Abstract
BackgroundMultifactorial diseases such as type 2 diabetes mellitus (T2DM), are driven by a complex network of interconnected mechanisms that translate to a diverse range of complications at the physiological level. To optimally treat T2DM, pharmacological interventions should, ideally, target key nodes in this network that act as determinants of disease progression.ResultsWe set out to discover key nodes in molecular networks based on the hepatic transcriptome dataset from a preclinical study in obese LDLR-/- mice recently published by Radonjic et al. Here, we focus on comparing efficacy of anti-diabetic dietary (DLI) and two drug treatments, namely PPARA agonist fenofibrate and LXR agonist T0901317. By combining knowledge-based and data-driven networks with a random walks based algorithm, we extracted network signatures that link the DLI and two drug interventions to dyslipidemia-related disease parameters.ConclusionsThis study identified specific and prioritized sets of key nodes in hepatic molecular networks underlying T2DM, uncovering pathways that are to be modulated by targeted T2DM drug interventions in order to modulate the complex disease phenotype.
Highlights
Multifactorial diseases such as type 2 diabetes mellitus (T2DM), are driven by a complex network of interconnected mechanisms that translate to a diverse range of complications at the physiological level
To improve our understanding and ability to intervene with complex multifactorial diseases such as type 2 diabetes mellitus (T2DM) it is important to investigate the molecular networks underlying the biological system and elucidate which and how interactions within this system contribute to pathology [1]
We included 16 measured disease parameters consisting of parameters relevant for insulin resistance, body and organ weights, atherosclerotic lesion area, plasma cholesterol, and plasma and liver triglycerides
Summary
Multifactorial diseases such as type 2 diabetes mellitus (T2DM), are driven by a complex network of interconnected mechanisms that translate to a diverse range of complications at the physiological level. To improve our understanding and ability to intervene with complex multifactorial diseases such as type 2 diabetes mellitus (T2DM) it is important to investigate the molecular networks underlying the biological system and elucidate which and how interactions within this system contribute to pathology [1] This will enable discovery of novel therapeutic pathways that trigger a specific cascade of processes underlying pathology development and subsequently optimally target a wide range of disease parameters. Data-driven network reconstruction methods, such as Weighted Gene Co-expression Analysis [13] can be used to extract co-regulated network modules that reduce dimensionality and identify biologically relevant patterns in the data These methods have proven to be of value in complex diseases, from defining a network-based inflammation signature common across diseases [14], to elucidating molecular mechanisms underlying autism in brain [15]
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