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Wang S, Chen H, Zheng Y, Li Z, Cui B, Zhao P, Zheng J, Lu R, Sun N. Transcriptomics- and metabolomics-based integration analyses revealed the potential pharmacological effects and functional pattern of in vivo Radix Paeoniae Alba administration. Chin Med 2020; 15:52. [PMID: 32489401 PMCID: PMC7245909 DOI: 10.1186/s13020-020-00330-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 05/12/2020] [Indexed: 01/10/2023] Open
Abstract
Background Radix Paeoniae Alba (RPA) and other natural medicines have remarkable curative effects and are widely used in traditional Chinese Medicine (TCM). However, due to their multi-component and multi-target characteristics, it is difficult to study the detailed pharmacological mechanisms for those natural medicines in vivo. Therefore, their real effects on organisms is still uncertain. Methods RPA was selected as research object, the present study was designed to study the complex mechanisms of RPA in vivo by integrating and interpreting the transcriptomic based RNA-seq and metabolomic based NMR spectrum after RPA administration in mice. A variety of dimension-reduction algorithms and classifier models were applied to the processing of high-throughput data. Results Among serum metabolites, the contents of PC and glucose were significantly increased, while the contents of various amino acids, lipids and their metabolites were significantly decreased in mice after RPA administration. Based on the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, differential analysis showed that the liver was the site where RPA exerted a significant effect, which confirmed the rationality of “meridian tropism” in the theory in TCM. In addition, RPA played a role in lipid metabolism by regulating genes encoding enzymes of the glycerolipid metabolism pathway, such as 1-acyl-sn-glycerol-3-phosphate acyltransferase (Agpat), phosphatidate phosphatase (Lpin), phospholipid phosphatase (Plpp) and endothelial lipase (Lipg). We also found that RPA regulates several substance addiction pathways in the brain, such as the cocaine addiction pathway, and the related targets were predicted based on the sequencing data from pathological model in the GEO database. The overall effective pattern of RPA was intuitively presented with a multidimensional radar map through a self-designed model which found that liver and brain were mainly regulated by RPA compared with the traditional meridian tropism theory. Conclusions Overall this study expanded the potential application of RPA and provided possible targets and directions for further mechanism study, meanwhile, it also established a multi-dimensional evaluation model to represent the overall effective pattern of TCM for the first time. In the future, such study based on the high-throughput data sets can be used to interpret the theory of TCM and to provide a valuable research model and clinical medication reference for the TCM researchers and doctors.
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Affiliation(s)
- Sining Wang
- Department of Pathology, School of Basic Medical Sciences, Shanghai University of Traditional Chinese Medicine, 1200 CaiLun Ave, Pudong, 201203 Shanghai China
| | - Huihua Chen
- Department of Pathology, School of Basic Medical Sciences, Shanghai University of Traditional Chinese Medicine, 1200 CaiLun Ave, Pudong, 201203 Shanghai China
| | - Yufan Zheng
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Fudan University, 130 DongAn Ave, Xuhui, 200032 Shanghai China
| | - Zhenyu Li
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Baiping Cui
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Fudan University, 130 DongAn Ave, Xuhui, 200032 Shanghai China
| | - Pei Zhao
- Public Laboratory Platform, School of Basic Medical Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiali Zheng
- Department of Pathology, School of Basic Medical Sciences, Shanghai University of Traditional Chinese Medicine, 1200 CaiLun Ave, Pudong, 201203 Shanghai China
| | - Rong Lu
- Department of Pathology, School of Basic Medical Sciences, Shanghai University of Traditional Chinese Medicine, 1200 CaiLun Ave, Pudong, 201203 Shanghai China
| | - Ning Sun
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Fudan University, 130 DongAn Ave, Xuhui, 200032 Shanghai China
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Zeng ISL, Lumley T. Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science). Bioinform Biol Insights 2018; 12:1177932218759292. [PMID: 29497285 PMCID: PMC5824897 DOI: 10.1177/1177932218759292] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2017] [Accepted: 01/24/2018] [Indexed: 12/14/2022] Open
Abstract
Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.
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Affiliation(s)
- Irene Sui Lan Zeng
- Department of Statistics, Faculty of Science, The University of Auckland, Auckland, New Zealand
| | - Thomas Lumley
- Department of Statistics, Faculty of Science, The University of Auckland, Auckland, New Zealand
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Zhao Y, Chung M, Johnson BA, Moreno CS, Long Q. Hierarchical Feature Selection Incorporating Known and Novel Biological Information: Identifying Genomic Features Related to Prostate Cancer Recurrence. J Am Stat Assoc 2017; 111:1427-1439. [PMID: 28435175 DOI: 10.1080/01621459.2016.1164051] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Our work is motivated by a prostate cancer study aimed at identifying mRNA and miRNA biomarkers that are predictive of cancer recurrence after prostatectomy. It has been shown in the literature that incorporating known biological information on pathway memberships and interactions among biomarkers improves feature selection of high-dimensional biomarkers in relation to disease risk. Biological information is often represented by graphs or networks, in which biomarkers are represented by nodes and interactions among them are represented by edges; however, biological information is often not fully known. For example, the role of microRNAs (miRNAs) in regulating gene expression is not fully understood and the miRNA regulatory network is not fully established, in which case new strategies are needed for feature selection. To this end, we treat unknown biological information as missing data (i.e., missing edges in graphs), different from commonly encountered missing data problems where variable values are missing. We propose a new concept of imputing unknown biological information based on observed data and define the imputed information as the novel biological information. In addition, we propose a hierarchical group penalty to encourage sparsity and feature selection at both the pathway level and the within-pathway level, which, combined with the imputation step, allows for incorporation of known and novel biological information. While it is applicable to general regression settings, we develop and investigate the proposed approach in the context of semiparametric accelerated failure time models motivated by our data example. Data application and simulation studies show that incorporation of novel biological information improves performance in risk prediction and feature selection and the proposed penalty outperforms the extensions of several existing penalties.
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Affiliation(s)
- Yize Zhao
- Postdoctoral Fellow, Statistical and Applied Mathematical Sciences Institute, Research Triangle Park, NC 27709
| | - Matthias Chung
- Assistant Professor, Department of Mathematics, Virginia Tech, Blacksburg, VA 24061
| | - Brent A Johnson
- Associate Professor, Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642
| | - Carlos S Moreno
- Associate Professor, Department of Pathology and Laboratory Medicine
| | - Qi Long
- Associate Professor, Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322
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Tomoiaga A, Westfall P, Donato M, Draghici S, Hassan S, Romero R, Tellaroli P. Pathway crosstalk effects: Shrinkage and disentanglement using a Bayesian hierarchical model. STATISTICS IN BIOSCIENCES 2016; 8:374-394. [PMID: 33456621 DOI: 10.1007/s12561-016-9160-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Identifying the biological pathways that are related to various clinical phenotypes is an important concern in biomedical research. Based on estimated expression levels and/or p-values, over-representation analysis (ORA) methods provide rankings of pathways, but they are tainted because pathways overlap. This crosstalk phenomenon has not been rigorously studied and classical ORA does not take into consideration: (i) that crosstalk effects in cases of overlapping pathways can cause incorrect rankings of pathways, (ii) that crosstalk effects can cause both excess type I errors and type II errors, (iii) that rankings of small pathways are unreliable and (iv) that type I error rates can be inflated due to multiple comparisons of pathways. We develop a Bayesian hierarchical model that addresses these problems, providing sensible estimates and rankings, and reducing error rates. We show, on both real and simulated data, that the results of our method are more accurate than the results produced by the classical over-representation analysis, providing a better understanding of the underlying biological phenomena involved in the phenotypes under study. The R code and the binary datasets for implementing the analyses described in this article are available online at: http://www.eng.wayne.edu/page.php?id=6402.
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Affiliation(s)
- Alin Tomoiaga
- Center for Advanced Analytics and Business Intelligence, Texas Tech University, Lubbock, TX 79409, U.S.A
| | - Peter Westfall
- Center for Advanced Analytics and Business Intelligence, Texas Tech University, Lubbock, TX 79409, U.S.A
| | - Michele Donato
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, 48201, MI, U.S.A
| | - Sorin Draghici
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, 48201, MI, U.S.A
| | - Sonia Hassan
- Perinatology Research Branch, Program for Perinatal Research and Obstetrics, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Detroit, MI, U.S.A
| | - Roberto Romero
- Perinatology Research Branch, Program for Perinatal Research and Obstetrics, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Detroit, MI, U.S.A
| | - Paola Tellaroli
- Department of Statistical Sciences, University of Padua, Italy
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Mikelsaar M, Sepp E, Štšepetova J, Songisepp E, Mändar R. Biodiversity of Intestinal Lactic Acid Bacteria in the Healthy Population. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 932:1-64. [DOI: 10.1007/5584_2016_3] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Yang Z, Michailidis G. A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data. Bioinformatics 2016; 32:1-8. [PMID: 26377073 PMCID: PMC5006236 DOI: 10.1093/bioinformatics/btv544] [Citation(s) in RCA: 94] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2015] [Revised: 09/08/2015] [Accepted: 09/09/2015] [Indexed: 12/15/2022] Open
Abstract
MOTIVATION Recent advances in high-throughput omics technologies have enabled biomedical researchers to collect large-scale genomic data. As a consequence, there has been growing interest in developing methods to integrate such data to obtain deeper insights regarding the underlying biological system. A key challenge for integrative studies is the heterogeneity present in the different omics data sources, which makes it difficult to discern the coordinated signal of interest from source-specific noise or extraneous effects. RESULTS We introduce a novel method of multi-modal data analysis that is designed for heterogeneous data based on non-negative matrix factorization. We provide an algorithm for jointly decomposing the data matrices involved that also includes a sparsity option for high-dimensional settings. The performance of the proposed method is evaluated on synthetic data and on real DNA methylation, gene expression and miRNA expression data from ovarian cancer samples obtained from The Cancer Genome Atlas. The results show the presence of common modules across patient samples linked to cancer-related pathways, as well as previously established ovarian cancer subtypes. AVAILABILITY AND IMPLEMENTATION The source code repository is publicly available at https://github.com/yangzi4/iNMF. CONTACT gmichail@umich.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zi Yang
- Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - George Michailidis
- Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA
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Siska C, Bowler R, Kechris K. The discordant method: a novel approach for differential correlation. ACTA ACUST UNITED AC 2015; 32:690-6. [PMID: 26520855 DOI: 10.1093/bioinformatics/btv633] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Accepted: 10/24/2015] [Indexed: 11/12/2022]
Abstract
MOTIVATION Current differential correlation methods are designed to determine molecular feature pairs that have the largest magnitude of difference between correlation coefficients. These methods do not easily capture molecular feature pairs that experience no correlation in one group but correlation in another, which may reflect certain types of biological interactions. We have developed a tool, the Discordant method, which categorizes the correlation types for each group to make this possible. RESULTS We compare the Discordant method to existing approaches using simulations and two biological datasets with different types of -omics data. In contrast to other methods, Discordant identifies phenotype-related features at a similar or higher rate while maintaining reasonable computational tractability and usability. AVAILABILITY AND IMPLEMENTATION R code and sample data are available at https://github.com/siskac/discordant CONTACT katerina.kechris@ucdenver.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Charlotte Siska
- Computational Bioscience Program, Department of Pharmacology, University of Colorado Denver
| | | | - Katerina Kechris
- Department of Biostatistics and Informatics, University of Colorado Denver, Denver, CO, USA
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Cavill R, Jennen D, Kleinjans J, Briedé JJ. Transcriptomic and metabolomic data integration. Brief Bioinform 2015; 17:891-901. [PMID: 26467821 DOI: 10.1093/bib/bbv090] [Citation(s) in RCA: 147] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Indexed: 01/12/2023] Open
Abstract
Many studies now produce parallel data sets from different omics technologies; however, the task of interpreting the acquired data in an integrated fashion is not trivial. This review covers those methods that have been used over the past decade to statistically integrate and interpret metabolomics and transcriptomic data sets. It defines four categories of approaches, correlation-based integration, concatenation-based integration, multivariate-based integration and pathway-based integration, into which all existing statistical methods fit. It also explores the choices in study design for generating samples for analysis by these omics technologies and the impact that these technical decisions have on the subsequent data analysis options.
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Sumner LW, Lei Z, Nikolau BJ, Saito K. Modern plant metabolomics: advanced natural product gene discoveries, improved technologies, and future prospects. Nat Prod Rep 2015; 32:212-29. [PMID: 25342293 DOI: 10.1039/c4np00072b] [Citation(s) in RCA: 138] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Plant metabolomics has matured and modern plant metabolomics has accelerated gene discoveries and the elucidation of a variety of plant natural product biosynthetic pathways. This review covers the approximate period of 2000 to 2014, and highlights specific examples of the discovery and characterization of novel genes and enzymes associated with the biosynthesis of natural products such as flavonoids, glucosinolates, terpenoids, and alkaloids. Additional examples of the integration of metabolomics with genome-based functional characterizations of plant natural products that are important to modern pharmaceutical technology are also reviewed. This article also provides a substantial review of recent technical advances in mass spectrometry imaging, nuclear magnetic resonance imaging, integrated LC-MS-SPE-NMR for metabolite identifications, and X-ray crystallography of microgram quantities for structural determinations. The review closes with a discussion on the future prospects of metabolomics related to crop species and herbal medicine.
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Affiliation(s)
- Lloyd W Sumner
- The Samuel Roberts Noble Foundation, Plant Biology Division, 2510 Sam Noble Parkway, Ardmore, OK, USA.
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10
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Pandey V, Sultan M, Kashofer K, Ralser M, Amstislavskiy V, Starmann J, Osprian I, Grimm C, Hache H, Yaspo ML, Sültmann H, Trauner M, Denk H, Zatloukal K, Lehrach H, Wierling C. Comparative analysis and modeling of the severity of steatohepatitis in DDC-treated mouse strains. PLoS One 2014; 9:e111006. [PMID: 25347188 PMCID: PMC4210132 DOI: 10.1371/journal.pone.0111006] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Accepted: 09/20/2014] [Indexed: 01/11/2023] Open
Abstract
Background Non-alcoholic fatty liver disease (NAFLD) has a broad spectrum of disease states ranging from mild steatosis characterized by an abnormal retention of lipids within liver cells to steatohepatitis (NASH) showing fat accumulation, inflammation, ballooning and degradation of hepatocytes, and fibrosis. Ultimately, steatohepatitis can result in liver cirrhosis and hepatocellular carcinoma. Methodology and Results In this study we have analyzed three different mouse strains, A/J, C57BL/6J, and PWD/PhJ, that show different degrees of steatohepatitis when administered a 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) containing diet. RNA-Seq gene expression analysis, protein analysis and metabolic profiling were applied to identify differentially expressed genes/proteins and perturbed metabolite levels of mouse liver samples upon DDC-treatment. Pathway analysis revealed alteration of arachidonic acid (AA) and S-adenosylmethionine (SAMe) metabolism upon other pathways. To understand metabolic changes of arachidonic acid metabolism in the light of disease expression profiles a kinetic model of this pathway was developed and optimized according to metabolite levels. Subsequently, the model was used to study in silico effects of potential drug targets for steatohepatitis. Conclusions We identified AA/eicosanoid metabolism as highly perturbed in DDC-induced mice using a combination of an experimental and in silico approach. Our analysis of the AA/eicosanoid metabolic pathway suggests that 5-hydroxyeicosatetraenoic acid (5-HETE), 15-hydroxyeicosatetraenoic acid (15-HETE) and prostaglandin D2 (PGD2) are perturbed in DDC mice. We further demonstrate that a dynamic model can be used for qualitative prediction of metabolic changes based on transcriptomics data in a disease-related context. Furthermore, SAMe metabolism was identified as being perturbed due to DDC treatment. Several genes as well as some metabolites of this module show differences between A/J and C57BL/6J on the one hand and PWD/PhJ on the other.
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Affiliation(s)
- Vikash Pandey
- Max Planck Institute for Molecular Genetics, Department Vertebrate Genomics, Berlin, Germany
| | - Marc Sultan
- Max Planck Institute for Molecular Genetics, Department Vertebrate Genomics, Berlin, Germany
| | - Karl Kashofer
- Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Meryem Ralser
- Max Planck Institute for Molecular Genetics, Department Vertebrate Genomics, Berlin, Germany
| | | | | | - Ingrid Osprian
- BIOCRATES Life Sciences AG, Innsbruck, Austria
- LKH Wagna, Department of Internal Medicine, Wagna, Austria
| | - Christina Grimm
- Rheumatology and Clinical Immunology, Charité-University Medicine, Berlin, Germany
| | - Hendrik Hache
- Max Planck Institute for Molecular Genetics, Department Vertebrate Genomics, Berlin, Germany
| | - Marie-Laure Yaspo
- Max Planck Institute for Molecular Genetics, Department Vertebrate Genomics, Berlin, Germany
- Dahlem Centre for Genome Research and Medical Systems Biology, Berlin, Germany
| | | | - Michael Trauner
- Hans Popper Laboratory of Molecular Hepatology, Division of Gastroenterology and Hepatology, Department of Medicine, Medical University of Vienna, Vienna, Austria
| | - Helmut Denk
- Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Kurt Zatloukal
- Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Hans Lehrach
- Max Planck Institute for Molecular Genetics, Department Vertebrate Genomics, Berlin, Germany
- Dahlem Centre for Genome Research and Medical Systems Biology, Berlin, Germany
| | - Christoph Wierling
- Max Planck Institute for Molecular Genetics, Department Vertebrate Genomics, Berlin, Germany
- * E-mail:
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Hur M, Campbell AA, Almeida-de-Macedo M, Li L, Ransom N, Jose A, Crispin M, Nikolau BJ, Wurtele ES. A global approach to analysis and interpretation of metabolic data for plant natural product discovery. Nat Prod Rep 2013; 30:565-83. [PMID: 23447050 PMCID: PMC3629923 DOI: 10.1039/c3np20111b] [Citation(s) in RCA: 83] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Discovering molecular components and their functionality is key to the development of hypotheses concerning the organization and regulation of metabolic networks. The iterative experimental testing of such hypotheses is the trajectory that can ultimately enable accurate computational modelling and prediction of metabolic outcomes. This information can be particularly important for understanding the biology of natural products, whose metabolism itself is often only poorly defined. Here, we describe factors that must be in place to optimize the use of metabolomics in predictive biology. A key to achieving this vision is a collection of accurate time-resolved and spatially defined metabolite abundance data and associated metadata. One formidable challenge associated with metabolite profiling is the complexity and analytical limits associated with comprehensively determining the metabolome of an organism. Further, for metabolomics data to be efficiently used by the research community, it must be curated in publicly available metabolomics databases. Such databases require clear, consistent formats, easy access to data and metadata, data download, and accessible computational tools to integrate genome system-scale datasets. Although transcriptomics and proteomics integrate the linear predictive power of the genome, the metabolome represents the nonlinear, final biochemical products of the genome, which results from the intricate system(s) that regulate genome expression. For example, the relationship of metabolomics data to the metabolic network is confounded by redundant connections between metabolites and gene-products. However, connections among metabolites are predictable through the rules of chemistry. Therefore, enhancing the ability to integrate the metabolome with anchor-points in the transcriptome and proteome will enhance the predictive power of genomics data. We detail a public database repository for metabolomics, tools and approaches for statistical analysis of metabolomics data, and methods for integrating these datasets with transcriptomic data to create hypotheses concerning specialized metabolisms that generate the diversity in natural product chemistry. We discuss the importance of close collaborations among biologists, chemists, computer scientists and statisticians throughout the development of such integrated metabolism-centric databases and software.
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Affiliation(s)
- Manhoi Hur
- Human Computer Interactions and Department of Genetics Development and Cell Biology, 2624 Howe Hall, Iowa State University, Ames, IA 50011, USA. Fax: +1 515 294 0803; Tel: +1 515 708 3232;
| | - Alexis Ann Campbell
- Biochemistry, Biophysics and Molecular Biology and Center for Biorenewable Chemicals and Center for Metabolic Biology, 3254 Molecular Biology Building, Iowa State University, Ames, IA 50010, USA. Fax: +1 515 294 9423; Tel: +1 515 294 0453;
| | - Marcia Almeida-de-Macedo
- Department of Genetics Development and Cell Biology, 2624 Howe Hall, Iowa State University, Ames, IA 50011, USA. Fax: +1 515 294 5530; Tel: +1 515 294 3738;
| | - Ling Li
- Department of Genetics Development and Cell Biology, 443 Bessey Hall Iowa State University, Ames, IA 50011, USA. Fax: +1 515 294 1337; Tel: +1 515 294 6236;
| | - Nick Ransom
- Department of Genetics Development and Cell Biology, 2624 Howe Hall, Iowa State University, Ames, IA 50011, USA. Fax: +1 515 294 0803; Tel: +1 515 708 3232;
| | - Adarsh Jose
- Bioinformatics and Computational Biology, Center for Biorenewable Chemicals, Iowa State University, Ames, IA 50010, USA. Fax: +1 515 294 1269; Tel: +1 515 230 3429;
| | - Matt Crispin
- Department of Genetics Development and Cell Biology, 443 Bessey Hall Iowa State University, Ames, IA 50011, USA. Fax: +1 515 294 1337; Tel: +1 515 294 6236;
| | - Basil J. Nikolau
- Biochemistry, Biophysics and Molecular Biology and Center for Biorenewable Chemicals and Center for Metabolic Biology, 3254 Molecular Biology Building, Iowa State University, Ames, IA 50010, USA. Fax: +1 515 294 9423; Tel: +1 515 294 0453;
| | - Eve Syrkin Wurtele
- Department of Genetics, Development and Cell Biology, Center for Metabolic Biology, and Center for Biorenewable Chemicals, 2624D Howe Hall, Iowa State University, Ames, IA 50011, USA. Fax: +1 515 294 0803; Tel: +1 515 708 3232;
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Inter-individual differences in response to dietary intervention: integrating omics platforms towards personalised dietary recommendations. Proc Nutr Soc 2013; 72:207-18. [PMID: 23388096 DOI: 10.1017/s0029665113000025] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Technologic advances now make it possible to collect large amounts of genetic, epigenetic, metabolomic and gut microbiome data. These data have the potential to transform approaches towards nutrition counselling by allowing us to recognise and embrace the metabolic, physiologic and genetic differences among individuals. The ultimate goal is to be able to integrate these multi-dimensional data so as to characterise the health status and disease risk of an individual and to provide personalised recommendations to maximise health. To this end, accurate and predictive systems-based measures of health are needed that incorporate molecular signatures of genes, transcripts, proteins, metabolites and microbes. Although we are making progress within each of these omics arenas, we have yet to integrate effectively multiple sources of biologic data so as to provide comprehensive phenotypic profiles. Observational studies have provided some insights into associative interactions between genetic or phenotypic variation and diet and their impact on health; however, very few human experimental studies have addressed these relationships. Dietary interventions that test prescribed diets in well-characterised study populations and that monitor system-wide responses (ideally using several omics platforms) are needed to make correlation-causation connections and to characterise phenotypes under controlled conditions. Given the growth in our knowledge, there is the potential to develop personalised dietary recommendations. However, developing these recommendations assumes that an improved understanding of the phenotypic complexities of individuals and their responses to the complexities of their diets will lead to a sustainable, effective approach to promote health and prevent disease - therein lies our challenge.
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