1
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Chang T, Gavelis GS, Brown JM, Stepanauskas R. Genomic representativeness and chimerism in large collections of SAGs and MAGs of marine prokaryoplankton. MICROBIOME 2024; 12:126. [PMID: 39010229 PMCID: PMC11247762 DOI: 10.1186/s40168-024-01848-3] [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: 02/09/2024] [Accepted: 05/28/2024] [Indexed: 07/17/2024]
Abstract
BACKGROUND Single amplified genomes (SAGs) and metagenome-assembled genomes (MAGs) are the predominant sources of information about the coding potential of uncultured microbial lineages, but their strengths and limitations remain poorly understood. Here, we performed a direct comparison of two previously published collections of thousands of SAGs and MAGs obtained from the same, global environment. RESULTS We found that SAGs were less prone to chimerism and more accurately reflected the relative abundance and the pangenome content of microbial lineages inhabiting the epipelagic of the tropical and subtropical ocean, as compared to MAGs. SAGs were also better suited to link genome information with taxa discovered through 16S rRNA amplicon analyses. Meanwhile, MAGs had the advantage of more readily recovering genomes of rare lineages. CONCLUSIONS Our analyses revealed the relative strengths and weaknesses of the two most commonly used genome recovery approaches in environmental microbiology. These considerations, as well as the need for better tools for genome quality assessment, should be taken into account when designing studies and interpreting data that involve SAGs or MAGs. Video Abstract.
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Affiliation(s)
- Tianyi Chang
- Bigelow Laboratory for Ocean Sciences, East Boothbay, Maine, 04544, USA
| | - Gregory S Gavelis
- Bigelow Laboratory for Ocean Sciences, East Boothbay, Maine, 04544, USA
| | - Julia M Brown
- Bigelow Laboratory for Ocean Sciences, East Boothbay, Maine, 04544, USA
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2
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Santos-Júnior CD, Torres MDT, Duan Y, Rodríguez Del Río Á, Schmidt TSB, Chong H, Fullam A, Kuhn M, Zhu C, Houseman A, Somborski J, Vines A, Zhao XM, Bork P, Huerta-Cepas J, de la Fuente-Nunez C, Coelho LP. Discovery of antimicrobial peptides in the global microbiome with machine learning. Cell 2024; 187:3761-3778.e16. [PMID: 38843834 DOI: 10.1016/j.cell.2024.05.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 04/11/2024] [Accepted: 05/06/2024] [Indexed: 06/25/2024]
Abstract
Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.
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Affiliation(s)
- Célio Dias Santos-Júnior
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China; Laboratory of Microbial Processes & Biodiversity - LMPB, Department of Hydrobiology, Universidade Federal de São Carlos - UFSCar, São Carlos, São Paulo 13565-905, Brazil
| | - Marcelo D T Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Yiqian Duan
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Álvaro Rodríguez Del Río
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Campus de Montegancedo-UPM, Pozuelo de Alarcón, 28223 Madrid, Spain
| | - Thomas S B Schmidt
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; APC Microbiome & School of Medicine, University College Cork, Cork, Ireland
| | - Hui Chong
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Anthony Fullam
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Michael Kuhn
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Chengkai Zhu
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Amy Houseman
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Jelena Somborski
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Anna Vines
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China; Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China; State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Max Delbrück Centre for Molecular Medicine, Berlin, Germany; Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Campus de Montegancedo-UPM, Pozuelo de Alarcón, 28223 Madrid, Spain
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
| | - Luis Pedro Coelho
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China; Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology, Translational Research Institute, Woolloongabba, QLD, Australia.
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3
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Li DD, Wang J, Jiang Y, Zhang P, Liu Y, Li YZ, Zhang Z. Quantifying functional redundancy in polysaccharide-degrading prokaryotic communities. MICROBIOME 2024; 12:120. [PMID: 38956705 PMCID: PMC11218364 DOI: 10.1186/s40168-024-01838-5] [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: 04/19/2022] [Accepted: 05/14/2024] [Indexed: 07/04/2024]
Abstract
BACKGROUND Functional redundancy (FR) is widely present, but there is no consensus on its formation process and influencing factors. Taxonomically distinct microorganisms possessing genes for the same function in a community lead to within-community FR, and distinct assemblies of microorganisms in different communities playing the same functional roles are termed between-community FR. We proposed two formulas to respectively quantify the degree of functional redundancy within and between communities and analyzed the FR degrees of carbohydrate degradation functions in global environment samples using the genetic information of glycoside hydrolases (GHs) encoded by prokaryotes. RESULTS Our results revealed that GHs are each encoded by multiple taxonomically distinct prokaryotes within a community, and the enzyme-encoding prokaryotes are further distinct between almost any community pairs. The within- and between-FR degrees are primarily affected by the alpha and beta community diversities, respectively, and are also affected by environmental factors (e.g., pH, temperature, and salinity). The FR degree of the prokaryotic community is determined by deterministic factors. CONCLUSIONS We conclude that the functional redundancy of GHs is a stabilized community characteristic. This study helps to determine the FR formation process and influencing factors and provides new insights into the relationships between prokaryotic community biodiversity and ecosystem functions. Video Abstract.
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Affiliation(s)
- Dan-Dan Li
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao, 266237, China
- Institute of Marine Science and Technology, Shandong University, Qingdao, 266237, China
| | - Jianing Wang
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao, 266237, China
| | - Yiru Jiang
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao, 266237, China
| | - Peng Zhang
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao, 266237, China
| | - Ya Liu
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao, 266237, China
| | - Yue-Zhong Li
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao, 266237, China.
| | - Zheng Zhang
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao, 266237, China.
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4
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Yi Y, Liang L, de Jong A, Kuipers OP. A systematic comparison of natural product potential, with an emphasis on RiPPs, by mining of bacteria of three large ecosystems. Genomics 2024; 116:110880. [PMID: 38857812 DOI: 10.1016/j.ygeno.2024.110880] [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: 01/08/2024] [Revised: 04/22/2024] [Accepted: 06/05/2024] [Indexed: 06/12/2024]
Abstract
The implementation of several global microbiome studies has yielded extensive insights into the biosynthetic potential of natural microbial communities. However, studies on the distribution of several classes of ribosomally synthesized and post-translationally modified peptides (RiPPs), non-ribosomal peptides (NRPs) and polyketides (PKs) in different large microbial ecosystems have been very limited. Here, we collected a large set of metagenome-assembled bacterial genomes from marine, freshwater and terrestrial ecosystems to investigate the biosynthetic potential of these bacteria. We demonstrate the utility of public dataset collections for revealing the different secondary metabolite biosynthetic potentials among these different living environments. We show that there is a higher occurrence of RiPPs in terrestrial systems, while in marine systems, we found relatively more terpene-, NRP-, and PK encoding gene clusters. Among the many new biosynthetic gene clusters (BGCs) identified, we analyzed various Nif-11-like and nitrile hydratase leader peptide (NHLP) containing gene clusters that would merit further study, including promising products, such as mersacidin-, LAP- and proteusin analogs. This research highlights the significance of public datasets in elucidating the biosynthetic potential of microbes in different living environments and underscores the wide bioengineering opportunities within the RiPP family.
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Affiliation(s)
- Yunhai Yi
- Department of Molecular Genetics, University of Groningen, Groningen 9747AG, the Netherlands
| | | | - Anne de Jong
- Department of Molecular Genetics, University of Groningen, Groningen 9747AG, the Netherlands
| | - Oscar P Kuipers
- Department of Molecular Genetics, University of Groningen, Groningen 9747AG, the Netherlands.
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5
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Tierney BT, Kim J, Overbey EG, Ryon KA, Foox J, Sierra MA, Bhattacharya C, Damle N, Najjar D, Park J, Garcia Medina JS, Houerbi N, Meydan C, Wain Hirschberg J, Qiu J, Kleinman AS, Al-Ghalith GA, MacKay M, Afshin EE, Dhir R, Borg J, Gatt C, Brereton N, Readhead BP, Beyaz S, Venkateswaran KJ, Wiseman K, Moreno J, Boddicker AM, Zhao J, Lajoie BR, Scott RT, Altomare A, Kruglyak S, Levy S, Church GM, Mason CE. Longitudinal multi-omics analysis of host microbiome architecture and immune responses during short-term spaceflight. Nat Microbiol 2024; 9:1661-1675. [PMID: 38862604 PMCID: PMC11222149 DOI: 10.1038/s41564-024-01635-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 02/09/2024] [Indexed: 06/13/2024]
Abstract
Maintenance of astronaut health during spaceflight will require monitoring and potentially modulating their microbiomes. However, documenting microbial shifts during spaceflight has been difficult due to mission constraints that lead to limited sampling and profiling. Here we executed a six-month longitudinal study to quantify the high-resolution human microbiome response to three days in orbit for four individuals. Using paired metagenomics and metatranscriptomics alongside single-nuclei immune cell profiling, we characterized time-dependent, multikingdom microbiome changes across 750 samples and 10 body sites before, during and after spaceflight at eight timepoints. We found that most alterations were transient across body sites; for example, viruses increased in skin sites mostly during flight. However, longer-term shifts were observed in the oral microbiome, including increased plaque-associated bacteria (for example, Fusobacteriota), which correlated with immune cell gene expression. Further, microbial genes associated with phage activity, toxin-antitoxin systems and stress response were enriched across multiple body sites. In total, this study reveals in-depth characterization of microbiome and immune response shifts experienced by astronauts during short-term spaceflight and the associated changes to the living environment, which can help guide future missions, spacecraft design and space habitat planning.
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Affiliation(s)
- Braden T Tierney
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - JangKeun Kim
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Eliah G Overbey
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- BioAstra, Inc., New York, NY, USA
- Center for STEM, University of Austin, Austin, TX, USA
| | - Krista A Ryon
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Jonathan Foox
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Maria A Sierra
- Tri-Institutional Biology and Medicine program, Weill Cornell Medicine, New York, NY, USA
| | - Chandrima Bhattacharya
- Tri-Institutional Biology and Medicine program, Weill Cornell Medicine, New York, NY, USA
| | - Namita Damle
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Deena Najjar
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jiwoon Park
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - J Sebastian Garcia Medina
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Tri-Institutional Biology and Medicine program, Weill Cornell Medicine, New York, NY, USA
| | - Nadia Houerbi
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Cem Meydan
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | | | - Jake Qiu
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Ashley S Kleinman
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | | | - Matthew MacKay
- Tri-Institutional Biology and Medicine program, Weill Cornell Medicine, New York, NY, USA
| | - Evan E Afshin
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Raja Dhir
- Seed Health, Inc., Venice, CA, USA
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Joseph Borg
- Department of Applied Biomedical Science, Faculty of Health Sciences, University of Malta, Msida, Malta
| | - Christine Gatt
- Department of Applied Biomedical Science, Faculty of Health Sciences, University of Malta, Msida, Malta
| | - Nicholas Brereton
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Benjamin P Readhead
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, USA
| | - Semir Beyaz
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | | | | | | | | | | | - Ryan T Scott
- KBR; Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, USA
| | | | | | | | - George M Church
- Harvard Medical School and the Wyss Institute, Boston, MA, USA
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
- BioAstra, Inc., New York, NY, USA.
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA.
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Coelho LP, Santos-Júnior CD, de la Fuente-Nunez C. Challenges in computational discovery of bioactive peptides in 'omics data. Proteomics 2024; 24:e2300105. [PMID: 38458994 DOI: 10.1002/pmic.202300105] [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: 10/13/2023] [Revised: 02/06/2024] [Accepted: 02/06/2024] [Indexed: 03/10/2024]
Abstract
Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.
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Affiliation(s)
- Luis Pedro Coelho
- Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology, Woolloongabba, Queensland, Australia
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
| | - Célio Dias Santos-Júnior
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
- Laboratory of Microbial Processes & Biodiversity - LMPB, Hydrobiology Department, Federal University of São Carlos - UFSCar, São Paulo, Brazil
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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7
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Giovannini M, Vieri W, Bosi E, Riccardi C, Lo Giudice A, Fani R, Fondi M, Perrin E. Functional Genomics of a Collection of Gammaproteobacteria Isolated from Antarctica. Mar Drugs 2024; 22:238. [PMID: 38921549 PMCID: PMC11205219 DOI: 10.3390/md22060238] [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: 04/22/2024] [Revised: 05/18/2024] [Accepted: 05/21/2024] [Indexed: 06/27/2024] Open
Abstract
Antarctica, one of the most extreme environments on Earth, hosts diverse microbial communities. These microbes have evolved and adapted to survive in these hostile conditions, but knowledge on the molecular mechanisms underlying this process remains limited. The Italian Collection of Antarctic Bacteria (Collezione Italiana Batteri Antartici (CIBAN)), managed by the University of Messina, represents a valuable repository of cold-adapted bacterial strains isolated from various Antarctic environments. In this study, we sequenced and analyzed the genomes of 58 marine Gammaproteobacteria strains from the CIBAN collection, which were isolated during Italian expeditions from 1990 to 2005. By employing genome-scale metrics, we taxonomically characterized these strains and assigned them to four distinct genera: Pseudomonas, Pseudoalteromonas, Shewanella, and Psychrobacter. Genome annotation revealed a previously untapped functional potential, including secondary metabolite biosynthetic gene clusters and antibiotic resistance genes. Phylogenomic analyses provided evolutionary insights, while assessment of cold-shock protein presence shed light on adaptation mechanisms. Our study emphasizes the significance of CIBAN as a resource for understanding Antarctic microbial life and its biotechnological potential. The genomic data unveil new horizons for insight into bacterial existence in Antarctica.
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Affiliation(s)
- Michele Giovannini
- Department of Biology, University of Florence, Via Madonna del Piano 6, I-50019 Sesto Fiorentino, Italy; (M.G.); (W.V.); (C.R.); (R.F.); (M.F.)
| | - Walter Vieri
- Department of Biology, University of Florence, Via Madonna del Piano 6, I-50019 Sesto Fiorentino, Italy; (M.G.); (W.V.); (C.R.); (R.F.); (M.F.)
| | - Emanuele Bosi
- Department of Earth, Environment and Life Sciences—DISTAV, University of Genoa, Corso Europa 26, I-16132 Genova, Italy;
| | - Christopher Riccardi
- Department of Biology, University of Florence, Via Madonna del Piano 6, I-50019 Sesto Fiorentino, Italy; (M.G.); (W.V.); (C.R.); (R.F.); (M.F.)
- Quantitative and Computational Biology Department, University of Southern California, Los Angeles, CA 90089, USA
| | - Angelina Lo Giudice
- Institute of Polar Sciences, National Research Council, (CNR.ISP), Spianata San Raineri 86, I-98122 Messina, Italy;
- Italian Collection of Antarctic Bacteria, National Antarctic Museum (CIBAN-MNA), I-98122 Messina, Italy
- NBFC, National Biodiversity Future Center, Piazza Marina 61, I-90133 Palermo, Italy
| | - Renato Fani
- Department of Biology, University of Florence, Via Madonna del Piano 6, I-50019 Sesto Fiorentino, Italy; (M.G.); (W.V.); (C.R.); (R.F.); (M.F.)
| | - Marco Fondi
- Department of Biology, University of Florence, Via Madonna del Piano 6, I-50019 Sesto Fiorentino, Italy; (M.G.); (W.V.); (C.R.); (R.F.); (M.F.)
| | - Elena Perrin
- Department of Biology, University of Florence, Via Madonna del Piano 6, I-50019 Sesto Fiorentino, Italy; (M.G.); (W.V.); (C.R.); (R.F.); (M.F.)
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8
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Lima LFO, Alker AT, Morris MM, Edwards RA, de Putron SJ, Dinsdale EA. Pre-Bleaching Coral Microbiome Is Enriched in Beneficial Taxa and Functions. Microorganisms 2024; 12:1005. [PMID: 38792833 PMCID: PMC11123844 DOI: 10.3390/microorganisms12051005] [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: 03/02/2024] [Revised: 04/26/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024] Open
Abstract
Coral reef health is tightly connected to the coral holobiont, which is the association between the coral animal and a diverse microbiome functioning as a unit. The coral holobiont depends on key services such as nitrogen and sulfur cycling mediated by the associated bacteria. However, these microbial services may be impaired in response to environmental changes, such as thermal stress. A perturbed microbiome may lead to coral bleaching and disease outbreaks, which have caused an unprecedented loss in coral cover worldwide, particularly correlated to a warming ocean. The response mechanisms of the coral holobiont under high temperatures are not completely understood, but the associated microbial community is a potential source of acquired heat-tolerance. Here we investigate the effects of increased temperature on the taxonomic and functional profiles of coral surface mucous layer (SML) microbiomes in relationship to coral-algal physiology. We used shotgun metagenomics in an experimental setting to understand the dynamics of microbial taxa and genes in the SML microbiome of the coral Pseudodiploria strigosa under heat treatment. The metagenomes of corals exposed to heat showed high similarity at the level of bacterial genera and functional genes related to nitrogen and sulfur metabolism and stress response. The coral SML microbiome responded to heat with an increase in the relative abundance of taxa with probiotic potential, and functional genes for nitrogen and sulfur acquisition. Coral-algal physiology significantly explained the variation in the microbiome at taxonomic and functional levels. These consistent and specific microbial taxa and gene functions that significantly increased in proportional abundance in corals exposed to heat are potentially beneficial to coral health and thermal resistance.
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Affiliation(s)
- Laís F. O. Lima
- Marine Biology, Scripps Institute of Oceanography, University of California San Diego, La Jolla, CA 92093, USA;
- San Diego State University, San Diego, CA 92182, USA
| | - Amanda T. Alker
- Innovative Genomics Institute, University of California, Berkeley, SA 5045, USA;
| | - Megan M. Morris
- Lawrence Livermore National Laboratory, Livermore, CA 94550, USA;
| | - Robert A. Edwards
- Flinders Accelerator Microbiome Exploration, Flinders University, Bedford Park, SA 5042, Australia;
| | | | - Elizabeth A. Dinsdale
- Flinders Accelerator Microbiome Exploration, Flinders University, Bedford Park, SA 5042, Australia;
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9
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Sekeresova Kralova J, Donic C, Dassa B, Livyatan I, Jansen PM, Ben-Dor S, Fidel L, Trzebanski S, Narunsky-Haziza L, Asraf O, Brenner O, Dafni H, Jona G, Boura-Halfon S, Stettner N, Segal E, Brunke S, Pilpel Y, Straussman R, Zeevi D, Bacher P, Hube B, Shlezinger N, Jung S. Competitive fungal commensalism mitigates candidiasis pathology. J Exp Med 2024; 221:e20231686. [PMID: 38497819 PMCID: PMC10949073 DOI: 10.1084/jem.20231686] [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: 09/14/2023] [Revised: 01/17/2024] [Accepted: 02/14/2024] [Indexed: 03/19/2024] Open
Abstract
The mycobiota are a critical part of the gut microbiome, but host-fungal interactions and specific functional contributions of commensal fungi to host fitness remain incompletely understood. Here, we report the identification of a new fungal commensal, Kazachstania heterogenica var. weizmannii, isolated from murine intestines. K. weizmannii exposure prevented Candida albicans colonization and significantly reduced the commensal C. albicans burden in colonized animals. Following immunosuppression of C. albicans colonized mice, competitive fungal commensalism thereby mitigated fatal candidiasis. Metagenome analysis revealed K. heterogenica or K. weizmannii presence among human commensals. Our results reveal competitive fungal commensalism within the intestinal microbiota, independent of bacteria and immune responses, that could bear potential therapeutic value for the management of C. albicans-mediated diseases.
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Affiliation(s)
| | - Catalina Donic
- Departments of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Bareket Dassa
- Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Ilana Livyatan
- Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Paul Mathias Jansen
- Department of Microbial Pathogenicity Mechanisms, Leibniz Institute for Natural Product Research and Infection Biology—Hans Knoell Institute Jena (HKI), Jena, Germany
| | - Shifra Ben-Dor
- Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Lena Fidel
- Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Sébastien Trzebanski
- Departments of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
| | | | - Omer Asraf
- Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Ori Brenner
- Veterinary Resources, Weizmann Institute of Science, Rehovot, Israel
| | - Hagit Dafni
- Veterinary Resources, Weizmann Institute of Science, Rehovot, Israel
| | - Ghil Jona
- Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Sigalit Boura-Halfon
- Departments of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Noa Stettner
- Veterinary Resources, Weizmann Institute of Science, Rehovot, Israel
| | - Eran Segal
- Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Sascha Brunke
- Department of Microbial Pathogenicity Mechanisms, Leibniz Institute for Natural Product Research and Infection Biology—Hans Knoell Institute Jena (HKI), Jena, Germany
| | - Yitzhak Pilpel
- Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Ravid Straussman
- Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - David Zeevi
- Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Petra Bacher
- Institute of Immunology, Christian-Albrecht-University of Kiel, Kiel, Germany
- Institute of Clinical Molecular Biology, Christian-Albrecht-University of Kiel, Kiel, Germany
| | - Bernhard Hube
- Department of Microbial Pathogenicity Mechanisms, Leibniz Institute for Natural Product Research and Infection Biology—Hans Knoell Institute Jena (HKI), Jena, Germany
- Institute of Microbiology, Friedrich Schiller University, Jena, Germany
| | - Neta Shlezinger
- The Robert H. Smith Faculty of Agriculture, Food and Environment The Hebrew University of Jerusalem, Rehovot, Israel
| | - Steffen Jung
- Departments of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
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10
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Dmitrijeva M, Tackmann J, Matias Rodrigues JF, Huerta-Cepas J, Coelho LP, von Mering C. A global survey of prokaryotic genomes reveals the eco-evolutionary pressures driving horizontal gene transfer. Nat Ecol Evol 2024; 8:986-998. [PMID: 38443606 DOI: 10.1038/s41559-024-02357-0] [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: 06/14/2023] [Accepted: 02/05/2024] [Indexed: 03/07/2024]
Abstract
Horizontal gene transfer, the exchange of genetic material through means other than reproduction, is a fundamental force in prokaryotic genome evolution. Genomic persistence of horizontally transferred genes has been shown to be influenced by both ecological and evolutionary factors. However, there is limited availability of ecological information about species other than the habitats from which they were isolated, which has prevented a deeper exploration of ecological contributions to horizontal gene transfer. Here we focus on transfers detected through comparison of individual gene trees to the species tree, assessing the distribution of gene-exchanging prokaryotes across over a million environmental sequencing samples. By analysing detected horizontal gene transfer events, we show distinct functional profiles for recent versus old events. Although most genes transferred are part of the accessory genome, genes transferred earlier in evolution tend to be more ubiquitous within present-day species. We find that co-occurring, interacting and high-abundance species tend to exchange more genes. Finally, we show that host-associated specialist species are most likely to exchange genes with other host-associated specialist species, whereas species found across different habitats have similar gene exchange rates irrespective of their preferred habitat. Our study covers an unprecedented scale of integrated horizontal gene transfer and environmental information, highlighting broad eco-evolutionary trends.
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Affiliation(s)
- Marija Dmitrijeva
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zürich, Zurich, Switzerland
- Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, Zurich, Switzerland
| | - Janko Tackmann
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zürich, Zurich, Switzerland
| | | | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Campus de Montegancedo-UPM, Madrid, Spain
| | - Luis Pedro Coelho
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology, Translational Research Institute, Woolloongabba, Queensland, Australia.
| | - Christian von Mering
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zürich, Zurich, Switzerland.
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11
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Harnpicharnchai P, Siriarchawatana P, Mayteeworakoon S, Ingsrisawang L, Likhitrattanapisal S, Eurwilaichitr L, Ingsriswang S. Interplay of xenobiotic-degrading and antibiotic-resistant microorganisms among the microbiome found in the air, handrail, and floor of the subway station. ENVIRONMENTAL RESEARCH 2024; 247:118269. [PMID: 38246293 DOI: 10.1016/j.envres.2024.118269] [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: 10/30/2023] [Revised: 01/11/2024] [Accepted: 01/18/2024] [Indexed: 01/23/2024]
Abstract
Investigating the quality of the subway environment, especially regarding antibiotic resistance genes (ARGs) and xenobiotics, conveys ecological and health impacts. In this study, compositions and relations of microorganisms harboring ARGs and xenobiotic degradation and metabolism genes (XDGs) in the Sukhumvit subway station (MRT-SKV) in Bangkok was assessed by analyzing the taxonomic and genetic diversity of the microbiome in the air and on the surfaces of floor and handrail. The major bacteria in the MRT-SKV (including Moraxella, which was abundant in the bioaerosol and handrail samples, and Staphylococcus, which was abundant in the bioaerosol samples) were found to contain both ARGs and XDGs. The co-abundance correlation network revealed notable relationships among bacteria harboring antibiotic resistance genes (ARGs) and xenobiotic degradation genes (XDGs). Significant associations were observed between ARGs linked to glycopeptide and fluoroquinolone resistance and genes associated with benzoate, styrene, and atrazine degradation pathways, as well as between ARGs related to cephamycin, cephalosporin, and MLS resistance and XDGs associated with the cytochrome P450-dependent drug metabolism pathway. These correlations suggested that selective pressure exerted by certain xenobiotics and antibiotics can simultaneously affect both ARGs and XDGs in the environment and should favor correlations and co-survival among ARG- and XDG-containing bacteria in the environments. The correlations may occur via shared mechanisms of resistance to both xenobiotics and antibiotics. Finally, different correlation pairs were seen in different niches (air, handrail, floor) of the subway environment or different geolocations. Thus, the relationship between ARG and XDG pairs most likely depends on the unique characteristics of the niches and on the prominent types of xenobiotics and antibiotics in the subway environment. The results indicated that interactions and connections between microbial communities can impact how they function. These microorganisms can have profound effects on accumulation of xenobiotics and ARGs in the MRT-SKV.
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Affiliation(s)
- Piyanun Harnpicharnchai
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Khlong Luang, Pathum Thani, Thailand
| | - Paopit Siriarchawatana
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Khlong Luang, Pathum Thani, Thailand
| | - Sermsiri Mayteeworakoon
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Khlong Luang, Pathum Thani, Thailand
| | - Lily Ingsrisawang
- Department of Statistics, Faculty of Science, Kasetsart University, Chatuchak, Bangkok, Thailand
| | - Somsak Likhitrattanapisal
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Khlong Luang, Pathum Thani, Thailand
| | - Lily Eurwilaichitr
- National Energy Technology Center, National Science and Technology Development Agency, Khlong Luang, Pathum Thani, Thailand
| | - Supawadee Ingsriswang
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Khlong Luang, Pathum Thani, Thailand.
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12
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Wu Y, Gao N, Sun C, Feng T, Liu Q, Chen WH. A compendium of ruminant gastrointestinal phage genomes revealed a higher proportion of lytic phages than in any other environments. MICROBIOME 2024; 12:69. [PMID: 38576042 PMCID: PMC10993611 DOI: 10.1186/s40168-024-01784-2] [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: 03/20/2023] [Accepted: 02/29/2024] [Indexed: 04/06/2024]
Abstract
BACKGROUND Ruminants are important livestock animals that have a unique digestive system comprising multiple stomach compartments. Despite significant progress in the study of microbiome in the gastrointestinal tract (GIT) sites of ruminants, we still lack an understanding of the viral community of ruminants. Here, we surveyed its viral ecology using 2333 samples from 10 sites along the GIT of 8 ruminant species. RESULTS We present the Unified Ruminant Phage Catalogue (URPC), a comprehensive survey of phages in the GITs of ruminants including 64,922 non-redundant phage genomes. We characterized the distributions of the phage genomes in different ruminants and GIT sites and found that most phages were organism-specific. We revealed that ~ 60% of the ruminant phages were lytic, which was the highest as compared with those in all other environments and certainly will facilitate their applications in microbial interventions. To further facilitate the future applications of the phages, we also constructed a comprehensive virus-bacteria/archaea interaction network and identified dozens of phages that may have lytic effects on methanogenic archaea. CONCLUSIONS The URPC dataset represents a useful resource for future microbial interventions to improve ruminant production and ecological environmental qualities. Phages have great potential for controlling pathogenic bacterial/archaeal species and reducing methane emissions. Our findings provide insights into the virome ecology research of the ruminant GIT and offer a starting point for future research on phage therapy in ruminants. Video Abstract.
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Affiliation(s)
- Yingjian Wu
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-Imaging, Center for Artificial Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Na Gao
- Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China
| | - Chuqing Sun
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-Imaging, Center for Artificial Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Tong Feng
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-Imaging, Center for Artificial Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Qingyou Liu
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan, 528225, China.
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning, 530005, China.
| | - Wei-Hua Chen
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-Imaging, Center for Artificial Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
- Institution of Medical Artificial Intelligence, Binzhou Medical University, Yantai, 264003, China.
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13
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Gschwind R, Petitjean M, Fournier C, Lao J, Clermont O, Nordmann P, Mellmann A, Denamur E, Poirel L, Ruppé E. Inter-phylum circulation of a beta-lactamase-encoding gene: a rare but observable event. Antimicrob Agents Chemother 2024; 68:e0145923. [PMID: 38441061 PMCID: PMC10989005 DOI: 10.1128/aac.01459-23] [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: 11/10/2023] [Accepted: 02/12/2024] [Indexed: 03/06/2024] Open
Abstract
Beta-lactamase-mediated degradation of beta-lactams is the most common mechanism of beta-lactam resistance in Gram-negative bacteria. Beta-lactamase-encoding genes can be transferred between closely related bacteria, but spontaneous inter-phylum transfers (between distantly related bacteria) have never been reported. Here, we describe an extended-spectrum beta-lactamase (ESBL)-encoding gene (blaMUN-1) shared between the Pseudomonadota and Bacteroidota phyla. An Escherichia coli strain was isolated from a patient in Münster (Germany). Its genome was sequenced. The ESBL-encoding gene (named blaMUN-1) was cloned, and the corresponding enzyme was characterized. The distribution of the gene among bacteria was investigated using the RefSeq Genomes database. The frequency and relative abundance of its closest homolog in the global microbial gene catalog (GMGC) were analyzed. The E. coli strain exhibited two distinct morphotypes. Each morphotype possessed two chromosomal copies of the blaMUN-1 gene, with one morphotype having two additional copies located on a phage-plasmid p0111. Each copy was located within a 7.6-kb genomic island associated with mobility. blaMUN-1 encoded for an extended-spectrum Ambler subclass A2 beta-lactamase with 43.0% amino acid identity to TLA-1. blaMUN-1 was found in species among the Bacteroidales order and in Sutterella wadsworthensis (Pseudomonadota). Its closest homolog in GMGC was detected frequently in human fecal samples. This is, to our knowledge, the first reported instance of inter-phylum transfer of an ESBL-encoding gene, between the Bacteroidota and Pseudomonadota phyla. Although the gene was frequently detected in the human gut, inter-phylum transfer was rare, indicating that inter-phylum barriers are effective in impeding the spread of ESBL-encoding genes, but not entirely impenetrable.
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Affiliation(s)
- Rémi Gschwind
- Université Paris Cité, INSERM, Université Sorbonne Paris Nord, IAME, Paris, France
| | - Marie Petitjean
- Université Paris Cité, INSERM, Université Sorbonne Paris Nord, IAME, Paris, France
- AP-HP, Hôpital Bichat, Laboratoire de Bactériologie, Paris, France
| | - Claudine Fournier
- Emerging Antibiotic Resistance, Medical and Molecular Microbiology, Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland
- Swiss National Reference Center for Emerging Antibiotic Resistance, Fribourg, Switzerland
- INSERM European Unit (IAME, France), University of Fribourg, Fribourg, Switzerland
| | - Julie Lao
- Université Paris Cité, INSERM, Université Sorbonne Paris Nord, IAME, Paris, France
| | - Olivier Clermont
- Université Paris Cité, INSERM, Université Sorbonne Paris Nord, IAME, Paris, France
| | - Patrice Nordmann
- Emerging Antibiotic Resistance, Medical and Molecular Microbiology, Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland
- Swiss National Reference Center for Emerging Antibiotic Resistance, Fribourg, Switzerland
- INSERM European Unit (IAME, France), University of Fribourg, Fribourg, Switzerland
- University of Lausanne, University Hospital Center, Lausanne, Switzerland
| | | | - Erick Denamur
- Université Paris Cité, INSERM, Université Sorbonne Paris Nord, IAME, Paris, France
- AP-HP, Hôpital Bichat, Laboratoire de Génétique Moléculaire, Paris, France
| | - Laurent Poirel
- Emerging Antibiotic Resistance, Medical and Molecular Microbiology, Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland
- Swiss National Reference Center for Emerging Antibiotic Resistance, Fribourg, Switzerland
- INSERM European Unit (IAME, France), University of Fribourg, Fribourg, Switzerland
- University of Lausanne, University Hospital Center, Lausanne, Switzerland
| | - Etienne Ruppé
- Université Paris Cité, INSERM, Université Sorbonne Paris Nord, IAME, Paris, France
- AP-HP, Hôpital Bichat, Laboratoire de Bactériologie, Paris, France
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14
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Baker BA, Gutiérrez-Preciado A, Rodríguez Del Río Á, McCarthy CGP, López-García P, Huerta-Cepas J, Susko E, Roger AJ, Eme L, Moreira D. Expanded phylogeny of extremely halophilic archaea shows multiple independent adaptations to hypersaline environments. Nat Microbiol 2024; 9:964-975. [PMID: 38519541 DOI: 10.1038/s41564-024-01647-4] [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: 06/30/2023] [Accepted: 02/20/2024] [Indexed: 03/25/2024]
Abstract
Extremely halophilic archaea (Haloarchaea, Nanohaloarchaeota, Methanonatronarchaeia and Halarchaeoplasmatales) thrive in saturating salt concentrations where they must maintain osmotic equilibrium with their environment. The evolutionary history of adaptations enabling salt tolerance remains poorly understood, in particular because the phylogeny of several lineages is conflicting. Here we present a resolved phylogeny of extremely halophilic archaea obtained using improved taxon sampling and state-of-the-art phylogenetic approaches designed to cope with the strong compositional biases of their proteomes. We describe two uncultured lineages, Afararchaeaceae and Asbonarchaeaceae, which break the long branches at the base of Haloarchaea and Nanohaloarchaeota, respectively. We obtained 13 metagenome-assembled genomes (MAGs) of these archaea from metagenomes of hypersaline aquatic systems of the Danakil Depression (Ethiopia). Our phylogenomic analyses including these taxa show that at least four independent adaptations to extreme halophily occurred during archaeal evolution. Gene-tree/species-tree reconciliation suggests that gene duplication and horizontal gene transfer played an important role in this process, for example, by spreading key genes (such as those encoding potassium transporters) across extremely halophilic lineages.
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Affiliation(s)
- Brittany A Baker
- Ecologie Systématique Evolution, CNRS, Université Paris-Saclay, AgroParisTech, Gif-sur-Yvette, France
| | - Ana Gutiérrez-Preciado
- Ecologie Systématique Evolution, CNRS, Université Paris-Saclay, AgroParisTech, Gif-sur-Yvette, France
| | - Álvaro Rodríguez Del Río
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Charley G P McCarthy
- Institute for Comparative Genomics, Dalhousie University, Halifax, Nova Scotia, Canada
- Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Purificación López-García
- Ecologie Systématique Evolution, CNRS, Université Paris-Saclay, AgroParisTech, Gif-sur-Yvette, France
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Edward Susko
- Institute for Comparative Genomics, Dalhousie University, Halifax, Nova Scotia, Canada
- Department of Mathematics and Statistics, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Andrew J Roger
- Institute for Comparative Genomics, Dalhousie University, Halifax, Nova Scotia, Canada
- Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Laura Eme
- Ecologie Systématique Evolution, CNRS, Université Paris-Saclay, AgroParisTech, Gif-sur-Yvette, France.
| | - David Moreira
- Ecologie Systématique Evolution, CNRS, Université Paris-Saclay, AgroParisTech, Gif-sur-Yvette, France.
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15
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Xu X, Feng Q, Zhang T, Gao Y, Cheng Q, Zhang W, Wu Q, Xu K, Li Y, Nguyen N, Taft DH, Mills DA, Lemay DG, Zhu W, Mao S, Zhang A, Xu K, Liu J. Infant age inversely correlates with gut carriage of resistance genes, reflecting modifications in microbial carbohydrate metabolism during early life. IMETA 2024; 3:e169. [PMID: 38882494 PMCID: PMC11170968 DOI: 10.1002/imt2.169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 11/23/2023] [Accepted: 12/06/2023] [Indexed: 06/18/2024]
Abstract
The infant gut microbiome is increasingly recognized as a reservoir of antibiotic resistance genes, yet the assembly of gut resistome in infants and its influencing factors remain largely unknown. We characterized resistome in 4132 metagenomes from 963 infants in six countries and 4285 resistance genes were observed. The inherent resistome pattern of healthy infants (N = 272) could be distinguished by two stages: a multicompound resistance phase (Months 0-7) and a tetracycline-mupirocin-β-lactam-dominant phase (Months 8-14). Microbial taxonomy explained 40.7% of the gut resistome of healthy infants, with Escherichia (25.5%) harboring the most resistance genes. In a further analysis with all available infants (N = 963), we found age was the strongest influencer on the resistome and was negatively correlated with the overall resistance during the first 3 years (p < 0.001). Using a random-forest approach, a set of 34 resistance genes could be used to predict age (R 2 = 68.0%). Leveraging microbial host inference analyses, we inferred the age-dependent assembly of infant resistome was a result of shifts in the gut microbiome, primarily driven by changes in taxa that disproportionately harbor resistance genes across taxa (e.g., Escherichia coli more frequently harbored resistance genes than other taxa). We performed metagenomic functional profiling and metagenomic assembled genome analyses whose results indicate that the development of gut resistome was driven by changes in microbial carbohydrate metabolism, with an increasing need for carbohydrate-active enzymes from Bacteroidota and a decreasing need for Pseudomonadota during infancy. Importantly, we observed increased acquired resistance genes over time, which was related to increased horizontal gene transfer in the developing infant gut microbiome. In summary, infant age was negatively correlated with antimicrobial resistance gene levels, reflecting a composition shift in the gut microbiome, likely driven by the changing need for microbial carbohydrate metabolism during early life.
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Affiliation(s)
- Xinming Xu
- Laboratory of Gastrointestinal Microbiology, College of Animal Science & Technology Nanjing Agricultural University Nanjing China
- Jiangsu Key Laboratory of Gastrointestinal Nutrition and Animal Health, National Center for International Research on Animal Gut Nutrition Nanjing Agricultural University Nanjing China
- Department of Nutrition and Food Hygiene, School of Public Health, Institute of Nutrition Fudan University Shanghai China
| | - Qingying Feng
- Laboratory of Gastrointestinal Microbiology, College of Animal Science & Technology Nanjing Agricultural University Nanjing China
- Jiangsu Key Laboratory of Gastrointestinal Nutrition and Animal Health, National Center for International Research on Animal Gut Nutrition Nanjing Agricultural University Nanjing China
- Biological Engineering Division Massachusetts Institute of Technology (MIT) Cambridge Massachusetts USA
| | - Tao Zhang
- Laboratory of Gastrointestinal Microbiology, College of Animal Science & Technology Nanjing Agricultural University Nanjing China
- Jiangsu Key Laboratory of Gastrointestinal Nutrition and Animal Health, National Center for International Research on Animal Gut Nutrition Nanjing Agricultural University Nanjing China
| | - Yunlong Gao
- Laboratory of Gastrointestinal Microbiology, College of Animal Science & Technology Nanjing Agricultural University Nanjing China
- Jiangsu Key Laboratory of Gastrointestinal Nutrition and Animal Health, National Center for International Research on Animal Gut Nutrition Nanjing Agricultural University Nanjing China
| | - Qu Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College Huazhong University of Science and Technology Wuhan China
| | - Wanqiu Zhang
- Laboratory of Gastrointestinal Microbiology, College of Animal Science & Technology Nanjing Agricultural University Nanjing China
- Jiangsu Key Laboratory of Gastrointestinal Nutrition and Animal Health, National Center for International Research on Animal Gut Nutrition Nanjing Agricultural University Nanjing China
| | - Qinglong Wu
- Department of Pathology and Immunology Baylor College of Medicine Houston Texas USA
| | - Ke Xu
- Department of Statistics University of Chicago Chicago Illinois
| | - Yucan Li
- State Key Laboratory of Genetic Engineering, Human Phenome Institute Fudan University Shanghai China
| | - Nhu Nguyen
- Department of Food Science and Technology University of California, Davis Davis California USA
| | - Diana H Taft
- Department of Food Science and Technology University of California, Davis Davis California USA
| | - David A Mills
- Department of Food Science and Technology University of California, Davis Davis California USA
- Department of Viticulture and Enology, Robert Mondavi Institute for Wine and Food Science University of California, Davis Davis California USA
| | - Danielle G Lemay
- USDA ARS Western Human Nutrition Research Center Davis California USA
| | - Weiyun Zhu
- Laboratory of Gastrointestinal Microbiology, College of Animal Science & Technology Nanjing Agricultural University Nanjing China
- Jiangsu Key Laboratory of Gastrointestinal Nutrition and Animal Health, National Center for International Research on Animal Gut Nutrition Nanjing Agricultural University Nanjing China
| | - Shengyong Mao
- Laboratory of Gastrointestinal Microbiology, College of Animal Science & Technology Nanjing Agricultural University Nanjing China
- Jiangsu Key Laboratory of Gastrointestinal Nutrition and Animal Health, National Center for International Research on Animal Gut Nutrition Nanjing Agricultural University Nanjing China
| | - Anyun Zhang
- Animal Disease Prevention and Food Safety Key Laboratory of Sichuan Province, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences Sichuan University Chengdu China
| | - Kelin Xu
- Department of Biostatistics, Key Laboratory of Public Health Safety, NHC Key Laboratory of Health Technology Assessment, School of Public Health Fudan University Shanghai China
| | - Jinxin Liu
- Laboratory of Gastrointestinal Microbiology, College of Animal Science & Technology Nanjing Agricultural University Nanjing China
- Jiangsu Key Laboratory of Gastrointestinal Nutrition and Animal Health, National Center for International Research on Animal Gut Nutrition Nanjing Agricultural University Nanjing China
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16
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Roy G, Prifti E, Belda E, Zucker JD. Deep learning methods in metagenomics: a review. Microb Genom 2024; 10. [PMID: 38630611 DOI: 10.1099/mgen.0.001231] [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] [Indexed: 04/19/2024] Open
Abstract
The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.
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Affiliation(s)
- Gaspar Roy
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
| | - Edi Prifti
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
- Sorbonne University, INSERM, Nutriomics, 91 bvd de l'hopital, 75013 Paris, France
| | - Eugeni Belda
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
- Sorbonne University, INSERM, Nutriomics, 91 bvd de l'hopital, 75013 Paris, France
| | - Jean-Daniel Zucker
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
- Sorbonne University, INSERM, Nutriomics, 91 bvd de l'hopital, 75013 Paris, France
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17
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Dörner PJ, Anandakumar H, Röwekamp I, Fiocca Vernengo F, Millet Pascual-Leone B, Krzanowski M, Sellmaier J, Brüning U, Fritsche-Guenther R, Pfannkuch L, Kurth F, Milek M, Igbokwe V, Löber U, Gutbier B, Holstein M, Heinz GA, Mashreghi MF, Schulte LN, Klatt AB, Caesar S, Wienhold SM, Offermanns S, Mack M, Witzenrath M, Jordan S, Beule D, Kirwan JA, Forslund SK, Wilck N, Bartolomaeus H, Heimesaat MM, Opitz B. Clinically used broad-spectrum antibiotics compromise inflammatory monocyte-dependent antibacterial defense in the lung. Nat Commun 2024; 15:2788. [PMID: 38555356 PMCID: PMC10981692 DOI: 10.1038/s41467-024-47149-z] [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: 05/17/2023] [Accepted: 03/20/2024] [Indexed: 04/02/2024] Open
Abstract
Hospital-acquired pneumonia (HAP) is associated with high mortality and costs, and frequently caused by multidrug-resistant (MDR) bacteria. Although prior antimicrobial therapy is a major risk factor for HAP, the underlying mechanism remains incompletely understood. Here, we demonstrate that antibiotic therapy in hospitalized patients is associated with decreased diversity of the gut microbiome and depletion of short-chain fatty acid (SCFA) producers. Infection experiments with mice transplanted with patient fecal material reveal that these antibiotic-induced microbiota perturbations impair pulmonary defense against MDR Klebsiella pneumoniae. This is dependent on inflammatory monocytes (IMs), whose fatty acid receptor (FFAR)2/3-controlled and phagolysosome-dependent antibacterial activity is compromized in mice transplanted with antibiotic-associated patient microbiota. Collectively, we characterize how clinically relevant antibiotics affect antimicrobial defense in the context of human microbiota, and reveal a critical impairment of IM´s antimicrobial activity. Our study provides additional arguments for the rational use of antibiotics and offers mechanistic insights for the development of novel prophylactic strategies to protect high-risk patients from HAP.
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Affiliation(s)
- Patrick J Dörner
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Harithaa Anandakumar
- Experimental and Clinical Research Center, a cooperation of Charité - Universitätsmedizin Berlin and Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
- Department of Nephrology and Internal Intensive Care Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ivo Röwekamp
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Facundo Fiocca Vernengo
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Belén Millet Pascual-Leone
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Marta Krzanowski
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Josua Sellmaier
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ulrike Brüning
- Metabolomics Platform, Berlin Institute of Health at Charité, Berlin, Germany
| | | | - Lennart Pfannkuch
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Florian Kurth
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Miha Milek
- Core Unit Bioinformatics, Berlin Institute of Health at Charité, Berlin, Germany
| | - Vanessa Igbokwe
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ulrike Löber
- Experimental and Clinical Research Center, a cooperation of Charité - Universitätsmedizin Berlin and Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
| | - Birgitt Gutbier
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Markus Holstein
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Gitta Anne Heinz
- German Rheumatism Research Center, a Leibniz Institute, Berlin, Germany
| | | | - Leon N Schulte
- Department of Medicine, Institute for Lung Research, Philipps University Marburg, Marburg, Germany
- German center for lung research (DZL), Marburg, Germany
| | - Ann-Brit Klatt
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sandra Caesar
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sandra-Maria Wienhold
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Stefan Offermanns
- Max-Planck-Institute for Heart and Lung Research, Bad Nauheim, Germany
| | - Matthias Mack
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Martin Witzenrath
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- German center for lung research (DZL), Berlin, Germany
| | - Stefan Jordan
- Institute of Microbiology, Infectious Diseases and Immunology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Dieter Beule
- Core Unit Bioinformatics, Berlin Institute of Health at Charité, Berlin, Germany
| | - Jennifer A Kirwan
- Metabolomics Platform, Berlin Institute of Health at Charité, Berlin, Germany
| | - Sofia K Forslund
- Experimental and Clinical Research Center, a cooperation of Charité - Universitätsmedizin Berlin and Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Nicola Wilck
- Experimental and Clinical Research Center, a cooperation of Charité - Universitätsmedizin Berlin and Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
- Department of Nephrology and Internal Intensive Care Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Hendrik Bartolomaeus
- Experimental and Clinical Research Center, a cooperation of Charité - Universitätsmedizin Berlin and Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
- Department of Nephrology and Internal Intensive Care Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Markus M Heimesaat
- Institute of Microbiology, Infectious Diseases and Immunology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Bastian Opitz
- Department of Infectious Diseases, Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- German center for lung research (DZL), Berlin, Germany.
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18
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Fierro Morales JC, Redfearn C, Titus MA, Roh-Johnson M. Reduced PaxillinB localization to cell-substrate adhesions promotes cell migration in Dictyostelium. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585764. [PMID: 38562712 PMCID: PMC10983970 DOI: 10.1101/2024.03.19.585764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Many cells adhere to extracellular matrix for efficient cell migration. This adhesion is mediated by focal adhesions, a protein complex linking the extracellular matrix to the intracellular cytoskeleton. Focal adhesions have been studied extensively in mesenchymal cells, but recent research in physiological contexts and amoeboid cells suggest focal adhesion regulation differs from the mesenchymal focal adhesion paradigm. We used Dictyostelium discoideum to uncover new mechanisms of focal adhesion regulation, as Dictyostelium are amoeboid cells that form focal adhesion-like structures for migration. We show that PaxillinB, the Dictyostelium homologue of Paxillin, localizes to dynamic focal adhesion-like structures during Dictyostelium migration. Unexpectedly, reduced PaxillinB recruitment to these structures increases Dictyostelium cell migration. Quantitative analysis of focal adhesion size and dynamics show that lack of PaxillinB recruitment to focal adhesions does not alter focal adhesion size, but rather increases focal adhesion turnover. These findings are in direct contrast to Paxillin function at focal adhesions during mesenchymal migration, challenging the established focal adhesion model.
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Affiliation(s)
| | - Chandler Redfearn
- Department of Kinesiology, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA
| | - Margaret A Titus
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Minna Roh-Johnson
- Department of Biochemistry, University of Utah, Salt Lake City, UT, 84112, USA
- Department of Kinesiology, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA
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19
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Cheng M, Luo S, Zhang P, Xiong G, Chen K, Jiang C, Yang F, Huang H, Yang P, Liu G, Zhang Y, Ba S, Yin P, Xiong J, Miao W, Ning K. A genome and gene catalog of the aquatic microbiomes of the Tibetan Plateau. Nat Commun 2024; 15:1438. [PMID: 38365793 PMCID: PMC10873407 DOI: 10.1038/s41467-024-45895-8] [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: 09/07/2023] [Accepted: 02/07/2024] [Indexed: 02/18/2024] Open
Abstract
The Tibetan Plateau supplies water to nearly 2 billion people in Asia, but climate change poses threats to its aquatic microbial resources. Here, we construct the Tibetan Plateau Microbial Catalog by sequencing 498 metagenomes from six water ecosystems (saline lakes, freshwater lakes, rivers, hot springs, wetlands and glaciers). Our catalog expands knowledge of regional genomic diversity by presenting 32,355 metagenome-assembled genomes that de-replicated into 10,723 representative genome-based species, of which 88% were unannotated. The catalog contains nearly 300 million non-redundant gene clusters, of which 15% novel, and 73,864 biosynthetic gene clusters, of which 50% novel, thus expanding known functional diversity. Using these data, we investigate the Tibetan Plateau aquatic microbiome's biogeography along a distance of 2,500 km and >5 km in altitude. Microbial compositional similarity and the shared gene count with the Tibetan Plateau microbiome decline along with distance and altitude difference, suggesting a dispersal pattern. The Tibetan Plateau Microbial Catalog stands as a substantial repository for high-altitude aquatic microbiome resources, providing potential for discovering novel lineages and functions, and bridging knowledge gaps in microbiome biogeography.
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Affiliation(s)
- Mingyue Cheng
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular Imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Shuai Luo
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Peng Zhang
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
- Laboratory of Tibetan Plateau Wetland and Watershed Ecosystem, College of Science, Tibet University, Lhasa, China
| | - Guangzhou Xiong
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular Imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Kai Chen
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
| | - Chuanqi Jiang
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
| | - Fangdian Yang
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
- Laboratory of Tibetan Plateau Wetland and Watershed Ecosystem, College of Science, Tibet University, Lhasa, China
| | - Hanhui Huang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular Imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Pengshuo Yang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular Imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Guanxi Liu
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular Imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Yuhao Zhang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular Imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Sang Ba
- Laboratory of Tibetan Plateau Wetland and Watershed Ecosystem, College of Science, Tibet University, Lhasa, China
| | - Ping Yin
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Jie Xiong
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China.
- Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture, Chinese Academy of Sciences, Wuhan, China.
| | - Wei Miao
- Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China.
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China.
- Laboratory of Tibetan Plateau Wetland and Watershed Ecosystem, College of Science, Tibet University, Lhasa, China.
- Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture, Chinese Academy of Sciences, Wuhan, China.
| | - Kang Ning
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular Imaging, Center of Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
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20
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Wirbel J, Bhatt AS, Probst AJ. The journey to understand previously unknown microbial genes. Nature 2024; 626:267-269. [PMID: 38291331 DOI: 10.1038/d41586-024-00077-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
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21
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Rodríguez Del Río Á, Giner-Lamia J, Cantalapiedra CP, Botas J, Deng Z, Hernández-Plaza A, Munar-Palmer M, Santamaría-Hernando S, Rodríguez-Herva JJ, Ruscheweyh HJ, Paoli L, Schmidt TSB, Sunagawa S, Bork P, López-Solanilla E, Coelho LP, Huerta-Cepas J. Functional and evolutionary significance of unknown genes from uncultivated taxa. Nature 2024; 626:377-384. [PMID: 38109938 PMCID: PMC10849945 DOI: 10.1038/s41586-023-06955-z] [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: 02/01/2022] [Accepted: 12/08/2023] [Indexed: 12/20/2023]
Abstract
Many of the Earth's microbes remain uncultured and understudied, limiting our understanding of the functional and evolutionary aspects of their genetic material, which remain largely overlooked in most metagenomic studies1. Here we analysed 149,842 environmental genomes from multiple habitats2-6 and compiled a curated catalogue of 404,085 functionally and evolutionarily significant novel (FESNov) gene families exclusive to uncultivated prokaryotic taxa. All FESNov families span multiple species, exhibit strong signals of purifying selection and qualify as new orthologous groups, thus nearly tripling the number of bacterial and archaeal gene families described to date. The FESNov catalogue is enriched in clade-specific traits, including 1,034 novel families that can distinguish entire uncultivated phyla, classes and orders, probably representing synapomorphies that facilitated their evolutionary divergence. Using genomic context analysis and structural alignments we predicted functional associations for 32.4% of FESNov families, including 4,349 high-confidence associations with important biological processes. These predictions provide a valuable hypothesis-driven framework that we used for experimental validatation of a new gene family involved in cell motility and a novel set of antimicrobial peptides. We also demonstrate that the relative abundance profiles of novel families can discriminate between environments and clinical conditions, leading to the discovery of potentially new biomarkers associated with colorectal cancer. We expect this work to enhance future metagenomics studies and expand our knowledge of the genetic repertory of uncultivated organisms.
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Affiliation(s)
- Álvaro Rodríguez Del Río
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Joaquín Giner-Lamia
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
- Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid (UPM), Madrid, Spain
- Departamento de Bioquímica Vegetal y Biología Molecular, Facultad de Biología, Instituto de Bioquímica Vegetal y Fotosíntesis (IBVF), Universidad de Sevilla-CSIC, Seville, Spain
| | - Carlos P Cantalapiedra
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Jorge Botas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Ziqi Deng
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Ana Hernández-Plaza
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Martí Munar-Palmer
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Saray Santamaría-Hernando
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - José J Rodríguez-Herva
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
- Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - Hans-Joachim Ruscheweyh
- Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, Zürich, Switzerland
| | - Lucas Paoli
- Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, Zürich, Switzerland
| | - Thomas S B Schmidt
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Shinichi Sunagawa
- Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, Zürich, Switzerland
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Emilia López-Solanilla
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
- Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - Luis Pedro Coelho
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Shanghai, China
- Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology, Translational Research Institute, Woolloongabba, Queensland, Australia
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain.
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22
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Park H, Joachimiak MP, Jungbluth SP, Yang Z, Riehl WJ, Canon RS, Arkin AP, Dehal PS. A bacterial sensor taxonomy across earth ecosystems for machine learning applications. mSystems 2024; 9:e0002623. [PMID: 38078749 PMCID: PMC10804942 DOI: 10.1128/msystems.00026-23] [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: 01/13/2023] [Accepted: 10/23/2023] [Indexed: 01/24/2024] Open
Abstract
Microbial communities have evolved to colonize all ecosystems of the planet, from the deep sea to the human gut. Microbes survive by sensing, responding, and adapting to immediate environmental cues. This process is driven by signal transduction proteins such as histidine kinases, which use their sensing domains to bind or otherwise detect environmental cues and "transduce" signals to adjust internal processes. We hypothesized that an ecosystem's unique stimuli leave a sensor "fingerprint," able to identify and shed insight on ecosystem conditions. To test this, we collected 20,712 publicly available metagenomes from Host-associated, Environmental, and Engineered ecosystems across the globe. We extracted and clustered the collection's nearly 18M unique sensory domains into 113,712 similar groupings with MMseqs2. We built gradient-boosted decision tree machine learning models and found we could classify the ecosystem type (accuracy: 87%) and predict the levels of different physical parameters (R2 score: 83%) using the sensor cluster abundance as features. Feature importance enables identification of the most predictive sensors to differentiate between ecosystems which can lead to mechanistic interpretations if the sensor domains are well annotated. To demonstrate this, a machine learning model was trained to predict patient's disease state and used to identify domains related to oxygen sensing present in a healthy gut but missing in patients with abnormal conditions. Moreover, since 98.7% of identified sensor domains are uncharacterized, importance ranking can be used to prioritize sensors to determine what ecosystem function they may be sensing. Furthermore, these new predictive sensors can function as targets for novel sensor engineering with applications in biotechnology, ecosystem maintenance, and medicine.IMPORTANCEMicrobes infect, colonize, and proliferate due to their ability to sense and respond quickly to their surroundings. In this research, we extract the sensory proteins from a diverse range of environmental, engineered, and host-associated metagenomes. We trained machine learning classifiers using sensors as features such that it is possible to predict the ecosystem for a metagenome from its sensor profile. We use the optimized model's feature importance to identify the most impactful and predictive sensors in different environments. We next use the sensor profile from human gut metagenomes to classify their disease states and explore which sensors can explain differences between diseases. The sensors most predictive of environmental labels here, most of which correspond to uncharacterized proteins, are a useful starting point for the discovery of important environment signals and the development of possible diagnostic interventions.
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Affiliation(s)
- Helen Park
- Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, China
- EPSRC/BBSRC Future Biomanufacturing Research Hub, EPSRC Synthetic Biology Research Centre SYNBIOCHEM Manchester Institute of Biotechnology and School of Chemistry, The University of Manchester, Manchester, United Kingdom
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Marcin P. Joachimiak
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Sean P. Jungbluth
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Ziming Yang
- Computational Science Initiative, Brookhaven National Laboratory, Upton, New York, USA
| | - William J. Riehl
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - R. Shane Canon
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
- National Energy Research Scientific Computing Center, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Adam P. Arkin
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
- Department of Bioengineering, University of California, Berkeley, California, USA
| | - Paramvir S. Dehal
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
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23
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Duan N, Hand E, Pheko M, Sharma S, Emiola A. Structure-guided discovery of anti-CRISPR and anti-phage defense proteins. Nat Commun 2024; 15:649. [PMID: 38245560 PMCID: PMC10799925 DOI: 10.1038/s41467-024-45068-7] [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: 11/24/2023] [Accepted: 01/12/2024] [Indexed: 01/22/2024] Open
Abstract
Bacteria use a variety of defense systems to protect themselves from phage infection. In turn, phages have evolved diverse counter-defense measures to overcome host defenses. Here, we use protein structural similarity and gene co-occurrence analyses to screen >66 million viral protein sequences and >330,000 metagenome-assembled genomes for the identification of anti-phage and counter-defense systems. We predict structures for ~300,000 proteins and perform large-scale, pairwise comparison to known anti-CRISPR (Acr) and anti-phage proteins to identify structural homologs that otherwise may not be uncovered using primary sequence search. This way, we identify a Bacteroidota phage Acr protein that inhibits Cas12a, and an Akkermansia muciniphila anti-phage defense protein, termed BxaP. Gene bxaP is found in loci encoding Bacteriophage Exclusion (BREX) and restriction-modification defense systems, but confers immunity independently. Our work highlights the advantage of combining protein structural features and gene co-localization information in studying host-phage interactions.
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Affiliation(s)
- Ning Duan
- Microbial Therapeutics Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Emily Hand
- Microbial Therapeutics Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Mannuku Pheko
- Microbial Therapeutics Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Shikha Sharma
- Microbial Therapeutics Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA
| | - Akintunde Emiola
- Microbial Therapeutics Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA.
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24
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Wong C. Largest genetic database of marine microbes could aid drug discovery. Nature 2024:10.1038/d41586-024-00133-5. [PMID: 38228931 DOI: 10.1038/d41586-024-00133-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
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25
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Yu XA, McLean C, Hehemann JH, Angeles-Albores D, Wu F, Muszyński A, Corzett CH, Azadi P, Kujawinski EB, Alm EJ, Polz MF. Low-level resource partitioning supports coexistence among functionally redundant bacteria during successional dynamics. THE ISME JOURNAL 2024; 18:wrad013. [PMID: 38365244 PMCID: PMC10811730 DOI: 10.1093/ismejo/wrad013] [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: 10/31/2023] [Revised: 11/09/2023] [Accepted: 12/05/2023] [Indexed: 02/18/2024]
Abstract
Members of microbial communities can substantially overlap in substrate use. However, what enables functionally redundant microorganisms to coassemble or even stably coexist remains poorly understood. Here, we show that during unstable successional dynamics on complex, natural organic matter, functionally redundant bacteria can coexist by partitioning low-concentration substrates even though they compete for one simple, dominant substrate. We allowed ocean microbial communities to self-assemble on leachates of the brown seaweed Fucus vesiculosus and then analyzed the competition among 10 taxonomically diverse isolates representing two distinct stages of the succession. All, but two isolates, exhibited an average of 90% ± 6% pairwise overlap in resource use, and functional redundancy of isolates from the same assembly stage was higher than that from between assembly stages, leading us to construct a simpler four-isolate community with two isolates from each of the early and late stages. We found that, although the short-term dynamics of the four-isolate communities in F. vesiculosus leachate was dependent on initial isolate ratios, in the long term, the four isolates stably coexist in F. vesiculosus leachate, albeit with some strains at low abundance. We therefore explored the potential for nonredundant substrate use by genomic content analysis and RNA expression patterns. This analysis revealed that the four isolates mainly differed in peripheral metabolic pathways, such as the ability to degrade pyrimidine, leucine, and tyrosine, as well as aromatic substrates. These results highlight the importance of fine-scale differences in metabolic strategies for supporting the frequently observed coexistence of large numbers of rare organisms in natural microbiomes.
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Affiliation(s)
- Xiaoqian Annie Yu
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
- Division of Microbial Ecology, Department of Microbiology and Ecosystems Science, Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna 1030, Austria
| | - Craig McLean
- Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, United States
- MIT/WHOI Joint Program in Oceanography/Applied Ocean Science and Engineering, Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, United States
| | - Jan-Hendrik Hehemann
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
| | - David Angeles-Albores
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
| | - Fuqing Wu
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
| | - Artur Muszyński
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, United States
| | - Christopher H Corzett
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
| | - Parastoo Azadi
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, United States
| | - Elizabeth B Kujawinski
- Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, United States
| | - Eric J Alm
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, United States
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
| | - Martin F Polz
- Division of Microbial Ecology, Department of Microbiology and Ecosystems Science, Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna 1030, Austria
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
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26
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Pan Z, Li DD, Li P, Geng Y, Jiang Y, Liu Y, Li YZ, Zhang Z. GDPF: a data resource for the distribution of prokaryotic protein families across the global biosphere. Nucleic Acids Res 2024; 52:D724-D731. [PMID: 37823598 PMCID: PMC10767866 DOI: 10.1093/nar/gkad869] [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: 08/11/2023] [Revised: 09/19/2023] [Accepted: 09/26/2023] [Indexed: 10/13/2023] Open
Abstract
Microorganisms encode most of the functions of life on Earth. However, conventional research has primarily focused on specific environments such as humans, soil and oceans, leaving the distribution of functional families throughout the global biosphere poorly comprehended. Here, we present the database of the global distribution of prokaryotic protein families (GDPF, http://bioinfo.qd.sdu.edu.cn/GDPF/), a data resource on the distribution of functional families across the global biosphere. GDPF provides global distribution information for 36 334 protein families, 19 734 superfamilies and 12 089 KEGG (Kyoto Encyclopedia of Genes and Genomes) orthologs from multiple source databases, covering typical environments such as soil, oceans, animals, plants and sediments. Users can browse, search and download the distribution data of each entry in 10 000 global microbial communities, as well as conduct comparative analysis of distribution disparities among multiple entries across various environments. The GDPF data resource contributes to uncovering the geographical distribution patterns, key influencing factors and macroecological principles of microbial functions at a global level, thereby promoting research in Earth ecology and human health.
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Affiliation(s)
- Zhuo Pan
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao 266237, China
| | - Dan-dan Li
- Institute of Marine Science and Technology, Shandong University, Qingdao 266237, China
| | - Peng Li
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao 266237, China
| | - Yu Geng
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao 266237, China
| | - Yiru Jiang
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao 266237, China
| | - Ya Liu
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao 266237, China
- Suzhou Research Institute, Shandong University, Suzhou 215123, China
| | - Yue-zhong Li
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao 266237, China
| | - Zheng Zhang
- State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, Qingdao 266237, China
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27
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Schmidt TSB, Fullam A, Ferretti P, Orakov A, Maistrenko OM, Ruscheweyh HJ, Letunic I, Duan Y, Van Rossum T, Sunagawa S, Mende DR, Finn RD, Kuhn M, Pedro Coelho L, Bork P. SPIRE: a Searchable, Planetary-scale mIcrobiome REsource. Nucleic Acids Res 2024; 52:D777-D783. [PMID: 37897342 PMCID: PMC10767986 DOI: 10.1093/nar/gkad943] [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: 08/18/2023] [Revised: 10/01/2023] [Accepted: 10/11/2023] [Indexed: 10/30/2023] Open
Abstract
Meta'omic data on microbial diversity and function accrue exponentially in public repositories, but derived information is often siloed according to data type, study or sampled microbial environment. Here we present SPIRE, a Searchable Planetary-scale mIcrobiome REsource that integrates various consistently processed metagenome-derived microbial data modalities across habitats, geography and phylogeny. SPIRE encompasses 99 146 metagenomic samples from 739 studies covering a wide array of microbial environments and augmented with manually-curated contextual data. Across a total metagenomic assembly of 16 Tbp, SPIRE comprises 35 billion predicted protein sequences and 1.16 million newly constructed metagenome-assembled genomes (MAGs) of medium or high quality. Beyond mapping to the high-quality genome reference provided by proGenomes3 (http://progenomes.embl.de), these novel MAGs form 92 134 novel species-level clusters, the majority of which are unclassified at species level using current tools. SPIRE enables taxonomic profiling of these species clusters via an updated, custom mOTUs database (https://motu-tool.org/) and includes several layers of functional annotation, as well as crosslinks to several (micro-)biological databases. The resource is accessible, searchable and browsable via http://spire.embl.de.
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Affiliation(s)
- Thomas S B Schmidt
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Anthony Fullam
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Pamela Ferretti
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Askarbek Orakov
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Oleksandr M Maistrenko
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Hans-Joachim Ruscheweyh
- Institute of Microbiology, Department of Biology and Swiss Institute of Bioinformatics, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland
| | - Ivica Letunic
- Biobyte solutions GmbH, Bothestr. 142, 69117 Heidelberg, Germany
| | - Yiqian Duan
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Thea Van Rossum
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Shinichi Sunagawa
- Institute of Microbiology, Department of Biology and Swiss Institute of Bioinformatics, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland
| | - Daniel R Mende
- Department of Medical Microbiology, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Robert D Finn
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Michael Kuhn
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Luis Pedro Coelho
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology, Translational Research Institute, Woolloongabba, Queensland, Australia
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Department of Bioinformatics, Biozentrum, University of Würzburg, 97074 Würzburg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
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28
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Burz SD, Causevic S, Dal Co A, Dmitrijeva M, Engel P, Garrido-Sanz D, Greub G, Hapfelmeier S, Hardt WD, Hatzimanikatis V, Heiman CM, Herzog MKM, Hockenberry A, Keel C, Keppler A, Lee SJ, Luneau J, Malfertheiner L, Mitri S, Ngyuen B, Oftadeh O, Pacheco AR, Peaudecerf F, Resch G, Ruscheweyh HJ, Sahin A, Sanders IR, Slack E, Sunagawa S, Tackmann J, Tecon R, Ugolini GS, Vacheron J, van der Meer JR, Vayena E, Vonaesch P, Vorholt JA. From microbiome composition to functional engineering, one step at a time. Microbiol Mol Biol Rev 2023; 87:e0006323. [PMID: 37947420 PMCID: PMC10732080 DOI: 10.1128/mmbr.00063-23] [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] [Indexed: 11/12/2023] Open
Abstract
SUMMARYCommunities of microorganisms (microbiota) are present in all habitats on Earth and are relevant for agriculture, health, and climate. Deciphering the mechanisms that determine microbiota dynamics and functioning within the context of their respective environments or hosts (the microbiomes) is crucially important. However, the sheer taxonomic, metabolic, functional, and spatial complexity of most microbiomes poses substantial challenges to advancing our knowledge of these mechanisms. While nucleic acid sequencing technologies can chart microbiota composition with high precision, we mostly lack information about the functional roles and interactions of each strain present in a given microbiome. This limits our ability to predict microbiome function in natural habitats and, in the case of dysfunction or dysbiosis, to redirect microbiomes onto stable paths. Here, we will discuss a systematic approach (dubbed the N+1/N-1 concept) to enable step-by-step dissection of microbiome assembly and functioning, as well as intervention procedures to introduce or eliminate one particular microbial strain at a time. The N+1/N-1 concept is informed by natural invasion events and selects culturable, genetically accessible microbes with well-annotated genomes to chart their proliferation or decline within defined synthetic and/or complex natural microbiota. This approach enables harnessing classical microbiological and diversity approaches, as well as omics tools and mathematical modeling to decipher the mechanisms underlying N+1/N-1 microbiota outcomes. Application of this concept further provides stepping stones and benchmarks for microbiome structure and function analyses and more complex microbiome intervention strategies.
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Affiliation(s)
- Sebastian Dan Burz
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Senka Causevic
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Alma Dal Co
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Marija Dmitrijeva
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Philipp Engel
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Daniel Garrido-Sanz
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Gilbert Greub
- Institut de microbiologie, CHUV University Hospital Lausanne, Lausanne, Switzerland
| | | | | | | | - Clara Margot Heiman
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | | | - Christoph Keel
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | - Soon-Jae Lee
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
| | - Julien Luneau
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Lukas Malfertheiner
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Sara Mitri
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Bidong Ngyuen
- Institute of Microbiology, ETH Zürich, Zürich, Switzerland
| | - Omid Oftadeh
- Laboratory of Computational Systems Biotechnology, EPF Lausanne, Lausanne, Switzerland
| | | | | | - Grégory Resch
- Center for Research and Innovation in Clinical Pharmaceutical Sciences, CHUV University Hospital Lausanne, Lausanne, Switzerland
| | | | - Asli Sahin
- Laboratory of Computational Systems Biotechnology, EPF Lausanne, Lausanne, Switzerland
| | - Ian R. Sanders
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
| | - Emma Slack
- Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland
| | | | - Janko Tackmann
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Robin Tecon
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | - Jordan Vacheron
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | | | - Evangelia Vayena
- Laboratory of Computational Systems Biotechnology, EPF Lausanne, Lausanne, Switzerland
| | - Pascale Vonaesch
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
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29
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Minot SS, Mayer-Blackwell K, Fiore-Gartland A, Johnson A, Self S, Bhatti P, Yao L, Liu L, Sun X, Jinfa Y, Kublin J. Strain-level characterization of health-associated bacterial consortia that colonize the human gut during infancy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.16.23300077. [PMID: 38168439 PMCID: PMC10760300 DOI: 10.1101/2023.12.16.23300077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Background The human gut microbiome develops rapidly during infancy, a key window of development coinciding with maturation of the adaptive immune system. However, little is known of the microbiome growth dynamics over the first few months of life and whether there are any generalizable patterns across human populations. We performed metagenomic sequencing on stool samples (n=94) from a cohort of infants (n=15) at monthly intervals in the first six months of life, augmenting our dataset with seven published studies for a total of 4,441 metagenomes from 1,162 infants. Results Strain-level de novo analysis was used to identify 592 of the most abundant organisms in the infant gut microbiome. Previously unrecognized consortia were identified which exhibited highly correlated abundances across samples and were composed of diverse species spanning multiple genera. Analysis of a cohort of infants with cystic fibrosis identified one such novel consortium of diverse Enterobacterales which was positively correlated with weight gain. While all studies showed an increased community stability during the first year of life, microbial dynamics varied widely in the first few months of life, both by study and by individual. Conclusion By augmenting published metagenomic datasets with data from a newly established cohort we were able to identify novel groups of organisms that are correlated with measures of robust human development. We hypothesize that the presence of these groups may impact human health in aggregate in ways that individual species may not in isolation.
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Affiliation(s)
| | | | - Andrew Fiore-Gartland
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, USA
| | - Andrew Johnson
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, USA
| | - Steven Self
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, USA
| | - Parveen Bhatti
- Cancer Control Research, BC Cancer Research Institute, Vancouver, BC, Canada
- Epidemiology Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, USA
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Lena Yao
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, USA
| | - Lili Liu
- Key Laboratory of Occupational Disease Prevention and Treatment, Guangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, China
| | - Xin Sun
- National Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yi Jinfa
- Nanhai Maternity and Child Healthcare Hospital of Foshan, Foshan, China
| | - James Kublin
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, USA
- HIV Vaccine Trials Network, Fred Hutchinson Cancer Center, Seattle, USA
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30
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Piton G, Allison SD, Bahram M, Hildebrand F, Martiny JBH, Treseder KK, Martiny AC. Life history strategies of soil bacterial communities across global terrestrial biomes. Nat Microbiol 2023; 8:2093-2102. [PMID: 37798477 DOI: 10.1038/s41564-023-01465-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 08/08/2023] [Indexed: 10/07/2023]
Abstract
The life history strategies of soil microbes determine their metabolic potential and their response to environmental changes. Yet these strategies remain poorly understood. Here we use shotgun metagenomes from terrestrial biomes to characterize overarching covariations of the genomic traits that capture dominant life history strategies in bacterial communities. The emerging patterns show a triangle of life history strategies shaped by two trait dimensions, supporting previous theoretical and isolate-based studies. The first dimension ranges from streamlined genomes with simple metabolisms to larger genomes and expanded metabolic capacities. As metabolic capacities expand, bacterial communities increasingly differentiate along a second dimension that reflects a trade-off between increasing capacities for environmental responsiveness or for nutrient recycling. Random forest analyses show that soil pH, C:N ratio and precipitation patterns together drive the dominant life history strategy of soil bacterial communities and their biogeographic distribution. Our findings provide a trait-based framework to compare life history strategies of soil bacteria.
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Affiliation(s)
- Gabin Piton
- Department of Earth System Science, University of California, Irvine, Irvine, CA, USA.
- Eco&Sols, University Montpellier, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France.
| | - Steven D Allison
- Department of Earth System Science, University of California, Irvine, Irvine, CA, USA
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA, USA
| | - Mohammad Bahram
- Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden
- Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia
| | - Falk Hildebrand
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich Research Park, Norwich, Norfolk, UK
- Digital Biology, Earlham Institute, Norwich Research Park, Norwich, Norfolk, UK
| | - Jennifer B H Martiny
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA, USA
| | - Kathleen K Treseder
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA, USA
| | - Adam C Martiny
- Department of Earth System Science, University of California, Irvine, Irvine, CA, USA
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA, USA
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31
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Manzano-Morales S, Liu Y, González-Bodí S, Huerta-Cepas J, Iranzo J. Comparison of gene clustering criteria reveals intrinsic uncertainty in pangenome analyses. Genome Biol 2023; 24:250. [PMID: 37904249 PMCID: PMC10614367 DOI: 10.1186/s13059-023-03089-3] [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: 12/19/2022] [Accepted: 10/16/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND A key step for comparative genomics is to group open reading frames into functionally and evolutionarily meaningful gene clusters. Gene clustering is complicated by intraspecific duplications and horizontal gene transfers that are frequent in prokaryotes. In consequence, gene clustering methods must deal with a trade-off between identifying vertically transmitted representatives of multicopy gene families, which are recognizable by synteny conservation, and retrieving complete sets of species-level orthologs. We studied the implications of adopting homology, orthology, or synteny conservation as formal criteria for gene clustering by performing comparative analyses of 125 prokaryotic pangenomes. RESULTS Clustering criteria affect pangenome functional characterization, core genome inference, and reconstruction of ancestral gene content to different extents. Species-wise estimates of pangenome and core genome sizes change by the same factor when using different clustering criteria, allowing robust cross-species comparisons regardless of the clustering criterion. However, cross-species comparisons of genome plasticity and functional profiles are substantially affected by inconsistencies among clustering criteria. Such inconsistencies are driven not only by mobile genetic elements, but also by genes involved in defense, secondary metabolism, and other accessory functions. In some pangenome features, the variability attributed to methodological inconsistencies can even exceed the effect sizes of ecological and phylogenetic variables. CONCLUSIONS Choosing an appropriate criterion for gene clustering is critical to conduct unbiased pangenome analyses. We provide practical guidelines to choose the right method depending on the research goals and the quality of genome assemblies, and a benchmarking dataset to assess the robustness and reproducibility of future comparative studies.
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Affiliation(s)
- Saioa Manzano-Morales
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
- Barcelona Supercomputing Centre (BSC-CNS) - Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Yang Liu
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
- Guangdong Province Key Laboratory of Microbial Signals and Disease Control, Integrative Microbiology Research Centre, South China Agricultural University, Guangzhou, China
| | - Sara González-Bodí
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain.
| | - Jaime Iranzo
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain.
- Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza, Spain.
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Tierney BT, Kim J, Overbey EG, Ryon KA, Foox J, Sierra M, Bhattacharya C, Damle N, Najjar D, Park J, Garcia Medina S, Houerbi N, Meydan C, Wain Hershberg J, Qiu J, Kleinman A, Al Ghalith G, MacKay M, Afshin EE, Dhir R, Borg J, Gatt C, Brereton N, Readhead B, Beyaz S, Venkateswaran KJ, Blease K, Moreno J, Boddicker A, Zhao J, Lajoie B, Scott RT, Altomare A, Kruglyak S, Levy S, Church G, Mason CE. Viral activation and ecological restructuring characterize a microbiome axis of spaceflight-associated immune activation. RESEARCH SQUARE 2023:rs.3.rs-2493867. [PMID: 37886447 PMCID: PMC10602132 DOI: 10.21203/rs.3.rs-2493867/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Maintenance of astronaut health during spaceflight will require monitoring and potentially modulating their microbiomes, which play a role in some space-derived health disorders. However, documenting the response of microbiota to spaceflight has been difficult thus far due to mission constraints that lead to limited sampling. Here, we executed a six-month longitudinal study centered on a three-day flight to quantify the high-resolution microbiome response to spaceflight. Via paired metagenomics and metatranscriptomics alongside single immune profiling, we resolved a microbiome "architecture" of spaceflight characterized by time-dependent and taxonomically divergent microbiome alterations across 750 samples and ten body sites. We observed pan-phyletic viral activation and signs of persistent changes that, in the oral microbiome, yielded plaque-associated pathobionts with strong associations to immune cell gene expression. Further, we found enrichments of microbial genes associated with antibiotic production, toxin-antitoxin systems, and stress response enriched universally across the body sites. We also used strain-level tracking to measure the potential propagation of microbial species from the crew members to each other and the environment, identifying microbes that were prone to seed the capsule surface and move between the crew. Finally, we identified associations between microbiome and host immune cell shifts, proposing both a microbiome axis of immune changes during flight as well as the sources of some of those changes. In summary, these datasets and methods reveal connections between crew immunology, the microbiome, and their likely drivers and lay the groundwork for future microbiome studies of spaceflight.
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Affiliation(s)
- Braden T. Tierney
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - JangKeun Kim
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Eliah G. Overbey
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Krista A. Ryon
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Jonathan Foox
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Maria Sierra
- Tri-Institutional Biology and Medicine program, Weill Cornell Medicine, New York, NY, USA
| | - Chandrima Bhattacharya
- Tri-Institutional Biology and Medicine program, Weill Cornell Medicine, New York, NY, USA
| | - Namita Damle
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Deena Najjar
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Jiwoon Park
- Tri-Institutional Biology and Medicine program, Weill Cornell Medicine, New York, NY, USA
| | | | - Nadia Houerbi
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Cem Meydan
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Jeremy Wain Hershberg
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Jake Qiu
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Ashley Kleinman
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | | | - Matthew MacKay
- Tri-Institutional Biology and Medicine program, Weill Cornell Medicine, New York, NY, USA
| | - Evan E Afshin
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Raja Dhir
- Seed Health, Inc, Venice, CA, USA
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Joseph Borg
- Department of Applied Biomedical Science, Faculty of Health Sciences, University of Malta, Msida, MSD2090, Malta
| | - Christine Gatt
- Department of Applied Biomedical Science, Faculty of Health Sciences, University of Malta, Msida, MSD2090, Malta
| | - Nicholas Brereton
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Ben Readhead
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, USA
| | - Semir Beyaz
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | | | | | | | | | | | - Ryan T. Scott
- KBR; Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, USA
| | | | | | | | - George Church
- Harvard Medical School and the Wyss Institute, Boston, MA, USA
| | - Christopher E. Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA
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Pavlopoulos GA, Baltoumas FA, Liu S, Selvitopi O, Camargo AP, Nayfach S, Azad A, Roux S, Call L, Ivanova NN, Chen IM, Paez-Espino D, Karatzas E, Iliopoulos I, Konstantinidis K, Tiedje JM, Pett-Ridge J, Baker D, Visel A, Ouzounis CA, Ovchinnikov S, Buluç A, Kyrpides NC. Unraveling the functional dark matter through global metagenomics. Nature 2023; 622:594-602. [PMID: 37821698 PMCID: PMC10584684 DOI: 10.1038/s41586-023-06583-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 08/30/2023] [Indexed: 10/13/2023]
Abstract
Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities1,2. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyse 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database3. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical and gene neighbourhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter.
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Affiliation(s)
- Georgios A Pavlopoulos
- Institute for Fundamental Biomedical Research, Biomedical Science Research Center Alexander Fleming, Vari, Greece.
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
- Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece.
| | - Fotis A Baltoumas
- Institute for Fundamental Biomedical Research, Biomedical Science Research Center Alexander Fleming, Vari, Greece
| | - Sirui Liu
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA, USA
| | - Oguz Selvitopi
- Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Antonio Pedro Camargo
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Stephen Nayfach
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Ariful Azad
- Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, USA
| | - Simon Roux
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Lee Call
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Natalia N Ivanova
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - I Min Chen
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - David Paez-Espino
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, Biomedical Science Research Center Alexander Fleming, Vari, Greece
| | - Ioannis Iliopoulos
- Department of Basic Sciences, School of Medicine, University of Crete, Heraklion, Greece
| | | | - James M Tiedje
- Center for Microbial Ecology, Michigan State University, East Lansing, MI, USA
| | - Jennifer Pett-Ridge
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Axel Visel
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Christos A Ouzounis
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Biological Computation & Process Laboratory, Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thessalonica, Greece
- Biological Computation & Computational Biology Group, Artificial Intelligence & Information Analysis Lab, School of Informatics, Aristotle University of Thessalonica, Thessalonica, Greece
| | - Sergey Ovchinnikov
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA, USA
| | - Aydin Buluç
- Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Nikos C Kyrpides
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
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Arikawa K, Hosokawa M. Uncultured prokaryotic genomes in the spotlight: An examination of publicly available data from metagenomics and single-cell genomics. Comput Struct Biotechnol J 2023; 21:4508-4518. [PMID: 37771751 PMCID: PMC10523443 DOI: 10.1016/j.csbj.2023.09.010] [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: 06/15/2023] [Revised: 09/10/2023] [Accepted: 09/10/2023] [Indexed: 09/30/2023] Open
Abstract
Owing to the ineffectiveness of traditional culture techniques for the vast majority of microbial species, culture-independent analyses utilizing next-generation sequencing and bioinformatics have become essential for gaining insight into microbial ecology and function. This mini-review focuses on two essential methods for obtaining genetic information from uncultured prokaryotes, metagenomics and single-cell genomics. We analyzed the registration status of uncultured prokaryotic genome data from major public databases and assessed the advantages and limitations of both the methods. Metagenomics generates a significant quantity of sequence data and multiple prokaryotic genomes using straightforward experimental procedures. However, in ecosystems with high microbial diversity, such as soil, most genes are presented as brief, disconnected contigs, and lack association of highly conserved genes and mobile genetic elements with individual species genomes. Although technically more challenging, single-cell genomics offers valuable insights into complex ecosystems by providing strain-resolved genomes, addressing issues in metagenomics. Recent technological advancements, such as long-read sequencing, machine learning algorithms, and in silico protein structure prediction, in combination with vast genomic data, have the potential to overcome the current technical challenges and facilitate a deeper understanding of uncultured microbial ecosystems and microbial dark matter genes and proteins. In light of this, it is imperative that continued innovation in both methods and technologies take place to create high-quality reference genome databases that will support future microbial research and industrial applications.
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Affiliation(s)
- Koji Arikawa
- Department of Life Science and Medical Bioscience, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
- bitBiome, Inc., 513 Wasedatsurumaki-cho, Shinjuku-ku, Tokyo 162-0041, Japan
| | - Masahito Hosokawa
- Department of Life Science and Medical Bioscience, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
- bitBiome, Inc., 513 Wasedatsurumaki-cho, Shinjuku-ku, Tokyo 162-0041, Japan
- Research Organization for Nano and Life Innovation, Waseda University, 513 Wasedatsurumaki-cho, Shinjuku-ku, Tokyo 162-0041, Japan
- Institute for Advanced Research of Biosystem Dynamics, Waseda Research Institute for Science and Engineering, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
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35
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Santos-Júnior CD, Der Torossian Torres M, Duan Y, del Río ÁR, Schmidt TS, Chong H, Fullam A, Kuhn M, Zhu C, Houseman A, Somborski J, Vines A, Zhao XM, Bork P, Huerta-Cepas J, de la Fuente-Nunez C, Coelho LP. Computational exploration of the global microbiome for antibiotic discovery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.31.555663. [PMID: 37693522 PMCID: PMC10491242 DOI: 10.1101/2023.08.31.555663] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine learning-based approach to predict prokaryotic antimicrobial peptides (AMPs) by leveraging a vast dataset of 63,410 metagenomes and 87,920 microbial genomes. This led to the creation of AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, the majority of which were previously unknown. We observed that AMP production varies by habitat, with animal-associated samples displaying the highest proportion of AMPs compared to other habitats. Furthermore, within different human-associated microbiota, strain-level differences were evident. To validate our predictions, we synthesized and experimentally tested 50 AMPs, demonstrating their efficacy against clinically relevant drug-resistant pathogens both in vitro and in vivo. These AMPs exhibited antibacterial activity by targeting the bacterial membrane. Additionally, AMPSphere provides valuable insights into the evolutionary origins of peptides. In conclusion, our approach identified AMP sequences within prokaryotic microbiomes, opening up new avenues for the discovery of antibiotics.
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Affiliation(s)
- Célio Dias Santos-Júnior
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
| | - Marcelo Der Torossian Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania; Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania; Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania; Philadelphia, Pennsylvania, United States of America
| | - Yiqian Duan
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
| | - Álvaro Rodríguez del Río
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Campus de Montegancedo-UPM, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Thomas S.B. Schmidt
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Hui Chong
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
| | - Anthony Fullam
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Michael Kuhn
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Chengkai Zhu
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
| | - Amy Houseman
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
| | - Jelena Somborski
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
| | - Anna Vines
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- International Human Phenome Institute, Shanghai, China
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Campus de Montegancedo-UPM, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania; Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania; Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania; Philadelphia, Pennsylvania, United States of America
| | - Luis Pedro Coelho
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
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Han Y, Zhang C, Zhao Z, Peng Y, Liao J, Jiang Q, Liu Q, Shao Z, Dong X. A comprehensive genomic catalog from global cold seeps. Sci Data 2023; 10:596. [PMID: 37684262 PMCID: PMC10491686 DOI: 10.1038/s41597-023-02521-4] [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: 04/12/2023] [Accepted: 08/30/2023] [Indexed: 09/10/2023] Open
Abstract
Cold seeps harbor abundant and diverse microbes with tremendous potential for biological applications and that have a significant influence on biogeochemical cycles. Although recent metagenomic studies have expanded our understanding of the community and function of seep microorganisms, knowledge of the diversity and genetic repertoire of global seep microbes is lacking. Here, we collected a compilation of 165 metagenomic datasets from 16 cold seep sites across the globe to construct a comprehensive gene and genome catalog. The non-redundant gene catalog comprised 147 million genes, and 36% of them could not be assigned to a function with the currently available databases. A total of 3,164 species-level representative metagenome-assembled genomes (MAGs) were obtained, most of which (94%) belonged to novel species. Of them, 81 ANME species were identified that cover all subclades except ANME-2d, and 23 syntrophic SRB species spanned the Seep-SRB1a, Seep-SRB1g, and Seep-SRB2 clades. The non-redundant gene and MAG catalog is a valuable resource that will aid in deepening our understanding of the functions of cold seep microbiomes.
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Affiliation(s)
- Yingchun Han
- Key Laboratory of Marine Genetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China
| | - Chuwen Zhang
- Key Laboratory of Marine Genetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China
| | - Zhuoming Zhao
- Key Laboratory of Marine Genetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China
| | - Yongyi Peng
- Key Laboratory of Marine Genetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China
- School of Marine Sciences, Sun Yat-Sen University, Zhuhai, 519082, China
| | - Jing Liao
- Key Laboratory of Marine Genetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China
| | - Qiuyun Jiang
- Key Laboratory of Marine Genetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China
| | - Qing Liu
- School of Marine Sciences, Sun Yat-Sen University, Zhuhai, 519082, China
| | - Zongze Shao
- Key Laboratory of Marine Genetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China
| | - Xiyang Dong
- Key Laboratory of Marine Genetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China.
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China.
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37
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Bengtsson-Palme J, Abramova A, Berendonk TU, Coelho LP, Forslund SK, Gschwind R, Heikinheimo A, Jarquín-Díaz VH, Khan AA, Klümper U, Löber U, Nekoro M, Osińska AD, Ugarcina Perovic S, Pitkänen T, Rødland EK, Ruppé E, Wasteson Y, Wester AL, Zahra R. Towards monitoring of antimicrobial resistance in the environment: For what reasons, how to implement it, and what are the data needs? ENVIRONMENT INTERNATIONAL 2023; 178:108089. [PMID: 37441817 DOI: 10.1016/j.envint.2023.108089] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 07/03/2023] [Accepted: 07/05/2023] [Indexed: 07/15/2023]
Abstract
Antimicrobial resistance (AMR) is a global threat to human and animal health and well-being. To understand AMR dynamics, it is important to monitor resistant bacteria and resistance genes in all relevant settings. However, while monitoring of AMR has been implemented in clinical and veterinary settings, comprehensive monitoring of AMR in the environment is almost completely lacking. Yet, the environmental dimension of AMR is critical for understanding the dissemination routes and selection of resistant microorganisms, as well as the human health risks related to environmental AMR. Here, we outline important knowledge gaps that impede implementation of environmental AMR monitoring. These include lack of knowledge of the 'normal' background levels of environmental AMR, definition of high-risk environments for transmission, and a poor understanding of the concentrations of antibiotics and other chemical agents that promote resistance selection. Furthermore, there is a lack of methods to detect resistance genes that are not already circulating among pathogens. We conclude that these knowledge gaps need to be addressed before routine monitoring for AMR in the environment can be implemented on a large scale. Yet, AMR monitoring data bridging different sectors is needed in order to fill these knowledge gaps, which means that some level of national, regional and global AMR surveillance in the environment must happen even without all scientific questions answered. With the possibilities opened up by rapidly advancing technologies, it is time to fill these knowledge gaps. Doing so will allow for specific actions against environmental AMR development and spread to pathogens and thereby safeguard the health and wellbeing of humans and animals.
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Affiliation(s)
- Johan Bengtsson-Palme
- Division of Systems and Synthetic Biology, Department of Life Sciences, SciLifeLab, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; Department of Infectious Diseases, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Guldhedsgatan 10, SE-413 46 Gothenburg, Sweden; Centre for Antibiotic Resistance Research (CARe) in Gothenburg, Sweden.
| | - Anna Abramova
- Division of Systems and Synthetic Biology, Department of Life Sciences, SciLifeLab, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; Department of Infectious Diseases, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Guldhedsgatan 10, SE-413 46 Gothenburg, Sweden; Centre for Antibiotic Resistance Research (CARe) in Gothenburg, Sweden
| | - Thomas U Berendonk
- Institute of Hydrobiology, Technische Universität Dresden, Zellescher Weg 40, 01217 Dresden, Germany
| | - Luis Pedro Coelho
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Sofia K Forslund
- Experimental and Clinical Research Center, a cooperation between the Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association and the Charité - Universitätsmedizin Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Experimental and Clinical Research Center, Lindenberger Weg 80, 13125 Berlin, Germany; Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany; DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany; Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Rémi Gschwind
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, IAME F-75018 Paris, France
| | - Annamari Heikinheimo
- University of Helsinki, Faculty of Veterinary Medicine, Department of Food Hygiene and Environmental Health, P.O.Box 66, FI-00014, Finland; Finnish Food Authority, P.O.Box 100, 00027 Seinäjoki, Finland
| | - Víctor Hugo Jarquín-Díaz
- Experimental and Clinical Research Center, a cooperation between the Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association and the Charité - Universitätsmedizin Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Experimental and Clinical Research Center, Lindenberger Weg 80, 13125 Berlin, Germany; Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Ayaz Ali Khan
- Department of Microbiology, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan; Department of Biotechnology, University of Malakand, Chakdara, Dir (Lower), Khyber Pakhtunkhwa, Pakistan
| | - Uli Klümper
- Institute of Hydrobiology, Technische Universität Dresden, Zellescher Weg 40, 01217 Dresden, Germany
| | - Ulrike Löber
- Experimental and Clinical Research Center, a cooperation between the Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association and the Charité - Universitätsmedizin Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Experimental and Clinical Research Center, Lindenberger Weg 80, 13125 Berlin, Germany; Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Marmar Nekoro
- Swedish Knowledge Centre on Pharmaceuticals in the Environment, Swedish Medical Products Agency, P.O Box 26, 751 03 Uppsala, Sweden
| | - Adriana D Osińska
- Norwegian University of Life Sciences, Faculty of Veterinary Medicine, Department of Paraclinical Sciences, P.O.Box 5003 NMBU, N-1432 Ås, Norway
| | - Svetlana Ugarcina Perovic
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Tarja Pitkänen
- University of Helsinki, Faculty of Veterinary Medicine, Department of Food Hygiene and Environmental Health, P.O.Box 66, FI-00014, Finland; Finnish Institute for Health and Welfare, Expert Microbiology Unit, P.O.Box 95, FI-70701 Kuopio, Finland
| | | | - Etienne Ruppé
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, IAME F-75018 Paris, France
| | - Yngvild Wasteson
- Norwegian University of Life Sciences, Faculty of Veterinary Medicine, Department of Paraclinical Sciences, P.O.Box 5003 NMBU, N-1432 Ås, Norway
| | | | - Rabaab Zahra
- Department of Microbiology, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan
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Pan S, Zhao XM, Coelho LP. SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing. Bioinformatics 2023; 39:i21-i29. [PMID: 37387171 PMCID: PMC10311329 DOI: 10.1093/bioinformatics/btad209] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION Metagenomic binning methods to reconstruct metagenome-assembled genomes (MAGs) from environmental samples have been widely used in large-scale metagenomic studies. The recently proposed semi-supervised binning method, SemiBin, achieved state-of-the-art binning results in several environments. However, this required annotating contigs, a computationally costly and potentially biased process. RESULTS We propose SemiBin2, which uses self-supervised learning to learn feature embeddings from the contigs. In simulated and real datasets, we show that self-supervised learning achieves better results than the semi-supervised learning used in SemiBin1 and that SemiBin2 outperforms other state-of-the-art binners. Compared to SemiBin1, SemiBin2 can reconstruct 8.3-21.5% more high-quality bins and requires only 25% of the running time and 11% of peak memory usage in real short-read sequencing samples. To extend SemiBin2 to long-read data, we also propose ensemble-based DBSCAN clustering algorithm, resulting in 13.1-26.3% more high-quality genomes than the second best binner for long-read data. AVAILABILITY AND IMPLEMENTATION SemiBin2 is available as open source software at https://github.com/BigDataBiology/SemiBin/ and the analysis scripts used in the study can be found at https://github.com/BigDataBiology/SemiBin2_benchmark.
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Affiliation(s)
- Shaojun Pan
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Luis Pedro Coelho
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China
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Wei B, Hu GA, Zhou ZY, Yu WC, Du AQ, Yang CL, Yu YL, Chen JW, Zhang HW, Wu Q, Xuan Q, Xu XW, Wang H. Global analysis of the biosynthetic chemical space of marine prokaryotes. MICROBIOME 2023; 11:144. [PMID: 37370187 DOI: 10.1186/s40168-023-01573-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 05/15/2023] [Indexed: 06/29/2023]
Abstract
BACKGROUND Marine prokaryotes are a rich source of novel bioactive secondary metabolites for drug discovery. Recent genome mining studies have revealed their great potential to bio-synthesize novel secondary metabolites. However, the exact biosynthetic chemical space encoded by the marine prokaryotes has yet to be systematically evaluated. RESULTS We first investigated the secondary metabolic potential of marine prokaryotes by analyzing the diversity and novelty of the biosynthetic gene clusters (BGCs) in 7541 prokaryotic genomes from cultivated and single cells, along with 26,363 newly assembled medium-to-high-quality genomes from marine environmental samples. To quantitatively evaluate the unexplored biosynthetic chemical space of marine prokaryotes, the clustering thresholds for constructing the biosynthetic gene cluster and molecular networks were optimized to reach a similar level of the chemical similarity between the gene cluster family (GCF)-encoded metabolites and molecular family (MF) scaffolds using the MIBiG database. The global genome mining analysis demonstrated that the predicted 70,011 BGCs were organized into 24,536 mostly new (99.5%) GCFs, while the reported marine prokaryotic natural products were only classified into 778 MFs at the optimized clustering thresholds. The number of MF scaffolds is only 3.2% of the number of GCF-encoded scaffolds, suggesting that at least 96.8% of the secondary metabolic potential in marine prokaryotes is untapped. The unexplored biosynthetic chemical space of marine prokaryotes was illustrated by the 88 potential novel antimicrobial peptides encoded by ribosomally synthesized and post-translationally modified peptide BGCs. Furthermore, a sea-water-derived Aquimarina strain was selected to illustrate the diverse biosynthetic chemical space through untargeted metabolomics and genomics approaches, which identified the potential biosynthetic pathways of a group of novel polyketides and two known compounds (didemnilactone B and macrolactin A 15-ketone). CONCLUSIONS The present bioinformatics and cheminformatics analyses highlight the promising potential to explore the biosynthetic chemical diversity of marine prokaryotes and provide valuable knowledge for the targeted discovery and biosynthesis of novel marine prokaryotic natural products. Video Abstract.
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Affiliation(s)
- Bin Wei
- College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Key Laboratory of Marine Fishery Resources Exploitment & Utilization of Zhejiang Province, Zhejiang University of Technology, Hangzhou, 310014, China
- Key Laboratory of Marine Ecosystem and Biogeochemistry, Ministry of Natural Resources & Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, 310012, China
| | - Gang-Ao Hu
- College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Key Laboratory of Marine Fishery Resources Exploitment & Utilization of Zhejiang Province, Zhejiang University of Technology, Hangzhou, 310014, China
| | - Zhen-Yi Zhou
- College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Key Laboratory of Marine Fishery Resources Exploitment & Utilization of Zhejiang Province, Zhejiang University of Technology, Hangzhou, 310014, China
| | - Wen-Chao Yu
- College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Key Laboratory of Marine Fishery Resources Exploitment & Utilization of Zhejiang Province, Zhejiang University of Technology, Hangzhou, 310014, China
| | - Ao-Qi Du
- College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Key Laboratory of Marine Fishery Resources Exploitment & Utilization of Zhejiang Province, Zhejiang University of Technology, Hangzhou, 310014, China
| | - Cai-Ling Yang
- College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Key Laboratory of Marine Fishery Resources Exploitment & Utilization of Zhejiang Province, Zhejiang University of Technology, Hangzhou, 310014, China
| | - Yan-Lei Yu
- College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Key Laboratory of Marine Fishery Resources Exploitment & Utilization of Zhejiang Province, Zhejiang University of Technology, Hangzhou, 310014, China
| | - Jian-Wei Chen
- College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Key Laboratory of Marine Fishery Resources Exploitment & Utilization of Zhejiang Province, Zhejiang University of Technology, Hangzhou, 310014, China
| | - Hua-Wei Zhang
- College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Key Laboratory of Marine Fishery Resources Exploitment & Utilization of Zhejiang Province, Zhejiang University of Technology, Hangzhou, 310014, China
| | - Qihao Wu
- Department of Chemistry, Institute of Biomolecular Design & Discovery, Yale University, West Haven, CT, 06516, USA
| | - Qi Xuan
- Institute of Cyberspace Security, College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.
| | - Xue-Wei Xu
- Key Laboratory of Marine Ecosystem and Biogeochemistry, Ministry of Natural Resources & Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, 310012, China.
| | - Hong Wang
- College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Key Laboratory of Marine Fishery Resources Exploitment & Utilization of Zhejiang Province, Zhejiang University of Technology, Hangzhou, 310014, China.
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Gschwind R, Ugarcina Perovic S, Weiss M, Petitjean M, Lao J, Coelho LP, Ruppé E. ResFinderFG v2.0: a database of antibiotic resistance genes obtained by functional metagenomics. Nucleic Acids Res 2023:7173762. [PMID: 37207327 DOI: 10.1093/nar/gkad384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 04/27/2023] [Accepted: 05/02/2023] [Indexed: 05/21/2023] Open
Abstract
Metagenomics can be used to monitor the spread of antibiotic resistance genes (ARGs). ARGs found in databases such as ResFinder and CARD primarily originate from culturable and pathogenic bacteria, while ARGs from non-culturable and non-pathogenic bacteria remain understudied. Functional metagenomics is based on phenotypic gene selection and can identify ARGs from non-culturable bacteria with a potentially low identity shared with known ARGs. In 2016, the ResFinderFG v1.0 database was created to collect ARGs from functional metagenomics studies. Here, we present the second version of the database, ResFinderFG v2.0, which is available on the Center of Genomic Epidemiology web server (https://cge.food.dtu.dk/services/ResFinderFG/). It comprises 3913 ARGs identified by functional metagenomics from 50 carefully curated datasets. We assessed its potential to detect ARGs in comparison to other popular databases in gut, soil and water (marine + freshwater) Global Microbial Gene Catalogues (https://gmgc.embl.de). ResFinderFG v2.0 allowed for the detection of ARGs that were not detected using other databases. These included ARGs conferring resistance to beta-lactams, cycline, phenicol, glycopeptide/cycloserine and trimethoprim/sulfonamide. Thus, ResFinderFG v2.0 can be used to identify ARGs differing from those found in conventional databases and therefore improve the description of resistomes.
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Affiliation(s)
- Rémi Gschwind
- University of Paris Cité, INSERM UMR 1137 IAME, F-75018Paris, France
| | - Svetlana Ugarcina Perovic
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai200433, China
| | - Maja Weiss
- Research Group for Genomic Epidemiology, Technical University of Denmark, Kgs, Lyngby 2800, Denmark
| | - Marie Petitjean
- University of Paris Cité, INSERM UMR 1137 IAME, F-75018Paris, France
| | - Julie Lao
- University of Paris Cité, INSERM UMR 1137 IAME, F-75018Paris, France
| | - Luis Pedro Coelho
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai200433, China
| | - Etienne Ruppé
- University of Paris Cité, INSERM UMR 1137 IAME, F-75018Paris, France
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41
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Zimmerman S, Tierney BT, Patel CJ, Kostic AD. Quantifying Shared and Unique Gene Content across 17 Microbial Ecosystems. mSystems 2023; 8:e0011823. [PMID: 37022232 PMCID: PMC10134805 DOI: 10.1128/msystems.00118-23] [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: 02/08/2023] [Accepted: 02/27/2023] [Indexed: 04/07/2023] Open
Abstract
Measuring microbial diversity is traditionally based on microbe taxonomy. Here, in contrast, we aimed to quantify heterogeneity in microbial gene content across 14,183 metagenomic samples spanning 17 ecologies, including 6 human associated, 7 nonhuman host associated, and 4 in other nonhuman host environments. In total, we identified 117,629,181 nonredundant genes. The vast majority of genes (66%) occurred in only one sample (i.e., "singletons"). In contrast, we found 1,864 sequences present in every metagenome, but not necessarily every bacterial genome. Additionally, we report data sets of other ecology-associated genes (e.g., abundant in only gut ecosystems) and simultaneously demonstrated that prior microbiome gene catalogs are both incomplete and inaccurately cluster microbial genetic life (e.g., at gene sequence identities that are too restrictive). We provide our results and the sets of environmentally differentiating genes described above at http://www.microbial-genes.bio. IMPORTANCE The amount of shared genetic elements has not been quantified between the human microbiome and other host- and non-host-associated microbiomes. Here, we made a gene catalog of 17 different microbial ecosystems and compared them. We show that most species shared between environment and human gut microbiomes are pathogens and that prior gene catalogs described as "nearly complete" are far from it. Additionally, over two-thirds of all genes only appear in a single sample, and only 1,864 genes (0.001%) are found in all types of metagenomes. These results highlight the large diversity between metagenomes and reveal a new, rare class of genes, those found in every type of metagenome, but not every microbial genome.
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Affiliation(s)
- Samuel Zimmerman
- Section on Pathophysiology and Molecular Pharmacology, Joslin Diabetes Center, Boston, Massachusetts, USA
- Section on Islet Cell and Regenerative Biology, Joslin Diabetes Center, Boston, Massachusetts, USA
- Department of Microbiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Braden T. Tierney
- Section on Pathophysiology and Molecular Pharmacology, Joslin Diabetes Center, Boston, Massachusetts, USA
- Section on Islet Cell and Regenerative Biology, Joslin Diabetes Center, Boston, Massachusetts, USA
- Department of Microbiology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Chirag J. Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Aleksandar D. Kostic
- Section on Pathophysiology and Molecular Pharmacology, Joslin Diabetes Center, Boston, Massachusetts, USA
- Section on Islet Cell and Regenerative Biology, Joslin Diabetes Center, Boston, Massachusetts, USA
- Department of Microbiology, Harvard Medical School, Boston, Massachusetts, USA
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42
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Yang P, Zhu X, Ning K. Microbiome-based enrichment pattern mining has enabled a deeper understanding of the biome-species-function relationship. Commun Biol 2023; 6:391. [PMID: 37037946 PMCID: PMC10085995 DOI: 10.1038/s42003-023-04753-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 03/24/2023] [Indexed: 04/12/2023] Open
Abstract
Microbes live in diverse habitats (i.e. biomes), yet their species and genes were biome-specific, forming enrichment patterns. These enrichment patterns have mirrored the biome-species-function relationship, which is shaped by ecological and evolutionary principles. However, a grand picture of these enrichment patterns, as well as the roles of external and internal factors in driving these enrichment patterns, remain largely unexamined. In this work, we have examined the enrichment patterns based on 1705 microbiome samples from four representative biomes (Engineered, Gut, Freshwater, and Soil). Moreover, an "enrichment sphere" model was constructed to elucidate the regulatory principles behind these patterns. The driving factors for this model were revealed based on two case studies: (1) The copper-resistance genes were enriched in Soil biomes, owing to the copper contamination and horizontal gene transfer. (2) The flagellum-related genes were enriched in the Freshwater biome, due to high fluidity and vertical gene accumulation. Furthermore, this enrichment sphere model has valuable applications, such as in biome identification for metagenome samples, and in guiding 3D structure modeling of proteins. In summary, the enrichment sphere model aims towards creating a bluebook of the biome-species-function relationships and be applied in many fields.
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Affiliation(s)
- Pengshuo Yang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
- Institute of Medical Genomics, Biomedical Sciences College, Shandong First Medical University, Shandong, 250117, China
| | - Xue Zhu
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Kang Ning
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
- Institute of Medical Genomics, Biomedical Sciences College, Shandong First Medical University, Shandong, 250117, China.
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Rodríguez-Gijón A, Buck M, Andersson AF, Izabel-Shen D, Nascimento FJA, Garcia SL. Linking prokaryotic genome size variation to metabolic potential and environment. ISME COMMUNICATIONS 2023; 3:25. [PMID: 36973336 PMCID: PMC10042847 DOI: 10.1038/s43705-023-00231-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 03/02/2023] [Accepted: 03/14/2023] [Indexed: 03/29/2023]
Abstract
While theories and models have appeared to explain genome size as a result of evolutionary processes, little work has shown that genome sizes carry ecological signatures. Our work delves into the ecological implications of microbial genome size variation in benthic and pelagic habitats across environmental gradients of the brackish Baltic Sea. While depth is significantly associated with genome size in benthic and pelagic brackish metagenomes, salinity is only correlated to genome size in benthic metagenomes. Overall, we confirm that prokaryotic genome sizes in Baltic sediments (3.47 Mbp) are significantly bigger than in the water column (2.96 Mbp). While benthic genomes have a higher number of functions than pelagic genomes, the smallest genomes coded for a higher number of module steps per Mbp for most of the functions irrespective of their environment. Some examples of this functions are amino acid metabolism and central carbohydrate metabolism. However, we observed that nitrogen metabolism was almost absent in pelagic genomes and was mostly present in benthic genomes. Finally, we also show that Bacteria inhabiting Baltic sediments and water column not only differ in taxonomy, but also in their metabolic potential, such as the Wood-Ljungdahl pathway or the presence of different hydrogenases. Our work shows how microbial genome size is linked to abiotic factors in the environment, metabolic potential and taxonomic identity of Bacteria and Archaea within aquatic ecosystems.
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Affiliation(s)
- Alejandro Rodríguez-Gijón
- Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, 106 91, Sweden.
- Science for Life Laboratory, Stockholm, Sweden.
| | - Moritz Buck
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Anders F Andersson
- Science for Life Laboratory, Stockholm, Sweden
- Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Dandan Izabel-Shen
- Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, 106 91, Sweden
| | - Francisco J A Nascimento
- Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, 106 91, Sweden
- Baltic Sea Centre, Stockholm University, Stockholm, Sweden
| | - Sarahi L Garcia
- Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, 106 91, Sweden.
- Science for Life Laboratory, Stockholm, Sweden.
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Dong X, Peng Y, Wang M, Woods L, Wu W, Wang Y, Xiao X, Li J, Jia K, Greening C, Shao Z, Hubert CRJ. Evolutionary ecology of microbial populations inhabiting deep sea sediments associated with cold seeps. Nat Commun 2023; 14:1127. [PMID: 36854684 PMCID: PMC9974965 DOI: 10.1038/s41467-023-36877-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 02/21/2023] [Indexed: 03/02/2023] Open
Abstract
Deep sea cold seep sediments host abundant and diverse microbial populations that significantly influence biogeochemical cycles. While numerous studies have revealed their community structure and functional capabilities, little is known about genetic heterogeneity within species. Here, we examine intraspecies diversity patterns of 39 abundant species identified in sediment layers down to 430 cm below the sea floor across six cold seep sites. These populations are grouped as aerobic methane-oxidizing bacteria, anaerobic methanotrophic archaea and sulfate-reducing bacteria. Different evolutionary trajectories are observed at the genomic level among these physiologically and phylogenetically diverse populations, with generally low rates of homologous recombination and strong purifying selection. Functional genes related to methane (pmoA and mcrA) and sulfate (dsrA) metabolisms are under strong purifying selection in most species investigated. These genes differ in evolutionary trajectories across phylogenetic clades but are functionally conserved across sites. Intrapopulation diversification of genomes and their mcrA and dsrA genes is depth-dependent and subject to different selection pressure throughout the sediment column redox zones at different sites. These results highlight the interplay between ecological processes and the evolution of key bacteria and archaea in deep sea cold seep extreme environments, shedding light on microbial adaptation in the subseafloor biosphere.
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Affiliation(s)
- Xiyang Dong
- Key Laboratory of Marine Genetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China.
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China.
| | - Yongyi Peng
- Key Laboratory of Marine Genetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China
- School of Marine Sciences, Sun Yat-Sen University, Zhuhai, 519082, China
| | - Muhua Wang
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China
- School of Marine Sciences, Sun Yat-Sen University, Zhuhai, 519082, China
| | - Laura Woods
- Department of Microbiology, Biomedicine Discovery Institute, Monash University, Clayton, VIC, 3800, Australia
| | - Wenxue Wu
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China
- School of Marine Sciences, Sun Yat-Sen University, Zhuhai, 519082, China
- State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570228, China
| | - Yong Wang
- Institute for Ocean Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - Xi Xiao
- Guangzhou Marine Geological Survey, China Geological Survey, Guangzhou, 510075, China
| | - Jiwei Li
- Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya, 572000, China
| | - Kuntong Jia
- School of Marine Sciences, Sun Yat-Sen University, Zhuhai, 519082, China
| | - Chris Greening
- Department of Microbiology, Biomedicine Discovery Institute, Monash University, Clayton, VIC, 3800, Australia
| | - Zongze Shao
- Key Laboratory of Marine Genetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China.
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China.
| | - Casey R J Hubert
- Department of Biological Sciences, University of Calgary, Calgary, AB, T2N 1N4, Canada
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Doane MP, Johnson CJ, Johri S, Kerr EN, Morris MM, Desantiago R, Turnlund AC, Goodman A, Mora M, Lima LFO, Nosal AP, Dinsdale EA. The Epidermal Microbiome Within an Aggregation of Leopard Sharks (Triakis semifasciata) Has Taxonomic Flexibility with Gene Functional Stability Across Three Time-points. MICROBIAL ECOLOGY 2023; 85:747-764. [PMID: 35129649 PMCID: PMC9957878 DOI: 10.1007/s00248-022-01969-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 01/17/2022] [Indexed: 05/06/2023]
Abstract
The epidermis of Chondrichthyan fishes consists of dermal denticles with production of minimal but protein-rich mucus that collectively, influence the attachment and biofilm development of microbes, facilitating a unique epidermal microbiome. Here, we use metagenomics to provide the taxonomic and functional characterization of the epidermal microbiome of the Triakis semifasciata (leopard shark) at three time-points collected across 4 years to identify links between microbial groups and host metabolism. Our aims include (1) describing the variation of microbiome taxa over time and identifying recurrent microbiome members (present across all time-points); (2) investigating the relationship between the recurrent and flexible taxa (those which are not found consistently across time-points); (3) describing the functional compositions of the microbiome which may suggest links with the host metabolism; and (4) identifying whether metabolic processes are shared across microbial genera or are unique to specific taxa. Microbial members of the microbiome showed high similarity between all individuals (Bray-Curtis similarity index = 82.7, where 0 = no overlap, 100 = total overlap) with the relative abundance of those members varying across sampling time-points, suggesting flexibility of taxa in the microbiome. One hundred and eighty-eight genera were identified as recurrent, including Pseudomonas, Erythrobacter, Alcanivorax, Marinobacter, and Sphingopxis being consistently abundant across time-points, while Limnobacter and Xyella exhibited switching patterns with high relative abundance in 2013, Sphingobium and Sphingomona in 2015, and Altermonas, Leeuwenhoekiella, Gramella, and Maribacter in 2017. Of the 188 genera identified as recurrent, the top 19 relatively abundant genera formed three recurrent groups. The microbiome also displayed high functional similarity between individuals (Bray-Curtis similarity index = 97.6) with gene function composition remaining consistent across all time-points. These results show that while the presence of microbial genera exhibits consistency across time-points, their abundances do fluctuate. Microbial functions however remain stable across time-points; thus, we suggest the leopard shark microbiomes exhibit functional redundancy. We show coexistence of microbes hosted in elasmobranch microbiomes that encode genes involved in utilizing nitrogen, but not fixing nitrogen, degrading urea, and resistant to heavy metal.
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Affiliation(s)
- Michael P. Doane
- College of Science and Engineering, Flinders University, Bedford Park, South Australia Australia
| | - Colton J. Johnson
- Department of Biology, San Diego State University, San Diego, CA USA
| | - Shaili Johri
- Hopkins Marine Station, Stanford University, Pacific Grove, CA USA
| | - Emma N. Kerr
- College of Science and Engineering, Flinders University, Bedford Park, South Australia Australia
| | | | - Ric Desantiago
- Department of Biology, San Diego State University, San Diego, CA USA
| | - Abigail C. Turnlund
- Australian Centre for Ecogenomics, University of Queensland, St Lucia, QLD Australia
| | - Asha Goodman
- Department of Biology, San Diego State University, San Diego, CA USA
| | - Maria Mora
- Department of Biology, San Diego State University, San Diego, CA USA
| | | | - Andrew P. Nosal
- Department of Environmental and Ocean Sciences, University of San Diego, San Diego, CA USA
- Scripps Institution of Oceanography, University of California – San Diego, CA La Jolla, USA
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Fullam A, Letunic I, Schmidt TSB, Ducarmon QR, Karcher N, Khedkar S, Kuhn M, Larralde M, Maistrenko OM, Malfertheiner L, Milanese A, Rodrigues JFM, Sanchis-López C, Schudoma C, Szklarczyk D, Sunagawa S, Zeller G, Huerta-Cepas J, von Mering C, Bork P, Mende DR. proGenomes3: approaching one million accurately and consistently annotated high-quality prokaryotic genomes. Nucleic Acids Res 2023; 51:D760-D766. [PMID: 36408900 PMCID: PMC9825469 DOI: 10.1093/nar/gkac1078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/15/2022] [Accepted: 11/07/2022] [Indexed: 11/22/2022] Open
Abstract
The interpretation of genomic, transcriptomic and other microbial 'omics data is highly dependent on the availability of well-annotated genomes. As the number of publicly available microbial genomes continues to increase exponentially, the need for quality control and consistent annotation is becoming critical. We present proGenomes3, a database of 907 388 high-quality genomes containing 4 billion genes that passed stringent criteria and have been consistently annotated using multiple functional and taxonomic databases including mobile genetic elements and biosynthetic gene clusters. proGenomes3 encompasses 41 171 species-level clusters, defined based on universal single copy marker genes, for which pan-genomes and contextual habitat annotations are provided. The database is available at http://progenomes.embl.de/.
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Affiliation(s)
- Anthony Fullam
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Ivica Letunic
- Biobyte solutions GmbH, Bothestr. 142, 69117 Heidelberg, Germany
| | - Thomas S B Schmidt
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Quinten R Ducarmon
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Nicolai Karcher
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Supriya Khedkar
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Michael Kuhn
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Martin Larralde
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Oleksandr M Maistrenko
- Royal Netherlands Institute for Sea Research (NIOZ), Department of Marine Microbiology & Biogeochemistry, 1797 SZ, 't Horntje (Texel), Netherlands
| | - Lukas Malfertheiner
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alessio Milanese
- Institute of Microbiology, Department of Biology and Swiss Institute of Bioinformatics, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland
| | | | - Claudia Sanchis-López
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Campus de Montegancedo-UPM, 28223, Pozuelo de Alarcón, Madrid, Spain
| | - Christian Schudoma
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Damian Szklarczyk
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Shinichi Sunagawa
- Institute of Microbiology, Department of Biology and Swiss Institute of Bioinformatics, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland
| | - Georg Zeller
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Campus de Montegancedo-UPM, 28223, Pozuelo de Alarcón, Madrid, Spain
| | - Christian von Mering
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.,Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany.,Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany.,Yonsei Frontier Lab (YFL), Yonsei University, 03722 Seoul, South Korea
| | - Daniel R Mende
- Department of Medical Microbiology, Amsterdam University Medical Centers, Amsterdam, The Netherlands
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47
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Hallin S. Environmental microbiology going computational-Predictive ecology and unpredicted discoveries. Environ Microbiol 2023; 25:111-114. [PMID: 36181387 PMCID: PMC10092848 DOI: 10.1111/1462-2920.16232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 09/28/2022] [Indexed: 01/21/2023]
Affiliation(s)
- Sara Hallin
- Swedish University of Agricultural Sciences, Department of Forest Mycology and Plant Pathology, Uppsala, Sweden
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48
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Kerr EN, Papudeshi B, Haggerty M, Wild N, Goodman AZ, Lima LFO, Hesse RD, Skye A, Mallawaarachchi V, Johri S, Parker S, Dinsdale EA. Stingray epidermal microbiomes are species-specific with local adaptations. Front Microbiol 2023; 14:1031711. [PMID: 36937279 PMCID: PMC10017458 DOI: 10.3389/fmicb.2023.1031711] [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: 08/30/2022] [Accepted: 02/07/2023] [Indexed: 03/06/2023] Open
Abstract
Marine host-associated microbiomes are affected by a combination of species-specific (e.g., host ancestry, genotype) and habitat-specific features (e.g., environmental physiochemistry and microbial biogeography). The stingray epidermis provides a gradient of characteristics from high dermal denticles coverage with low mucus to reduce dermal denticles and high levels of mucus. Here we investigate the effects of host phylogeny and habitat by comparing the epidermal microbiomes of Myliobatis californica (bat rays) with a mucus rich epidermis, and Urobatis halleri (round rays) with a mucus reduced epidermis from two locations, Los Angeles and San Diego, California (a 150 km distance). We found that host microbiomes are species-specific and distinct from the water column, however composition of M. californica microbiomes showed more variability between individuals compared to U. halleri. The variability in the microbiome of M. californica caused the microbial taxa to be similar across locations, while U. halleri microbiomes were distinct across locations. Despite taxonomic differences, Shannon diversity is the same across the two locations in U. halleri microbiomes suggesting the taxonomic composition are locally adapted, but diversity is maintained by the host. Myliobatis californica and U. halleri microbiomes maintain functional similarity across Los Angeles and San Diego and each ray showed several unique functional genes. Myliobatis californica has a greater relative abundance of RNA Polymerase III-like genes in the microbiome than U. halleri, suggesting specific adaptations to a heavy mucus environment. Construction of Metagenome Assembled Genomes (MAGs) identified novel microbial species within Rhodobacteraceae, Moraxellaceae, Caulobacteraceae, Alcanivoracaceae and Gammaproteobacteria. All MAGs had a high abundance of active RNA processing genes, heavy metal, and antibiotic resistant genes, suggesting the stingray mucus supports high microbial growth rates, which may drive high levels of competition within the microbiomes increasing the antimicrobial properties of the microbes.
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Affiliation(s)
- Emma N. Kerr
- Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Adelaide, SA, Australia
- *Correspondence: Emma N. Kerr,
| | - Bhavya Papudeshi
- Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Adelaide, SA, Australia
| | - Miranda Haggerty
- California Department of Fish and Wildlife, San Diego, CA, United States
| | - Natasha Wild
- Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Adelaide, SA, Australia
| | - Asha Z. Goodman
- Department of Biology, San Diego State University, San Diego, CA, United States
| | - Lais F. O. Lima
- Department of Biology, San Diego State University, San Diego, CA, United States
| | - Ryan D. Hesse
- Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Adelaide, SA, Australia
| | - Amber Skye
- Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Adelaide, SA, Australia
| | - Vijini Mallawaarachchi
- Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Adelaide, SA, Australia
| | - Shaili Johri
- Hopkins Maine Station, Stanford University, Stanford, CA, United States
| | - Sophia Parker
- Department of Biology, San Diego State University, San Diego, CA, United States
| | - Elizabeth A. Dinsdale
- Flinders Accelerator for Microbiome Exploration, College of Science and Engineering, Flinders University, Adelaide, SA, Australia
- Elizabeth A. Dinsdale,
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49
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Liu H, Jiang S, Ou J, Tang J, Lu Y, Wei Y. Investigation of soil microbiota reveals variable dominant species at different land areas in China. BIOTECHNOL BIOTEC EQ 2022. [DOI: 10.1080/13102818.2022.2071634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Affiliation(s)
- Hai Liu
- Criminal technology corps of Henan Provincial Public Security Bureau, Zhengzhou, Henan Province, China
| | - Shan Jiang
- State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, PR China
| | - Jintao Ou
- Key Laboratory of Advanced Drug Preparation Technologies, Ministry of Education, School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
- Laboratory of Synthetic Biology, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
| | - Jinfeng Tang
- Laboratory of Synthetic Biology, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
- Key Laboratory for Water Quality and Conservation of Pearl River Delta, Ministry of Education, School of Environmental Science and Engineering, Linköping University – Guangzhou University Research Center on Urban Sustainable Development, Guangzhou, People’s Republic of China
| | - Yang Lu
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
| | - Yongjun Wei
- Key Laboratory of Advanced Drug Preparation Technologies, Ministry of Education, School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
- Laboratory of Synthetic Biology, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
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50
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Wang Z, Yang T, Mei X, Wang N, Li X, Yang Q, Dong C, Jiang G, Lin J, Xu Y, Shen Q, Jousset A, Banerjee S. Bio-Organic Fertilizer Promotes Pear Yield by Shaping the Rhizosphere Microbiome Composition and Functions. Microbiol Spectr 2022; 10:e0357222. [PMID: 36453930 PMCID: PMC9769518 DOI: 10.1128/spectrum.03572-22] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 11/11/2022] [Indexed: 12/03/2022] Open
Abstract
Bio-organic fertilizers (BOF) containing both organic amendments and beneficial microorganisms have been consistently shown to improve soils fertility and yield. However, the exact mechanisms which link amendments and yields remain disputed, and the complexity of bio-organic fertilizers may work in parallel in several ways. BOF may directly improve yield by replenishing soil nutrients or introducing beneficial microbial genes or indirectly by altering the soil microbiome to enrich native beneficial microorganisms. In this work, we aim to disentangle the relative contributions of direct and indirect effects on pear yield. We treated pear trees with either chemical fertilizer or organic fertilizer with/without the plant-beneficial bacterium Bacillus velezensis SQR9. We then assessed, in detail, soil physicochemical and biological properties (metagenome sequencing) as well as pear yield. We then evaluated the relative importance of direct and indirect effects of soil amendments on pear yield. Both organic treatments increased plant yield by up to 20%, with the addition of bacteria tripling the increase driven by organic fertilizer alone. This increase could be linked to alterations in soil physicochemical properties, bacterial community function, and metabolism. Supplementation of organic fertilizer SQR9 increased rhizosphere microbiome richness and functional diversity. Fertilizer-sensitive microbes and functions responded as whole guilds. Pear yield was most positively associated with the Mitsuaria- and Actinoplanes-dominated ecological clusters and with gene clusters involved in ion transport and secondary metabolite biosynthesis. Together, these results suggested that bio-organic fertilizers mainly act indirectly on plant yield by creating soil chemical properties which promote a plant-beneficial microbiome. IMPORTANCE Bio-organic fertilization is a widely used, eco-friendly, sustainable approach to increasing plant productivity in the agriculture and fruit industries. However, it remains unclear whether the promotion of fruit productivity is related to specific changes in microbial inoculants, the resident microbiome, and/or the physicochemical properties of rhizosphere soils. We found that bio-organic fertilizers alter soil chemical properties, thus manipulating specific microbial taxa and functions within the rhizosphere microbiome of pear plants to promote yield. Our work unveils the ecological mechanisms which underlie the beneficial impacts of bio-organic fertilizers on yield promotion in fruit orchards, which may help in the design of more efficient biofertilizers to promote sustainable fruit production.
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Affiliation(s)
- Zhonghua Wang
- Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization, Key Laboratory of Plant immunity, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing, China
- Institute of Pomology, Jiangsu Academy of Agricultural Sciences, Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Nanjing, China
| | - Tianjie Yang
- Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization, Key Laboratory of Plant immunity, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing, China
| | - Xinlan Mei
- Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization, Key Laboratory of Plant immunity, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing, China
| | - Ningqi Wang
- Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization, Key Laboratory of Plant immunity, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing, China
| | - Xiaogang Li
- Institute of Pomology, Jiangsu Academy of Agricultural Sciences, Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Nanjing, China
| | - Qingsong Yang
- Institute of Pomology, Jiangsu Academy of Agricultural Sciences, Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Nanjing, China
| | - Caixia Dong
- Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization, Key Laboratory of Plant immunity, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing, China
| | - Gaofei Jiang
- Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization, Key Laboratory of Plant immunity, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing, China
| | - Jing Lin
- Institute of Pomology, Jiangsu Academy of Agricultural Sciences, Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Nanjing, China
| | - Yangchun Xu
- Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization, Key Laboratory of Plant immunity, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing, China
| | - Qirong Shen
- Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization, Key Laboratory of Plant immunity, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing, China
| | - Alexandre Jousset
- Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization, Key Laboratory of Plant immunity, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing, China
| | - Samiran Banerjee
- Department of Microbiological Sciences, North Dakota State University, Fargo, North Dakota, USA
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