Relating protein adduction to gene expression changes: a systems approach

Lipoxin A4 yields an electrophilic 15-oxo metabolite that mediates FPR2 receptor-independent anti-inflammatory signaling

The enzymatic oxidation of arachidonic acid is proposed to yield trihydroxytetraene species (termed lipoxins) that resolve inflammation via ligand activation of the formyl peptide receptor, FPR2. While cell and murine models activate signaling responses to synthetic lipoxins, primarily 5S,6R,15S-trihydroxy-7E,9E,11Z,13E-eicosatetraenoic acid (lipoxin A4, LXA4), there are expanding concerns about the biological formation, detection and signaling mechanisms ascribed to LXA4 and related di- and tri-hydroxy ω-6 and ω-3 fatty acids. Herein, the generation and actions of LXA4 and its primary 15-oxo metabolite were assessed in control, LPS-activated and arachidonic acid supplemented RAW 264.7 macrophages. Despite protein expression of all enzymes required for LXA4 synthesis, both LXA4 and its 15-oxo-LXA4 metabolite were undetectable. Moreover, synthetic LXA4 and the membrane permeable 15-oxo-LXA4 methyl ester that is rapidly de-esterified to 15-oxo-LXA4, displayed no ligand activity for the putative LXA4 receptor FPR2, as opposed to the FPR2 ligand WKYMVm. Alternatively, 15-oxo-LXA4, an electrophilic α,β-unsaturated ketone, alkylates nucleophilic amino acids such as cysteine to modulate redox-sensitive transcriptional regulatory protein and enzyme function. 15-oxo-LXA4 activated nuclear factor (erythroid related factor 2)-like 2 (Nrf2)-regulated gene expression of anti-inflammatory and repair genes and inhibited nuclear factor (NF)-κB-regulated pro-inflammatory mediator expression. LXA4 did not impact these macrophage anti-inflammatory and repair responses. In summary, these data show an absence of macrophage LXA4 formation and receptor-mediated signaling actions. Rather, if LXA4 were present in sufficient concentrations, this, and other more abundant mono- and poly-hydroxylated unsaturated fatty acids can be readily oxidized to electrophilic α,β-unsaturated ketone products that modulate the redox-sensitive cysteine proteome via G-protein coupled receptor-independent mechanisms.

Complex Disease Genes Identification Using a Heterogeneous Network Embedding Approach

Finding the causal relation between a gene and a disease using experimental approaches is a time-consuming and expensive task. However, computational approaches are cost-efficient methods for identifying candidate genes. This article proposes a new heterogeneous biological network embedding approach, named NetEM, to identify disease-associated genes. To evaluate NetEM, we examine six complex diseases, including peroxisomal disorders, sarcoma, grave's disease, lysosomal storage diseases, blood coagulation disorders, and cardiomyopathy hypertrophic. Our experiments indicate that NetEM outperforms three well-known state-of-the-art algorithms: Cardigan, DIAMOnD and GeneWanderer, in identifying disease genes. We examine TCGA data of Invasive Lobular Breast Cancer and CPTAC data of human glioblastoma as other case studies to evaluate NetEM using real data. This evaluation also indicates the validity of the method. The source codes of NetEM and data are available in the supplementary of this article.

PyGNA: a unified framework for geneset network analysis

BACKGROUND

Gene and protein interaction experiments provide unique opportunities to study the molecular wiring of a cell. Integrating high-throughput functional genomics data with this information can help identifying networks associated with complex diseases and phenotypes.

RESULTS

Here we introduce an integrated statistical framework to test network properties of single and multiple genesets under different interaction models. We implemented this framework as an open-source software, called Python Geneset Network Analysis (PyGNA). Our software is designed for easy integration into existing analysis pipelines and to generate high quality figures and reports. We also developed PyGNA to take advantage of multi-core systems to generate calibrated null distributions on large datasets. We then present the results of extensive benchmarking of the tests implemented in PyGNA and a use case inspired by RNA sequencing data analysis, showing how PyGNA can be easily integrated to study biological networks. PyGNA is available at http://github.com/stracquadaniolab/pygna and can be easily installed using the PyPi or Anaconda package managers, and Docker.

CONCLUSIONS

We present a tool for network-aware geneset analysis. PyGNA can either be readily used and easily integrated into existing high-performance data analysis pipelines or as a Python package to implement new tests and analyses. With the increasing availability of population-scale omic data, PyGNA provides a viable approach for large scale geneset network analysis.

Identification of Regulatory Modules That Stratify Lupus Disease Mechanism through Integrating Multi-Omics Data

Although recent advances in genetic studies have shed light on systemic lupus erythematosus (SLE), its detailed mechanisms remain elusive. In this study, using datasets on SLE transcriptomic profiles, we identified 750 differentially expressed genes (DEGs) in T and B lymphocytes and peripheral blood cells. Using transcription factor (TF) binding data derived from chromatin immunoprecipitation sequencing (ChIP-seq) experiments from the Encyclopedia of DNA Elements (ENCODE) project, we inferred networks of co-regulated genes (NcRGs) based on binding profiles of the upregulated DEGs by significantly enriched TFs. Modularization analysis of NcRGs identified co-regulatory modules among the DEGs and master TFs vital for each module. Remarkably, the co-regulatory modules stratified the common SLE interferon (IFN) signature and revealed SLE pathogenesis pathways, including the complement cascade, cell cycle regulation, NETosis, and epigenetic regulation. By integrative analyses of disease-associated genes (DAGs), DEGs, and enriched TFs, as well as proteins interacting with them, we identified a hierarchical regulatory cascade with TFs regulated by DAGs, which in turn regulates gene expression. Integrative analysis of multi-omics data provided valuable molecular insights into the molecular mechanisms of SLE.

Histone Adduction and Its Functional Impact on Epigenetics

Bioactive electrophiles generated from the oxidation of endogenous and exogenous compounds are a contributing factor in numerous disease states. Their toxicity is largely attributed to the covalent modification of cellular nucleophiles, including protein and DNA. With regard to protein modification, the side-chains of Cys, His, Lys, and Arg residues are critical targets. This results in the generation of undesired protein post-translational modifications (PTMs) that can trigger dire cellular consequences. Notably, histones are Lys- and Arg-rich proteins, providing a fertile source for adduction by both exogenous and endogenous electrophiles. The regulation of histone PTMs plays a critical role in the regulation of chromatin structure and thus gene expression. This perspective focuses on the role of electrophilic protein adduction within the context of chromatin and its potential consequences on cellular law and order.

Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction

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.

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

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.

SNRFCB: sub-network based random forest classifier for predicting chemotherapy benefit on survival for cancer treatment

Adjuvant chemotherapy (CTX) should be individualized to provide potential survival benefit and avoid potential harm to cancer patients. Our goal was to establish a computational approach for making personalized estimates of the survival benefit from adjuvant CTX. We developed Sub-Network based Random Forest classifier for predicting Chemotherapy Benefit (SNRFCB) based gene expression datasets of lung cancer. The SNRFCB approach was then validated in independent test cohorts for identifying chemotherapy responder cohorts and chemotherapy non-responder cohorts. SNRFCB involved the pre-selection of gene sub-network signatures based on the mutations and on protein-protein interaction data as well as the application of the random forest algorithm to gene expression datasets. Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer patients in the chemotherapy responder group (P = 0.008), but it was not beneficial to patients in the chemotherapy non-responder group (P = 0.657). Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer squamous cell carcinoma (SQCC) subtype patients in the chemotherapy responder cohorts (P = 0.024), but it was not beneficial to patients in the chemotherapy non-responder cohorts (P = 0.383). SNRFCB improved prediction performance as compared to the machine learning method, support vector machine (SVM). To test the general applicability of the predictive model, we further applied the SNRFCB approach to human breast cancer datasets and also observed superior performance. SNRFCB could provide recurrent probability for individual patients and identify which patients may benefit from adjuvant CTX in clinical trials.

4-Hydroxy-nonenal-A Bioactive Lipid Peroxidation Product

This review on recent research advances of the lipid peroxidation product 4-hydroxy-nonenal (HNE) has four major topics: I. the formation of HNE in various organs and tissues, II. the diverse biochemical reactions with Michael adduct formation as the most prominent one, III. the endogenous targets of HNE, primarily peptides and proteins (here the mechanisms of covalent adduct formation are described and the (patho-) physiological consequences discussed), and IV. the metabolism of HNE leading to a great number of degradation products, some of which are excreted in urine and may serve as non-invasive biomarkers of oxidative stress.

Network-based sub-network signatures unveil the potential for acute myeloid leukemia therapy

Although gene expression profiling studies of acute myeloid leukemia (AML) patients have provided key insights into potential diagnostic and prognostic markers and therapeutic targets, it is not clear that the patterns of molecular heterogeneity affect the tumor biology and respond to the treatment. We hypothesized that network-based gene expression signatures of AML represent the mechanistically important genes and may improve the predicted performance of prognosis and clinical outcome. We provided the random walk with restart (RWR) analysis to discover the sub-network of genomic alterations. The RWR approach integrates the signature genes derived from the random forest (RF) analysis as "seeds" to identify genes critical to the AML recurrence phenotype. To test whether the 81-gene biomarkers could predict AML recurrence, we developed Survival Support Vector Machine (SSVM) models using a gene expression dataset and test on an independent dataset. The random forest classifier was built based on 81-gene biomarkers to separate the AML patients into "recurrence" and "non-recurrence" groups. The 81-gene biomarkers showed significant enrichment related to cancer pathophysiology and provided good coverage of sub-network biomarkers and AML-related signaling pathways. The SSVM-based score was significantly associated with overall survival (hazard ratio [HR], 2.16; 95% confidence interval [CI], 1.18-3.97; p = 0.01). Similar results were obtained with reversed training and testing datasets (hazard ratio [HR], 1.6; 95% confidence interval [CI], 1.08-2.37; p = 0.02). The 81-gene biomarker based RF classifier improved classification performance. Overall, 81-gene biomarkers might be useful prognostic and predictive molecular markers to predict the clinical outcome of AML patients.

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

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.

A deep understanding of a drug’s mechanisms of actions is essential not only in the discovery of new treatments but also in minimizing adverse effects. Here, we develop a computational framework, the Drug-specific Signaling Pathway Network (DSPathNet), to reconstruct a comprehensive signaling pathway network (SPNetwork) impacted by a particular drug. To illustrate this computational approach, we used metformin, an anti-diabetic drug, as an example. Starting from collecting the metformin-related upstream genes and inferring the metformin-related downstream genes, we built one metformin-specific SPNetwork via random walk based algorithms. Our evaluation of the metformin-specific SPNetwork by using disease genes and genotyping data from genome-wide association studies showed that our DSPathNet approach was efficient to synopsize drug’s key components and their relationship involved in the type 2 diabetes and cancer, even the metformin anticancer activity. This work presents a novel computational framework for constructing individual drug-specific signal transduction networks. Furthermore, its successful application to the drug metformin provides some valuable insights into the mode of metformin action, which will facilitate our understanding of the molecular mechanisms underlying drug treatments, disease pathogenesis, and identification of novel drug targets and repurposed drugs.

Quantitative chemoproteomics for site-specific analysis of protein alkylation by 4-hydroxy-2-nonenal in cells

Protein alkylation by 4-hydroxy-2-nonenal (HNE), an endogenous lipid derived electrophile, contributes to stress signaling and cellular toxicity. Although previous work has identified protein targets for HNE alkylation, the sequence specificity of alkylation and dynamics in a cellular context remain largely unexplored. We developed a new quantitative chemoproteomic platform, which uses isotopically tagged, photocleavable azido-biotin reagents to selectively capture and quantify the cellular targets labeled by the alkynyl analogue of HNE (aHNE). Our analyses site-specifically identified and quantified 398 aHNE protein alkylation events (386 cysteine sites and 12 histidine sites) in intact cells. This data set expands by at least an order of magnitude the number of such modification sites previously reported. Although adducts formed by Michael addition are thought to be largely irreversible, we found that most aHNE modifications are lost rapidly in situ. Moreover, aHNE adduct turnover occurs only in intact cells and loss rates are site-selective. This quantitative chemoproteomics platform provides a versatile general approach to map bioorthogonal-chemically engineered post-translational modifications and their cellular dynamics in a site-specific and unbiased manner.

Alkylation damage by lipid electrophiles targets functional protein systems

Protein alkylation by reactive electrophiles contributes to chemical toxicities and oxidative stress, but the functional impact of alkylation damage across proteomes is poorly understood. We used Click chemistry and shotgun proteomics to profile the accumulation of proteome damage in human cells treated with lipid electrophile probes. Protein target profiles revealed three damage susceptibility classes, as well as proteins that were highly resistant to alkylation. Damage occurred selectively across functional protein interaction networks, with the most highly alkylation-susceptible proteins mapping to networks involved in cytoskeletal regulation. Proteins with lower damage susceptibility mapped to networks involved in protein synthesis and turnover and were alkylated only at electrophile concentrations that caused significant toxicity. Hierarchical susceptibility of proteome systems to alkylation may allow cells to survive sublethal damage while protecting critical cell functions.

Deciphering genomic alterations in colorectal cancer through transcriptional subtype-based network analysis

Both transcriptional subtype and signaling network analyses have proved useful in cancer genomics research. However, these two approaches are usually applied in isolation in existing studies. We reason that deciphering genomic alterations based on cancer transcriptional subtypes may help reveal subtype-specific driver networks and provide insights for the development of personalized therapeutic strategies. In this study, we defined transcriptional subtypes for colorectal cancer (CRC) and identified driver networks/pathways for each subtype. Applying consensus clustering to a patient cohort with 1173 samples identified three transcriptional subtypes, which were validated in an independent cohort with 485 samples. The three subtypes were characterized by different transcriptional programs related to normal adult colon, early colon embryonic development, and epithelial mesenchymal transition, respectively. They also showed statistically different clinical outcomes. For each subtype, we mapped somatic mutation and copy number variation data onto an integrated signaling network and identified subtype-specific driver networks using a random walk-based strategy. We found that genomic alterations in the Wnt signaling pathway were common among all three subtypes; however, unique combinations of pathway alterations including Wnt, VEGF and Notch drove distinct molecular and clinical phenotypes in different CRC subtypes. Our results provide a coherent and integrated picture of human CRC that links genomic alterations to molecular and clinical consequences, and which provides insights for the development of personalized therapeutic strategies for different CRC subtypes.

RNA-seq data analysis at the gene and CDS levels provides a comprehensive view of transcriptome responses induced by 4-hydroxynonenal

Reactive electrophiles produced during oxidative stress, such as 4-hydroxynonenal (HNE), are increasingly recognized as contributing factors in a variety of degenerative and inflammatory diseases. Here we used the RNA-seq technology to characterize transcriptome responses in RKO cells induced by HNE at subcytotoxic and cytotoxic doses. RNA-seq analysis rediscovered most of the differentially expressed genes reported by microarray studies and also identified novel gene responses. Interestingly, differential expression detection at the coding DNA sequence (CDS) level helped to further improve the consistency between the two technologies, suggesting the utility and importance of the CDS level analysis. RNA-seq data analysis combining gene and CDS levels yielded an informative and comprehensive picture of gradually evolving response networks with increasing HNE doses, from cell protection against oxidative injury at low dose, initiation of cell apoptosis and DNA damage at intermediate dose to significant deregulation of cellular functions at high dose. These evolving dose-dependent pathway changes, which cannot be observed by the gene level analysis alone, clearly reveal the HNE cytotoxic effect and are supported by IC50 experiments. Additionally, differential expression at the CDS level provides new insights into isoform regulation mechanisms. Taken together, our data demonstrate the power of RNA-seq to identify subtle transcriptome changes and to characterize effects induced by HNE through the generation of high-resolution data coupled with differential analysis at both gene and CDS levels.

Integrative genomics analysis identifies candidate drivers at 3q26-29 amplicon in squamous cell carcinoma of the lung

PURPOSE

Chromosome 3q26-29 is a critical region of genomic amplification in lung squamous cell carcinomas (SCC). Identification of candidate drivers in this region could help uncover new mechanisms in the pathogenesis and potentially new targets in SCC of the lung.

EXPERIMENTAL DESIGN

We conducted a meta-analysis of seven independent datasets containing a total of 593 human primary SCC samples to identify consensus candidate drivers in 3q26-29 amplicon. Through integrating protein-protein interaction network information, we further filtered for candidates that may function together in a network. Computationally predicted candidates were validated using RNA interference (RNAi) knockdown and cell viability assays. Clinical relevance of the experimentally supported drivers was evaluated in an independent cohort of 52 lung SCC patients using survival analysis.

RESULTS

The meta-analysis identified 20 consensus candidates, among which four (SENP2, DCUN1D1, DVL3, and UBXN7) are involved in a small protein-protein interaction network. Knocking down any of the four proteins led to cell growth inhibition of the 3q26-29-amplified SCC. Moreover, knocking down of SENP2 resulted in the most significant cell growth inhibition and downregulation of DCUN1D1 and DVL3. Importantly, a gene expression signature composed of SENP2, DCUN1D1, and DVL3 stratified patients into subgroups with different response to adjuvant chemotherapy.

CONCLUSION

Together, our findings show that SENP2, DCUN1D1, and DVL3 are candidate driver genes in the 3q26-29 amplicon of SCC, providing novel insights into the molecular mechanisms of disease progression and may have significant implication in the management of SCC of the lung.

SAMNet: a network-based approach to integrate multi-dimensional high throughput datasets

The rapid development of high throughput biotechnologies has led to an onslaught of data describing genetic perturbations and changes in mRNA and protein levels in the cell. Because each assay provides a one-dimensional snapshot of active signaling pathways, it has become desirable to perform multiple assays (e.g. mRNA expression and phospho-proteomics) to measure a single condition. However, as experiments expand to accommodate various cellular conditions, proper analysis and interpretation of these data have become more challenging. Here we introduce a novel approach called SAMNet, for Simultaneous Analysis of Multiple Networks, that is able to interpret diverse assays over multiple perturbations. The algorithm uses a constrained optimization approach to integrate mRNA expression data with upstream genes, selecting edges in the protein-protein interaction network that best explain the changes across all perturbations. The result is a putative set of protein interactions that succinctly summarizes the results from all experiments, highlighting the network elements unique to each perturbation. We evaluated SAMNet in both yeast and human datasets. The yeast dataset measured the cellular response to seven different transition metals, and the human dataset measured cellular changes in four different lung cancer models of Epithelial-Mesenchymal Transition (EMT), a crucial process in tumor metastasis. SAMNet was able to identify canonical yeast metal-processing genes unique to each commodity in the yeast dataset, as well as human genes such as β-catenin and TCF7L2/TCF4 that are required for EMT signaling but escaped detection in the mRNA and phospho-proteomic data. Moreover, SAMNet also highlighted drugs likely to modulate EMT, identifying a series of less canonical genes known to be affected by the BCR-ABL inhibitor imatinib (Gleevec), suggesting a possible influence of this drug on EMT.

Peptidyl-prolyl cis/trans-isomerase A1 (Pin1) is a target for modification by lipid electrophiles

Oxidation of membrane phospholipids is associated with inflammation, neurodegenerative disease, and cancer. Oxyradical damage to phospholipids results in the production of reactive aldehydes that adduct proteins and modulate their function. 4-Hydroxynonenal (HNE), a common product of oxidative damage to lipids, adducts proteins at exposed Cys, His, or Lys residues. Here, we demonstrate that peptidyl-prolyl cis/trans-isomerase A1 (Pin1), an enzyme that catalyzes the conversion of the peptide bond of pSer/pThr-Pro moieties in signaling proteins from cis to trans, is highly susceptible to HNE modification. Incubation of purified Pin1 with HNE followed by MALDI-TOF/TOF mass spectrometry resulted in detection of Michael adducts at the active site residues His-157 and Cys-113. Time and concentration dependencies indicate that Cys-113 is the primary site of HNE modification. Pin1 was adducted in MDA-MB-231 breast cancer cells treated with 8-alkynyl-HNE as judged by click chemistry conjugation with biotin followed by streptavidin-based pulldown and Western blotting with anti-Pin1 antibody. Furthermore, orbitrap MS data support the adduction of Cys-113 in the Pin1 active site upon HNE treatment of MDA-MB-231 cells. siRNA knockdown of Pin1 in MDA-MB-231 cells partially protected the cells from HNE-induced toxicity. Recent studies indicate that Pin1 is an important molecular target for the chemopreventive effects of green tea polyphenols. The present study establishes that it is also a target for electrophilic modification by products of lipid peroxidation.

Network analysis of the focal adhesion to invadopodia transition identifies a PI3K-PKCα invasive signaling axis

In cancer, deregulated signaling can produce an invasive cellular phenotype. We modeled the invasive transition as a theoretical switch between two cytoskeletal structures: focal adhesions and extracellular matrix-degrading invadopodia. We constructed molecular interaction networks of each structure and identified upstream regulatory hubs through computational analyses. We compared these regulatory hubs to the status of signaling components from head and neck carcinomas, which led us to analyze phosphatidylinositol 3-kinase (PI3K) and protein kinase C α (PKCα). Consistent with previous studies, PI3K activity promoted both the formation and the activity of invadopodia. We found that PI3K induction of invadopodia was increased by overexpression of SH2 (Src homology 2) domain-containing inositol 5'-phosphatase 2 (SHIP2), which converts the phosphatidylinositol 3,4,5-trisphosphate [PI(3,4,5)P(3)] that is produced by PI3K activity to phosphatidylinositol 3,4-bisphosphate [PI(3,4)P(2)], which is believed to promote invadopodia formation. Knockdown of PKCα had divergent effects on invadopodia formation, depending on the status of PI3K. Loss of PKCα inhibited invadopodia formation in cells with wild-type PI3K pathway status. Conversely, in cells with constitutively active PI3K (through activating PI3K mutants or lacking the endogenous opposing enzyme PTEN), PKCα knockdown increased invadopodia formation. Mechanistic studies revealed a negative feedback loop from PKCα that dampened PI3K activity and invasive behavior in cells with genetic hyperactivation of the PI3K pathway. These studies demonstrated the potential of network modeling as a discovery tool and identified PI3K and PKCα as interacting regulators of invasive behavior.

A network-based gene expression signature informs prognosis and treatment for colorectal cancer patients

Background

Several studies have reported gene expression signatures that predict recurrence risk in stage II and III colorectal cancer (CRC) patients with minimal gene membership overlap and undefined biological relevance. The goal of this study was to investigate biological themes underlying these signatures, to infer genes of potential mechanistic importance to the CRC recurrence phenotype and to test whether accurate prognostic models can be developed using mechanistically important genes.

Methods and Findings

We investigated eight published CRC gene expression signatures and found no functional convergence in Gene Ontology enrichment analysis. Using a random walk-based approach, we integrated these signatures and publicly available somatic mutation data on a protein-protein interaction network and inferred 487 genes that were plausible candidate molecular underpinnings for the CRC recurrence phenotype. We named the list of 487 genes a NEM signature because it integrated information from Network, Expression, and Mutation. The signature showed significant enrichment in four biological processes closely related to cancer pathophysiology and provided good coverage of known oncogenes, tumor suppressors, and CRC-related signaling pathways. A NEM signature-based Survival Support Vector Machine prognostic model was trained using a microarray gene expression dataset and tested on an independent dataset. The model-based scores showed a 75.7% concordance with the real survival data and separated patients into two groups with significantly different relapse-free survival (p = 0.002). Similar results were obtained with reversed training and testing datasets (p = 0.007). Furthermore, adjuvant chemotherapy was significantly associated with prolonged survival of the high-risk patients (p = 0.006), but not beneficial to the low-risk patients (p = 0.491).

Conclusions

The NEM signature not only reflects CRC biology but also informs patient prognosis and treatment response. Thus, the network-based data integration method provides a convergence between biological relevance and clinical usefulness in gene signature development.

Cell signalling by reactive lipid species: new concepts and molecular mechanisms

The process of lipid peroxidation is widespread in biology and is mediated through both enzymatic and non-enzymatic pathways. A significant proportion of the oxidized lipid products are electrophilic in nature, the RLS (reactive lipid species), and react with cellular nucleophiles such as the amino acids cysteine, lysine and histidine. Cell signalling by electrophiles appears to be limited to the modification of cysteine residues in proteins, whereas non-specific toxic effects involve modification of other nucleophiles. RLS have been found to participate in several physiological pathways including resolution of inflammation, cell death and induction of cellular antioxidants through the modification of specific signalling proteins. The covalent modification of proteins endows some unique features to this signalling mechanism which we have termed the ‘covalent advantage’. For example, covalent modification of signalling proteins allows for the accumulation of a signal over time. The activation of cell signalling pathways by electrophiles is hierarchical and depends on a complex interaction of factors such as the intrinsic chemical reactivity of the electrophile, the intracellular domain to which it is exposed and steric factors. This introduces the concept of electrophilic signalling domains in which the production of the lipid electrophile is in close proximity to the thiol-containing signalling protein. In addition, we propose that the role of glutathione and associated enzymes is to insulate the signalling domain from uncontrolled electrophilic stress. The persistence of the signal is in turn regulated by the proteasomal pathway which may itself be subject to redox regulation by RLS. Cell death mediated by RLS is associated with bioenergetic dysfunction, and the damaged proteins are probably removed by the lysosome-autophagy pathway.

Protein modification by oxidized phospholipids and hydrolytically released lipid electrophiles: Investigating cellular responses

Oxygen is essential for the growth and function of mammalian cells. However, imbalances in oxygen or abnormalities in the ability of a cell to respond to oxygen levels can result in oxidative stress. Oxidative stress plays an important role in a number of diseases including atherosclerosis, rheumatoid arthritis, cancer, neurodegenerative diseases and asthma. When membrane lipids are exposed to high levels of oxygen or derived oxidants, they undergo lipid peroxidation to generate oxidized phospholipids (oxPL). Continual exposure to oxidants and decomposition of oxPL results in the formation of reactive electrophiles, such as 4-hydroxy-2-nonenal (HNE). Reactive lipid electrophiles have been shown to covalently modify DNA and proteins. Furthermore, exposure of cells to lipid electrophiles results in the activation of cytoprotective signaling pathways in order to promote cell survival and recovery from oxidant stress. However, if not properly managed by cellular detoxification mechanisms, the continual exposure of cells to electrophiles results in cytotoxicity. The following perspective will discuss the biological importance of lipid electrophile protein adducts including current strategies employed to identify and isolate protein adducts of lipid electrophiles as well as approaches to define cellular signaling mechanisms altered upon exposure to electrophiles. This article is part of a Special Issue entitled: Oxidized phospholipids-their properties and interactions with proteins.