1
|
Zhang Y, Bhosle A, Bae S, McIver LJ, Pishchany G, Accorsi EK, Thompson KN, Arze C, Wang Y, Subramanian A, Kearney SM, Pawluk A, Plichta DR, Rahnavard A, Shafquat A, Xavier RJ, Vlamakis H, Garrett WS, Krueger A, Huttenhower C, Franzosa EA. Discovery of bioactive microbial gene products in inflammatory bowel disease. Nature 2022; 606:754-760. [PMID: 35614211 PMCID: PMC9913614 DOI: 10.1038/s41586-022-04648-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 03/15/2022] [Indexed: 01/26/2023]
Abstract
Microbial communities and their associated bioactive compounds1-3 are often disrupted in conditions such as the inflammatory bowel diseases (IBD)4. However, even in well-characterized environments (for example, the human gastrointestinal tract), more than one-third of microbial proteins are uncharacterized and often expected to be bioactive5-7. Here we systematically identified more than 340,000 protein families as potentially bioactive with respect to gut inflammation during IBD, about half of which have not to our knowledge been functionally characterized previously on the basis of homology or experiment. To validate prioritized microbial proteins, we used a combination of metagenomics, metatranscriptomics and metaproteomics to provide evidence of bioactivity for a subset of proteins that are involved in host and microbial cell-cell communication in the microbiome; for example, proteins associated with adherence or invasion processes, and extracellular von Willebrand-like factors. Predictions from high-throughput data were validated using targeted experiments that revealed the differential immunogenicity of prioritized Enterobacteriaceae pilins and the contribution of homologues of von Willebrand factors to the formation of Bacteroides biofilms in a manner dependent on mucin levels. This methodology, which we term MetaWIBELE (workflow to identify novel bioactive elements in the microbiome), is generalizable to other environmental communities and human phenotypes. The prioritized results provide thousands of candidate microbial proteins that are likely to interact with the host immune system in IBD, thus expanding our understanding of potentially bioactive gene products in chronic disease states and offering a rational compendium of possible therapeutic compounds and targets.
Collapse
Affiliation(s)
- Yancong Zhang
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Amrisha Bhosle
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Sena Bae
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Lauren J. McIver
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Gleb Pishchany
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Emma K. Accorsi
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Kelsey N. Thompson
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Cesar Arze
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Ya Wang
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Ayshwarya Subramanian
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Sean M. Kearney
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - April Pawluk
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Damian R. Plichta
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ali Rahnavard
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Afrah Shafquat
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Ramnik J. Xavier
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA,Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hera Vlamakis
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Wendy S. Garrett
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Andy Krueger
- Takeda Pharmaceutical Company Limited, Cambridge, MA, USA
| | - Curtis Huttenhower
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. .,Harvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, MA, USA. .,Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Eric A. Franzosa
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA,Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA,These authors jointly supervised this work: Curtis Huttenhower & Eric A. Franzosa
| |
Collapse
|
2
|
Podlesny D, Arze C, Dörner E, Verma S, Dutta S, Walter J, Fricke WF. Metagenomic strain detection with SameStr: identification of a persisting core gut microbiota transferable by fecal transplantation. Microbiome 2022; 10:53. [PMID: 35337386 PMCID: PMC8951724 DOI: 10.1186/s40168-022-01251-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 02/24/2022] [Indexed: 05/13/2023]
Abstract
BACKGROUND The understanding of how microbiomes assemble, function, and evolve requires metagenomic tools that can resolve microbiota compositions at the strain level. However, the identification and tracking of microbial strains in fecal metagenomes is challenging and available tools variably classify subspecies lineages, which affects their applicability to infer microbial persistence and transfer. RESULTS We introduce SameStr, a bioinformatic tool that identifies shared strains in metagenomes by determining single-nucleotide variants (SNV) in species-specific marker genes, which are compared based on a maximum variant profile similarity. We validated SameStr on mock strain populations, available human fecal metagenomes from healthy individuals and newly generated data from recurrent Clostridioides difficile infection (rCDI) patients treated with fecal microbiota transplantation (FMT). SameStr demonstrated enhanced sensitivity to detect shared dominant and subdominant strains in related samples (where strain persistence or transfer would be expected) when compared to other tools, while being robust against false-positive shared strain calls between unrelated samples (where neither strain persistence nor transfer would be expected). We applied SameStr to identify strains that are stably maintained in fecal microbiomes of healthy adults over time (strain persistence) and that successfully engraft in rCDI patients after FMT (strain engraftment). Taxonomy-dependent strain persistence and engraftment frequencies were positively correlated, indicating that a specific core microbiota of intestinal species is adapted to be competitive both in healthy microbiomes and during post-FMT microbiome assembly. We explored other use cases for strain-level microbiota profiling, as a metagenomics quality control measure and to identify individuals based on the persisting core gut microbiota. CONCLUSION SameStr provides for a robust identification of shared strains in metagenomic sequence data with sufficient specificity and sensitivity to examine strain persistence, transfer, and engraftment in human fecal microbiomes. Our findings identify a persisting healthy adult core gut microbiota, which should be further studied to shed light on microbiota contributions to chronic diseases. Video abstract.
Collapse
Affiliation(s)
- Daniel Podlesny
- Department of Microbiome Research and Applied Bioinformatics, University of Hohenheim, Stuttgart, Germany.
| | - Cesar Arze
- Department of Microbiome Research and Applied Bioinformatics, University of Hohenheim, Stuttgart, Germany
- Current address: Ring Therapeutics, Cambridge, MA, USA
| | - Elisabeth Dörner
- Department of Microbiome Research and Applied Bioinformatics, University of Hohenheim, Stuttgart, Germany
| | - Sandeep Verma
- Division of Gastroenterology, Sinai Hospital of Baltimore, Baltimore, MD, USA
| | - Sudhir Dutta
- Division of Gastroenterology, Sinai Hospital of Baltimore, Baltimore, MD, USA
| | - Jens Walter
- APC Microbiome Ireland, School of Microbiology, and Department of Medicine, University College Cork, Cork, Ireland
| | - W Florian Fricke
- Department of Microbiome Research and Applied Bioinformatics, University of Hohenheim, Stuttgart, Germany.
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
| |
Collapse
|
3
|
Bubeck AM, Preiss L, Jung A, Dörner E, Podlesny D, Kulis M, Maddox C, Arze C, Zörb C, Merkt N, Fricke WF. Bacterial microbiota diversity and composition in red and white wines correlate with plant-derived DNA contributions and botrytis infection. Sci Rep 2020; 10:13828. [PMID: 32796896 PMCID: PMC7427798 DOI: 10.1038/s41598-020-70535-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 07/27/2020] [Indexed: 12/18/2022] Open
Abstract
Wine is a globally produced, marketed and consumed alcoholic beverage, which is valued for its aromatic and qualitative complexity and variation. These properties are partially attributable to the bacterial involvement in the fermentation process. However, the organizational principles and dynamic changes of the bacterial wine microbiota remain poorly understood, especially in the context of red and white wine variations and environmental stress factors. Here, we determined relative and absolute bacterial microbiota compositions from six distinct cultivars during the first week of fermentation by quantitative and qualitative 16S rRNA gene amplification and amplicon sequencing. All wines harboured complex and variable bacterial communities, with Tatumella as the most abundant genus across all batches, but red wines were characterized by higher bacterial diversity and increased relative and absolute abundance of lactic and acetic acid bacteria (LAB/AAB) and bacterial taxa of predicted environmental origin. Microbial diversity was positively correlated with plant-derived DNA concentrations in the wine and Botrytis cinerea infection before harvest. Our findings suggest that exogenous factors, such as procedural differences between red and white wine production and environmental stress on grape integrity, can increase bacterial diversity and specific bacterial taxa in wine, with potential consequences for wine quality and aroma.
Collapse
Affiliation(s)
- Alena M Bubeck
- Department of Microbiome Research and Applied Bioinformatics, Institute of Nutritional Sciences, University of Hohenheim, Stuttgart, Germany
| | - Lena Preiss
- Department of Microbiome Research and Applied Bioinformatics, Institute of Nutritional Sciences, University of Hohenheim, Stuttgart, Germany
| | - Anna Jung
- Department of Microbiome Research and Applied Bioinformatics, Institute of Nutritional Sciences, University of Hohenheim, Stuttgart, Germany
| | - Elisabeth Dörner
- Department of Microbiome Research and Applied Bioinformatics, Institute of Nutritional Sciences, University of Hohenheim, Stuttgart, Germany
| | - Daniel Podlesny
- Department of Microbiome Research and Applied Bioinformatics, Institute of Nutritional Sciences, University of Hohenheim, Stuttgart, Germany
| | - Marija Kulis
- Department of Microbiome Research and Applied Bioinformatics, Institute of Nutritional Sciences, University of Hohenheim, Stuttgart, Germany
| | - Cynthia Maddox
- Department of Microbiome Research and Applied Bioinformatics, Institute of Nutritional Sciences, University of Hohenheim, Stuttgart, Germany.,Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.,Personal Genome Diagnostics, Baltimore, MD, USA
| | - Cesar Arze
- Department of Microbiome Research and Applied Bioinformatics, Institute of Nutritional Sciences, University of Hohenheim, Stuttgart, Germany.,Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.,Ring Therapeutics, Cambridge, MA, USA
| | - Christian Zörb
- Department of Plant Quality and Viticulture, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany
| | - Nikolaus Merkt
- Department of Plant Quality and Viticulture, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany
| | - W Florian Fricke
- Department of Microbiome Research and Applied Bioinformatics, Institute of Nutritional Sciences, University of Hohenheim, Stuttgart, Germany. .,Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
| |
Collapse
|
4
|
Nguyen LH, Ma W, Wang DD, Cao Y, Mallick H, Gerbaba TK, Lloyd-Price J, Abu-Ali G, Hall AB, Sikavi D, Drew DA, Mehta RS, Arze C, Joshi AD, Yan Y, Branck T, DuLong C, Ivey KL, Ogino S, Rimm EB, Song M, Garrett WS, Izard J, Huttenhower C, Chan AT. Association Between Sulfur-Metabolizing Bacterial Communities in Stool and Risk of Distal Colorectal Cancer in Men. Gastroenterology 2020; 158:1313-1325. [PMID: 31972239 PMCID: PMC7384232 DOI: 10.1053/j.gastro.2019.12.029] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 12/06/2019] [Accepted: 12/24/2019] [Indexed: 12/20/2022]
Abstract
BACKGROUND & AIMS Sulfur-metabolizing microbes, which convert dietary sources of sulfur into genotoxic hydrogen sulfide (H2S), have been associated with development of colorectal cancer (CRC). We identified a dietary pattern associated with sulfur-metabolizing bacteria in stool and then investigated its association with risk of incident CRC using data from a large prospective study of men. METHODS We collected data from 51,529 men enrolled in the Health Professionals Follow-up Study since 1986 to determine the association between sulfur-metabolizing bacteria in stool and risk of CRC over 26 years of follow-up. First, in a subcohort of 307 healthy men, we profiled serial stool metagenomes and metatranscriptomes and assessed diet using semiquantitative food frequency questionnaires to identify food groups associated with 43 bacterial species involved in sulfur metabolism. We used these data to develop a sulfur microbial dietary score. We then used Cox proportional hazards modeling to evaluate adherence to this pattern among eligible individuals (n = 48,246) from 1986 through 2012 with risk for incident CRC. RESULTS Foods associated with higher sulfur microbial diet scores included increased consumption of processed meats and low-calorie drinks and lower consumption of vegetables and legumes. Increased sulfur microbial diet scores were associated with risk of distal colon and rectal cancers, after adjusting for other risk factors (multivariable relative risk, highest vs lowest quartile, 1.43; 95% confidence interval 1.14-1.81; P-trend = .002). In contrast, sulfur microbial diet scores were not associated with risk of proximal colon cancer (multivariable relative risk 0.86; 95% CI 0.65-1.14; P-trend = .31). CONCLUSIONS In an analysis of participants in the Health Professionals Follow-up Study, we found that long-term adherence to a dietary pattern associated with sulfur-metabolizing bacteria in stool was associated with an increased risk of distal CRC. Further studies are needed to determine how sulfur-metabolizing bacteria might contribute to CRC pathogenesis.
Collapse
Affiliation(s)
- Long H Nguyen
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Wenjie Ma
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Dong D Wang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Yin Cao
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis, Missouri; Siteman Cancer Center, Washington University School of Medicine, St Louis, Missouri
| | - Himel Mallick
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts; Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Teklu K Gerbaba
- Department of Food Science & Technology, University of Nebraska, Lincoln, Nebraska
| | - Jason Lloyd-Price
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts; Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Galeb Abu-Ali
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - A Brantley Hall
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Daniel Sikavi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - David A Drew
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Raaj S Mehta
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Cesar Arze
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Amit D Joshi
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Yan Yan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Tobyn Branck
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Casey DuLong
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Kerry L Ivey
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts; South Australian Health and Medical Research Institute, Microbiome & Host Health Programme, Precision Medicine Theme, South Australia, Australia
| | - Shuji Ogino
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts; Cancer Immunology and Cancer Epidemiology Programs, Dana-Farber Harvard Cancer Center, Boston, Massachusetts; Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Eric B Rimm
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Mingyang Song
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Wendy S Garrett
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts; Department of Medicine, Dana-Farber Cancer Institute, Boston, Massachusetts; Department of Immunology and Infectious Disease, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Jacques Izard
- Department of Food Science & Technology, University of Nebraska, Lincoln, Nebraska; Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, Nebraska
| | - Curtis Huttenhower
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts; Broad Institute of MIT and Harvard, Cambridge, Massachusetts.
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Broad Institute of MIT and Harvard, Cambridge, Massachusetts; Department of Immunology and Infectious Disease, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
| |
Collapse
|
5
|
Lloyd-Price J, Arze C, Ananthakrishnan AN, Schirmer M, Avila-Pacheco J, Poon TW, Andrews E, Ajami NJ, Bonham KS, Brislawn CJ, Casero D, Courtney H, Gonzalez A, Graeber TG, Hall AB, Lake K, Landers CJ, Mallick H, Plichta DR, Prasad M, Rahnavard G, Sauk J, Shungin D, Vázquez-Baeza Y, White RA, Braun J, Denson LA, Jansson JK, Knight R, Kugathasan S, McGovern DPB, Petrosino JF, Stappenbeck TS, Winter HS, Clish CB, Franzosa EA, Vlamakis H, Xavier RJ, Huttenhower C. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 2019; 569:655-662. [PMID: 31142855 PMCID: PMC6650278 DOI: 10.1038/s41586-019-1237-9] [Citation(s) in RCA: 1328] [Impact Index Per Article: 265.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 04/16/2019] [Indexed: 12/11/2022]
Abstract
Inflammatory bowel diseases, which include Crohn’s disease and ulcerative colitis, affect several million individuals worldwide. Crohn’s disease and ulcerative colitis are complex diseases that are heterogeneous at the clinical, immunological, molecular, genetic, and microbial levels. Individual contributing factors have been the focus of extensive research. As part of the Integrative Human Microbiome Project (HMP2 or iHMP), we followed 132 subjects for one year each to generate integrated longitudinal molecular profiles of host and microbial activity during disease (up to 24 time points each; in total 2,965 stool, biopsy, and blood specimens). Here we present the results, which provide a comprehensive view of functional dysbiosis in the gut microbiome during inflammatory bowel disease activity. We demonstrate a characteristic increase in facultative anaerobes at the expense of obligate anaerobes, as well as molecular disruptions in microbial transcription (for example, among clostridia), metabolite pools (acylcarnitines, bile acids, and short-chain fatty acids), and levels of antibodies in host serum. Periods of disease activity were also marked by increases in temporal variability, with characteristic taxonomic, functional, and biochemical shifts. Finally, integrative analysis identified microbial, biochemical, and host factors central to this dysregulation. The study’s infrastructure resources, results, and data, which are available through the Inflammatory Bowel Disease Multi’omics Database (http://ibdmdb.org), provide the most comprehensive description to date of host and microbial activities in inflammatory bowel diseases. The Inflammatory Bowel Disease Multi’omics Database includes longitudinal data encompassing a multitude of analyses of stool, blood and biopsies of more than 100 individuals, and provides a comprehensive description of host and microbial activities in inflammatory bowel diseases.
Collapse
Affiliation(s)
- Jason Lloyd-Price
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Cesar Arze
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | | | - Melanie Schirmer
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Gastroenterology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Tiffany W Poon
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Nadim J Ajami
- Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA
| | - Kevin S Bonham
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Colin J Brislawn
- Earth and Biological Sciences Directorate, Pacific Northwest National Lab, Richland, WA, USA
| | - David Casero
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Holly Courtney
- Gastroenterology, Massachusetts General Hospital, Boston, MA, USA
| | - Antonio Gonzalez
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Thomas G Graeber
- Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, CA, USA
| | - A Brantley Hall
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathleen Lake
- Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Carol J Landers
- F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Himel Mallick
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Damian R Plichta
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mahadev Prasad
- Department of Pediatrics, Emory University, Atlanta, GA, USA
| | - Gholamali Rahnavard
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Jenny Sauk
- Vatche and Tamar Manoukian Division of Digestive Diseases, University of California Los Angeles, Los Angeles, CA, USA
| | - Dmitry Shungin
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Odontology, Umeå University, Umeå, Sweden
| | - Yoshiki Vázquez-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
| | - Richard A White
- Earth and Biological Sciences Directorate, Pacific Northwest National Lab, Richland, WA, USA
| | | | - Jonathan Braun
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Lee A Denson
- Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Janet K Jansson
- Earth and Biological Sciences Directorate, Pacific Northwest National Lab, Richland, WA, USA
| | - Rob Knight
- 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.,Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | | | - Dermot P B McGovern
- F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Joseph F Petrosino
- Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA
| | | | - Harland S Winter
- Department of Pediatrics, MassGeneral Hospital for Children, Boston, MA, USA.,Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Clary B Clish
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Eric A Franzosa
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Hera Vlamakis
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ramnik J Xavier
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Gastroenterology, Massachusetts General Hospital, Boston, MA, USA.,Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Curtis Huttenhower
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
| |
Collapse
|
6
|
Agrawal S, Arze C, Adkins RS, Crabtree J, Riley D, Vangala M, Galens K, Fraser CM, Tettelin H, White O, Angiuoli SV, Mahurkar A, Fricke WF. CloVR-Comparative: automated, cloud-enabled comparative microbial genome sequence analysis pipeline. BMC Genomics 2017; 18:332. [PMID: 28449639 PMCID: PMC5408420 DOI: 10.1186/s12864-017-3717-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Accepted: 04/21/2017] [Indexed: 11/11/2022] Open
Abstract
Background The benefit of increasing genomic sequence data to the scientific community depends on easy-to-use, scalable bioinformatics support. CloVR-Comparative combines commonly used bioinformatics tools into an intuitive, automated, and cloud-enabled analysis pipeline for comparative microbial genomics. Results CloVR-Comparative runs on annotated complete or draft genome sequences that are uploaded by the user or selected via a taxonomic tree-based user interface and downloaded from NCBI. CloVR-Comparative runs reference-free multiple whole-genome alignments to determine unique, shared and core coding sequences (CDSs) and single nucleotide polymorphisms (SNPs). Output includes short summary reports and detailed text-based results files, graphical visualizations (phylogenetic trees, circular figures), and a database file linked to the Sybil comparative genome browser. Data up- and download, pipeline configuration and monitoring, and access to Sybil are managed through CloVR-Comparative web interface. CloVR-Comparative and Sybil are distributed as part of the CloVR virtual appliance, which runs on local computers or the Amazon EC2 cloud. Representative datasets (e.g. 40 draft and complete Escherichia coli genomes) are processed in <36 h on a local desktop or at a cost of <$20 on EC2. Conclusions CloVR-Comparative allows anybody with Internet access to run comparative genomics projects, while eliminating the need for on-site computational resources and expertise. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3717-3) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
| | - Cesar Arze
- Institute for Genome Sciences, Baltimore, MD, USA
| | | | | | - David Riley
- Institute for Genome Sciences, Baltimore, MD, USA
| | | | - Kevin Galens
- Institute for Genome Sciences, Baltimore, MD, USA
| | - Claire M Fraser
- Institute for Genome Sciences, Baltimore, MD, USA.,Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Hervé Tettelin
- Institute for Genome Sciences, Baltimore, MD, USA.,Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Owen White
- Institute for Genome Sciences, Baltimore, MD, USA.,Department of Epidemiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | | | - W Florian Fricke
- Institute for Genome Sciences, Baltimore, MD, USA. .,Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD, USA. .,Department of Nutrigenomics, University of Hohenheim, Stuttgart, Germany.
| |
Collapse
|
7
|
Silva JC, Egan A, Arze C, Spouge JL, Harris DG. A new method for estimating species age supports the coexistence of malaria parasites and their Mammalian hosts. Mol Biol Evol 2015; 32:1354-64. [PMID: 25589738 PMCID: PMC4408405 DOI: 10.1093/molbev/msv005] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Species in the genus Plasmodium cause malaria in humans and infect a variety of mammals and other vertebrates. Currently, estimated ages for several mammalian Plasmodium parasites differ by as much as one order of magnitude, an inaccuracy that frustrates reliable estimation of evolutionary rates of disease-related traits. We developed a novel statistical approach to dating the relative age of evolutionary lineages, based on Total Least Squares regression. We validated this lineage dating approach by applying it to the genus Drosophila. Using data from the Drosophila 12 Genomes project, our approach accurately reconstructs the age of well-established Drosophila clades, including the speciation event that led to the subgenera Drosophila and Sophophora, and age of the melanogaster species subgroup. We applied this approach to hundreds of loci from seven mammalian Plasmodium species. We demonstrate the existence of a molecular clock specific to individual Plasmodium proteins, and estimate the relative age of mammalian-infecting Plasmodium. These analyses indicate that: 1) the split between the human parasite Plasmodium vivax and P. knowlesi, from Old World monkeys, occurred 6.1 times earlier than that between P. falciparum and P. reichenowi, parasites of humans and chimpanzees, respectively; and 2) mammalian Plasmodium parasites originated 22 times earlier than the split between P. falciparum and P. reichenowi. Calibrating the absolute divergence times for Plasmodium with eukaryotic substitution rates, we show that the split between P. falciparum and P. reichenowi occurred 3.0-5.5 Ma, and that mammalian Plasmodium parasites originated over 64 Ma. Our results indicate that mammalian-infecting Plasmodium evolved contemporaneously with their hosts, with little evidence for parasite host-switching on an evolutionary scale, and provide a solid timeframe within which to place the evolution of new Plasmodium species.
Collapse
Affiliation(s)
- Joana C Silva
- Institute for Genome Sciences, University of Maryland School of Medicine Department of Microbiology and Immunology, University of Maryland School of Medicine
| | - Amy Egan
- Institute for Genome Sciences, University of Maryland School of Medicine
| | - Cesar Arze
- Institute for Genome Sciences, University of Maryland School of Medicine
| | - John L Spouge
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD
| | - David G Harris
- Department of Applied Mathematics and Statistics, University of Maryland, College Park
| |
Collapse
|
8
|
Kibbe WA, Arze C, Felix V, Mitraka E, Bolton E, Fu G, Mungall CJ, Binder JX, Malone J, Vasant D, Parkinson H, Schriml LM. Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Res 2014; 43:D1071-8. [PMID: 25348409 PMCID: PMC4383880 DOI: 10.1093/nar/gku1011] [Citation(s) in RCA: 366] [Impact Index Per Article: 36.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
The current version of the Human Disease Ontology (DO) (http://www.disease-ontology.org) database expands the utility of the ontology for the examination and comparison of genetic variation, phenotype, protein, drug and epitope data through the lens of human disease. DO is a biomedical resource of standardized common and rare disease concepts with stable identifiers organized by disease etiology. The content of DO has had 192 revisions since 2012, including the addition of 760 terms. Thirty-two percent of all terms now include definitions. DO has expanded the number and diversity of research communities and community members by 50+ during the past two years. These community members actively submit term requests, coordinate biomedical resource disease representation and provide expert curation guidance. Since the DO 2012 NAR paper, there have been hundreds of term requests and a steady increase in the number of DO listserv members, twitter followers and DO website usage. DO is moving to a multi-editor model utilizing Protégé to curate DO in web ontology language. This will enable closer collaboration with the Human Phenotype Ontology, EBI's Ontology Working Group, Mouse Genome Informatics and the Monarch Initiative among others, and enhance DO's current asserted view and multiple inferred views through reasoning.
Collapse
Affiliation(s)
- Warren A Kibbe
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20850, USA
| | - Cesar Arze
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Victor Felix
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Elvira Mitraka
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Evan Bolton
- PubChem, National Center for Biotechnology Information, National Library of Medicine National Institutes of Health Department of Health and Human Services 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Gang Fu
- PubChem, National Center for Biotechnology Information, National Library of Medicine National Institutes of Health Department of Health and Human Services 8600 Rockville Pike, Bethesda, MD 20894, USA
| | | | - Janos X Binder
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, 69117, Germany Bioinformatics Core Facility, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, 4362, Luxembourg
| | - James Malone
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Drashtti Vasant
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Lynn M Schriml
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| |
Collapse
|
9
|
Takala-Harrison S, Jacob CG, Arze C, Cummings MP, Silva JC, Dondorp AM, Fukuda MM, Hien TT, Mayxay M, Noedl H, Nosten F, Kyaw MP, Nhien NTT, Imwong M, Bethell D, Se Y, Lon C, Tyner SD, Saunders DL, Ariey F, Mercereau-Puijalon O, Menard D, Newton PN, Khanthavong M, Hongvanthong B, Starzengruber P, Fuehrer HP, Swoboda P, Khan WA, Phyo AP, Nyunt MM, Nyunt MH, Brown TS, Adams M, Pepin CS, Bailey J, Tan JC, Ferdig MT, Clark TG, Miotto O, MacInnis B, Kwiatkowski DP, White NJ, Ringwald P, Plowe CV. Independent emergence of artemisinin resistance mutations among Plasmodium falciparum in Southeast Asia. J Infect Dis 2014; 211:670-9. [PMID: 25180241 DOI: 10.1093/infdis/jiu491] [Citation(s) in RCA: 325] [Impact Index Per Article: 32.5] [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: 12/31/2022] Open
Abstract
BACKGROUND The emergence of artemisinin-resistant Plasmodium falciparum in Southeast Asia threatens malaria treatment efficacy. Mutations in a kelch protein encoded on P. falciparum chromosome 13 (K13) have been associated with resistance in vitro and in field samples from Cambodia. METHODS P. falciparum infections from artesunate efficacy trials in Bangladesh, Cambodia, Laos, Myanmar, and Vietnam were genotyped at 33 716 genome-wide single-nucleotide polymorphisms (SNPs). Linear mixed models were used to test associations between parasite genotypes and parasite clearance half-lives following artesunate treatment. K13 mutations were tested for association with artemisinin resistance, and extended haplotypes on chromosome 13 were examined to determine whether mutations arose focally and spread or whether they emerged independently. RESULTS The presence of nonreference K13 alleles was associated with prolonged parasite clearance half-life (P = 1.97 × 10(-12)). Parasites with a mutation in any of the K13 kelch domains displayed longer parasite clearance half-lives than parasites with wild-type alleles. Haplotype analysis revealed both population-specific emergence of mutations and independent emergence of the same mutation in different geographic areas. CONCLUSIONS K13 appears to be a major determinant of artemisinin resistance throughout Southeast Asia. While we found some evidence of spreading resistance, there was no evidence of resistance moving westward from Cambodia into Myanmar.
Collapse
Affiliation(s)
| | | | - Cesar Arze
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore
| | - Michael P Cummings
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park
| | - Joana C Silva
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore
| | | | - Mark M Fukuda
- Armed Forces Research Institute of Medical Sciences, Bangkok
| | - Tran Tinh Hien
- Center for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Mayfong Mayxay
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital Faculty of Postgraduate Studies, University of Health Sciences Centre for Tropical Medicine, Nuffield Department of Medicine, University of Oxford
| | - Harald Noedl
- Institute of Specific Prophylaxis and Tropical Medicine, Medical University of Vienna, Austria
| | - Francois Nosten
- Mahidol-Oxford Tropical Medicine Research Unit Centre for Tropical Medicine, Nuffield Department of Medicine, University of Oxford
| | - Myat P Kyaw
- Department of Medical Research (Lower Myanmar), Yangon
| | - Nguyen Thanh Thuy Nhien
- Center for Tropical Medicine, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Mallika Imwong
- Department of Molecular Tropical Medicine and Genetics, Faculty of Tropical Medicine, Mahidol University
| | - Delia Bethell
- Armed Forces Research Institute of Medical Sciences, Bangkok
| | - Youry Se
- Armed Forces Research Institute of Medical Sciences
| | - Chanthap Lon
- Armed Forces Research Institute of Medical Sciences
| | - Stuart D Tyner
- Armed Forces Research Institute of Medical Sciences, Bangkok
| | | | | | | | - Didier Menard
- Malaria Molecular Epidemiology Unit, Institut Pasteur du Cambodge, Phnom Penh, Cambodia
| | - Paul N Newton
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital Centre for Tropical Medicine, Nuffield Department of Medicine, University of Oxford
| | | | | | - Peter Starzengruber
- Institute of Specific Prophylaxis and Tropical Medicine, Medical University of Vienna, Austria
| | - Hans-Peter Fuehrer
- Institute of Specific Prophylaxis and Tropical Medicine, Medical University of Vienna, Austria
| | - Paul Swoboda
- Institute of Specific Prophylaxis and Tropical Medicine, Medical University of Vienna, Austria
| | - Wasif A Khan
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Aung Pyae Phyo
- Mahidol-Oxford Tropical Medicine Research Unit Shoklo Malaria Research Unit Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand
| | - Myaing M Nyunt
- Howard Hughes Medical Institute/Center for Vaccine Development
| | - Myat H Nyunt
- Department of Medical Research (Lower Myanmar), Yangon
| | - Tyler S Brown
- Howard Hughes Medical Institute/Center for Vaccine Development
| | - Matthew Adams
- Howard Hughes Medical Institute/Center for Vaccine Development
| | | | - Jason Bailey
- Howard Hughes Medical Institute/Center for Vaccine Development
| | - John C Tan
- Research and Development, Roche NimbleGen, Madison, Wisconsin
| | - Michael T Ferdig
- Eck Institute for Global Health, Department of Biological Sciences, University of Notre Dame, Indiana
| | - Taane G Clark
- Faculty of Epidemiology and Population Health Faculty Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine
| | - Olivo Miotto
- Mahidol-Oxford Tropical Medicine Research Unit MRC Centre for Genomics and Global Health, Oxford University and Wellcome Trust Sanger Institute Malaria Programme, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
| | - Bronwyn MacInnis
- Malaria Programme, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
| | - Dominic P Kwiatkowski
- MRC Centre for Genomics and Global Health, Oxford University and Wellcome Trust Sanger Institute Malaria Programme, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
| | | | - Pascal Ringwald
- Drug Resistance and Containment Unit, Global Malaria Programme, World Health Organization, Geneva, Switzerland
| | | |
Collapse
|
10
|
Venkatesan M, Gadalla NB, Stepniewska K, Dahal P, Nsanzabana C, Moriera C, Price RN, Mårtensson A, Rosenthal PJ, Dorsey G, Sutherland CJ, Guérin P, Davis TME, Ménard D, Adam I, Ademowo G, Arze C, Baliraine FN, Berens-Riha N, Björkman A, Borrmann S, Checchi F, Desai M, Dhorda M, Djimdé AA, El-Sayed BB, Eshetu T, Eyase F, Falade C, Faucher JF, Fröberg G, Grivoyannis A, Hamour S, Houzé S, Johnson J, Kamugisha E, Kariuki S, Kiechel JR, Kironde F, Kofoed PE, LeBras J, Malmberg M, Mwai L, Ngasala B, Nosten F, Nsobya SL, Nzila A, Oguike M, Otienoburu SD, Ogutu B, Ouédraogo JB, Piola P, Rombo L, Schramm B, Somé AF, Thwing J, Ursing J, Wong RPM, Zeynudin A, Zongo I, Plowe CV, Sibley CH. Polymorphisms in Plasmodium falciparum chloroquine resistance transporter and multidrug resistance 1 genes: parasite risk factors that affect treatment outcomes for P. falciparum malaria after artemether-lumefantrine and artesunate-amodiaquine. Am J Trop Med Hyg 2014; 91:833-843. [PMID: 25048375 PMCID: PMC4183414 DOI: 10.4269/ajtmh.14-0031] [Citation(s) in RCA: 184] [Impact Index Per Article: 18.4] [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] [Indexed: 01/12/2023] Open
Abstract
Adequate clinical and parasitologic cure by artemisinin combination therapies relies on the artemisinin component and the partner drug. Polymorphisms in the Plasmodium falciparum chloroquine resistance transporter (pfcrt) and P. falciparum multidrug resistance 1 (pfmdr1) genes are associated with decreased sensitivity to amodiaquine and lumefantrine, but effects of these polymorphisms on therapeutic responses to artesunate-amodiaquine (ASAQ) and artemether-lumefantrine (AL) have not been clearly defined. Individual patient data from 31 clinical trials were harmonized and pooled by using standardized methods from the WorldWide Antimalarial Resistance Network. Data for more than 7,000 patients were analyzed to assess relationships between parasite polymorphisms in pfcrt and pfmdr1 and clinically relevant outcomes after treatment with AL or ASAQ. Presence of the pfmdr1 gene N86 (adjusted hazards ratio = 4.74, 95% confidence interval = 2.29 – 9.78, P < 0.001) and increased pfmdr1 copy number (adjusted hazards ratio = 6.52, 95% confidence interval = 2.36–17.97, P < 0.001) were significant independent risk factors for recrudescence in patients treated with AL. AL and ASAQ exerted opposing selective effects on single-nucleotide polymorphisms in pfcrt and pfmdr1. Monitoring selection and responding to emerging signs of drug resistance are critical tools for preserving efficacy of artemisinin combination therapies; determination of the prevalence of at least pfcrt K76T and pfmdr1 N86Y should now be routine.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Carol Hopkins Sibley
- *Address correspondence to Carol Hopkins Sibley, Department of Genome Sciences, University of Washington, Box 355065, Seattle, WA 98195. E-mail:
| | | |
Collapse
|
11
|
Schriml LM, Arze C, Nadendla S, Chang YWW, Mazaitis M, Felix V, Feng G, Kibbe WA. Disease Ontology: a backbone for disease semantic integration. Nucleic Acids Res 2011; 40:D940-6. [PMID: 22080554 PMCID: PMC3245088 DOI: 10.1093/nar/gkr972] [Citation(s) in RCA: 498] [Impact Index Per Article: 38.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
The Disease Ontology (DO) database (http://disease-ontology.org) represents a comprehensive knowledge base of 8043 inherited, developmental and acquired human diseases (DO version 3, revision 2510). The DO web browser has been designed for speed, efficiency and robustness through the use of a graph database. Full-text contextual searching functionality using Lucene allows the querying of name, synonym, definition, DOID and cross-reference (xrefs) with complex Boolean search strings. The DO semantically integrates disease and medical vocabularies through extensive cross mapping and integration of MeSH, ICD, NCI's thesaurus, SNOMED CT and OMIM disease-specific terms and identifiers. The DO is utilized for disease annotation by major biomedical databases (e.g. Array Express, NIF, IEDB), as a standard representation of human disease in biomedical ontologies (e.g. IDO, Cell line ontology, NIFSTD ontology, Experimental Factor Ontology, Influenza Ontology), and as an ontological cross mappings resource between DO, MeSH and OMIM (e.g. GeneWiki). The DO project (http://diseaseontology.sf.net) has been incorporated into open source tools (e.g. Gene Answers, FunDO) to connect gene and disease biomedical data through the lens of human disease. The next iteration of the DO web browser will integrate DO's extended relations and logical definition representation along with these biomedical resource cross-mappings.
Collapse
Affiliation(s)
- Lynn Marie Schriml
- Department of Epidemiology and Public Health, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
| | | | | | | | | | | | | | | |
Collapse
|
12
|
Angiuoli SV, Matalka M, Gussman A, Galens K, Vangala M, Riley DR, Arze C, White JR, White O, Fricke WF. CloVR: a virtual machine for automated and portable sequence analysis from the desktop using cloud computing. BMC Bioinformatics 2011; 12:356. [PMID: 21878105 PMCID: PMC3228541 DOI: 10.1186/1471-2105-12-356] [Citation(s) in RCA: 227] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2011] [Accepted: 08/30/2011] [Indexed: 11/23/2022] Open
Abstract
Background Next-generation sequencing technologies have decentralized sequence acquisition, increasing the demand for new bioinformatics tools that are easy to use, portable across multiple platforms, and scalable for high-throughput applications. Cloud computing platforms provide on-demand access to computing infrastructure over the Internet and can be used in combination with custom built virtual machines to distribute pre-packaged with pre-configured software. Results We describe the Cloud Virtual Resource, CloVR, a new desktop application for push-button automated sequence analysis that can utilize cloud computing resources. CloVR is implemented as a single portable virtual machine (VM) that provides several automated analysis pipelines for microbial genomics, including 16S, whole genome and metagenome sequence analysis. The CloVR VM runs on a personal computer, utilizes local computer resources and requires minimal installation, addressing key challenges in deploying bioinformatics workflows. In addition CloVR supports use of remote cloud computing resources to improve performance for large-scale sequence processing. In a case study, we demonstrate the use of CloVR to automatically process next-generation sequencing data on multiple cloud computing platforms. Conclusion The CloVR VM and associated architecture lowers the barrier of entry for utilizing complex analysis protocols on both local single- and multi-core computers and cloud systems for high throughput data processing.
Collapse
Affiliation(s)
- Samuel V Angiuoli
- Institute for Genome Sciences (IGS), University of Maryland School of Medicine, Baltimore, Maryland, USA.
| | | | | | | | | | | | | | | | | | | |
Collapse
|
13
|
Schriml LM, Arze C, Nadendla S, Ganapathy A, Felix V, Mahurkar A, Phillippy K, Gussman A, Angiuoli S, Ghedin E, White O, Hall N. GeMInA, Genomic Metadata for Infectious Agents, a geospatial surveillance pathogen database. Nucleic Acids Res 2010; 38:D754-64. [PMID: 19850722 PMCID: PMC2808878 DOI: 10.1093/nar/gkp832] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2009] [Revised: 09/16/2009] [Accepted: 09/18/2009] [Indexed: 11/13/2022] Open
Abstract
The Gemina system (http://gemina.igs.umaryland.edu) identifies, standardizes and integrates the outbreak metadata for the breadth of NIAID category A-C viral and bacterial pathogens, thereby providing an investigative and surveillance tool describing the Who [Host], What [Disease, Symptom], When [Date], Where [Location] and How [Pathogen, Environmental Source, Reservoir, Transmission Method] for each pathogen. The Gemina database will provide a greater understanding of the interactions of viral and bacterial pathogens with their hosts and infectious diseases through in-depth literature text-mining, integrated outbreak metadata, outbreak surveillance tools, extensive ontology development, metadata curation and representative genomic sequence identification and standards development. The Gemina web interface provides metadata selection and retrieval of a pathogen's; Infection Systems (Pathogen, Host, Disease, Transmission Method and Anatomy) and Incidents (Location and Date) along with a hosts Age and Gender. The Gemina system provides an integrated investigative and geospatial surveillance system connecting pathogens, pathogen products and disease anchored on the taxonomic ID of the pathogen and host to identify the breadth of hosts and diseases known for these pathogens, to identify the extent of outbreak locations, and to identify unique genomic regions with the DNA Signature Insignia Detection Tool.
Collapse
Affiliation(s)
- Lynn M Schriml
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|