Introduction
Atherosclerotic cardiovascular disease (CVD) involves peripheral circulation, the brain, and the heart. The disease is a primary health issue in many countries around the world (Sawhney, et al., 2012). CVD develops through complex processes involving novel risk markers and common risk factors. Decades of research have demonstrated that a sibling’s or parent’s family history is linked to the development of atherosclerotic CVD (Gupta et al., 2013; Nelson, 2013; Imes and Lewis, 2014), which manifests as peripheral arterial disorder, stroke, and coronary heart disorder (CHD). Various Mendelian disorders have also been shown to contribute to atherosclerotic CVD. Although mutations occur rarely, they may have a significant relative risk (Arnett et al., 2007). In the last century, considerable progress has been made to reduce mortality from CVD. The advances have resulted from the identification of CVD’s risk factors, treatment of the factors, and amelioration of care for patients suffering from acute syndromes (Palomaki et al., 2010). In spite of the successes, cardiovascular disease is expected to be a leading cause of morbidity and death worldwide (Kullo and Cooper, 2010). Initially, CHD manifests as a devastating condition, such as myocardial infarction or sudden death, and many patients present with symptoms of an acute coronary syndrome at relatively younger ages or without the involvement of common risk factors. In particular, early-onset CHD occurs frequently in urban areas of developing nations, especially in the Southeast Asia (Krishnan, 2012). Therefore, it is imperative to refine the methods of early detection in order to treat cardiovascular disease in time and ameliorate global health.
Predicting cardiovascular events focuses on risk factors such as smoking, dyslipidemia, male sex, and age. Since such risk factors can be prevalent in the population, conventional algorithms for risk-prediction have an inadequate accuracy (Kullo and Cooper, 2010). Consequently, there is an urgent need for new tools that can predict a person’s risk of developing CVD. Typically, cardiovascular disease in humans results from an interplay of various environmental, epigenetic, and genetic factors. Traditionally, the resulting phenotypes have been described using different imaging techniques, biochemical markers, and clinical descriptors. Current technologies in cardiology attempt to prevent illness by altering established risk elements and treating the manifest disease (Ordovas and Smith, 2010). Sometimes, the techniques are employed after the development of complications and the occurrence of irreversible tissue modifications. Furthermore, health care professionals administer modern therapies uniformly across the heterogeneous spectrum associated with genetic background, disease severity, and etiology (Ouzounian et al., 2007). While large-scale platforms of investigation become increasingly mainstream, medical professionals have the unique opportunity to increase their medical armamentarium and incorporate molecular phenotypes for distinguishing subtle disease sub-classifications and allowing improvements in the tailoring of therapeutics and prevention strategies.
Although drugs for treating risk factors like hypertension and dyslipidemia have been introduced over the last few decades, identifying new biomarkers to ensure timely detection of CVD has continued to lag behind. Recently, considerable progress has occurred in the areas of proteomics and genomics. According to Kullo and Cooper (2010), the advancement will most likely result in a more accurate prediction of cardiovascular risk. As such, an understanding of the variants of genetic susceptibility and novel plasma markers may offer a more precise approximation of cardiovascular risk. Moreover, it may help in defining pathways that are perturbed in each patient and lead to the identification of targets for clinical intervention. Ultimately, the approach may allow healthcare practitioners to individualize the care of patients with CVD. Accordingly, the present study discusses the role of proteomics and genomics in the diagnosis of CVD, as well as the assessment of cardiovascular risk. Furthermore, the investigation examines the promotion of the understanding of CVD through advances in the two fields of proteomics and genomics. It is hypothesized that the application of proteomics and genomics to new diagnostic tools has the potential to refine the assessment of cardiovascular risk, allow timely intervention, and lead to significant improvements in global health.
Genomics and Proteomics as Tools for Profiling CVD
Genomics can be described as the characterization of the human genome with the primary goal of determining the DNA sequence and the links between different areas of the genome. Variations in the genome or genetic repertoire, in addition to interactions with environmental factors, lead to clinical, physiological, biochemical, and molecular manifestations described as the phenome. Research has demonstrated that the product of environment and genetic interaction, which influence protein-protein interactions, makes considerable contributions to the phenome (Piran et al., 2012). The parameters progress rapidly when environmental impacts on the genome create additional stress, irregular protein production, and structural instability of heart chambers. Such processes are often in their advanced stages when symptoms of CVD become evident. Hence, various genetic biomarkers can be employed to determine a person’s genetic predisposition and delineate the progression of CVD (Fig. 1). Imaging technologies can also detect cardiac functional and structural alterations as the disorder progresses. Such changes occur prior to the onset of disease symptoms. Therefore, the use of genomics can allow early intervention.
Figure 1. Factors Associated with Disease Progression (Source: Piran et al., 2012). The figure shows how biomarkers reflect disease phenotype, as well as the interaction between environmental and genetic factors, during the progression of cardiovascular disease.
Presently, genomic research has made rapid progress in the area of cardiovascular medicine. For example, the recent “completion of the Human Genome Project” has created a high expectation for significant “insights into the polygenetic architecture of complex human diseases,” including CVD (Schnabel et al., 2012). Genome-wide investigations utilizing unbiased approaches have also generated valuable insights into the complex processes involved in the development of CVD. In particular, studies have demonstrated that family history is a personalized and well-proven genomic tool for capturing environmental and gene interactions, as well as allowing the personalization of disease prevention or treatment (Schnabel et al., 2012). Generally, CVD has a major heritable component with a high-risk family making up a significant proportion of the early cases of CVD (Lara-Pezzi et al., 2012). For instance, a “history of early CHD in a first-degree relative approximately doubles the risk of CHD, although the reported relative risk ranges from 1.3 to 11.3” (Kullo and Cooper, 2010). CHD’s familial clustering can be explained partly by the heritable variations in known risk factors for CHD (Kullo and Ding, 2007), which can then be quantified using current algorithms of risk-prediction. Nevertheless, existing evidence indicates that a family history makes risk contributions that are independent of conventional risk factors. Therefore, researchers have suggested that numerous undiscovered variants of genetic susceptibility, which mediate CHD’s familial clustering, exist (Kullo and Cooper, 2010). Consequently, agnostic and candidate-gene genomic approaches may assist in identifying the genetic variants associated with a person’s susceptibility to the disease (Padmanabhan et al., 2010).
Several companies are already marketing the “genotyping of disease-susceptibility variants to the public” (Kullo and Cooper, 2010). After genotyping, consumers receive reports showing estimates of their cardiovascular risk. Given the increase in genetic testing with results being sent directly to consumers, there is a growing need to examine how predictive risk assessment can be integrated into clinical practice (Sawhney et al., 2012). Additionally, although genetic evaluation may enhance the accuracy of a person’s risk profile and promote early diagnosis of CVD, research has not yet determined whether the testing will ameliorate outcomes (Kullo and Cooper, 2010; Wung et al., 2013). Furthermore, the expectations that single protein markers will provide significant incremental information for promoting the early diagnosis of CVD are unrealistic (Kim et al., 2010). Since CVD is often a complex disorder, a multimarker strategy can yield more information than methods that utilize single markers. For instance, a multimarker technique can identify perturbations in CVD’s etiologic pathways. Nonetheless, unbiased approaches are necessary to enhance the identification of novel biomarkers that show no correlation with known candidate markers but influence cardiovascular risk. One such approach may involve the application of proteomics to biomarker discovery. Typically, proteomics refers to the examination of a genome’s protein complement or the proteome. The proteome includes every protein that occurs in tissues. On average, human genome has 5 to 7 protein isoforms in each open reading frame. Researchers have confirmed that about “30,000 human genes encode for nearly one million proteins” (Ouzounian et al., 2007). Also, alternative splicing makes a significant contribution to the diversity of proteins and occurs in about 36 to 60 percent of human genes. Other mechanisms involved in the process include the editing of the pre-messenger RNA, polyadenylation, and the utilization of transcription start sites. Protein diversity is also compounded by post-translational modifications of proteins. The alterations include N-myristylation, prenylation, acetylation, carboxymethylation, N-methylation, hydroxylation, glycosylation, sulphation, and phosphorylation (Ouzounian et al., 2007).
Proteins influence nearly all cellular functions and, hence, dictate the phenotypes of particular organs, tissues, or cells. Accordingly, they represent a person’s genetic composition, as well as gene-environment and gene-gene interactions. Proteomes vary under different physiological conditions and show pronounced modifications in aging. Often, chronic illnesses alter protein levels through processes such as “specific gene up- or down-regulation, isoform switching, or de novo protein synthesis” (Ouzounian et al., 2007). Usually, the period of acute illness is insufficient to disrupt synthetic protein machinery. Therefore, the most frequently observed protein alteration mechanisms are post-translational alterations.
Several techniques have been developed to analyze the proteome. For example, the introduction of “two-dimensional polyacrylamide gel electrophoresis (2-DE)” nearly thirty years ago allowed the concurrent separation of large protein numbers by both isoelectric points and molecular mass (Ouzounian et al., 2007). However, the use of 2-DE to separate membrane-associated or low-abundance proteins can be difficult. Complementary approaches employ “isotope-coded affinity tags, which selectively label cysteine residues of peptide fragments following tryptic digest of the protein sample” (Ouzounian et al., 2007). Consequently, smaller peptides are detected easily, and protein composition changes are quantified effectively. Gel-free techniques may include approaches that utilize “tryptic digests of complex sets of proteins, followed by peptide sequencing in a tandem mass spectrometer for identification” (Ouzounian et al., 2007). In recent years, however, researchers have employed advanced systems of “liquid chromatography-mass spectroscopy” to determine the abundance of numerous proteins (Ouzounian et al., 2007). Although proteomic technology has progressed rapidly in recent decades, several areas still require improvement. In particular, there is a need to improve the identification, quantification, and detection of proteins that have low abundance. Furthermore, the distribution of proteins in subcellular structures and tissues should be assessed more effectively. Post-translational changes also require better characterization.
Use of Proteomics and Genomics in New Diagnostic Tools
Proteomic and genomic approaches offer complementary and valuable insights into the pathways of cardiovascular disease. Traditionally, genetic disorders like Marfan syndrome and cystic fibrosis have been described as single-gene and early-onset entities that an offspring inherits from its parent (Ouzounian et al., 2007). For example, in 50% of the cases of hypertrophic cardiomyopathy, the offspring inherits the disease as autosomal dominant traits. Similarly, researchers have identified the monogenic causes of numerous other cardiovascular diseases, including atrial fibrillation (Lubitz et al., 2010; Mahida et al., 2011; Tucker and Ellinor, 2014), arrhythmogenic cardiomyopathy (Cahill et al., 2013), long QT disorder (Schwartz et al., 2012), dilated cardiomyopathy (Dellefave and McNally, 2010), coronary disease (Sasidhar et al., 2014; Dai et al., 2016), and dyslipidemia (Weissglas-Volkov and Pajukanta, 2010; Larach et al., 2013). Basically, humans are often distinguished from each other by about 0.1 percent difference in their genome’s nucleotide sequence. Usually, the differences occur as variations in a base pair identified as single nucleotide polymorphism (SNP). SNPs are known to generate only minor changes in protein function or concentration (Ouzounian et al., 2007). Thus, the simultaneous presence of several SNPs determines a person’s susceptibility to the development of diseases like polygenic syndromes. Evidence has further demonstrated the occurrence of complex diseases that lack monogenic transmission (Ouzounian et al., 2007). Such conditions include heterogeneous disorders like atherosclerosis, myocardial infarction, and heart failure. In some genetic syndromes, genotypes confer disease susceptibility, but the progression or development of the disease depends on gene interactions with each other and the environment. Although the phenotype-genotype correlation is sometimes unclear, incorporating genomic and proteomic data can elucidate the correlation while accounting for environmental and epigenetic influences (Ouzounian et al., 2007).
Proteomic investigations have extended and confirmed the results of their functional and stable genomic studies. In particular, proteomic analyses have been carried out on samples of cardiac biopsy to identify the novel markers associated with the rejection of cardiac allografts. In addition, up-regulated proteins such as alphaB-crystallin and tropomyosin have been confirmed in the sera of patients (Ouzounian et al., 2007). Also, studies involving models of ischemia-reperfusion have reported significant alterations in functional groups, especially stress response, energy metabolism, and redox regulation, as well as cytoskeletal and sarcomeric proteins. Researchers have further performed proteomic analyses of “pharmacological protection to ischemia-reperfusion injury” to gain further insight into preconditioned phenotypes (Ouzounian et al., 2007). The studies have revealed that most proteins take part in the energetics of mitochondria. Other investigations have characterized cardiac 26S proteasomes and offered information that is essential to the understanding of the important system of protein degradation in myocardia (Ouzounian et al., 2007).
Role of Genomics and Proteomics in the Personalization of Care
Proteomic data and genome sequences are becoming increasingly available to investigators for a variety of pathologies and normal genes. Characterization of such proteomes and genomes is often crucial for the individualization of therapeutic interventions. For example, the provision of personalized care to cardiovascular patients involves the utilization of information regarding the genetic makeup of individuals to facilitate the tailoring of strategies for detecting, treating, and preventing cardiovascular disease (Ouzounian et al., 2007). The use of new diagnostic tools can allow healthcare professionals to intervene at earlier phases of disease development or progression, as well as ensure the implementation of tailored therapies that are designed for a particular individual. Thus, personalized medicine requires the integration of molecular phenotyping, dynamic and stable genomics, and clinical information (Ouzounian et al., 2007; Lee et al., 2012). Subsequently, the data can be translated into valuable applications using bioinformatics and, hence, lead to improved treatment, prognostication, prediction, and diagnosis (Fig. 2).
Figure 2. Requirements for Personalized Medicine (Source: Ouzounian et al., 2007). Integrating molecular phenotyping and clinical information, as well as dynamic and stable genomics, facilitates the development of individualized medicine.
Therefore, personalized cardiovascular medicine needs multidisciplinary approaches involving different teams that work together (Fig. 3). The methods can help to address major challenges that the medical profession continues to face. For instance, modern healthcare models are considered to be inefficient, reactive, and expensive, especially due to their focus on “‘one-size-fits-all’ treatments for events of late stage diseases” (Lee et al., 2012). There are also issues in the determination of treatment or risk effects using proteomic and genomic techniques (Roberts et al., 2013). Another challenge that confronts the health care profession is the development of analytical platforms and informatics for arranging putative risk markers into clinically useful panels (Ouzounian et al., 2007). An ideal multimarker panel or biomarker would enhance diagnosis, offer independent prognostic data, and provide a reflection of ongoing modifications in the clinical condition of a patient. Ideal biomarkers would also guide the management of cardiovascular disease and ameliorate clinical outcomes. Segmenting complex illnesses into simple sub-classifications using signature profiles and novel biomarkers is an effective approach that is expected to improve the understanding of many common diseases (Ouzounian et al., 2007). The method will facilitate the separation of mechanistically similar diseases into distinct groups during their early stages of pathophysiological progression.
Figure 3. Application of Genomics-based Information to Personalized Cardiovascular Medicine (Source: Lee et al., 2012). During a chronic disease’s time course (line), treatment occurs at the “typical current intervention” point, which represents the phase of disease progression that is extremely difficult to treat. The figure also shows that novel biomarkers based on genomics can facilitate the identification of baseline risks or an early initiating event in the disease.
The largest area of clinical utilization of genomics and proteomics is pharmacotherapy, where the ultimate objective involves maximizing response and minimizing toxicity. Genotype-based pharmacotherapies have the potential to improve drug safety and efficacy significantly (Johnson and Cavallari, 2013; Rincon et al., 2015). Also, molecularly targeted interventions may have a more direct impact on responsive subgroups. In addition to therapies that target individual molecules, the use of proteomic profiles has the potential to reveal different protein nodes in the interconnected pathways associated with disease-causing aberrant signaling. The information may allow the identification of multiple pharmacological targets that can be used to shut down aberrant signaling in causal pathways. Such multipronged approach can address redundancies that are common in various biological systems (Ouzounian et al., 2007). Therefore, future therapeutic approaches may target entire pathways instead of individual proteins or genes. Genomic and proteomic profiling may also predict or monitor drug reactions more closely. Accordingly, improving the understanding of genetic variations that create differences in pharmacokinetics and pharmacodynamics will be a critical step towards the personalization of therapies.
Conclusion
This study has supported the view that the application of proteomics and genomics to new diagnostic tools has the potential to refine the assessment of cardiovascular risk, allow timely intervention, and lead to significant improvements in global health. Such advances are likely to identify new risk factors, as well as promote the development of targeted therapy and personalized risk stratification (Kullo and Cooper, 2010). Furthermore, risk assessment in each person can be carried out using tests for proteomic and genomic markers and allow the implementation of therapeutic and preventive measures based on the risk profile of an individual. Genomic and proteomic profiling may also facilitate the monitoring and prediction of adverse drug reactions. Thus, proteomics and genomics offer complementary and valuable insights into the pathways of CVD. As a result, they play crucial roles in risk assessment, diagnosis, and management of CVD.
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