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

Zheng, Lirong; Srivastava, Shireesh; Mittal, Sheenu; Yang, Xuerui; Sheng, Lufang; Chan, Christina

doi:10.1186/1471-2105-8-202

Cited by 17 publications

(18 citation statements)

References 60 publications

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“…A simplified toxicity relevant network was reconstructed as shown in Figure 2. A more detailed network is shown in reference [6]. The network was used to predict the effect of perturbing a gene on the liver toxicity.…”

Section: Resultsmentioning

confidence: 99%

“…The reconstructed network was perturbed to identify i) which genes, when perturbed, had an impact on altering the cytotoxic phenotype in the palmitate cultures, and ii) how perturbing a gene (node) affected the other genes in the network. More details can be found in our published paper [6]. TIPS © was further extended to identify genes relevant to multiple cellular responses, e.g., multiple metabolites, in a separate study [11].…”

Section: Methodsmentioning

confidence: 99%

“…Gene targets, such as NADH dehydrogenase, were identified and experimentally confirmed to have significant effects on reducing the toxic signal, reactive oxygen species (ROS), and ultimately toxicity levels in palmitate treated liver cells. The details of the approaches discussed in this paper are published elsewhere [4-6]. The objective of this paper is to provide an overall picture of how systems biology approaches may be used to integrate multiple-source information for novel biological discoveries.…”

Section: Introductionmentioning

confidence: 99%

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Systems biology for identifying liver toxicity pathways

Zheng

Chan

2009

BMC Proc

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Drug-induced liver toxicity is one of the leading causes of acute liver failure in the United States, exceeding all other causes combined. The objective of this paper is to describe systems biology methods for identifying pathways involved in liver toxicity induced by free fatty acids (FFA) and tumor necrosis factor (TNF)-α in human hepatoblastoma cells (HepG2/C3A). Systems biology approaches were developed to integrate multi-level data, i.e., gene expression, metabolite profile, toxicity measurements and a priori knowledge to identify gene targets for modulating liver toxicity. Targets that modulate liver toxicity, in vitro, were computationally predicted and some targets were experimentally validated.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Systems biology for identifying liver toxicity pathways

Zheng

Chan

2009

BMC Proc

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“…63,64 The large data sets that are collected in metabolomics experiments makes them effective tools in the interpretation of metabolomic data. Genetic algorithms mimic biological evolution (through concepts such as mutation, inheritance, breeding, natural selection, etc.)…”

Section: Data Analysis Methods and Softwarementioning

confidence: 99%

Metabolomic Fingerprinting: Challenges and Opportunities

Kosmides

Kamisoglu

Calvano

et al. 2013

Crit Rev Biomed Eng

124

101

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Systems biology has primarily focused on studying genomics, transcriptomics, and proteomics and their dynamic interactions. These, however, represent only the potential for a biological outcome since the ultimate phenotype at the level of the eventually produced metabolites is not taken into consideration. The emerging field of metabolomics provides complementary guidance toward an integrated approach to this problem: It allows global profiling of the metabolites of a cell, tissue, or host and presents information on the actual end points of a response. A wide range of data collection methods are currently used and allow the extraction of global or tissue-specific metabolic profiles. The great amount and complexity of data that are collected require multivariate analysis techniques, but the increasing amount of work in this field has made easy-to-use analysis programs readily available. Metabolomics has already shown great potential in drug toxicity studies, disease modeling, and diagnostics and may be integrated with genomic and proteomic data in the future to provide in-depth understanding of systems, pathways, and their functionally dynamic interactions. In this review we discuss the current state of the art of metabolomics, its applications, and future potential.

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“…While biology-driven interpretations have been more commonly reported, data-driven techniques have been utilized to study diverse areas, such as hepatocellular metabolism77, the unique effects of anti-inflammatory compounds on macrophage cells78, and the identification of regulatory metabolites and gene transcription in glucosinolate and anthocyanin biosynthesis79. Another comprehensive example is a ‘pharmaco-metabolomic’ approach in which pre-dose metabolite profiles of urine and chemometrics were used to predict toxicity responses to acetaminophen in an in vivo rat model80.…”

Section: Data Integration Between -Omics Technologiesmentioning

confidence: 99%

Systems biology and functional genomics approaches for the identification of cellular responses to drug toxicity

Harrill

Rusyn

2008

Expert Opinion on Drug Metabolism & Toxicology

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Extensive growth in the field of molecular biology in recent decades has led to the development of new and powerful experimental and computational tools that enable the analysis of complex biological responses to chemical exposure on both a functional and structural genetic level. The ability to profile global responses on a transcriptional level has become a valuable resource in the science of toxicology and attempts are now being made to further understand toxicity mechanisms by incorporating metabolomics and proteomics approaches. In addition, recent progress in understanding the extent of the genetic diversity within and between species allows us to take a fresh look at research on genetic polymorphisms that may influence an individual's susceptibility to toxicity. Whereas new technologies have the potential to make a sizeable impact on our understanding of the mechanisms of toxicity, considerable challenges remain to be addressed, especially with regard to the regulatory acceptance and successful integration of omics data. This review highlights recent advancements in the application of functional and structural genomics techniques to chemical hazard identification and characterization, and to the understanding of the interindividual differences in susceptibility to adverse drug reactions.

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

Cited by 17 publications

References 60 publications

Systems biology for identifying liver toxicity pathways

Systems biology for identifying liver toxicity pathways

Metabolomic Fingerprinting: Challenges and Opportunities

Systems biology and functional genomics approaches for the identification of cellular responses to drug toxicity

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