PURPOSE: High-throughput profiling of metabolic status (metabolomics) allows for the assessment of small-molecule metabolites that may participate in exercise-induced biochemical pathways and corresponding cardiometabolic risk modification. We sought to describe the changes in a diverse set of plasma metabolite profiles in patients undergoing chronic exercise training and assess the relationship between metabolites and cardiometabolic response to exercise.
METHODS: A secondary analysis was performed in 216 middle-age abdominally obese men and women (mean ± SD, 52.4 ± 8.0 yr) randomized into one of four groups varying in exercise amount and intensity for 6-month duration: high amount high intensity, high amount low intensity, low amount low intensity, and control. One hundred forty-seven metabolites were profiled by liquid chromatography-tandem mass spectrometry.
RESULTS: No significant differences in metabolite changes between specific exercise groups were observed; therefore, subsequent analyses were collapsed across exercise groups. There were no significant differences in metabolite changes between the exercise and control groups after 24 wk at a Bonferroni-adjusted statistical significance (P < 3.0 × 10). Seven metabolites changed in the exercise group compared with the control group at P < 0.05. Changes in several metabolites from distinct metabolic pathways were associated with change in cardiometabolic risk traits, and three baseline metabolite levels predicted changes in cardiometabolic risk traits.
CONCLUSIONS: Metabolomic profiling revealed no significant plasma metabolite changes between exercise and control after 24 wk at Bonferroni significance. However, we identified circulating biomarkers that were predictive or reflective of improvements in cardiometabolic traits in the exercise group.
BACKGROUND: Emerging proteomic technologies using novel affinity-based reagents allow for efficient multiplexing with high-sample throughput. To identify early biomarkers of myocardial injury, we recently applied an aptamer-based proteomic profiling platform that measures 1129 proteins to samples from patients undergoing septal alcohol ablation for hypertrophic cardiomyopathy, a human model of planned myocardial injury. Here, we examined the scalability of this approach using a markedly expanded platform to study a far broader range of human proteins in the context of myocardial injury.
METHODS: We applied a highly multiplexed, expanded proteomic technique that uses single-stranded DNA aptamers to assay 4783 human proteins (4137 distinct human gene targets) to derivation and validation cohorts of planned myocardial injury, individuals with spontaneous myocardial infarction, and at-risk controls.
RESULTS: We found 376 target proteins that significantly changed in the blood after planned myocardial injury in a derivation cohort (n=20; P<1.05E-05, 1-way repeated measures analysis of variance, Bonferroni threshold). Two hundred forty-seven of these proteins were validated in an independent planned myocardial injury cohort (n=15; P<1.33E-04, 1-way repeated measures analysis of variance); >90% were directionally consistent and reached nominal significance in the validation cohort. Among the validated proteins that were increased within 1 hour after planned myocardial injury, 29 were also elevated in patients with spontaneous myocardial infarction (n=63; P<6.17E-04). Many of the novel markers identified in our study are intracellular proteins not previously identified in the peripheral circulation or have functional roles relevant to myocardial injury. For example, the cardiac LIM protein, cysteine- and glycine-rich protein 3, is thought to mediate cardiac mechanotransduction and stress responses, whereas the mitochondrial ATP synthase F0 subunit component is a vasoactive peptide on its release from cells. Last, we performed aptamer-affinity enrichment coupled with mass spectrometry to technically verify aptamer specificity for a subset of the new biomarkers.
CONCLUSIONS: Our results demonstrate the feasibility of large-scale aptamer multiplexing at a level that has not previously been reported and with sample throughput that greatly exceeds other existing proteomic methods. The expanded aptamer-based proteomic platform provides a unique opportunity for biomarker and pathway discovery after myocardial injury.
BACKGROUND: We recently identified 156 proteins in human plasma that were each associated with the net Framingham Cardiovascular Disease Risk Score using an aptamer-based proteomic platform in Framingham Heart Study Offspring participants. Here we hypothesized that performing genome-wide association studies and exome array analyses on the levels of each of these 156 proteins might identify genetic determinants of risk-associated circulating factors and provide insights into early cardiovascular pathophysiology.
METHODS: We studied the association of genetic variants with the plasma levels of each of the 156 Framingham Cardiovascular Disease Risk Score-associated proteins using linear mixed-effects models in 2 population-based cohorts. We performed discovery analyses on plasma samples from 759 participants of the Framingham Heart Study Offspring cohort, an observational study of the offspring of the original Framingham Heart Study and their spouses, and validated these findings in plasma samples from 1421 participants of the MDCS (Malmö Diet and Cancer Study). To evaluate the utility of this strategy in identifying new biological pathways relevant to cardiovascular disease pathophysiology, we performed studies in a cell-model system to experimentally validate the functional significance of an especially novel genetic association with circulating apolipoprotein E levels.
RESULTS: We identified 120 locus-protein associations in genome-wide analyses and 41 associations in exome array analyses, the majority of which have not been described previously. These loci explained up to 66% of interindividual plasma protein-level variation and, on average, accounted for 3 times the amount of variation explained by common clinical factors, such as age, sex, and diabetes mellitus status. We described overlap among many of these loci and cardiovascular disease genetic risk variants. Finally, we experimentally validated a novel association between circulating apolipoprotein E levels and the transcription factor phosphatase 1G. Knockdown of phosphatase 1G in a human liver cell model resulted in decreased apolipoprotein E transcription and apolipoprotein E protein levels in cultured supernatants.
CONCLUSIONS: We identified dozens of novel genetic determinants of proteins associated with the Framingham Cardiovascular Disease Risk Score and experimentally validated a new role for phosphatase 1G in lipoprotein biology. Further, genome-wide and exome array data for each protein have been made publicly available as a resource for cardiovascular disease research.
BACKGROUND: Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a "virtual proteomic" approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals.
METHODS: We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651).
RESULTS: In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-β predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08-1.58; P=0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66-0.94; P=0.008) per 1-SD increment in platelet-derived growth factor receptor-β.
CONCLUSIONS: We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.
BACKGROUND: Single-stranded DNA aptamers are oligonucleotides of ≈50 base pairs in length selected for their ability to bind proteins with high specificity and affinity. Emerging DNA aptamer-based technologies may address limitations of existing proteomic techniques, including low sample throughput, which have hindered proteomic analyses of large cohorts.
METHODS: To identify early biomarkers of myocardial injury, we applied an aptamer-based proteomic platform that measures 1129 proteins to a clinically relevant perturbational model of planned myocardial infarction (PMI), patients undergoing septal ablation for hypertrophic cardiomyopathy. Blood samples were obtained before and at 10 and 60 minutes after PMI, and protein changes were assessed by repeated-measures analysis of variance. The generalizability of our PMI findings was evaluated in a spontaneous myocardial infarction cohort (Wilcoxon rank-sum). We then tested the platform's ability to detect associations between proteins and Framingham Risk Score components in the Framingham Heart Study, performing regression analyses for each protein versus each clinical trait.
RESULTS: We found 217 proteins that significantly changed in the peripheral vein blood after PMI in a derivation cohort (n=15; P<5.70E-5). Seventy-nine of these proteins were validated in an independent PMI cohort (n=15; P<2.30E-4); >85% were directionally consistent and reached nominal significance. We detected many protein changes that are novel in the context of myocardial injury, including Dickkopf-related protein 4, a WNT pathway inhibitor (peak increase 124%, P=1.29E-15) and cripto, a growth factor important in cardiac development (peak increase 64%, P=1.74E-4). Among the 40 validated proteins that increased within 1 hour after PMI, 23 were also elevated in patients with spontaneous myocardial infarction (n=46; P<0.05). Framingham Heart Study analyses revealed 156 significant protein associations with the Framingham Risk Score (n=899), including aminoacylase 1 (β=0.3386, P=2.54E-22) and trigger factor 2 (β=0.2846, P=5.71E-17). Furthermore, we developed a novel workflow integrating DNA-based immunoaffinity with mass spectrometry to analytically validate aptamer specificity.
CONCLUSIONS: Our results highlight an emerging proteomics tool capable of profiling >1000 low-abundance analytes with high sensitivity and high precision, applicable both to well-phenotyped perturbational studies and large human cohorts, as well.
The voltage-gated potassium (Kv) channel Kv1.5 mediates the I(Kur) repolarizing current in human atrial myocytes and regulates vascular tone in multiple peripheral vascular beds. Understanding the complex regulation of Kv1.5 function is of substantial interest because it represents a promising pharmacological target for the treatment of atrial fibrillation and hypoxic pulmonary hypertension. Herein we demonstrate that posttranslational modification of Kv1.5 by small ubiquitin-like modifier (SUMO) proteins modulates Kv1.5 function. We have identified two membrane-proximal and highly conserved cytoplasmic sequences in Kv1.5 that conform to established SUMO modification sites in transcription factors. We find that Kv1.5 interacts specifically with the SUMO-conjugating enzyme Ubc9 and is a target for modification by SUMO-1, -2, and -3 in vivo. In addition, purified recombinant Kv1.5 serves as a substrate in a minimal in vitro reconstituted SUMOylation reaction. The SUMO-specific proteases SENP2 and Ulp1 efficiently deconjugate SUMO from Kv1.5 in vivo and in vitro, and disruption of the two identified target motifs results in a loss of the major SUMO-conjugated forms of Kv1.5. In whole-cell patch-clamp electrophysiological studies, loss of Kv1.5 SUMOylation, by either disruption of the conjugation sites or expression of the SUMO protease SENP2, leads to a selective approximately 15-mV hyperpolarizing shift in the voltage dependence of steady-state inactivation. Reversible control of voltage-sensitive channels through SUMOylation constitutes a unique and likely widespread mechanism for adaptive tuning of the electrical excitability of cells.