Now showing 1 - 3 of 3
  • Publication
    An Integrative Computational Approach for a Prioritization of Key Transcription Regulators Associated With Nanomaterial-Induced Toxicity
    A rapid increase of new nanomaterial products poses new challenges for their risk assessment. Current traditional methods for estimating potential adverse health effect of nanomaterials (NMs) are complex, time consuming and expensive. In order to develop new prediction tests for nanotoxicity evaluation, a systems biology approach and data from high-throughput omics experiments can be used. We present a computational approach that combines reverse engineering techniques, network analysis and pathway enrichment analysis for inferring the transcriptional regulation landscape and its functional interpretation. To illustrate this approach, we used published transcriptomic data derived from mice lung tissue exposed to carbon nanotubes (NM-401 and NRCWE-26). Because fibrosis is the most common adverse effect of these NMs, we included in our analysis the data for bleomycin (BLM) treatment, which is a well-known fibrosis inducer. We inferred gene regulatory networks for each NM and BLM to capture functional hierarchical regulatory structures between genes and their regulators. Despite the different nature of the lung injury caused by nanoparticles and BLM, we identified several conserved core regulators for all agents. We reason that these regulators can be considered as early predictors of toxic responses after NMs exposure. This integrative approach, which refines traditional methods of transcriptomic analysis, can be useful for prioritization of potential core regulators and generation of new hypothesis about mechanisms of nanoparticles toxicity.
    Scopus© Citations 7  543
  • Publication
    Characterization of the renal cortical transcriptome following Roux-en-Y gastric bypass surgery in experimental diabetic kidney disease
    Introduction Roux-en-Y gastric bypass surgery (RYGB) reduces albuminuria and the long-term incidence of end-stage renal disease in patients with obesity and diabetes. Preclinical modeling in experimental diabetic kidney disease demonstrates that improvements in glomerular structure likely underpin these findings. Research design and methods In adult male Zucker diabetic fatty (ZDF) rats, we profiled the effect of RYGB on weight and metabolic control as well biochemical, structural and ultrastructural indices of diabetic renal injury. Furthermore, we sequenced the renal cortical transcriptome in these rats and used bioinformatic pathway analyses to characterize the transcriptional alterations governing the renal reparative response to RYGB. Results In parallel with improvements in weight and metabolic control, RYGB reduced albuminuria, glomerulomegaly, podocyte stress and podocyte foot process effacement. Pathway analysis of RYGB-induced transcriptomic changes in the renal cortex highlighted correction of disease-associated alterations in fibrosis, inflammation and biological oxidation pathways. RYGB reversed disease-associated changes in the expression of transforming growth factor (TGF)-β superfamily genes that strongly correlated with improvements in structural measures of glomerulopathy. Conclusions Improved glomerular structure in ZDF rats following RYGB is underpinned by pathway level changes, including interruption of the TGF-β-driven early profibrotic programme. Our data provide an important layer of experimental support for clinical evidence demonstrating that RYGB arrests renal damage in patients with obesity and type 2 diabetes.
    Scopus© Citations 10  133
  • Publication
    Reconstructing static and dynamic models of signaling pathways using Modular Response Analysis
    In this review we discuss the origination and evolution of Modular Response Analysis (MRA), which is a physics-based method for reconstructing quantitative topological models of biochemical pathways. We first focus on the core theory of MRA, demonstrating how both the direction and the strength of local, causal connections between network modules can be precisely inferred from the global responses of the entire network to a sufficient number of perturbations, under certain conditions. Subsequently, we analyze statistical reformulations of MRA and show how MRA is used to build and calibrate mechanistic models of biological networks. We further discuss what sets MRA apart from other network reconstruction methods and outline future directions for MRA-based methods of network reconstruction.
    Scopus© Citations 16  340