1
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Jin DM, Morton JT, Bonneau R. Meta-analysis of the human gut microbiome uncovers shared and distinct microbial signatures between diseases. bioRxiv 2024:2024.02.27.582333. [PMID: 38464323 PMCID: PMC10925178 DOI: 10.1101/2024.02.27.582333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Microbiome studies have revealed gut microbiota's potential impact on complex diseases. However, many studies often focus on one disease per cohort. We developed a meta-analysis workflow for gut microbiome profiles and analyzed shotgun metagenomic data covering 11 diseases. Using interpretable machine learning and differential abundance analysis, our findings reinforce the generalization of binary classifiers for Crohn's disease (CD) and colorectal cancer (CRC) to hold-out cohorts and highlight the key microbes driving these classifications. We identified high microbial similarity in disease pairs like CD vs ulcerative colitis (UC), CD vs CRC, Parkinson's disease vs type 2 diabetes (T2D), and schizophrenia vs T2D. We also found strong inverse correlations in Alzheimer's disease vs CD and UC. These findings detected by our pipeline provide valuable insights into these diseases.
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Affiliation(s)
- Dong-Min Jin
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - James T. Morton
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Richard Bonneau
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- Genentech, New York, NY, USA
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2
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Hamamsy T, Barot M, Morton JT, Steinegger M, Bonneau R, Cho K. Learning sequence, structure, and function representations of proteins with language models. bioRxiv 2023:2023.11.26.568742. [PMID: 38045331 PMCID: PMC10690258 DOI: 10.1101/2023.11.26.568742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
The sequence-structure-function relationships that ultimately generate the diversity of extant observed proteins is complex, as proteins bridge the gap between multiple informational and physical scales involved in nearly all cellular processes. One limitation of existing protein annotation databases such as UniProt is that less than 1% of proteins have experimentally verified functions, and computational methods are needed to fill in the missing information. Here, we demonstrate that a multi-aspect framework based on protein language models can learn sequence-structure-function representations of amino acid sequences, and can provide the foundation for sensitive sequence-structure-function aware protein sequence search and annotation. Based on this model, we introduce a multi-aspect information retrieval system for proteins, Protein-Vec, covering sequence, structure, and function aspects, that enables computational protein annotation and function prediction at tree-of-life scales.
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3
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McDonald D, Jiang Y, Balaban M, Cantrell K, Zhu Q, Gonzalez A, Morton JT, Nicolaou G, Parks DH, Karst SM, Albertsen M, Hugenholtz P, DeSantis T, Song SJ, Bartko A, Havulinna AS, Jousilahti P, Cheng S, Inouye M, Niiranen T, Jain M, Salomaa V, Lahti L, Mirarab S, Knight R. Author Correction: Greengenes2 unifies microbial data in a single reference tree. Nat Biotechnol 2023:10.1038/s41587-023-02026-w. [PMID: 37853258 DOI: 10.1038/s41587-023-02026-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Affiliation(s)
- Daniel McDonald
- Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA
| | - Yueyu Jiang
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Metin Balaban
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - Kalen Cantrell
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Qiyun Zhu
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
- Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, USA
| | - Antonio Gonzalez
- Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA
| | - James T Morton
- Biostatistics & Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Giorgia Nicolaou
- Halicioglu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Donovan H Parks
- Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, Queensland, Australia
| | - Søren M Karst
- Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA
| | - Mads Albertsen
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Philip Hugenholtz
- Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, Queensland, Australia
| | - Todd DeSantis
- Department of Informatics, Second Genome, Brisbane, CA, USA
| | - Se Jin Song
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Andrew Bartko
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Aki S Havulinna
- Finnish Institute for Health and Welfare, Helsinki, Finland
- Institute for Molecular Medicine Finland, FIMM-HiLIFE, Helsinki, Finland
| | | | - Susan Cheng
- Division of Cardiology, Brigham and Women's Hospital, Boston, MA, USA
- Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Teemu Niiranen
- Finnish Institute for Health and Welfare, Helsinki, Finland
- Division of Medicine, Turku University Hospital and University of Turku, Turku, Finland
| | - Mohit Jain
- Sapient Bioanalytics, LLC, San Diego, CA, USA
| | - Veikko Salomaa
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Siavash Mirarab
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
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4
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Hamamsy T, Morton JT, Blackwell R, Berenberg D, Carriero N, Gligorijevic V, Strauss CEM, Leman JK, Cho K, Bonneau R. Protein remote homology detection and structural alignment using deep learning. Nat Biotechnol 2023:10.1038/s41587-023-01917-2. [PMID: 37679542 DOI: 10.1038/s41587-023-01917-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 07/26/2023] [Indexed: 09/09/2023]
Abstract
Exploiting sequence-structure-function relationships in biotechnology requires improved methods for aligning proteins that have low sequence similarity to previously annotated proteins. We develop two deep learning methods to address this gap, TM-Vec and DeepBLAST. TM-Vec allows searching for structure-structure similarities in large sequence databases. It is trained to accurately predict TM-scores as a metric of structural similarity directly from sequence pairs without the need for intermediate computation or solution of structures. Once structurally similar proteins have been identified, DeepBLAST can structurally align proteins using only sequence information by identifying structurally homologous regions between proteins. It outperforms traditional sequence alignment methods and performs similarly to structure-based alignment methods. We show the merits of TM-Vec and DeepBLAST on a variety of datasets, including better identification of remotely homologous proteins compared with state-of-the-art sequence alignment and structure prediction methods.
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Affiliation(s)
- Tymor Hamamsy
- Center for Data Science, New York University, New York, NY, USA
| | - James T Morton
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Robert Blackwell
- Scientific Computing Core, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Daniel Berenberg
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
- Prescient Design, New York, NY, USA
| | - Nicholas Carriero
- Scientific Computing Core, Flatiron Institute, Simons Foundation, New York, NY, USA
| | | | | | - Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Kyunghyun Cho
- Center for Data Science, New York University, New York, NY, USA.
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA.
- Prescient Design, New York, NY, USA.
- CIFAR, Toronto, Ontario, Canada.
| | - Richard Bonneau
- Center for Data Science, New York University, New York, NY, USA.
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA.
- Prescient Design, New York, NY, USA.
- Department of Biology, New York University, New York, NY, USA.
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5
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McDonald D, Jiang Y, Balaban M, Cantrell K, Zhu Q, Gonzalez A, Morton JT, Nicolaou G, Parks DH, Karst SM, Albertsen M, Hugenholtz P, DeSantis T, Song SJ, Bartko A, Havulinna AS, Jousilahti P, Cheng S, Inouye M, Niiranen T, Jain M, Salomaa V, Lahti L, Mirarab S, Knight R. Greengenes2 unifies microbial data in a single reference tree. Nat Biotechnol 2023:10.1038/s41587-023-01845-1. [PMID: 37500913 PMCID: PMC10818020 DOI: 10.1038/s41587-023-01845-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 05/25/2023] [Indexed: 07/29/2023]
Abstract
Studies using 16S rRNA and shotgun metagenomics typically yield different results, usually attributed to PCR amplification biases. We introduce Greengenes2, a reference tree that unifies genomic and 16S rRNA databases in a consistent, integrated resource. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same samples agree in principal coordinates space, taxonomy and phenotype effect size when analyzed with the same tree.
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Affiliation(s)
- Daniel McDonald
- Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA
| | - Yueyu Jiang
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Metin Balaban
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - Kalen Cantrell
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Qiyun Zhu
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
- Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, USA
| | - Antonio Gonzalez
- Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA
| | - James T Morton
- Biostatistics & Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Giorgia Nicolaou
- Halicioglu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | - Donovan H Parks
- Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, Queensland, Australia
| | - Søren M Karst
- Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA
| | - Mads Albertsen
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Philip Hugenholtz
- Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, Queensland, Australia
| | - Todd DeSantis
- Department of Informatics, Second Genome, Brisbane, CA, USA
| | - Se Jin Song
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Andrew Bartko
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Aki S Havulinna
- Finnish Institute for Health and Welfare, Helsinki, Finland
- Institute for Molecular Medicine Finland, FIMM-HiLIFE, Helsinki, Finland
| | | | - Susan Cheng
- Division of Cardiology, Brigham and Women's Hospital, Boston, MA, USA
- Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Teemu Niiranen
- Finnish Institute for Health and Welfare, Helsinki, Finland
- Division of Medicine, Turku University Hospital and University of Turku, Turku, Finland
| | - Mohit Jain
- Sapient Bioanalytics, LLC, San Diego, CA, USA
| | - Veikko Salomaa
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Siavash Mirarab
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
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6
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Morton JT, Jin DM, Mills RH, Shao Y, Rahman G, McDonald D, Zhu Q, Balaban M, Jiang Y, Cantrell K, Gonzalez A, Carmel J, Frankiensztajn LM, Martin-Brevet S, Berding K, Needham BD, Zurita MF, David M, Averina OV, Kovtun AS, Noto A, Mussap M, Wang M, Frank DN, Li E, Zhou W, Fanos V, Danilenko VN, Wall DP, Cárdenas P, Baldeón ME, Jacquemont S, Koren O, Elliott E, Xavier RJ, Mazmanian SK, Knight R, Gilbert JA, Donovan SM, Lawley TD, Carpenter B, Bonneau R, Taroncher-Oldenburg G. Multi-level analysis of the gut-brain axis shows autism spectrum disorder-associated molecular and microbial profiles. Nat Neurosci 2023:10.1038/s41593-023-01361-0. [PMID: 37365313 DOI: 10.1038/s41593-023-01361-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 05/13/2023] [Indexed: 06/28/2023]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut-brain axis (GBA) has been implicated in ASD although with limited reproducibility across studies. In this study, we developed a Bayesian differential ranking algorithm to identify ASD-associated molecular and taxa profiles across 10 cross-sectional microbiome datasets and 15 other datasets, including dietary patterns, metabolomics, cytokine profiles and human brain gene expression profiles. We found a functional architecture along the GBA that correlates with heterogeneity of ASD phenotypes, and it is characterized by ASD-associated amino acid, carbohydrate and lipid profiles predominantly encoded by microbial species in the genera Prevotella, Bifidobacterium, Desulfovibrio and Bacteroides and correlates with brain gene expression changes, restrictive dietary patterns and pro-inflammatory cytokine profiles. The functional architecture revealed in age-matched and sex-matched cohorts is not present in sibling-matched cohorts. We also show a strong association between temporal changes in microbiome composition and ASD phenotypes. In summary, we propose a framework to leverage multi-omic datasets from well-defined cohorts and investigate how the GBA influences ASD.
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Affiliation(s)
- James T Morton
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Biostatistics & Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Dong-Min Jin
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, USA
| | | | - Yan Shao
- Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, UK
| | - Gibraan Rahman
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Daniel McDonald
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Qiyun Zhu
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
- Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, USA
| | - Metin Balaban
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Yueyu Jiang
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Kalen Cantrell
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Antonio Gonzalez
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Julie Carmel
- Azrieli Faculty of Medicine, Bar Ilan University, Safed, Israel
| | | | - Sandra Martin-Brevet
- Laboratory for Research in Neuroimaging, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Kirsten Berding
- Division of Nutritional Sciences, University of Illinois, Urbana, IL, USA
| | - Brittany D Needham
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - María Fernanda Zurita
- Microbiology Institute and Health Science College, Universidad San Francisco de Quito, Quito, Ecuador
| | - Maude David
- Departments of Microbiology & Pharmaceutical Sciences, Oregon State University, Corvallis, OR, USA
| | - Olga V Averina
- Vavilov Institute of General Genetics Russian Academy of Sciences, Moscow, Russia
| | - Alexey S Kovtun
- Vavilov Institute of General Genetics Russian Academy of Sciences, Moscow, Russia
- Skolkovo Institute of Science and Technology, Skolkovo, Russia
| | - Antonio Noto
- Department of Biomedical Sciences, School of Medicine, University of Cagliari, Cagliari, Italy
| | - Michele Mussap
- Laboratory Medicine, Department of Surgical Sciences, School of Medicine, University of Cagliari, Cagliari, Italy
| | - Mingbang Wang
- Shanghai Key Laboratory of Birth Defects, Division of Neonatology, Children's Hospital of Fudan University, National Center for Children's Health, Shanghai, China
- Microbiome Therapy Center, South China Hospital, Health Science Center, Shenzhen University, Shenzhen, China
| | - Daniel N Frank
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Ellen Li
- Department of Medicine, Division of Gastroenterology and Hepatology, Stony Brook University, Stony Brook, NY, USA
| | - Wenhao Zhou
- Shanghai Key Laboratory of Birth Defects, Division of Neonatology, Children's Hospital of Fudan University, National Center for Children's Health, Shanghai, China
| | - Vassilios Fanos
- Neonatal Intensive Care Unit and Neonatal Pathology, Department of Surgical Sciences, School of Medicine, University of Cagliari, Cagliari, Italy
| | - Valery N Danilenko
- Vavilov Institute of General Genetics Russian Academy of Sciences, Moscow, Russia
| | - Dennis P Wall
- Pediatrics (Systems Medicine), Biomedical Data Science, and Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Paúl Cárdenas
- Institute of Microbiology, COCIBA, Universidad San Francisco de Quito, Quito, Ecuador
| | - Manuel E Baldeón
- Facultad de Ciencias Médicas, de la Salud y la Vida, Universidad Internacional del Ecuador, Quito, Ecuador
| | - Sébastien Jacquemont
- Sainte Justine Hospital Research Center, Montréal, QC, Canada
- Department of Pediatrics, Université de Montréal, Montréal, QC, Canada
| | - Omry Koren
- Azrieli Faculty of Medicine, Bar Ilan University, Safed, Israel
| | - Evan Elliott
- Azrieli Faculty of Medicine, Bar Ilan University, Safed, Israel
- The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar Ilan University, Ramat Gan, Israel
| | - Ramnik J Xavier
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
- Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital, Boston, MA, USA
| | - Sarkis K Mazmanian
- Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Rob Knight
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, California, USA
| | - Jack A Gilbert
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, California, USA
- Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA
| | - Sharon M Donovan
- Division of Nutritional Sciences, University of Illinois, Urbana, IL, USA
| | - Trevor D Lawley
- Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, UK
| | - Bob Carpenter
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, USA
- Prescient Design, a Genentech Accelerator, New York, NY, USA
| | - Gaspar Taroncher-Oldenburg
- Gaspar Taroncher Consulting, Philadelphia, PA, USA.
- Simons Foundation Autism Research Initiative, Simons Foundation, New York, NY, USA.
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7
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Debelius JW, Engstrand L, Matussek A, Brusselaers N, Morton JT, Stenmarker M, Olsen RS. The Local Tumor Microbiome Is Associated with Survival in Late-Stage Colorectal Cancer Patients. Microbiol Spectr 2023; 11:e0506622. [PMID: 37042765 PMCID: PMC10269740 DOI: 10.1128/spectrum.05066-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/27/2023] [Indexed: 04/13/2023] Open
Abstract
The gut microbiome is associated with survival in colorectal cancer. Single organisms have been identified as markers of poor prognosis. However, in situ imaging of tumors demonstrate a polymicrobial tumor-associated community. To understand the role of these polymicrobial communities in survival, we conducted a nested case-control study in late-stage cancer patients undergoing resection for primary adenocarcinoma. The microbiome of paired tumor and adjacent normal tissue samples was profiled using 16S rRNA sequencing. We found a consistent difference in the microbiome between paired tumor and adjacent tissue, despite strong individual microbial identities. Furthermore, a larger difference between normal and tumor tissue was associated with prognosis: patients with shorter survival had a larger difference between normal and tumor tissue. Within the tumor tissue, we identified a 39-member community statistic associated with survival; for every log2-fold increase in this value, an individual's odds of survival increased by 20% (odds ratio survival 1.20; 95% confidence interval = 1.04 to 1.33). Our results suggest that a polymicrobial tumor-specific microbiome is associated with survival in late-stage colorectal cancer patients. IMPORTANCE Microbiome studies in colorectal cancer (CRC) have primarily focused on the role of single organisms in cancer progression. Recent work has identified specific organisms throughout the intestinal tract, which may affect survival; however, the results are inconsistent. We found differences between the tumor microbiome and the microbiome of the rest of the intestine in patients, and the magnitude of this difference was associated with survival, or, the more like a healthy gut a tumor looked, the better a patient's prognosis. Our results suggest that future microbiome-based interventions to affect survival in CRC will need to target the tumor community.
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Affiliation(s)
- Justine W. Debelius
- Centre for Translational Microbiome Research, Department of Microbiology, Tumor, and Cell Biology, Karolinska Institutet, Solna, Sweden
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Lars Engstrand
- Centre for Translational Microbiome Research, Department of Microbiology, Tumor, and Cell Biology, Karolinska Institutet, Solna, Sweden
| | - Andreas Matussek
- Laboratory Medicine, Jönköping Region County, Department of Clinical and Experimental Medicine, Linköping University, Jönköping, Sweden
- Division of Laboratory Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Microbiology, Division of Laboratory Medicine, Oslo University Hospital, Oslo, Norway
| | - Nele Brusselaers
- Centre for Translational Microbiome Research, Department of Microbiology, Tumor, and Cell Biology, Karolinska Institutet, Solna, Sweden
- Global Health Institute, Antwerp University, Antwerp, Belgium
- Department of Head and Skin, Ghent University, Ghent, Belgium
| | - James T. Morton
- Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA
| | - Margaretha Stenmarker
- Futurum/Department of Pediatrics, Jönköping Region County, Jönköping, Sweden
- Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
- Institute of Clinical Sciences, Department of Paediatrics, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Renate S. Olsen
- Centre for Translational Microbiome Research, Department of Microbiology, Tumor, and Cell Biology, Karolinska Institutet, Solna, Sweden
- Pathology Laboratory, Department of Laboratory Medicine, Jönköping Region County, Jönköping, Sweden
- Department of Pathology, Division of Laboratory Medicine, Oslo University Hospital, Norwegian Radium Hospital, Oslo, Norway
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8
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Rahman G, Morton JT, Martino C, Sepich-Poore GD, Allaband C, Guccione C, Chen Y, Hakim D, Estaki M, Knight R. BIRDMAn: A Bayesian differential abundance framework that enables robust inference of host-microbe associations. bioRxiv 2023:2023.01.30.526328. [PMID: 36778470 PMCID: PMC9915500 DOI: 10.1101/2023.01.30.526328] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Quantifying the differential abundance (DA) of specific taxa among experimental groups in microbiome studies is challenging due to data characteristics (e.g., compositionality, sparsity) and specific study designs (e.g., repeated measures, meta-analysis, cross-over). Here we present BIRDMAn (Bayesian Inferential Regression for Differential Microbiome Analysis), a flexible DA method that can account for microbiome data characteristics and diverse experimental designs. Simulations show that BIRDMAn models are robust to uneven sequencing depth and provide a >20-fold improvement in statistical power over existing methods. We then use BIRDMAn to identify antibiotic-mediated perturbations undetected by other DA methods due to subject-level heterogeneity. Finally, we demonstrate how BIRDMAn can construct state-of-the-art cancer-type classifiers using The Cancer Genome Atlas (TCGA) dataset, with substantial accuracy improvements over random forests and existing DA tools across multiple sequencing centers. Collectively, BIRDMAn extracts more informative biological signals while accounting for study-specific experimental conditions than existing approaches.
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Affiliation(s)
- Gibraan Rahman
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - James T Morton
- Biostatistics & Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Cameron Martino
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | | | - Celeste Allaband
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Caitlin Guccione
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA
| | - Yang Chen
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Department of Dermatology, University of California San Diego, La Jolla, CA, USA
- Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA
| | - Daniel Hakim
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - Mehrbod Estaki
- Department of Physiology & Pharmacology, University of Calgary, Calgary, Canada
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California, USA
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9
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Dallago C, Schütze K, Heinzinger M, Olenyi T, Littmann M, Lu AX, Yang KK, Min S, Yoon S, Morton JT, Rost B. Learned Embeddings from Deep Learning to Visualize and Predict Protein Sets. Curr Protoc 2021; 1:e113. [PMID: 33961736 DOI: 10.1002/cpz1.113] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Models from machine learning (ML) or artificial intelligence (AI) increasingly assist in guiding experimental design and decision making in molecular biology and medicine. Recently, Language Models (LMs) have been adapted from Natural Language Processing (NLP) to encode the implicit language written in protein sequences. Protein LMs show enormous potential in generating descriptive representations (embeddings) for proteins from just their sequences, in a fraction of the time with respect to previous approaches, yet with comparable or improved predictive ability. Researchers have trained a variety of protein LMs that are likely to illuminate different angles of the protein language. By leveraging the bio_embeddings pipeline and modules, simple and reproducible workflows can be laid out to generate protein embeddings and rich visualizations. Embeddings can then be leveraged as input features through machine learning libraries to develop methods predicting particular aspects of protein function and structure. Beyond the workflows included here, embeddings have been leveraged as proxies to traditional homology-based inference and even to align similar protein sequences. A wealth of possibilities remain for researchers to harness through the tools provided in the following protocols. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. The following protocols are included in this manuscript: Basic Protocol 1: Generic use of the bio_embeddings pipeline to plot protein sequences and annotations Basic Protocol 2: Generate embeddings from protein sequences using the bio_embeddings pipeline Basic Protocol 3: Overlay sequence annotations onto a protein space visualization Basic Protocol 4: Train a machine learning classifier on protein embeddings Alternate Protocol 1: Generate 3D instead of 2D visualizations Alternate Protocol 2: Visualize protein solubility instead of protein subcellular localization Support Protocol: Join embedding generation and sequence space visualization in a pipeline.
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Affiliation(s)
- Christian Dallago
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology, Garching/Munich, Germany.,TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Garching/Munich, Germany
| | - Konstantin Schütze
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology, Garching/Munich, Germany
| | - Michael Heinzinger
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology, Garching/Munich, Germany.,TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Garching/Munich, Germany
| | - Tobias Olenyi
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology, Garching/Munich, Germany
| | - Maria Littmann
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology, Garching/Munich, Germany.,TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Garching/Munich, Germany
| | - Amy X Lu
- Department of Computer Science, University of Toronto, Toronto, Canada & Vector Institute
| | - Kevin K Yang
- Microsoft Research New England, Cambridge, Massachusetts
| | - Seonwoo Min
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
| | - Sungroh Yoon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea.,Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
| | - James T Morton
- Center for Computational Biology, Flatiron Institute, New York, New York
| | - Burkhard Rost
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology, Garching/Munich, Germany.,Institute for Advanced Study (TUM-IAS), Garching/Munich, Germany.,TUM School of Life Sciences Weihenstephan (WZW), Freising, Germany.,Columbia University, Department of Biochemistry and Molecular Biophysics, New York, New York.,New York Consortium on Membrane Protein Structure (NYCOMPS), New York, New York
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10
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Aksenov AA, Laponogov I, Zhang Z, Doran SLF, Belluomo I, Veselkov D, Bittremieux W, Nothias LF, Nothias-Esposito M, Maloney KN, Misra BB, Melnik AV, Smirnov A, Du X, Jones KL, Dorrestein K, Panitchpakdi M, Ernst M, van der Hooft JJJ, Gonzalez M, Carazzone C, Amézquita A, Callewaert C, Morton JT, Quinn RA, Bouslimani A, Orio AA, Petras D, Smania AM, Couvillion SP, Burnet MC, Nicora CD, Zink E, Metz TO, Artaev V, Humston-Fulmer E, Gregor R, Meijler MM, Mizrahi I, Eyal S, Anderson B, Dutton R, Lugan R, Boulch PL, Guitton Y, Prevost S, Poirier A, Dervilly G, Le Bizec B, Fait A, Persi NS, Song C, Gashu K, Coras R, Guma M, Manasson J, Scher JU, Barupal DK, Alseekh S, Fernie AR, Mirnezami R, Vasiliou V, Schmid R, Borisov RS, Kulikova LN, Knight R, Wang M, Hanna GB, Dorrestein PC, Veselkov K. Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data. Nat Biotechnol 2021; 39:169-173. [PMID: 33169034 PMCID: PMC7971188 DOI: 10.1038/s41587-020-0700-3] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 08/26/2020] [Accepted: 09/09/2020] [Indexed: 12/23/2022]
Abstract
We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.
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Affiliation(s)
- Alexander A Aksenov
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - Ivan Laponogov
- Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK
| | - Zheng Zhang
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Sophie L F Doran
- Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK
| | - Ilaria Belluomo
- Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK
| | - Dennis Veselkov
- Intelligify Limited, London, UK
- Department of Computing, Imperial College, South Kensington Campus, London, UK
| | - Wout Bittremieux
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Louis Felix Nothias
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - Mélissa Nothias-Esposito
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - Katherine N Maloney
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Department of Chemistry, Point Loma Nazarene University, San Diego, CA, USA
| | - Biswapriya B Misra
- Center for Precision Medicine, Department of Internal Medicine, Section of Molecular Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Alexey V Melnik
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Aleksandr Smirnov
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Xiuxia Du
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Kenneth L Jones
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Kathleen Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - Morgan Panitchpakdi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Madeleine Ernst
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Section for Clinical Mass Spectrometry, Department of Congenital Disorders, Danish Center for Neonatal Screening, Statens Serum Institut, Copenhagen, Denmark
| | - Justin J J van der Hooft
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Bioinformatics Group, Wageningen University, Wageningen, the Netherlands
| | - Mabel Gonzalez
- Department of Chemistry, Universidad de los Andes, Bogotá, Colombia
| | - Chiara Carazzone
- Department of Chemistry, Universidad de los Andes, Bogotá, Colombia
| | - Adolfo Amézquita
- Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
| | - Chris Callewaert
- Center for Microbial Ecology and Technology, Ghent, Belgium
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - James T Morton
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Robert A Quinn
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
| | - Amina Bouslimani
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - Andrea Albarracín Orio
- IRNASUS, Universidad Católica de Córdoba, CONICET, Facultad de Ciencias Agropecuarias, Córdoba, Argentina
| | - Daniel Petras
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - Andrea M Smania
- Universidad Nacional de Córdoba, Facultad de Ciencias Químicas, Departamento de Química Biológica Ranwel Caputto, Córdoba, Argentina
- CONICET, Universidad Nacional de Córdoba, Centro de Investigaciones en Química Biológica de Córdoba (CIQUIBIC), Córdoba, Argentina
| | - Sneha P Couvillion
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Meagan C Burnet
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Carrie D Nicora
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Erika Zink
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | | | | | - Rachel Gregor
- Department of Chemistry and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Michael M Meijler
- Department of Chemistry and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Itzhak Mizrahi
- Department of Life Sciences and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Stav Eyal
- Department of Life Sciences and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Brooke Anderson
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Rachel Dutton
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Raphaël Lugan
- UMR Qualisud, Université d'Avignon et des Pays du Vaucluse, Agrosciences, Avignon, France
| | - Pauline Le Boulch
- UMR Qualisud, Université d'Avignon et des Pays du Vaucluse, Agrosciences, Avignon, France
| | - Yann Guitton
- Laboratoire d'Etude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris, INRAe, Nantes, France
| | - Stephanie Prevost
- Laboratoire d'Etude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris, INRAe, Nantes, France
| | - Audrey Poirier
- Laboratoire d'Etude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris, INRAe, Nantes, France
| | - Gaud Dervilly
- Laboratoire d'Etude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris, INRAe, Nantes, France
| | - Bruno Le Bizec
- Laboratoire d'Etude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris, INRAe, Nantes, France
| | - Aaron Fait
- The French Associates Institute for Agriculture and Biotechnology of Dryland, The Jacob Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, Sede Boqer Campus, Beer Sheva, Israel
| | - Noga Sikron Persi
- The French Associates Institute for Agriculture and Biotechnology of Dryland, The Jacob Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, Sede Boqer Campus, Beer Sheva, Israel
| | - Chao Song
- The French Associates Institute for Agriculture and Biotechnology of Dryland, The Jacob Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, Sede Boqer Campus, Beer Sheva, Israel
| | - Kelem Gashu
- The French Associates Institute for Agriculture and Biotechnology of Dryland, The Jacob Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, Sede Boqer Campus, Beer Sheva, Israel
| | - Roxana Coras
- Division of Rheumatology, Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Monica Guma
- Division of Rheumatology, Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Julia Manasson
- Division of Rheumatology, Department of Medicine, New York University School of Medicine, New York, NY, USA
| | - Jose U Scher
- Division of Rheumatology, Department of Medicine, New York University School of Medicine, New York, NY, USA
| | - Dinesh Kumar Barupal
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saleh Alseekh
- Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany
- Center of Plant Systems Biology and Biotechnology (CPSBB), Plovdiv, Bulgaria
| | - Alisdair R Fernie
- Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany
- Center of Plant Systems Biology and Biotechnology (CPSBB), Plovdiv, Bulgaria
| | - Reza Mirnezami
- Department of Colorectal Surgery, Royal Free Hospital NHS Foundation Trust, Hampstead, London, UK
| | - Vasilis Vasiliou
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA
| | - Robin Schmid
- Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany
| | - Roman S Borisov
- A.V. Topchiev Institute of Petrochemical Synthesis RAS, Moscow, Russian Federation
| | - Larisa N Kulikova
- Рeoples' Friendship University of Russia (RUDN University), Moscow, Russian Federation
| | - Rob Knight
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Mingxun Wang
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - George B Hanna
- Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK
| | - Pieter C Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA.
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA.
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA.
| | - Kirill Veselkov
- Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK.
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11
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Aksenov AA, Laponogov I, Zhang Z, Doran SLF, Belluomo I, Veselkov D, Bittremieux W, Nothias LF, Nothias-Esposito M, Maloney KN, Misra BB, Melnik AV, Smirnov A, Du X, Jones KL, Dorrestein K, Panitchpakdi M, Ernst M, van der Hooft JJJ, Gonzalez M, Carazzone C, Amézquita A, Callewaert C, Morton JT, Quinn RA, Bouslimani A, Orio AA, Petras D, Smania AM, Couvillion SP, Burnet MC, Nicora CD, Zink E, Metz TO, Artaev V, Humston-Fulmer E, Gregor R, Meijler MM, Mizrahi I, Eyal S, Anderson B, Dutton R, Lugan R, Boulch PL, Guitton Y, Prevost S, Poirier A, Dervilly G, Le Bizec B, Fait A, Persi NS, Song C, Gashu K, Coras R, Guma M, Manasson J, Scher JU, Barupal DK, Alseekh S, Fernie AR, Mirnezami R, Vasiliou V, Schmid R, Borisov RS, Kulikova LN, Knight R, Wang M, Hanna GB, Dorrestein PC, Veselkov K. Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data. Nat Biotechnol 2021. [PMID: 33169034 DOI: 10.1038/s41587-41020-40700-41583] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.
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Affiliation(s)
- Alexander A Aksenov
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - Ivan Laponogov
- Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK
| | - Zheng Zhang
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Sophie L F Doran
- Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK
| | - Ilaria Belluomo
- Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK
| | - Dennis Veselkov
- Intelligify Limited, London, UK
- Department of Computing, Imperial College, South Kensington Campus, London, UK
| | - Wout Bittremieux
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Louis Felix Nothias
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - Mélissa Nothias-Esposito
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - Katherine N Maloney
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Department of Chemistry, Point Loma Nazarene University, San Diego, CA, USA
| | - Biswapriya B Misra
- Center for Precision Medicine, Department of Internal Medicine, Section of Molecular Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Alexey V Melnik
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Aleksandr Smirnov
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Xiuxia Du
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Kenneth L Jones
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Kathleen Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - Morgan Panitchpakdi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Madeleine Ernst
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Section for Clinical Mass Spectrometry, Department of Congenital Disorders, Danish Center for Neonatal Screening, Statens Serum Institut, Copenhagen, Denmark
| | - Justin J J van der Hooft
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Bioinformatics Group, Wageningen University, Wageningen, the Netherlands
| | - Mabel Gonzalez
- Department of Chemistry, Universidad de los Andes, Bogotá, Colombia
| | - Chiara Carazzone
- Department of Chemistry, Universidad de los Andes, Bogotá, Colombia
| | - Adolfo Amézquita
- Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
| | - Chris Callewaert
- Center for Microbial Ecology and Technology, Ghent, Belgium
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - James T Morton
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Robert A Quinn
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
| | - Amina Bouslimani
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - Andrea Albarracín Orio
- IRNASUS, Universidad Católica de Córdoba, CONICET, Facultad de Ciencias Agropecuarias, Córdoba, Argentina
| | - Daniel Petras
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - Andrea M Smania
- Universidad Nacional de Córdoba, Facultad de Ciencias Químicas, Departamento de Química Biológica Ranwel Caputto, Córdoba, Argentina
- CONICET, Universidad Nacional de Córdoba, Centro de Investigaciones en Química Biológica de Córdoba (CIQUIBIC), Córdoba, Argentina
| | - Sneha P Couvillion
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Meagan C Burnet
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Carrie D Nicora
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Erika Zink
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | | | | | - Rachel Gregor
- Department of Chemistry and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Michael M Meijler
- Department of Chemistry and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Itzhak Mizrahi
- Department of Life Sciences and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Stav Eyal
- Department of Life Sciences and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Brooke Anderson
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Rachel Dutton
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Raphaël Lugan
- UMR Qualisud, Université d'Avignon et des Pays du Vaucluse, Agrosciences, Avignon, France
| | - Pauline Le Boulch
- UMR Qualisud, Université d'Avignon et des Pays du Vaucluse, Agrosciences, Avignon, France
| | - Yann Guitton
- Laboratoire d'Etude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris, INRAe, Nantes, France
| | - Stephanie Prevost
- Laboratoire d'Etude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris, INRAe, Nantes, France
| | - Audrey Poirier
- Laboratoire d'Etude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris, INRAe, Nantes, France
| | - Gaud Dervilly
- Laboratoire d'Etude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris, INRAe, Nantes, France
| | - Bruno Le Bizec
- Laboratoire d'Etude des Résidus et Contaminants dans les Aliments (LABERCA), Oniris, INRAe, Nantes, France
| | - Aaron Fait
- The French Associates Institute for Agriculture and Biotechnology of Dryland, The Jacob Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, Sede Boqer Campus, Beer Sheva, Israel
| | - Noga Sikron Persi
- The French Associates Institute for Agriculture and Biotechnology of Dryland, The Jacob Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, Sede Boqer Campus, Beer Sheva, Israel
| | - Chao Song
- The French Associates Institute for Agriculture and Biotechnology of Dryland, The Jacob Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, Sede Boqer Campus, Beer Sheva, Israel
| | - Kelem Gashu
- The French Associates Institute for Agriculture and Biotechnology of Dryland, The Jacob Blaustein Institutes for Desert Research, Ben Gurion University of the Negev, Sede Boqer Campus, Beer Sheva, Israel
| | - Roxana Coras
- Division of Rheumatology, Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Monica Guma
- Division of Rheumatology, Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Julia Manasson
- Division of Rheumatology, Department of Medicine, New York University School of Medicine, New York, NY, USA
| | - Jose U Scher
- Division of Rheumatology, Department of Medicine, New York University School of Medicine, New York, NY, USA
| | - Dinesh Kumar Barupal
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saleh Alseekh
- Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany
- Center of Plant Systems Biology and Biotechnology (CPSBB), Plovdiv, Bulgaria
| | - Alisdair R Fernie
- Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany
- Center of Plant Systems Biology and Biotechnology (CPSBB), Plovdiv, Bulgaria
| | - Reza Mirnezami
- Department of Colorectal Surgery, Royal Free Hospital NHS Foundation Trust, Hampstead, London, UK
| | - Vasilis Vasiliou
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA
| | - Robin Schmid
- Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany
| | - Roman S Borisov
- A.V. Topchiev Institute of Petrochemical Synthesis RAS, Moscow, Russian Federation
| | - Larisa N Kulikova
- Рeoples' Friendship University of Russia (RUDN University), Moscow, Russian Federation
| | - Rob Knight
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Mingxun Wang
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA
| | - George B Hanna
- Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK
| | - Pieter C Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA.
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California,San Diego, La Jolla, CA, USA.
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA.
| | - Kirill Veselkov
- Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK.
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12
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Martino C, Shenhav L, Marotz CA, Armstrong G, McDonald D, Vázquez-Baeza Y, Morton JT, Jiang L, Dominguez-Bello MG, Swafford AD, Halperin E, Knight R. Context-aware dimensionality reduction deconvolutes gut microbial community dynamics. Nat Biotechnol 2021; 39:165-168. [PMID: 32868914 PMCID: PMC7878194 DOI: 10.1038/s41587-020-0660-7] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 08/03/2020] [Indexed: 11/27/2022]
Abstract
The translational power of human microbiome studies is limited by high interindividual variation. We describe a dimensionality reduction tool, compositional tensor factorization (CTF), that incorporates information from the same host across multiple samples to reveal patterns driving differences in microbial composition across phenotypes. CTF identifies robust patterns in sparse compositional datasets, allowing for the detection of microbial changes associated with specific phenotypes that are reproducible across datasets.
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Affiliation(s)
- Cameron Martino
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Liat Shenhav
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
| | - Clarisse A Marotz
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - George Armstrong
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Yoshiki Vázquez-Baeza
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
- Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - James T Morton
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Lingjing Jiang
- Division of Biostatistics, University of California San Diego, La Jolla, CA, USA
| | - Maria Gloria Dominguez-Bello
- Department of Biochemistry and Microbiology, Rutgers University New Brunswick, New Brunswick, NJ, USA
- Department of Anthropology, Rutgers University, New Brunswick, NJ, USA
| | - Austin D Swafford
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
| | - Eran Halperin
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA
- Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Institute of Precision Health, University of California, Los Angeles, CA, USA
| | - Rob Knight
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA.
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
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13
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Baker JL, Morton JT, Dinis M, Alvarez R, Tran NC, Knight R, Edlund A. Deep metagenomics examines the oral microbiome during dental caries, revealing novel taxa and co-occurrences with host molecules. Genome Res 2020; 31:64-74. [PMID: 33239396 PMCID: PMC7849383 DOI: 10.1101/gr.265645.120] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 11/23/2020] [Indexed: 12/25/2022]
Abstract
Dental caries, the most common chronic infectious disease worldwide, has a complex etiology involving the interplay of microbial and host factors that are not completely understood. In this study, the oral microbiome and 38 host cytokines and chemokines were analyzed across 23 children with caries and 24 children with healthy dentition. De novo assembly of metagenomic sequencing obtained 527 metagenome-assembled genomes (MAGs), representing 150 bacterial species. Forty-two of these species had no genomes in public repositories, thereby representing novel taxa. These new genomes greatly expanded the known pangenomes of many oral clades, including the enigmatic Saccharibacteria clades G3 and G6, which had distinct functional repertoires compared to other oral Saccharibacteria. Saccharibacteria are understood to be obligate epibionts, which are dependent on host bacteria. These data suggest that the various Saccharibacteria clades may rely on their hosts for highly distinct metabolic requirements, which would have significant evolutionary and ecological implications. Across the study group, Rothia, Neisseria, and Haemophilus spp. were associated with good dental health, whereas Prevotella spp., Streptococcus mutans, and Human herpesvirus 4 (Epstein-Barr virus [EBV]) were more prevalent in children with caries. Finally, 10 of the host immunological markers were significantly elevated in the caries group, and co-occurrence analysis provided an atlas of potential relationships between microbes and host immunological molecules. Overall, this study illustrated the oral microbiome at an unprecedented resolution and contributed several leads for further study that will increase the understanding of caries pathogenesis and guide therapeutic development.
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Affiliation(s)
- Jonathon L Baker
- Genomic Medicine Group, J. Craig Venter Institute, La Jolla, California 92037, USA
| | - James T Morton
- Systems Biology Group, Flatiron Institute, New York, New York 10010, USA
| | - Márcia Dinis
- Section of Pediatric Dentistry, UCLA School of Dentistry, Los Angeles, California 90095-1668, USA
| | - Ruth Alvarez
- Section of Pediatric Dentistry, UCLA School of Dentistry, Los Angeles, California 90095-1668, USA
| | - Nini C Tran
- Section of Pediatric Dentistry, UCLA School of Dentistry, Los Angeles, California 90095-1668, USA
| | - Rob Knight
- Center for Microbiome Innovation, University of California at San Diego, La Jolla, California 92161, USA.,Department of Pediatrics, University of California at San Diego, La Jolla, California 92161, USA.,Department of Computer Science and Engineering, University of California at San Diego, La Jolla, California 92093, USA.,Department of Bioengineering, University of California at San Diego, La Jolla, California 92093, USA
| | - Anna Edlund
- Genomic Medicine Group, J. Craig Venter Institute, La Jolla, California 92037, USA.,Department of Pediatrics, University of California at San Diego, La Jolla, California 92161, USA
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14
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Tsay JCJ, Wu BG, Sulaiman I, Gershner K, Schluger R, Li Y, Yie TA, Meyn P, Olsen E, Perez L, Franca B, Carpenito J, Iizumi T, El-Ashmawy M, Badri M, Morton JT, Shen N, He L, Michaud G, Rafeq S, Bessich JL, Smith RL, Sauthoff H, Felner K, Pillai R, Zavitsanou AM, Koralov SB, Mezzano V, Loomis CA, Moreira AL, Moore W, Tsirigos A, Heguy A, Rom WN, Sterman DH, Pass HI, Clemente JC, Li H, Bonneau R, Wong KK, Papagiannakopoulos T, Segal LN. Lower Airway Dysbiosis Affects Lung Cancer Progression. Cancer Discov 2020; 11:293-307. [PMID: 33177060 DOI: 10.1158/2159-8290.cd-20-0263] [Citation(s) in RCA: 121] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 09/15/2020] [Accepted: 10/27/2020] [Indexed: 11/16/2022]
Abstract
In lung cancer, enrichment of the lower airway microbiota with oral commensals commonly occurs, and ex vivo models support that some of these bacteria can trigger host transcriptomic signatures associated with carcinogenesis. Here, we show that this lower airway dysbiotic signature was more prevalent in the stage IIIB-IV tumor-node-metastasis lung cancer group and is associated with poor prognosis, as shown by decreased survival among subjects with early-stage disease (I-IIIA) and worse tumor progression as measured by RECIST scores among subjects with stage IIIB-IV disease. In addition, this lower airway microbiota signature was associated with upregulation of the IL17, PI3K, MAPK, and ERK pathways in airway transcriptome, and we identified Veillonella parvula as the most abundant taxon driving this association. In a KP lung cancer model, lower airway dysbiosis with V. parvula led to decreased survival, increased tumor burden, IL17 inflammatory phenotype, and activation of checkpoint inhibitor markers. SIGNIFICANCE: Multiple lines of investigation have shown that the gut microbiota affects host immune response to immunotherapy in cancer. Here, we support that the local airway microbiota modulates the host immune tone in lung cancer, affecting tumor progression and prognosis.See related commentary by Zitvogel and Kroemer, p. 224.This article is highlighted in the In This Issue feature, p. 211.
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Affiliation(s)
- Jun-Chieh J Tsay
- Division of Pulmonary and Critical Care Medicine, New York University School of Medicine, New York, New York.,Division of Pulmonary and Critical Care Medicine, VA New York Harbor Healthcare System, New York, New York
| | - Benjamin G Wu
- Division of Pulmonary and Critical Care Medicine, New York University School of Medicine, New York, New York.,Division of Pulmonary and Critical Care Medicine, VA New York Harbor Healthcare System, New York, New York
| | - Imran Sulaiman
- Division of Pulmonary and Critical Care Medicine, New York University School of Medicine, New York, New York
| | - Katherine Gershner
- Section of Pulmonary, Critical Care, Allergy and Immunology, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Rosemary Schluger
- Division of Pulmonary and Critical Care Medicine, New York University School of Medicine, New York, New York
| | - Yonghua Li
- Division of Pulmonary and Critical Care Medicine, New York University School of Medicine, New York, New York
| | - Ting-An Yie
- Division of Pulmonary and Critical Care Medicine, New York University School of Medicine, New York, New York
| | - Peter Meyn
- NYU Langone Genomic Technology Center, New York University School of Medicine, New York, New York
| | - Evan Olsen
- Division of Pulmonary and Critical Care Medicine, New York University School of Medicine, New York, New York
| | - Luisannay Perez
- Division of Pulmonary and Critical Care Medicine, New York University School of Medicine, New York, New York
| | - Brendan Franca
- Division of Pulmonary and Critical Care Medicine, New York University School of Medicine, New York, New York
| | - Joseph Carpenito
- Division of Pulmonary and Critical Care Medicine, New York University School of Medicine, New York, New York
| | - Tadasu Iizumi
- Division of Pulmonary and Critical Care Medicine, New York University School of Medicine, New York, New York
| | - Mariam El-Ashmawy
- Department of Medicine, New York University School of Medicine, New York, New York
| | - Michelle Badri
- Department of Biology, New York University, New York, New York
| | - James T Morton
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, New York
| | - Nan Shen
- Department of Genetics and Genomic Sciences and Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Linchen He
- Department of Population Health, New York University School of Medicine, New York, New York
| | - Gaetane Michaud
- Division of Pulmonary and Critical Care Medicine, New York University School of Medicine, New York, New York
| | - Samaan Rafeq
- Division of Pulmonary and Critical Care Medicine, New York University School of Medicine, New York, New York
| | - Jamie L Bessich
- Division of Pulmonary and Critical Care Medicine, New York University School of Medicine, New York, New York
| | - Robert L Smith
- Division of Pulmonary and Critical Care Medicine, VA New York Harbor Healthcare System, New York, New York
| | - Harald Sauthoff
- Division of Pulmonary and Critical Care Medicine, VA New York Harbor Healthcare System, New York, New York
| | - Kevin Felner
- Division of Pulmonary and Critical Care Medicine, VA New York Harbor Healthcare System, New York, New York
| | - Ray Pillai
- Division of Pulmonary and Critical Care Medicine, New York University School of Medicine, New York, New York
| | | | - Sergei B Koralov
- Department of Pathology, New York University School of Medicine, New York, New York
| | - Valeria Mezzano
- Department of Pathology, New York University School of Medicine, New York, New York
| | - Cynthia A Loomis
- Department of Pathology, New York University School of Medicine, New York, New York
| | - Andre L Moreira
- Department of Pathology, New York University School of Medicine, New York, New York
| | - William Moore
- Department of Radiology, New York University School of Medicine, New York, New York
| | - Aristotelis Tsirigos
- Department of Pathology, New York University School of Medicine, New York, New York
| | - Adriana Heguy
- NYU Langone Genomic Technology Center, New York University School of Medicine, New York, New York.,Department of Pathology, New York University School of Medicine, New York, New York
| | - William N Rom
- Division of Pulmonary and Critical Care Medicine, New York University School of Medicine, New York, New York
| | - Daniel H Sterman
- Division of Pulmonary and Critical Care Medicine, New York University School of Medicine, New York, New York
| | - Harvey I Pass
- Department of Cardiothoracic Surgery, New York University School of Medicine, New York, New York
| | - Jose C Clemente
- Department of Genetics and Genomic Sciences and Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Huilin Li
- Department of Population Health, New York University School of Medicine, New York, New York
| | - Richard Bonneau
- Department of Biology, New York University, New York, New York.,Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, New York.,Center for Data Science, New York University School of Medicine, New York, New York
| | - Kwok-Kin Wong
- Division of Hematology and Oncology, New York University School of Medicine, New York, New York
| | | | - Leopoldo N Segal
- Division of Pulmonary and Critical Care Medicine, New York University School of Medicine, New York, New York.
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15
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Fedarko MW, Martino C, Morton JT, González A, Rahman G, Marotz CA, Minich JJ, Allen EE, Knight R. Visualizing 'omic feature rankings and log-ratios using Qurro. NAR Genom Bioinform 2020; 2:lqaa023. [PMID: 32391521 PMCID: PMC7194218 DOI: 10.1093/nargab/lqaa023] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 03/11/2020] [Accepted: 03/31/2020] [Indexed: 12/30/2022] Open
Abstract
Many tools for dealing with compositional ' 'omics' data produce feature-wise values that can be ranked in order to describe features' associations with some sort of variation. These values include differentials (which describe features' associations with specified covariates) and feature loadings (which describe features' associations with variation along a given axis in a biplot). Although prior work has discussed the use of these 'rankings' as a starting point for exploring the log-ratios of particularly high- or low-ranked features, such exploratory analyses have previously been done using custom code to visualize feature rankings and the log-ratios of interest. This approach is laborious, prone to errors and raises questions about reproducibility. To address these problems we introduce Qurro, a tool that interactively visualizes a plot of feature rankings (a 'rank plot') alongside a plot of selected features' log-ratios within samples (a 'sample plot'). Qurro's interface includes various controls that allow users to select features from along the rank plot to compute a log-ratio; this action updates both the rank plot (through highlighting selected features) and the sample plot (through displaying the current log-ratios of samples). Here, we demonstrate how this unique interface helps users explore feature rankings and log-ratios simply and effectively.
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Affiliation(s)
- Marcus W Fedarko
- Department of Computer Science and Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Center for Microbiome Innovation, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Cameron Martino
- Center for Microbiome Innovation, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - James T Morton
- Flatiron Institute, Simons Foundation, 162 Fifth Avenue, New York City, NY 10010, USA
| | - Antonio González
- Department of Pediatrics, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Gibraan Rahman
- Bioinformatics and Systems Biology Program, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Clarisse A Marotz
- Department of Biomedical Sciences, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Jeremiah J Minich
- Marine Biology Research Division, Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Eric E Allen
- Center for Microbiome Innovation, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Marine Biology Research Division, Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Biological Sciences, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Rob Knight
- Department of Computer Science and Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Center for Microbiome Innovation, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
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16
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Zhu Q, Mai U, Pfeiffer W, Janssen S, Asnicar F, Sanders JG, Belda-Ferre P, Al-Ghalith GA, Kopylova E, McDonald D, Kosciolek T, Yin JB, Huang S, Salam N, Jiao JY, Wu Z, Xu ZZ, Cantrell K, Yang Y, Sayyari E, Rabiee M, Morton JT, Podell S, Knights D, Li WJ, Huttenhower C, Segata N, Smarr L, Mirarab S, Knight R. Phylogenomics of 10,575 genomes reveals evolutionary proximity between domains Bacteria and Archaea. Nat Commun 2019; 10:5477. [PMID: 31792218 PMCID: PMC6889312 DOI: 10.1038/s41467-019-13443-4] [Citation(s) in RCA: 121] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Accepted: 11/06/2019] [Indexed: 11/10/2022] Open
Abstract
Rapid growth of genome data provides opportunities for updating microbial evolutionary relationships, but this is challenged by the discordant evolution of individual genes. Here we build a reference phylogeny of 10,575 evenly-sampled bacterial and archaeal genomes, based on a comprehensive set of 381 markers, using multiple strategies. Our trees indicate remarkably closer evolutionary proximity between Archaea and Bacteria than previous estimates that were limited to fewer "core" genes, such as the ribosomal proteins. The robustness of the results was tested with respect to several variables, including taxon and site sampling, amino acid substitution heterogeneity and saturation, non-vertical evolution, and the impact of exclusion of candidate phyla radiation (CPR) taxa. Our results provide an updated view of domain-level relationships.
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Affiliation(s)
- Qiyun Zhu
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Uyen Mai
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Wayne Pfeiffer
- San Diego Supercomputer Center, University of California San Diego, La Jolla, CA, USA
| | - Stefan Janssen
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Algorithmic Bioinformatics, Department of Biology and Chemistry, Justus Liebig University Gießen, Giessen, Germany
| | | | - Jon G Sanders
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Pedro Belda-Ferre
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Gabriel A Al-Ghalith
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Evguenia Kopylova
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Tomasz Kosciolek
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
| | - John B Yin
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
- Department of Mathematics, University of California San Diego, La Jolla, CA, USA
| | - Shi Huang
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Single-Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong, China
| | - Nimaichand Salam
- State Key Laboratory of Biocontrol and Guangdong Key Laboratory of Plant Resources, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Jian-Yu Jiao
- State Key Laboratory of Biocontrol and Guangdong Key Laboratory of Plant Resources, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Zijun Wu
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA
| | - Zhenjiang Z Xu
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Kalen Cantrell
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Yimeng Yang
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Erfan Sayyari
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Maryam Rabiee
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - James T Morton
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Sheila Podell
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Dan Knights
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Wen-Jun Li
- State Key Laboratory of Biocontrol and Guangdong Key Laboratory of Plant Resources, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Curtis Huttenhower
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nicola Segata
- Department CIBIO, University of Trento, Trento, Italy
| | - Larry Smarr
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
- California Institute for Telecommunications and Information Technology, University of California San Diego, La Jolla, CA, USA
| | - Siavash Mirarab
- Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
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17
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Morton JT, Aksenov AA, Nothias LF, Foulds JR, Quinn RA, Badri MH, Swenson TL, Van Goethem MW, Northen TR, Vazquez-Baeza Y, Wang M, Bokulich NA, Watters A, Song SJ, Bonneau R, Dorrestein PC, Knight R. Learning representations of microbe-metabolite interactions. Nat Methods 2019; 16:1306-1314. [PMID: 31686038 PMCID: PMC6884698 DOI: 10.1038/s41592-019-0616-3] [Citation(s) in RCA: 124] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 09/19/2019] [Indexed: 12/26/2022]
Abstract
Integrating multiomics datasets is critical for microbiome research; however, inferring interactions across omics datasets has multiple statistical challenges. We solve this problem by using neural networks (https://github.com/biocore/mmvec) to estimate the conditional probability that each molecule is present given the presence of a specific microorganism. We show with known environmental (desert soil biocrust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe-metabolite relationships, and demonstrate how the method can discover relationships between microbially produced metabolites and inflammatory bowel disease.
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Affiliation(s)
- James T Morton
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Alexander A Aksenov
- Collaborative Mass Spectrometry Innovaftion Center, University of California, San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Louis Felix Nothias
- Collaborative Mass Spectrometry Innovaftion Center, University of California, San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - James R Foulds
- Department of Information Systems, University of Maryland Baltimore County, Baltimore, MD, USA
| | - Robert A Quinn
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
| | | | - Tami L Swenson
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Marc W Van Goethem
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Trent R Northen
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- DOE Joint Genome Institute, Walnut Creek, CA, USA
| | - Yoshiki Vazquez-Baeza
- Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
| | - Mingxun Wang
- Collaborative Mass Spectrometry Innovaftion Center, University of California, San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Nicholas A Bokulich
- The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
| | - Aaron Watters
- Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Se Jin Song
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
| | - Richard Bonneau
- Department of Biology, New York University, New York, NY, USA
- Flatiron Institute, Simons Foundation, New York, NY, USA
- Computer Science Department, Courant Institute, New York, NY, USA
- Center For Data Science, New York University, New York, NY, USA
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovaftion Center, University of California, San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Rob Knight
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA.
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
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18
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Cao L, Gurevich A, Alexander KL, Naman CB, Leão T, Glukhov E, Luzzatto-Knaan T, Vargas F, Quinn R, Bouslimani A, Nothias LF, Singh NK, Sanders JG, Benitez RAS, Thompson LR, Hamid MN, Morton JT, Mikheenko A, Shlemov A, Korobeynikov A, Friedberg I, Knight R, Venkateswaran K, Gerwick WH, Gerwick L, Dorrestein PC, Pevzner PA, Mohimani H. MetaMiner: A Scalable Peptidogenomics Approach for Discovery of Ribosomal Peptide Natural Products with Blind Modifications from Microbial Communities. Cell Syst 2019; 9:600-608.e4. [PMID: 31629686 DOI: 10.1016/j.cels.2019.09.004] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 04/23/2019] [Accepted: 09/12/2019] [Indexed: 12/22/2022]
Abstract
Ribosomally synthesized and post-translationally modified peptides (RiPPs) are an important class of natural products that contain antibiotics and a variety of other bioactive compounds. The existing methods for discovery of RiPPs by combining genome mining and computational mass spectrometry are limited to discovering specific classes of RiPPs from small datasets, and these methods fail to handle unknown post-translational modifications. Here, we present MetaMiner, a software tool for addressing these challenges that is compatible with large-scale screening platforms for natural product discovery. After searching millions of spectra in the Global Natural Products Social (GNPS) molecular networking infrastructure against just eight genomic and metagenomic datasets, MetaMiner discovered 31 known and seven unknown RiPPs from diverse microbial communities, including human microbiome and lichen microbiome, and microorganisms isolated from the International Space Station.
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Affiliation(s)
- Liu Cao
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Alexey Gurevich
- Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
| | - Kelsey L Alexander
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA; Department of Chemistry and Biochemistry, University of California, San Diego, San Diego, CA, USA
| | - C Benjamin Naman
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA; Li Dak Sum Yip Yio Chin Kenneth Li Marine Biopharmaceutical Research Center, Department of Marine Pharmacy, College of Food and Pharmaceutical Sciences, Ningbo University, Ningbo, Zhejiang, China
| | - Tiago Leão
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
| | - Evgenia Glukhov
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
| | - Tal Luzzatto-Knaan
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
| | - Fernando Vargas
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
| | - Robby Quinn
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
| | - Amina Bouslimani
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
| | - Louis Felix Nothias
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
| | - Nitin K Singh
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Jon G Sanders
- Department of Pediatrics, University of California, San Diego School of Medicine, San Diego, CA, USA
| | - Rodolfo A S Benitez
- Department of Pediatrics, University of California, San Diego School of Medicine, San Diego, CA, USA
| | - Luke R Thompson
- Department of Biological Sciences and Northern Gulf Institute, University of Southern Mississippi, Hattiesburg, MS, USA; Ocean Chemistry and Ecosystems Division, Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, stationed at Southwest Fisheries Science Center, La Jolla, CA, USA
| | - Md-Nafiz Hamid
- Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA, USA; Interdepartmental program in Bioinformatics and Computational Biology, Iowa State University, Ames, IA, USA
| | - James T Morton
- Department of Pediatrics, University of California, San Diego School of Medicine, San Diego, CA, USA; Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA, USA
| | - Alla Mikheenko
- Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
| | - Alexander Shlemov
- Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
| | - Anton Korobeynikov
- Center for Algorithmic Biotechnology, Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia; Department of Mathematics and Mechanics, St. Petersburg State University, St. Petersburg, Russia
| | - Iddo Friedberg
- Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, IA, USA; Interdepartmental program in Bioinformatics and Computational Biology, Iowa State University, Ames, IA, USA
| | - Rob Knight
- Department of Pediatrics, University of California, San Diego School of Medicine, San Diego, CA, USA; Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA, USA; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, San Diego, CA, USA; Department of Bioengineering, University of California, San Diego, San Diego, CA, USA
| | | | - William H Gerwick
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
| | - Lena Gerwick
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA, USA; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, San Diego, CA, USA
| | - Pavel A Pevzner
- Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA, USA; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, San Diego, CA, USA
| | - Hosein Mohimani
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA, USA.
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19
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Morton JT, Marotz C, Washburne A, Silverman J, Zaramela LS, Edlund A, Zengler K, Knight R. Establishing microbial composition measurement standards with reference frames. Nat Commun 2019; 10:2719. [PMID: 31222023 PMCID: PMC6586903 DOI: 10.1038/s41467-019-10656-5] [Citation(s) in RCA: 328] [Impact Index Per Article: 65.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 05/14/2019] [Indexed: 12/30/2022] Open
Abstract
Differential abundance analysis is controversial throughout microbiome research. Gold standard approaches require laborious measurements of total microbial load, or absolute number of microorganisms, to accurately determine taxonomic shifts. Therefore, most studies rely on relative abundance data. Here, we demonstrate common pitfalls in comparing relative abundance across samples and identify two solutions that reveal microbial changes without the need to estimate total microbial load. We define the notion of "reference frames", which provide deep intuition about the compositional nature of microbiome data. In an oral time series experiment, reference frames alleviate false positives and produce consistent results on both raw and cell-count normalized data. Furthermore, reference frames identify consistent, differentially abundant microbes previously undetected in two independent published datasets from subjects with atopic dermatitis. These methods allow reassessment of published relative abundance data to reveal reproducible microbial changes from standard sequencing output without the need for new assays.
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Affiliation(s)
- James T Morton
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Clarisse Marotz
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Alex Washburne
- Department of Microbiology and Immunology, Montana State University, Bozeman, MT, 59717, USA
| | - Justin Silverman
- Program in Computational Biology and Bioinformatics, Duke University, Durham, 27708, USA
- Medical Scientist Training Program, Duke University, Durham, 27708, USA
- Center for Genomic and Computational Biology, Duke University, Durham, 27708, USA
| | - Livia S Zaramela
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Anna Edlund
- J. Craig Venter Institute, Genomic Medicine Group, La Jolla, CA, 92037, USA
| | - Karsten Zengler
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA.
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, 92093, USA.
| | - Rob Knight
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA.
- Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA, 92093, USA.
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, 92093, USA.
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20
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Washburne AD, Silverman JD, Morton JT, Becker DJ, Crowley D, Mukherjee S, David LA, Plowright RK. Phylofactorization: a graph partitioning algorithm to identify phylogenetic scales of ecological data. ECOL MONOGR 2019. [DOI: 10.1002/ecm.1353] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Alex D. Washburne
- Department of Microbiology and Immunology Montana State University Bozeman Montana 59717 USA
| | - Justin D. Silverman
- Program for Computational Biology and Bioinformatics Duke University Durham North Carolina 27708 USA
- Center for Genomic and Computational Biology Duke University Durham North Carolina 27708 USA
| | - James T. Morton
- Department of Computer Science University of California San Diego La Jolla California 92037 USA
- Department of Pediatrics University of California San Diego La Jolla California 92037 USA
| | - Daniel J. Becker
- Department of Microbiology and Immunology Montana State University Bozeman Montana 59717 USA
| | - Daniel Crowley
- Department of Microbiology and Immunology Montana State University Bozeman Montana 59717 USA
| | - Sayan Mukherjee
- Center for Genomic and Computational Biology Duke University Durham North Carolina 27708 USA
- Department of Statistical Science, Mathematics, and Computer Science Duke University Durham North Carolina 27708 USA
| | - Lawrence A. David
- Center for Genomic and Computational Biology Duke University Durham North Carolina 27708 USA
| | - Raina K. Plowright
- Department of Microbiology and Immunology Montana State University Bozeman Montana 59717 USA
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21
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Martino C, Morton JT, Marotz CA, Thompson LR, Tripathi A, Knight R, Zengler K. A Novel Sparse Compositional Technique Reveals Microbial Perturbations. mSystems 2019; 4:e00016-19. [PMID: 30801021 PMCID: PMC6372836 DOI: 10.1128/msystems.00016-19] [Citation(s) in RCA: 211] [Impact Index Per Article: 42.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Accepted: 01/11/2019] [Indexed: 12/17/2022] Open
Abstract
The central aims of many host or environmental microbiome studies are to elucidate factors associated with microbial community compositions and to relate microbial features to outcomes. However, these aims are often complicated by difficulties stemming from high-dimensionality, non-normality, sparsity, and the compositional nature of microbiome data sets. A key tool in microbiome analysis is beta diversity, defined by the distances between microbial samples. Many different distance metrics have been proposed, all with varying discriminatory power on data with differing characteristics. Here, we propose a compositional beta diversity metric rooted in a centered log-ratio transformation and matrix completion called robust Aitchison PCA. We demonstrate the benefits of compositional transformations upstream of beta diversity calculations through simulations. Additionally, we demonstrate improved effect size, classification accuracy, and robustness to sequencing depth over the current methods on several decreased sample subsets of real microbiome data sets. Finally, we highlight the ability of this new beta diversity metric to retain the feature loadings linked to sample ordinations revealing salient intercommunity niche feature importance. IMPORTANCE By accounting for the sparse compositional nature of microbiome data sets, robust Aitchison PCA can yield high discriminatory power and salient feature ranking between microbial niches. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/biocore/DEICODE; additionally, a QIIME 2 plugin is provided to perform this analysis at https://library.qiime2.org/plugins/deicode/.
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Affiliation(s)
- Cameron Martino
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, California, USA
| | - James T. Morton
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Clarisse A. Marotz
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Luke R. Thompson
- Department of Biological Sciences and Northern Gulf Institute, University of Southern Mississippi, Hattiesburg, Mississippi, USA
- Ocean Chemistry and Ecosystems Division, Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, stationed at Southwest Fisheries Science Center, La Jolla, California, USA
| | - Anupriya Tripathi
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, California, USA
| | - Karsten Zengler
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, California, USA
- Department of Bioengineering, University of California San Diego, La Jolla, California, USA
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22
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Quinn RA, Comstock W, Zhang T, Morton JT, da Silva R, Tran A, Aksenov A, Nothias LF, Wangpraseurt D, Melnik AV, Ackermann G, Conrad D, Klapper I, Knight R, Dorrestein PC. Niche partitioning of a pathogenic microbiome driven by chemical gradients. Sci Adv 2018; 4:eaau1908. [PMID: 30263961 PMCID: PMC6157970 DOI: 10.1126/sciadv.aau1908] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 08/10/2018] [Indexed: 05/25/2023]
Abstract
Environmental microbial communities are stratified by chemical gradients that shape the structure and function of these systems. Similar chemical gradients exist in the human body, but how they influence these microbial systems is more poorly understood. Understanding these effects can be particularly important for dysbiotic shifts in microbiome structure that are often associated with disease. We show that pH and oxygen strongly partition the microbial community from a diseased human lung into two mutually exclusive communities of pathogens and anaerobes. Antimicrobial treatment disrupted this chemical partitioning, causing complex death, survival, and resistance outcomes that were highly dependent on the individual microorganism and on community stratification. These effects were mathematically modeled, enabling a predictive understanding of this complex polymicrobial system. Harnessing the power of these chemical gradients could be a drug-free method of shaping microbial communities in the human body from undesirable dysbiotic states.
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Affiliation(s)
- Robert A. Quinn
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California at San Diego, La Jolla, CA 92093, USA
- Center for Microbiome Innovation, University of California at San Diego, La Jolla, CA 92093, USA
| | - William Comstock
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California at San Diego, La Jolla, CA 92093, USA
| | - Tianyu Zhang
- Department of Mathematical Sciences, Montana State University, Bozeman, MT 59717, USA
| | - James T. Morton
- Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA
| | - Ricardo da Silva
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California at San Diego, La Jolla, CA 92093, USA
| | - Alda Tran
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California at San Diego, La Jolla, CA 92093, USA
| | - Alexander Aksenov
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California at San Diego, La Jolla, CA 92093, USA
- Center for Microbiome Innovation, University of California at San Diego, La Jolla, CA 92093, USA
| | - Louis-Felix Nothias
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California at San Diego, La Jolla, CA 92093, USA
| | - Daniel Wangpraseurt
- Department of Chemistry, University of Cambridge, Cambridge, UK
- Scripps Institution of Oceanography, University of California at San Diego, La Jolla, CA 92093, USA
| | - Alexey V. Melnik
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California at San Diego, La Jolla, CA 92093, USA
| | - Gail Ackermann
- Department of Pediatrics, University of California at San Diego, La Jolla, CA 92093, USA
| | - Douglas Conrad
- Department of Medicine, University of California at San Diego, La Jolla, CA 92093, USA
| | - Isaac Klapper
- Department of Mathematics, Temple University, Philadelphia, PA 19122, USA
| | - Rob Knight
- Center for Microbiome Innovation, University of California at San Diego, La Jolla, CA 92093, USA
- Department of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California at San Diego, La Jolla, CA 92093, USA
| | - Pieter C. Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California at San Diego, La Jolla, CA 92093, USA
- Center for Microbiome Innovation, University of California at San Diego, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California at San Diego, La Jolla, CA 92093, USA
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23
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Lavrinienko A, Mappes T, Tukalenko E, Mousseau TA, Møller AP, Knight R, Morton JT, Thompson LR, Watts PC. Environmental radiation alters the gut microbiome of the bank vole Myodes glareolus. ISME J 2018; 12:2801-2806. [PMID: 29988064 PMCID: PMC6193954 DOI: 10.1038/s41396-018-0214-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Revised: 05/16/2018] [Accepted: 06/11/2018] [Indexed: 02/07/2023]
Abstract
Gut microbiota composition depends on many factors, although the impact of environmental pollution is largely unknown. We used amplicon sequencing of bacterial 16S rRNA genes to quantify whether anthropogenic radionuclides at Chernobyl (Ukraine) impact the gut microbiome of the bank vole Myodes glareolus. Exposure to elevated levels of environmental radionuclides had no detectable effect on the gut community richness but was associated with an almost two-fold increase in the Firmicutes:Bacteroidetes ratio. Animals inhabiting uncontaminated areas had remarkably similar gut communities irrespective of their proximity to the nuclear power plant. Hence, samples could be classified to high-radiation or low-radiation sites based solely on microbial community with >90% accuracy. Radiation-associated bacteria had distinct inferred functional profiles, including pathways involved in degradation, assimilation and transport of carbohydrates, xenobiotics biodegradation, and DNA repair. Our results suggest that exposure to environmental radionuclides significantly alters vertebrate gut microbiota.
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Affiliation(s)
- Anton Lavrinienko
- Department of Ecology and Genetics, University of Oulu, 90014, Oulu, Finland.
| | - Tapio Mappes
- Department of Biological and Environmental Science, University of Jyväskylä, 40014, Jyväskylä, Finland
| | - Eugene Tukalenko
- Department of Biological and Environmental Science, University of Jyväskylä, 40014, Jyväskylä, Finland.,Institute of Biology and Medicine, Taras Shevchenko National University of Kyiv, Kyiv, 03022, Ukraine
| | - Timothy A Mousseau
- Department of Biological Sciences, University of South Carolina, Columbia, SC, 29208, USA
| | - Anders P Møller
- Ecologie Systématique Evolution, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91405, Orsay Cedex, France
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92037, USA.,Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, 92037, USA.,Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, 92037, USA
| | - James T Morton
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92037, USA.,Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, 92037, USA
| | - Luke R Thompson
- Department of Biological Sciences and Northern Gulf Institute, University of Southern Mississippi, Hattiesburg, MS, USA.,Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, stationed at Southwest Fisheries Science Center, National Marine Fisheries Service, La Jolla, CA, USA
| | - Phillip C Watts
- Department of Ecology and Genetics, University of Oulu, 90014, Oulu, Finland
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24
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Washburne AD, Morton JT, Sanders J, McDonald D, Zhu Q, Oliverio AM, Knight R. Methods for phylogenetic analysis of microbiome data. Nat Microbiol 2018; 3:652-661. [PMID: 29795540 DOI: 10.1038/s41564-018-0156-0] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 03/27/2018] [Indexed: 02/07/2023]
Abstract
How does knowing the evolutionary history of microorganisms affect our analysis of microbiological datasets? Depending on the research question, the common ancestry of microorganisms can be a source of confounding variation, or a scaffolding used for inference. For example, when performing regression on traits, common ancestry is a source of dependence among observations, whereas when searching for clades with correlated abundances, common ancestry is the scaffolding for inference. The common ancestry of microorganisms and their genes are organized in trees-phylogenies-which can and should be incorporated into analyses of microbial datasets. While there has been a recent expansion of phylogenetically informed analytical tools, little guidance exists for which method best answers which biological questions. Here, we review methods for phylogeny-aware analyses of microbiome datasets, considerations for choosing the appropriate method and challenges inherent in these methods. We introduce a conceptual organization of these tools, breaking them down into phylogenetic comparative methods, ancestral state reconstruction and analysis of phylogenetic variables and distances, and provide examples in Supplementary Online Tutorials. Careful consideration of the research question and ecological and evolutionary assumptions will help researchers choose a phylogeny and appropriate methods to produce accurate, biologically informative and previously unreported insights.
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Affiliation(s)
- Alex D Washburne
- Department of Microbiology and Immunology, Montana State University, Bozeman, MT, USA.
| | - James T Morton
- Department of Computer Science, University of California San Diego, La Jolla, CA, USA.,Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Jon Sanders
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Qiyun Zhu
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Angela M Oliverio
- Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO, USA.,Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
| | - Rob Knight
- Department of Computer Science, University of California San Diego, La Jolla, CA, USA.,Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
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25
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Kapono CA, Morton JT, Bouslimani A, Melnik AV, Orlinsky K, Knaan TL, Garg N, Vázquez-Baeza Y, Protsyuk I, Janssen S, Zhu Q, Alexandrov T, Smarr L, Knight R, Dorrestein PC. Creating a 3D microbial and chemical snapshot of a human habitat. Sci Rep 2018; 8:3669. [PMID: 29487294 PMCID: PMC5829137 DOI: 10.1038/s41598-018-21541-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 02/05/2018] [Indexed: 12/22/2022] Open
Abstract
One of the goals of forensic science is to identify individuals and their lifestyle by analyzing the trace signatures left behind in built environments. Here, microbiome and metabolomic methods were used to see how its occupants used an office and to also gain insights into the lifestyle characteristics such as diet, medications, and personal care products of the occupants. 3D molecular cartography, a molecular visualization technology, was used in combination with mass spectrometry and microbial inventories to highlight human-environmental interactions. Molecular signatures were correlated with the individuals as well as their interactions with this indoor environment. There are person-specific chemical and microbial signatures associated with this environment that directly relate who had touched objects such as computers, computer mice, cell phones, desk phone, table or desks. By combining molecular and microbial investigation forensic strategies, this study offers novel insights to investigators who value the reconstructing of human lifestyle and characterization of human environmental interaction.
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Affiliation(s)
- Clifford A Kapono
- Department of Chemistry, University of California San Diego, La Jolla, CA, USA
| | - James T Morton
- Department of Computer of Science and Engineering, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Amina Bouslimani
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California at San Diego, La Jolla, CA, USA
| | - Alexey V Melnik
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California at San Diego, La Jolla, CA, USA
| | - Kayla Orlinsky
- Department of Computer of Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Tal Luzzatto Knaan
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California at San Diego, La Jolla, CA, USA
| | - Neha Garg
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California at San Diego, La Jolla, CA, USA
| | - Yoshiki Vázquez-Baeza
- Department of Computer of Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Ivan Protsyuk
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117, Heidelberg, Germany
| | - Stefan Janssen
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Qiyun Zhu
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Theodore Alexandrov
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California at San Diego, La Jolla, CA, USA
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117, Heidelberg, Germany
| | - Larry Smarr
- Department of Computer of Science and Engineering, University of California San Diego, La Jolla, CA, USA
- California Institute for Telecommunications and Information Technology, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
| | - Rob Knight
- Department of Computer of Science and Engineering, University of California San Diego, La Jolla, CA, USA.
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA.
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California at San Diego, La Jolla, CA, USA.
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA.
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26
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Thompson LR, Sanders JG, McDonald D, Amir A, Ladau J, Locey KJ, Prill RJ, Tripathi A, Gibbons SM, Ackermann G, Navas-Molina JA, Janssen S, Kopylova E, Vázquez-Baeza Y, González A, Morton JT, Mirarab S, Zech Xu Z, Jiang L, Haroon MF, Kanbar J, Zhu Q, Jin Song S, Kosciolek T, Bokulich NA, Lefler J, Brislawn CJ, Humphrey G, Owens SM, Hampton-Marcell J, Berg-Lyons D, McKenzie V, Fierer N, Fuhrman JA, Clauset A, Stevens RL, Shade A, Pollard KS, Goodwin KD, Jansson JK, Gilbert JA, Knight R. A communal catalogue reveals Earth's multiscale microbial diversity. Nature 2017; 551:457-463. [PMID: 29088705 PMCID: PMC6192678 DOI: 10.1038/nature24621] [Citation(s) in RCA: 1219] [Impact Index Per Article: 174.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Accepted: 10/10/2017] [Indexed: 02/07/2023]
Abstract
Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth's microbial diversity.
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Affiliation(s)
- Luke R Thompson
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA.,Department of Biological Sciences and Northern Gulf Institute, University of Southern Mississippi, Hattiesburg, Mississippi, USA.,Ocean Chemistry and Ecosystems Division, Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, stationed at Southwest Fisheries Science Center, La Jolla, California, USA
| | - Jon G Sanders
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Amnon Amir
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Joshua Ladau
- The Gladstone Institutes and University of California San Francisco, San Francisco, California, USA
| | - Kenneth J Locey
- Department of Biology, Indiana University, Bloomington, Indiana, USA
| | - Robert J Prill
- Industrial and Applied Genomics, IBM Almaden Research Center, San Jose, California, USA
| | - Anupriya Tripathi
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA.,Division of Biological Sciences, University of California San Diego, La Jolla, California, USA.,Skaggs School of Pharmacy, University of California San Diego, La Jolla, California, USA
| | - Sean M Gibbons
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Gail Ackermann
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Jose A Navas-Molina
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA.,Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Stefan Janssen
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Evguenia Kopylova
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Yoshiki Vázquez-Baeza
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA.,Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Antonio González
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - James T Morton
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA.,Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Siavash Mirarab
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California, USA
| | - Zhenjiang Zech Xu
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Lingjing Jiang
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA.,Department of Family Medicine and Public Health, University of California San Diego, La Jolla, California, USA
| | - Mohamed F Haroon
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA
| | - Jad Kanbar
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Qiyun Zhu
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Se Jin Song
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Tomasz Kosciolek
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Nicholas A Bokulich
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, USA
| | - Joshua Lefler
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Colin J Brislawn
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Gregory Humphrey
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Sarah M Owens
- Biosciences Division, Argonne National Laboratory, Argonne, Illinois, USA
| | - Jarrad Hampton-Marcell
- Biosciences Division, Argonne National Laboratory, Argonne, Illinois, USA.,Department of Biological Sciences, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Donna Berg-Lyons
- BioFrontiers Institute, University of Colorado, Boulder, Colorado, USA
| | - Valerie McKenzie
- Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA
| | - Noah Fierer
- Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, USA.,Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USA
| | - Jed A Fuhrman
- Department of Biological Sciences, University of Southern California, Los Angeles, California, USA
| | - Aaron Clauset
- BioFrontiers Institute, University of Colorado, Boulder, Colorado, USA.,Department of Computer Science, University of Colorado, Boulder, Colorado, USA
| | - Rick L Stevens
- Computing, Environment and Life Sciences, Argonne National Laboratory, Argonne, Illinois, USA.,Department of Computer Science, University of Chicago, Chicago, Illinois, USA
| | - Ashley Shade
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan, USA.,Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA.,Program in Ecology, Evolutionary Biology and Behavior, Michigan State University, East Lansing, Michigan, USA
| | - Katherine S Pollard
- The Gladstone Institutes and University of California San Francisco, San Francisco, California, USA
| | - Kelly D Goodwin
- Ocean Chemistry and Ecosystems Division, Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, stationed at Southwest Fisheries Science Center, La Jolla, California, USA
| | - Janet K Jansson
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Jack A Gilbert
- Biosciences Division, Argonne National Laboratory, Argonne, Illinois, USA.,Department of Surgery, University of Chicago, Chicago, Illinois, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA.,Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA.,Center for Microbiome Innovation, University of California San Diego, La Jolla, California, USA
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27
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McCall LI, Morton JT, Bernatchez JA, de Siqueira-Neto JL, Knight R, Dorrestein PC, McKerrow JH. Mass Spectrometry-Based Chemical Cartography of a Cardiac Parasitic Infection. Anal Chem 2017; 89:10414-10421. [PMID: 28892370 PMCID: PMC6298790 DOI: 10.1021/acs.analchem.7b02423] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Trypanosoma cruzi parasites are the causative agents of Chagas disease, a leading infectious form of heart failure whose pathogenesis is still not fully characterized. In this work, we applied untargeted liquid chromatography-tandem mass spectrometry to heart sections from T. cruzi-infected and uninfected mice. We combined molecular networking and three-dimensional modeling to generate chemical cartographical heart models. This approach revealed for the first time preferential parasite localization to the base of the heart and regiospecific distributions of nucleoside derivatives and eicosanoids, which we correlated to tissue-damaging immune responses. We further detected novel cardiac chemical signatures related to the severity and ultimate outcome of the infection. These signatures included differential representation of higher- vs lower-molecular-weight carnitine and phosphatidylcholine family members in specific cardiac regions of mice infected with lethal or nonlethal T. cruzi strains and doses. Overall, this work provides new insights into Chagas disease pathogenesis and presents an analytical chemistry approach that can be broadly applied to the study of host-microbe interactions.
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Affiliation(s)
- Laura-Isobel McCall
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093
- Present address: Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma, 73019-5251
| | - James T. Morton
- Department of Computer Science, University of California San Diego, La Jolla, CA 92093
| | - Jean A. Bernatchez
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093
| | - Jair Lage de Siqueira-Neto
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093
| | - Rob Knight
- Department of Computer Science, University of California San Diego, La Jolla, CA 92093
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093
| | - Pieter C. Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, California 92093
| | - James H. McKerrow
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093
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28
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Vázquez-Baeza Y, Callewaert C, Debelius J, Hyde E, Marotz C, Morton JT, Swafford A, Vrbanac A, Dorrestein PC, Knight R. Impacts of the Human Gut Microbiome on Therapeutics. Annu Rev Pharmacol Toxicol 2017; 58:253-270. [PMID: 28968189 DOI: 10.1146/annurev-pharmtox-042017-031849] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The human microbiome contains a vast source of genetic and biochemical variation, and its impacts on therapeutic responses are just beginning to be understood. This expanded understanding is especially important because the human microbiome differs far more among different people than does the human genome, and it is also dramatically easier to change. Here, we describe some of the major factors driving differences in the human microbiome among individuals and populations. We then describe some of the many ways in which gut microbes modify the action of specific chemotherapeutic agents, including nonsteroidal anti-inflammatory drugs and cardiac glycosides, and outline the potential of fecal microbiota transplant as a therapeutic. Intriguingly, microbes also alter how hosts respond to therapeutic agents through various pathways acting at distal sites. Finally, we discuss some of the computational and practical issues surrounding use of the microbiome to stratify individuals for drug response, and we envision a future where the microbiome will be modified to increase everyone's potential to benefit from therapy.
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Affiliation(s)
- Yoshiki Vázquez-Baeza
- Department of Computer Science and Engineering, University of California, San Diego, California 92093, USA;
| | - Chris Callewaert
- Department of Pediatrics, University of California, San Diego, California 92093, USA
| | - Justine Debelius
- Department of Pediatrics, University of California, San Diego, California 92093, USA
| | - Embriette Hyde
- Department of Pediatrics, University of California, San Diego, California 92093, USA
| | - Clarisse Marotz
- Biomedical Sciences, University of California, San Diego, California 92093, USA
| | - James T Morton
- Department of Computer Science and Engineering, University of California, San Diego, California 92093, USA;
| | - Austin Swafford
- Center for Microbiome Innovation, University of California, San Diego, California 92093, USA
| | - Alison Vrbanac
- Biomedical Sciences, University of California, San Diego, California 92093, USA
| | - Pieter C Dorrestein
- Pharmacy and Pharmaceutical Sciences, University of California, San Diego, California 92093, USA
| | - Rob Knight
- Department of Computer Science and Engineering, University of California, San Diego, California 92093, USA; .,Department of Pediatrics, University of California, San Diego, California 92093, USA.,Center for Microbiome Innovation, University of California, San Diego, California 92093, USA
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29
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Vázquez-Baeza Y, Gonzalez A, Smarr L, McDonald D, Morton JT, Navas-Molina JA, Knight R. Bringing the Dynamic Microbiome to Life with Animations. Cell Host Microbe 2017; 21:7-10. [PMID: 28081445 DOI: 10.1016/j.chom.2016.12.009] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Our bodies and natural environment contain complex microbial communities, colloquially termed microbiomes. We previously created a web-based application, EMPeror, for visualizing ordinations derived from comparisons of these microbiome communities. We have now improved EMPeror to create interactive animations that connect successive samples to highlight patterns over time.
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Affiliation(s)
- Yoshiki Vázquez-Baeza
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093-0404, USA
| | - Antonio Gonzalez
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Larry Smarr
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093-0404, USA; California Institute for Telecommunications and Information Technology, University of California, San Diego, La Jolla, CA 92093-0436, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - James T Morton
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093-0404, USA
| | - Jose A Navas-Molina
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093-0404, USA
| | - Rob Knight
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093-0404, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA.
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30
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Abstract
Microbes affect drug responses, but mechanisms remain elusive. Two papers in Cell exploit C. elegans to infer anticancer drug mechanisms. Through high-throughput screens of drug-microbe-host interactions, García-González et al. (2017) and Scott et al. (2017) determine that bacterial metabolism underpins fluoropyrimidine cytotoxicity, providing a paradigm for unraveling bacterial mechanisms in drug metabolism.
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Affiliation(s)
- Alison Vrbanac
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Justine W Debelius
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Lingjing Jiang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - James T Morton
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Pieter Dorrestein
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Rob Knight
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA.
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31
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Amir A, McDonald D, Navas-Molina JA, Kopylova E, Morton JT, Zech Xu Z, Kightley EP, Thompson LR, Hyde ER, Gonzalez A, Knight R. Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns. mSystems 2017; 2:e00191-16. [PMID: 28289731 PMCID: PMC5340863 DOI: 10.1128/msystems.00191-00116 10.1128/msystems.00191-16] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Accepted: 12/14/2016] [Indexed: 11/23/2023] Open
Abstract
High-throughput sequencing of 16S ribosomal RNA gene amplicons has facilitated understanding of complex microbial communities, but the inherent noise in PCR and DNA sequencing limits differentiation of closely related bacteria. Although many scientific questions can be addressed with broad taxonomic profiles, clinical, food safety, and some ecological applications require higher specificity. Here we introduce a novel sub-operational-taxonomic-unit (sOTU) approach, Deblur, that uses error profiles to obtain putative error-free sequences from Illumina MiSeq and HiSeq sequencing platforms. Deblur substantially reduces computational demands relative to similar sOTU methods and does so with similar or better sensitivity and specificity. Using simulations, mock mixtures, and real data sets, we detected closely related bacterial sequences with single nucleotide differences while removing false positives and maintaining stability in detection, suggesting that Deblur is limited only by read length and diversity within the amplicon sequences. Because Deblur operates on a per-sample level, it scales to modern data sets and meta-analyses. To highlight Deblur's ability to integrate data sets, we include an interactive exploration of its application to multiple distinct sequencing rounds of the American Gut Project. Deblur is open source under the Berkeley Software Distribution (BSD) license, easily installable, and downloadable from https://github.com/biocore/deblur. IMPORTANCE Deblur provides a rapid and sensitive means to assess ecological patterns driven by differentiation of closely related taxa. This algorithm provides a solution to the problem of identifying real ecological differences between taxa whose amplicons differ by a single base pair, is applicable in an automated fashion to large-scale sequencing data sets, and can integrate sequencing runs collected over time.
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Affiliation(s)
- Amnon Amir
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Jose A. Navas-Molina
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Evguenia Kopylova
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - James T. Morton
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Zhenjiang Zech Xu
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Eric P. Kightley
- Department of Applied Mathematics, and Interdisciplinary Quantitative Biology Graduate Program, University of Colorado Boulder, Boulder, Colorado, USA
| | - Luke R. Thompson
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Embriette R. Hyde
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Antonio Gonzalez
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
- Center for Microbiome Innovation, University of California San Diego, San Diego, California, USA
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32
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Hill-Burns EM, Debelius JW, Morton JT, Wissemann WT, Lewis MR, Wallen ZD, Peddada SD, Factor SA, Molho E, Zabetian CP, Knight R, Payami H. Parkinson's disease and Parkinson's disease medications have distinct signatures of the gut microbiome. Mov Disord 2017; 32:739-749. [PMID: 28195358 DOI: 10.1002/mds.26942] [Citation(s) in RCA: 538] [Impact Index Per Article: 76.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 01/06/2017] [Accepted: 01/11/2017] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND There is mounting evidence for a connection between the gut and Parkinson's disease (PD). Dysbiosis of gut microbiota could explain several features of PD. OBJECTIVE The objective of this study was to determine if PD involves dysbiosis of gut microbiome, disentangle effects of confounders, and identify candidate taxa and functional pathways to guide research. METHODS A total of 197 PD cases and 130 controls were studied. Microbial composition was determined by 16S rRNA gene sequencing of DNA extracted from stool. Metadata were collected on 39 potential confounders including medications, diet, gastrointestinal symptoms, and demographics. Statistical analyses were conducted while controlling for potential confounders and correcting for multiple testing. We tested differences in the overall microbial composition, taxa abundance, and functional pathways. RESULTS Independent microbial signatures were detected for PD (P = 4E-5), participants' region of residence within the United States (P = 3E-3), age (P = 0.03), sex (P = 1E-3), and dietary fruits/vegetables (P = 0.01). Among patients, independent signals were detected for catechol-O-methyltransferase-inhibitors (P = 4E-4), anticholinergics (P = 5E-3), and possibly carbidopa/levodopa (P = 0.05). We found significantly altered abundances of the Bifidobacteriaceae, Christensenellaceae, [Tissierellaceae], Lachnospiraceae, Lactobacillaceae, Pasteurellaceae, and Verrucomicrobiaceae families. Functional predictions revealed changes in numerous pathways, including the metabolism of plant-derived compounds and xenobiotics degradation. CONCLUSION PD is accompanied by dysbiosis of gut microbiome. Results coalesce divergent findings of prior studies, reveal altered abundance of several taxa, nominate functional pathways, and demonstrate independent effects of PD medications on the microbiome. The findings provide new leads and testable hypotheses on the pathophysiology and treatment of PD. © 2017 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Erin M Hill-Burns
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Justine W Debelius
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - James T Morton
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - William T Wissemann
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Matthew R Lewis
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Zachary D Wallen
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Shyamal D Peddada
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Stewart A Factor
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Eric Molho
- Department of Neurology, Albany Medical College, Albany, New York, USA
| | - Cyrus P Zabetian
- Veterans Affairs Puget Sound Health Care System and Department of Neurology, University of Washington, Seattle, Washington, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA.,Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA.,Center for Microbiome Innovation, University of California San Diego, La Jolla, California, USA
| | - Haydeh Payami
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA.,Center for Genomic Medicine, HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA
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Morton JT, Sanders J, Quinn RA, McDonald D, Gonzalez A, Vázquez-Baeza Y, Navas-Molina JA, Song SJ, Metcalf JL, Hyde ER, Lladser M, Dorrestein PC, Knight R. Balance Trees Reveal Microbial Niche Differentiation. mSystems 2017; 2:e00162-16. [PMID: 28144630 PMCID: PMC5264246 DOI: 10.1128/msystems.00162-16] [Citation(s) in RCA: 168] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Accepted: 11/22/2016] [Indexed: 11/20/2022] Open
Abstract
Advances in sequencing technologies have enabled novel insights into microbial niche differentiation, from analyzing environmental samples to understanding human diseases and informing dietary studies. However, identifying the microbial taxa that differentiate these samples can be challenging. These issues stem from the compositional nature of 16S rRNA gene data (or, more generally, taxon or functional gene data); the changes in the relative abundance of one taxon influence the apparent abundances of the others. Here we acknowledge that inferring properties of individual bacteria is a difficult problem and instead introduce the concept of balances to infer meaningful properties of subcommunities, rather than properties of individual species. We show that balances can yield insights about niche differentiation across multiple microbial environments, including soil environments and lung sputum. These techniques have the potential to reshape how we carry out future ecological analyses aimed at revealing differences in relative taxonomic abundances across different samples. IMPORTANCE By explicitly accounting for the compositional nature of 16S rRNA gene data through the concept of balances, balance trees yield novel biological insights into niche differentiation. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/biocore/gneiss. Author Video: An author video summary of this article is available.
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Affiliation(s)
- James T. Morton
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Jon Sanders
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Robert A. Quinn
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy, University of California San Diego, La Jolla, California, USA, and Department of Animal Sciences, Colorado State University, Fort Collins, Colorado, USA
| | - Daniel McDonald
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Antonio Gonzalez
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Yoshiki Vázquez-Baeza
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Jose A. Navas-Molina
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Se Jin Song
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Jessica L. Metcalf
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy, University of California San Diego, La Jolla, California, USA, and Department of Animal Sciences, Colorado State University, Fort Collins, Colorado, USA
| | - Embriette R. Hyde
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Manuel Lladser
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, Colorado, USA
| | - Pieter C. Dorrestein
- Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
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Morton JT, Abrudan P, Figueroa N, Liang C, Karro JE. SCOPE++: sequence classification of homoPolymer emissions. Genomics 2014; 104:157-62. [PMID: 25087770 DOI: 10.1016/j.ygeno.2014.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2014] [Revised: 07/04/2014] [Accepted: 07/17/2014] [Indexed: 10/25/2022]
Abstract
BACKGROUND mRNA polyadenylation, the addition of a poly(A) tail to the 3'-end of pre-mRNA, is a process critical to gene expression and regulation in eukaryotes. To understand the molecular mechanisms governing polyadenylation and other relevant biological processes, it is important to identify these poly(A) tails accurately in transcriptome sequencing data and differentiate them from artificial adapter sequences added in the sequencing process. But the annotation of these tails is complicated by the presence of sequencing errors and post-transcriptional modifications. While determining that a tail is present in a given transcript fragment is straight-forward, these obfuscations make the problem of boundary identification a challenge; conventional seed-and-extend algorithms struggle to accurately identify these poly(A) tail end-points. Further, all existing tools that we are aware of focus exclusively on the trimming of poly(A) tails, failing to provide the detailed information needed for studying the polyadenylation process. RESULTS We have created SCOPE++, an open-source tool for finding the precise border of poly(A) tails and other homopolymers in raw mRNA sequence reads. Based on a Hidden Markov Model (HMM) approach, SCOPE++ accurately identifies specific homopolymer sequences in error-prone EST/cDNA data or RNA-Seq data at a speed appropriate for large sequence sets. CONCLUSIONS We demonstrate that our tool can precisely identify poly(A) tails with near perfect accuracy at the speed required for high-throughput applications, providing a valuable resource for polyadenylation research.
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Affiliation(s)
- James T Morton
- Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA.
| | | | - Nathanial Figueroa
- Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA.
| | - Chun Liang
- Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA; Department of Biology, Miami University, Oxford, OH, USA.
| | - John E Karro
- Department of Computer Science and Software Engineering, Miami University, Oxford, OH, USA; Department of Microbiology, Miami University, Oxford, OH, USA; Department of Statistics, Miami University, Oxford, OH, USA.
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