Ben Cosgrove
 Ben Cosgrove CV email Epernicus Ph.D. Student Biological Engineering Division webpage Massachusetts Institute of Technology webpage
"Quantitative Analysis of Cytokine-Induced Hepatocyte Proliferation, Apoptosis, and Toxicity" Many therapeutic agents, including viral gene therapy vectors and small molecule pharmaceutical compounds, are confounded by liver toxicity due to, in part, relationships with inflammatory stimuli in eliciting hepatocyte toxicity and/or death. Our work focuses on developing physiologically relevant in vitro approaches to quantitatively assess how hepatocytes regulate, through the activities of intracellular and extracellular signaling networks, cell fate decisions related to proliferation, survival, apoptosis, and differentiated function following cytokine stimulation in the presence of viral gene therapy agents or small molecule drugs. Initially, we examined the role of a specific inflammatory cytokine, tumor necrosis factor alpha (TNF), which regulates both hepatocyte proliferation and apoptosis in vivo. We have shown that TNF stimulates hepatocyte proliferation in vitro through a contingent, inducible autocrine cascade containing the growth factor TGF-alpha and the cytokines IL-1alpha/beta. This TGF-alpha-IL-1 autocrine cascade regulates TNF-induced hepatocyte proliferation in a self-limiting manner as TGF-alpha positively regulates proliferation while also upregulating autocrine IL-1 release, which negatively regulates proliferation. Similarly, we have shown that TNF potently stimulates apoptosis in hepatocytes infected with a replication-deficient adenovirus in vitro as mediated by the same TGF-alpha-IL-1 autocrine cascade. Currently, we are developing in vitro models of idiosyncratic drug hepatotoxicity by examining the interactions between multiple pharmaceutical compounds and inflammatory cytokines. In this work, we aim to elucidate how certain idiosyncratic hepatotoxic drugs exhibit synergistic toxicity relationships with inflammatory cytokines by collecting systems-level intracellular signaling data (using multiplexed kinase activity assays and bead-based phosphoprotein detection schemes) and phenotypic cell response data (using imaging-based assays of differentiated function and high-throughput cell necrosis and apoptosis assays). This data set will be used to develop statistical signaling-outcome models through partial least squares regression (PLSR) approaches to identify and predict key signaling activities that regulate a diverse set of hepatocyte toxicity phenotypes and to inform future therapeutic strategies.
|
