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Parente E, Ricciardi A. A Comprehensive View of Food Microbiota: Introducing FoodMicrobionet v5. Foods 2024; 13:1689. [PMID: 38890917 PMCID: PMC11171936 DOI: 10.3390/foods13111689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 05/21/2024] [Accepted: 05/24/2024] [Indexed: 06/20/2024] Open
Abstract
Amplicon-targeted metagenomics is now the standard approach for the study of the composition and dynamics of food microbial communities. Hundreds of papers on this subject have been published in scientific journals and the information is dispersed in a variety of sources, while raw sequences and their metadata are available in public repositories for some, but not all, of the published studies. A limited number of web resources and databases allow scientists to access this wealth of information but their level of annotation on studies and samples varies. Here, we report on the release of FoodMicrobionet v5, a comprehensive database of metataxonomic studies on bacterial and fungal communities of foods. The current version of the database includes 251 published studies (11 focusing on fungal microbiota, 230 on bacterial microbiota, and 10 providing data for both bacterial and fungal microbiota) and 14,035 samples with data on bacteria and 1114 samples with data on fungi. The new structure of the database is compatible with interactive apps and scripts developed for previous versions and allows scientists, R&D personnel in industries and regulators to access a wealth of information on food microbial communities.
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Affiliation(s)
- Eugenio Parente
- Scuola di Scienze Agrarie, Forestali, Alimentari ed Ambientali, Università degli Studi della Basilicata, 85100 Potenza, Italy;
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2
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Wang B, Hu K, Li C, Zhang Y, Hu C, Liu Z, Ding J, Chen L, Zhang W, Fang J, Zhang H. Geographic distribution of bacterial communities of inland waters in China. ENVIRONMENTAL RESEARCH 2024; 249:118337. [PMID: 38325783 DOI: 10.1016/j.envres.2024.118337] [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: 12/01/2023] [Revised: 01/08/2024] [Accepted: 01/27/2024] [Indexed: 02/09/2024]
Abstract
Microorganisms are integral to freshwater ecological functions and, reciprocally, their activity and diversity are shaped by the ecosystem state. Yet, the diversity of bacterial community and its driving factors at a large scale remain elusive. To bridge this knowledge gap, we delved into an analysis of 16S RNA gene sequences extracted from 929 water samples across China. Our analyses revealed that inland water bacterial communities showed a weak latitudinal diversity gradient. We found 530 bacterial genera with high relative abundance of hgcI clade. Among them, 29 core bacterial genera were identified, that is strongly linked to mean annual temperature and nutrient loadings. We also detected a non-linear response of bacterial network complexity to the increasing of human pressure. Mantel analysis suggested that MAT, HPI and P loading were the major factors driving bacterial communities in inland waters. The map of taxa abundance showed that the abundant CL500-29 marine group in eastern and southern China indicated high eutrophication risk. Our findings enhance our understanding of the diversity and large-scale biogeographic pattern of bacterial communities of inland waters and have important implications for microbial ecology.
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Affiliation(s)
- Binhao Wang
- School of Engineering, Hangzhou Normal University, Hangzhou, 310018, China
| | - Kaiming Hu
- School of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou, 311121, China
| | - Chuqiao Li
- School of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou, 311121, China
| | - Yinan Zhang
- School of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou, 311121, China
| | - Chao Hu
- School of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou, 311121, China
| | - Zhiquan Liu
- School of Engineering, Hangzhou Normal University, Hangzhou, 310018, China
| | - Jiafeng Ding
- School of Engineering, Hangzhou Normal University, Hangzhou, 310018, China
| | - Lin Chen
- Hangzhou Xixi National Wetland Park Ecology & Culture Research Center, Hangzhou, 310030, China; Zhejiang Xixi Wetland Ecosystem National Observation and Research Station, Hangzhou, 310030, China
| | - Wei Zhang
- Hangzhou Xixi National Wetland Park Ecology & Culture Research Center, Hangzhou, 310030, China; Zhejiang Xixi Wetland Ecosystem National Observation and Research Station, Hangzhou, 310030, China
| | - Jing Fang
- Hangzhou Xixi National Wetland Park Ecology & Culture Research Center, Hangzhou, 310030, China; Zhejiang Xixi Wetland Ecosystem National Observation and Research Station, Hangzhou, 310030, China
| | - Hangjun Zhang
- School of Engineering, Hangzhou Normal University, Hangzhou, 310018, China; Hangzhou International Urbanology Research Center and Center for Zhejiang Urban Governance Studies, Hangzhou, 311121, China.
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Holm JB, Gajer P, Ravel J. SpeciateIT and vSpeciateDB: Novel, fast and accurate per sequence 16S rRNA gene taxonomic classification of vaginal microbiota. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.18.590089. [PMID: 38712229 PMCID: PMC11071307 DOI: 10.1101/2024.04.18.590089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Clustering of sequences into operational taxonomic units (OTUs) and denoising methods are a mainstream stopgap to taxonomically classifying large numbers of 16S rRNA gene sequences. We developed speciateIT, a novel taxonomic classification tool which rapidly and accurately classifies individual amplicon sequences (https://github.com/Ravel-Laboratory/speciateIT). Environment-specific reference databases generally yield optimal taxonomic assignment. To this end, we also present vSpeciateDB, a custom reference database for the taxonomic classification of 16S rRNA gene amplicon sequences from vaginal microbiota. We show that speciateIT requires minimal computational resources relative to other algorithms and, when combined with vSpeciateDB, affords accurate species level classification in an environment-specific manner.
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Affiliation(s)
- Johanna B Holm
- Department of Microbiology & Immunology, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, 21201 USA
| | - Pawel Gajer
- Department of Microbiology & Immunology, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, 21201 USA
| | - Jacques Ravel
- Department of Microbiology & Immunology, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, 21201 USA
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Niya B, Yaakoubi K, Beraich FZ, Arouch M, Meftah Kadmiri I. Current status and future developments of assessing microbiome composition and dynamics in anaerobic digestion systems using metagenomic approaches. Heliyon 2024; 10:e28221. [PMID: 38560681 PMCID: PMC10979216 DOI: 10.1016/j.heliyon.2024.e28221] [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: 08/17/2023] [Revised: 03/12/2024] [Accepted: 03/13/2024] [Indexed: 04/04/2024] Open
Abstract
The metagenomic approach stands as a powerful technique for examining the composition of microbial communities and their involvement in various anaerobic digestion (AD) systems. Understanding the structure, function, and dynamics of microbial communities becomes pivotal for optimizing the biogas process, enhancing its stability and improving overall performance. Currently, taxonomic profiling of biogas-producing communities relies mainly on high-throughput 16S rRNA sequencing, offering insights into the bacterial and archaeal structures of AD assemblages and their correlations with fed substrates and process parameters. To delve even deeper, shotgun and genome-centric metagenomic approaches are employed to recover individual genomes from the metagenome. This provides a nuanced understanding of collective functionalities, interspecies interactions, and microbial associations with abiotic factors. The application of OMICs in AD systems holds the potential to revolutionize the field, leading to more efficient and sustainable waste management practices particularly through the implementation of precision anaerobic digestion systems. As ongoing research in this area progresses, anticipations are high for further exciting developments in the future. This review serves to explore the current landscape of metagenomic analyses, with focus on advancing our comprehension and critically evaluating biases and recommendations in the analysis of microbial communities in anaerobic digesters. Its objective is to explore how contemporary metagenomic approaches can be effectively applied to enhance our understanding and contribute to the refinement of the AD process. This marks a substantial stride towards achieving a more comprehensive understanding of anaerobic digestion systems.
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Affiliation(s)
- Btissam Niya
- Plant and Microbial Biotechnology Center, Moroccan Foundation of Advanced Science Innovation and Research MAScIR, Mohammed VI Polytechnic University (UM6P), Lot 660, Hay Moulay Rachid, 43150, Benguerir, Morocco
- Engineering, Industrial Management & Innovation Laboratory IMII, Faculty of Science and Technics (FST), Hassan 1st University of Settat, Morocco
| | - Kaoutar Yaakoubi
- Plant and Microbial Biotechnology Center, Moroccan Foundation of Advanced Science Innovation and Research MAScIR, Mohammed VI Polytechnic University (UM6P), Lot 660, Hay Moulay Rachid, 43150, Benguerir, Morocco
| | - Fatima Zahra Beraich
- Biodome.sarl, Research and Development Design Office of Biogas Technology, Casablanca, Morocco
| | - Moha Arouch
- Engineering, Industrial Management & Innovation Laboratory IMII, Faculty of Science and Technics (FST), Hassan 1st University of Settat, Morocco
| | - Issam Meftah Kadmiri
- Plant and Microbial Biotechnology Center, Moroccan Foundation of Advanced Science Innovation and Research MAScIR, Mohammed VI Polytechnic University (UM6P), Lot 660, Hay Moulay Rachid, 43150, Benguerir, Morocco
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Ribeiro AJM, Riziotis IG, Borkakoti N, Thornton JM. Enzyme function and evolution through the lens of bioinformatics. Biochem J 2023; 480:1845-1863. [PMID: 37991346 PMCID: PMC10754289 DOI: 10.1042/bcj20220405] [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: 07/20/2023] [Revised: 11/09/2023] [Accepted: 11/14/2023] [Indexed: 11/23/2023]
Abstract
Enzymes have been shaped by evolution over billions of years to catalyse the chemical reactions that support life on earth. Dispersed in the literature, or organised in online databases, knowledge about enzymes can be structured in distinct dimensions, either related to their quality as biological macromolecules, such as their sequence and structure, or related to their chemical functions, such as the catalytic site, kinetics, mechanism, and overall reaction. The evolution of enzymes can only be understood when each of these dimensions is considered. In addition, many of the properties of enzymes only make sense in the light of evolution. We start this review by outlining the main paradigms of enzyme evolution, including gene duplication and divergence, convergent evolution, and evolution by recombination of domains. In the second part, we overview the current collective knowledge about enzymes, as organised by different types of data and collected in several databases. We also highlight some increasingly powerful computational tools that can be used to close gaps in understanding, in particular for types of data that require laborious experimental protocols. We believe that recent advances in protein structure prediction will be a powerful catalyst for the prediction of binding, mechanism, and ultimately, chemical reactions. A comprehensive mapping of enzyme function and evolution may be attainable in the near future.
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Affiliation(s)
- Antonio J. M. Ribeiro
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, U.K
| | - Ioannis G. Riziotis
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, U.K
| | - Neera Borkakoti
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, U.K
| | - Janet M. Thornton
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, U.K
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Kaur H, Kaur G, Gupta T, Mittal D, Ali SA. Integrating Omics Technologies for a Comprehensive Understanding of the Microbiome and Its Impact on Cattle Production. BIOLOGY 2023; 12:1200. [PMID: 37759599 PMCID: PMC10525894 DOI: 10.3390/biology12091200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/16/2023] [Accepted: 08/29/2023] [Indexed: 09/29/2023]
Abstract
Ruminant production holds a pivotal position within the global animal production and agricultural sectors. As population growth escalates, posing environmental challenges, a heightened emphasis is directed toward refining ruminant production systems. Recent investigations underscore the connection between the composition and functionality of the rumen microbiome and economically advantageous traits in cattle. Consequently, the development of innovative strategies to enhance cattle feed efficiency, while curbing environmental and financial burdens, becomes imperative. The advent of omics technologies has yielded fresh insights into metabolic health fluctuations in dairy cattle, consequently enhancing nutritional management practices. The pivotal role of the rumen microbiome in augmenting feeding efficiency by transforming low-quality feedstuffs into energy substrates for the host is underscored. This microbial community assumes focal importance within gut microbiome studies, contributing indispensably to plant fiber digestion, as well as influencing production and health variability in ruminants. Instances of compromised animal welfare can substantially modulate the microbiological composition of the rumen, thereby influencing production rates. A comprehensive global approach that targets both cattle and their rumen microbiota is paramount for enhancing feed efficiency and optimizing rumen fermentation processes. This review article underscores the factors that contribute to the establishment or restoration of the rumen microbiome post perturbations and the intricacies of host-microbiome interactions. We accentuate the elements responsible for responsible host-microbiome interactions and practical applications in the domains of animal health and production. Moreover, meticulous scrutiny of the microbiome and its consequential effects on cattle production systems greatly contributes to forging more sustainable and resilient food production systems, thereby mitigating the adverse environmental impact.
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Affiliation(s)
- Harpreet Kaur
- Division of Biochemistry, ICAR-National Dairy Research Institute (ICAR-NDRI), Karnal 132001, India
| | - Gurjeet Kaur
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia
- Mark Wainwright Analytical Centre, Bioanalytical Mass Spectrometry Facility, University of New South Wales, Sydney, NSW 2052, Australia
- Steno Diabetes Center Copenhagen, DK-2730 Herlev, Denmark
| | - Taruna Gupta
- Division of Biochemistry, ICAR-National Dairy Research Institute (ICAR-NDRI), Karnal 132001, India
| | - Deepti Mittal
- Division of Biochemistry, ICAR-National Dairy Research Institute (ICAR-NDRI), Karnal 132001, India
| | - Syed Azmal Ali
- Cell Biology and Proteomics Lab, Animal Biotechnology Center, ICAR-National Dairy Research Institute (ICAR-NDRI), Karnal 132001, India
- Division Proteomics of Stem Cells and Cancer, German Cancer Research Center, 69120 Heidelberg, Germany
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Sengupta P, Sivabalan SKM, Mahesh A, Palanikumar I, Kuppa Baskaran DK, Raman K. Big Data for a Small World: A Review on Databases and Resources for Studying Microbiomes. J Indian Inst Sci 2023; 103:1-17. [PMID: 37362854 PMCID: PMC10073628 DOI: 10.1007/s41745-023-00370-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 03/05/2023] [Indexed: 06/28/2023]
Abstract
Microorganisms are ubiquitous in nature and form complex community networks to survive in various environments. This community structure depends on numerous factors like nutrient availability, abiotic factors like temperature and pH as well as microbial composition. Categorising accessible biomes according to their habitats would help in understanding the complexity of the environment-specific communities. Owing to the recent improvements in sequencing facilities, researchers have started to explore diverse microbiomes rapidly and attempts have been made to study microbial crosstalk. However, different metagenomics sampling, preprocessing, and annotation methods make it difficult to compare multiple studies and hinder the recycling of data. Huge datasets originating from these experiments demand systematic computational methods to extract biological information beyond microbial compositions. Further exploration of microbial co-occurring patterns across the biomes could help us in designing cross-biome experiments. In this review, we catalogue databases with system-specific microbiomes, discussing publicly available common databases as well as specialised databases for a range of microbiomes. If the new datasets generated in the future could maintain at least biome-specific annotation, then researchers could use those contemporary tools for relevant and bias-free analysis of complex metagenomics data.
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Affiliation(s)
- Pratyay Sengupta
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu 600036 India
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu 600036 India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu 600036 India
| | | | - Amrita Mahesh
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu 600036 India
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu 600036 India
| | - Indumathi Palanikumar
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu 600036 India
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu 600036 India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu 600036 India
| | - Dinesh Kumar Kuppa Baskaran
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu 600036 India
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu 600036 India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu 600036 India
| | - Karthik Raman
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu 600036 India
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu 600036 India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu 600036 India
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Chen Y, Knight R, Gallo RL. Evolving approaches to profiling the microbiome in skin disease. Front Immunol 2023; 14:1151527. [PMID: 37081873 PMCID: PMC10110978 DOI: 10.3389/fimmu.2023.1151527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 03/14/2023] [Indexed: 04/22/2023] Open
Abstract
Despite its harsh and dry environment, human skin is home to diverse microbes, including bacteria, fungi, viruses, and microscopic mites. These microbes form communities that may exist at the skin surface, deeper skin layers, and within microhabitats such as the hair follicle and sweat glands, allowing complex interactions with the host immune system. Imbalances in the skin microbiome, known as dysbiosis, have been linked to various inflammatory skin disorders, including atopic dermatitis, acne, and psoriasis. The roles of abundant commensal bacteria belonging to Staphylococcus and Cutibacterium taxa and the fungi Malassezia, where particular species or strains can benefit the host or cause disease, are increasingly appreciated in skin disorders. Furthermore, recent research suggests that the interactions between microorganisms and the host's immune system on the skin can have distant and systemic effects on the body, such as on the gut and brain, known as the "skin-gut" or "skin-brain" axes. Studies on the microbiome in skin disease have typically relied on 16S rRNA gene sequencing methods, which cannot provide accurate information about species or strains of microorganisms on the skin. However, advancing technologies, including metagenomics and other functional 'omic' approaches, have great potential to provide more comprehensive and detailed information about the skin microbiome in health and disease. Additionally, inter-species and multi-kingdom interactions can cause cascading shifts towards dysbiosis and are crucial but yet-to-be-explored aspects of many skin disorders. Better understanding these complex dynamics will require meta-omic studies complemented with experiments and clinical trials to confirm function. Evolving how we profile the skin microbiome alongside technological advances is essential to exploring such relationships. This review presents the current and emerging methods and their findings for profiling skin microbes to advance our understanding of the microbiome in skin disease.
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Affiliation(s)
- Yang Chen
- Department of Dermatology, University of California San Diego, La Jolla, CA, United States
- Department of Pediatrics, University of California San Diego, La Jolla, CA, United States
- Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA, United States
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, CA, United States
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, United States
- Department of Bioengineering, University of California San Diego, La Jolla, CA, United States
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, United States
| | - Richard L. Gallo
- Department of Dermatology, University of California San Diego, La Jolla, CA, United States
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, United States
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Lu J, Shu Y, Zhang H, Zhang S, Zhu C, Ding W, Zhang W. The Landscape of Global Ocean Microbiome: From Bacterioplankton to Biofilms. Int J Mol Sci 2023; 24:ijms24076491. [PMID: 37047466 PMCID: PMC10095273 DOI: 10.3390/ijms24076491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 03/26/2023] [Accepted: 03/27/2023] [Indexed: 04/01/2023] Open
Abstract
The development of metagenomics has opened up a new era in the study of marine microbiota, which play important roles in biogeochemical cycles. In recent years, the global ocean sampling expeditions have spurred this research field toward a deeper understanding of the microbial diversities and functions spanning various lifestyles, planktonic (free-living) or sessile (biofilm-associated). In this review, we deliver a comprehensive summary of marine microbiome datasets generated in global ocean expeditions conducted over the last 20 years, including the Sorcerer II GOS Expedition, the Tara Oceans project, the bioGEOTRACES project, the Micro B3 project, the Bio-GO-SHIP project, and the Marine Biofilms. These datasets have revealed unprecedented insights into the microscopic life in our oceans and led to the publication of world-leading research. We also note the progress of metatranscriptomics and metaproteomics, which are confined to local marine microbiota. Furthermore, approaches to transforming the global ocean microbiome datasets are highlighted, and the state-of-the-art techniques that can be combined with data analyses, which can present fresh perspectives on marine molecular ecology and microbiology, are proposed.
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Affiliation(s)
- Jie Lu
- Institute of Evolution & Marine Biodiversity, Ocean University of China, Qingdao 266100, China
| | - Yi Shu
- MOE Key Laboratory of Marine Genetics and Breeding, Ocean University of China, Qingdao 266100, China;
| | - Heng Zhang
- College of Marine Life Sciences, Ocean University of China, Qingdao 266100, China
| | - Shangxian Zhang
- Haide College, Ocean University of China, Qingdao 266100, China
| | - Chengrui Zhu
- Haide College, Ocean University of China, Qingdao 266100, China
| | - Wei Ding
- MOE Key Laboratory of Marine Genetics and Breeding, Ocean University of China, Qingdao 266100, China;
- College of Marine Life Sciences, Ocean University of China, Qingdao 266100, China
- Haide College, Ocean University of China, Qingdao 266100, China
- Correspondence: (W.D.); (W.Z.)
| | - Weipeng Zhang
- Institute of Evolution & Marine Biodiversity, Ocean University of China, Qingdao 266100, China
- College of Marine Life Sciences, Ocean University of China, Qingdao 266100, China
- Haide College, Ocean University of China, Qingdao 266100, China
- Correspondence: (W.D.); (W.Z.)
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Richardson L, Allen B, Baldi G, Beracochea M, Bileschi M, Burdett T, Burgin J, Caballero-Pérez J, Cochrane G, Colwell L, Curtis T, Escobar-Zepeda A, Gurbich T, Kale V, Korobeynikov A, Raj S, Rogers A, Sakharova E, Sanchez S, Wilkinson D, Finn R. MGnify: the microbiome sequence data analysis resource in 2023. Nucleic Acids Res 2022; 51:D753-D759. [PMID: 36477304 PMCID: PMC9825492 DOI: 10.1093/nar/gkac1080] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 10/19/2022] [Accepted: 11/01/2022] [Indexed: 12/12/2022] Open
Abstract
The MGnify platform (https://www.ebi.ac.uk/metagenomics) facilitates the assembly, analysis and archiving of microbiome-derived nucleic acid sequences. The platform provides access to taxonomic assignments and functional annotations for nearly half a million analyses covering metabarcoding, metatranscriptomic, and metagenomic datasets, which are derived from a wide range of different environments. Over the past 3 years, MGnify has not only grown in terms of the number of datasets contained but also increased the breadth of analyses provided, such as the analysis of long-read sequences. The MGnify protein database now exceeds 2.4 billion non-redundant sequences predicted from metagenomic assemblies. This collection is now organised into a relational database making it possible to understand the genomic context of the protein through navigation back to the source assembly and sample metadata, marking a major improvement. To extend beyond the functional annotations already provided in MGnify, we have applied deep learning-based annotation methods. The technology underlying MGnify's Application Programming Interface (API) and website has been upgraded, and we have enabled the ability to perform downstream analysis of the MGnify data through the introduction of a coupled Jupyter Lab environment.
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Affiliation(s)
- Lorna Richardson
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Ben Allen
- School of Engineering, Newcastle University, Newcastle upon Tyne, UK
| | - Germana Baldi
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Martin Beracochea
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - Tony Burdett
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Josephine Burgin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Juan Caballero-Pérez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Guy Cochrane
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Lucy J Colwell
- Google Research, Brain Team, Mountain View, CA, USA,Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Tom Curtis
- School of Engineering, Newcastle University, Newcastle upon Tyne, UK
| | - Alejandra Escobar-Zepeda
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Tatiana A Gurbich
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Varsha Kale
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Anton Korobeynikov
- Center for Algorithmic Biotechnology, St Petersburg State University, St Petersburg, Russia
| | - Shriya Raj
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Alexander B Rogers
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Ekaterina Sakharova
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Santiago Sanchez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - Robert D Finn
- To whom correspondence should be addressed. Tel: +44 1223 492679;
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