A systems approach to identifying toxicity pathways
In this study, a systems approach was developed to integrate gene expression and metabolic profiles to identify pathways related to a metabolic function. The approach first extracts features from the metabolite profiles to discriminate different phenotypes; and then selects genes relevant to these features with genetic algorithm coupled partial least squares analysis and independent component analysis; and finally reconstructs the pathways from the selected subset of genes using Bayesian network analysis.
We applied the approach to investigate the cytotoxcity related pathways in a human hepatoblastoma cell line (HepG2/C3A). In this cellular system, saturated fatty acid and acute TNFa exposure were found to be cytotoxic, although chronic TNFa exposure was found to be cytoprotective. Genes encoding biomolecules in pathways involved in regulating cytotoxicity were selected and the pathways were reconstructed. Several pathways were experimentally validated by perturbing the pathways and measuring their protein levels. Finally, the BN network analysis was applied to predict toxicity level and the effects of perturbing genes, e.g. stearoyl-CoA desaturase and Bcl-2 on toxicity level were simulated using the model. The results illustrate that the integration of gene expression and metabolite data by our framework provides informative and testable hypothesis on the mechanism(s) of palmitate-induced cytotoxicity.
A hierarchical framework was further developed to incorporate a priori knowledge in a framework to identify the toxicity pathways. The hierarchical framework consisted of three stages. 1) Different phenotypes induced by TNF-a and FFA were characterized, for example, a cytotoxic phenotype was characterized by the level of lactate dehydrogenase (LDH) release. Important metabolites that contribute to separating the phenotype were identified using a discriminant analysis of the twenty seven metabolites. Relevant metabolic pathways were suggested by the identified metabolites. 2) cDNA microarray was used to measure the global gene expression changes induced by FFAs and TNF-a. Gene set enrichment analysis (GSEA) of the microarray data identified the relevant pathways in addition to those identified by the metabolite analysis. The pathways identified to be enriched included mitochondria-related pathways, oxidative-stress-related pathways and fatty acid metabolism. 3) Finally, the gene expression and metabolite profiles were integrated with a regression model to identify the genes most relevant to the metabolites identified in the first step to be highly correlated with the cytotoxicity. The cytotoxic and cytoprotective pathways identified, as well as the experimentally validated pathways will be discussed.
Finally we studied the dynamic properties of the toxicity pathways. Cells continuously reprogram gene regulatory networks when they sense the changes in the environmental conditions. Reconstruct the dynamic network is essential to understand the regulatory mechanism of progression of diseases. Gene expression profiles of HepG2/C3A cells were measured after 24, 48, and 72 hours of exposure to FFA and TNF-a. To identify the underlying pathways, module map analysis was used to identify the important genes. 522 biologically meaningful gene sets were first defined based upon their functional category or pathway, as defined by the MsigDB database. The functional gene modules were extracted from the 522 gene sets based upon the expression profile with Genomica. The module maps at different time points were compared to identify the dynamics of the modules that are important to the cytotoxic phenotype. The profiles of the genes in the modules identified to be involved in activating/inducing cytotoxicity along with the profile of the phenotype were used for network reconstruction to identify potential targets that can be perturbed to modulate the cytotoxicity. Some of the targets identified to be important to palmitate induced cytotoxcity were experimentally validated.
Therapeutic engineering
Combination therapy for cancer
Described herein is a network biology approach useful for the identification of multiple therapeutic targets, which can be targeted simultaneously using an agent (or a plurality of agents) to modulate cellular phenotypes, or in combination with pharmaceutical compounds to improve the drug sensitivity and/or reduce the drug doses for minimal side effects. The approach described herein relies on first identifying the mediators of a condition of interest, and second, selecting gene combinations that are in competing/parallel pathways.
Systems biology analysis of neurodegenerative diseases
Amyotrophic lateral sclerosis (ALS) is one of the most common neurodegenerative diseases, characterized by degeneration of motor neurons in brain and spinal cord, leading to muscle atrophy and eventually death. Extensive studies have been undertaken to understand the pathogenesis of ALS. However, the underlying mechanisms of selective degeneration of motor neurons in ALS are still incompletely understood so far. It is the objective of this study to apply systems biology approaches to integrate interactome data with mircroarray gene expression profiles to identify the causal factors of ALS. Interactome and transcriptome profile of both ALS patients and mice model were integrated to identify active pathways that were differentiated in ALS disease condition. Therapeutic targets were predicted and validated experimentally.
Applicable peer reviewed publications
1. Li, Z., Srivastava, S., Findlan, R., Chan C., “Using dynamic gene module map analysis to identify targets that modulate free fatty acid and tumore necrosis factor (TNF)-a induced cytotoxicity” (2008) Biotechnology Progress, 24, 29-37.
2. Srivastava, S.#, Li, Z.#, Yang, X., Yedwabnick M., Shaw S., Chan C. “Identification of genes that regulate multiple celluar processes/response in the context of lipotoxicity to hepatoma cells” (2007) BMC Genomics, 8:364. # co-first author.
3. Li, Z. Srivastava, S., Mittal, S., Yang, X., Sheng, L., Chan, C., “ Identifying active pathways by integrating gene expression and phenotypic profiles”, (2007) BMC Bioinformatics, 8:101.
4.Li, Z.#, Srivastava, S.#, Yang, X., Mittal, S., Norton, P., Resau, J., Brian, H., Chan, C., “A Hierarchical Approach to Identify Pathways that Confer Cytotoxicity in HepG2 Cells from Metabolic and Gene Expression Profiles”, (2007) BMC Systems Biology, 1:21.# co-first author.
5.Li, Z., Shaw, S. M., Yedwabnick, M. J., Chan, C. “Using a state-space model with hidden variables to infer transcription factor activities”, (2006) Bioinformatics 22(6): 747-754
6.Walton, S.P., Li, Z., and Chan, C., “Biological network analyses: computational genomics and systems approaches” (2006) Molecular Simulation, 32 (3-4): 203-209.
7.Patil, S., Li, Z., and Chan, C., “Approaches to Optimizing Cellular Function of Engineered Tissue: Cellular to Tissue Informatics", in Advances in Biochemical Engineering /
Biotechnology, eds. K. Lee and D. Kaplan, Springer, 2006, volume 102, p. 139-159.
8.Li, Z., and Chan, C., "Inferring pathways and networks with a Bayesian framework", (2004) FASEB J 18(6): 746-8 (published on-line Feb 6, 2004, doi:10.1096/fj.03-0475fje).
9.Li, Z., and Chan, C., "Integrating gene expression and metabolic profiles", (2004) Journal Biological Chemistry 279: 27124-27137.
10.Li, Z., Yarmush, M. L., and Chan, C., "Insulin concentration during pre-conditioning mediates the regulation of urea synthesis in primary rat hepatocytes during exposure to amino acid supplemented plasma", (2004) Tissue Engineering 10(11):1737-1746.
11.Li, Z., and Chan, C., "Extracting novel information from gene expression data”, (2004) Trends in Biotechnology, 22: 381-383.