Evaluating Subtle Pathological Changes in Early Myocardial Ischemia Using Spectral Histopathology

Early myocardial ischemia (EMI) is morphologically challenging, and the results from conventional histological staining may be subjective, imprecise, or even silent. The size of myocardial necrosis determines the acute and long-term mortality of EMI. The precise diagnosis of myocardial ischemia is critical for both clinical management and forensic investigation. Fourier transform infrared (FTIR) spectroscopic imaging is a highly sensitive tool for detecting protein conformations and imaging protein profiles. The aim of this study was to evaluate the application of FTIR imaging with multivariate analysis to detect biochemical changes in the protein conformation in the early phase of myocardial ischemia and to visually classify different disease states. The spectra and curve fitting results revealed that the total protein content decreased significantly in the EMI group and that the α-helix content of the secondary protein structure continuously decreased as ischemia progressed, while the β-sheet content increased. Differences in the control and EMI groups and perfused and ischemic myocardium were confirmed using principal component analysis and partial least squares discriminant analysis. Next, two support vector machine classifiers were effectively created. The accuracy, recall, and precision were 99.98, 99.96, and 100.00%, respectively, to differentiate the EMI group from the control group and 99.25, 98.95, and 99.54%, respectively, to differentiate perfused and ischemic myocardium. Ultimately, high EMI diagnostic accuracy was achieved with 100.00% recall and 100.00% precision, and ischemic myocardium diagnostic accuracy was achieved with 99.30% recall and 99.53% precision for the test set. This pilot study demonstrated that FTIR imaging is a powerful automated quantitative analysis tool to detect EMI without morphological changes and will improve diagnostic accuracy and patient prognosis.

Omics and Cardiometabolic Disease Risk Prediction

Risk assessments are integral for the prevention and management of cardiometabolic disease (CMD). However, individuals may develop CMD without traditional risk factors, necessitating the development of novel biomarkers to aid risk prediction. The emergence of omic technologies, including genomics, proteomics, and metabolomics, has allowed for assessment of orthogonal measures of cardiometabolic risk, potentially improving the ability for novel biomarkers to refine disease risk assessments. While omics has shed light on novel mechanisms for the development of CMD, its adoption in clinical practice faces significant challenges. We review select omic technologies and cardiometabolic investigations for risk prediction, while highlighting challenges and opportunities for translating findings to clinical practice.

Biomarkers in heart failure: the past, current and future

Despite the enhanced knowledge of the pathophysiology of heart failure (HF), it still remains a serious syndrome with substantial morbidity, mortality, and frequent hospitalizations. These are due to the current improvements in other cardiovascular diseases (like myocardial infarction), the aging population, and growing prevalence of comorbidities. Biomarker-guided management has brought a new dimension in prognostication, diagnosis, and therapy options. Following the recommendation of natriuretic peptides (B-type natriuretic peptide and N-terminal-proBNP), many other biomarkers have been thoroughly studied to reflect different pathophysiological processes (such as fibrosis, inflammation, myocardial injury, and remodeling) in HF and some of them (like cardiac troponins, soluble suppression of tumorigenesis-2, and galectin 3) have subsequently been recommended to aid in the diagnosis and prognostication in HF. Consequently, multi-marker approach has also been approved owing to the varied nature of HF syndrome. In this review, we discussed the guidelines available for HF biomarkers, procedures for evaluating novel markers, and the utilities of both emerging and established biomarkers for risk stratification, diagnosis, and management of HF in the clinics. We later looked at how the rapidly emerging field-OMICs, can help transform HF biomarkers discoveries and establishment.

Somewhere over the sex differences rainbow of myocardial infarction remodeling: hormones, chromosomes, inflammasome, oh my

Introduction: Cardiovascular disease is a major cause of death in both men and women. While women are protected until the onset of menopause, after menopause women have increased risk of adverse cardiovascular disease events. Animal models of myocardial infarction recapitulate many of the sex differences observed in humans, and proteomics evaluations offer mechanistic insights to explain sex differences.Areas covered: In this review, we will discuss how proteomics has helped us understand the hormonal, chromosomal, and immune mechanisms behind sex differences in response to ischemic injury and the development of heart failure.Expert opinion: There are a number of ways in which proteomics has and will continue to facilitate our understanding of sex differences in cardiac remodeling after myocardial infarction.

Time Series Characteristics of Serum Branched-Chain Amino Acids for Early Diagnosis of Chronic Heart Failure

Chronic heart failure (CHF) is an ongoing clinical syndrome with cardiac dysfunction that can be traced to alterations in cardiac metabolism. The identification of metabolic biomarkers in easily accessible fluids to improve the early diagnosis of CHF has been elusive to date. In this study, we took multidimensional analytical techniques to discover potentially new diagnostic biomarkers by focusing on the dynamic changes of metabolites in serum during the progression of CHF. Using mass-spectrometry-based untargeted metabolomics, we identified 23 cardiac metabolites that were altered in a rat model of myocardial infarction induced CHF. Among these differential metabolites, branched-chain amino acids (BCAAs) in serum, especially leucine and valine, showed a high capability to differentiate between CHF and sham-operated rats, of which area under the receiver operating characteristic curve was greater than 0.75. Combining with targeted analysis of the amino acids and related proteins and genes, we confirmed that BCAA metabolic pathway was significantly inhibited in rat failing hearts. On the basis of the time series data of serum samples, we characterized the fluctuation pattern of circulating BCAAs by the disease progression model. Finally, the time-resolved diagnostic potential of serum BCAAs was evaluated by the machine-learning-based classifier, and high diagnostic accuracy of 93.75% was achieved within 3 weeks after surgery. These findings provide a promising metabolic signature that can be further exploited for CHF early diagnostic development.

Clinical veterinary proteomics: Techniques and approaches to decipher the animal plasma proteome

Over the last two decades, technological advancements in the field of proteomics have advanced our understanding of the complex biological systems of living organisms. Techniques based on mass spectrometry (MS) have emerged as powerful tools to contextualise existing genomic information and to create quantitative protein profiles from plasma, tissues or cell lines of various species. Proteomic approaches have been used increasingly in veterinary science to investigate biological processes responsible for growth, reproduction and pathological events. However, the adoption of proteomic approaches by veterinary investigators lags behind that of researchers in the human medical field. Furthermore, in contrast to human proteomics studies, interpretation of veterinary proteomic data is difficult due to the limited protein databases available for many animal species. This review article examines the current use of advanced proteomics techniques for evaluation of animal health and welfare and covers the current status of clinical veterinary proteomics research, including successful protein identification and data interpretation studies. It includes a description of an emerging tool, sequential window acquisition of all theoretical fragment ion mass spectra (SWATH-MS), available on selected mass spectrometry instruments. This newly developed data acquisition technique combines advantages of discovery and targeted proteomics approaches, and thus has the potential to advance the veterinary proteomics field by enhancing identification and reproducibility of proteomics data.

Peptidomic Analysis of Rat Plasma: Proteolysis in Hemorrhagic Shock

It has been previously shown that intestinal proteases translocate into the circulation during hemorrhagic shock and contribute to proteolysis in distal organs. However, consequences of this phenomenon have not previously been investigated using high-throughput approaches. Here, a shotgun label-free quantitative proteomic approach was utilized to compare the peptidome of plasma samples from healthy and hemorrhagic shock rats to verify the possible role of uncontrolled proteolytic activity in shock. Plasma was collected from rats after hemorrhagic shock (HS) consisting of 2-h hypovolemia followed by 2-h reperfusion, and from healthy control (CTRL) rats. A new two-step enrichment method was applied to selectively extract peptides and low molecular weight proteins from plasma, and directly analyze these samples by tandem mass spectrometry. One hundred twenty-six circulating peptides were identified in CTRL and 295 in HS animals. Ninety-six peptides were present in both conditions; of these, 57 increased and 30 decreased in shock. In total, 256 peptides were increased or present only in HS confirming a general increase in proteolytic activity in shock. Analysis of the proteases that potentially generated the identified peptides suggests that the larger relative contribution to the proteolytic activity in shock is due to chymotryptic-like proteases. These results provide quantitative confirmation that extensive, system-wide proteolysis is part of the complex pathologic phenomena occurring in hemorrhagic shock.

Dysfunctional High-Density Lipoprotein: An Innovative Target for Proteomics and Lipidomics

High-Density Lipoprotein-Cholesterol (HDL-C) is regarded as an important protective factor against cardiovascular disease, with abundant evidence of an inverse relationship between its serum levels and risk of cardiovascular disease, as well as various antiatherogenic, antioxidant, and anti-inflammatory properties. Nevertheless, observations of hereditary syndromes featuring scant HDL-C concentration in absence of premature atherosclerotic disease suggest HDL-C levels may not be the best predictor of cardiovascular disease. Indeed, the beneficial effects of HDL may not depend solely on their concentration, but also on their quality. Distinct subfractions of this lipoprotein appear to be constituted by specific protein-lipid conglomerates necessary for different physiologic and pathophysiologic functions. However, in a chronic inflammatory microenvironment, diverse components of the HDL proteome and lipid core suffer alterations, which propel a shift towards a dysfunctional state, where HDL-C becomes proatherogenic, prooxidant, and proinflammatory. This heterogeneity highlights the need for further specialized molecular studies in this aspect, in order to achieve a better understanding of this dysfunctional state; with an emphasis on the potential role for proteomics and lipidomics as valuable methods in the search of novel therapeutic approaches for cardiovascular disease.

Site-Specific Secretome Map Evidences VSMC-Related Markers of Coronary Atherosclerosis Grade and Extent in the Hypercholesterolemic Swine

A major drawback in coronary atherosclerosis (ATS) research is the difficulty of investigating early phase of plaque growth and related features in the clinical context. In this study, secreted proteins from atherosclerotic coronary arteries in a hypercholesterolemic swine model were characterized by a proteomics approach and their expression was correlated to site-specific ATS stage and extent. A wide coronary artery map of secreted proteins has been obtained in high fat (HF) diet induced ATS swine model and a significantly different expression of many proteins related to vascular smooth muscle cell (VSMC) activation/migration has been identified. Significant associations with ATS stage of HF coronary lesions were found for several VSMC-derived proteins and validated for chitinase 3 like protein 1 (CHI3L1) by tissue immunoexpression. A direct correlation (R(2) = 0.85) was evidenced with intima to media thickness ratio values and ELISA confirmed the higher blood concentrations of CHI3L1 in HF cases. These findings confirmed the pivotal role of VSMCs in coronary plaque development and demonstrated a strong site-specific relation between VSMC-secreted CHI3L1 and lesion grade, suggesting that this protein could be proposed as a useful biomarker for diagnosing and staging of atherosclerotic lesions in coronary artery disease.

Animal Models and "Omics" Technologies for Identification of Novel Biomarkers and Drug Targets to Prevent Heart Failure

It is now accepted that heart failure (HF) is a complex multifunctional disease rather than simply a hemodynamic dysfunction. Despite its complexity, stressed cardiomyocytes often follow conserved patterns of structural remodelling in order to adapt, survive, and regenerate. When cardiac adaptations cannot cope with mechanical, ischemic, and metabolic loads efficiently or become chronically activated, as, for example, after infection, then the ongoing structural remodelling and dedifferentiation often lead to compromised pump function and patient death. It is, therefore, of major importance to understand key events in the progression from a compensatory left ventricular (LV) systolic dysfunction to a decompensatory LV systolic dysfunction and HF. To achieve this, various animal models in combination with an “omics” toolbox can be used. These approaches will ultimately lead to the identification of an arsenal of biomarkers and therapeutic targets which have the potential to shape the medicine of the future.

Hold or fold--proteins in advanced heart failure and myocardial recovery

Advanced heart failure (AHF) describes the subset of heart failure patients refractory to conventional medical therapy. For some AHF patients, the use of mechanical circulatory support (MCS) provides an intermediary "bridge" step for transplant-eligible patients or an alternative therapy for transplant-ineligible patients. Over the past 20 years, clinical observations have revealed that approximately 1% of patients with MCS undergo significant reverse remodeling to the point where the device can be explanted. Unfortunately, it is unclear why some patients experience durable, sustained myocardial remission, while others redevelop heart failure (i.e. which hearts "hold" and which hearts "fold"). In this review, we outline unmet clinical needs related to treating patients with MCS, provide an overview of protein dynamics in the reverse-remodeling process, and propose specific areas where we expect MS and proteomic analyses will have significant impact on our understanding of disease progression, molecular mechanisms of recovery, and provide new markers with prognostic value that can positively impact patient care. Complimentary perspectives are provided with the goal of making this important topic accessible and relevant to both a clinical and basic science audience, as the intersection of these disciplines is required to advance the field.

Proteomics reveals potential non-neuronal cholinergic receptor-effectors in endothelial cells

AIM

The non-neuronal acetylcholine system (NNAS) in endothelial cells participates in modulating endothelial function, vascular tone, angiogenesis and inflammation, thus plays a critical role in cardiovascular diseases. In this study, we used a proteomic approach to study potential downstream receptor-effectors of NNAS that were involved in regulating cellular function in endothelial cells.

METHODS

Human umbilical vein endothelial cells were incubated in the presence of acetylcholine, oxotremorine, pilocarpine or nicotine at the concentration of 10 μmol/L for 12 h, and the expressed proteins in the cells were separated and identified with two-dimensional electrophoresis (2-DE) and LC-MS. The protein spots with the largest changes were identified by LC-MS. Biowork software was used for database search of the peptide mass fingerprints.

RESULTS

Over 1200 polypeptides were reproducibly detected in 2-DE with a pH range of 3-10. Acetylcholine, oxotremorine, pilocarpine and nicotine treatment caused 16, 9, 8 and 9 protein spots, respectively, expressed differentially. Four protein spots were identified as destrin, FK506 binding protein 1A (FKBP1A), macrophage migration inhibitory factor (MIF) and profilin-1. Western blotting analyses showed that treatment of the cells with cholinergic agonists significantly decreased the expression of destrin, FKBP1A and MIF, and increased the expression of profilin-1.

CONCLUSION

A set of proteins differentially expressed in endothelial cells in response to cholinergic agonists may have important implications for the downstream biological effects of NNAS.

Development and analytical comparison of microflow and nanoflow liquid chromatography/mass spectrometry procedures for quantification of cardiac troponin T in mouse hearts

Three procedures for the quantification of cardiac troponin T (cTnT) based on liquid chromatography/mass spectrometry (LC/MS) were developed, validated and compared. The procedures were applied to estimate the cTnT content in the hearts of wild type mice C57BL/6J (WT) and double knock-outs for apolipoprotein E and receptor for LDL (AL KO). Three variants of the procedure proposed include microflow, direct injection nanoflow and preconcentration nanoflow LC/MS. Troponin T tryptic peptide YEINVLR and its analog (internal standard) were monitored in a multiple reaction monitoring mode using triple quadrupole mass detector with electrospray (ESI) ion sources. The preconcentration nanoflow LC/MS method offered the best sensitivity with a lower limit of quantification (LLOQ) of 0.25 fg µL(-1) and a minimal matrix effect. The LLOQ value was 8 times better, compared with that in direct injection nanoflow LC/MS and 200 times better than in microflow LC/MS. The accuracy or precision for all three methods were not different. Separation time in the direct injection nanoflow (8 min) was equivalent to the microflow method (6 min). The cTnT contents in the mice hearts measured by the methods developed by the present authors were not different between the WT and AL KO. We conclude that nanoflow LC/MS based quantitative proteomics offers fundamentally better sensitivities while maintaining analytical quality and separation times equivalent to microflow procedures.

Quantitative proteomics in cardiovascular research: global and targeted strategies

Extensive technical advances in the past decade have substantially expanded quantitative proteomics in cardiovascular research. This has great promise for elucidating the mechanisms of cardiovascular diseases and the discovery of cardiac biomarkers used for diagnosis and treatment evaluation. Global and targeted proteomics are the two major avenues of quantitative proteomics. While global approaches enable unbiased discovery of altered proteins via relative quantification at the proteome level, targeted techniques provide higher sensitivity and accuracy, and are capable of multiplexed absolute quantification in numerous clinical/biological samples. While promising, technical challenges need to be overcome to enable full utilization of these techniques in cardiovascular medicine. Here, we discuss recent advances in quantitative proteomics and summarize applications in cardiovascular research with an emphasis on biomarker discovery and elucidating molecular mechanisms of disease. We propose the integration of global and targeted strategies as a high-throughput pipeline for cardiovascular proteomics. Targeted approaches enable rapid, extensive validation of biomarker candidates discovered by global proteomics. These approaches provide a promising alternative to immunoassays and other low-throughput means currently used for limited validation.

Proteomic profiling of the dystrophin-deficient mdx phenocopy of dystrophinopathy-associated cardiomyopathy

Cardiorespiratory complications are frequent symptoms of Duchenne muscular dystrophy, a neuromuscular disorder caused by primary abnormalities in the dystrophin gene. Loss of cardiac dystrophin initially leads to changes in dystrophin-associated glycoproteins and subsequently triggers secondarily sarcolemmal disintegration, fibre necrosis, fibrosis, fatty tissue replacement, and interstitial inflammation. This results in progressive cardiac disease, which is the cause of death in a considerable number of patients afflicted with X-linked muscular dystrophy. In order to better define the molecular pathogenesis of this type of cardiomyopathy, several studies have applied mass spectrometry-based proteomics to determine proteome-wide alterations in dystrophinopathy-associated cardiomyopathy. Proteomic studies included both gel-based and label-free mass spectrometric surveys of dystrophin-deficient heart muscle from the established mdx animal model of dystrophinopathy. Comparative cardiac proteomics revealed novel changes in proteins associated with mitochondrial energy metabolism, glycolysis, signaling, iron binding, antibody response, fibre contraction, basal lamina stabilisation, and cytoskeletal organisation. This review summarizes the importance of studying cardiomyopathy within the field of muscular dystrophy research, outlines key features of the mdx heart and its suitability as a model system for studying cardiac pathogenesis, and discusses the impact of recent proteomic findings for exploring molecular and cellular aspects of cardiac abnormalities in inherited muscular dystrophies.

Identification of osteoarthritis biomarkers by proteomic analysis of synovial fluid

OBJECTIVE

To use proteomic analysis to identify novel candidate biomarker proteins in synovial fluid for the differential diagnosis of osteoarthritis and rheumatoid arthritis.

METHODS

Synovial fluid samples were analysed using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Data were used to generate an artificial neural network (ANN). The identification of one protein peak was confirmed via Western blotting.

RESULTS

Fluid samples were analysed from 36 patients with osteoarthritis and 24 with rheumatoid arthritis. In total, three protein peaks (mass-to-charge ratio [m/z] 3893, 10,576 and 14,175 Da) were identified as potential biomarkers for osteoarthritis. The ANN differentiated between osteoarthritis and rheumatoid arthritis with a sensitivity of 89.4% and a specificity of 91.2%. The protein peak at m/z 10 576 was identified as S100 calcium binding protein A12 (S100A12).

CONCLUSIONS

A combination of SELDI-TOF-MS and ANN identified osteoarthritis biomarkers. SELDI-TOF-MS may be a useful tool in the screening of synovial fluid for osteoarthritis diagnosis.

Proteomic analysis of venous thromboembolism: an update

Venous thromboembolism is a complex, multifactorial disorder, the pathogenesis of which typically involves a variety of inherited or acquired factors. The multifactorial etiology of this disease and the partial correlation between genotype and prothrombotic phenotype limit greatly the value of genetic analysis in assessing thrombotic risk. The integration of several new 'omics' techniques enables a multifaceted and holistic approach to the study of venous thrombotic processes and pave the way to the search and identification of novel blood biomarkers and/or effectors of thrombus formation that can also be the possible future target of new anticoagulant and thrombolytic therapies for more personalized medicine. This review provides a comprehensive overview of the latest candidate proteomic biomarkers of venous thrombosis and of the proteomics studies relevant to its pathophysiology, some of which seem to confirm the existence of a common physiopathological basis for venous thromboembolism and atherothrombosis.

Novel network biomarkers profile based coronary artery disease risk stratification in Asian Indians

BACKGROUND

Multi-marker approaches for risk prediction in coronary artery disease (CAD) have been inconsistent due to biased selection of specific know biomarkers. We have assessed the global proteome of CAD-affected and unaffected subjects, and developed a pathway network model for elucidating the mechanism and risk prediction for CAD.

MATERIALS AND METHODS

A total of 252 samples (112 CAD-affected without family history and 140 true controls) were analyzed by Surface-Enhanced Laser Desorption/Ionization Time of Flight Mass Spectrometry (SELDI-TOF-MS) by using CM10 cationic chips and bioinformatics tools.

RESULTS

Out of 36 significant peaks in SELDI-TOF MS, nine peaks could do better discrimination of CAD subjects and controls (area under the curve (AUC) of 0.963) based on the Support Vector Machine (SVM) feature selection method. Of the nine peaks used in the model for discrimination of CAD-affected and unaffected, the m/z corresponding to 22,859 was identified as stress-related protein HSP27 and was shown to be highly associated with CAD (odds ratio of 3.47). The 36 biomarker peaks were identified and a network profile was constructed showing the functional association between different pathways in CAD.

CONCLUSION

Based on our data, proteome profiling with SELDI-TOF MS and SVM feature selection methods can be used for novel network biomarker discovery and risk stratification in CAD. The functional associations of the identified novel biomarkers suggest that they play an important role in the development of disease.

Proteomic analysis of proteins eliminated by low-density lipoprotein apheresis

Low-density lipoprotein apheresis (LDL-A) treatment has been shown to decrease serum LDL cholesterol levels and prevent cardiovascular events in homozygous patients with familial hypercholesterolemia. Recently, LDL-A treatment has been suggested to have beneficial effects beyond the removal of LDL particles. In this study, to clarify the preventive effects of LDL-A treatment on atherosclerosis, the waste fluid from the adsorption columns was analyzed. The waste fluid of LDL adsorption columns was analyzed by two-dimensional electrophoresis followed by mass spectrometry. Serum concentrations of the newly identified proteins before and after LDL-A treatment were measured by enzyme-linked immunosorbent assay. We identified 48 kinds of proteins in the waste fluid of LDL adsorption columns, including coagulation factors, thrombogenic factors, complement factors, inflammatory factors and adhesion molecules. In addition to the proteins that were reported to be removed by LDL-A treatment, we newly identified several proteins that have some significant roles in the development of atherosclerosis, including vitronectin and apolipoprotein C-III (Apo C-III). The serum levels of vitronectin and Apo C-III decreased by 82.4% and 54.8%, respectively, after a single LDL-A treatment. While Apo C-III was removed with very low-density lipoprotein (VLDL) and LDL, vitronectin was removed without association with lipoproteins. The removal of proteins observed in the waste fluid has a certain impact on their serum levels, and this may be related to the efficacy of LDL-A treatment. Proteomic analysis of the waste fluid of LDL adsorption columns may provide a rational means of assessing the effects of LDL-A treatment.

Protein target quantification decision tree

The utility of mass spectrometry-(MS-) based proteomic platforms and their clinical applications have become an emerging field in proteomics in recent years. Owing to its selectivity and sensitivity, MS has become a key technological platform in proteomic research. Using this platform, a large number of potential biomarker candidates for specific diseases have been reported. However, due to lack of validation, none has been approved for use in clinical settings by the Food and Drug Administration (FDA). Successful candidate verification and validation will facilitate the development of potential biomarkers, leading to better strategies for disease diagnostics, prognostics, and treatment. With the recent new developments in mass spectrometers, high sensitivity, high resolution, and high mass accuracy can be achieved. This greatly enhances the capabilities of protein biomarker validation. In this paper, we describe and discuss recent developments and applications of targeted proteomics methods for biomarker validation.

Five years later: the current status of the use of proteomics and transcriptomics in EMF research

The World Health Organization's and Radiation and Nuclear Safety Authority's "Workshop on Application of Proteomics and Transcriptomics in Electromagnetic Fields Research" was held in Helsinki in the October/November 2005. As a consequence of this meeting, Proteomics journal published in 2006 a special issue "Application of Proteomics and Transcriptomics in EMF Research" (Vol. 6 No. 17; Guest Editor: D. Leszczynski). This Proteomics issue presented the status of research, of the effects of electromagnetic fields (EMF) using proteomics and transcriptomics methods, present in 2005. The current overview/opinion article presents the status of research in this area by reviewing all studies that were published by the end of 2010. The review work was a part of the European Cooperation in the Field of Scientific and Technical Research (COST) Action BM0704 that created a structure in which researchers in the field of EMF and health shared knowledge and information. The review was prepared by the members of the COST Action BM0704 task group on the high-throughput screening techniques and electromagnetic fields (TG-HTST-EMF).

The role of molecular chaperonins in warm ischemia and reperfusion injury in the steatotic liver: a proteomic study

BACKGROUND

The molecular basis of the increased susceptibility of steatotic livers to warm ischemia/reperfusion (I/R) injury during transplantation remains undefined. Animal model for warm I/R injury was induced in obese Zucker rats. Lean Zucker rats provided controls. Two dimensional differential gel electrophoresis was performed with liver protein extracts. Protein features with significant abundance ratios (p < 0.01) between the two cohorts were selected and analyzed with HPLC/MS. Proteins were identified by Uniprot database. Interactive protein networks were generated using Ingenuity Pathway Analysis and GRANITE software.

RESULTS

The relative abundance of 105 proteins was observed in warm I/R injury. Functional grouping revealed four categories of importance: molecular chaperones/endoplasmic reticulum (ER) stress, oxidative stress, metabolism, and cell structure. Hypoxia up-regulated 1, calcium binding protein 1, calreticulin, heat shock protein (HSP) 60, HSP-90, and protein disulfide isomerase 3 were chaperonins significantly (p < 0.01) down-regulated and only one chaperonin, HSP-1 was significantly upregulated in steatotic liver following I/R.

CONCLUSION

Down-regulation of the chaperones identified in this analysis may contribute to the increased ER stress and, consequently, apoptosis and necrosis. This study provides an initial platform for future investigation of the role of chaperones and therapeutic targets for increasing the viability of steatotic liver allografts.

Impact of Next-Generation Sequencing (NGS) technology on cardiovascular disease research

In recent years, hundreds of gene loci associated with multiple cardiovascular pathologies and traits have been identified through high-throughput Next-Generation Sequencing (NGS) technology. Due to the increasing efficiency and decreasing cost of NGS, rapid progresses anticipated in the field of CVD research. This review summarizes the main strategies of CV research with NGS at the level of genomics, transcriptomics, epigenetics, and proteomics.

Comprehensive analysis of protein modifications by top-down mass spectrometry

Mass spectrometry (MS)-based proteomics is playing an increasingly important role in cardiovascular research. Proteomics includes identification and quantification of proteins and the characterization of protein modifications, such as posttranslational modifications and sequence variants. The conventional bottom-up approach, involving proteolytic digestion of proteins into small peptides before MS analysis, is routinely used for protein identification and quantification with high throughput and automation. Nevertheless, it has limitations in the analysis of protein modifications, mainly because of the partial sequence coverage and loss of connections among modifications on disparate portions of a protein. An alternative approach, top-down MS, has emerged as a powerful tool for the analysis of protein modifications. The top-down approach analyzes whole proteins directly, providing a "bird's-eye" view of all existing modifications. Subsequently, each modified protein form can be isolated and fragmented in the mass spectrometer to locate the modification site. The incorporation of the nonergodic dissociation methods, such as electron-capture dissociation (ECD), greatly enhances the top-down capabilities. ECD is especially useful for mapping labile posttranslational modifications that are well preserved during the ECD fragmentation process. Top-down MS with ECD has been successfully applied to cardiovascular research, with the unique advantages in unraveling the molecular complexity, quantifying modified protein forms, complete mapping of modifications with full-sequence coverage, discovering unexpected modifications, identifying and quantifying positional isomers, and determining the order of multiple modifications. Nevertheless, top-down MS still needs to overcome some technical challenges to realize its full potential. Herein, we reviewed the advantages and challenges of the top-down method, with a focus on its application in cardiovascular research.

Advancing the sensitivity of selected reaction monitoring-based targeted quantitative proteomics

Selected reaction monitoring (SRM) - also known as multiple reaction monitoring (MRM) - has emerged as a promising high-throughput targeted protein quantification technology for candidate biomarker verification and systems biology applications. A major bottleneck for current SRM technology, however, is insufficient sensitivity for, e.g. detecting low-abundance biomarkers likely present at the low ng/mL to pg/mL range in human blood plasma or serum, or extremely low-abundance signaling proteins in cells or tissues. Herein, we review recent advances in methods and technologies, including front-end immunoaffinity depletion, fractionation, selective enrichment of target proteins/peptides including posttranslational modifications, as well as advances in MS instrumentation which have significantly enhanced the overall sensitivity of SRM assays and enabled the detection of low-abundance proteins at low- to sub-ng/mL level in human blood plasma or serum. General perspectives on the potential of achieving sufficient sensitivity for detection of pg/mL level proteins in plasma are also discussed.

Proteomic analysis of effluents from perfused human heart for transplantation: identification of potential biomarkers for ischemic heart damage

Background

Biomarkers released from the heart at early stage of ischemia are very important to diagnosis of ischemic heart disease and salvage myocytes from death. Known specific markers for blood tests including CK-MB, cardiac troponin T (cTnT) and cardiac troponin I (cTnI) are released after the onset of significant necrosis instead of early ischemia. Thus, they are not good biomarkers to diagnose myocardial injury before necrosis happens. Therefore, in this study, we performed proteomic analysis on effluents from perfused human hearts of donors at different ischemic time.

Results

After global ischemia for 0 min, 30 min and 60 min at 4°C, effluents from five perfused hearts were analyzed respectively, by High performance liquid chromatography-Chip-Mass spectrometry (HPLC-Chip-MS) system. Total 196 highly reliable proteins were identified. 107 proteins were identified at the beginning of ischemia, 174 and 175 proteins at ischemic 30 min and ischemic 60 min, respectively. With the exception of cardiac troponin I and T, all known biomarkers for myocardial ischemia were detected in our study. However, there were four glycolytic enzymes and two targets of matrix metalloproteinase released significantly from the heart when ischemic time was increasing. These proteins were L-lactate dehydrogenase B(LDHB), glyceraldehyde-3-phosphate dehydrogenase, glucose-6-phosphate isomerase (GPI), phosphoglycerate mutase 2 (PGAM2), gelsolin and isoform 8 of titin. PGAM2, LDHB and titin were measured with enzyme-linked immunosorbent assays kits. The mean concentrations of LDHB and PGAM2 in samples showed an increasing trend when ischemic time was extending. In addition, 33% identified proteins are involved in metabolism. Protein to protein interaction network analysis showed glycolytic enzymes, such as isoform alpha-enolase of alpha-enolase, isoform 1 of triosephosphate isomerase and glyceraldehyde-3-phosphate dehydrogenase, had more connections than other proteins in myocardial metabolism during ischemia.

Conclusion

It is the first time to use effluents of human perfused heart to study the proteins released during myocardial ischemia by HPLC-Chip-MS system. There might be many potential biomarkers for mild ischemic injury in myocardium, especially isoform 8 of titin and M-type of PGAM2 that are more specific in the cardiac tissue than in the others. Furthermore, glycolysis is one of the important conversions during early ischemia in myocardium. This finding may provide new insight into pathology and biology of myocardial ischemia, and potential diagnostic and therapeutic biomarkers.

Release of tissue-specific proteins into coronary perfusate as a model for biomarker discovery in myocardial ischemia/reperfusion injury

Diagnosis of acute coronary syndromes is based on protein biomarkers, such as the cardiac troponins (cTnI/cTnT) and creatine kinase (CK-MB) that are released into the circulation. Biomarker discovery is focused on identifying very low abundance tissue-derived analytes from within albumin-rich plasma, in which the wide dynamic range of the native protein complement hinders classical proteomic investigations. We employed an ex vivo rabbit model of myocardial ischemia/reperfusion (I/R) injury using Langendorff buffer perfusion. Nonrecirculating perfusate was collected over a temporal profile of 60 min reperfusion following brief, reversible ischemia (15 min; 15I/60R) for comparison with irreversible I/R (60I/60R). Perfusate proteins were separated using two-dimensional gel electrophoresis (2-DE) and identified by mass spectrometry (MS), revealing 26 tissue-specific proteins released during reperfusion post-15I. Proteins released during irreversible I/R (60I/60R) were profiled using gel-based (2-DE and one-dimensional gel electrophoresis coupled to liquid chromatography and tandem mass spectrometry; geLC-MS) and gel-free (LC-MS/MS) methods. A total of 192 tissue-specific proteins were identified during reperfusion post-60I. Identified proteins included those previously associated with I/R (myoglobin, CK-MB, cTnI, and cTnT), in addition to examples currently under investigation in large cohort studies (heart-type fatty acid binding protein; FABPH). The postischemic release profile of a novel cardiac-specific protein, cysteine and glycine-rich protein 3 (Csrp3; cardiac LIM domain protein) was validated by Western blot analysis. We also identified Csrp3 in serum from 6 of 8 patients postreperfusion following acute myocardial infarction. These studies indicate that animal modeling of biomarker release using ex vivo buffer perfused tissue to limit the presence of obfuscating plasma proteins may identify candidates for further study in humans.

Rationale and design of the Leipzig (LIFE) Heart Study: phenotyping and cardiovascular characteristics of patients with coronary artery disease

Objective

We established the Leipzig (LIFE) Heart Study, a biobank and database of patients with different stages of coronary artery disease (CAD) for studies of clinical, metabolic, cellular and genetic factors of cardiovascular diseases.

Design

The Leipzig (LIFE) Heart Study (NCT00497887) is an ongoing observational angiographic study including subjects with different entities of CAD. Cohort 1, patients undergoing first-time diagnostic coronary angiography due to suspected stable CAD with previously untreated coronary arteries. Cohort 2, patients with acute myocardial infarction (MI) requiring percutaneous revascularization. Cohort 3, patients with known left main coronary artery disease (LMCAD).

Results

We present preliminary results of demographics and phenotyping based on a 4-years analysis of a total of 3,165 subjects. Cohort 1 (n = 2,274) shows the typical distribution of elective coronary angiography cohorts with 43% cases with obstructive CAD and 37% normal angiograms. Cohorts 2 and 3 consist of 590 and 301 subjects, respectively, adding patients with severe forms of CAD. The suitability of the database and biobank to perform association studies was confirmed by replication of the CAD susceptibility locus on chromosome 9p21 (OR per allele: 1.55 (any CAD), 1.54 (MI), 1.74 (LMCAD), p<10⁻⁶, respectively). A novel finding was that patients with LMCAD had a stronger association with 9p21 than patients with obstructive CAD without LMCAD (OR 1.22, p = 0.042). In contrast, 9p21 did not associate with myocardial infarction in excess of stable CAD.

Conclusion

The Leipzig (LIFE) Heart Study provides a basis to identify molecular targets related to atherogenesis and associated metabolic disorders. The study may contribute to an improvement of individual prediction, prevention, and treatment of CAD.

Proteomic analysis of plasma samples from patients with acute myocardial infarction identifies haptoglobin as a potential prognostic biomarker

Prognosis of clinical outcome following myocardial infarction is variable and difficult to predict. We have analyzed the plasma proteome of thirty patients with acute myocardial infarction to search for new prognostic biomarkers. Proteomic analyses of blood samples were performed by 2-D-DiGE after plasma depletion of albumin and immunoglobulins G. New York Heart Association (NYHA) class determined at 1-year follow-up was used to identify patients with heart failure. Principal component analysis and hierarchical clustering of proteomic data revealed that patients could be separated into 3 groups. The 22 differentially expressed proteins involved in this grouping were identified as haptoglobin (Hp) and respective isoforms. The 3 groups of patients had distinct Hp isoforms: patients from group 1 had the α1-α1, patients from group 2 the α2-α1, and patients from group 3 the α2-α2 genotype. This classification was also associated with different total plasma levels of Hp. The presence of the α2 genotype and low plasma levels of Hp was associated with a higher NYHA class and therefore with a detrimental functional outcome after myocardial infarction. A plasma level of Hp below 1.4g/L predicted the occurrence of heart failure (NYHA 2, 3, 4) at 1-year with 100% sensitivity.

A pipeline that integrates the discovery and verification of plasma protein biomarkers reveals candidate markers for cardiovascular disease

We developed a pipeline to integrate the proteomic technologies used from the discovery to the verification stages of plasma biomarker identification and applied it to identify early biomarkers of cardiac injury from the blood of patients undergoing a therapeutic, planned myocardial infarction (PMI) for treatment of hypertrophic cardiomyopathy. Sampling of blood directly from patient hearts before, during and after controlled myocardial injury ensured enrichment for candidate biomarkers and allowed patients to serve as their own biological controls. LC-MS/MS analyses detected 121 highly differentially expressed proteins, including previously credentialed markers of cardiovascular disease and >100 novel candidate biomarkers for myocardial infarction (MI). Accurate inclusion mass screening (AIMS) qualified a subset of the candidates based on highly specific, targeted detection in peripheral plasma, including some markers unlikely to have been identified without this step. Analyses of peripheral plasma from controls and patients with PMI or spontaneous MI by quantitative multiple reaction monitoring mass spectrometry or immunoassays suggest that the candidate biomarkers may be specific to MI. This study demonstrates that modern proteomic technologies, when coherently integrated, can yield novel cardiovascular biomarkers meriting further evaluation in large, heterogeneous cohorts.

Divide and conquer: the application of organelle proteomics to heart failure

Chronic heart failure is a worldwide cause of mortality and morbidity and is the final outcome of a number of different etiologies. This reflects both the complexity of the disease and our incomplete understanding of its underlying molecular mechanisms. One experimental approach to address this is to study subcellular organelles and how their functions are activated and synchronized under physiological and pathological conditions. In this review, we discuss the application of proteomic technologies to organelles and how this has deepened our perception of the cellular proteome and its alterations with heart failure. The use of proteomics to monitor protein quantity and posttranslational modifications has revealed a highly intricate and sophisticated level of protein regulation. Posttranslational modifications have the potential to regulate organelle function and interplay most likely by targeting both structural and signaling proteins throughout the cell, ultimately coordinating their responses. The potentials and limitations of existing proteomic technologies are also discussed emphasizing that the development of novel methods will enhance our ability to further investigate organelles and decode intracellular communication.

Computational models reduce complexity and accelerate insight into cardiac signaling networks

Cardiac signaling networks exhibit considerable complexity in size and connectivity. The intrinsic complexity of these networks complicates the interpretation of experimental findings. This motivates new methods for investigating the mechanisms regulating cardiac signaling networks and the consequences these networks have on cardiac physiology and disease. Next-generation experimental techniques are also generating a wealth of genomic and proteomic data that can be difficult to analyze or interpret. Computational models are poised to play a key role in addressing these challenges. Computational models have a long history in contributing to the understanding of cardiac physiology and are useful for identifying biological mechanisms, inferring multiscale consequences to cell signaling activities and reducing the complexity of large data sets. Models also integrate well with experimental studies to explain experimental observations and generate new hypotheses. Here, we review the contributions computational modeling approaches have made to the analysis of cardiac signaling networks and forecast opportunities for computational models to accelerate cardiac signaling research.

Combinatorial peptide libraries facilitate development of multiple reaction monitoring assays for low-abundance proteins

Low-abundance proteins present in biological fluids are often considered an attractive source of new disease biomarkers. Since such proteins are poorly observed in proteome-scale discovery experiments due to an overwhelming mass of high-abundance proteins, the development of quantitative multiple reaction monitoring (MRM) assays for low-abundance proteins is a challenging task. Here, we present a strategy that facilitates the development of MRM assays for large numbers of unpurified low-abundance proteins. Our discovery strategy is based on the reduction of the dynamic range of protein concentrations in biological fluids by means of one-bead one-compound combinatorial peptide libraries (CPL). Our 2D-LC-MS/MS approach allowed us to identify a total of 484 unique proteins in ovarian cancer ascites, and 216 proteins were assigned as low-abundance ones. Interestingly, 74 of those proteins have never been previously described in ascites fluid. Treatment with CPL allowed identification of a significantly higher number of unique peptides for low-abundance proteins and provided important empirical fragmentation information for development of MRM assays. Finally, we confirmed that MRM assays worked for 30 low-abundance proteins in the unfractionated ascites digest. Using a multiplexed MRM method, relative amounts of five proteins (kallikrein 6, metalloproteinase inhibitor 1, macrophage migration inhibitory factor, follistatin-related protein, and mesothelin) were determined in a set of ovarian cancer ascites. Multiplexed MRM assays targeting large numbers of proteins can be used to develop comprehensive panels of biomarkers with high sensitivity and selectivity, and to study complex protein networks.

Proteomics and mass spectrometry: what have we learned about the heart?

The emergence of new platforms for the discovery of innovative therapeutics has provided a means for diagnosing cardiac disease in its early stages. Taking into consideration the global health burden of cardiac disease, clinicians require innovations in medical diagnostics that can be used for risk stratification. Proteomic based studies offer an avenue for the discovery of proteins that are differentially regulated during disease; such proteins could serve as novel biomarkers of the disease state. For instance, in clinical practice, the abundance of such biomarkers in blood could be correlated with the severity of the disease state. As such, early detection of biomarkers would enable an improvement in patient prognosis. In this review, we outline advancements in various proteomic platforms used to study the disease proteome and their applications to the field of clinical medicine. Specifically, we highlight the contributions of proteomic-based profiling experiments to the analysis of cardiovascular diseases.

Decreased serine207 phosphorylation of troponin T as a biomarker for left ventricular remodelling after myocardial infarction

AIMS

Chronic heart failure following myocardial infarction (MI) is characterized by progressive left ventricular remodelling (LVR). Despite significant improvements in MI management, LVR remains a frequent complication. Although several risk factors have been identified, such as infarct size, LVR is difficult to predict in clinical practice.

METHODS AND RESULTS

Using a rat model of MI and phosphoproteomic technology, we discovered that remodelling is associated with decreased levels of myocardial and plasma serine(208)-phosphorylated troponin T (TnT). To confirm the association in human plasma, we developed new specific polyclonal antibodies against human/rat serine(207/208)-phosphorylated TnT and tested plasma obtained in the first week after MI from patients with low, intermediate, and high remodelling a year later. We observed a significant decrease of serine(207)-phosphorylated TnT and of the serine(207)-phosphorylated TnT/total TnT ratio in those with intermediate or high LVR. These differences remained statistically significant when adjusted for other determinants of LVR. In contrast, baseline B-type natriuretic peptide levels were not associated with LVR.

CONCLUSION

The level of circulating phosphorylated TnT could be a new biomarker of LVR.

Identification of cardiac myosin-binding protein C as a candidate biomarker of myocardial infarction by proteomics analysis

Acute myocardial infarction (AMI) is a common cause of death for which effective treatments are available provided that diagnosis is rapid. The current diagnostic gold standards are circulating cardiac troponins I and T. However, their slow release delays diagnosis, and their persistence limits their utility in the identification of reinfarction. The aim was to identify candidate biomarkers of AMI. Isolated mouse hearts were perfused with oxygenated protein-free buffer, and coronary effluent was collected after ischemia or during matched normoxic perfusion. Effluents were analyzed using proteomics approaches based on one- or two-dimensional initial separation. Of the 459 proteins identified after ischemia with one-dimensional separation, 320 were not detected in the control coronary effluent. Among these were all classic existing biomarkers of AMI. We also identified the cardiac isoform of myosin-binding protein C in its full-length form and as a 40-kDa degradation product. This protein was not detected in the other murine organs examined, increased markedly with even trivial myocardial infarction, and could be detected in the plasma after myocardial infarction in vivo, a profile compatible with a biomarker of AMI. Two-dimensional fluorescence DIGE of ischemic and control coronary effluents identified more than 200 asymmetric spots verified by swapping dyes. Once again existing biomarkers of injury were confirmed as well as posttranslational modifications of antioxidant proteins such as peroxiredoxins. Perfusing hearts with protein-free buffers provides a platform of graded ischemic injury that allows detailed analysis of protein release and identification of candidate cardiac biomarkers like myosin-binding protein C.

Integrating post-genomic approaches as a strategy to advance our understanding of health and disease

Following the publication of the complete human genomic sequence, the post-genomic era is driven by the need to extract useful information from genomic data. Genomics, transcriptomics, proteomics, metabolomics, epidemiological data and microbial data provide different angles to our understanding of gene-environment interactions and the determinants of disease and health. Our goal and our challenge are to integrate these very different types of data and perspectives of disease into a global model suitable for dissecting the mechanisms of disease and for predicting novel therapeutic strategies. This review aims to highlight the need for and problems with complex data integration, and proposes a framework for data integration. While there are many obstacles to overcome, biological models based upon multiple datasets will probably become the basis that drives future biomedical research.