance from existing practices (e.g., fishing, waste disposal, oil and gas extraction) as well as emerging industries such as deep-seabed mining. Current scientific tools may not be adequate for monitoring and assessing subsequent changes to biodiversity. In this paper, we evaluate the scientific and budgetary trade-offs associated with morphology-based taxonomy and metabarcoding approaches to biodiversity surveys in the context of nascent deep-seabed mining for polymetallic nodules in the Clarion-Clipperton Zone, the area of most intense interest. For the dominant taxa of benthic meiofauna, we discuss the types of information produced by these methods and use cost-effectiveness analysis to compare their abilities to yield biological and ecological data for use in environmental assessment and management. On the basis of our evaluation, morphology-based taxonomy is less cost-effective than metabarcoding but offers scientific advantages, such as the generation of density, biomass, and size structure data. Approaches that combine the two methods during the environmental assessment phase of commercial activities may facilitate future biodiversity monitoring and assessment for deep-seabed mining and for other activities in remote deep-sea habitats, for which taxonomic data and expertise are limited. Integr Environ Assess Manag 2022;18:655-663. © 2021 SETAC.
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Affiliation(s)
- Jennifer T Le
- Center for Marine Biodiversity and Conservation, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA
| | - Lisa A Levin
- Center for Marine Biodiversity and Conservation, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA
| | - Franck Lejzerowicz
- Jacobs School of Engineering, University of California San Diego, La Jolla, USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, USA
- Department of Pediatrics, University of California San Diego, La Jolla, USA
| | - Tristan Cordier
- Department of Genetics & Evolution, University of Geneva, Geneva, Switzerland
- NORCE Climate, NORCE Norwegian Research Centre AS, Bjerknes Centre for Climate Research, Bergen, Norway
| | - Andrew J Gooday
- National Oceanography Centre, Southampton, UK
- Life Sciences Department, Natural History Museum, London, UK
| | - Jan Pawlowski
- Department of Genetics & Evolution, University of Geneva, Geneva, Switzerland
- Institute of Oceanology, Polish Academy of Sciences, Sopot, Poland
- ID-Gene Ecodiagnostics, Campus Biotech Innovation Park, Geneva, Switzerland
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28
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Qiu X, Lu Q, Jia C, Dai Y, Ouyang S, Wu X. The Effects of Water Level Fluctuation on Zooplankton Communities in Shahu Lake Based on DNA Metabarcoding and Morphological Methods. Animals (Basel) 2022; 12:ani12080950. [PMID: 35454197 PMCID: PMC9025402 DOI: 10.3390/ani12080950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 04/02/2022] [Accepted: 04/04/2022] [Indexed: 02/05/2023] Open
Abstract
Background: The water level of Poyang Lake (China) fluctuates seasonally. Shahu Lake, a smaller body of water connected to Poyang Lake during the wet season, is separated in the dry season. Due to a special fishing method termed ‘lake enclosed in autumn’, the water level is lowered and reaches its lowest point in January, which is <0.5 m deep in the middle of the lake. Our research investigated the effect of water level changes on the zooplankton community composition in Shahu Lake. Methods: We used both DNA metabarcoding method (MBC) (18S rRNA gene V4 region) and morphological method (MOI) to track the zooplankton community structure over four seasons in Shahu Lake (China). Results: Totals of 90 and 98 species of zooplankton were detected by MOI and MBC, respectively, with rotifers being the main zooplankton component. The α-diversity index of both methods increased from spring to summer and decreased from summer to autumn, reaching the lowest value in winter. NMDS and a cluster analysis showed that all zooplankton communities detected by MOI and MBC were significantly separated by season. The zooplankton community in winter was separated from that of the other three seasons, but the summer and autumn communities were more similar. Conclusions: Changes in the water level had significant effects on the zooplankton community composition. We found that MBC was more able to detect the differences in the zooplankton composition than MOI. MBC also had more advantages in copepod recognition. In our study, 37 species of copepods were detected by MBC, but only 11 species were detected by MOI. We concluded that MBC should be used to research the seasonal variations of zooplankton.
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Affiliation(s)
- Xuemei Qiu
- School of Life Science, Nanchang University, Nanchang 330036, China; (X.Q.); (Q.L.); (C.J.); (Y.D.); (S.O.)
- School of Life Science, Jiangxi Science and Technology Normal University, Nanchang 330013, China
| | - Quanfeng Lu
- School of Life Science, Nanchang University, Nanchang 330036, China; (X.Q.); (Q.L.); (C.J.); (Y.D.); (S.O.)
| | - Chenchen Jia
- School of Life Science, Nanchang University, Nanchang 330036, China; (X.Q.); (Q.L.); (C.J.); (Y.D.); (S.O.)
| | - Yuting Dai
- School of Life Science, Nanchang University, Nanchang 330036, China; (X.Q.); (Q.L.); (C.J.); (Y.D.); (S.O.)
| | - Shan Ouyang
- School of Life Science, Nanchang University, Nanchang 330036, China; (X.Q.); (Q.L.); (C.J.); (Y.D.); (S.O.)
| | - Xiaoping Wu
- School of Life Science, Nanchang University, Nanchang 330036, China; (X.Q.); (Q.L.); (C.J.); (Y.D.); (S.O.)
- Correspondence:
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29
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Zhang J, Yu F, Hu X, Gao Y, Qu Q. Multifeature superposition analysis of the effects of microplastics on microbial communities in realistic environments. ENVIRONMENT INTERNATIONAL 2022; 162:107172. [PMID: 35290867 DOI: 10.1016/j.envint.2022.107172] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/04/2022] [Accepted: 03/02/2022] [Indexed: 06/14/2023]
Abstract
Microplastic (MP) contamination has become an increasingly serious environmental problem. However, the risks of MP contamination in complex global climatic and geographic scenarios remain unclear. We established a multifeature superposition analysis boosting (MFAB) machine learning (ML) approach to address the above knowledge gap. MFAB-ML identified and predicted the importance, interaction networks and superposition effects of multiple features, including 34 characteristic variables (e.g., MP contamination and climatic and geographic variables), from 1354 samples distributed globally. MFAB-ML analysis achieved realistic and significant results, in some cases even opposite to those obtained using a single or a few features, revealing the importance of considering complicated scenarios. We found that the microbial diversity in East Asian seas will continually decrease due to the superposition effects of MPs with ocean warming; for example, the Chao1 index will decrease by 10.32% by 2065. The present work provides a powerful approach to identify and predict the multifeature superposition effects of pollutants on realistic environments in complicated climatic and geographic scenarios, overcoming the bias from general studies.
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Affiliation(s)
- Jing Zhang
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education), Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Fubo Yu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education), Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Xiangang Hu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education), Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
| | - Yiming Gao
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education), Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Qian Qu
- Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education), Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
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30
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Advancement of Metatranscriptomics towards Productive Agriculture and Sustainable Environment: A Review. Int J Mol Sci 2022; 23:ijms23073737. [PMID: 35409097 PMCID: PMC8998989 DOI: 10.3390/ijms23073737] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/19/2022] [Accepted: 03/26/2022] [Indexed: 01/19/2023] Open
Abstract
While chemical fertilisers and pesticides indeed enhance agricultural productivity, their excessive usage has been detrimental to environmental health. In addressing this matter, the use of environmental microbiomes has been greatly favoured as a ‘greener’ alternative to these inorganic chemicals’ application. Challenged by a significant proportion of unidentified microbiomes with unknown ecological functions, advanced high throughput metatranscriptomics is prudent to overcome the technological limitations in unfolding the previously undiscovered functional profiles of the beneficial microbiomes. Under this context, this review begins by summarising (1) the evolution of next-generation sequencing and metatranscriptomics in leveraging the microbiome transcriptome profiles through whole gene expression profiling. Next, the current environmental metatranscriptomics studies are reviewed, with the discussion centred on (2) the emerging application of the beneficial microbiomes in developing fertile soils and (3) the development of disease-suppressive soils as greener alternatives against biotic stress. As sustainable agriculture focuses not only on crop productivity but also long-term environmental sustainability, the second half of the review highlights the metatranscriptomics’ contribution in (4) revolutionising the pollution monitoring systems via specific bioindicators. Overall, growing knowledge on the complex microbiome functional profiles is imperative to unlock the unlimited potential of agricultural microbiome-based practices, which we believe hold the key to productive agriculture and sustainable environment.
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31
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Suzzi AL, Gaston TF, McKenzie L, Mazumder D, Huggett MJ. Tracking the impacts of nutrient inputs on estuary ecosystem function. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 811:152405. [PMID: 34923003 DOI: 10.1016/j.scitotenv.2021.152405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 12/10/2021] [Accepted: 12/10/2021] [Indexed: 06/14/2023]
Abstract
Estuaries are one of the most impacted coastal environments globally, subjected to multiple stressors from urban, industry and coastal development. With increasing anthropogenic activity surrounding estuarine systems, sewage inputs have become a common concern. Stable isotope analysis provides a well-established tool to investigate the incorporation of nitrogen into marine organisms and identify major nutrient sources. Benthic macroinvertebrate communities are often used as bioindicators in ecological studies as they typically display predictable responses to anthropogenic pressures, however have a suite of limitations and costs associated with their use. 16S rDNA amplicon sequencing techniques allow for investigation of the microbial communities inhabiting complex environmental samples, with potential as a tool in the ecological assessment of pollution. These communities have not yet been adequately considered for ecological studies and biomonitoring, with a need to better understand interactions with environmental stressors and implications for ecosystem function. This study used a combination of stable isotope analysis to trace the uptake of anthropogenic nitrogen in biota, traditional assessment of benthic macroinvertebrate communities, and 16S rDNA genotyping of benthic microbial communities. Stable isotope analysis of seagrass and epiphytes identified multiple treated and untreated sewage inputs, ranges of 5.2-7.2‰ and 1.9-4.0‰ for δ15N respectively, as the dominant nitrogen source at specific locations. The benthic macroinvertebrate community reflected these inputs with shifts in dominant taxa and high abundances of polychaetes at some sites. Microbial communities provided a sensitive indication of impact with a breadth of information not available using traditional techniques. Composition and predicted function reflected sewage inputs, particularly within sediments, with the relative abundance of specific taxa and putative pathogens linked to these inputs. This research supports the growing body of evidence that benthic microbial communities respond rapidly to anthropogenic stressors and have potential as a monitoring tool in urban estuarine systems.
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Affiliation(s)
- Alessandra L Suzzi
- College of Engineering, Science and Environment, University of Newcastle, Ourimbah, NSW, Australia.
| | - Troy F Gaston
- College of Engineering, Science and Environment, University of Newcastle, Ourimbah, NSW, Australia
| | - Louise McKenzie
- College of Engineering, Science and Environment, University of Newcastle, Ourimbah, NSW, Australia; Hunter Water Corporation, Newcastle, NSW, Australia
| | - Debashish Mazumder
- Australian Nuclear Science and Technology Organisation (ANSTO), Sydney, NSW, Australia
| | - Megan J Huggett
- College of Engineering, Science and Environment, University of Newcastle, Ourimbah, NSW, Australia; Centre for Marine Ecosystems Research, School of Science, Edith Cowan University, Joondalup, WA, Australia
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Jin L, Gerson JR, Rocca JD, Bernhardt ES, Simonin M. Alkaline mine drainage drives stream sediment microbial community structure and function. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 805:150189. [PMID: 34818783 DOI: 10.1016/j.scitotenv.2021.150189] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 08/24/2021] [Accepted: 09/03/2021] [Indexed: 06/13/2023]
Abstract
With advances in eDNA metabarcoding, environmental microbiomes are increasingly used as cost-effective tools for monitoring ecosystem health. Stream ecosystems in Central Appalachia, heavily impacted by alkaline drainage from mountaintop coal mining, present ideal opportunities for biomonitoring using stream microbiomes, but the structural and functional responses of microbial communities in different environmental compartments are not well understood. We investigated sediment microbiomes in mining impacted streams to determine how community composition and function respond to mining and to look for potential microbial bioindicators. Using 16s rRNA gene amplicon sequencing, we found that mining leads to shifts in microbial community structure, with the phylum Planctomycetes enriched by 1-6% at mined sites. We observed ~51% increase in species richness in bulk sediments. In contrast, of the 31 predicted metabolic pathways that changed significantly with mining, 23 responded negatively. Mining explained 15-18% of the variance in community structure and S, Se, %C and %N were the main drivers of community and functional pathway composition. We identified 12 microbial indicators prevalent in the ecosystem and sensitive to mining. Overall, alkaline mountaintop mining drainage causes a restructuration of the sediment microbiome, and our study identified promising microbial indicators for the long-term monitoring of these impacted streams.
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Affiliation(s)
- Lingrong Jin
- Department of Biology, Duke University, Durham, NC 27708, United States.
| | | | - Jennifer D Rocca
- Department of Biology, Duke University, Durham, NC 27708, United States
| | - Emily S Bernhardt
- Department of Biology, Duke University, Durham, NC 27708, United States.
| | - Marie Simonin
- Department of Biology, Duke University, Durham, NC 27708, United States.
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Bourhane Z, Lanzén A, Cagnon C, Ben Said O, Mahmoudi E, Coulon F, Atai E, Borja A, Cravo-Laureau C, Duran R. Microbial diversity alteration reveals biomarkers of contamination in soil-river-lake continuum. JOURNAL OF HAZARDOUS MATERIALS 2022; 421:126789. [PMID: 34365235 DOI: 10.1016/j.jhazmat.2021.126789] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 07/27/2021] [Accepted: 07/28/2021] [Indexed: 05/21/2023]
Abstract
Microbial communities inhabiting soil-water-sediment continuum in coastal areas provide important ecosystem services. Their adaptation in response to environmental stressors, particularly mitigating the impact of pollutants discharged from human activities, has been considered for the development of microbial biomonitoring tools, but their use is still in the infancy. Here, chemical and molecular (16S rRNA gene metabarcoding) approaches were combined in order to determine the impact of pollutants on microbial assemblages inhabiting the aquatic network of a soil-water-sediment continuum around the Ichkeul Lake (Tunisia), an area highly impacted by human activities. Samples were collected within the soil-river-lake continuum at three stations in dry (summer) and wet (winter) seasons. The contaminant pressure index (PI), which integrates Polycyclic aromatic hydrocarbons (PAHs), alkanes, Organochlorine pesticides (OCPs) and metal contents, and the microbial pressure index microgAMBI, based on bacterial community structure, showed significant correlation with contamination level and differences between seasons. The comparison of prokaryotic communities further revealed specific assemblages for soil, river and lake sediments. Correlation analyses identified potential "specialist" genera for the different compartments, whose abundances were correlated with the pollutant type found. Additionally, PICRUSt analysis revealed the metabolic potential for pollutant transformation or degradation of the identified "specialist" species, providing information to estimate the recovery capacity of the ecosystem. Such findings offer the possibility to define a relevant set of microbial indicators for assessing the effects of human activities on aquatic ecosystems. Microbial indicators, including the detection of "specialist" and sensitive taxa, and their functional capacity, might be useful, in combination with integrative microbial indices, to constitute accurate biomonitoring tools for the management and restoration of complex coastal aquatic systems.
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Affiliation(s)
- Zeina Bourhane
- Université de Pau et des Pays de l'Adour, UPPA/E2S, IPREM CNRS 5254, Pau, France
| | - Anders Lanzén
- AZTI, Marine Research, Basque Research and Technology Alliance (BRTA), Herrera Kaia, Portualdea z/g, 20110 Pasaia, Gipuzkoa, Spain; IKERBASQUE, Basque Foundation for Science, E-48011 Bilbao, Spain
| | - Christine Cagnon
- Université de Pau et des Pays de l'Adour, UPPA/E2S, IPREM CNRS 5254, Pau, France
| | - Olfa Ben Said
- Laboratoire de Biosurveillance de l'Environnement, Faculté des Sciences de Bizerte, LBE, Tunisia
| | - Ezzeddine Mahmoudi
- Laboratoire de Biosurveillance de l'Environnement, Faculté des Sciences de Bizerte, LBE, Tunisia
| | - Frederic Coulon
- Cranfield University, School of Water, Energy and Environment, Cranfield MK430AL, UK
| | - Emmanuel Atai
- Cranfield University, School of Water, Energy and Environment, Cranfield MK430AL, UK
| | - Angel Borja
- AZTI, Marine Research, Basque Research and Technology Alliance (BRTA), Herrera Kaia, Portualdea z/g, 20110 Pasaia, Gipuzkoa, Spain; King Abdulaziz University, Faculty of Marine Sciences, Jeddah, Saudi Arabia
| | | | - Robert Duran
- Université de Pau et des Pays de l'Adour, UPPA/E2S, IPREM CNRS 5254, Pau, France.
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Li F, Zhang Y, Altermatt F, Zhang X. Consideration of Multitrophic Biodiversity and Ecosystem Functions Improves Indices on River Ecological Status. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:16434-16444. [PMID: 34882399 DOI: 10.1021/acs.est.1c05899] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Biological quality elements have been developed worldwide to assess whether a water body is in a good status or not. However, current studies mainly focus on a single taxonomic group or a small set of species, often limited by methods of morphological identification, and lack further aspects of biodiversity (e.g., across taxa and multiple attributes) and ecosystem functions. Here, we advance a framework for assessing the river's ecological status based on complete biodiversity data measured by environmental DNA (eDNA) metabarcoding and measurements of ecosystem functions in addition to physicochemical elements across a large riverine system in China. We identified 40 indicators of biodiversity and ecosystem functions, covering five taxonomic groups from bacteria to invertebrates, and associated with multiple attributes of biodiversity and ecosystem functions. Our data show that human impact on ecosystems could be accurately predicted by these eDNA-based indicators and ecosystem functions, using cross-validation with a known stressor gradient. Moreover, indices based on these indicators of biodiversity and ecosystem functions not only distinguish the physicochemical characteristics of the sites but also improve the assessment accuracy of 20-30% for the river's ecological status. Overall, by incorporating eDNA-based biodiversity with physicochemical and ecosystem functional elements, the multidimensional perspectives of ecosystem states provide additional information to protect and maintain a good ecological status of rivers.
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Affiliation(s)
- Feilong Li
- State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, P. R. China
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou 510006, P. R. China
| | - Yan Zhang
- State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, P. R. China
| | - Florian Altermatt
- Department of Aquatic Ecology, Eawag: Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, CH-8600 Dübendorf, Switzerland
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
| | - Xiaowei Zhang
- State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, P. R. China
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Radulovici AE, Vieira PE, Duarte S, Teixeira MAL, Borges LMS, Deagle BE, Majaneva S, Redmond N, Schultz JA, Costa FO. Revision and annotation of DNA barcode records for marine invertebrates: report of the 8th iBOL conference hackathon. METABARCODING AND METAGENOMICS 2021. [DOI: 10.3897/mbmg.5.67862] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
The accuracy of specimen identification through DNA barcoding and metabarcoding relies on reference libraries containing records with reliable taxonomy and sequence quality. The considerable growth in barcode data requires stringent data curation, especially in taxonomically difficult groups such as marine invertebrates. A major effort in curating marine barcode data in the Barcode of Life Data Systems (BOLD) was undertaken during the 8th International Barcode of Life Conference (Trondheim, Norway, 2019). Major taxonomic groups (crustaceans, echinoderms, molluscs, and polychaetes) were reviewed to identify those which had disagreement between Linnaean names and Barcode Index Numbers (BINs). The records with disagreement were annotated with four tags: a) MIS-ID (misidentified, mislabeled, or contaminated records), b) AMBIG (ambiguous records unresolved with the existing data), c) COMPLEX (species names occurring in multiple BINs), and d) SHARE (barcodes shared between species). A total of 83,712 specimen records corresponding to 7,576 species were reviewed and 39% of the species were tagged (7% MIS-ID, 17% AMBIG, 14% COMPLEX, and 1% SHARE). High percentages (>50%) of AMBIG tags were recorded in gastropods, whereas COMPLEX tags dominated in crustaceans and polychaetes. The high proportion of tagged species reflects either flaws in the barcoding workflow (e.g., misidentification, cross-contamination) or taxonomic difficulties (e.g., synonyms, undescribed species). Although data curation is essential for barcode applications, such manual attempts to examine large datasets are unsustainable and automated solutions are extremely desirable.
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Simonin M, Rocca JD, Gerson JR, Moore E, Brooks AC, Czaplicki L, Ross MRV, Fierer N, Craine JM, Bernhardt ES. Consistent declines in aquatic biodiversity across diverse domains of life in rivers impacted by surface coal mining. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02389. [PMID: 34142402 DOI: 10.1002/eap.2389] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 02/04/2021] [Indexed: 06/12/2023]
Abstract
The rivers of Appalachia (United States) are among the most biologically diverse freshwater ecosystems in the temperate zone and are home to numerous endemic aquatic organisms. Throughout the Central Appalachian ecoregion, extensive surface coal mines generate alkaline mine drainage that raises the pH, salinity, and trace element concentrations in downstream waters. Previous regional assessments have found significant declines in stream macroinvertebrate and fish communities after draining these mined areas. Here, we expand these assessments with a more comprehensive evaluation across a broad range of organisms (bacteria, algae, macroinvertebrates, all eukaryotes, and fish) using high-throughput amplicon sequencing of environmental DNA (eDNA). We collected water samples from 93 streams in Central Appalachia (West Virginia, United States) spanning a gradient of mountaintop coal mining intensity and legacy to assess how this land use alters downstream water chemistry and affects aquatic biodiversity. For each group of organisms, we identified the sensitive and tolerant taxa along the gradient and calculated stream specific conductivity thresholds in which large synchronous declines in diversity were observed. Streams below mining operations had steep declines in diversity (-18 to -41%) and substantial shifts in community composition that were consistent across multiple taxonomic groups. Overall, large synchronous declines in bacterial, algal, and macroinvertebrate communities occurred even at low levels of mining impact at stream specific conductivity thresholds of 150-200 µS/cm that are substantially below the current U.S. Environmental Protection Agency aquatic life benchmark of 300 µS/cm for Central Appalachian streams. We show that extensive coal surface mining activities led to the extirpation of 40% of biodiversity from impacted rivers throughout the region and that current water quality criteria are likely not protective for many groups of aquatic organisms.
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Affiliation(s)
- Marie Simonin
- Biology Department, Duke University, Durham, North Carolina, 27708, USA
- Institut Agro, University of Angers, INRAE, IRHS, SFR 4207 QuaSaV, Angers, 49000, France
| | - Jennifer D Rocca
- Biology Department, Duke University, Durham, North Carolina, 27708, USA
- Department of Plant and Microbial Ecology, North Carolina State University, Raleigh, North Carolina, 27695, USA
| | | | - Eric Moore
- Biology Department, Duke University, Durham, North Carolina, 27708, USA
- Department of Natural Resources and the Environment, Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, Connecticut, 06269, USA
| | - Alexander C Brooks
- Natural Resources and Ecology Laboratory, Colorado State University, Fort Collins, Colorado, 80523, USA
| | | | - Matthew R V Ross
- Natural Resources and Ecology Laboratory, Colorado State University, Fort Collins, Colorado, 80523, USA
| | - Noah Fierer
- Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, 80309, USA
- Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, 80309, USA
| | | | - Emily S Bernhardt
- Biology Department, Duke University, Durham, North Carolina, 27708, USA
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Pawlowski J, Bonin A, Boyer F, Cordier T, Taberlet P. Environmental DNA for biomonitoring. Mol Ecol 2021; 30:2931-2936. [PMID: 34176165 PMCID: PMC8451586 DOI: 10.1111/mec.16023] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 06/10/2021] [Indexed: 12/17/2022]
Affiliation(s)
- Jan Pawlowski
- Department of Genetics and EvolutionUniversity of GenevaGenevaSwitzerland
- Institute of OceanologyPolish Academy of SciencesSopotPoland
- ID‐Gene EcodiagnosticsGenevaSwitzerland
| | - Aurélie Bonin
- Department of Environmental Science and PolicyUniversità degli Studi di MilanoMilanItaly
| | - Frédéric Boyer
- Laboratoire d'Ecologie Alpine (LECA)CNRSUniversité Grenoble AlpesGrenobleFrance
| | - Tristan Cordier
- Department of Genetics and EvolutionUniversity of GenevaGenevaSwitzerland
- NORCE ClimateNORCE Norwegian Research Centre ASBjerknes Centre for Climate ResearchBergenNorway
| | - Pierre Taberlet
- Laboratoire d'Ecologie Alpine (LECA)CNRSUniversité Grenoble AlpesGrenobleFrance
- Tromsø MuseumUiT – The Arctic University of NorwayTromsøNorway
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38
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Cordier T, Alonso‐Sáez L, Apothéloz‐Perret‐Gentil L, Aylagas E, Bohan DA, Bouchez A, Chariton A, Creer S, Frühe L, Keck F, Keeley N, Laroche O, Leese F, Pochon X, Stoeck T, Pawlowski J, Lanzén A. Ecosystems monitoring powered by environmental genomics: A review of current strategies with an implementation roadmap. Mol Ecol 2021; 30:2937-2958. [PMID: 32416615 PMCID: PMC8358956 DOI: 10.1111/mec.15472] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 04/25/2020] [Accepted: 05/06/2020] [Indexed: 01/02/2023]
Abstract
A decade after environmental scientists integrated high-throughput sequencing technologies in their toolbox, the genomics-based monitoring of anthropogenic impacts on the biodiversity and functioning of ecosystems is yet to be implemented by regulatory frameworks. Despite the broadly acknowledged potential of environmental genomics to this end, technical limitations and conceptual issues still stand in the way of its broad application by end-users. In addition, the multiplicity of potential implementation strategies may contribute to a perception that the routine application of this methodology is premature or "in development", hence restraining regulators from binding these tools into legal frameworks. Here, we review recent implementations of environmental genomics-based methods, applied to the biomonitoring of ecosystems. By taking a general overview, without narrowing our perspective to particular habitats or groups of organisms, this paper aims to compare, review and discuss the strengths and limitations of four general implementation strategies of environmental genomics for monitoring: (a) Taxonomy-based analyses focused on identification of known bioindicators or described taxa; (b) De novo bioindicator analyses; (c) Structural community metrics including inferred ecological networks; and (d) Functional community metrics (metagenomics or metatranscriptomics). We emphasise the utility of the three latter strategies to integrate meiofauna and microorganisms that are not traditionally utilised in biomonitoring because of difficult taxonomic identification. Finally, we propose a roadmap for the implementation of environmental genomics into routine monitoring programmes that leverage recent analytical advancements, while pointing out current limitations and future research needs.
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Affiliation(s)
- Tristan Cordier
- Department of Genetics and EvolutionScience IIIUniversity of GenevaGenevaSwitzerland
| | - Laura Alonso‐Sáez
- AZTIMarine ResearchBasque Research and Technology Alliance (BRTA)Spain
| | | | - Eva Aylagas
- Red Sea Research Center (RSRC)Biological and Environmental Sciences and Engineering (BESE)King Abdullah University of Science and Technology (KAUST)ThuwalSaudi Arabia
| | - David A. Bohan
- AgroécologieINRAEUniversity of BourgogneUniversity Bourgogne Franche‐ComtéDijonFrance
| | | | - Anthony Chariton
- Department of Biological SciencesMacquarie UniversitySydneyNSWAustralia
| | - Simon Creer
- School of Natural SciencesBangor UniversityGwyneddUK
| | - Larissa Frühe
- Department of EcologyTechnische Universität KaiserslauternKaiserslauternGermany
| | | | - Nigel Keeley
- Benthic Resources and Processes GroupInstitute of Marine ResearchTromsøNorway
| | - Olivier Laroche
- Benthic Resources and Processes GroupInstitute of Marine ResearchTromsøNorway
| | - Florian Leese
- Aquatic Ecosystem ResearchFaculty of BiologyUniversity of Duisburg‐EssenEssenGermany
- Centre for Water and Environmental Research (ZWU)University of Duisburg‐EssenEssenGermany
| | - Xavier Pochon
- Coastal & Freshwater GroupCawthron InstituteNelsonNew Zealand
- Institute of Marine ScienceUniversity of AucklandWarkworthNew Zealand
| | - Thorsten Stoeck
- Department of EcologyTechnische Universität KaiserslauternKaiserslauternGermany
| | - Jan Pawlowski
- Department of Genetics and EvolutionScience IIIUniversity of GenevaGenevaSwitzerland
- ID‐Gene EcodiagnosticsGenevaSwitzerland
- Institute of OceanologyPolish Academy of SciencesSopotPoland
| | - Anders Lanzén
- AZTIMarine ResearchBasque Research and Technology Alliance (BRTA)Spain
- Basque Foundation for ScienceIKERBASQUEBilbaoSpain
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39
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Tardy V, Bonnineau C, Bouchez A, Miège C, Masson M, Jeannin P, Pesce S. A pilot experiment to assess the efficiency of pharmaceutical plant wastewater treatment and the decreasing effluent toxicity to periphytic biofilms. JOURNAL OF HAZARDOUS MATERIALS 2021; 411:125121. [PMID: 33858096 DOI: 10.1016/j.jhazmat.2021.125121] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 12/17/2020] [Accepted: 01/08/2021] [Indexed: 06/12/2023]
Abstract
Pharmaceutical industry effluents are complex and highly variable in time. Assessing the efficiency of a pharmaceutical industry wastewater treatment plant (WWTP) and the resulting decrease in effluent toxicity and ecological risk is thus not straightforward. We set up an original in situ pilot directly connected to a pharmaceutical WWTP to monitor the chronic toxicity of successive effluents using natural periphytic biofilms. Their structural and functional responses to effluent exposure were assessed by combining (i) a molecular approach to characterize the bacterial and diatom diversity and (ii) functional measurements of photosynthetic and enzyme activities. Effluent contamination by pharmaceuticals strongly decreased after the quaternary treatment (activated carbon). Most of the structural biological characteristics improved with cumulative WWTP treatment (bacterial diversity, microbial genetic structure, and biological diatom index), showing community recovery along the treatment process. However, functional parameters did not show clear links with treatment steps, suggesting that microbial activities were not solely driven by pharmaceuticals produced during the experimental period. Operationally, this type of pilot system offers a useful tool for biomonitoring approaches and offers new approaches for industrial managers to assess the ecological risk of production effluents in receiving water.
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Affiliation(s)
| | | | - Agnès Bouchez
- INRAE, USMB, UMR CARRTEL, 74200 Thonon-les-Bains, France
| | | | | | - Pierric Jeannin
- SANOFI, Central Laboratory of Environment & Safety, route d'Avignon, 30390 Aramon, France
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Alam I, Kamau AA, Ngugi DK, Gojobori T, Duarte CM, Bajic VB. KAUST Metagenomic Analysis Platform (KMAP), enabling access to massive analytics of re-annotated metagenomic data. Sci Rep 2021; 11:11511. [PMID: 34075103 PMCID: PMC8169707 DOI: 10.1038/s41598-021-90799-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 05/18/2021] [Indexed: 11/09/2022] Open
Abstract
Exponential rise of metagenomics sequencing is delivering massive functional environmental genomics data. However, this also generates a procedural bottleneck for on-going re-analysis as reference databases grow and methods improve, and analyses need be updated for consistency, which require acceess to increasingly demanding bioinformatic and computational resources. Here, we present the KAUST Metagenomic Analysis Platform (KMAP), a new integrated open web-based tool for the comprehensive exploration of shotgun metagenomic data. We illustrate the capacities KMAP provides through the re-assembly of ~ 27,000 public metagenomic samples captured in ~ 450 studies sampled across ~ 77 diverse habitats. A small subset of these metagenomic assemblies is used in this pilot study grouped into 36 new habitat-specific gene catalogs, all based on full-length (complete) genes. Extensive taxonomic and gene annotations are stored in Gene Information Tables (GITs), a simple tractable data integration format useful for analysis through command line or for database management. KMAP pilot study provides the exploration and comparison of microbial GITs across different habitats with over 275 million genes. KMAP access to data and analyses is available at https://www.cbrc.kaust.edu.sa/aamg/kmap.start .
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Affiliation(s)
- Intikhab Alam
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
| | - Allan Anthony Kamau
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - David Kamanda Ngugi
- Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Inhoffenstraße 7B, 38124, Brunswick, Germany
| | - Takashi Gojobori
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Carlos M Duarte
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.,Red Sea Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Vladimir B Bajic
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
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41
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Survey of artificial intelligence approaches in the study of anthropogenic impacts on symbiotic organisms – a holistic view. Symbiosis 2021. [DOI: 10.1007/s13199-021-00778-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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42
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Dully V, Wilding TA, Mühlhaus T, Stoeck T. Identifying the minimum amplicon sequence depth to adequately predict classes in eDNA-based marine biomonitoring using supervised machine learning. Comput Struct Biotechnol J 2021; 19:2256-2268. [PMID: 33995917 PMCID: PMC8093828 DOI: 10.1016/j.csbj.2021.04.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/30/2021] [Accepted: 04/01/2021] [Indexed: 01/04/2023] Open
Abstract
Environmental DNA metabarcoding is a powerful approach for use in biomonitoring and impact assessments. Amplicon-based eDNA sequence data are characteristically highly divergent in sequencing depth (total reads per sample) as influenced inter alia by the number of samples simultaneously analyzed per sequencing run. The random forest (RF) machine learning algorithm has been successfully employed to accurately classify unknown samples into monitoring categories. To employ RF to eDNA data, and avoid sequencing-depth artifacts, sequence data across samples are normalized using rarefaction, a process that inherently loses information. The aim of this study was to inform future sampling designs in terms of the relationship between sampling depth and RF accuracy. We analyzed three published and one new bacterial amplicon datasets, using a RF, based initially on the maximal rarefied data available (minimum mean of > 30,000 reads across all datasets) to give our baseline performance. We then evaluated the RF classification success based on increasingly rarefied datasets. We found that extreme to moderate rarefaction (50-5000 sequences per sample) was sufficient to achieve prediction performance commensurate to the full data, depending on the classification task. We did not find that the number of classification classes, data balance across classes, or the total number of sequences or samples, were associated with predictive accuracy. We identified the ability of the training data to adequately characterize the classes being mapped as the most important criterion and discuss how this finding can inform future sampling design for eDNA based biomonitoring to reduce costs and computation time.
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Key Words
- 16S rRNA
- AMBI, AZTI's marine biotic index
- ASV, Amplicon Sequence Variants
- AZE, allowable zone of effect, intermediate impact zone
- BI, biotic index
- BallWa, ballast water dataset
- BasCo, Basque coast dataset
- Biomonitoring
- CE, cage edge
- CV, Coefficient of Variance
- DADA2, Divisive Amplicon Denoising Algorithm
- EQ, environmental quality
- Environmental DNA
- FM, full model
- MDS, multidimensional scaling
- Machine learning
- Marine
- NEB, New England Biolabs
- NW, north west
- NorSa, Norway salmon dataset
- OOB-error, out-of-bag error estimate
- PCR, polymerase chain reaction
- REF, reference site
- RF, random forest algorithm
- SML, supervised machine learning
- ScoSa, Scottish salmon farm dataset
- V3-V4, hypervariable gene regions of the 16s rRNA
- bp, base pairs
- eDNA, environmental deoxyribonucleic acid
- microgAMBI, AZTI's marine biotic index based on microbial genes
- mtry, numbers of variables tried at each split
- n, number
- rRNA, small subunit prokaryotic ribosomal ribonucleic acid
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Affiliation(s)
- Verena Dully
- Technische Universität Kaiserslautern, Ecology, D-67663 Kaiserslautern, Germany
| | - Thomas A. Wilding
- Scottish Association for Marine Science, Scottish Marine Institute, Oban, Scotland, United Kingdom
| | - Timo Mühlhaus
- Technische Universität Kaiserslautern, Computational Systems Biology, D-67663 Kaiserslautern, Germany
| | - Thorsten Stoeck
- Technische Universität Kaiserslautern, Ecology, D-67663 Kaiserslautern, Germany
- Corresponding author.
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Sperlea T, Kreuder N, Beisser D, Hattab G, Boenigk J, Heider D. Quantification of the covariation of lake microbiomes and environmental variables using a machine learning-based framework. Mol Ecol 2021; 30:2131-2144. [PMID: 33682183 DOI: 10.1111/mec.15872] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 02/17/2021] [Accepted: 02/24/2021] [Indexed: 12/12/2022]
Abstract
It is known that microorganisms are essential for the functioning of ecosystems, but the extent to which microorganisms respond to different environmental variables in their natural habitats is not clear. In the current study, we present a methodological framework to quantify the covariation of the microbial community of a habitat and environmental variables of this habitat. It is built on theoretical considerations of systems ecology, makes use of state-of-the-art machine learning techniques and can be used to identify bioindicators. We apply the framework to a data set containing operational taxonomic units (OTUs) as well as more than twenty physicochemical and geographic variables measured in a large-scale survey of European lakes. While a large part of variation (up to 61%) in many environmental variables can be explained by microbial community composition, some variables do not show significant covariation with the microbial lake community. Moreover, we have identified OTUs that act as "multitask" bioindicators, i.e., that are indicative for multiple environmental variables, and thus could be candidates for lake water monitoring schemes. Our results represent, for the first time, a quantification of the covariation of the lake microbiome and a wide array of environmental variables for lake ecosystems. Building on the results and methodology presented here, it will be possible to identify microbial taxa and processes that are essential for functioning and stability of lake ecosystems.
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Affiliation(s)
- Theodor Sperlea
- Faculty of Mathematics and Computer Science, University of Marburg, Marburg (Lahn), Germany
| | - Nico Kreuder
- Department of Biodiversity, Center for Water and Environmental Research, University of Duisburg-Essen, Essen, Germany
| | - Daniela Beisser
- Department of Biodiversity, Center for Water and Environmental Research, University of Duisburg-Essen, Essen, Germany
| | - Georges Hattab
- Faculty of Mathematics and Computer Science, University of Marburg, Marburg (Lahn), Germany
| | - Jens Boenigk
- Department of Biodiversity, Center for Water and Environmental Research, University of Duisburg-Essen, Essen, Germany
| | - Dominik Heider
- Faculty of Mathematics and Computer Science, University of Marburg, Marburg (Lahn), Germany
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44
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Ruuskanen MO, Sommeria-Klein G, Havulinna AS, Niiranen TJ, Lahti L. Modelling spatial patterns in host-associated microbial communities. Environ Microbiol 2021; 23:2374-2388. [PMID: 33734553 DOI: 10.1111/1462-2920.15462] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 03/02/2021] [Accepted: 03/11/2021] [Indexed: 12/12/2022]
Abstract
Microbial communities exhibit spatial structure at different scales, due to constant interactions with their environment and dispersal limitation. While this spatial structure is often considered in studies focusing on free-living environmental communities, it has received less attention in the context of host-associated microbial communities or microbiota. The wider adoption of methods accounting for spatial variation in these communities will help to address open questions in basic microbial ecology as well as realize the full potential of microbiome-aided medicine. Here, we first overview known factors affecting the composition of microbiota across diverse host types and at different scales, with a focus on the human gut as one of the most actively studied microbiota. We outline a number of topical open questions in the field related to spatial variation and patterns. We then review the existing methodology for the spatial modelling of microbiota. We suggest that methodology from related fields, such as systems biology and macro-organismal ecology, could be adapted to obtain more accurate models of spatial structure. We further posit that methodological developments in the spatial modelling and analysis of microbiota could in turn broadly benefit theoretical and applied ecology and contribute to the development of novel industrial and clinical applications.
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Affiliation(s)
- Matti O Ruuskanen
- Department of Internal Medicine, University of Turku, Turku, Finland.,Finnish Institute for Health and Welfare, Helsinki, Finland
| | | | - Aki S Havulinna
- Finnish Institute for Health and Welfare, Helsinki, Finland.,Institute for Molecular Medicine Finland, FIMM-HiLIFE, Helsinki, Finland
| | - Teemu J Niiranen
- Department of Internal Medicine, University of Turku, Turku, Finland.,Finnish Institute for Health and Welfare, Helsinki, Finland.,Division of Medicine, Turku University Hospital, Turku, Finland
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
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45
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Nugent CM, Elliott TA, Ratnasingham S, Hebert PDN, Adamowicz SJ. Debar: A sequence-by-sequence denoiser for COI-5P DNA barcode data. Mol Ecol Resour 2021; 21:2832-2846. [PMID: 33749132 DOI: 10.1111/1755-0998.13384] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 03/05/2021] [Indexed: 12/14/2022]
Abstract
DNA barcoding and metabarcoding are now widely used to advance species discovery and biodiversity assessments. High-throughput sequencing (HTS) has expanded the volume and scope of these analyses, but elevated error rates introduce noise into sequence records that can inflate estimates of biodiversity. Denoising -the separation of biological signal from instrument (technical) noise-of barcode and metabarcode data currently employs abundance-based methods which do not capitalize on the highly conserved structure of the cytochrome c oxidase subunit I (COI) region employed as the animal barcode. This manuscript introduces debar, an R package that utilizes a profile hidden Markov model to denoise indel errors in COI sequences introduced by instrument error. In silico studies demonstrated that debar recognized 95% of artificially introduced indels in COI sequences. When applied to real-world data, debar reduced indel errors in circular consensus sequences obtained with the Sequel platform by 75%, and those generated on the Ion Torrent S5 by 94%. The false correction rate was less than 0.1%, indicating that debar is receptive to the majority of true COI variation in the animal kingdom. In conclusion, the debar package improves DNA barcode and metabarcode workflows by aiding the generation of more accurate sequences aiding the characterization of species diversity.
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Affiliation(s)
- Cameron M Nugent
- Department of Integrative Biology, University of Guelph, Guelph, ON, Canada.,Centre for Biodiversity Genomics, University of Guelph, Guelph, ON, Canada
| | - Tyler A Elliott
- Centre for Biodiversity Genomics, University of Guelph, Guelph, ON, Canada
| | | | - Paul D N Hebert
- Centre for Biodiversity Genomics, University of Guelph, Guelph, ON, Canada
| | - Sarah J Adamowicz
- Department of Integrative Biology, University of Guelph, Guelph, ON, Canada
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46
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Sehnal L, Brammer-Robbins E, Wormington AM, Blaha L, Bisesi J, Larkin I, Martyniuk CJ, Simonin M, Adamovsky O. Microbiome Composition and Function in Aquatic Vertebrates: Small Organisms Making Big Impacts on Aquatic Animal Health. Front Microbiol 2021; 12:567408. [PMID: 33776947 PMCID: PMC7995652 DOI: 10.3389/fmicb.2021.567408] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 02/05/2021] [Indexed: 01/03/2023] Open
Abstract
Aquatic ecosystems are under increasing stress from global anthropogenic and natural changes, including climate change, eutrophication, ocean acidification, and pollution. In this critical review, we synthesize research on the microbiota of aquatic vertebrates and discuss the impact of emerging stressors on aquatic microbial communities using two case studies, that of toxic cyanobacteria and microplastics. Most studies to date are focused on host-associated microbiomes of individual organisms, however, few studies take an integrative approach to examine aquatic vertebrate microbiomes by considering both host-associated and free-living microbiota within an ecosystem. We highlight what is known about microbiota in aquatic ecosystems, with a focus on the interface between water, fish, and marine mammals. Though microbiomes in water vary with geography, temperature, depth, and other factors, core microbial functions such as primary production, nitrogen cycling, and nutrient metabolism are often conserved across aquatic environments. We outline knowledge on the composition and function of tissue-specific microbiomes in fish and marine mammals and discuss the environmental factors influencing their structure. The microbiota of aquatic mammals and fish are highly unique to species and a delicate balance between respiratory, skin, and gastrointestinal microbiota exists within the host. In aquatic vertebrates, water conditions and ecological niche are driving factors behind microbial composition and function. We also generate a comprehensive catalog of marine mammal and fish microbial genera, revealing commonalities in composition and function among aquatic species, and discuss the potential use of microbiomes as indicators of health and ecological status of aquatic ecosystems. We also discuss the importance of a focus on the functional relevance of microbial communities in relation to organism physiology and their ability to overcome stressors related to global change. Understanding the dynamic relationship between aquatic microbiota and the animals they colonize is critical for monitoring water quality and population health.
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Affiliation(s)
- Ludek Sehnal
- RECETOX, Faculty of Science, Masaryk University, Brno, Czechia
| | - Elizabeth Brammer-Robbins
- Department of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, United States.,Department of Physiological Sciences, University of Florida, Gainesville, FL, United States.,Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL, United States
| | - Alexis M Wormington
- Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL, United States.,Department of Environmental and Global Health, University of Florida, Gainesville, FL, United States
| | - Ludek Blaha
- RECETOX, Faculty of Science, Masaryk University, Brno, Czechia
| | - Joe Bisesi
- Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL, United States.,Department of Environmental and Global Health, University of Florida, Gainesville, FL, United States
| | - Iske Larkin
- Department of Large Animal Clinical Sciences, University of Florida, Gainesville, FL, United States
| | - Christopher J Martyniuk
- Department of Physiological Sciences, University of Florida, Gainesville, FL, United States.,Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL, United States
| | - Marie Simonin
- Univ Angers, Institut Agro, INRAE, IRHS, SFR QUASAV, Angers, France
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Sagova-Mareckova M, Boenigk J, Bouchez A, Cermakova K, Chonova T, Cordier T, Eisendle U, Elersek T, Fazi S, Fleituch T, Frühe L, Gajdosova M, Graupner N, Haegerbaeumer A, Kelly AM, Kopecky J, Leese F, Nõges P, Orlic S, Panksep K, Pawlowski J, Petrusek A, Piggott JJ, Rusch JC, Salis R, Schenk J, Simek K, Stovicek A, Strand DA, Vasquez MI, Vrålstad T, Zlatkovic S, Zupancic M, Stoeck T. Expanding ecological assessment by integrating microorganisms into routine freshwater biomonitoring. WATER RESEARCH 2021; 191:116767. [PMID: 33418487 DOI: 10.1016/j.watres.2020.116767] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 12/14/2020] [Accepted: 12/19/2020] [Indexed: 06/12/2023]
Abstract
Bioindication has become an indispensable part of water quality monitoring in most countries of the world, with the presence and abundance of bioindicator taxa, mostly multicellular eukaryotes, used for biotic indices. In contrast, microbes (bacteria, archaea and protists) are seldom used as bioindicators in routine assessments, although they have been recognized for their importance in environmental processes. Recently, the use of molecular methods has revealed unexpected diversity within known functional groups and novel metabolic pathways that are particularly important in energy and nutrient cycling. In various habitats, microbial communities respond to eutrophication, metals, and natural or anthropogenic organic pollutants through changes in diversity and function. In this review, we evaluated the common trends in these changes, documenting that they have value as bioindicators and can be used not only for monitoring but also for improving our understanding of the major processes in lotic and lentic environments. Current knowledge provides a solid foundation for exploiting microbial taxa, community structures and diversity, as well as functional genes, in novel monitoring programs. These microbial community measures can also be combined into biotic indices, improving the resolution of individual bioindicators. Here, we assess particular molecular approaches complemented by advanced bioinformatic analysis, as these are the most promising with respect to detailed bioindication value. We conclude that microbial community dynamics are a missing link important for our understanding of rapid changes in the structure and function of aquatic ecosystems, and should be addressed in the future environmental monitoring of freshwater ecosystems.
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Affiliation(s)
- M Sagova-Mareckova
- Dept. of Microbiology, Nutrition and Dietetics, Czech University of Life Sciences, Kamýcká 129, Prague 6, 16500, Czechia.
| | - J Boenigk
- Biodiversity, University of Duisburg-Essen, Universitaetsstraße 5, 45141 Essen, Germany
| | - A Bouchez
- UMR CARRTEL, INRAE, UMR Carrtel, 75 av. de Corzent, FR-74203 Thonon les Bains cedex, France; University Savoie Mont-Blanc, UMR CARRTEL, FR-73370 Le Bourget du Lac, France
| | - K Cermakova
- ID-Gene Ecodiagnostics, Campus Biotech Innovation Park, 15, av. Sécheron, 1202 Geneva, Switzerland
| | - T Chonova
- UMR CARRTEL, INRAE, UMR Carrtel, 75 av. de Corzent, FR-74203 Thonon les Bains cedex, France; University Savoie Mont-Blanc, UMR CARRTEL, FR-73370 Le Bourget du Lac, France
| | - T Cordier
- Department of Genetics and Evolution, University of Geneva, Science III, 4 Boulevard d'Yvoy, 1205 Geneva, Switzerland
| | - U Eisendle
- University of Salzburg, Hellbrunnerstraße 34, 5020 Salzburg, Austria
| | - T Elersek
- National Institute of Biology, Vecna pot 111, SI-1000 Ljubljana, Slovenia
| | - S Fazi
- Water Research Institute, National Research Council of Italy (IRSA-CNR), Via Salaria km 29,300 - C.P. 10, 00015 Monterotondo St., Rome, Italy
| | - T Fleituch
- Institute of Nature Conservation, Polish Academy of Sciences, ul. Adama Mickiewicza 33, 31-120 Krakow, Poland
| | - L Frühe
- Ecology Group, Technische Universität Kaiserslautern, D-67663 Kaiserslautern, Germany
| | - M Gajdosova
- Dept. of Ecology, Faculty of Science, Charles University, Viničná 7, 12844 Prague, Czechia
| | - N Graupner
- Biodiversity, University of Duisburg-Essen, Universitaetsstraße 5, 45141 Essen, Germany
| | - A Haegerbaeumer
- Dept. of Animal Ecology, Bielefeld University, Konsequenz 45, 33615 Bielefeld, Germany
| | - A-M Kelly
- School of Natural Sciences, Trinity College Dublin, University of Dublin, College Green, Dublin 2, D02 PN40, Ireland
| | - J Kopecky
- Epidemiology and Ecology of Microoganisms, Crop Research Institute, Drnovská 507, 16106 Prague 6, Czechia
| | - F Leese
- Biodiversity, University of Duisburg-Essen, Universitaetsstraße 5, 45141 Essen, Germany; Aquatic Ecosystem Resarch, University of Duisburg-Essen, Universitaetsstrasse 5 D-45141 Essen, Germany
| | - P Nõges
- Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, Tartu 51006, Estonia
| | - S Orlic
- Institute Ruđer Bošković, Bijenička 54, 10000 Zagreb, Croatia; Center of Excellence for Science and Technology Integrating Mediterranean, Bijenička 54,10 000 Zagreb, Croatia
| | - K Panksep
- Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, Tartu 51006, Estonia
| | - J Pawlowski
- ID-Gene Ecodiagnostics, Campus Biotech Innovation Park, 15, av. Sécheron, 1202 Geneva, Switzerland; Department of Genetics and Evolution, University of Geneva, Science III, 4 Boulevard d'Yvoy, 1205 Geneva, Switzerland; Institute of Oceanology, Polish Academy of Sciences, Powstańców Warszawy 55, 81-712 Sopot, Poland
| | - A Petrusek
- Dept. of Ecology, Faculty of Science, Charles University, Viničná 7, 12844 Prague, Czechia
| | - J J Piggott
- School of Natural Sciences, Trinity College Dublin, University of Dublin, College Green, Dublin 2, D02 PN40, Ireland
| | - J C Rusch
- Norwegian Veterinary Institute, P.O. Box 750, Sentrum, NO-0106 Oslo, Norway; Department of Biosciences, University of Oslo, P.O. Box 1066, Blindern, NO-0316 Oslo, Norway
| | - R Salis
- Department of Biology, Faculty of Science, Lund University, Sölvegatan 37, 223 62 Lund, Sweden
| | - J Schenk
- Dept. of Animal Ecology, Bielefeld University, Konsequenz 45, 33615 Bielefeld, Germany
| | - K Simek
- Institute of Hydrobiology, Biology Centre CAS, Branišovská 31, 370 05 České Budějovice, Czechia
| | - A Stovicek
- Dept. of Microbiology, Nutrition and Dietetics, Czech University of Life Sciences, Kamýcká 129, Prague 6, 16500, Czechia
| | - D A Strand
- Norwegian Veterinary Institute, P.O. Box 750, Sentrum, NO-0106 Oslo, Norway
| | - M I Vasquez
- Department of Chemical Engineering, Cyprus University of Technology, 30 Arch. Kyprianos Str., 3036 Limassol, Cyprus
| | - T Vrålstad
- Norwegian Veterinary Institute, P.O. Box 750, Sentrum, NO-0106 Oslo, Norway
| | - S Zlatkovic
- Ministry of Environmental Protection, Omladinskih brigada 1, 11070 Belgrade, Serbia; Agency "Akvatorija", 11. krajiške divizije 49, 11090 Belgrade, Serbia
| | - M Zupancic
- National Institute of Biology, Vecna pot 111, SI-1000 Ljubljana, Slovenia
| | - T Stoeck
- Ecology Group, Technische Universität Kaiserslautern, D-67663 Kaiserslautern, Germany
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Pin L, Eiler A, Fazi S, Friberg N. Two different approaches of microbial community structure characterization in riverine epilithic biofilms under multiple stressors conditions: Developing molecular indicators. Mol Ecol Resour 2021; 21:1200-1215. [PMID: 33529477 DOI: 10.1111/1755-0998.13341] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 01/25/2021] [Accepted: 01/26/2021] [Indexed: 01/04/2023]
Abstract
Microbial communities are major players in the biogeochemical processes and ecosystem functioning of river networks. Despite their importance in the ecosystem, biomonitoring tools relying on prokaryotes are still lacking. Only a few studies have employed both metabarcoding and quantitative techniques such as catalysed reported deposition fluorescence in situ hybridization (CARD-FISH) to analyse prokaryotic communities of epilithic biofilms in river ecosystems. We intended to investigate the efficacy of both techniques in detecting changes in microbial community structure associated with environmental drivers. We report a significant correlation between the prokaryotic community composition and pH in rivers from two different geographical areas in Norway. Both CARD-FISH and metabarcoding data were following the pattern of the environmental variables, but the main feature distinguishing the community composition was the regional difference itself. Beta-dispersion analyses on both CARD-FISH abundance and metabarcoding data revealed higher accuracy of metabarcoding to differentiate regions and river systems. The CARD-FISH results showed high variability, even for samples within the same river, probably due to some unmeasured microscale ecological variability which we could not resolve. We also present a statistical method, which uses variation coefficient and overall prevalence of taxonomic groups, to detect possible biological indicators among prokaryotes using metabarcoding data. The development of new prokaryotic bioindicators would benefit from both techniques used in this study, but metabarcoding seems to be faster and more reliable than CARD-FISH for large scale bio-assessment.
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Affiliation(s)
- Lorenzo Pin
- Norsk Institutt for Vannforskning (NIVA), Oslo, Norway.,Section for Aquatic Biology and Toxicology, Department of Biosciences, Centre for Biogeochemistry in the Anthropocene, University of Oslo, Norway
| | - Alexander Eiler
- Section for Aquatic Biology and Toxicology, Department of Biosciences, Centre for Biogeochemistry in the Anthropocene, University of Oslo, Norway.,eDNA solutions AB, Mölndal, Sweden
| | - Stefano Fazi
- Water Research Institute, IRSA-CNR, Monterotondo, Roma, Italy
| | - Nikolai Friberg
- Norsk Institutt for Vannforskning (NIVA), Oslo, Norway.,Freshwater Biological Section, University of Copenhagen, Copenhagen, Denmark.,School of Geography, University of Leeds, Leeds, UK
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49
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The Seagrass Holobiont: What We Know and What We Still Need to Disclose for Its Possible Use as an Ecological Indicator. WATER 2021. [DOI: 10.3390/w13040406] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Microbes and seagrass establish symbiotic relationships constituting a functional unit called the holobiont that reacts as a whole to environmental changes. Recent studies have shown that the seagrass microbial associated community varies according to host species, environmental conditions and the host’s health status, suggesting that the microbial communities respond rapidly to environmental disturbances and changes. These changes, dynamics of which are still far from being clear, could represent a sensitive monitoring tool and ecological indicator to detect early stages of seagrass stress. In this review, the state of art on seagrass holobiont is discussed in this perspective, with the aim of disentangling the influence of different factors in shaping it. As an example, we expand on the widely studied Halophila stipulacea’s associated microbial community, highlighting the changing and the constant components of the associated microbes, in different environmental conditions. These studies represent a pivotal contribution to understanding the holobiont’s dynamics and variability pattern, and to the potential development of ecological/ecotoxicological indices. The influences of the host’s physiological and environmental status in changing the seagrass holobiont, alongside the bioinformatic tools for data analysis, are key topics that need to be deepened, in order to use the seagrass-microbial interactions as a source of ecological information.
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50
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Ghannam RB, Techtmann SM. Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring. Comput Struct Biotechnol J 2021; 19:1092-1107. [PMID: 33680353 PMCID: PMC7892807 DOI: 10.1016/j.csbj.2021.01.028] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 01/16/2021] [Accepted: 01/18/2021] [Indexed: 01/04/2023] Open
Abstract
Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data. However, often machine learning models can be used as black boxes to predict a specific outcome, with little understanding of how the models arrived at predictions. Complex machine learning algorithms often may value higher accuracy and performance at the sacrifice of interpretability. In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be interpretable. Here we review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understanding microbial communities.
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Key Words
- 16S rRNA
- ANN, Artificial Neural Networks
- ASV, Amplicon Sequence Variant
- AUC, Area Under the Curve
- Forensics
- GB, Gradient Boosting
- ML, Machine Learning
- Machine learning
- Marker genes
- Metagenomics
- PCoA, Principal Coordinate Analysis
- RF, Random Forests
- ROC, Receiver Operating Characteristic
- SML, Supervised Machine Learning
- SVM, Support Vector Machines
- USML, Unsupervised Machine Learning
- tSNE, t-distributed Stochastic Neighbor Embedding
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Affiliation(s)
- Ryan B. Ghannam
- Department of Biological Sciences, Michigan Technological University, Houghton MI, United States
| | - Stephen M. Techtmann
- Department of Biological Sciences, Michigan Technological University, Houghton MI, United States
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