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Lu S, Zeng H, Xiong F, Yao M, He S. Advances in environmental DNA monitoring: standardization, automation, and emerging technologies in aquatic ecosystems. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2493-5. [PMID: 38512561 DOI: 10.1007/s11427-023-2493-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 10/30/2023] [Indexed: 03/23/2024]
Abstract
Environmental DNA (eDNA) monitoring, a rapidly advancing technique for assessing biodiversity and ecosystem health, offers a noninvasive approach for detecting and quantifying species from various environmental samples. In this review, a comprehensive overview of current eDNA collection and detection technologies is provided, emphasizing the necessity for standardization and automation in aquatic ecological monitoring. Furthermore, the intricacies of water bodies, from streams to the deep sea, and the associated challenges they pose for eDNA capture and analysis are explored. The paper delineates three primary eDNA survey methods, namely, bringing back water, bringing back filters, and bringing back data, each with specific advantages and constraints in terms of labor, transport, and data acquisition. Additionally, innovations in eDNA sampling equipment, including autonomous drones, subsurface samplers, and in-situ filtration devices, and their applications in monitoring diverse taxa are discussed. Moreover, recent advancements in species-specific detection and eDNA metabarcoding are addressed, highlighting the integration of novel techniques such as CRISPR-Cas and nanopore sequencing that enable precise and rapid detection of biodiversity. The implications of environmental RNA and epigenetic modifications are considered for future applications in providing nuanced ecological data. Lastly, the review stresses the critical role of standardization and automation in enhancing data consistency and comparability for robust long-term biomonitoring. We propose that the amalgamation of these technologies represents a paradigm shift in ecological monitoring, aligning with the urgent call for biodiversity conservation and sustainable management of aquatic ecosystems.
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Affiliation(s)
- Suxiang Lu
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China
| | - Honghui Zeng
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China
| | - Fan Xiong
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China
| | - Meng Yao
- Institute of Ecology, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China.
- School of Life Sciences, Peking University, Beijing, 100871, China.
| | - Shunping He
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China.
- Institute of Deep Sea Science and Engineering, Chinese Academy of Sciences, Sanya, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.
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Urban L, Perlas A, Francino O, Martí‐Carreras J, Muga BA, Mwangi JW, Boykin Okalebo L, Stanton JL, Black A, Waipara N, Fontsere C, Eccles D, Urel H, Reska T, Morales HE, Palmada‐Flores M, Marques‐Bonet T, Watsa M, Libke Z, Erkenswick G, van Oosterhout C. Real-time genomics for One Health. Mol Syst Biol 2023; 19:e11686. [PMID: 37325891 PMCID: PMC10407731 DOI: 10.15252/msb.202311686] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/31/2023] [Accepted: 06/02/2023] [Indexed: 06/17/2023] Open
Abstract
The ongoing degradation of natural systems and other environmental changes has put our society at a crossroad with respect to our future relationship with our planet. While the concept of One Health describes how human health is inextricably linked with environmental health, many of these complex interdependencies are still not well-understood. Here, we describe how the advent of real-time genomic analyses can benefit One Health and how it can enable timely, in-depth ecosystem health assessments. We introduce nanopore sequencing as the only disruptive technology that currently allows for real-time genomic analyses and that is already being used worldwide to improve the accessibility and versatility of genomic sequencing. We showcase real-time genomic studies on zoonotic disease, food security, environmental microbiome, emerging pathogens, and their antimicrobial resistances, and on environmental health itself - from genomic resource creation for wildlife conservation to the monitoring of biodiversity, invasive species, and wildlife trafficking. We stress why equitable access to real-time genomics in the context of One Health will be paramount and discuss related practical, legal, and ethical limitations.
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Affiliation(s)
- Lara Urban
- Helmholtz AI, Helmholtz Zentrum MuenchenNeuherbergGermany
- Helmholtz Pioneer Campus, Helmholtz Zentrum MuenchenNeuherbergGermany
- School of Life Sciences, Technical University of MunichFreisingGermany
| | - Albert Perlas
- Helmholtz AI, Helmholtz Zentrum MuenchenNeuherbergGermany
- Helmholtz Pioneer Campus, Helmholtz Zentrum MuenchenNeuherbergGermany
| | - Olga Francino
- Nano1Health SL, Parc de Recerca UABCampus Universitat Autònoma de BarcelonaBarcelonaSpain
| | - Joan Martí‐Carreras
- Nano1Health SL, Parc de Recerca UABCampus Universitat Autònoma de BarcelonaBarcelonaSpain
| | - Brenda A Muga
- Department of AnatomyUniversity of OtagoDunedinNew Zealand
| | | | | | | | - Amanda Black
- Bioprotection AotearoaLincoln UniversityLincolnNew Zealand
| | | | - Claudia Fontsere
- Center for Evolutionary HologenomicsThe Globe Institute, University of CopenhagenCopenhagenDenmark
| | - David Eccles
- Hugh Green Cytometry CentreMalaghan Institute of Medical ResearchWellingtonNew Zealand
| | - Harika Urel
- Helmholtz AI, Helmholtz Zentrum MuenchenNeuherbergGermany
- Helmholtz Pioneer Campus, Helmholtz Zentrum MuenchenNeuherbergGermany
- School of Life Sciences, Technical University of MunichFreisingGermany
| | - Tim Reska
- Helmholtz AI, Helmholtz Zentrum MuenchenNeuherbergGermany
- Helmholtz Pioneer Campus, Helmholtz Zentrum MuenchenNeuherbergGermany
- School of Life Sciences, Technical University of MunichFreisingGermany
| | - Hernán E Morales
- Center for Evolutionary HologenomicsThe Globe Institute, University of CopenhagenCopenhagenDenmark
- Department of Biology, Ecology BuildingLund UniversityLundSweden
| | - Marc Palmada‐Flores
- Institute of Evolutionary BiologyUniversitat Pompeu Fabra‐CSIC, PRBBBarcelonaSpain
| | - Tomas Marques‐Bonet
- Institute of Evolutionary BiologyUniversitat Pompeu Fabra‐CSIC, PRBBBarcelonaSpain
- Catalan Institution of Research and Advanced Studies (ICREA)BarcelonaSpain
- CNAGCentre of Genomic AnalysisBarcelonaSpain
- Institut Català de Paleontologia Miquel CrusafontUniversitat Autònoma de BarcelonaBarcelonaSpain
| | | | - Zane Libke
- Instituto Nacional de BiodiversidadQuitoEcuador
- Fundación Sumak Kawsay In SituCantón MeraEcuador
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Zorz J, Li C, Chakraborty A, Gittins DA, Surcon T, Morrison N, Bennett R, MacDonald A, Hubert CRJ. SituSeq: an offline protocol for rapid and remote Nanopore 16S rRNA amplicon sequence analysis. ISME COMMUNICATIONS 2023; 3:33. [PMID: 37081077 PMCID: PMC10119094 DOI: 10.1038/s43705-023-00239-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/27/2023] [Accepted: 04/04/2023] [Indexed: 04/22/2023]
Abstract
Microbiome analysis through 16S rRNA gene sequencing is a crucial tool for understanding the microbial ecology of any habitat or ecosystem. However, workflows require large equipment, stable internet, and extensive computing power such that most of the work is performed far away from sample collection in both space and time. Performing amplicon sequencing and analysis at sample collection would have positive implications in many instances including remote fieldwork and point-of-care medical diagnoses. Here we present SituSeq, an offline and portable workflow for the sequencing and analysis of 16S rRNA gene amplicons using Nanopore sequencing and a standard laptop computer. SituSeq was validated by comparing Nanopore 16S rRNA gene amplicons, Illumina 16S rRNA gene amplicons, and Illumina metagenomes, sequenced using the same environmental DNA. Comparisons revealed consistent community composition, ecological trends, and sequence identity across platforms. Correlation between the abundance of taxa in each taxonomic level in Illumina and Nanopore data sets was high (Pearson's r > 0.9), and over 70% of Illumina 16S rRNA gene sequences matched a Nanopore sequence with greater than 97% sequence identity. On board a research vessel on the open ocean, SituSeq was used to analyze amplicon sequences from deep sea sediments less than 2 h after sequencing, and 8 h after sample collection. The rapidly available results informed decisions about subsequent sampling in near real-time while the offshore expedition was still underway. SituSeq is a portable and user-friendly workflow that helps to bring the power of microbial genomics and diagnostics to many more researchers and situations.
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Affiliation(s)
- Jackie Zorz
- Department of Biological Sciences, University of Calgary, Calgary, AB, Canada.
| | - Carmen Li
- Department of Biological Sciences, University of Calgary, Calgary, AB, Canada
| | - Anirban Chakraborty
- Department of Biological Sciences, Idaho State University, Pocatello, ID, USA
| | - Daniel A Gittins
- Department of Biological Sciences, University of Calgary, Calgary, AB, Canada
| | - Taylor Surcon
- Department of Biological Sciences, University of Calgary, Calgary, AB, Canada
| | - Natasha Morrison
- Department of Natural Resources and Renewables, Government of Nova Scotia, Halifax, NS, Canada
| | - Robbie Bennett
- Natural Resources Canada, Geological Survey of Canada-Atlantic, Dartmouth, NS, Canada
| | - Adam MacDonald
- Department of Natural Resources and Renewables, Government of Nova Scotia, Halifax, NS, Canada
| | - Casey R J Hubert
- Department of Biological Sciences, University of Calgary, Calgary, AB, Canada
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Barquero A, Marini S, Boucher C, Ruiz J, Prosperi M. KARGAMobile: Android app for portable, real-time, easily interpretable analysis of antibiotic resistance genes via nanopore sequencing. Front Bioeng Biotechnol 2022; 10:1016408. [PMID: 36324897 PMCID: PMC9618647 DOI: 10.3389/fbioe.2022.1016408] [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: 08/10/2022] [Accepted: 09/27/2022] [Indexed: 02/03/2023] Open
Abstract
Nanopore technology enables portable, real-time sequencing of microbial populations from clinical and ecological samples. An emerging healthcare application for Nanopore includes point-of-care, timely identification of antibiotic resistance genes (ARGs) to help developing targeted treatments of bacterial infections, and monitoring resistant outbreaks in the environment. While several computational tools exist for classifying ARGs from sequencing data, to date (2022) none have been developed for mobile devices. We present here KARGAMobile, a mobile app for portable, real-time, easily interpretable analysis of ARGs from Nanopore sequencing. KARGAMobile is the porting of an existing ARG identification tool named KARGA; it retains the same algorithmic structure, but it is optimized for mobile devices. Specifically, KARGAMobile employs a compressed ARG reference database and different internal data structures to save RAM usage. The KARGAMobile app features a friendly graphical user interface that guides through file browsing, loading, parameter setup, and process execution. More importantly, the output files are post-processed to create visual, printable and shareable reports, aiding users to interpret the ARG findings. The difference in classification performance between KARGAMobile and KARGA is minimal (96.2% vs. 96.9% f-measure on semi-synthetic datasets of 1 million reads with known resistance ground truth). Using real Nanopore experiments, KARGAMobile processes on average 1 GB data every 23-48 min (targeted sequencing - metagenomics), with peak RAM usage below 500MB, independently from input file sizes, and an average temperature of 49°C after 1 h of continuous data processing. KARGAMobile is written in Java and is available at https://github.com/Ruiz-HCI-Lab/KargaMobile under the MIT license.
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Affiliation(s)
- Alexander Barquero
- Department of Computer Science and Information and Engineering, University of Florida, Gainesville, FL, United States
| | - Simone Marini
- Department of Epidemiology, University of Florida, Gainesville, FL, United States,Department of Pathology, University of Florida, Gainesville, FL, United States
| | - Christina Boucher
- Department of Computer Science and Information and Engineering, University of Florida, Gainesville, FL, United States
| | - Jaime Ruiz
- Department of Computer Science and Information and Engineering, University of Florida, Gainesville, FL, United States
| | - Mattia Prosperi
- Department of Epidemiology, University of Florida, Gainesville, FL, United States,*Correspondence: Mattia Prosperi,
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