The complexity of higher organisms is regulated through the coordinated control of biological networks, including miRNAs. Computational networks have previously been generated using miRNA array data. For the first time, we have applied a similar approach to circulating miRNAs.
The complexity of higher organisms is regulated through the coordinated control of biological networks, including miRNAs. Computational networks have previously been generated using miRNA array data. For the first time, we have applied a similar approach to circulating miRNAs. In such networks, nodes represent distinct miRNAs while edges represent computationally derived relationships, such as co-expression, between miRNA pairs. A network-driven analysis offers additional insights compared with studying up- or down-regulation of individual miRNAs alone. First, availability of graph clustering algorithms such as Markov clustering allows identification of miRNA modules that are likely to be involved in the same biological process or delineation of proximal miRNA pairs. Second, integration of differential expression profiling with network topology contribution of a single miRNA to the stability of the co-expression network may provide a more reliable alternative to target prioritization and subsequent validation. Finally, representation of biological data as networks of relationships facilitates data integration across multiple levels of biological complexity and may define contribution of specific miRNAs to systems-wide properties of disease. While network biology is a powerful approach for studying relationships between genes, proteins, and diseases, its application in the biomarker field is at an early stage and further analytical and experimental efforts will be needed to substantiate its utility. Network inference algorithms could be particularly useful to characterize circulating miRNA networks, to unravel rewiring of miRNA profiles under pathological conditions and to test for potential associations of miRNA clusters with disease endpoints. There are currently no good soluble biomarkers, which could be used to accurately identify subjects who are at risk of developing acute manifestations of cardiovascular disease. Despite extensive studies and development of several risk prediction models, traditional risk factors fail to predict cardiovascular events in a large group of cases (25-50%). Inflammatory markers such as high-sensitivity C-reactive protein are widely used but lack specificity for the vasculature. Advanced imaging techniques are expensive and not suitable for population-wide screening. Besides, atherosclerosis is a diffuse disorder with various local and systemic manifestations. Circulating miRNAs could be attractive biomarkers. They are easily accessible, relatively stable and in some instances tissue specific. On the other hand, the point of care test for cTnI and cTnT can already provide results within minutes of bleeding the patient. Laboratory-based assays such as hsTnT are faster than qPCR-based assays, and the latter will not be of clinical value unless they outperform existing tests for myocardial injury. However, qPCR-based assays are used routinely for the diagnosis of viral infections. Undoubtedly, there is a clinical need for biomarkers of vascular injury that could complement the assessment of traditional risk factors in monitoring vascular health and stratifying patients according to risk and treatment response. It remains to be seen whether miRNAs can fulfil this need and improve risk prediction. At least, circulating miRNAs may offer new insights into the mechanisms and systemic manifestations of cardiovascular disease. Source: Anna Zampetaki, Peter Willeit, Ignat Drozdov, Stephan Kiechl, Manuel Mayr