PUBLICATIONS
β-Aminoisobutyric Acid Induces Browning of White Fat and Hepatic β-oxidation and is Inversely Correlated with Cardiometabolic Risk Factors
Roberts LD, Boström P, O’Sullivan JF, Schinzel RT, Lewis GD, Dejam A, Lee Y-K, Palma MJ, Calhoun S, Georgiadi A, Chen M-H, Ramachandran VS, Larson MG, Bouchard C, Rankinen T, Souza AL, Clish CB, Wang TJ, Estall JL, Soukas AA, Cowan CA, Spiegelman BM, Gerszten RE. β-Aminoisobutyric Acid Induces Browning of White Fat and Hepatic β-oxidation and is Inversely Correlated with Cardiometabolic Risk Factors. Cell Metabolism. 2014 Jan 7;19(1):96-108. PMCID: in progress
The transcriptional coactivator peroxisome proliferator activated receptor-gamma coactivator-1a (PGC-1a) regulates metabolic genes in skeletal muscle and contributes to the response of muscle to exercise. Muscle PGC-1a transgenic expression and exercise both increase the expression of thermogenic genes within white adipose. How the PGC- 1a-mediated response to exercise in muscle conveys signals to other tissues remains incompletely defined. We employed a metabolomic approach to examine metabolites secreted from myocytes with forced expression of PGC-1a, and identified b-aminoisobutyric acid (BAIBA) as a small molecule myokine. BAIBA increases the expression of brown adipocyte-specific genes in white adipocytes and b-oxidation in hepatocytes both in vitro and in vivo through a PPARa-mediated mechanism, induces a brown adipose-like phenotype in human pluripotent stem cells, and improves glucose homeostasis in mice. In humans, plasma BAIBA concentrations are increased with exercise and inversely associated with metabolic risk factors. BAIBA may thus contribute to exercise-induced protection from metabolic diseases.
2-Aminoadipic acid is a biomarker for diabetes risk
Wang TJ, Ngo D, Psychogios N, Dejam A, Larson MG, Vasan RS, Ghorbani A, O’Sullivan J, Cheng S, Rhee EP, Sinha S, McCabe E, Fox CS, O’Donnell CJ, Ho J, Florez J, Magnusson M, Pierce K, Souza AL, Yu Y, Carter C, Light P, Melander O, Clish CB, Gerszten RE. 2-Aminoadipic acid is a biomarker for diabetes risk. Journal of Clinical Investigation. 2013 Oct 1;123(10):4309-17. PMCID: in progress
Improvements in metabolite-profiling techniques are providing increased breadth of coverage of the human metabolome and may highlight biomarkers and pathways in common diseases such as diabetes. Using a metabolomics platform that analyzes intermediary organic acids, purines, pyrimidines, and other compounds, we performed a nested case-control study of 188 individuals who developed diabetes and 188 propensity-matched controls from 2,422 normoglycemic participants followed for 12 years in the Framingham Heart Study. The metabolite 2-aminoadipic acid (2-AAA) was most strongly associated with the risk of developing diabetes. Individuals with 2-AAA concentrations in the top quartile had greater than a 4-fold risk of developing diabetes. Levels of 2-AAA were not well correlated with other metabolite biomarkers of diabetes, such as branched chain amino acids and aromatic amino acids, suggesting they report on a distinct pathophysiological pathway. In experimental studies, administration of 2-AAA lowered fasting plasma glucose levels in mice fed both standard chow and high-fat diets. Further, 2-AAA treatment enhanced insulin secretion from a pancreatic β cell line as well as murine and human islets. These data highlight a metabolite not previously associated with diabetes risk that is increased up to 12 years before the onset of overt disease. Our findings suggest that 2-AAA is a marker of diabetes risk and a potential modulator of glucose homeostasis in humans.
A Genome-Wide Association Study of the Human Metabolome in a Community-Based Cohort
Rhee EP, Ho JE, Chen M-H, Dongxiao Shen D, Cheng S, Larson MG, Ghorbani A, Shi X, Helenius IT, O’Donnell CJ, Souza AL, Deik A, Pierce K, Bullock K, Walford GA, Vasan RS, Florez JC, Clish C, Yeh J-R, Wang TJ*, Gerszten RE*. (*denotes co-senior authors). A Genome-Wide Association Study of the Human Metabolome in a Community-Based Cohort. Cell Metabolism. 2013 Jul 2;18(1):130-43. PMCID: in progress
Because metabolites are hypothesized to play key roles as markers and effectors of cardiometabolic diseases, recent studies have sought to annotate the genetic determinants of circulating metabolite levels. We report a genome-wide association study (GWAS) of 217 plasma metabolites, including >100 not measured in prior GWAS, in 2076 participants of the Framingham Heart Study (FHS). For the majority of analytes, we find that estimated heritability explains >20% of interindividual variation, and that variation attributable to heritable factors is greater than that attributable to clinical factors. Further, we identify 31 genetic loci associated with plasma metabolites, including 23 that have not previously been reported. Importantly, we include GWAS results for all surveyed metabolites and demonstrate how this information highlights a role for AGXT2 in cholesterol ester and triacylglycerol metabolism. Thus, our study outlines the relative contributions of inherited and clinical factors on the plasma metabolome and provides a resource for metabolism research.
Metabolite Profiling Identifies Pathways Associated With Metabolic Risk in Humans
Cheng S, Rhee EP, Larson MG, Lewis GD, McCabe EL, Shen D, Palma MJ, Roberts LD, Dejam A, Souza AL, Deik AA, Magnusson M, Fox CS, O'Donnell CJ, Vasan RS, Melander O, Clish CB, Gerszten RE*, Wang TJ* (*denotes co-senior authors). Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation. 2012;125:2222-31. PMCID:PMC3376658
Background—Although metabolic risk factors are known to cluster in individuals who are prone to developing diabetes mellitus and cardiovascular disease, the underlying biological mechanisms remain poorly understood.
Methods and Results—To identify pathways associated with cardiometabolic risk, we used liquid chromatography/mass spectrometry to determine the plasma concentrations of 45 distinct metabolites and to examine their relation to cardiometabolic risk in the Framingham Heart Study (FHS; n1015) and the Malmo¨ Diet and Cancer Study (MDC; n746). We then interrogated significant findings in experimental models of cardiovascular and metabolic disease. We observed that metabolic risk factors (obesity, insulin resistance, high blood pressure, and dyslipidemia) were associated with multiple metabolites, including branched-chain amino acids, other hydrophobic amino acids, tryptophan breakdown products, and nucleotide metabolites. We observed strong associations of insulin resistance traits with glutamine (standardized regression coefficients, 0.04 to 0.22 per 1-SD change in log-glutamine; P0.001), glutamate (0.05 to 0.14; P0.001), and the glutamine-toglutamate ratio (0.05 to 0.20; P0.001) in the discovery sample (FHS); similar associations were observed in the replication sample (MDC). High glutamine-to-glutamate ratio was associated with lower risk of incident diabetes mellitus in FHS (odds ratio, 0.79; adjusted P0.03) but not in MDC. In experimental models, administration of glutamine in mice led to both increased glucose tolerance (P0.01) and decreased blood pressure (P0.05).
Conclusions—Biochemical profiling identified circulating metabolites not previously associated with metabolic traits. Experimentally interrogating one of these pathways demonstrated that excess glutamine relative to glutamate, resulting from exogenous administration, is associated with reduced metabolic risk in mice.
A pipeline that integrates the discovery and verification of plasma protein biomarkers reveals candidate markers for cardiovascular disease
Addona T, Shi X, Keshishian H, Mani DR, Burgess M, Gillette M, Klauser K, Shen D, Lewis G, Farrell L, Fifer M, Sabatine MS, Gerszten RE*, Carr S* (denotes co-senior authors). A pipeline that integrates the discovery and verification of plasma protein biomarkers reveals candidate markers for cardiovascular disease. Nat Biotechnol. 2011;29:635-43. PMCID:PMC3366591
The absolute bottleneck in proteomics is the transition from discovery-based protein identifications to the establishment of techniques for validation of large numbers of protein candidates. This manuscript integrates two novel technologies to triage over one hundred protein biomarker candidates of human myocardial injury from human plasma into dozens of quantitative assays. These novel techniques yielded new protein biomarkers of myocardial injury that add diagnostic information on top of traditional biomarkers and clinical risk factors. This study demonstrates that modern proteomic technologies when coherently integrated can yield novel cardiovascular biomarkers meriting further evaluation in large, heterogeneous patient cohorts.
Metabolite profiles and the risk of developing diabetes
Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, Lewis GD, Fox CS, Jacques PF, Ferandez C, O’Donnell CJ, Carr SA, Mootha VK, Florez JC, Souza A, Melander O, Clish CB, Gerszten RE. Metabolite profiles and the risk of developing diabetes. Nat Med. 2011; 17:448-54. PMCID: PMC3126616
Metabolic diseases present particular difficulty for clinicians because they are often present for years before becoming clinically apparent. We investigated whether metabolite profiles can predict the development of diabetes in the Framingham Heart Study. Five branched-chain and aromatic amino acids had highly-significant associations with future diabetes, while a combination of three amino acids strongly predicted future diabetes by up to 12 years (>5-fold increased risk for individuals in top quartile). Our findings in over 1100 individuals underscore the potential importance of amino acid metabolism early in the pathogenesis of diabetes, and suggest that amino acid profiles could aid in diabetes risk assessment.
Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans
Rhee EP, Cheng S, Larson MG, Walford GA, Lewis GD, McCabe E, Yang E, Farrell L, Fox CS, O’Donnell CJ, Carr SA, Vasan RS, Florez JC, Clish CB, Wang TJ, Gerszten RE. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J Clin Invest. 2011;121:1402-11. PMCID: PMC3069773
In a “lipidomics” analysis in the Framingham Heart Study, we found that lipids of relatively lower carbon number and double bond content were associated with an increased risk of diabetes, whereas lipids of higher carbon number and double bond content were associated with decreased risk. To explore potential mechanisms that modulate the distribution of plasma lipids, we also performed lipid profiling in the setting of perturbational experiments in MGH patients, including oral glucose tolerance testing, pharmacologic interventions such and acute exercise testing. Lipids associated with increased diabetes risk (particularly triacylglycerols or TAGs) fell in response to insulin action; in turn, these TAGs were elevated in the setting of insulin resistance. These studies identify a novel relationship between lipid acyl-chain content and diabetes risk, demonstrate how lipidomic profiling could also aid in clinical risk assessment beyond standard risk factors, and highlight enzymes including specific lipid elongases and desaturases for further exploration in the context of diabetes.
MicroRNA-33 and the SREBP Host Genes Cooperate to Control Cholesterol Homeostatsis
Najafi-Shoushtari SH, Kristo F, Li Y, Shioda T, Cohen DE, Gerszten RE, Näär AM. MicroRNA-33 and the SREBP Host Genes Cooperate to Control Cholesterol Homeostatsis. Science. 2010;328:1566-9. PMCID: PMC3840500
Because microRNAs exert coordinated effects on a multitude of targets and frequently affect entire biological pathways, they can co-ordinate changes in complex processes. In this collaborative study with the Näär group, we led the in vivo murine studies demonstrating that the miR-33a/b microRNAs modulate cholesterol/lipid homeostasis. We demonstrated that they act in concert with their host genes, the SREBP transcription factors, to regulate the expression of genes involved in cholesterol trafficking, fatty acid degradation, and energy homeostasis.
Metabolic Signatures of Exercise in Human Plasma
Lewis GD, Farrell L, Wood MJ, Martinovic M, Arany Z, Rowe GC, Souza A, Cheng S, McCabe EL, Yang E, Shi X, Deo R, Roth FP, Asnani A, Rhee EP, Systrom DM, Semigran MJ, Vasan RS, Carr SA, Wang TJ, Sabatine MS, Clish CB, Gerszten RE. Metabolic Signatures of Exercise in Human Plasma. Science Transl Med. 2010;2:33. PMCID: PMC3010398
Exercise is known to protect against cardiovascular and metabolic diseases and to predict long term survival, but how these effects occur is not entirely clear. We found new metabolic changes triggered by exercise, as well as signatures that clearly distinguish more-fit from less-fit individuals. Using our sensitive metabolic profiling method, we were able to detect metabolites involved in energy metabolism in blood that were previously thought to be confined within cells. A combination of metabolites that increased in plasma in response to exercise upregulated the expression of nur77, a transcriptional regulator of glucose utilization and lipid metabolism genes in skeletal muscle. Metabolic profiling during exercise therefore provided plasma signatures of exercise performance and cardiovascular disease susceptibility, in addition to highlighting molecular pathways that may modulate its salutary effects.
Metabolite profiling of blood from individuals undergoing planned myocardial infarction reveals early markers of myocardial injury
Lewis GD, Wei R, Liu E, Yang E, Shi X, Martinovic M, Farrell L, Asnani A, Cyrille M, Ramanathan A, Shaham O, Berriz G, Lowry PA, Palacios IF, Tasan M, Roth FP, Min J, Baumgartner C, Keshishian H, Addona T, Mootha VK, Rosenzweig A, Carr SA, Fifer MA, Sabatine MS, Gerszten RE.Metabolite profiling of blood from individuals undergoing planned myocardial infarction reveals early markers of myocardial injury. J Clin Invest.2008;118:3503-12. PMCID: PMC2525696
Emerging metabolomic tools have created the opportunity to establish metabolic signatures of myocardial injury. We applied a mass spectrometry–based metabolite profiling platform to 36 patients undergoing alcohol septal ablation treatment for hypertrophic obstructive cardiomyopathy, a human model of planned myocardial infarction (PMI). Serial blood samples were obtained before and at various intervals after PMI, with patients undergoing elective diagnostic coronary angiography and patients with spontaneous myocardial infarction (SMI) serving as negative and positive controls, respectively. We identified changes in circulating levels of metabolites participating in pyrimidine metabolism, the tricarboxylic acid cycle and its upstream contributors, and the pentose phosphate pathway. Alterations in levels of multiple metabolites were detected as early as 10 minutes after PMI in an initial derivation group and were validated in a second, independent group of PMI patients. A PMI-derived metabolic signature consisting of aconitic acid, hypoxanthine, trimethylamine N-oxide, and threonine differentiated patients with SMI from those undergoing diagnostic coronary angiography with high accuracy, and coronary sinus sampling distinguished cardiac-derived from peripheral metabolic changes. Our results identify a role for metabolic profiling in the early detection of myocardial injury and suggest that similar approaches may be used for detection or prediction of other disease states.