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Mallick H, Porwal A, Saha S, Basak P, Svetnik V, Paul E. An integrated Bayesian framework for multi-omics prediction and classification. Stat Med 2024; 43:983-1002. [PMID: 38146838 DOI: 10.1002/sim.9953] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 10/06/2023] [Accepted: 10/24/2023] [Indexed: 12/27/2023]
Abstract
With the growing commonality of multi-omics datasets, there is now increasing evidence that integrated omics profiles lead to more efficient discovery of clinically actionable biomarkers that enable better disease outcome prediction and patient stratification. Several methods exist to perform host phenotype prediction from cross-sectional, single-omics data modalities but decentralized frameworks that jointly analyze multiple time-dependent omics data to highlight the integrative and dynamic impact of repeatedly measured biomarkers are currently limited. In this article, we propose a novel Bayesian ensemble method to consolidate prediction by combining information across several longitudinal and cross-sectional omics data layers. Unlike existing frequentist paradigms, our approach enables uncertainty quantification in prediction as well as interval estimation for a variety of quantities of interest based on posterior summaries. We apply our method to four published multi-omics datasets and demonstrate that it recapitulates known biology in addition to providing novel insights while also outperforming existing methods in estimation, prediction, and uncertainty quantification. Our open-source software is publicly available at https://github.com/himelmallick/IntegratedLearner.
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Affiliation(s)
- Himel Mallick
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, 10065, New York, USA
- Department of Statistics and Data Science, Cornell University, Ithaca, New York, USA
| | - Anupreet Porwal
- Department of Statistics, University of Washington, Seattle, Washington, USA
| | - Satabdi Saha
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Piyali Basak
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, USA
| | - Vladimir Svetnik
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, USA
| | - Erina Paul
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, USA
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2
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Paul E, Mallick H. Unified reciprocal LASSO estimation via least squares approximation. COMMUN STAT-SIMUL C 2022. [DOI: 10.1080/03610918.2022.2146723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Affiliation(s)
- Erina Paul
- Biostatistics and Research Decision Sciences, Merck & Co., Inc, Rahway, New Jersey, USA
| | - Himel Mallick
- Biostatistics and Research Decision Sciences, Merck & Co., Inc, Rahway, New Jersey, USA
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3
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Ma S, Shungin D, Mallick H, Schirmer M, Nguyen LH, Kolde R, Franzosa E, Vlamakis H, Xavier R, Huttenhower C. Population structure discovery in meta-analyzed microbial communities and inflammatory bowel disease using MMUPHin. Genome Biol 2022; 23:208. [PMID: 36192803 PMCID: PMC9531436 DOI: 10.1186/s13059-022-02753-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [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: 07/23/2021] [Accepted: 08/19/2022] [Indexed: 01/19/2023] Open
Abstract
Microbiome studies of inflammatory bowel diseases (IBD) have achieved a scale for meta-analysis of dysbioses among populations. To enable microbial community meta-analyses generally, we develop MMUPHin for normalization, statistical meta-analysis, and population structure discovery using microbial taxonomic and functional profiles. Applying it to ten IBD cohorts, we identify consistent associations, including novel taxa such as Acinetobacter and Turicibacter, and additional exposure and interaction effects. A single gradient of dysbiosis severity is favored over discrete types to summarize IBD microbiome population structure. These results provide a benchmark for characterization of IBD and a framework for meta-analysis of any microbial communities.
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Affiliation(s)
- Siyuan Ma
- grid.38142.3c000000041936754XHarvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Dmitry Shungin
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Himel Mallick
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Melanie Schirmer
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Long H. Nguyen
- grid.32224.350000 0004 0386 9924Massachusetts General Hospital, Boston, MA USA
| | - Raivo Kolde
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Eric Franzosa
- grid.38142.3c000000041936754XHarvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Hera Vlamakis
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Ramnik Xavier
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Curtis Huttenhower
- grid.38142.3c000000041936754XHarvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, MA USA
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4
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Mallick H, Chatterjee S, Chowdhury S, Chatterjee S, Rahnavard A, Hicks SC. Differential expression of single-cell RNA-seq data using Tweedie models. Stat Med 2022; 41:3492-3510. [PMID: 35656596 PMCID: PMC9288986 DOI: 10.1002/sim.9430] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 04/21/2022] [Accepted: 04/22/2022] [Indexed: 12/13/2022]
Abstract
The performance of computational methods and software to identify differentially expressed features in single-cell RNA-sequencing (scRNA-seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA-seq expression features. To model the technological variability in cross-platform scRNA-seq data, here we propose to use Tweedie generalized linear models that can flexibly capture a large dynamic range of observed scRNA-seq expression profiles across experimental platforms induced by platform- and gene-specific statistical properties such as heavy tails, sparsity, and gene expression distributions. We also propose a zero-inflated Tweedie model that allows zero probability mass to exceed a traditional Tweedie distribution to model zero-inflated scRNA-seq data with excessive zero counts. Using both synthetic and published plate- and droplet-based scRNA-seq datasets, we perform a systematic benchmark evaluation of more than 10 representative DE methods and demonstrate that our method (Tweedieverse) outperforms the state-of-the-art DE approaches across experimental platforms in terms of statistical power and false discovery rate control. Our open-source software (R/Bioconductor package) is available at https://github.com/himelmallick/Tweedieverse.
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Affiliation(s)
- Himel Mallick
- Biostatistics and Research Decision Sciences, Merck &
Co., Inc., Rahway, NJ 07065, USA
| | - Suvo Chatterjee
- Epidemiology Branch, Division of Intramural Population
Health Research, Eunice Kennedy Shriver National Institute of Child
Health and Human Development, National Institutes of Health, Bethesda, MD 20892,
USA
| | - Shrabanti Chowdhury
- Department of Genetics and Genomic Sciences and Icahn
Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount
Sinai, New York, NY 10029, USA
| | - Saptarshi Chatterjee
- Department of Statistics, Data and Analytics, Eli Lilly
& Company, Indianapolis, IN 46225, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of
Biostatistics and Bioinformatics, Milken Institute School of Public Health, The
George Washington University, Washington, DC 20052, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School
of Public Health, Baltimore, MD 21205, USA
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5
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Mallick H, An L, Chen M, Wang P, Zhao N. Editorial: Methods for Single-Cell and Microbiome Sequencing Data. Front Genet 2022; 13:920191. [PMID: 35734426 PMCID: PMC9208326 DOI: 10.3389/fgene.2022.920191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 04/26/2022] [Indexed: 11/23/2022] Open
Affiliation(s)
- Himel Mallick
- Biostatistics and Research Decision Sciences, Merck & Co.Inc., Rahway, NJ, United States
- *Correspondence: Himel Mallick,
| | - Lingling An
- Interdisciplinary Program in Statistics and Data Science, The University of Arizona, Tucson, AZ, United States
- Department of Epidemiology and Biostatistics, The University of Arizona, Tucson, AZ, United States
- Department of Biosystems Engineering, The University of Arizona, Tucson, AZ, United States
| | - Mengjie Chen
- Department of Human Genetics and Department of Medicine, University of Chicago, Chicago, IL, United States
| | - Pei Wang
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Ni Zhao
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States
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6
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Mallick H, Rahnavard A, McIver LJ, Ma S, Zhang Y, Nguyen LH, Tickle TL, Weingart G, Ren B, Schwager EH, Chatterjee S, Thompson KN, Wilkinson JE, Subramanian A, Lu Y, Waldron L, Paulson JN, Franzosa EA, Bravo HC, Huttenhower C. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput Biol 2021; 17:e1009442. [PMID: 34784344 PMCID: PMC8714082 DOI: 10.1371/journal.pcbi.1009442] [Citation(s) in RCA: 540] [Impact Index Per Article: 180.0] [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: 08/05/2021] [Revised: 12/28/2021] [Accepted: 09/09/2021] [Indexed: 12/13/2022] Open
Abstract
It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2's linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles.
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Affiliation(s)
- Himel Mallick
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington DC, United States of America
| | - Lauren J. McIver
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Siyuan Ma
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Yancong Zhang
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Long H. Nguyen
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Timothy L. Tickle
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - George Weingart
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Boyu Ren
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Emma H. Schwager
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Suvo Chatterjee
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Kelsey N. Thompson
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Jeremy E. Wilkinson
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Ayshwarya Subramanian
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Yiren Lu
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Levi Waldron
- Department of Epidemiology and Biostatistics, CUNY School of Public Health, New York City, New York, United States of America
| | - Joseph N. Paulson
- Department of Biostatistics, Product Development, Genentech, Inc., South San Francisco, California, United States of America
| | - Eric A. Franzosa
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Hector Corrada Bravo
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America
| | - Curtis Huttenhower
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
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7
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Mallick H, Rahnavard A, McIver LJ, Ma S, Zhang Y, Nguyen LH, Tickle TL, Weingart G, Ren B, Schwager EH, Chatterjee S, Thompson KN, Wilkinson JE, Subramanian A, Lu Y, Waldron L, Paulson JN, Franzosa EA, Bravo HC, Huttenhower C. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput Biol 2021. [PMID: 34784344 DOI: 10.1101/2021.01.20.427420v1] [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: 05/07/2023] Open
Abstract
It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2's linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles.
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Affiliation(s)
- Himel Mallick
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington DC, United States of America
| | - Lauren J McIver
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Siyuan Ma
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Yancong Zhang
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Long H Nguyen
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Timothy L Tickle
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - George Weingart
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Boyu Ren
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Emma H Schwager
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Suvo Chatterjee
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Kelsey N Thompson
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Jeremy E Wilkinson
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Ayshwarya Subramanian
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Yiren Lu
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Levi Waldron
- Department of Epidemiology and Biostatistics, CUNY School of Public Health, New York City, New York, United States of America
| | - Joseph N Paulson
- Department of Biostatistics, Product Development, Genentech, Inc., South San Francisco, California, United States of America
| | - Eric A Franzosa
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
| | - Hector Corrada Bravo
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America
| | - Curtis Huttenhower
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America
- The Broad Institute, Cambridge, Massachusetts, United States of America
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8
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Ma S, Ren B, Mallick H, Moon YS, Schwager E, Maharjan S, Tickle TL, Lu Y, Carmody RN, Franzosa EA, Janson L, Huttenhower C. A statistical model for describing and simulating microbial community profiles. PLoS Comput Biol 2021; 17:e1008913. [PMID: 34516542 PMCID: PMC8491899 DOI: 10.1371/journal.pcbi.1008913] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [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: 03/23/2021] [Revised: 10/05/2021] [Accepted: 08/19/2021] [Indexed: 12/26/2022] Open
Abstract
Many methods have been developed for statistical analysis of microbial community profiles, but due to the complex nature of typical microbiome measurements (e.g. sparsity, zero-inflation, non-independence, and compositionality) and of the associated underlying biology, it is difficult to compare or evaluate such methods within a single systematic framework. To address this challenge, we developed SparseDOSSA (Sparse Data Observations for the Simulation of Synthetic Abundances): a statistical model of microbial ecological population structure, which can be used to parameterize real-world microbial community profiles and to simulate new, realistic profiles of known structure for methods evaluation. Specifically, SparseDOSSA's model captures marginal microbial feature abundances as a zero-inflated log-normal distribution, with additional model components for absolute cell counts and the sequence read generation process, microbe-microbe, and microbe-environment interactions. Together, these allow fully known covariance structure between synthetic features (i.e. "taxa") or between features and "phenotypes" to be simulated for method benchmarking. Here, we demonstrate SparseDOSSA's performance for 1) accurately modeling human-associated microbial population profiles; 2) generating synthetic communities with controlled population and ecological structures; 3) spiking-in true positive synthetic associations to benchmark analysis methods; and 4) recapitulating an end-to-end mouse microbiome feeding experiment. Together, these represent the most common analysis types in assessment of real microbial community environmental and epidemiological statistics, thus demonstrating SparseDOSSA's utility as a general-purpose aid for modeling communities and evaluating quantitative methods. An open-source implementation is available at http://huttenhower.sph.harvard.edu/sparsedossa2.
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Affiliation(s)
- Siyuan Ma
- Harvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Broad Institute, Cambridge, Massachusetts, United States of America
| | - Boyu Ren
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Broad Institute, Cambridge, Massachusetts, United States of America
| | - Himel Mallick
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Broad Institute, Cambridge, Massachusetts, United States of America
| | - Yo Sup Moon
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Emma Schwager
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Sagun Maharjan
- Harvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Broad Institute, Cambridge, Massachusetts, United States of America
| | - Timothy L. Tickle
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Broad Institute, Cambridge, Massachusetts, United States of America
| | - Yiren Lu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Rachel N. Carmody
- Department of Human Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Eric A. Franzosa
- Harvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Broad Institute, Cambridge, Massachusetts, United States of America
| | - Lucas Janson
- Department of Statistics, Harvard University, Cambridge, Massachusetts, United States of America
| | - Curtis Huttenhower
- Harvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Broad Institute, Cambridge, Massachusetts, United States of America
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
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9
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Aggarwal K, Akhtar N, Mallick H. Sleep quality mediates the relationship between risk of obstructive sleep apnea and acute stress in young adults. J Physiol Pharmacol 2021; 72. [PMID: 34272347 DOI: 10.26402/jpp.2021.1.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 02/26/2021] [Indexed: 11/03/2022]
Abstract
Intermittent hypoxia and transient arousals in obstructive sleep apnea (OSA) can lead to poor sleep quality and acute stress. Rising levels of obesity and increased incidence of OSA in young adults predisposes them to acute stress. We propose a mediation model to assess if risk of OSA is associated with acute stress and if the relationship between risk for OSA and acute stress is mediated by sleep quality. 493 healthy individuals (F = 237, M = 256) from 18 - 25 years of age (mean age 20.3 ± 1.53 years) were screened for OSA, sleep quality and acute stress using STOP-BANG questionnaire, Pittsburg Sleep Quality Index and American Psychiatry Association's National Stressful Events Survey Acute Stress Disorder Short Scale (NSESS-S), respectively. 73 participants (17.3%) were found at an intermediate and high risk of OSA by STOP BANG questionnaire. 79 (16%) participants reported level of stress as 'None'. Mild, moderate and severe stress was present in 248 (50.3%), 109 (22.1%), 51 (10.3%) and 16 (3.2%) participants, respectively. The odds of having severe and extreme stress among those at risk of sleep apnea is 2.18 times higher than that among those not at risk of sleep apnea (OR: 2.18, 95%, confidence interval: 1.37-3.51). Sobel test established that the relationship between OSA and acute stress is mediated by sleep quality. Sleep quality mediates the relationship between risk for sleep apnea and acute stress. This highlights the importance of screening for OSA in young adults, particularly young men with high BMI, presenting with high stress levels.
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Affiliation(s)
- K Aggarwal
- India Institute of Medical Sciences, New Delhi, India
| | - N Akhtar
- Department of Physiology, All India Institute of Medical Sciences, New Delhi, India.
| | - H Mallick
- Department of Physiology, Faculty of Medicine and Health Sciences, SGT University, Gurgaon, Haryana, India
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10
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Mallick H, Alhamzawi R, Paul E, Svetnik V. The reciprocal Bayesian LASSO. Stat Med 2021; 40:4830-4849. [PMID: 34126655 DOI: 10.1002/sim.9098] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 05/19/2021] [Accepted: 05/27/2021] [Indexed: 11/08/2022]
Abstract
A reciprocal LASSO (rLASSO) regularization employs a decreasing penalty function as opposed to conventional penalization approaches that use increasing penalties on the coefficients, leading to stronger parsimony and superior model selection relative to traditional shrinkage methods. Here we consider a fully Bayesian formulation of the rLASSO problem, which is based on the observation that the rLASSO estimate for linear regression parameters can be interpreted as a Bayesian posterior mode estimate when the regression parameters are assigned independent inverse Laplace priors. Bayesian inference from this posterior is possible using an expanded hierarchy motivated by a scale mixture of double Pareto or truncated normal distributions. On simulated and real datasets, we show that the Bayesian formulation outperforms its classical cousin in estimation, prediction, and variable selection across a wide range of scenarios while offering the advantage of posterior inference. Finally, we discuss other variants of this new approach and provide a unified framework for variable selection using flexible reciprocal penalties. All methods described in this article are publicly available as an R package at: https://github.com/himelmallick/BayesRecipe.
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Affiliation(s)
- Himel Mallick
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, USA
| | - Rahim Alhamzawi
- Department of Statistics, University of Al-Qadisiyah, Al Diwaniyah, Iraq.,Center for Scientific Research and Development, Nawroz University, Duhok, Iraq
| | - Erina Paul
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, USA
| | - Vladimir Svetnik
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, USA
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11
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Rahnavard A, Chatterjee S, Sayoldin B, Crandall KA, Tekola-Ayele F, Mallick H. Omics community detection using multi-resolution clustering. Bioinformatics 2021; 37:3588-3594. [PMID: 33974004 PMCID: PMC8545346 DOI: 10.1093/bioinformatics/btab317] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/23/2021] [Accepted: 04/26/2021] [Indexed: 12/26/2022] Open
Abstract
MOTIVATION The discovery of biologically interpretable and clinically actionable communities in heterogeneous omics data is a necessary first step towards deriving mechanistic insights into complex biological phenomena. Here we present a novel clustering approach, omeClust, for community detection in omics profiles by simultaneously incorporating similarities among measurements and the overall complex structure of the data. RESULTS We show that omeClust outperforms published methods in inferring the true community structure as measured by both sensitivity and misclassification rate on simulated datasets. We further validated omeClust in diverse, multiple omics datasets, revealing new communities and functionally related groups in microbial strains, cell line gene expression patterns, and fetal genomic variation. We also derived enrichment scores attributable to putatively meaningful biological factors in these datasets that can serve as hypothesis generators facilitating new sets of testable hypotheses. AVAILABILITY omeClust is open-source software, and the implementation is available online at http://github.com/omicsEye/omeClust. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Suvo Chatterjee
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA
| | - Bahar Sayoldin
- School of Systems Biology, George Mason University, Fairfax, VA 22030, USA
| | - Keith A Crandall
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA
| | - Fasil Tekola-Ayele
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA
| | - Himel Mallick
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA
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12
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Mallick H, Bucci V, An L. Editorial: Statistical and Computational Methods for Microbiome Multi-Omics Data. Front Genet 2020; 11:927. [PMID: 33061936 PMCID: PMC7477107 DOI: 10.3389/fgene.2020.00927] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Accepted: 07/24/2020] [Indexed: 11/16/2022] Open
Affiliation(s)
- Himel Mallick
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ, United States
| | - Vanni Bucci
- Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, MA, United States
| | - Lingling An
- Interdisciplinary Program in Statistics and Data Science, The University of Arizona, Tucson, AZ, United States.,Department of Epidemiology and Biostatistics, The University of Arizona, Tucson, AZ, United States.,Department of Biosystems Engineering, The University of Arizona, Tucson, AZ, United States
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13
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Affiliation(s)
- Rahim Alhamzawi
- Department of Statistics, University of Al-Qadisiyah, Al Diwaniyah, Iraq
- Center for Scientific Research and Development, Nawroz University, Duhok, Iraq
| | - Himel Mallick
- Biostatistics and Research Decision Sciences, Merck & Co., Inc, Rahway, NJ, USA
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14
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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.
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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.
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15
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Quinn RA, Melnik AV, Vrbanac A, Fu T, Patras KA, Christy MP, Bodai Z, Belda-Ferre P, Tripathi A, Chung LK, Downes M, Welch RD, Quinn M, Humphrey G, Panitchpakdi M, Weldon KC, Aksenov A, da Silva R, Avila-Pacheco J, Clish C, Bae S, Mallick H, Franzosa EA, Lloyd-Price J, Bussell R, Thron T, Nelson AT, Wang M, Leszczynski E, Vargas F, Gauglitz JM, Meehan MJ, Gentry E, Arthur TD, Komor AC, Poulsen O, Boland BS, Chang JT, Sandborn WJ, Lim M, Garg N, Lumeng JC, Xavier RJ, Kazmierczak BI, Jain R, Egan M, Rhee KE, Ferguson D, Raffatellu M, Vlamakis H, Haddad GG, Siegel D, Huttenhower C, Mazmanian SK, Evans RM, Nizet V, Knight R, Dorrestein PC. Global chemical effects of the microbiome include new bile-acid conjugations. Nature 2020; 579:123-129. [PMID: 32103176 PMCID: PMC7252668 DOI: 10.1038/s41586-020-2047-9] [Citation(s) in RCA: 266] [Impact Index Per Article: 66.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Accepted: 01/03/2020] [Indexed: 12/26/2022]
Abstract
A mosaic of cross-phylum chemical interactions occurs between all metazoans and their microbiomes. A number of molecular families that are known to be produced by the microbiome have a marked effect on the balance between health and disease1-9. Considering the diversity of the human microbiome (which numbers over 40,000 operational taxonomic units10), the effect of the microbiome on the chemistry of an entire animal remains underexplored. Here we use mass spectrometry informatics and data visualization approaches11-13 to provide an assessment of the effects of the microbiome on the chemistry of an entire mammal by comparing metabolomics data from germ-free and specific-pathogen-free mice. We found that the microbiota affects the chemistry of all organs. This included the amino acid conjugations of host bile acids that were used to produce phenylalanocholic acid, tyrosocholic acid and leucocholic acid, which have not previously been characterized despite extensive research on bile-acid chemistry14. These bile-acid conjugates were also found in humans, and were enriched in patients with inflammatory bowel disease or cystic fibrosis. These compounds agonized the farnesoid X receptor in vitro, and mice gavaged with the compounds showed reduced expression of bile-acid synthesis genes in vivo. Further studies are required to confirm whether these compounds have a physiological role in the host, and whether they contribute to gut diseases that are associated with microbiome dysbiosis.
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Affiliation(s)
- Robert A Quinn
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.,Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
| | - Alexey V Melnik
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Alison Vrbanac
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Ting Fu
- Gene Expression Laboratory, Salk Institute for Biological Studies, San Diego, CA, USA
| | - Kathryn A Patras
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Mitchell P Christy
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Zsolt Bodai
- Department of Chemistry and Biochemistry, University of California San Diego, San Diego, CA, USA
| | - Pedro Belda-Ferre
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Anupriya Tripathi
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.,Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Lawton K Chung
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Michael Downes
- Gene Expression Laboratory, Salk Institute for Biological Studies, San Diego, CA, USA
| | - Ryan D Welch
- Gene Expression Laboratory, Salk Institute for Biological Studies, San Diego, CA, USA
| | - Melissa Quinn
- Department of Kinesiology, Michigan State University, East Lansing, MI, USA
| | - Greg Humphrey
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Morgan Panitchpakdi
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Kelly C Weldon
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.,UCSD Center for Microbiome Innovation, University of California San Diego, San Diego, CA, USA
| | - Alexander Aksenov
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Ricardo da Silva
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | | | - Clary Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sena Bae
- Department of Biostatistics, 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
| | - Himel Mallick
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Eric A Franzosa
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Jason Lloyd-Price
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Robert Bussell
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Taren Thron
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Andrew T Nelson
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Mingxun Wang
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Eric Leszczynski
- Department of Kinesiology, Michigan State University, East Lansing, MI, USA
| | - Fernando Vargas
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Julia M Gauglitz
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Michael J Meehan
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Emily Gentry
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Timothy D Arthur
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alexis C Komor
- Department of Chemistry and Biochemistry, University of California San Diego, San Diego, CA, USA
| | - Orit Poulsen
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Brigid S Boland
- Division of Gastroenterology, Department of Medicine, University of California San Diego, San Diego, CA, USA
| | - John T Chang
- Division of Gastroenterology, Department of Medicine, University of California San Diego, San Diego, CA, USA
| | - William J Sandborn
- Division of Gastroenterology, Department of Medicine, University of California San Diego, San Diego, CA, USA
| | - Meerana Lim
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Neha Garg
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, USA.,Emory-Children's Cystic Fibrosis Center, Atlanta, GA, USA
| | - Julie C Lumeng
- Department of Pediatrics, University of Michigan, Ann Arbor, MI, USA
| | | | | | - Ruchi Jain
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Marie Egan
- Department of Pediatrics, Yale School of Medicine, New Haven, CT, USA
| | - Kyung E Rhee
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - David Ferguson
- Department of Kinesiology, Michigan State University, East Lansing, MI, USA
| | - Manuela Raffatellu
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Hera Vlamakis
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gabriel G Haddad
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Dionicio Siegel
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA
| | - Curtis Huttenhower
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Sarkis K Mazmanian
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Ronald M Evans
- Gene Expression Laboratory, Salk Institute for Biological Studies, San Diego, CA, USA.,Howard Hughes Medical Institute, The Salk Institute for Biological Studies, San Diego, CA, USA
| | - Victor Nizet
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.,Department of Pediatrics, University of California San Diego, San Diego, CA, USA.,UCSD Center for Microbiome Innovation, University of California San Diego, San Diego, CA, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA.,UCSD Center for Microbiome Innovation, University of California San Diego, San Diego, CA, USA.,Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, USA.,Department of Engineering, 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. .,Department of Pediatrics, University of California San Diego, San Diego, CA, USA. .,UCSD Center for Microbiome Innovation, University of California San Diego, San Diego, CA, USA.
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16
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Mallick H, Franzosa EA, Mclver LJ, Banerjee S, Sirota-Madi A, Kostic AD, Clish CB, Vlamakis H, Xavier RJ, Huttenhower C. Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences. Nat Commun 2019; 10:3136. [PMID: 31316056 PMCID: PMC6637180 DOI: 10.1038/s41467-019-10927-1] [Citation(s) in RCA: 129] [Impact Index Per Article: 25.8] [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: 11/17/2018] [Accepted: 06/06/2019] [Indexed: 02/07/2023] Open
Abstract
Microbial community metabolomics, particularly in the human gut, are beginning to provide a new route to identify functions and ecology disrupted in disease. However, these data can be costly and difficult to obtain at scale, while amplicon or shotgun metagenomic sequencing data are readily available for populations of many thousands. Here, we describe a computational approach to predict potentially unobserved metabolites in new microbial communities, given a model trained on paired metabolomes and metagenomes from the environment of interest. Focusing on two independent human gut microbiome datasets, we demonstrate that our framework successfully recovers community metabolic trends for more than 50% of associated metabolites. Similar accuracy is maintained using amplicon profiles of coral-associated, murine gut, and human vaginal microbiomes. We also provide an expected performance score to guide application of the model in new samples. Our results thus demonstrate that this 'predictive metabolomic' approach can aid in experimental design and provide useful insights into the thousands of community profiles for which only metagenomes are currently available.
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Affiliation(s)
- Himel Mallick
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Eric A Franzosa
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Lauren J Mclver
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Soumya Banerjee
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Alexandra Sirota-Madi
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Aleksandar D Kostic
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Hera Vlamakis
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Ramnik J Xavier
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
- Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
- Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Curtis Huttenhower
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA.
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17
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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.
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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.
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18
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Franzosa EA, Sirota-Madi A, Avila-Pacheco J, Fornelos N, Haiser HJ, Reinker S, Vatanen T, Hall AB, Mallick H, McIver LJ, Sauk JS, Wilson RG, Stevens BW, Scott JM, Pierce K, Deik AA, Bullock K, Imhann F, Porter JA, Zhernakova A, Fu J, Weersma RK, Wijmenga C, Clish CB, Vlamakis H, Huttenhower C, Xavier RJ. Author Correction: Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat Microbiol 2019; 4:898. [PMID: 30971771 DOI: 10.1038/s41564-019-0442-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In the Supplementary Tables 2, 4 and 6 originally published with this Article, the authors mistakenly included sample identifiers in the form of UMCGs rather than UMCG IBDs in the validation cohort; this has now been amended.
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Affiliation(s)
- Eric A Franzosa
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
| | | | | | | | - Henry J Haiser
- Novartis Institute for Biomedical Research Inc, Cambridge, MA, USA
| | - Stefan Reinker
- Novartis Institute for Biomedical Research Inc, Cambridge, MA, USA
| | - Tommi Vatanen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Himel Mallick
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
| | - Lauren J McIver
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
| | - Jenny S Sauk
- Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Robin G Wilson
- Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Betsy W Stevens
- Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Kerry Pierce
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amy A Deik
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kevin Bullock
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Floris Imhann
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.,Department of Genetics, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
| | - Jeffrey A Porter
- Novartis Institute for Biomedical Research Inc, Basel, Switzerland
| | - Alexandra Zhernakova
- Department of Genetics, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
| | - Jingyuan Fu
- Department of Genetics, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.,Department of Pediatrics, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
| | - Rinse K Weersma
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
| | - Cisca Wijmenga
- Department of Genetics, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands.,Department of Immunology, K.G. Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, Norway
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hera Vlamakis
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Curtis Huttenhower
- Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.
| | - Ramnik J Xavier
- 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.
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19
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Franzosa EA, Sirota-Madi A, Avila-Pacheco J, Fornelos N, Haiser HJ, Reinker S, Vatanen T, Hall AB, Mallick H, McIver LJ, Sauk JS, Wilson RG, Stevens BW, Scott JM, Pierce K, Deik AA, Bullock K, Imhann F, Porter JA, Zhernakova A, Fu J, Weersma RK, Wijmenga C, Clish CB, Vlamakis H, Huttenhower C, Xavier RJ. Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat Microbiol 2019; 4:293-305. [PMID: 30531976 PMCID: PMC6342642 DOI: 10.1038/s41564-018-0306-4] [Citation(s) in RCA: 881] [Impact Index Per Article: 176.2] [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: 11/16/2017] [Accepted: 10/25/2018] [Indexed: 12/13/2022]
Abstract
The inflammatory bowel diseases (IBDs), which include Crohn's disease (CD) and ulcerative colitis (UC), are multifactorial chronic conditions of the gastrointestinal tract. While IBD has been associated with dramatic changes in the gut microbiota, changes in the gut metabolome-the molecular interface between host and microbiota-are less well understood. To address this gap, we performed untargeted metabolomic and shotgun metagenomic profiling of cross-sectional stool samples from discovery (n = 155) and validation (n = 65) cohorts of CD, UC and non-IBD control patients. Metabolomic and metagenomic profiles were broadly correlated with faecal calprotectin levels (a measure of gut inflammation). Across >8,000 measured metabolite features, we identified chemicals and chemical classes that were differentially abundant in IBD, including enrichments for sphingolipids and bile acids, and depletions for triacylglycerols and tetrapyrroles. While > 50% of differentially abundant metabolite features were uncharacterized, many could be assigned putative roles through metabolomic 'guilt by association' (covariation with known metabolites). Differentially abundant species and functions from the metagenomic profiles reflected adaptation to oxidative stress in the IBD gut, and were individually consistent with previous findings. Integrating these data, however, we identified 122 robust associations between differentially abundant species and well-characterized differentially abundant metabolites, indicating possible mechanistic relationships that are perturbed in IBD. Finally, we found that metabolome- and metagenome-based classifiers of IBD status were highly accurate and, like the vast majority of individual trends, generalized well to the independent validation cohort. Our findings thus provide an improved understanding of perturbations of the microbiome-metabolome interface in IBD, including identification of many potential diagnostic and therapeutic targets.
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Affiliation(s)
- Eric A Franzosa
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
| | | | | | | | - Henry J Haiser
- Novartis Institute for Biomedical Research Inc., Cambridge, MA, USA
| | - Stefan Reinker
- Novartis Institute for Biomedical Research Inc., Cambridge, MA, USA
| | - Tommi Vatanen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Himel Mallick
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
| | - Lauren J McIver
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
| | - Jenny S Sauk
- Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Robin G Wilson
- Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Betsy W Stevens
- Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Kerry Pierce
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amy A Deik
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kevin Bullock
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Floris Imhann
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
- Department of Genetics, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
| | - Jeffrey A Porter
- Novartis Institute for Biomedical Research Inc., Basel, Switzerland
| | - Alexandra Zhernakova
- Department of Genetics, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
| | - Jingyuan Fu
- Department of Genetics, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
- Department of Pediatrics, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
| | - Rinse K Weersma
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
| | - Cisca Wijmenga
- Department of Genetics, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
- Department of Immunology, K.G. Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, Norway
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hera Vlamakis
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Curtis Huttenhower
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.
| | - Ramnik J Xavier
- 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.
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20
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Chowdhury S, Chatterjee S, Mallick H, Banerjee P, Garai B. Group regularization for zero-inflated poisson regression models with an application to insurance ratemaking. J Appl Stat 2018. [DOI: 10.1080/02664763.2018.1555232] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Shrabanti Chowdhury
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Himel Mallick
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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21
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Chatterjee S, Chowdhury S, Mallick H, Banerjee P, Garai B. Group regularization for zero-inflated negative binomial regression models with an application to health care demand in Germany. Stat Med 2018; 37:3012-3026. [PMID: 29900575 DOI: 10.1002/sim.7804] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [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: 01/19/2018] [Revised: 03/21/2018] [Accepted: 04/12/2018] [Indexed: 11/10/2022]
Abstract
In many biomedical applications, covariates are naturally grouped, with variables in the same group being systematically related or statistically correlated. Under such settings, variable selection must be conducted at both group and individual variable levels. Motivated by the widespread availability of zero-inflated count outcomes and grouped covariates in many practical applications, we consider group regularization for zero-inflated negative binomial regression models. Using a least squares approximation of the mixture likelihood and a variety of group-wise penalties on the coefficients, we propose a unified algorithm (Gooogle: Group Regularization for Zero-inflated Count Regression Models) to efficiently compute the entire regularization path of the estimators. We investigate the finite sample performance of these methods through extensive simulation experiments and the analysis of a German health care demand dataset. Finally, we derive theoretical properties of these methods under reasonable assumptions, which further provides deeper insight into the asymptotic behavior of these approaches. The open source software implementation of this method is publicly available at: https://github.com/himelmallick/Gooogle.
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Affiliation(s)
- Saptarshi Chatterjee
- Division of Statistics, Department of Mathematical Sciences, Northern Illinois University, DeKalb, IL, 60115, USA
| | - Shrabanti Chowdhury
- Center for Molecular Medicine and Genetics, School of Medicine, Wayne State University, Detroit, MI, 48202, USA
| | - Himel Mallick
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | | | - Broti Garai
- Monsanto Company, Chesterfield, MO, 63017, USA
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22
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Borren NZ, Conway G, Garber JJ, Khalili H, Budree S, Mallick H, Yajnik V, Xavier RJ, Ananthakrishnan AN. Differences in Clinical Course, Genetics, and the Microbiome Between Familial and Sporadic Inflammatory Bowel Diseases. J Crohns Colitis 2018; 12:525-531. [PMID: 29145572 PMCID: PMC6018966 DOI: 10.1093/ecco-jcc/jjx154] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2017] [Accepted: 11/13/2017] [Indexed: 12/31/2022]
Abstract
BACKGROUND AND AIM Family history is the strongest risk factor for developing Crohn's disease [CD] or ulcerative colitis [UC]. We investigated whether the proximity of relationship with the affected relative and concordance for type of inflammatory bowel disease [IBD] modifies the effect of family history on phenotype and disease severity. METHOD This cross-sectional study included patients with a confirmed diagnosis of IBD in a clinical registry. Family history of IBD was assessed by a questionnaire ascertaining presence of disease in a first-first-degree, second-second-degree or distant relative. Our primary outcomes were disease phenotype as per the Montreal classification and severity measured by need for immunomodulator, biologic, or surgical therapy. Genotyping was performed on the Immunochip and faecal samples were subjected to 16S rRNA microbiome sequencing. RESULTS Our study included 2136 patients with IBD [1197 CD, 939 UC]. Just under one-third [32%] of cases ere familial IBD [17% first-degree, 21% second-degree]. Familial IBD was diagnosed at an earlier age, both in CD [26 vs 28 years, p = 0.0006] and UC [29 vs 32 years, p = 0.01]. Among CD patients, a positive family history for CD was associated with an increased risk for complicated disease in the presence of an affected family member (odds ratio [OR] 1.48, 95% confidence interval [CI] 1.07-2.03). However, this effect was significant only for first-degree relatives [OR 1.82, 95% CI 1.19-2.78]. CONCLUSIONS A family history of CD in first-degree relatives was associated with complicated CD. Family history discordant for type of IBD or in distant relatives did not influence disease phenotype or natural history.
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Affiliation(s)
- Nienke Z Borren
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA,Harvard Medical School, Boston, MA, USA
| | - Grace Conway
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA
| | - John J Garber
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA,Harvard Medical School, Boston, MA, USA
| | - Hamed Khalili
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA,Harvard Medical School, Boston, MA, USA
| | - Shrish Budree
- Department of Pediatrics, University of Cape Town, Cape Town, South Africa; OpenBiome, Cambridge, MA, USA; The Discovery Foundation, Johannesburg, South Africa
| | - Himel Mallick
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Vijay Yajnik
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA,Harvard Medical School, Boston, MA, USA
| | - Ramnik J Xavier
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA,Harvard Medical School, Boston, MA, USA
| | - Ashwin N Ananthakrishnan
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA,Harvard Medical School, Boston, MA, USA,Corresponding author: Ashwin N. Ananthakrishnan, MD, MPH, Massachusetts General Hospital Crohn’s and Colitis Center, 165 Cambridge Street, 9th Floor Boston, MA 02114, USA.
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23
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Mehta RS, Abu-Ali GS, Drew DA, Lloyd-Price J, Subramanian A, Lochhead P, Joshi AD, Ivey KL, Khalili H, Brown GT, DuLong C, Song M, Nguyen LH, Mallick H, Rimm EB, Izard J, Huttenhower C, Chan AT. Stability of the human faecal microbiome in a cohort of adult men. Nat Microbiol 2018; 3:347-355. [PMID: 29335554 PMCID: PMC6016839 DOI: 10.1038/s41564-017-0096-0] [Citation(s) in RCA: 144] [Impact Index Per Article: 24.0] [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: 03/27/2017] [Accepted: 12/11/2017] [Indexed: 12/30/2022]
Abstract
Characterizing the stability of the gut microbiome is important to exploit it as a therapeutic target and diagnostic biomarker. We metagenomically and metatranscriptomically sequenced the faecal microbiomes of 308 participants in the Health Professionals Follow-Up Study. Participants provided four stool samples-one pair collected 24-72 h apart and a second pair ~6 months later. Within-person taxonomic and functional variation was consistently lower than between-person variation over time. In contrast, metatranscriptomic profiles were comparably variable within and between subjects due to higher within-subject longitudinal variation. Metagenomic instability accounted for ~74% of corresponding metatranscriptomic instability. The rest was probably attributable to sources such as regulation. Among the pathways that were differentially regulated, most were consistently over- or under-transcribed at each time point. Together, these results suggest that a single measurement of the faecal microbiome can provide long-term information regarding organismal composition and functional potential, but repeated or short-term measures may be necessary for dynamic features identified by metatranscriptomics.
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Affiliation(s)
- Raaj S Mehta
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Galeb S Abu-Ali
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- The Broad Institute, Cambridge, MA, USA
| | - David A Drew
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jason Lloyd-Price
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- The Broad Institute, Cambridge, MA, USA
| | - Ayshwarya Subramanian
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- The Broad Institute, Cambridge, MA, USA
| | - Paul Lochhead
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Amit D Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Kerry L Ivey
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- South Australian Health and Medical Research Institute, Infection and Immunity Theme, School of Medicine, Flinders University, Adelaide, Australia
| | - Hamed Khalili
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Gordon T Brown
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Casey DuLong
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Mingyang Song
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Long H Nguyen
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Himel Mallick
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- The Broad Institute, Cambridge, MA, USA
| | - Eric B Rimm
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Curtis Huttenhower
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
- The Broad Institute, Cambridge, MA, USA.
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.
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Abu-Ali GS, Mehta RS, Lloyd-Price J, Mallick H, Branck T, Ivey KL, Drew DA, DuLong C, Rimm E, Izard J, Chan AT, Huttenhower C. Metatranscriptome of human faecal microbial communities in a cohort of adult men. Nat Microbiol 2018; 3:356-366. [PMID: 29335555 PMCID: PMC6557121 DOI: 10.1038/s41564-017-0084-4] [Citation(s) in RCA: 125] [Impact Index Per Article: 20.8] [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: 03/27/2017] [Accepted: 11/23/2017] [Indexed: 02/07/2023]
Abstract
The gut microbiome is intimately related to human health, but it is not yet known which functional activities are driven by specific microbes’ ecological configurations or transcription. We report a large-scale investigation of 372 human fecal metatranscriptomes and 929 metagenomes from a subset of 308 men in the Health Professionals Follow-up Study. We identified a metatranscriptomic “core” universally transcribed over time and across participants, often by different microbes. In contrast to the housekeeping functions enriched in this core, a “variable” metatranscriptome included specialized pathways that were differentially expressed both across participants and among microbes. Finally, longitudinal metagenomic profiles allowed ecological interaction network reconstruction, which remained stable over the six-month timespan, as did strain tracking within and between participants. These results provide an initial characterization of human fecal microbial ecology into core, subject-specific, microbe-specific, and temporally-variable transcription, and they differentiate metagenomically versus metatranscriptomically informative aspects of the human fecal microbiome.
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Affiliation(s)
- Galeb S Abu-Ali
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA, USA.,The Broad Institute, Cambridge, MA, USA
| | - Raaj S Mehta
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jason Lloyd-Price
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA, USA.,The Broad Institute, Cambridge, MA, USA
| | - Himel Mallick
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA, USA.,The Broad Institute, Cambridge, MA, USA
| | - Tobyn Branck
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA, USA.,U.S. Army Natick Soldier Systems Center in Natick, Natick, MA, USA
| | - Kerry L Ivey
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA.,South Australian Health and Medical Research Institute, Infection and Immunity Theme, School of Medicine, Flinders University, Adelaide, South Australia, Australia
| | - David A Drew
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Casey DuLong
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Eric Rimm
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Andrew T Chan
- The Broad Institute, Cambridge, MA, USA. .,Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. .,Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. .,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
| | - Curtis Huttenhower
- Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA, USA. .,The Broad Institute, Cambridge, MA, USA.
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25
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Mallick H, Ma S, Franzosa EA, Vatanen T, Morgan XC, Huttenhower C. Experimental design and quantitative analysis of microbial community multiomics. Genome Biol 2017; 18:228. [PMID: 29187204 PMCID: PMC5708111 DOI: 10.1186/s13059-017-1359-z] [Citation(s) in RCA: 112] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Studies of the microbiome have become increasingly sophisticated, and multiple sequence-based, molecular methods as well as culture-based methods exist for population-scale microbiome profiles. To link the resulting host and microbial data types to human health, several experimental design considerations, data analysis challenges, and statistical epidemiological approaches must be addressed. Here, we survey current best practices for experimental design in microbiome molecular epidemiology, including technologies for generating, analyzing, and integrating microbiome multiomics data. We highlight studies that have identified molecular bioactives that influence human health, and we suggest steps for scaling translational microbiome research to high-throughput target discovery across large populations.
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Affiliation(s)
- Himel Mallick
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Siyuan Ma
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Eric A Franzosa
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Tommi Vatanen
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Xochitl C Morgan
- Department of Microbiology and Immunology, The University of Otago, Dunedin, New Zealand
| | - Curtis Huttenhower
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA. .,Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
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26
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Schwager E, Mallick H, Ventz S, Huttenhower C. A Bayesian method for detecting pairwise associations in compositional data. PLoS Comput Biol 2017; 13:e1005852. [PMID: 29140991 PMCID: PMC5706738 DOI: 10.1371/journal.pcbi.1005852] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [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] [Received: 05/24/2017] [Revised: 11/29/2017] [Accepted: 10/25/2017] [Indexed: 12/12/2022] Open
Abstract
Compositional data consist of vectors of proportions normalized to a constant sum from a basis of unobserved counts. The sum constraint makes inference on correlations between unconstrained features challenging due to the information loss from normalization. However, such correlations are of long-standing interest in fields including ecology. We propose a novel Bayesian framework (BAnOCC: Bayesian Analysis of Compositional Covariance) to estimate a sparse precision matrix through a LASSO prior. The resulting posterior, generated by MCMC sampling, allows uncertainty quantification of any function of the precision matrix, including the correlation matrix. We also use a first-order Taylor expansion to approximate the transformation from the unobserved counts to the composition in order to investigate what characteristics of the unobserved counts can make the correlations more or less difficult to infer. On simulated datasets, we show that BAnOCC infers the true network as well as previous methods while offering the advantage of posterior inference. Larger and more realistic simulated datasets further showed that BAnOCC performs well as measured by type I and type II error rates. Finally, we apply BAnOCC to a microbial ecology dataset from the Human Microbiome Project, which in addition to reproducing established ecological results revealed unique, competition-based roles for Proteobacteria in multiple distinct habitats.
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Affiliation(s)
- Emma Schwager
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Himel Mallick
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Steffen Ventz
- Department of Computer Science and Statistics, University of Rhode Island, Kingstown, Rhode Island, United States of America
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
| | - Curtis Huttenhower
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
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Abstract
Classical bridge regression is known to possess many desirable statistical properties such as oracle, sparsity, and unbiasedness. One outstanding disadvantage of bridge regularization, however, is that it lacks a systematic approach to inference, reducing its flexibility in practical applications. In this study, we propose bridge regression from a Bayesian perspective. Unlike classical bridge regression that summarizes inference using a single point estimate, the proposed Bayesian method provides uncertainty estimates of the regression parameters, allowing coherent inference through the posterior distribution. Under a sparsity assumption non the high-dimensional parameter, we provide sufficient conditions for strong posterior consistency of the Bayesian bridge prior. On simulated datasets, we show that the proposed method performs well compared to several competing methods across a wide range of scenarios. Application to two real datasets further revealed that the proposed method performs as well as or better than published methods while offering the advantage of posterior inference.
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Affiliation(s)
- Himel Mallick
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Nengjun Yi
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
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28
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Abstract
A Bayesian bi-level variable selection method (BAGB: Bayesian Analysis of Group Bridge) is developed for regularized regression and classification. This new development is motivated by grouped data, where generic variables can be divided into multiple groups, with variables in the same group being mechanistically related or statistically correlated. As an alternative to frequentist group variable selection methods, BAGB incorporates structural information among predictors through a group-wise shrinkage prior. Posterior computation proceeds via an efficient MCMC algorithm. In addition to the usual ease-of-interpretation of hierarchical linear models, the Bayesian formulation produces valid standard errors, a feature that is notably absent in the frequentist framework. Empirical evidence of the attractiveness of the method is illustrated by extensive Monte Carlo simulations and real data analysis. Finally, several extensions of this new approach are presented, providing a unified framework for bi-level variable selection in general models with flexible penalties.
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Affiliation(s)
- Himel Mallick
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Nengjun Yi
- Section on Statistical Genetics, Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, 1665 University Boulevard, Birmingham, AL 35294, USA
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29
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Zhang X, Mallick H, Tang Z, Zhang L, Cui X, Benson AK, Yi N. Negative binomial mixed models for analyzing microbiome count data. BMC Bioinformatics 2017; 18:4. [PMID: 28049409 PMCID: PMC5209949 DOI: 10.1186/s12859-016-1441-7] [Citation(s) in RCA: 86] [Impact Index Per Article: 12.3] [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: 08/26/2016] [Accepted: 12/21/2016] [Indexed: 12/21/2022] Open
Abstract
Background Recent advances in next-generation sequencing (NGS) technology enable researchers to collect a large volume of metagenomic sequencing data. These data provide valuable resources for investigating interactions between the microbiome and host environmental/clinical factors. In addition to the well-known properties of microbiome count measurements, for example, varied total sequence reads across samples, over-dispersion and zero-inflation, microbiome studies usually collect samples with hierarchical structures, which introduce correlation among the samples and thus further complicate the analysis and interpretation of microbiome count data. Results In this article, we propose negative binomial mixed models (NBMMs) for detecting the association between the microbiome and host environmental/clinical factors for correlated microbiome count data. Although having not dealt with zero-inflation, the proposed mixed-effects models account for correlation among the samples by incorporating random effects into the commonly used fixed-effects negative binomial model, and can efficiently handle over-dispersion and varying total reads. We have developed a flexible and efficient IWLS (Iterative Weighted Least Squares) algorithm to fit the proposed NBMMs by taking advantage of the standard procedure for fitting the linear mixed models. Conclusions We evaluate and demonstrate the proposed method via extensive simulation studies and the application to mouse gut microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of both empirical power and Type I error. The method has been incorporated into the freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/ and http://github.com/abbyyan3/BhGLM), providing a useful tool for analyzing microbiome data.
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Affiliation(s)
- Xinyan Zhang
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, 35294-0022, USA
| | - Himel Mallick
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.,Program in Medical and Population Genetics, the Broad Institute, Cambridge, MA, 02142, USA
| | - Zaixiang Tang
- Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, China
| | - Lei Zhang
- Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, China
| | - Xiangqin Cui
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, 35294-0022, USA
| | - Andrew K Benson
- Department of Food Science and Technology and Core for Applied Genomics and Ecology, University of Nebraska, Lincoln, NE, 68583, USA
| | - Nengjun Yi
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, 35294-0022, USA.
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30
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Mallick H, Tiwari HK. EM Adaptive LASSO-A Multilocus Modeling Strategy for Detecting SNPs Associated with Zero-inflated Count Phenotypes. Front Genet 2016; 7:32. [PMID: 27066062 PMCID: PMC4811966 DOI: 10.3389/fgene.2016.00032] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [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: 06/22/2015] [Accepted: 02/22/2016] [Indexed: 11/13/2022] Open
Abstract
Count data are increasingly ubiquitous in genetic association studies, where it is possible to observe excess zero counts as compared to what is expected based on standard assumptions. For instance, in rheumatology, data are usually collected in multiple joints within a person or multiple sub-regions of a joint, and it is not uncommon that the phenotypes contain enormous number of zeroes due to the presence of excessive zero counts in majority of patients. Most existing statistical methods assume that the count phenotypes follow one of these four distributions with appropriate dispersion-handling mechanisms: Poisson, Zero-inflated Poisson (ZIP), Negative Binomial, and Zero-inflated Negative Binomial (ZINB). However, little is known about their implications in genetic association studies. Also, there is a relative paucity of literature on their usefulness with respect to model misspecification and variable selection. In this article, we have investigated the performance of several state-of-the-art approaches for handling zero-inflated count data along with a novel penalized regression approach with an adaptive LASSO penalty, by simulating data under a variety of disease models and linkage disequilibrium patterns. By taking into account data-adaptive weights in the estimation procedure, the proposed method provides greater flexibility in multi-SNP modeling of zero-inflated count phenotypes. A fast coordinate descent algorithm nested within an EM (expectation-maximization) algorithm is implemented for estimating the model parameters and conducting variable selection simultaneously. Results show that the proposed method has optimal performance in the presence of multicollinearity, as measured by both prediction accuracy and empirical power, which is especially apparent as the sample size increases. Moreover, the Type I error rates become more or less uncontrollable for the competing methods when a model is misspecified, a phenomenon routinely encountered in practice.
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Affiliation(s)
- Himel Mallick
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard UniversityBoston, MA, USA; Program of Medical and Population Genetics, Broad Institute of MIT and HarvardCambridge, MA, USA
| | - Hemant K Tiwari
- Section on Statistical Genetics, Department of Biostatistics, School of Public Health, University of Alabama at Birmingham Birmingham, AL, USA
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31
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Renfro LA, Mallick H, An MW, Sargent DJ, Mandrekar SJ. Clinical trial designs incorporating predictive biomarkers. Cancer Treat Rev 2016; 43:74-82. [PMID: 26827695 DOI: 10.1016/j.ctrv.2015.12.008] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2015] [Revised: 12/26/2015] [Accepted: 12/29/2015] [Indexed: 01/13/2023]
Abstract
Development of oncologic therapies has traditionally been performed in a sequence of clinical trials intended to assess safety (phase I), preliminary efficacy (phase II), and improvement over the standard of care (phase III) in homogeneous (in terms of tumor type and disease stage) patient populations. As cancer has become increasingly understood on the molecular level, newer "targeted" drugs that inhibit specific cancer cell growth and survival mechanisms have increased the need for new clinical trial designs, wherein pertinent questions on the relationship between patient biomarkers and response to treatment can be answered. Herein, we review the clinical trial design literature from initial to more recently proposed designs for targeted agents or those treatments hypothesized to have enhanced effectiveness within patient subgroups (e.g., those with a certain biomarker value or who harbor a certain genetic tumor mutation). We also describe a number of real clinical trials where biomarker-based designs have been utilized, including a discussion of their respective advantages and challenges. As cancers become further categorized and/or reclassified according to individual patient and tumor features, we anticipate a continued need for novel trial designs to keep pace with the changing frontier of clinical cancer research.
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Affiliation(s)
- Lindsay A Renfro
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA.
| | - Himel Mallick
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA; The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ming-Wen An
- Department of Mathematics and Statistics, Vassar College, Poughkeepsie, NY, USA
| | - Daniel J Sargent
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Sumithra J Mandrekar
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
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Yadav R, Vishwakarma L, Mallick H. Role of preoptic area thermo TRPV1 channel in sleep and thermoregulation. Sleep Med 2015. [DOI: 10.1016/j.sleep.2015.02.171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Weige CC, Birtwistle MR, Mallick H, Yi N, Berrong Z, Cloessner E, Duff K, Tidwell J, Clendenning M, Wilkerson B, Farrell C, Bunz F, Ji H, Shtutman M, Creek KE, Banister CE, Buckhaults PJ. Transcriptomes and shRNA suppressors in a TP53 allele-specific model of early-onset colon cancer in African Americans. Mol Cancer Res 2014; 12:1029-41. [PMID: 24743655 DOI: 10.1158/1541-7786.mcr-13-0286-t] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
UNLABELLED African Americans are disproportionately affected by early-onset, high-grade malignancies. A fraction of this cancer health disparity can be explained by genetic differences between individuals of African or European descent. Here the wild-type Pro/Pro genotype at the TP53Pro72Arg (P72R) polymorphism (SNP: rs1042522) is more frequent in African Americans with cancer than in African Americans without cancer (51% vs. 37%), and is associated with a significant increase in the rates of cancer diagnosis in African Americans. To test the hypothesis that Tp53 allele-specific gene expression may contribute to African American cancer disparities, TP53 hemizygous knockout variants were generated and characterized in the RKO colon carcinoma cell line, which is wild type for TP53 and heterozygous at the TP53Pro72Arg locus. Transcriptome profiling, using RNAseq, in response to the DNA-damaging agent etoposide revealed a large number of Tp53-regulated transcripts, but also a subset of transcripts that were TP53Pro72Arg allele specific. In addition, a shRNA-library suppressor screen for Tp53 allele-specific escape from Tp53-induced arrest was performed. Several novel RNAi suppressors of Tp53 were identified, one of which, PRDM1β (BLIMP-1), was confirmed to be an Arg-specific transcript. Prdm1β silences target genes by recruiting H3K9 trimethyl (H3K9me3) repressive chromatin marks, and is necessary for stem cell differentiation. These results reveal a novel model for African American cancer disparity, in which the TP53 codon 72 allele influences lifetime cancer risk by driving damaged cells to differentiation through an epigenetic mechanism involving gene silencing. IMPLICATIONS TP53 P72R polymorphism significantly contributes to increased African American cancer disparity.
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Affiliation(s)
| | - Marc R Birtwistle
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Himel Mallick
- Biostatistics, The University of Alabama at Birmingham, Birmingham, Alabama
| | - Nengjun Yi
- Biostatistics, The University of Alabama at Birmingham, Birmingham, Alabama
| | - Zuzana Berrong
- Department of Biochemistry and Molecular Biology, Georgia Regents University, Augusta, Georgia
| | - Emily Cloessner
- Department of Biochemistry and Molecular Biology, Georgia Regents University, Augusta, Georgia
| | - Keely Duff
- Department of Biochemistry and Molecular Biology, Georgia Regents University, Augusta, Georgia
| | - Josephine Tidwell
- Department of Biochemistry and Molecular Biology, Georgia Regents University, Augusta, Georgia
| | - Megan Clendenning
- Department of Biochemistry and Molecular Biology, Georgia Regents University, Augusta, Georgia
| | - Brent Wilkerson
- Department of Otolaryngology-Head and Neck Surgery University of California, Davis, Sacramento, California
| | - Christopher Farrell
- Department of Pharmaceutical and Administrative Sciences School of Pharmacy, Presbyterian College, Clinton
| | - Fred Bunz
- Department of Radiation Oncology and Molecular Radiation Sciences, The Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Hao Ji
- Department of Drug Discovery and Biomedical Sciences South Carolina College of Pharmacy, University of South Carolina, Columbia, South Carolina; and
| | - Michael Shtutman
- Department of Drug Discovery and Biomedical Sciences South Carolina College of Pharmacy, University of South Carolina, Columbia, South Carolina; and
| | - Kim E Creek
- Department of Drug Discovery and Biomedical Sciences South Carolina College of Pharmacy, University of South Carolina, Columbia, South Carolina; and
| | - Carolyn E Banister
- Department of Drug Discovery and Biomedical Sciences South Carolina College of Pharmacy, University of South Carolina, Columbia, South Carolina; and
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Lakoski SG, Mallick H, McClure LA, Safford M, Kissela B, Howard G, Cushman M. A risk algorithm for assessing short-term mortality for obese black and white men and women. Obesity (Silver Spring) 2014; 22:1142-8. [PMID: 24115735 PMCID: PMC5036400 DOI: 10.1002/oby.20622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2013] [Revised: 09/09/2013] [Accepted: 09/09/2013] [Indexed: 11/08/2022]
Abstract
OBJECTIVE To develop and validate a mortality risk algorithm for obese black and white men and women to elucidate risk factors prognostic of short-term mortality among obese persons. METHODS Prospective cohort study. Reasons for geographic and racial differences in stroke (REGARDS) study, is a cohort of black and white men and women aged ≥45 years. Obese (≥30 kg m(-2) ) participants in REGARDS (n = 11 288) were randomly assigned to the derivation data set or an independent validation set. RESULTS During the mean follow-up period of 4.9 years, 8.9% (n = 504) in the derivation cohort and 8.7% (n = 492) in the validation cohort died. The best-fitting model based on data from the derivation cohort included demographic (age, sex), coronary heart disease (CHD) conditions (diabetes, systolic blood pressure, history of CHD), health behaviors (smoking, physical activity, alcohol use), and socioeconomic variables (income, use of physician services). The C-statistic when the model was applied to the validation cohort was 0.80. Observed and predicted rates of mortality were similar across deciles of mortality risk by race. CONCLUSIONS A risk algorithm was established and validated to predict mortality among black and white obese subjects based on CHD risk factors, behavioral risk factors, and socioeconomic status.
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Affiliation(s)
- Susan G. Lakoski
- Department of Medicine, University of Vermont, Burlington, Vermont, USA
| | - Himel Mallick
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Leslie A. McClure
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Monika Safford
- Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Brett Kissela
- Department of Neurology, University of Cincinnati, Cincinnati, Ohio, USA
| | - George Howard
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Mary Cushman
- Department of Medicine, University of Vermont, Burlington, Vermont, USA
- Department of Pathology, University of Vermont, Burlington, Vermont, USA
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35
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Abstract
Multiple comparisons or multiple testing has been viewed as a thorny issue in genetic association studies aiming to detect disease-associated genetic variants from a large number of genotyped variants. We alleviate the problem of multiple comparisons by proposing a hierarchical modeling approach that is fundamentally different from the existing methods. The proposed hierarchical models simultaneously fit as many variables as possible and shrink unimportant effects towards zero. Thus, the hierarchical models yield more efficient estimates of parameters than the traditional methods that analyze genetic variants separately, and also coherently address the multiple comparisons problem due to largely reducing the effective number of genetic effects and the number of statistically "significant" effects. We develop a method for computing the effective number of genetic effects in hierarchical generalized linear models, and propose a new adjustment for multiple comparisons, the hierarchical Bonferroni correction, based on the effective number of genetic effects. Our approach not only increases the power to detect disease-associated variants but also controls the Type I error. We illustrate and evaluate our method with real and simulated data sets from genetic association studies. The method has been implemented in our freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/).
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Affiliation(s)
- Nengjun Yi
- Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham, Birmingham, AL 35294
| | - Shizhong Xu
- Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
| | - Xiang-Yang Lou
- Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham, Birmingham, AL 35294
| | - Himel Mallick
- Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham, Birmingham, AL 35294
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36
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Abstract
Park and Casella (2008) provided the Bayesian lasso for linear models by assigning scale mixture of normal (SMN) priors on the parameters and independent exponential priors on their variances. In this paper, we propose an alternative Bayesian analysis of the lasso problem. A different hierarchical formulation of Bayesian lasso is introduced by utilizing the scale mixture of uniform (SMU) representation of the Laplace density. We consider a fully Bayesian treatment that leads to a new Gibbs sampler with tractable full conditional posterior distributions. Empirical results and real data analyses show that the new algorithm has good mixing property and performs comparably to the existing Bayesian method in terms of both prediction accuracy and variable selection. An ECM algorithm is provided to compute the MAP estimates of the parameters. Easy extension to general models is also briefly discussed.
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Affiliation(s)
- Himel Mallick
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Nengjun Yi
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
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37
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Kelleher J, Bhat R, Salas AA, Addis D, Mills EC, Mallick H, Tripathi A, Pruitt EP, Roane C, McNair T, Owen J, Ambalavanan N, Carlo WA. Oronasopharyngeal suction versus wiping of the mouth and nose at birth: a randomised equivalency trial. Lancet 2013; 382:326-30. [PMID: 23739521 DOI: 10.1016/s0140-6736(13)60775-8] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
BACKGROUND Wiping of the mouth and nose at birth is an alternative method to oronasopharyngeal suction in delivery-room management of neonates, but whether these methods have equivalent effectiveness is unclear. METHODS For this randomised equivalency trial, neonates delivered at 35 weeks' gestation or later at the University of Alabama at Birmingham Hospital, Birmingham, AL, USA, between October, 2010, and November, 2011, were eligible. Before birth, neonates were randomly assigned gentle wiping of the face, mouth (implemented by the paediatric or obstetric resident), and nose with a towel (wipe group) or suction with a bulb syringe of the mouth and nostrils (suction group). The primary outcome was the respiratory rate in the first 24 h after birth. We hypothesised that respiratory rates would differ by fewer than 4 breaths per min between groups. Analysis was by intention to treat. This study is registered with ClinicalTrials.gov, number NCT01197807. FINDINGS 506 neonates born at a median of 39 weeks' gestation (IQR 38-40) were randomised. Three parents withdrew consent and 15 non-vigorous neonates with meconium-stained amniotic fluid were excluded. Among the 488 treated neonates, the mean respiratory rates in the first 24 h were 51 (SD 8) breaths per min in the wipe group and 50 (6) breaths per min in the suction group (difference of means 1 breath per min, 95% CI -2 to 0, p<0·001). INTERPRETATION Wiping the nose and mouth has equivalent efficacy to routine use of oronasopharyngeal suction in neonates born at or beyond 35 weeks' gestation. FUNDING None.
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Affiliation(s)
- John Kelleher
- Department of Pediatrics, University of Alabama at Birmingham, Birmingham, AL, USA
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Kelleher J, Mallick H, Soltau TD, Harmon CM, Dimmitt RA. Mortality and intestinal failure in surgical necrotizing enterocolitis. J Pediatr Surg 2013; 48:568-72. [PMID: 23480914 DOI: 10.1016/j.jpedsurg.2012.11.028] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2012] [Revised: 10/19/2012] [Accepted: 11/08/2012] [Indexed: 02/05/2023]
Abstract
BACKGROUND/PURPOSE To examine whether as initial surgical intervention for necrotizing enterocolitis, primary peritoneal drainage as compared to primary laparotomy is associated with increased mortality or intestinal failure. METHODS Retrospective observational study of 240 infants with surgical necrotizing enterocolitis. RESULTS There was no difference concerning the composite outcome of mortality before discharge or survival with intestinal failure after adjusting for known covariates (Odds Ratio 1.73, 95% CI 0.88, 3.40). More surviving infants in the peritoneal drainage with subsequent salvage or secondary laparotomy had intestinal failure compared to those who received a peritoneal drain without subsequent laparotomy and survived (12% vs. 14% vs. 1%, p=0.015). CONCLUSIONS There is no difference between peritoneal drainage and laparotomy in infants with surgical necrotizing enterocolitis concerning the combined outcome of mortality or survival with intestinal failure. There is increased intestinal failure in surviving infants treated with peritoneal drain with either subsequent salvage or secondary laparotomy compared to peritoneal drainage alone.
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Affiliation(s)
- John Kelleher
- Department of Pediatrics, University of Alabama at Birmingham, Birmingham, AL 35249-7335, USA.
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Abstract
In this article, we present a selective overview of some recent developments in Bayesian model and variable selection methods for high dimensional linear models. While most of the reviews in literature are based on conventional methods, we focus on recently developed methods, which have proven to be successful in dealing with high dimensional variable selection. First, we give a brief overview of the traditional model selection methods (viz. Mallow's Cp, AIC, BIC, DIC), followed by a discussion on some recently developed methods (viz. EBIC, regularization), which have occupied the minds of many statisticians. Then, we review high dimensional Bayesian methods with a particular emphasis on Bayesian regularization methods, which have been used extensively in recent years. We conclude by briefly addressing the asymptotic behaviors of Bayesian variable selection methods for high dimensional linear models under different regularity conditions.
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Affiliation(s)
- Himel Mallick
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Nengjun Yi
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
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Ray B, Mallick H, Kumar VM. Role of the medial preoptic area in thermal preference of rats. Indian J Physiol Pharmacol 2001; 45:445-50. [PMID: 11883151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 04/17/2023]
Abstract
This study was conducted to find out whether the medial preoptic area (mPOA) plays a role in the selection of ambient temperature by rats. Adult male Wistar rats were kept in an environmental chamber having three interconnected compartments, maintained at three different temperatures (18 degrees, 24 degrees and 30 degrees C) in which the animals could move freely from one compartment to the other. Normal rats preferred to stay at the chamber maintained at 24 degrees C for most of the time, during day and night. The temperature preference shifted to 30 degrees C after the mPOA of these rats had been lesioned by local administration of 5 micrograms of N-methyl D-aspartic acid (NMDA) in 0.2 microliter distilled water. The results of the study suggest that the mPOA acts as a fine tuning center for homeostatic regulation of thermal balance, including selection of appropriate thermal environment. It is proposed that after the mPOA lesion, the animal cannot assess properly the energy status of the body and thereby prefers a higher ambient temperature.
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Affiliation(s)
- B Ray
- Department of Physiology, All India Institute of Medical Sciences, New Delhi-110 029
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Abstract
This study was aimed at investigating the role of the adrenergic mechanism in the medial preoptic area (mPOA) in sexual arousal and copulatory performance. Saline, norepinephrine (NE), phenoxybenzamine (PBZ) and propranolol (PROP) were injected into the mPOA in different groups of rats. NE application (3 micrograms) facilitated the male sexual behavior by increasing sexual arousal and copulatory performance. On the other hand, application of PROP and PBZ produced inhibition of male sexual behavior. Effects produced by low doses of PROP were more significant than PBZ. The results, viewed in the light of other available reports, suggest that the mPOA beta-adrenergic mechanism is important in the elaboration of male sexual behavior.
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Affiliation(s)
- H Mallick
- Department of Physiology, All India Institute of Medical Sciences, New Delhi
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Abstract
Adrenergic alpha 2 agonist (clonidine) and its antagonist (yohimbine) were locally applied to the medial preoptic area (mPOA), to find out the role of alpha 2 receptors at this brain region in the regulation of sleep-wakefulness. Clonidine produced arousal, whereas yohimbine induced sleep in freely moving animals. Behavioural arousal produced by clonidine administration was accompanied by EEG synchronization. The alpha 2 receptor as the probable site of action of externally applied norepinephrine (NE), is discussed.
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Affiliation(s)
- V Ramesh
- Department of Physiology, All India Institute of Medical Sciences, New Delhi
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John J, Kumar VM, Gopinath G, Ramesh V, Mallick H. Changes in sleep-wakefulness after kainic acid lesion of the preoptic area in rats. Jpn J Physiol 1994; 44:231-42. [PMID: 7823414 DOI: 10.2170/jjphysiol.44.231] [Citation(s) in RCA: 53] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The role of the preoptic area (POA) neurons in the regulation of sleep-wakefulness (S-W) has been investigated in this study. The cell-specific neurotoxin, kainic acid (KA), was injected (0.8 microgram in 0.2 microliter) intracerebrally for lesioning of the POA. S-W was assessed (on the basis of EEG, EMG, and EOG recordings) for a day before bilateral lesion of the POA, and for 3 weeks after the lesion. There was an increase in wakefulness, and a decrease in all the stages of sleep after KA lesion of the POA. The reduction in deep slow wave sleep (S2) and REM sleep (PS) were more marked than light slow wave sleep (S1), and these had not shown any recovery even after 3 weeks of lesion. Two days after the lesion, the reduction in sleep was much more marked during the daytime than at night. There was an increase in locomotor activity, especially during the daytime, though it was only statistically significant on the 6th and the 10th day after the lesion. This study shows that the POA neurons are involved in the induction and maintenance of sleep. The lesion did not have a long lasting effect on the circadian distribution of sleep but the changes in locomotor activity seem to persist for a longer period.
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Affiliation(s)
- J John
- Department of Physiology, All India Institute of Medical Sciences, New Delhi
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Prasad LS, Sinha KP, Sen DK, Mallick H. A study on immunoglobulins in Indian children. Indian J Med Res 1971; 59:107-14. [PMID: 4102395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
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Sinha HB, Mukherji AK, Mallick H, Achari AG. A report of investigation of the antigenic property of placenta in normal pregnancy, pre-eclamptic toxaemia and eclampsia. J Indian Med Assoc 1968; 50:456-7. [PMID: 5704862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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