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Koci O, Russell RK, Shaikh MG, Edwards C, Gerasimidis K, Ijaz UZ. CViewer: a Java-based statistical framework for integration of shotgun metagenomics with other omics datasets. MICROBIOME 2024; 12:117. [PMID: 38951915 PMCID: PMC11218139 DOI: 10.1186/s40168-024-01834-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 05/09/2024] [Indexed: 07/03/2024]
Abstract
BACKGROUND Shotgun metagenomics for microbial community survey recovers enormous amount of information for microbial genomes that include their abundances, taxonomic, and phylogenetic information, as well as their genomic makeup, the latter of which then helps retrieve their function based on annotated gene products, mRNA, protein, and metabolites. Within the context of a specific hypothesis, additional modalities are often included, to give host-microbiome interaction. For example, in human-associated microbiome projects, it has become increasingly common to include host immunology through flow cytometry. Whilst there are plenty of software approaches available, some that utilize marker-based and assembly-based approaches, for downstream statistical analyses, there is still a dearth of statistical tools that help consolidate all such information in a single platform. By virtue of stringent computational requirements, the statistical workflow is often passive with limited visual exploration. RESULTS In this study, we have developed a Java-based statistical framework ( https://github.com/KociOrges/cviewer ) to explore shotgun metagenomics data, which integrates seamlessly with conventional pipelines and offers exploratory as well as hypothesis-driven analyses. The end product is a highly interactive toolkit with a multiple document interface, which makes it easier for a person without specialized knowledge to perform analysis of multiomics datasets and unravel biologically relevant patterns. We have designed algorithms based on frequently used numerical ecology and machine learning principles, with value-driven from integrated omics tools which not only find correlations amongst different datasets but also provide discrimination based on case-control relationships. CONCLUSIONS CViewer was used to analyse two distinct metagenomic datasets with varying complexities. These include a dietary intervention study to understand Crohn's disease changes during a dietary treatment to include remission, as well as a gut microbiome profile for an obesity dataset comparing subjects who suffer from obesity of different aetiologies and against controls who were lean. Complete analyses of both studies in CViewer then provide very powerful mechanistic insights that corroborate with the published literature and demonstrate its full potential. Video Abstract.
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Affiliation(s)
- Orges Koci
- Human Nutrition, School of Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow Royal Infirmary, Glasgow, G4 0SF, UK
| | - Richard K Russell
- Department of Paediatric Gastroenterology, Hepatology and Nutrition, Royal Hospital for Children & Young People, Edinburgh, EH16 4TJ, UK
| | - M Guftar Shaikh
- Department of Endocrinology, Royal Hospital for Children, Glasgow, 1345 Govan Rd., Glasgow, G51 4T, UK
| | - Christine Edwards
- Human Nutrition, School of Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow Royal Infirmary, Glasgow, G4 0SF, UK
| | - Konstantinos Gerasimidis
- Human Nutrition, School of Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow Royal Infirmary, Glasgow, G4 0SF, UK
| | - Umer Zeeshan Ijaz
- Water & Environment Research Group, University of Glasgow, Mazumdar-Shaw Advanced Research Centre, Glasgow, G11 6EW, UK.
- National University of Ireland, Galway, University Road, Galway, H91 TK33, Ireland.
- Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, L69 7BE, UK.
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Valdes C, Stebliankin V, Ruiz-Perez D, Park JI, Lee H, Narasimhan G. Microbiome maps: Hilbert curve visualizations of metagenomic profiles. FRONTIERS IN BIOINFORMATICS 2023; 3:1154588. [PMID: 37405310 PMCID: PMC10317073 DOI: 10.3389/fbinf.2023.1154588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/22/2023] [Indexed: 07/06/2023] Open
Abstract
Abundance profiles from metagenomic sequencing data synthesize information from billions of sequenced reads coming from thousands of microbial genomes. Analyzing and understanding these profiles can be a challenge since the data they represent are complex. Particularly challenging is their visualization, as existing techniques are inadequate when the taxa number is in the thousands. We present a technique, and accompanying software, for the visualization of metagenomic abundance profiles using a space-filling curve that transforms a profile into an interactive 2D image. We created Jasper, an easy to use tool for the visualization and exploration of metagenomic profiles from DNA sequencing data. It orders taxa using a space-filling Hilbert curve, and creates a "Microbiome Map", where each position in the image represents the abundance of a single taxon from a reference collection. Jasper can order taxa in multiple ways, and the resulting microbiome maps can highlight "hot spots" of microbes that are dominant in taxonomic clades or biological conditions. We use Jasper to visualize samples from a variety of microbiome studies, and discuss ways in which microbiome maps can be an invaluable tool to visualize spatial, temporal, disease, and differential profiles. Our approach can create detailed microbiome maps involving hundreds of thousands of microbial reference genomes with the potential to unravel latent relationships (taxonomic, spatio-temporal, functional, and other) that could remain hidden using traditional visualization techniques. The maps can also be converted into animated movies that bring to life the dynamicity of microbiomes.
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Affiliation(s)
- Camilo Valdes
- Lawrence Livermore National Laboratory, Physical and Life Sciences Directorate, Livermore, CA, United States
| | - Vitalii Stebliankin
- Bioinformatics Research Group (BioRG), Florida International University, Miami, FL, United States
| | - Daniel Ruiz-Perez
- Bioinformatics Research Group (BioRG), Florida International University, Miami, FL, United States
| | - Ji In Park
- Department of Medicine, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon, Gangwon-do, Republic of Korea
| | - Hajeong Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Giri Narasimhan
- Bioinformatics Research Group (BioRG), Florida International University, Miami, FL, United States
- Biomolecular Sciences Institute, Florida International University, Miami, FL, United States
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3
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Baltoumas FA, Karatzas E, Paez-Espino D, Venetsianou NK, Aplakidou E, Oulas A, Finn RD, Ovchinnikov S, Pafilis E, Kyrpides NC, Pavlopoulos GA. Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters. FRONTIERS IN BIOINFORMATICS 2023; 3:1157956. [PMID: 36959975 PMCID: PMC10029925 DOI: 10.3389/fbinf.2023.1157956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 02/21/2023] [Indexed: 03/06/2023] Open
Abstract
Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.
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Affiliation(s)
- Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - David Paez-Espino
- Lawrence Berkeley National Laboratory, DOE Joint Genome Institute, Berkeley, CA, United States
| | - Nefeli K. Venetsianou
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - Eleni Aplakidou
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - Anastasis Oulas
- The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Robert D. Finn
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, United Kingdom
| | - Sergey Ovchinnikov
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA, United States
| | - Evangelos Pafilis
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion, Greece
| | - Nikos C. Kyrpides
- Lawrence Berkeley National Laboratory, DOE Joint Genome Institute, Berkeley, CA, United States
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
- Center of New Biotechnologies and Precision Medicine, Department of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, Athens, Greece
- Hellenic Army Academy, Vari, Greece
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Abdelsalam NA, Elshora H, El-Hadidi M. Interactive Web-Based Services for Metagenomic Data Analysis and Comparisons. Methods Mol Biol 2023; 2649:133-174. [PMID: 37258861 DOI: 10.1007/978-1-0716-3072-3_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Recently, sequencing technologies have become readily available, and scientists are more motivated to conduct metagenomic research to unveil the potential of a myriad of ecosystems and biomes. Metagenomics studies the composition and functions of microbial communities and paves the way to multiple applications in medicine, industry, and ecology. Nonetheless, the immense amount of sequencing data of metagenomics research and the few user-friendly analysis tools and pipelines carry a new challenge to the data analysis.Web-based bioinformatics tools are now being developed to facilitate the analysis of complex metagenomic data without prior knowledge of any programming languages or special installation. Specialized web tools help answer researchers' main questions on the taxonomic classification, functional capabilities, discrepancies between two ecosystems, and the probable functional correlations between the members of a specific microbial community. With an Internet connection and a few clicks, researchers can conveniently and efficiently analyze the metagenomic datasets, summarize results, and visualize key information on the composition and the functional potential of metagenomic samples under study. This chapter provides a simple guide to a few of the fundamental web-based services used for metagenomic data analyses, such as BV-BRC, RDP, MG-RAST, MicrobiomeAnalyst, METAGENassist, and MGnify.
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Affiliation(s)
- Nehal Adel Abdelsalam
- University of Science and Technology, Zewail City, Giza, Egypt
- Department of Microbiology and Immunology, Faculty of Pharmacy, Cairo University, Cairo, Egypt
| | - Hajar Elshora
- Bioinformatics Group, Center for Informatics Sciences (CIS), Nile University, Giza, Egypt
- Biomedical Informatics Program, School of Information Technology and Computer Science, Nile University, Giza, Egypt
| | - Mohamed El-Hadidi
- Bioinformatics Group, Center for Informatics Sciences (CIS), Nile University, Giza, Egypt.
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Ghosh A, Saha S. Meta-analysis of sputum microbiome studies identifies airway disease-specific taxonomic and functional signatures. J Med Microbiol 2022; 72. [PMID: 36748565 DOI: 10.1099/jmm.0.001617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Introduction. Studying taxonomic and functional signatures of respiratory microbiomes provide a better understanding of airway diseases.Gap Statement. Several human airway metagenomics studies have identified taxonomic and functional features restricted to a single disease condition and the findings are not comparable across airway diseases due to use of different samples, NGS platforms, and bioinformatics databases and tools.Aim. To study the microbial taxonomic and functional components of sputum microbiome across airway diseases and healthy smokers.Methodology. Here, 57 whole metagenome shotgun sequencing (WMSS) runs coming from the sputum of five airway diseases: asthma, bronchiectasis, chronic obstructive pulmonary diseases (COPD), cystic fibrosis (CF), tuberculosis (TB), and healthy smokers as the control were reanalysed using a common WMSS analysis pipeline.Results. Shannon's index (alpha diversity) of the healthy smoker group was the highest among all. The beta diversity showed that the sputum microbiome is distinct in major airway diseases such as asthma, COPD and cystic fibrosis. The microbial composition based on differential analysis showed that there are specific markers for each airway disease like Acinetobacter bereziniae as a marker for COPD and Achromobacter xylosoxidans as a marker of cystic fibrosis. Pathways and metabolites identified from the sputum microbiome of these five diseases and healthy smokers also show specific markers. 'ppGpp biosynthesis' and 'purine ribonucleosides degradation' pathways were identified as differential markers for bronchiectasis and COPD. In this meta-analysis, besides bacteria kingdom, Aspergillus fumigatus was detected in asthma and COPD, and Roseolovirus human betaherpesvirus 7 was detected in COPD. Our analysis showed that the majority of the gene families specific to the drug-resistant associated genes were detected from opportunistic pathogens across all the groups.Conclusion. In summary, the specific species in the sputum of airway diseases along with the microbial features like specific gene families, pathways, and metabolites were identified which can be explored for better diagnosis and therapy.
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Affiliation(s)
- Abhirupa Ghosh
- Division of Bioinformatics, Bose Institute, Kolkata - 700091, India
| | - Sudipto Saha
- Division of Bioinformatics, Bose Institute, Kolkata - 700091, India
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6
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Yang C, Peng C, Jin H, You L, Wang J, Xu H, Sun Z. Comparison of the composition and function of the gut microbiome in herdsmen from two pasture regions, Hongyuan and Xilingol. Food Sci Nutr 2021; 9:3258-3268. [PMID: 34136190 PMCID: PMC8194741 DOI: 10.1002/fsn3.2290] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 03/24/2021] [Accepted: 03/29/2021] [Indexed: 12/19/2022] Open
Abstract
There is a close relationship between the gut microbiome and health in humans including regulation of immunity and energy metabolism. This study investigated differences in the gut microbiome of herdsmen from two regions: Hongyuan pasture in Sichuan and Xilingol pasture in Inner Mongolia. We found significant differences in the gut microbiome between the two groups. The main discriminatory species between the two groups were Bifidobacterium longum, Bifidobacterium breve, Phascolarctobacterium succinatutens, Prevotella stercorea, Prevotella copri, Eubacterium biforme, and Fusobacterium prausnitzii. The abundances of Bifidobacterium longum and Bifidobacterium breve were significantly lower in the gut microbiomes of Hongyuan herdsmen than in the gut microbiomes of Xilingol herdsmen. Functional metagenomic analysis showed that more genes were enriched in glycoside hydrolase and transposase in the gut microbiome of Hongyuan herdsmen compared with Xilingol herdsmen, suggesting a higher energy demand in the gut microbiome of Hongyuan herdsmen. Significantly more genes associated with glycolysis, starch degradation, and sucrose degradation were also found in the gut microbiome of Hong yuan herdsmen compared with Xilingol herdsmen. These results indicate that herdsmen from different pastoral regions had distinct gut microbiome composition and functions.
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Affiliation(s)
- Chengcong Yang
- Key Laboratory of Dairy Biotechnology and EngineeringKey Laboratory of Dairy Products ProcessingInner Mongolia Agricultural UniversityHohhotChina
- Key Laboratory of Dairy Products ProcessingMinistry of Agriculture and Rural AffairsInner Mongolia Agricultural UniversityInner MongoliaChina
| | - Chuantao Peng
- Key Laboratory of Dairy Biotechnology and EngineeringKey Laboratory of Dairy Products ProcessingInner Mongolia Agricultural UniversityHohhotChina
- Key Laboratory of Dairy Products ProcessingMinistry of Agriculture and Rural AffairsInner Mongolia Agricultural UniversityInner MongoliaChina
- Qingdao Special Food Research InstituteQingdao Agricultural UniversityQingdaoChina
| | - Hao Jin
- Key Laboratory of Dairy Biotechnology and EngineeringKey Laboratory of Dairy Products ProcessingInner Mongolia Agricultural UniversityHohhotChina
- Key Laboratory of Dairy Products ProcessingMinistry of Agriculture and Rural AffairsInner Mongolia Agricultural UniversityInner MongoliaChina
| | - Lijun You
- Key Laboratory of Dairy Biotechnology and EngineeringKey Laboratory of Dairy Products ProcessingInner Mongolia Agricultural UniversityHohhotChina
- Key Laboratory of Dairy Products ProcessingMinistry of Agriculture and Rural AffairsInner Mongolia Agricultural UniversityInner MongoliaChina
| | - Jiao Wang
- Key Laboratory of Dairy Biotechnology and EngineeringKey Laboratory of Dairy Products ProcessingInner Mongolia Agricultural UniversityHohhotChina
- Key Laboratory of Dairy Products ProcessingMinistry of Agriculture and Rural AffairsInner Mongolia Agricultural UniversityInner MongoliaChina
| | - Haiyan Xu
- Key Laboratory of Dairy Biotechnology and EngineeringKey Laboratory of Dairy Products ProcessingInner Mongolia Agricultural UniversityHohhotChina
- Key Laboratory of Dairy Products ProcessingMinistry of Agriculture and Rural AffairsInner Mongolia Agricultural UniversityInner MongoliaChina
| | - Zhihong Sun
- Key Laboratory of Dairy Biotechnology and EngineeringKey Laboratory of Dairy Products ProcessingInner Mongolia Agricultural UniversityHohhotChina
- Key Laboratory of Dairy Products ProcessingMinistry of Agriculture and Rural AffairsInner Mongolia Agricultural UniversityInner MongoliaChina
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7
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Yeung F, Chen YH, Lin JD, Leung JM, McCauley C, Devlin JC, Hansen C, Cronkite A, Stephens Z, Drake-Dunn C, Fulmer Y, Shopsin B, Ruggles KV, Round JL, Loke P, Graham AL, Cadwell K. Altered Immunity of Laboratory Mice in the Natural Environment Is Associated with Fungal Colonization. Cell Host Microbe 2020; 27:809-822.e6. [PMID: 32209432 DOI: 10.1016/j.chom.2020.02.015] [Citation(s) in RCA: 111] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 12/11/2019] [Accepted: 02/28/2020] [Indexed: 12/21/2022]
Abstract
Free-living mammals, such as humans and wild mice, display heightened immune activation compared with artificially maintained laboratory mice. These differences are partially attributed to microbial exposure as laboratory mice infected with pathogens exhibit immune profiles more closely resembling that of free-living animals. Here, we examine how colonization by microorganisms within the natural environment contributes to immune system maturation by releasing inbred laboratory mice into an outdoor enclosure. In addition to enhancing differentiation of T cell populations previously associated with pathogen exposure, outdoor release increased circulating granulocytes. However, these "rewilded" mice were not infected by pathogens previously implicated in immune activation. Rather, immune system changes were associated with altered microbiota composition with notable increases in intestinal fungi. Fungi isolated from rewilded mice were sufficient in increasing circulating granulocytes. These findings establish a model to investigate how the natural environment impacts immune development and show that sustained fungal exposure impacts granulocyte numbers.
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Affiliation(s)
- Frank Yeung
- Kimmel Center for Biology and Medicine at the Skirball Institute, New York University Grossman School of Medicine, New York, NY 10016, USA; Sackler Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Ying-Han Chen
- Kimmel Center for Biology and Medicine at the Skirball Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Jian-Da Lin
- Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jacqueline M Leung
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Caroline McCauley
- Department of Microbiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Joseph C Devlin
- Sackler Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Microbiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Christina Hansen
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Alex Cronkite
- Kimmel Center for Biology and Medicine at the Skirball Institute, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Microbiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Zac Stephens
- Division of Microbiology and Immunology, Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - Charlotte Drake-Dunn
- Kimmel Center for Biology and Medicine at the Skirball Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Yi Fulmer
- Department of Microbiology, New York University Grossman School of Medicine, New York, NY 10016, USA; Division of Infectious Disease, Department of Medicine, New York University Langone Health, New York, NY 10016, USA
| | - Bo Shopsin
- Department of Microbiology, New York University Grossman School of Medicine, New York, NY 10016, USA; Division of Infectious Disease, Department of Medicine, New York University Langone Health, New York, NY 10016, USA
| | - Kelly V Ruggles
- Division of Translational Medicine, Department of Medicine, New York University Langone Health, New York, NY 10016, USA; Applied Bioinformatics Laboratories, New York Unversity Grossman School of Medicine, New York, NY 10016, USA
| | - June L Round
- Division of Microbiology and Immunology, Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84112, USA
| | - P'ng Loke
- Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Andrea L Graham
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA.
| | - Ken Cadwell
- Kimmel Center for Biology and Medicine at the Skirball Institute, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Microbiology, New York University Grossman School of Medicine, New York, NY 10016, USA; Division of Gastroenterology and Hepatology, Department of Medicine, New York University Langone Health, New York, NY 10016, USA.
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8
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Ventura RE, Iizumi T, Battaglia T, Liu M, Perez-Perez GI, Herbert J, Blaser MJ. Gut microbiome of treatment-naïve MS patients of different ethnicities early in disease course. Sci Rep 2019; 9:16396. [PMID: 31705027 PMCID: PMC6841666 DOI: 10.1038/s41598-019-52894-z] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 10/22/2019] [Indexed: 12/30/2022] Open
Abstract
Although the intestinal microbiome has been increasingly implicated in autoimmune diseases, much is unknown about its roles in Multiple Sclerosis (MS). Our aim was to compare the microbiome between treatment-naïve MS subjects early in their disease course and controls, and between Caucasian (CA), Hispanic (HA), and African American (AA) MS subjects. From fecal samples, we performed 16S rRNA V4 sequencing and analysis from 45 MS subjects (15 CA, 16 HA, 14 AA) and 44 matched healthy controls, and whole metagenomic shotgun sequencing from 24 MS subjects (all newly diagnosed, treatment-naïve, and steroid-free) and 24 controls. In all three ethnic groups, there was an increased relative abundance of the same single genus, Clostridium, compared to ethnicity-matched controls. Analysis of microbiota networks showed significant changes in the network characteristics between combined MS cohorts and controls, suggesting global differences not restricted to individual taxa. Metagenomic analysis revealed significant enrichment of individual species within Clostridia as well as particular functional pathways in the MS subjects. The increased relative abundance of Clostridia in all three early MS cohorts compared to controls provides candidate taxa for further study as biomarkers or as etiologic agents in MS.
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Affiliation(s)
- R E Ventura
- Department of Neurology, New York University School of Medicine, New York, NY, USA.
| | - T Iizumi
- Department of Medicine, New York University School of Medicine, New York, NY, USA
- Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, NJ, USA
| | - T Battaglia
- Department of Medicine, New York University School of Medicine, New York, NY, USA
| | - Menghan Liu
- Department of Medicine, New York University School of Medicine, New York, NY, USA
| | - G I Perez-Perez
- Department of Medicine, New York University School of Medicine, New York, NY, USA
| | - J Herbert
- Department of Neurology, New York University School of Medicine, New York, NY, USA
| | - M J Blaser
- Department of Medicine, New York University School of Medicine, New York, NY, USA
- Department of Microbiology, New York University School of Medicine, New York, NY, USA
- Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, NJ, USA
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9
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Dong X, Strous M. An Integrated Pipeline for Annotation and Visualization of Metagenomic Contigs. Front Genet 2019; 10:999. [PMID: 31681429 PMCID: PMC6803454 DOI: 10.3389/fgene.2019.00999] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 09/20/2019] [Indexed: 12/16/2022] Open
Abstract
Here, we describe MetaErg, a standalone and fully automated metagenome and metaproteome annotation pipeline. Annotation of metagenomes is challenging. First, metagenomes contain sequence data of many organisms from all domains of life. Second, many of these are from understudied lineages, encoding genes with low similarity to experimentally validated reference genes. Third, assembly and binning are not perfect, sometimes resulting in artifactual hybrid contigs or genomes. To address these challenges, MetaErg provides graphical summaries of annotation outcomes, both for the complete metagenome and for individual metagenome-assembled genomes (MAGs). It performs a comprehensive annotation of each gene, including taxonomic classification, enabling functional inferences despite low similarity to reference genes, as well as detection of potential assembly or binning artifacts. When provided with metaproteome information, it visualizes gene and pathway activity using sequencing coverage and proteomic spectral counts, respectively. For visualization, MetaErg provides an HTML interface, bringing all annotation results together, and producing sortable and searchable tables, collapsible trees, and other graphic representations enabling intuitive navigation of complex data. MetaErg, implemented in Perl, HTML, and JavaScript, is a fully open source application, distributed under Academic Free License at https://github.com/xiaoli-dong/metaerg. MetaErg is also available as a docker image at https://hub.docker.com/r/xiaolidong/docker-metaerg.
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Affiliation(s)
- Xiaoli Dong
- Department of Geoscience, University of Calgary, Calgary, AB, Canada
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