Molecular signatures and the study of gene expression profiles in inflammatory heart diseases

Department of Internal Medicine - Cardiology, Biomedical Research Center, University Hospital Giessen and Marburg and Philipps-University Marburg, Hans-Meerwein-Str., 35043, Marburg, Germany, .
Herz (Impact Factor: 0.69). 08/2012; 37(6):619-626. DOI: 10.1007/s00059-012-3662-5
Source: PubMed


Myocarditis, a common heart disease pathologically defined as an inflammatory reaction of the myocardium, is most frequently caused by infectious agents, including viruses and bacteria, and may develop in later stages into dilated cardiomyopathy (DCM). Several studies have identified inflammatory components engaged in the transition from acute myocarditis to chronic DCM, and there is growing evidence that myocarditis and DCM are closely related. Novel technological advances in genomic screening have gained insight into molecular and cellular mechanisms involved the pathogenesis of inflammatory heart disease and, in particular, in the development of systolic dysfunction resulting from DCM. Detection of differential gene expression profiles have become valid tools in the study of inflammatory heart disease. Molecular signatures are defined as individual sets of genes, mRNA transcripts, proteins, genetic variations or other variables, which can be used as markers for a particular phenotype. These signatures may be useful for clinical diagnosis or risk assessment and, in addition, may help to identify molecules not previously known to be involved in the pathogenesis of these disease conditions.
Microarray analyses have dramatically refined our knowledge about tissue-specific gene expression patterns, simply by being able to study thousands of genes simultaneously in a single experiment. In the field of cardiovascular research, microarrays are increasingly used in the study of end-stage cardiomyopathies, such as DCM, that ultimately lead to symptoms of heart failure. By means of microarray analysis, a set of differentially expressed genes can be detected, among them are transcripts coding for sarcomeric and extracellular matrix proteins, stress response and inflammatory proteins as well as transcription factors and translational regulators. Expression profiling may be particularly helpful to improve the differential diagnosis of heart failure and enable novel insight into selected molecular pathways.

1 Follower
11 Reads
  • Source
    • "The transcriptome is the full complement of RNA produced in response to signaling cues processed by, and transcribed from, the underlying genome, and technology and methods employed for genomic deconstruction are applicable to transcriptome resolution. Comprehensive transcript analysis is an attractive option for biomarker identification, as panels of differentially expressed genes (DEGs) are used to establish indices of disease progression (35). Prioritized gene lists can be further analyzed for gene ontology enrichment and bionetwork analyses to respectively identify and quantitate the molecular gestalt underlying normal or diseased phenotype progression (36). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Development of innovative high throughput technologies has enabled a variety of molecular landscapes to be interrogated with an unprecedented degree of detail. Emergence of next generation nucleotide sequencing methods, advanced proteomic techniques, and metabolic profiling approaches continue to produce a wealth of biological data that captures molecular frameworks underlying phenotype. The advent of these novel technologies has significant translational applications, as investigators can now explore molecular underpinnings of developmental states with a high degree of resolution. Application of these leading-edge techniques to patient samples has been successfully used to unmask nuanced molecular details of disease vs healthy tissue, which may provide novel targets for palliative intervention. To enhance such approaches, concomitant development of algorithms to reprogram differentiated cells in order to recapitulate pluripotent capacity offers a distinct advantage to advancing diagnostic methodology. Bioinformatic deconvolution of several "-omic" layers extracted from reprogrammed patient cells, could, in principle, provide a means by which the evolution of individual pathology can be developmentally monitored. Significant logistic challenges face current implementation of this novel paradigm of patient treatment and care, however, several of these limitations have been successfully addressed through continuous development of cutting edge in silico archiving and processing methods. Comprehensive elucidation of genomic, transcriptomic, proteomic, and metabolomic networks that define normal and pathological states, in combination with reprogrammed patient cells are thus poised to become high value resources in modern diagnosis and prognosis of patient disease.
    Full-text · Article · Aug 2013 · Croatian Medical Journal

  • No preview · Article · Nov 2012 · Herz
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Background: Myocarditis is an inflammatory disease of the cardiac muscle and is mainly caused by viral infections. Viral myocarditis has been proposed to be divided into 3 phases: the acute viral phase, the subacute immune phase, and the chronic cardiac remodeling phase. Although individualized therapy should be applied depending on the phase, no clinical or experimental studies have found biomarkers that distinguish between the 3 phases. Theiler's murine encephalomyelitis virus belongs to the genus Cardiovirus and can cause myocarditis in susceptible mouse strains. Methods and results: Using this novel model for viral myocarditis induced with Theiler's murine encephalomyelitis virus, we conducted multivariate analysis including echocardiography, serum troponin and viral RNA titration, and microarray to identify the biomarker candidates that can discriminate the 3 phases. Using C3H mice infected with Theiler's murine encephalomyelitis virus on 4, 7, and 60 days post infection, we conducted bioinformatics analyses, including principal component analysis and k-means clustering of microarray data, because our traditional cardiac and serum assays, including 2-way comparison of microarray data, did not lead to the identification of a single biomarker. Principal component analysis separated heart samples clearly between the groups of 4, 7, and 60 days post infection. Representative genes contributing to the separation were as follows: 4 and 7 days post infection, innate immunity-related genes, such as Irf7 and Cxcl9; 7 and 60 days post infection, acquired immunity-related genes, such as Cd3g and H2-Aa; and cardiac remodeling-related genes, such as Mmp12 and Gpnmb. Conclusions: Sets of molecules, not single molecules, identified by unsupervised principal component analysis, were found to be useful as phase-specific biomarkers.
    Full-text · Article · Jul 2014 · Circulation Cardiovascular Genetics