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1.  An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma 
Cell  2011;147(2):370-381.
Summary
By analyzing gene expression data in gliobastoma in combination with matched microRNA profiles, we have uncovered a post-transcriptional regulation layer of surprising magnitude, comprising over 248,000 microRNA (miR)-mediated interactions. These include ~7,000 genes whose transcripts act as miR ‘sponges’ and 148 genes that act through alternative, non-sponge interactions. Biochemical analyses in cell lines confirmed that this network regulates established drivers of tumor initiation and subtype, including PTEN, PDGFRA, RB1, VEGFA, STAT3, and RUNX1, suggesting that these interactions mediate crosstalk between canonical oncogenic pathways. RNA silencing of 13 microRNA-mediated PTEN regulators, whose locus deletions are predictive of PTEN expression variability, was sufficient to downregulate PTEN in a 3′ UTR-dependent manner and to increase tumor-cell growth rates. Thus, this miR-mediated network provides a mechanistic, experimentally validated rationale for the loss of PTEN expression in a large number of glioma samples with an intact PTEN locus.
doi:10.1016/j.cell.2011.09.041
PMCID: PMC3214599  PMID: 22000015
2.  Synergy Analysis Reveals Association between Insulin Signaling and Desmoplakin Expression in Palmitate Treated HepG2 Cells 
PLoS ONE  2011;6(11):e28138.
The regulation of complex cellular activities in palmitate treated HepG2 cells, and the ensuing cytotoxic phenotype, involves cooperative interactions between genes. While previous approaches have largely focused on identifying individual target genes, elucidating interacting genes has thus far remained elusive. We applied the concept of information synergy to reconstruct a “gene-cooperativity” network for palmititate-induced cytotoxicity in liver cells. Our approach integrated gene expression data with metabolic profiles to select a subset of genes for network reconstruction. Subsequent analysis of the network revealed insulin signaling as the most significantly enriched pathway, and desmoplakin (DSP) as its top neighbor. We determined that palmitate significantly reduces DSP expression, and treatment with insulin restores the lost expression of DSP. Insulin resistance is a common pathological feature of fatty liver and related ailments, whereas loss of DSP has been noted in liver carcinoma. Reduced DSP expression can lead to loss of cell-cell adhesion via desmosomes, and disrupt the keratin intermediate filament network. Our findings suggest that DSP expression may be perturbed by palmitate and, along with insulin resistance, may play a role in palmitate induced cytotoxicity, and serve as potential targets for further studies on non-alcoholic fatty liver disease (NAFLD).
doi:10.1371/journal.pone.0028138
PMCID: PMC3223234  PMID: 22132232
3.  The Double-stranded RNA–dependent Protein Kinase Differentially Regulates Insulin Receptor Substrates 1 and 2 in HepG2 Cells 
Molecular Biology of the Cell  2010;21(19):3449-3458.
The RNA-dependent protein kinase (PKR), initially known as a virus infection response protein, is found to differentially regulate two major players in the insulin signaling pathway, IRS1 and IRS2. PKR up-regulates the inhibitory phosphorylation of IRS1 and the expression of IRS2 at the transcriptional level.
Initially identified to be activated upon virus infection, the double-stranded RNA–dependent protein kinase (PKR) is best known for triggering cell defense responses by phosphorylating eIF-2α, thus suppressing RNA translation. We as well as others showed that the phosphorylation of PKR is down-regulated by insulin. In the present study, we further uncovered a novel function of PKR in regulating the IRS proteins. We found that PKR up-regulates the inhibitory phosphorylation of IRS1 at Ser312, which suppresses the tyrosine phosphorylation of IRS1. This effect of PKR on the phosphorylation of IRS1 is mediated by two other protein kinases, JNK and IKK. In contrast, PKR regulates IRS2, another major IRS family protein in the liver, at the transcriptional rather than the posttranslational level, and this effect is mediated by the transcription factor, FoxO1, which has been previously shown to be regulated by insulin and plays a significant role in glucose homeostasis and energy metabolism. In summary, we found for the first time that initially known as a virus infection response gene, PKR regulates the upstream central transmitters of insulin signaling, IRS1 and IRS2, through different mechanisms.
doi:10.1091/mbc.E10-06-0481
PMCID: PMC2947480  PMID: 20685959
4.  Reconstruct modular phenotype-specific gene networks by knowledge-driven matrix factorization 
Bioinformatics  2009;25(17):2236-2243.
Motivation: Reconstructing gene networks from microarray data has provided mechanistic information on cellular processes. A popular structure learning method, Bayesian network inference, has been used to determine network topology despite its shortcomings, i.e. the high-computational cost when analyzing a large number of genes and the inefficiency in exploiting prior knowledge, such as the co-regulation information of the genes. To address these limitations, we are introducing an alternative method, knowledge-driven matrix factorization (KMF) framework, to reconstruct phenotype-specific modular gene networks.
Results: Considering the reconstruction of gene network as a matrix factorization problem, we first use the gene expression data to estimate a correlation matrix, and then factorize the correlation matrix to recover the gene modules and the interactions between them. Prior knowledge from Gene Ontology is integrated into the matrix factorization. We applied this KMF algorithm to hepatocellular carcinoma (HepG2) cells treated with free fatty acids (FFAs). By comparing the module networks for the different conditions, we identified the specific modules that are involved in conferring the cytotoxic phenotype induced by palmitate. Further analysis of the gene modules of the different conditions suggested individual genes that play important roles in palmitate-induced cytotoxicity. In summary, KMF can efficiently integrate gene expression data with prior knowledge, thereby providing a powerful method of reconstructing phenotype-specific gene networks and valuable insights into the mechanisms that govern the phenotype.
Contact: krischan@msu.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btp376
PMCID: PMC2800351  PMID: 19542155
5.  A Dynamic Analysis of IRS-PKR Signaling in Liver Cells: A Discrete Modeling Approach 
PLoS ONE  2009;4(12):e8040.
A major challenge in systems biology is to develop a detailed dynamic understanding of the functions and behaviors in a particular cellular system, which depends on the elements and their inter-relationships in a specific network. Computational modeling plays an integral part in the study of network dynamics and uncovering the underlying mechanisms. Here we proposed a systematic approach that incorporates discrete dynamic modeling and experimental data to reconstruct a phenotype-specific network of cell signaling. A dynamic analysis of the insulin signaling system in liver cells provides a proof-of-concept application of the proposed methodology. Our group recently identified that double-stranded RNA-dependent protein kinase (PKR) plays an important role in the insulin signaling network. The dynamic behavior of the insulin signaling network is tuned by a variety of feedback pathways, many of which have the potential to cross talk with PKR. Given the complexity of insulin signaling, it is inefficient to experimentally test all possible interactions in the network to determine which pathways are functioning in our cell system. Our discrete dynamic model provides an in silico model framework that integrates potential interactions and assesses the contributions of the various interactions on the dynamic behavior of the signaling network. Simulations with the model generated testable hypothesis on the response of the network upon perturbation, which were experimentally evaluated to identify the pathways that function in our particular liver cell system. The modeling in combination with the experimental results enhanced our understanding of the insulin signaling dynamics and aided in generating a context-specific signaling network.
doi:10.1371/journal.pone.0008040
PMCID: PMC2779448  PMID: 19956598
6.  Repression of PKR Mediates Palmitate-Induced Apoptosis in HepG2 Cells through Bcl-2 
Cell research  2009;19(4):469-486.
In the present study we found that double-stranded RNA-dependent protein kinase (PKR) regulates the protein expression level and the phosphorylation of Bcl-2 and exploits an anti-apoptotic role in human hepatocellular carcinoma cells (HepG2). Saturated free fatty acids (FFAs), e.g. palmitate, have been shown to induce cellular apoptosis in various types of cells by different mechanisms. We found palmitate down-regulates the activity of PKR, and thereby decreases the protein level of Bcl-2, mediated, in part, by the NF-κB transcription factor. In addition to the protein level of Bcl-2, the phosphorylation of Bcl-2 at different amino acid residues, such as Ser70 and Ser87, is also important in regulating cellular apoptosis. The decrease in the phosphorylation of Bcl-2 at Ser70 upon exposure to palmitate is mediated by PKR and possibly JNK, while the phosphorylation of Bcl-2 at Ser87 is not affected by palmitate or PKR. In summary, PKR mediates the regulation of the protein level and the phosphorylation status of Bcl-2, providing a novel mechanism of palmitate-induced apoptosis in HepG2 cells.
doi:10.1038/cr.2009.25
PMCID: PMC2664847  PMID: 19259124
palmitate; apoptosis; liver cell; HepG2; PKR; Bcl-2; phosphorylation of Bcl-2; NF-kappaB; JNK
7.  Identification of genes that regulate multiple cellular processes/responses in the context of lipotoxicity to hepatoma cells 
BMC Genomics  2007;8:364.
Background
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 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.
Results
As 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).
Conclusion
These 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.
doi:10.1186/1471-2164-8-364
PMCID: PMC2110894  PMID: 17925029
8.  A Three Stage Integrative Pathway Search (TIPS©) framework to identify toxicity relevant genes and pathways 
BMC Bioinformatics  2007;8:202.
Background
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 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.
Results
This 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.
Conclusion
The 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 .
doi:10.1186/1471-2105-8-202
PMCID: PMC1906836  PMID: 17570844
9.  A hierarchical approach employing metabolic and gene expression profiles to identify the pathways that confer cytotoxicity in HepG2 cells 
BMC Systems Biology  2007;1:21.
Background
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 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.
Results
A 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.
Conclusion
The 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.
doi:10.1186/1752-0509-1-21
PMCID: PMC1885270  PMID: 17498300

Results 1-9 (9)