Correlation network analysis reveals relationships between diet-induced changes in human gut microbiota and metabolic health

Background:Recent evidence suggests that the gut microbiota plays an important role in human metabolism and energy homeostasis and is therefore a relevant factor in the assessment of metabolic health and flexibility. Understanding of these host–microbiome interactions aids the design of nutritional strategies that act via modulation of the microbiota. Nevertheless, relating gut microbiota composition to host health states remains challenging because of the sheer complexity of these ecosystems and the large degrees of interindividual variation in human microbiota composition.Methods:We assessed fecal microbiota composition and host response patterns of metabolic and inflammatory markers in 10 apparently healthy men subjected to a high-fat high-caloric diet (HFHC, 1300 kcal/day extra) for 4 weeks. DNA was isolated from stool and barcoded 16S rRNA gene amplicons were sequenced. Metabolic health parameters, including anthropomorphic and blood parameters, where determined at t=0 and t=4 weeks.Results:A correlation network approach revealed diet-induced changes in Bacteroides levels related to changes in carbohydrate oxidation rates, whereas the change in Firmicutes correlates with changes in fat oxidation. These results were confirmed by multivariate models. We identified correlations between microbial diversity indices and several inflammation-related host parameters that suggest a relation between diet-induced changes in gut microbiota diversity and inflammatory processes.Conclusions:This approach allowed us to identify significant correlations between abundances of microbial taxa and diet-induced shifts in several metabolic health parameters. Constructed correlation networks provide an overview of these relations, revealing groups of correlations that are of particular interest for explaining host health aspects through changes in the gut microbiota.

[1]  Alexander R. Pico,et al.  Finding the Right Questions: Exploratory Pathway Analysis to Enhance Biological Discovery in Large Datasets , 2010, PLoS biology.

[2]  J. Bouwman,et al.  The human gastrointestinal microbiota--an unexplored frontier for pharmaceutical discovery. , 2012, Pharmacological research.

[3]  H. Flint,et al.  Human colonic microbiota associated with diet, obesity and weight loss , 2008, International Journal of Obesity.

[4]  R. Knight,et al.  Analysis of the Gut Microbiota in the Old Order Amish and Its Relation to the Metabolic Syndrome , 2012, PloS one.

[5]  W. Bossert,et al.  The Measurement of Diversity , 2001 .

[6]  Bernard Henrissat,et al.  Effects of Diet on Resource Utilization by a Model Human Gut Microbiota Containing Bacteroides cellulosilyticus WH2, a Symbiont with an Extensive Glycobiome , 2013, PLoS biology.

[7]  C. Manichanh,et al.  Reduced diversity of faecal microbiota in Crohn’s disease revealed by a metagenomic approach , 2005, Gut.

[8]  Les Dethlefsen,et al.  The Pervasive Effects of an Antibiotic on the Human Gut Microbiota, as Revealed by Deep 16S rRNA Sequencing , 2008, PLoS biology.

[9]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[10]  Masahito Watanabe,et al.  IN MICE , 2009 .

[11]  H. Flint,et al.  Reduced Dietary Intake of Carbohydrates by Obese Subjects Results in Decreased Concentrations of Butyrate and Butyrate-Producing Bacteria in Feces , 2006, Applied and Environmental Microbiology.

[12]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[13]  P. Bork,et al.  Enterotypes of the human gut microbiome , 2011, Nature.

[14]  Krzysztof Trzciński,et al.  Deep Sequencing Analyses of Low Density Microbial Communities: Working at the Boundary of Accurate Microbiota Detection , 2012, PloS one.

[15]  F. Bushman,et al.  Linking Long-Term Dietary Patterns with Gut Microbial Enterotypes , 2011, Science.

[16]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[17]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[18]  Mihai Pop,et al.  Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples , 2009, PLoS Comput. Biol..

[19]  P. Turnbaugh,et al.  Microbial ecology: Human gut microbes associated with obesity , 2006, Nature.

[20]  Nathalie M. Delzenne,et al.  Targeting gut microbiota in obesity: effects of prebiotics and probiotics , 2011, Nature Reviews Endocrinology.

[21]  Andrew J. Hoffman,et al.  The Effect of the Undigested Fraction of Maize Products on the Activity and Composition of the Microbiota Determined in a Dynamic in Vitro Model of the Human Proximal Large Intestine , 2009, Journal of the American College of Nutrition.

[22]  S. Sørensen,et al.  Gut Microbiota in Human Adults with Type 2 Diabetes Differs from Non-Diabetic Adults , 2010, PloS one.

[23]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[24]  F. Bäckhed,et al.  Obesity alters gut microbial ecology. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[25]  J. Clemente,et al.  Human gut microbiome viewed across age and geography , 2012, Nature.

[26]  Tatjana van Strien,et al.  The Dutch Eating Behavior Questionnaire (DEBQ) for assessment of restrained, emotional, and external eating behavior , 1986 .

[27]  A. Smilde,et al.  Fusion of mass spectrometry-based metabolomics data. , 2005, Analytical chemistry.

[28]  M. Kendall.  A NEW MEASURE OF RANK CORRELATION , 1938 .

[29]  A. Schwiertz,et al.  Microbiota and SCFA in Lean and Overweight Healthy Subjects , 2010, Obesity.

[30]  Ting Wang,et al.  The gut microbiota as an environmental factor that regulates fat storage. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[31]  R. Knight,et al.  Fast UniFrac: Facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data , 2009, The ISME Journal.

[32]  Curtis Huttenhower,et al.  Microbial Co-occurrence Relationships in the Human Microbiome , 2012, PLoS Comput. Biol..

[33]  T. V. Steenbergen,et al.  Butyrate: a cytotoxin for Vero cells produced by Bacteroides gingivalis and Bacteroides asaccharolyticus , 2004, Antonie van Leeuwenhoek.

[34]  G. Macfarlane,et al.  The control and consequences of bacterial fermentation in the human colon. , 1991, The Journal of applied bacteriology.

[35]  H. Tilg,et al.  Non-alcoholic steatohepatitis: a microbiota-driven disease , 2013, Trends in Endocrinology & Metabolism.

[36]  R. Bibiloni,et al.  Changes in Gut Microbiota Control Metabolic Endotoxemia-Induced Inflammation in High-Fat Diet–Induced Obesity and Diabetes in Mice , 2008, Diabetes.

[37]  Martin Hartmann,et al.  Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities , 2009, Applied and Environmental Microbiology.

[38]  A. Fodor,et al.  Gut microbial diversity is reduced by the probiotic VSL#3 and correlates with decreased TNBS‐induced colitis , 2011, Inflammatory bowel diseases.

[39]  D. Floreano,et al.  Revealing strengths and weaknesses of methods for gene network inference , 2010, Proceedings of the National Academy of Sciences.

[40]  C E Shannon,et al.  The mathematical theory of communication. 1963. , 1997, M.D. computing : computers in medical practice.

[41]  K. Venema.  Role of gut microbiota in the control of energy and carbohydrate metabolism , 2010, Current opinion in clinical nutrition and metabolic care.

[42]  E. Mardis,et al.  An obesity-associated gut microbiome with increased capacity for energy harvest , 2006, Nature.

[43]  E. Mittge,et al.  Evidence for a core gut microbiota in the zebrafish , 2011, The ISME Journal.

[44]  Roger E Bumgarner,et al.  Construction of regulatory networks using expression time-series data of a genotyped population , 2011, Proceedings of the National Academy of Sciences.

[45]  Muriel Médard,et al.  Network deconvolution as a general method to distinguish direct dependencies in networks , 2013, Nature Biotechnology.

[46]  A. Barabasi,et al.  Network medicine : a network-based approach to human disease , 2010 .

[47]  E. Ravussin,et al.  Metabolic flexibility and insulin resistance. , 2008, American journal of physiology. Endocrinology and metabolism.

[48]  W. Cefalu,et al.  Butyrate Improves Insulin Sensitivity and Increases Energy Expenditure in Mice , 2009, Diabetes.