Shireesh Srivastava

Hydrogen peroxide and hydroxyl radicals mediate palmitate-induced cytotoxicity to hepatoma cells: Relation to mitochondrial permeability transition

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 release as well as a loss of mitochondrial potential. The cytotoxicity of palmitate was significantly reduced by treating with scavengers of hydrogen peroxide, hydroxyl radical and the spin trap alpha-(4-pyridyl-1-oxide)-N-tert-butyl nitrone (POBN). Superoxide dismutase (SOD) mimics, nitric oxide scavenger, and inhibitor of de novo ceramide synthesis had no effect on the toxicity. MPT-inhibitor, cyclosporine, prevented the loss of mitochondrial potential but did not reduce the cytotoxicity. In contrast, inhibiting mitochondrial complexes I and III reduced the early potential loss and the cytotoxicity. These results suggest that palmitate-cytotoxicity to hepatoma cells is mediated through the production of H2O2 and *OH and independent of MPT.

A hierarchical approach employing metabolic and gene expression profiles to identify the pathways that confer cytotoxicity in HepG2 cells.

BackgroundFree 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 processes associated with the toxicity of saturated FFA and TNF-α, the metabolic and gene expression profiles were measured to characterize the cellular states. A computational model was developed to integrate these disparate data to reveal the underlying pathways and mechanisms involved in saturated fatty acid toxicity.ResultsA hierarchical framework consisting of three stages was developed to identify the processes and genes that regulate the toxicity. First, discriminant analysis identified that fatty acid oxidation and intracellular triglyceride accumulation were the most relevant in differentiating the cytotoxic phenotype. Second, gene set enrichment analysis (GSEA) was applied to the cDNA microarray data to identify the transcriptionally altered pathways and processes. Finally, the genes and gene sets that regulate the metabolic responses identified in step 1 were identified by integrating the expression of the enriched gene sets and the metabolic profiles with a multi-block partial least squares (MBPLS) regression model.ConclusionThe hierarchical approach suggested potential mechanisms involved in mediating the cytotoxic and cytoprotective pathways, as well as identified novel targets, such as NADH dehydrogenases, aldehyde dehydrogenases 1A1 (ALDH1A1) and endothelial membrane protein 3 (EMP3) as modulator of the toxic phenotypes. These predictions, as well as, some specific targets that were suggested by the analysis were experimentally validated.

A Three Stage Integrative Pathway Search (TIPS) framework to identify toxicity relevant genes and pathways.

BackgroundThe 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 overcome these shortcomings, we developed a Three Stage Integrative Pathway Search (TIPS©) approach to reconstruct context-specific active pathways involved in conferring a specific phenotype, from limited amount of perturbation data. The approach was tested on human liver cells to identify pathways that confer cytotoxicity.ResultsThis paper presents a systems approach that integrates gene expression and cytotoxicity profiles to identify a network of pathways involved in free fatty acid (FFA) and tumor necrosis factor-α (TNF-α) induced cytotoxicity in human hepatoblastoma cells (HepG2/C3A). Cytotoxicity relevant genes were first identified and then used to reconstruct a network using Bayesian network (BN) analysis. BN inference was used subsequently to predict the effects of perturbing a gene on the other genes in the network and on the cytotoxicity. These predictions were subsequently confirmed through the published literature and further experiments.ConclusionThe TIPS© approach is able to reconstruct active pathways that confer a particular phenotype by integrating gene expression and phenotypic profiles. A web-based version of TIPS© that performs the analysis described herein can be accessed at http://www.egr.msu.edu/tips.

Using Dynamic Gene Module Map Analysis To Identify Targets That Modulate Free Fatty Acid Induced Cytotoxicity

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 cytotoxic. The pathways activated by the different FFAs at the different time points were identified using global gene module map analysis. Unsaturated FFAs exerted transcriptional regulation mainly within the first 24 h, whereas saturated FFA, palmitate, regulated energy production pathways, such as the electron transport chain (ETC) and tricarboxylic acid cycle, within the first 24 h. In the next 24 h, palmitate up‐regulated 36 cell death relevant pathways and down‐regulated several protective pathways, such as the pentose phosphate pathway and glutathione‐related pathways. In the final 24 h, the FFAs did not induce significant transcriptional regulation. We hypothesized that palmitate induced cytotoxicity by first perturbing metabolic pathways in the initial 24 h, resulting in changes to factors, such as metabolites or signaling molecules, which subsequently triggered cell death relevant pathways in the next 24 h. The uptake and release of 27 metabolites were measured to further elucidate the metabolic changes in the first 24 h. It was determined that ketone bodies such as β‐hydroxybutyrate and acetoacetate were important in separating the toxic from the nontoxic phenotypes. A regression model was used to identify the genes relevant to these metabolites. Some of the genes identified to be important were experimentally validated. It was found that ETC genes such as NADH dehydrogenase and succinate dehydrogenase were involved in palmitate induced cytotoxicity.

Identification of genes that regulate multiple cellular processes/responses in the context of lipotoxicity to hepatoma cells

BackgroundIn 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 shown to reduce the lipotoxicity of saturated FFA. We hypothesized that the genes which simultaneously regulate lipid accumulation as well as cytotoxicity may provide better targets to counter lipotoxicity of saturated FFA.ResultsAs a model system to test this hypothesis, human hepatoblastoma cells (HepG2) were exposed to elevated physiological levels of FFAs and TNF-α. Triglyceride (TG) accumulation, toxicity and the genomic responses to the treatments were measured. Here, we present a framework to identify such genes in the context of lipotoxicity. The aim of the current study is to identify the genes that could be altered to treat or ameliorate the cellular responses affected by a complex disease rather than to identify the causal genes. Genes that regulate the TG accumulation, cytotoxicity or both were identified by a modified genetic algorithm partial least squares (GA/PLS) analysis. The analyses identified NADH dehydrogenase and mitogen activated protein kinases (MAPKs) as important regulators of both cytotoxicity and lipid accumulation in response to FFA and TNF-α exposure. In agreement with the predictions, inhibiting NADH dehydrogenase and c-Jun N-terminal kinase (JNK) reduced cytotoxicity significantly and increased intracellular TG accumulation. Inhibiting another MAPK pathway, the extracellular signal regulated kinase (ERK), on the other hand, improved the cytotoxicity without changing TG accumulation. Much greater reduction in the toxicity was observed upon inhibiting the NADH dehydrogenase and MAPK (which were identified by the dual-response analysis), than for the stearoyl-CoA desaturase (SCD) activation (which was identified for the TG-alone analysis).ConclusionThese results demonstrate the applicability of GA/PLS in identifying the genes that regulate multiple cellular responses of interest and that genes regulating multiple cellular responses may be better candidates for countering complex diseases.

A Knowledge Driven Regression Model for Gene Expression and Microarray Analysis

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 significantly larger than the number of conditions. To resolve this problem, we present a knowledge driven regression model that incorporates the knowledge of genes from the Gene Ontology (GO) database into the linear regression model. It is based on the assumption that two genes are likely to be assigned similar weights when they share similar sets of GO codes. Empirical studies show that the proposed knowledge driven regression model is effective in reducing the regression errors, and furthermore effective in identifying genes that are relevant to a given metabolite