Shireesh Srivastava

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Chronic exposure to elevated levels of free fatty acids (FFAs) has been shown to cause cell death (lipotoxicity), but the underlying mechanisms of lipotoxicity in hepatocytes remain unclear. We have previously shown that the saturated FFAs cause much greater toxicity to human hepatoma cells (HepG2) than the unsaturated ones (Srivastava and Chan, 2007). In(More)
Free fatty acids (FFA) and tumor necrosis factor alpha (TNF-α) have been implicated in the pathogenesis of many obesity-related metabolic disorders. When human hepatoblastoma cells (HepG2) were exposed to different types of FFA and TNF-α, saturated fatty acid was found to be cytotoxic and its toxicity was exacerbated by TNF-α. In order to identify the(More)
We studied the toxicological responses of a human hepatoblastoma cell line (HepG2/C3A) to various free fatty acids (FFA) in order to identify the relation between reactive oxygen species (ROS) production and mitochondrial permeability transition (MPT). Exposure to the saturated FFA, palmitate, led to a time-dependent ROS production and hydrogen peroxide(More)
The ability to obtain profiles of gene expressions, proteins and metabolites with the advent of high throughput technologies has advanced the study of pathway and network reconstruction. Genome-wide network reconstruction requires either interaction measurements or large amount of perturbation data, often not available for mammalian cell systems. To(More)
In order to devise efficient treatments for complex, multi-factorial diseases, it is important to identify the genes which regulate multiple cellular processes. Exposure to elevated levels of free fatty acids (FFAs) and tumor necrosis factor alpha (TNF-α) alters multiple cellular processes, causing lipotoxicity. Intracellular lipid accumulation has been(More)
The objective of this study was to identify pathways that regulate the cytotoxicity induced by free fatty acids (FFAs) in human hepatoblastoma cells (HepG2/C3A). Gene expression profiles of HepG2/C3A cells were obtained at three time points, after 24, 48, and 72 h of exposure to different types of FFA. Saturated fatty acid (palmitate) was found to be(More)
BACKGROUND Among the primary goals of microarray analysis is the identification of genes that could distinguish between different phenotypes (feature selection). Previous studies indicate that incorporating prior information of the genes' function could help identify physiologically relevant features. However, current methods that incorporate prior(More)
The linear regression model has been widely used in the analysis of gene expression and microarray data to identify a subset of genes that are important to a given metabolic function. One of the key challenges in applying the linear regression model to gene expression data analysis arises from the sparse data problem, in which the number of genes is(More)
Reconstructing gene networks from micro-array data can provide information on the mechanisms that govern cellular processes. Numerous studies have been devoted to addressing this problem. A popular method is to view the gene network as a Bayesian inference network, and to apply structure learning methods to determine the topology of the gene network. There(More)
Numerous mathematical methods have been developed to reconstruct biological networks, for example gene (1,2), metabolic (3), and protein-protein (4,5) networks, from experimental data. Reconstructing pathways and networks provides a framework for predictive modeling and hypotheses testing to gain more insight into living organisms, disease mechanisms, and(More)