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Góngora E, Lirette AO, Freyria NJ, Greer CW, Whyte LG. Metagenomic survey reveals hydrocarbon biodegradation potential of Canadian high Arctic beaches. ENVIRONMENTAL MICROBIOME 2024; 19:72. [PMID: 39294752 PMCID: PMC11411865 DOI: 10.1186/s40793-024-00616-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 09/03/2024] [Indexed: 09/21/2024]
Abstract
BACKGROUND Decreasing sea ice coverage across the Arctic Ocean due to climate change is expected to increase shipping activity through previously inaccessible shipping routes, including the Northwest Passage (NWP). Changing weather conditions typically encountered in the Arctic will still pose a risk for ships which could lead to an accident and the uncontrolled release of hydrocarbons onto NWP shorelines. We performed a metagenomic survey to characterize the microbial communities of various NWP shorelines and to determine whether there is a metabolic potential for hydrocarbon degradation in these microbiomes. RESULTS We observed taxonomic and functional gene evidence supporting the potential of NWP beach microbes to degrade various types of hydrocarbons. The metagenomic and metagenome-assembled genome (MAG) taxonomy showed that known hydrocarbon-degrading taxa are present in these beaches. Additionally, we detected the presence of biomarker genes of aerobic and anaerobic degradation pathways of alkane and aromatic hydrocarbons along with complete degradation pathways for aerobic alkane degradation. Alkane degradation genes were present in all samples and were also more abundant (33.8 ± 34.5 hits per million genes, HPM) than their aromatic hydrocarbon counterparts (11.7 ± 12.3 HPM). Due to the ubiquity of MAGs from the genus Rhodococcus (23.8% of the MAGs), we compared our MAGs with Rhodococcus genomes from NWP isolates obtained using hydrocarbons as the carbon source to corroborate our results and to develop a pangenome of Arctic Rhodococcus. Our analysis revealed that the biodegradation of alkanes is part of the core pangenome of this genus. We also detected nitrogen and sulfur pathways as additional energy sources and electron donors as well as carbon pathways providing alternative carbon sources. These pathways occur in the absence of hydrocarbons allowing microbes to survive in these nutrient-poor beaches. CONCLUSIONS Our metagenomic analyses detected the genetic potential for hydrocarbon biodegradation in these NWP shoreline microbiomes. Alkane metabolism was the most prevalent type of hydrocarbon degradation observed in these tidal beach ecosystems. Our results indicate that bioremediation could be used as a cleanup strategy, but the addition of adequate amounts of N and P fertilizers, should be considered to help bacteria overcome the oligotrophic nature of NWP shorelines.
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Affiliation(s)
- Esteban Góngora
- Natural Resource Sciences, McGill University, 21111 Lakeshore Road, Sainte-Anne-de-Bellevue, QC, Canada.
| | - Antoine-O Lirette
- Natural Resource Sciences, McGill University, 21111 Lakeshore Road, Sainte-Anne-de-Bellevue, QC, Canada
| | - Nastasia J Freyria
- Natural Resource Sciences, McGill University, 21111 Lakeshore Road, Sainte-Anne-de-Bellevue, QC, Canada
| | - Charles W Greer
- Natural Resource Sciences, McGill University, 21111 Lakeshore Road, Sainte-Anne-de-Bellevue, QC, Canada
- Energy, Mining and Environment Research Centre, National Research Council Canada, 6100 Royalmount Avenue, Montreal, QC, Canada
| | - Lyle G Whyte
- Natural Resource Sciences, McGill University, 21111 Lakeshore Road, Sainte-Anne-de-Bellevue, QC, Canada
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Dippenaar A, Costa Conceição E, Wells F, Loubser J, Mann B, De Diego Fuertes M, Rennie V, Warren RM, Van Rie A. Exploring the potential of Oxford Nanopore Technologies sequencing for Mycobacterium tuberculosis sequencing: An assessment of R10 flowcells and V14 chemistry. PLoS One 2024; 19:e0303938. [PMID: 38843147 PMCID: PMC11156342 DOI: 10.1371/journal.pone.0303938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 05/03/2024] [Indexed: 06/09/2024] Open
Abstract
Oxford Nanopore Technologies (ONT) sequencing is a promising technology. We assessed the performance of the new ONT R10 flowcells and V14 rapid sequencing chemistry for Mtb whole genome sequencing of Mycobacterium tuberculosis (Mtb) DNA extracted from clinical primary liquid cultures (CPLCs). Using the recommended protocols for MinION Mk1C, R10.4.1 MinION flowcells, and the ONT Rapid Sequencing Kit V14 on six CPLC samples, we obtained a pooled library yield of 10.9 ng/μl, generated 1.94 Gb of sequenced bases and 214k reads after 48h in a first sequencing run. Only half (49%) of all generated reads met the Phred Quality score threshold (>8). To assess if the low data output and sequence quality were due to impurities present in DNA extracted directly from CPLCs, we added a pre-library preparation bead-clean-up step and included purified DNA obtained from an Mtb subculture as a control sample in a second sequencing run. The library yield for DNA extracted from four CPLCs and one Mtb subculture (control) was similar (10.0 ng/μl), 2.38 Gb of bases and 822k reads were produced. The quality was slightly better with 66% of the produced reads having a Phred Quality >8. A third run of DNA from six CPLCs with bead clean-up pre-processing produced a low library yield (±1 Gb of bases, 166k reads) of low quality (51% of reads with a Phred Quality score >8). A median depth of coverage above 10× was only achieved for five of 17 (29%) sequenced libraries. Compared to Illumina WGS of the same samples, accurate lineage predictions and full drug resistance profiles from the generated ONT data could not be determined by TBProfiler. Further optimization of the V14 ONT rapid sequencing chemistry and library preparation protocol is needed for clinical Mtb WGS applications.
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Affiliation(s)
- Anzaan Dippenaar
- Department of Family Medicine and Population Health, Global Health Institute, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Emilyn Costa Conceição
- Division of Molecular Biology and Human Genetics, South African Medical Research Council Centre for Tuberculosis Research, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Felicia Wells
- Division of Molecular Biology and Human Genetics, South African Medical Research Council Centre for Tuberculosis Research, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Johannes Loubser
- Division of Molecular Biology and Human Genetics, South African Medical Research Council Centre for Tuberculosis Research, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Brendon Mann
- Division of Molecular Biology and Human Genetics, South African Medical Research Council Centre for Tuberculosis Research, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Miguel De Diego Fuertes
- Department of Family Medicine and Population Health, Global Health Institute, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Vincent Rennie
- Department of Family Medicine and Population Health, Global Health Institute, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Robin Mark Warren
- Division of Molecular Biology and Human Genetics, South African Medical Research Council Centre for Tuberculosis Research, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Annelies Van Rie
- Department of Family Medicine and Population Health, Global Health Institute, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
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Reska T, Pozdniakova S, Borràs S, Perlas A, Sauerborn E, Cañas L, Schloter M, Rodó X, Wang Y, Winkler B, Schnitzler JP, Urban L. Air monitoring by nanopore sequencing. ISME COMMUNICATIONS 2024; 4:ycae099. [PMID: 39081363 PMCID: PMC11287864 DOI: 10.1093/ismeco/ycae099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 07/12/2024] [Accepted: 07/15/2024] [Indexed: 08/02/2024]
Abstract
While the air microbiome and its diversity are essential for human health and ecosystem resilience, comprehensive air microbial diversity monitoring has remained rare, so that little is known about the air microbiome's composition, distribution, or functionality. Here we show that nanopore sequencing-based metagenomics can robustly assess the air microbiome in combination with active air sampling through liquid impingement and tailored computational analysis. We provide fast and portable laboratory and computational approaches for air microbiome profiling, which we leverage to robustly assess the taxonomic composition of the core air microbiome of a controlled greenhouse environment and of a natural outdoor environment. We show that long-read sequencing can resolve species-level annotations and specific ecosystem functions through de novo metagenomic assemblies despite the low amount of fragmented DNA used as an input for nanopore sequencing. We then apply our pipeline to assess the diversity and variability of an urban air microbiome, using Barcelona, Spain, as an example; this randomized experiment gives first insights into the presence of highly stable location-specific air microbiomes within the city's boundaries, and showcases the robust microbial assessments that can be achieved through automatable, fast, and portable nanopore sequencing technology.
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Affiliation(s)
- Tim Reska
- Helmholtz AI, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Technical University of Munich, School of Life Sciences, 85354 Freising, Germany
| | - Sofya Pozdniakova
- AIRLAB, Climate and Health (CLIMA) group, ISGlobal, 08003 Barcelona, Spain
| | - Sílvia Borràs
- AIRLAB, Climate and Health (CLIMA) group, ISGlobal, 08003 Barcelona, Spain
| | - Albert Perlas
- Helmholtz AI, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Ela Sauerborn
- Helmholtz AI, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Technical University of Munich, School of Life Sciences, 85354 Freising, Germany
| | - Lídia Cañas
- AIRLAB, Climate and Health (CLIMA) group, ISGlobal, 08003 Barcelona, Spain
| | - Michael Schloter
- Technical University of Munich, School of Life Sciences, 85354 Freising, Germany
- Institute of Comparative Microbiome Analysis, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Xavier Rodó
- AIRLAB, Climate and Health (CLIMA) group, ISGlobal, 08003 Barcelona, Spain
- Catalan Institution for Research and Advanced Studies, ICREA, 08010 Barcelona, Spain
| | - Yuanyuan Wang
- Technical University of Munich, School of Engineering and Design, 80333 Munich, Germany
| | - Barbro Winkler
- Research Unit Environmental Simulation (EUS), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Jörg-Peter Schnitzler
- Research Unit Environmental Simulation (EUS), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Lara Urban
- Helmholtz AI, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Technical University of Munich, School of Life Sciences, 85354 Freising, Germany
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Liu CW, Tsutsui H. Sample-to-answer sensing technologies for nucleic acid preparation and detection in the field. SLAS Technol 2023; 28:302-323. [PMID: 37302751 DOI: 10.1016/j.slast.2023.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/16/2023] [Accepted: 06/06/2023] [Indexed: 06/13/2023]
Abstract
Efficient sample preparation and accurate disease diagnosis under field conditions are of great importance for the early intervention of diseases in humans, animals, and plants. However, in-field preparation of high-quality nucleic acids from various specimens for downstream analyses, such as amplification and sequencing, is challenging. Thus, developing and adapting sample lysis and nucleic acid extraction protocols suitable for portable formats have drawn significant attention. Similarly, various nucleic acid amplification techniques and detection methods have also been explored. Combining these functions in an integrated platform has resulted in emergent sample-to-answer sensing systems that allow effective disease detection and analyses outside a laboratory. Such devices have a vast potential to improve healthcare in resource-limited settings, low-cost and distributed surveillance of diseases in food and agriculture industries, environmental monitoring, and defense against biological warfare and terrorism. This paper reviews recent advances in portable sample preparation technologies and facile detection methods that have been / or could be adopted into novel sample-to-answer devices. In addition, recent developments and challenges of commercial kits and devices targeting on-site diagnosis of various plant diseases are discussed.
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Affiliation(s)
- Chia-Wei Liu
- Department of Mechanical Engineering, University of California, Riverside, CA 92521, USA
| | - Hideaki Tsutsui
- Department of Mechanical Engineering, University of California, Riverside, CA 92521, USA; Department of Bioengineering, University of California, Riverside, CA 92521, USA.
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Touchette D, Maggiori C, Altshuler I, Tettenborn A, Bourdages LJ, Magnuson E, Blenner-Hassett O, Raymond-Bouchard I, Ellery A, Whyte LG. Microbial Characterization of Arctic Glacial Ice Cores with a Semiautomated Life Detection System. ASTROBIOLOGY 2023; 23:756-768. [PMID: 37126945 DOI: 10.1089/ast.2022.0130] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
The search for extant microbial life will be a major focus of future astrobiology missions; however, no direct extant life detection instrumentation is included in current missions to Mars. In this study, we developed the semiautomated MicroLife detection platform that collects and processes environmental samples, detects biosignatures, and characterizes microbial activity. This platform is composed of a drill for sample collection, a redox dye colorimetric system for microbial metabolic activity detection and assessment (μMAMA [microfluidics Microbial Activity MicroAssay]), and a MinION sequencer for biosignature detection and characterization of microbial communities. The MicroLife platform was field-tested on White Glacier on Axel Heiberg Island in the Canadian high Arctic, with two extracted ice cores. The μMAMA successfully detected microbial metabolism from the ice cores within 1 day of incubation. The MinION sequencing of the ice cores and the positive μMAMA card identified a microbial community consistent with cold and oligotrophic environments. Furthermore, isolation and identification of microbial isolates from the μMAMA card corroborated the MinION sequencing. Together, these analyses support the MicroLife platform's efficacy in identifying microbes natively present in cryoenvironments and detecting their metabolic activity. Given our MicroLife platform's size and low energy requirements, it could be incorporated into a future landed platform or rovers for life detection.
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Affiliation(s)
- David Touchette
- Department of Natural Resource Sciences, McGill University, Sainte-Anne-de-Bellevue, Canada
- McGill Space Institute, Montréal, Canada
- Environmental Engineering Institute, River Ecosystems Laboratory, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Catherine Maggiori
- Department of Natural Resource Sciences, McGill University, Sainte-Anne-de-Bellevue, Canada
- McGill Space Institute, Montréal, Canada
| | - Ianina Altshuler
- Department of Natural Resource Sciences, McGill University, Sainte-Anne-de-Bellevue, Canada
- Environmental Engineering Institute, MACE Laboratory, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Alex Tettenborn
- Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, Canada
| | - Louis-Jacques Bourdages
- Department of Natural Resource Sciences, McGill University, Sainte-Anne-de-Bellevue, Canada
- Department of Mechanical Engineering, Faculty of Engineering, McGill University, Montréal, Canada
| | - Elisse Magnuson
- Department of Natural Resource Sciences, McGill University, Sainte-Anne-de-Bellevue, Canada
| | - Olivia Blenner-Hassett
- Department of Natural Resource Sciences, McGill University, Sainte-Anne-de-Bellevue, Canada
- McGill Space Institute, Montréal, Canada
| | - Isabelle Raymond-Bouchard
- Department of Natural Resource Sciences, McGill University, Sainte-Anne-de-Bellevue, Canada
- McGill Space Institute, Montréal, Canada
| | - Alex Ellery
- Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, Canada
| | - Lyle G Whyte
- Department of Natural Resource Sciences, McGill University, Sainte-Anne-de-Bellevue, Canada
- McGill Space Institute, Montréal, Canada
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Prudnikow L, Pannicke B, Wünschiers R. A primer on pollen assignment by nanopore-based DNA sequencing. Front Ecol Evol 2023. [DOI: 10.3389/fevo.2023.1112929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/15/2023] Open
Abstract
The possibility to identify plants based on the taxonomic information coming from their pollen grains offers many applications within various biological disciplines. In the past and depending on the application or research in question, pollen origin was analyzed by microscopy, usually preceded by chemical treatment methods. This procedure for identification of pollen grains is both time-consuming and requires expert knowledge of morphological features. Additionally, these microscopically recognizable features usually have a low resolution at species-level. Since a few decades, DNA has been used for the identification of pollen taxa, as sequencing technologies evolved both in their handling and affordability. We discuss advantages and challenges of pollen DNA analyses compared to traditional methods. With readers with little experience in this field in mind, we present a hands-on primer for genetic pollen analysis by nanopore sequencing. As our lab mainly works with pollen collected within agroecological research projects, we focus on pollen collected by pollinating insects. We briefly consider sample collection, storage and processing in the laboratory as well as bioinformatic aspects. Currently, pollen metabarcoding is mostly conducted with next-generation sequencing methods that generate short sequence reads (<1 kb). Increasingly, however, pollen DNA analysis is carried out using the long-read generating (several kb), low-budget and mobile MinION nanopore sequencing platform by Oxford Nanopore Technologies. Therefore, we are focusing on aspects for palynology with the MinION DNA sequencing device.
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Machine Learning-Based Models for Detection of Biomarkers of Autoimmune Diseases by Fragmentation and Analysis of miRNA Sequences. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Thanks to high-throughput data technology, microRNA analysis studies have evolved in early disease detection. This work introduces two complete models to detect the biomarkers of two autoimmune diseases, multiple sclerosis and rheumatoid arthritis, via miRNA analysis. Based on work the authors published previously, both introduced models involve complete pipelines of text mining methods, integrated with traditional machine learning methods, and LSTM deep learning. This work also studies the fragmentation of miRNA sequences to reduce the needed processing time and computational power. Moreover, this work studies the impact of obtaining two different library preparation kits (NEBNEXT and NEXTFLEX) on the detection accuracy for rheumatoid arthritis. Additional experiments are applied to the proposed models based on three different transcriptomic datasets. The results denote that the transcriptomic fragmentation model reported a biomarker detection accuracy of 96.45% on a sequence fragment size of 0.2, indicating a significant reduction in execution power while retaining biomarker detection accuracy. On the other hand, the LSTM model obtained a promising detection accuracy of 72%, implying savings in feature engineering processing. Additionally, the fragmentation model and the LSTM model reported 22.4% and 87.5% less execution time than work in the literature, respectively, denoting a considerable execution power reduction.
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