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Aptamer-based proteomic profiling reveals novel candidate biomarkers of cardiovascular disease

ABSTRACT: Ngo D, Sinha S, Shen D, Kuhn E, Keyes M, Shi X, Benson M, O'Sullivan J, Keshishian H, Farrell L, Fifer MA, Vasan RS, Sabatine M, Larson MG, Carr SA, Wang TJ, Gerszten RE. Circulation 2016. Emerging proteomic technologies are beginning to permit the systematic characterization of human plasma samples. DNA-aptamer based technologies may address limitations of existing proteomic techniques, including low sample throughput, which have prevented proteomic analyses of large cohorts. We applied an aptamer-based proteomic technology that measures 1,129 proteins to identify potential novel biomarkers of cardiovascular disease. We found very early markers of myocardial injury in a robust, clinically-relevant perturbational model of planned myocardial injury, patients undergoing septal ablation for hypertrophic cardiomyopathy. We then tested whether the platform could detect novel associations between proteins and cardiovascular risk factors in individuals without overt cardiovascular disease using archived samples from the Framingham Heart Study. Further, we developed a new workflow integrating DNA-based immunoaffinity with mass spectrometry to analytically validate the specificity of aptamer findings. Our results highlight an emerging proteomics tool capable of profiling over one thousand low abundance analytes with high sensitivity and high precision, applicable both to well-phenotyped perturbational studies as well as large human cohorts.

INSTRUMENT(S): TSQ Quantiva, Q Exactive

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Steven A. Carr

PROVIDER: MSV000079908 | MassIVE | Thu Jul 14 09:01:00 BST 2016

REPOSITORIES: MassIVE

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Project description:Risk stratification and management of acute myocardial infarction patients continue to be challenging despite considerable efforts made in the last decades by many clinicians and researchers. The aim of this study was to investigate the metabolomic fingerprint of acute myocardial infarction using nuclear magnetic resonance spectroscopy on patient serum samples and to evaluate the possible role of metabolomics in the prognostic stratification of acute myocardial infarction patients. <div>In total, 978 acute myocardial infarction patients were enrolled in this study; of these, 146 died and 832 survived during 2 years of follow-up after the acute myocardial infarction. Serum samples were analyzed via high-resolution 1H-nuclear magnetic resonance spectroscopy and the spectra were used to characterize the metabolic fingerprint of patients. Multivariate statistics were used to create a prognostic model for the prediction of death within 2 years after the cardiovascular event. </div><div>In the training set, metabolomics showed significant differential clustering of the two outcomes cohorts. A prognostic risk model predicted death with 76.9% sensitivity, 79.5% specificity, and 78.2% accuracy, and an area under the receiver operating characteristics curve of 0.859. These results were reproduced in the validation set, obtaining 72.6% sensitivity, 72.6% specificity, and 72.6% accuracy. Cox models were used to compare the known prognostic factors (for example, Global Registry of Acute Coronary Events score, age, sex, Killip class) with the metabolomic random forest risk score. In the univariate analysis, many prognostic factors were statistically associated with the outcomes; among them, the random forest score calculated from the nuclear magnetic resonance data showed a statistically relevant hazard ratio of 6.45 (p = 2.16×10-16). Moreover, in the multivariate regression only age, dyslipidemia, previous cerebrovascular disease, Killip class, and random forest score remained statistically significant, demonstrating their independence from the other variables. </div><div>For the first time, metabolomic profiling technologies were used to discriminate between patients with different outcomes after an acute myocardial infarction. These technologies seem to be a valid and accurate addition to standard stratification based on clinical and biohumoral parameters.</div>

Project description:Background On-pump cardiac surgery provokes a predictable perioperative myocardial ischaemia–reperfusion injury which is associated with poor clinical outcomes. We determined the occurrence of time-of-the-day variation in perioperative myocardial injury in patients undergoing aortic valve replacement and its molecular mechanisms. Methods We studied the incidence of major adverse cardiac events in a prospective observational single-centre cohort study of patients with severe aortic stenosis and preserved left ventricular ejection fraction (>50%) who were referred to our cardiovascular surgery department at Lille University Hospital (Lille, France) for aortic valve replacement and underwent surgery in the morning or afternoon. Patients were matched into pairs by propensity score. We also did a randomised study, in which we evaluated perioperative myocardial injury and myocardial samples of patients randomly assigned (1:1) via permuted block randomisation (block size of eight) to undergo isolated aortic valve replacement surgery either in the morning or afternoon. We also evaluated human and rodent myocardium in ex-vivo hypoxia–reoxygenation models and did a transcriptomic analysis in myocardial samples from the randomised patients to identify the signalling pathway(s) involved. The primary objective of the study was to assess whether myocardial tolerance of ischaemia–reperfusion differed depending on the timing of aortic valve replacement surgery (morning vs afternoon), as measured by the occurrence of major adverse cardiovascular events (cardiovascular death, myocardial infarction, and admission to hospital for acute heart failure). The randomised study is registered with ClinicalTrials.gov, number NCT02812901. Findings In the cohort study (n=596 patients in matched pairs who underwent either morning surgery [n=298] or afternoon surgery [n=298]), during the 500 days following aortic valve replacement, the incidence of major adverse cardiac events was lower in the afternoon surgery group than in the morning group: hazard ratio 0·50 (95% CI 0·32–0·77; p=0·0021). In the randomised study, 88 patients were randomly assigned to undergo surgery in the morning (n=44) or afternoon (n=44); perioperative myocardial injury assessed with the geometric mean of perioperative cardiac troponin T release was significantly lower in the afternoon group than in the morning group (estimated ratio of geometric means for afternoon to morning of 0·79 [95% CI 0·68–0·93; p=0·0045]). Ex-vivo analysis of human myocardium revealed an intrinsic morning–afternoon variation in hypoxia–reoxygenation tolerance, concomitant with transcriptional alterations in circadian gene expression with the nuclear receptor Rev-Erbα being highest in the morning. In a mouse Langendorff model of hypoxia–reoxygenation myocardial injury, Rev-Erbα gene deletion or antagonist treatment reduced injury at the time of sleep-to-wake transition, through an increase in the expression of the ischaemia–reperfusion injury modulator CDKN1a/p21. Interpretation Perioperative myocardial injury is transcriptionally orchestrated by the circadian clock in patients undergoing aortic valve replacement, and Rev-Erbα antagonism seems to be a pharmacological strategy for cardioprotection. Afternoon surgery might provide perioperative myocardial protection and lead to improved patient outcomes compared with morning surgery.

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Aptamer-based proteomic profiling reveals novel candidate biomarkers of cardiovascular disease

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