Relating protein adduction to gene expression changes: a systems approach

Zhang, Bing; Shi, Zhiao; Duncan, Dexter T.; Prodduturi, Naresh; Marnett, Lawrence J.; Liebler, Daniel C.

doi:10.1039/c1mb05014a

Cited by 30 publications

(50 citation statements)

References 50 publications

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“…More specifically, in the first step, we utilized the software NetWalker to expand metformin upstream genes and downstream genes for longitudinal conduction [116]. The NetWalker implements the random walk with a starting probability.…”

Section: Methodsmentioning

confidence: 99%

Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action

et al. 2015

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A drug exerts its effects typically through a signal transduction cascade, which is non-linear and involves intertwined networks of multiple signaling pathways. Construction of such a signaling pathway network (SPNetwork) can enable identification of novel drug targets and deep understanding of drug action. However, it is challenging to synopsize critical components of these interwoven pathways into one network. To tackle this issue, we developed a novel computational framework, the Drug-specific Signaling Pathway Network (DSPathNet). The DSPathNet amalgamates the prior drug knowledge and drug-induced gene expression via random walk algorithms. Using the drug metformin, we illustrated this framework and obtained one metformin-specific SPNetwork containing 477 nodes and 1,366 edges. To evaluate this network, we performed the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D) and cancer, one T2D genome-wide association study (GWAS) dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D on metformin. The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival. Furthermore, from the metformin SPNetwork and common genes to T2D and cancer, we generated a subnetwork to highlight the molecule crosstalk between T2D and cancer. The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin’s antidiabetic and anticancer effects. Some results are supported by previous studies. In summary, our study 1) develops a novel framework to construct drug-specific signal transduction networks; 2) provides insights into the molecular mode of metformin; 3) serves a model for exploring signaling pathways to facilitate understanding of drug action, disease pathogenesis, and identification of drug targets.

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Section: Methodsmentioning

confidence: 99%

Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action

et al. 2015

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“…Zhang et al (44) tried to remove this effect by assessing the statistical significance of the scores. To evaluate whether considering network topology could improve network-based function prediction, we combined two statistic metrics, localP and edgeP, with the rank ratio metric for assessing the significance of the priority scores (see supplemental Text S2).…”

Section: Discussionmentioning

confidence: 99%

“…The final score of a gene in the network was defined by iterating the above equation until Σ i =1 n | p i t +1 − p i t | fell below the predefined threshold of 1 × 10 −6 , as previously described (43, 44). A higher score of a gene represents a closer relationship between the gene and the seed genes.…”

Section: Methodsmentioning

confidence: 99%

Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction

Wang

Carr

et al. 2017

Molecular & Cellular Proteomics

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129

104

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Coexpression of mRNAs under multiple conditions is commonly used to infer cofunctionality of their gene products despite well-known limitations of this “guilt-by-association” (GBA) approach. Recent advancements in mass spectrometry-based proteomic technologies have enabled global expression profiling at the protein level; however, whether proteome profiling data can outperform transcriptome profiling data for coexpression based gene function prediction has not been systematically investigated. Here, we address this question by constructing and analyzing mRNA and protein coexpression networks for three cancer types with matched mRNA and protein profiling data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Our analyses revealed a marked difference in wiring between the mRNA and protein coexpression networks. Whereas protein coexpression was driven primarily by functional similarity between coexpressed genes, mRNA coexpression was driven by both cofunction and chromosomal colocalization of the genes. Functionally coherent mRNA modules were more likely to have their edges preserved in corresponding protein networks than functionally incoherent mRNA modules. Proteomic data strengthened the link between gene expression and function for at least 75% of Gene Ontology (GO) biological processes and 90% of KEGG pathways. A web application Gene2Net (http://cptac.gene2net.org) developed based on the three protein coexpression networks revealed novel gene-function relationships, such as linking ERBB2 (HER2) to lipid biosynthetic process in breast cancer, identifying PLG as a new gene involved in complement activation, and identifying AEBP1 as a new epithelial-mesenchymal transition (EMT) marker. Our results demonstrate that proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction. Proteomics should be integrated if not preferred in gene function and human disease studies.

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“…A systems analysis approach that integrates proteomic and gene expression data revealed that treatment of cells with HNE not only results in modification of CDK2 but also leads to significant decreases in the genes controlled by CDK2 activation. 22 These data suggest that HNE modification of CDK2 could result in cell cycle arrest at the G1/S-phase transition. Here, we show that modification of recombinant CDK2 by HNE disrupts its kinase activity.…”

Section: Introductionmentioning

confidence: 90%

Covalent Modification of CDK2 by 4-Hydroxynonenal as a Mechanism of Inhibition of Cell Cycle Progression

Camarillo

Rose

Galligan

et al. 2016

Chem. Res. Toxicol.

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Oxidative stress is a contributing factor in a number of chronic diseases, including cancer, atherosclerosis, and neurodegenerative diseases. Lipid peroxidation that occurs during periods of oxidative stress results in the formation of lipid electrophiles, which can modify a multitude of proteins in the cell. 4-Hydroxy-2-nonenal (HNE) is one of the most well-studied lipid electrophiles and has previously been shown to arrest cells at the G1/S transition. Recently, proteomic data have shown that HNE is capable of covalently modifying CDK2, the kinase responsible for the G1/S transition. Here, we identify the sites adducted by HNE using recombinant CDK2 and show that HNE treatment suppresses the kinase activity of the enzyme. We further identify sites of adduction in HNE-treated intact human colorectal carcinoma cells (RKO) and show that HNE-dependent modification in cells is long-lived, disrupts CDK2 function, and correlates with a delay of progression of the cells into S-phase. We propose that adduction of CDK2 by HNE directly alters its activity, contributing to the cell cycle delay.

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Relating protein adduction to gene expression changes: a systems approach

Cited by 30 publications

References 50 publications

Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action

Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action

Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction

Covalent Modification of CDK2 by 4-Hydroxynonenal as a Mechanism of Inhibition of Cell Cycle Progression

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