Lipidomic profiling identifies signatures of metabolic risk.

BACKGROUND: Metabolic syndrome (MetS), the clustering of metabolic risk factors, is associated with cardiovascular disease risk. We sought to determine if dysregulation of the lipidome may contribute to metabolic risk factors. METHODS: We measured 154 circulating lipid species in 658 participants from the Framingham Heart Study (FHS) using liquid chromatography-tandem mass spectrometry and tested for associations with obesity, dysglycemia, and dyslipidemia. Independent external validation was sought in three independent cohorts. Follow-up data from the FHS were used to test for lipid metabolites associated with longitudinal changes in metabolic risk factors. RESULTS: Thirty-nine lipids were associated with obesity and eight with dysglycemia in the FHS. Of 32 lipids that were available for replication for obesity and six for dyslipidemia, 28 (88%) replicated for obesity and five (83%) for dysglycemia. Four lipids were associated with longitudinal changes in body mass index and four were associated with changes in fasting blood glucose in the FHS. CONCLUSIONS: We identified and replicated several novel lipid biomarkers of key metabolic traits. The lipid moieties identified in this study are involved in biological pathways of metabolic risk and can be explored for prognostic and therapeutic utility.

Metabolite Signatures of Metabolic Risk Factors and their Longitudinal Changes.

CONTEXT: Metabolic dysregulation underlies key metabolic risk factors­obesity, dyslipidemia, and dysglycemia. OBJECTIVE: To uncover mechanistic links between metabolomic dysregulation and metabolic risk by testing metabolite associations with risk factors cross-sectionally and with risk factor changes over time. DESIGN: Cross-sectional­discovery samples (n = 650; age, 36­69 years) from the Framingham Heart Study (FHS) and replication samples (n = 670; age, 61­76 years) from the BioImage Study, both following a factorial design sampled from high vs low strata of body mass index, lipids, and glucose. Longitudinal­FHS participants (n = 554) with 5­7 years of follow-up for risk factor changes. SETTING: Observational studies. PARTICIPANTS: Cross-sectional samples with or without obesity, dysglycemia, and dyslipidemia, excluding prevalent cardiovascular disease and diabetes or dyslipidemia treatment. Age- and sex-matched by group. INTERVENTIONS: None. MAIN OUTCOME MEASURE(S): Gas chromatography-mass spectrometry detected 119 plasma metabolites. Cross-sectional associations with obesity, dyslipidemia, and dysglycemia were tested in discovery, with external replication of 37 metabolites. Single- and multi-metabolite markers were tested for association with longitudinal changes in risk factors. RESULTS: Cross-sectional metabolite associations were identified with obesity (n = 26), dyslipidemia (n = 21), and dysglycemia (n = 11) in discovery. Glutamic acid, lactic acid, and sitosterol associated with all three risk factors in meta-analysis (P < 4.5 × 10−4). Metabolites associated with longitudinal risk factor changes were enriched for bioactive lipids. Multi-metabolite panels explained 2.5­15.3% of longitudinal changes in metabolic traits. CONCLUSIONS: Cross-sectional results implicated dysregulated glutamate cycling and amino acid metabolism in metabolic risk. Certain bioactive lipids were associated with risk factors cross-sectionally and over time, suggesting their upstream role in risk factor progression. Functional studies are needed to validate findings and facilitate translation into treatments or preventive measures.

Multiple imputation and analysis for high-dimensional incomplete proteomics data.

Multivariable analysis of proteomics data using standard statistical models is hindered by the presence of incomplete data. We faced this issue in a nested case-control study of 135 incident cases of myocardial infarction and 135 pair-matched controls from the Framingham Heart Study Offspring cohort. Plasma protein markers (K = 861) were measured on the case-control pairs (N = 135), and the majority of proteins had missing expression values for a subset of samples. In the setting of many more variables than observations (K ≫ N), we explored and documented the feasibility of multiple imputation approaches along with subsequent analysis of the imputed data sets. Initially, we selected proteins with complete expression data (K = 261) and randomly masked some values as the basis of simulation to tune the imputation and analysis process. We randomly shuffled proteins into several bins, performed multiple imputation within each bin, and followed up with stepwise selection using conditional logistic regression within each bin. This process was repeated hundreds of times. We determined the optimal method of multiple imputation, number of proteins per bin, and number of random shuffles using several performance statistics. We then applied this method to 544 proteins with incomplete expression data (≤ 40% missing values), from which we identified a panel of seven proteins that were jointly associated with myocardial infarction.

Proteomic analysis in type 2 diabetes patients before and after a very low calorie diet reveals potential disease state and intervention specific biomarkers.

UNLABELLED: Very low calorie diets (VLCD) with and without exercise programs lead to major metabolic improvements in obese type 2 diabetes patients. The mechanisms underlying these improvements have so far not been elucidated fully. To further investigate the mechanisms of a VLCD with or without exercise and to uncover possible biomarkers associated with these interventions, blood samples were collected from 27 obese type 2 diabetes patients before and after a 16-week VLCD (Modifast ∼ 450 kcal/day). Thirteen of these patients followed an exercise program in addition to the VCLD. Plasma was obtained from 27 lean and 27 obese controls as well. Proteomic analysis was performed using mass spectrometry (MS) and targeted multiple reaction monitoring (MRM) and a large scale isobaric tags for relative and absolute quantitation (iTRAQ) approach. After the 16-week VLCD, there was a significant decrease in body weight and HbA1c in all patients, without differences between the two intervention groups. Targeted MRM analysis revealed differences in several proteins, which could be divided in diabetes-associated (fibrinogen, transthyretin), obesity-associated (complement C3), and diet-associated markers (apolipoproteins, especially apolipoprotein A-IV). To further investigate the effects of exercise, large scale iTRAQ analysis was performed. However, no proteins were found showing an exercise effect. Thus, in this study, specific proteins were found to be differentially expressed in type 2 diabetes patients versus controls and before and after a VLCD. These proteins are potential disease state and intervention specific biomarkers. TRIAL REGISTRATION: Controlled-Trials.com ISRCTN76920690.

Plasma galectin-3 and heart failure outcomes in MADIT-CRT (multicenter automatic defibrillator implantation trial with cardiac resynchronization therapy).

BACKGROUND: Elevated circulating levels of the protein galectin-3, a mediator of fibrogenesis, have previously been associated with adverse outcomes in heart failure (HF) patients and appear to modify response to certain pharmacologic therapies. This study investigated the relationship between galectin-3 level and clinical outcomes in HF patients randomized to implantable cardioverter defibrillator (ICD-only) or cardiac resynchronization therapy (CRT-D). METHODS AND RESULTS: Plasma galectin-3 concentrations were measured in 654 New York Heart Association functional class I/II patients participating in the MADIT-CRT trial. A heterogeneity of response was detected between pre-implantation galectin-3 and randomization group (CRT-D or ICD-only) on the primary MADIT-CRT trial end point of nonfatal HF event or death (P = .045). Among patients with baseline galectin-3 levels in the top quartile of the distribution, CRT-D was associated with a 65% reduction in risk of the primary end point (hazard ratio [HR] 0.35, 95% confidence interval [CI] 0.19-0.67), whereas among patients with lower baseline galectin-3 values CRT-D was associated with a 25% decrease in risk (HR 0.75, 95% CI. 0.51-1.11). Baseline galectin-3 level also was observed to be an independent predictor of the primary end point (multivariable adjusted HR per log unit increase: 1.55; 95% CI 1.01-2.38; P = .043). CONCLUSIONS: Elevated galectin-3 was found to be an independent predictor of adverse HF outcome in patients with mildly symptomatic HF. A significant interaction of device randomization group with pre-implantation galectin-3 level was detected, with HF patients with the highest baseline galectin-3 levels deriving a disproportionately larger benefit from CRT-D.

Elevated plasma galectin-3 is associated with near-term rehospitalization in heart failure: a pooled analysis of 3 clinical trials.

BACKGROUND: Rehospitalization is a major cause for heart failure (HF)-related morbidity and is associated with considerable loss of quality of life and costs. The rate of unplanned rehospitalization in patients with HF is unacceptably high; current risk stratification to identify patients at risk for rehospitalization is inadequate. We evaluated whether measurement of galectin-3 would be helpful in identifying patients at such risk. METHODS: We analyzed pooled data from patients (n = 902) enrolled in 3 cohorts (COACH, n = 592; PRIDE, n = 181; and UMD H-23258, n = 129) originally admitted because of HF. Mean patient age was between 61.6 and 72.9 years across the cohorts, with a wide range of left ventricular ejection fraction. Galectin-3 levels were measured during index admission. We used fixed and random-effects models, as well as continuous and categorical reclassification statistics to assess the association of baseline galectin-3 levels with risk of postdischarge rehospitalization at different time points and the composite end point all-cause mortality and rehospitalization. RESULTS: Compared with patients with galectin-3 concentrations less than 17.8 ng/mL, those with results exceeding this value were significantly more likely to be rehospitalized for HF at 30, 60, 90, and 120 days after discharge, with odds ratios (ORs) of 2.80 (95% CI 1.41-5.57), 2.61 (95% CI 1.46-4.65), 3.01 (95% CI 1.79-5.05), and 2.79 (95% CI 1.75-4.45), respectively. After adjustment for age, gender, New York Heart Association class, renal function (estimated glomerular filtration rate), left ventricular ejection fraction, and B-type natriuretic peptide, galectin-3 remained an independent predictor of HF rehospitalization. The addition of galectin-3 to risk models significantly reclassified patient risk of postdischarge rehospitalization and fatal event at each time point (continuous net reclassification improvement at 30 days of +42.6% [95% CI +19.9%-65.4%], P < .001). CONCLUSIONS: Among patients hospitalized for HF, plasma galectin-3 concentration is useful for the prediction of near-term rehospitalization.

Protein biomarkers of new-onset cardiovascular disease: prospective study from the systems approach to biomarker research in cardiovascular disease initiative.

OBJECTIVE: Incorporation of novel plasma protein biomarkers may improve current models for prediction of atherosclerotic cardiovascular disease (ASCVD) risk. APPROACH AND RESULTS: We used discovery mass spectrometry (MS) to determine plasma concentrations of 861 proteins in 135 myocardial infarction (MI) cases and 135 matched controls. Then, we measured 59 markers by targeted MS in 336 ASCVD case-control pairs. Associations with MI or ASCVD were tested in single-marker and multiple-marker analyses adjusted for established ASCVD risk factors. Twelve single markers from discovery MS were associated with MI incidence (at P<0.01), adjusting for clinical risk factors. Seven proteins in aggregate (cyclophilin A, cluster of differentiation 5 molecule [CD5] antigen-like, cell-surface glycoprotein mucin cell surface associated protein 18 [MUC-18], collagen-α 1 [XVIII] chain, salivary α-amylase 1, C-reactive protein, and multimerin-2) were highly associated with MI (P<0.0001) and significantly improved its prediction compared with a model with clinical risk factors alone (C-statistic of 0.71 versus 0.84). Through targeted MS, 12 single proteins were predictors of ASCVD (at P<0.05) after adjusting for established risk factors. In multiple-marker analyses, 4 proteins in combination (α-1-acid glycoprotein 1, paraoxonase 1, tetranectin, and CD5 antigen-like) predicted incident ASCVD (P<0.0001) and moderately improved the C-statistic from the model with clinical covariates alone (C-statistic of 0.69 versus 0.73). CONCLUSIONS: Proteomics profiling identified single- and multiple-marker protein panels that are associated with new-onset ASCVD and may lead to a better understanding of underlying disease mechanisms. Our findings include many novel protein biomarkers that, if externally validated, may improve risk assessment for MI and ASCVD.

Prognostic value of changes in galectin-3 levels over time in patients with heart failure: data from CORONA and COACH.

BACKGROUND: In several cross-sectional analyses, circulating baseline levels of galectin-3, a protein involved in myocardial fibrosis and remodeling, have been associated with increased risk for morbidity and mortality in patients with heart failure (HF). The importance and clinical use of repeated measurements of galectin-3 have not yet been reported. METHODS AND RESULTS: Plasma galectin-3 was measured at baseline and at 3 months in patients enrolled in the Controlled Rosuvastatin Multinational Trial in Heart Failure (CORONA) trial (n=1329), and at baseline and at 6 months in patients enrolled in the Coordinating Study Evaluating Outcomes of Advising and Counseling Failure (COACH) trial (n=324). Patient results were analyzed by categorical and percentage changes in galectin-3 level. A threshold value of 17.8 ng/mL or 15% change from baseline was used to categorize patients. Increasing galectin-3 levels over time, from a low to high galectin-3 category, were associated with significantly more HF hospitalization and mortality compared with stable or decreasing galectin-3 levels (hazard ratio in CORONA, 1.60; 95% confidence interval, 1.13-2.25; P=0.007; hazard ratio in COACH, 2.38; 95% confidence interval, 1.02-5.55; P=0.046). In addition, patients whose galectin-3 increased by >15% between measurements had a 50% higher relative hazard of adverse event than those whose galectin-3 stayed within ±15% of the baseline value, independent of age, sex, diabetes mellitus, left ventricular ejection fraction, renal function, medication (ß-blocker, angiotensin converting enzyme inhibitor, and angiotensin receptor blocker), and N-terminal probrain natriuretic peptide (hazard ratio in CORONA, 1.50; 95% confidence interval, 1.17-1.92; P=0.001). The impact of changing galectin-3 levels on other secondary end points was comparable. CONCLUSIONS: In 2 large cohorts of patients with chronic and acute decompensated HF, repeated measurements of galectin-3 level provided important and significant prognostic value in identifying patients with HF at elevated risk for subsequent HF morbidity and mortality.

Baseline and serial measurements of galectin-3 in patients with heart failure: relationship to prognosis and effect of treatment with valsartan in the Val-HeFT.

AIMS: This study was conducted to determine whether galectin-3, a ß-galactoside-binding lectin, plays a role in the pathogenesis of heart failure (HF). METHODS AND RESULTS: Galectin-3 was measured at baseline (n = 1650), after 4 months (n = 1346), and after 12 months (n = 1097) in the Valsartan Heart Failure Trial (Val-HeFT). Galectin-3 levels at baseline ranged from 4.8 to 53 ng/mL. Higher levels were associated with features of worse HF. In a fully adjusted Cox regression model comprising 23 other prognostic variables, baseline galectin-3 was not associated with the risks of all-cause mortality, the composite of the first morbid event, or hospitalization for HF. However, when changes in galectin-3 over time were examined, the increases in galectin-3 between baseline and 4 months were independently and significantly associated with the risks of subsequent all-cause mortality, first morbid event, and hospitalizations for HF, even after adjusting for all baseline and concurrent changes in all variables including estimated glomerular filtration rate (eGFR) and NT-proBNP. The strongest correlate of galectin-3 levels was eGFR, which accounted for 20% of the variability in galectin-3 levels at baseline. There was a significant interaction (P = 0.03) between baseline galectin-3 and the effect of valsartan on hospitalizations for HF. Valsartan caused a significant 44% reduction in hospitalizations for HF in patients with galectin-3 levels below the median level of 16.2 ng/mL, but not in patients with levels above the median. CONCLUSIONS: Galectin-3 levels are elevated in a substantial proportion of patients with HF, particularly those with more severe HF and renal dysfunction. Galectin-3 increased over time in this cohort, and the increase was independently associated with worse outcomes. Valsartan use was associated with a reduction in hospitalizations for HF in patients with low galectin-3, but not in those with higher levels of galectin-3.

Carotid plaque burden as a measure of subclinical atherosclerosis: comparison with other tests for subclinical arterial disease in the High Risk Plaque BioImage study.

OBJECTIVES: The purpose of this study was to compare carotid plaque burden, carotid intima-media thickness (cIMT), ankle-brachial index (ABI), and abdominal aortic diameter (AAD) to coronary artery calcium score (CACS) in people without known cardiovascular disease. BACKGROUND: The clinical utility of risk factors to predict cardiovascular events is limited. Detection of subclinical atherosclerosis by noninvasive tests such as CACS, cIMT, carotid plaque burden, AAD, and ABI may improve risk prediction above that of established risk scoring models, namely, Framingham Risk Score. METHODS: The High Risk Plaque BioImage study investigated 6.101 asymptomatic persons and reports baseline CACS, cIMT, ABI, and AAD. In addition, we present findings from a new 3-dimensional-based ultrasound approach, where the carotid artery was investigated in cross section from proximal in the neck to as distal as possible. From the resulting 10-s video, plaque was outlined on cross-sectional images and all plaque areas were summarized into "plaque burden." RESULTS: The mean age was 68.8 years, and 65.3% of subjects had intermediate Framingham Risk Score (6% to 20% 10-year risk). Carotid plaques were identified in 78% of cases, abnormal ABI in 10%, AAD >20 mm in 28%, and coronary calcium in 68% of participants. Carotid plaque burden was found to correlate stronger with CACS (chi-square 450, p < 0.0001) than did cIMT (chi-square 24, p < 0.0001), AAD (chi-square 2.9, p = 0.091), and ABI (chi-square 35.2, p < 0.0001). CONCLUSIONS: In the BioImage study, a new 3-dimensional-based ultrasound method identified more carotid plaques than in previous studies. Compared to other methods, carotid plaque burden was the strongest cross-sectional predictor of CACS, and its clinical utility as predictor of future cardiovascular events is being evaluated in the BioImage study. (BioImage Study: A Clinical Study of Burden of Atherosclerotic Disease in an At-Risk Population; NCT00738725).

Galectin-3 predicts response to statin therapy in the Controlled Rosuvastatin Multinational Trial in Heart Failure (CORONA).

AIMS: To investigate whether plasma galectin-3, a mediator of fibrogenesis, can identify patients with chronic heart failure (HF) for whom statins are effective. METHODS AND RESULTS: Patients with ischaemic systolic HF enrolled in the Controlled Rosuvastatin Multinational Trial in Heart Failure (CORONA) were randomly assigned to 10 mg/day of rosuvastatin or placebo. Galectin-3 was measured in plasma. The primary outcome was cardiovascular death, myocardial infarction, or stroke. Of 1492 patients, 411 had a primary event during a median follow-up of 32.8 months. There was an interaction between baseline galectin-3 and rosuvastatin on the primary endpoint (P-value for interaction = 0.036). Among patients with below the median plasma concentrations of galectin-3 (≤ 19.0 ng/mL), those assigned to rosuvastatin had a lower primary event rate [hazard ratio (HR) 0.65; 95% confidence interval (CI), 0.46-0.92; P= 0.014], lower total mortality (HR 0.70; 95% CI, 0.50-0.98; P= 0.038), and lower event rate of all-cause mortality and HF hospitalizations (HR 0.72; 95% CI, 0.54-0.98; P= 0.017) compared with placebo, but no benefit was observed in patients with higher levels of galectin-3. The combination of concurrently low concentrations of galectin-3 and N-terminal pro-B-type natriuretic peptide (<102.7 pmol/L) identified patients with a large benefit with rosuvastatin (HR 0.33; 95% CI, 0.16-0.67; P= 0.002). CONCLUSION: Patients with systolic HF of ischaemic aetiology who have galectin-3 values <19.0 ng/mL may benefit from rosuvastatin treatment. However, the data from this post hoc analysis should be interpreted with caution since the overall results of the CORONA study did not show a significant effect on the primary endpoint.

Semi-targeted plasma proteomics discovery workflow utilizing two-stage protein depletion and off-line LC-MALDI MS/MS.

A quantitative proteomics workflow was implemented that provides extended plasma protein coverage by extensive protein depletion in combination with the sensitivity and breadth of analysis of two-dimensional LC-MS/MS shotgun analysis. Abundant proteins were depleted by a two-stage process using IgY and Supermix depletion columns in series. Samples are then extensively fractionated by two-dimensional chromatography with fractions directly deposited onto MALDI plates. Decoupling sample fractionation from mass spectrometry facilitates a targeted MS/MS precursor selection strategy that maximizes measurement of a consistent set of peptides across experiments. Multiplexed stable isotope labeling provides quantification relative to a common reference sample and ensures an identical set of peptides measured in the set of samples (set of eight) combined in a single experiment. The more extensive protein depletion provided by the addition of the Supermix column did not compromise overall reproducibility of the measurements or the ability to reliably detect changes in protein levels between samples. The implementation of this workflow is presented for a case study aimed at generating molecular signatures for prediction of first heart attack.

Multi-center determination of galectin-3 assay performance characteristics: Anatomy of a novel assay for use in heart failure.

BACKGROUND: Galectin-3 is a carbohydrate binding protein that plays many important regulatory roles in inflammation, immunity and cancer. Recent studies indicate that galectin-3 is a mediator of heart failure development and progression. Development of an improved assay for galectin-3 measurement was necessary for appropriate clinical assessment. Key analytical performance characteristics, the reference distribution and association of galectin-3 levels with clinical outcome in heart failure patients are defined. METHODS: A two-site ELISA test was examined for measurement matrix, imprecision, limits of blank, detection, and quantitation, as well as linearity, high-dose hook effect, storage stability, cross-reactivity with nine similar compounds, interference with 22 common medications and icterus, lipemia and hemolysis, all in accordance with CLSI guidance. Also the effects of human anti-mouse antibodies and rheumatoid factor on recovery of galectin-3 were investigated. The reference interval was determined for 1092 ostensibly healthy individuals. The association of galectin-3 concentrations with an outcome of mortality was examined in a preliminary analysis of 129 acute decompensated heart failure patients. RESULTS: Galectin-3 results were equivalent when measured in serum or EDTA plasma. Imprecision studies demonstrated that the total CV was <10% at a low concentration of 6 ng/mL, 7% near the mid-level concentration of 21 ng/mL, and 15% at the high level of 70 ng/mL. The limit of blank was 0.86 ng/mL, the limit of detection was 1.13 ng/mL, and the limit of quantitation was 0.96 ng/mL. The linear measurement range was 0.96-130 ng/mL and there was no high-dose hook at levels up to 500 ng/mL. No cross-reactivity with nine compounds structurally related to galectin-3 and no interference from 22 common medications, icterus or lipemia was found. Hemolysis and presence of human anti-mouse antibodies or rheumatoid factor were found to be potential sources of interference. Samples can be stored for up to 15 days at either 22-28 degrees C or 2-8 degrees C before analysis; measurements are stable after storage at -20 degrees C or -70 degrees C for at least 6 months and through six freeze-thaw cycles. The 90th, 95th and 97.5th percentile of the normal reference interval was 17.6, 20.3 and 22.1 ng/mL, respectively. Galectin-3 in the acute decompensated heart failure patients ranged from 4.0 to 75 ng/mL; using a cutpoint of 22.1 ng/mL in an unadjusted analysis, galectin-3 values were associated with an outcome of death (p=0.035). Galectin-3 is associated with severity of heart failure as indicated by NYHA Class (p-value for trend, 0.017). CONCLUSION: This ELISA for galectin-3 measurement demonstrated acceptable analytical characteristics and was associated with mortality in a preliminary unadjusted analysis.

Risk factors for near-term myocardial infarction in apparently healthy men and women.

BACKGROUND: Limited information is available regarding risk factors for the near-term (4 years) onset of myocardial infarction (MI). We evaluated established cardiovascular risk factors and putative circulating biomarkers as predictors for MI within 4 years of measurement. METHODS: We conducted a matched, nested case-control study (252 cases and 499 controls) drawing on 45 735 men and women participating in the Copenhagen City Heart Study and the Copenhagen General Population Study. Established risk factors and 17 putative biomarkers, including inflammation-sensitive plasma proteins (C-reactive protein, fibrinogen, alpha(l)-antitrypsin, complement 3), apolipoproteins (A1, B, E, B/A1 ratio), markers of iron overload (iron, transferrin, transferrin saturation), creatinine, alkaline phosphatase, gamma-glutamyl transpeptidase, and leukocytes (lymphocyte count, neutrophil count, neutrophil/lymphocyte ratio) were assessed. RESULTS: Among women and men, only 13% and 50%, respectively, of those with near-term MI were classified as high risk by Framingham risk score at baseline. After adjustment for established risk factors, odds ratios for near-term MI, which compared highest to lowest quintiles, were 2.87(95% CI 1.51-5.48; P = 0.001) for alpha(l)-antitrypsin, 2.84(1.42-5.67; P = 0.003) for C-reactive protein, 1.97(1.09-3.57; P = 0.03) for creatinine, 1.99(1.09-3.65; P = 0.03) for fibrinogen, and 0.37(0.19-0.73; P = 0.004) for iron. The corresponding odds ratio for all biomarkers combined was 7.24 (3.28-16.0; P < 0.001). CONCLUSIONS: We identified 5 biomarkers associated with increased near-term risk of MI independently of established risk factors. All putative biomarkers combined explained a 7-fold increase in the odds of near-term MI.

Correlation network analysis for data integration and biomarker selection.

High-throughput biomolecular profiling techniques such as transcriptomics, proteomics and metabolomics are increasingly being used in in vivo studies to recognize and characterize effects of xenobiotics on organs and systems. Of particular interest are biomarkers of treatment-related effects which are detectable in easily accessible biological fluids such as blood. A fundamental challenge in such biomarker studies is selecting among the plethora of biomolecular changes induced by a compound and revealed by molecular profiling, to identify biomarkers which are exclusively or predominantly due to specific processes. In this work we present a cross-compartment correlation network approach, involving no a priori supervision or design, to integrate proteomic, metabolomic and transcriptomic data for selecting circulating biomarkers. The case study we present is the identification of biomarkers of drug-induced hepatic toxicity effects in a rodent model. Biomolecular profiling of both blood plasma and liver tissue from Wistar Hannover rats administered a toxic compound yielded many hundreds of statistically significant molecular changes. We exploited drug-induced correlations between blood plasma analytes and liver tissue molecules across study animals in order to nominate selected plasma molecules as biomarkers of drug-induced hepatic alterations of lipid metabolism and urea cycle processes.

Lost in translation? Role of metabolomics in solving translational problems in drug discovery and development.

Too few drug discovery projects generate a marketed drug product, often because preclinical studies fail to predict the clinical experience with a drug candidate. Improving the success of preclinical-to-clinical translation is of paramount importance in optimizing the pharmaceutical value chain. Here, we advance the case for a molecular systems approach to crossing the preclinical-to-clinical translational chasm and for metabolomic analysis of readily accessible bodyfluids as a key technology in translational activities.:

Integrative biological analysis of the APOE*3-leiden transgenic mouse.

Integrative (or systems biology) is a new approach to analyzing biological entities as integrated systems of genetic, genomic, protein, metabolite, cellular, and pathway events that are in flux and interdependent. Here, we demonstrate the application of intregrative biological analysis to a mammalian disease model, the apolipoprotein E3-Leiden (APO*E3) transgenic mouse. Mice selected for the study were fed a normal chow diet and sacrificed at 9 weeks of age-conditions under which they develop only mild type I and II atherosclerotic lesions. Hepatic mRNA expression analysis showed a 25% decrease in APO A1 and a 43% increase in liver fatty acid binding protein expression between transgenic and wild type control mice, while there was no change in PPAR-alpha expression. On-line high performance liquid chromatography-mass spectrometry quantitative profiling of tryptic digests of soluble liver proteins and liver lipids, coupled with principle component analysis, enabled rapid identification of early protein and metabolite markers of disease pathology. These included a 44% increase in L-FABP in transgenic animals compared to controls, as well as an increase in triglycerides and select bioactive lysophosphatidylcholine species. A correlation analysis of identified genes, proteins, and lipids was used to construct an interaction network. Taken together, these results indicate that integrative biology is a powerful tool for rapid identification of early markers and key components of pathophysiologic processes, and constitute the first application of this approach to a mammalian system.

Phenotype characterisation using integrated gene transcript, protein and metabolite profiling.

Multifactorial diseases present a significant challenge for functional genomics. Owing to their multiple compartmental effects and complex biomolecular activities, such diseases cannot be adequately characterised by changes in single components, nor can pathophysiological changes be understood by observing gene transcripts alone. Instead, a pattern of subtle changes is observed in multifactorial diseases across multiple tissues and organs with complex associations between corresponding gene, protein and metabolite levels. This article presents methods for exploratory and integrative analysis of pathophysiological changes at the biomolecular level. In particular, novel approaches are introduced for the following challenges: (i) data processing and analysis methods for proteomic and metabolomic data obtained by electrospray ionisation (ESI) liquid chromatography-tandem mass spectrometry (LC/MS); (ii) association analysis of integrated gene, protein and metabolite patterns that are most descriptive of pathophysiological changes; and (iii) interpretation of results obtained from association analyses in the context of known biological processes. These novel approaches are illustrated with the apolipoprotein E3-Leiden transgenic mouse model, a commonly used model of atherosclerosis. We seek to gain insight into the early responses of disease onset and progression by determining and identifying--well in advance of pathogenic manifestations of disease--the sets of gene transcripts, proteins and metabolites, along with their putative relationships in the transgenic model and associated wild-type cohort. Our results corroborate previous findings and extend predictions for three processes in atherosclerosis: aberrant lipid metabolism, inflammation, and tissue development and maintenance.

Methods for the differential integrative omic analysis of plasma from a transgenic disease animal model.

Multitiered quantitative analysis of biological systems is rapidly becoming the desired approach to study hierarchical functional interactions between proteins and metabolites. We describe here a novel systematic approach to analyze organisms with complex metabolic regulatory networks. By using precise analytical methods to measure biochemical constituents and their relative abundance in whole plasma of transgenic ApoE*3-Leiden mice and an isogenic wild-type control group, simultaneous snapshots of metabolic and protein states were obtained. Novel data processing and multivariate analysis tools such as Impurity Resolution Software (IMPRESS) and Windows-based linear fit program (WINLIN) were used to compare protein and metabolic profiles in parallel. Canonical correlations of the resulting data show quantitative relationships between heterogeneous components in the TG animals. These results, obtained solely from whole plasma analysis allowed us, in a rapid manner, to corroborate previous findings as well as find new events pertaining to dominant and peripheral events in lipoprotein metabolism of a genetically modified mammalian organism in relation to ApoE3, a key mediator of lipoprotein metabolism.