Revealing signaling pathway deregulation by using gene expression signatures and regulatory motif analysis

The heterotrimeric G-protein beta subunit Gpb1 controls hyphal growth under low oxygen conditions through the protein kinase A pathway and is essential for virulence in the fungus Mucor circinelloides

Mucor circinelloides, a dimorphic opportunistic pathogen, expresses three heterotrimeric G-protein beta subunits (Gpb1, Gpb2 and Gpb3). The Gpb1-encoding gene is up-regulated during mycelial growth compared with that in the spore or yeast stage. gpb1 deletion mutation analysis revealed its relevance for an adequate development during the dimorphic transition and for hyphal growth under low oxygen concentrations. Infection assays in mice indicated a phenotype with considerably reduced virulence and tissue invasiveness in the deletion mutants (Δgpb1) and decreased host inflammatory response. This finding could be attributed to the reduced filamentous growth in animal tissues compared with that of the wild-type strain. Mutation in a regulatory subunit of cAMP-dependent protein kinase A (PKA) subunit (PkaR1) resulted in similar phenotypes to Δgpb1. The defects exhibited by the Δgpb1 strain were genetically suppressed by pkaR1 overexpression, indicating that the PKA pathway is controlled by Gpb1 in M. circinelloides. Moreover, during growth under low oxygen levels, cAMP levels were much higher in the Δgpb1 than in the wild-type strain, but similar to those in the ΔpkaR1 strain. These findings reveal that M. circinelloides possesses a signal transduction pathway through which the Gpb1 heterotrimeric G subunit and PkaR1 control mycelial growth in response to low oxygen levels.

Gene expression patterns in transgenic mouse models of hypertrophic cardiomyopathy caused by mutations in myosin regulatory light chain

Using microarray and bioinformatics, we examined the gene expression profiles in transgenic mouse hearts expressing mutations in the myosin regulatory light chain shown to cause hypertrophic cardiomyopathy (HCM). We focused on two malignant RLC-mutations, Arginine 58→Glutamine (R58Q) and Aspartic Acid 166 → Valine (D166V), and one benign, Lysine 104 → Glutamic Acid (K104E)-mutation. Datasets of differentially expressed genes for each of three mutants were compared to those observed in wild-type (WT) hearts. The changes in the mutant vs. WT samples were shown as fold-change (FC), with stringency FC ≥ 2. Based on the gene profiles, we have identified the major signaling pathways that underlie the R58Q-, D166V- and K104E-HCM phenotypes. The correlations between different genotypes were also studied using network-based algorithms. Genes with strong correlations were clustered into one group and the central gene networks were identified for each HCM mutant. The overall gene expression patterns in all mutants were distinct from the WT profiles. Both malignant mutations shared certain classes of genes that were up or downregulated, but most similarities were noted between D166V and K104E mice, with R58Q hearts showing a distinct gene expression pattern. Our data suggest that all three HCM mice lead to cardiomyopathy in a mutation-specific manner and thus develop HCM through diverse mechanisms.

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.

Stratification and delineation of gastric cancer signaling by in vitro transcription factor activity profiling and integrative genomics

Integrative functional genomic approaches are helpful in delineating the complex dysregulations in cancers. In the present study, in vitro activity profiling of 45 signaling pathway driven transcription factors in eight gastric cancer cell lines and direct comparison with genome-wide profiles of gastric tumors were performed and the integration resulted in the identification of three categories of factors/pathways: i) highly activated signaling pathways that stem from mutations are the critical oncogenic drivers, ii) constitutively activated stress responsive pathways which are activated not due to genetic alterations, and iii) consistently down-regulated nuclear receptor responsive factors. This functional profiling helps in discriminating therapeutic targets and signaling interactions.

Genome-wide signatures of transcription factor activity: connecting transcription factors, disease, and small molecules

Identifying transcription factors (TF) involved in producing a genome-wide transcriptional profile is an essential step in building mechanistic model that can explain observed gene expression data. We developed a statistical framework for constructing genome-wide signatures of TF activity, and for using such signatures in the analysis of gene expression data produced by complex transcriptional regulatory programs. Our framework integrates ChIP-seq data and appropriately matched gene expression profiles to identify True REGulatory (TREG) TF-gene interactions. It provides genome-wide quantification of the likelihood of regulatory TF-gene interaction that can be used to either identify regulated genes, or as genome-wide signature of TF activity. To effectively use ChIP-seq data, we introduce a novel statistical model that integrates information from all binding "peaks" within 2 Mb window around a gene's transcription start site (TSS), and provides gene-level binding scores and probabilities of regulatory interaction. In the second step we integrate these binding scores and regulatory probabilities with gene expression data to assess the likelihood of True REGulatory (TREG) TF-gene interactions. We demonstrate the advantages of TREG framework in identifying genes regulated by two TFs with widely different distribution of functional binding events (ERα and E2f1). We also show that TREG signatures of TF activity vastly improve our ability to detect involvement of ERα in producing complex diseases-related transcriptional profiles. Through a large study of disease-related transcriptional signatures and transcriptional signatures of drug activity, we demonstrate that increase in statistical power associated with the use of TREG signatures makes the crucial difference in identifying key targets for treatment, and drugs to use for treatment. All methods are implemented in an open-source R package treg. The package also contains all data used in the analysis including 494 TREG binding profiles based on ENCODE ChIP-seq data. The treg package can be downloaded at http://GenomicsPortals.org.

Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties

Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC₅₀ values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC₅₀ values in a 8-fold cross-validation and an independent blind test with coefficient of determination R² of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R² of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC₅₀ values rather than experimentally measuring them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity.

Transcriptional data: a new gateway to drug repositioning?

Recent advances in computational biology suggest that any perturbation to the transcriptional programme of the cell can be summarised by a proper ‘signature’: a set of genes combined with a pattern of expression. Therefore, it should be possible to generate proxies of clinicopathological phenotypes and drug effects through signatures acquired via DNA microarray technology.

Gene expression signatures have recently been assembled and compared through genome-wide metrics, unveiling unexpected drug–disease and drug–drug ‘connections’ by matching corresponding signatures. Consequently, novel applications for existing drugs have been predicted and experimentally validated.

Here, we describe related methods, case studies and resources while discussing challenges and benefits of exploiting existing repositories of microarray data that could serve as a search space for systematic drug repositioning.

Evidence for a genetic role in varicose veins and chronic venous insufficiency

There is a strong body of circumstantial evidence which implicates genetics in the aetiology and pathology of varicose veins and venous ulcer disease. The aim of this review is to consider the current knowledge of the genetic associations and the ways in which new genetic technologies may be applied to advancing our understanding of the cause and progression of these venous diseases. A number of publications have used a candidate gene approach to identify genes implicated in venous disease. Although these studies have opened up important new insights, there has been a general failure to replicate results in an independent cohort of patients. With our limited knowledge of the biological pathways involved in the pathogenesis of venous disease we are not in a strong position to formulate truly erudite a priori candidate gene hypothesis-directed studies. A genome-wide association study should therefore be considered to help further our understanding of the genetic basis of venous disease. Due to the large sample sizes required for discovery and validation, using the new generations of molecular technologies, it will be necessary to form collaborating groups in order to successfully advance the field of venous disease genetics.

Penalized regression elucidates aberration hotspots mediating subtype-specific transcriptional responses in breast cancer

MOTIVATION

Copy number alterations (CNAs) associated with cancer are known to contribute to genomic instability and gene deregulation. Integrating CNAs with gene expression helps to elucidate the mechanisms by which CNAs act and to identify the transcriptional downstream targets of CNAs. Such analyses can help to sort functional driver events from the many accompanying passenger alterations. However, the way CNAs affect gene expression can vary in different cellular contexts, for example between different subtypes of the same cancer. Thus, it is important to develop computational approaches capable of inferring differential connectivity of regulatory networks in different cellular contexts.

RESULTS

We propose a statistical deregulation model that integrates copy number and expression data of different disease subtypes to jointly model common and differential regulatory relationships. Our model not only identifies CNAs driving gene expression changes, but at the same time also predicts differences in regulation that distinguish one cancer subtype from the other. We implement our model in a penalized regression framework and demonstrate in a simulation study the feasibility and accuracy of our approach. Subsequently, we show that this model can identify both known and novel aspects of cross-talk between the ER and NOTCH pathways in ER-negative-specific deregulations, when compared with ER-positive breast cancer. This flexible model can be applied on other modalities such as methylation or microRNA and expression to disentangle cancer signaling pathways.

AVAILABILITY

The Bioconductor-compliant R package DANCE is available from www.markowetzlab.org/software/

CONTACT

yinyin.yuan@cancer.org.uk; florian.markowetz@cancer.org.uk.

BAYESIAN HIERARCHICAL MODELING FOR SIGNALING PATHWAY INFERENCE FROM SINGLE CELL INTERVENTIONAL DATA

Recent technological advances have made it possible to simultaneously measure multiple protein activities at the single cell level. With such data collected under different stimulatory or inhibitory conditions, it is possible to infer the causal relationships among proteins from single cell interventional data. In this article we propose a Bayesian hierarchical modeling framework to infer the signaling pathway based on the posterior distributions of parameters in the model. Under this framework, we consider network sparsity and model the existence of an association between two proteins both at the overall level across all experiments and at each individual experimental level. This allows us to infer the pairs of proteins that are associated with each other and their causal relationships. We also explicitly consider both intrinsic noise and measurement error. Markov chain Monte Carlo is implemented for statistical inference. We demonstrate that this hierarchical modeling can effectively pool information from different interventional experiments through simulation studies and real data analysis.

Relating protein adduction to gene expression changes: a systems approach

Modification of proteins by reactive electrophiles such as the 4-hydroxy-2-nonenal (HNE) plays a critical role in oxidant-associated human diseases. However, little is known about protein adduction and the mechanism by which protein damage elicits adaptive effects and toxicity. We developed a systems approach for relating protein adduction to gene expression changes through the integration of protein adduction, gene expression, protein-DNA interaction, and protein-protein interaction data. Using a random walk strategy, we expanded a list of responsive transcription factors inferred from gene expression studies to upstream signaling networks, which in turn allowed overlaying protein adduction data on the network for the prediction of stress sensors and their associated regulatory mechanisms. We demonstrated the general applicability of transcription factor-based signaling network inference using 103 known pathways. Applying our workflow on gene expression and protein adduction data from HNE-treatment not only rediscovered known mechanisms of electrophile stress but also generated novel hypotheses regarding protein damage sensors. Although developed for analyzing protein adduction data, the framework can be easily adapted for phosphoproteomics and other types of protein modification data.

Comparative survival analysis of breast cancer microarray studies identifies important prognostic genetic pathways

BACKGROUND

An estimated 12% of females in the United States will develop breast cancer in their lifetime. Although, there are advances in treatment options including surgery and chemotherapy, breast cancer is still the second most lethal cancer in women. Thus, there is a clear need for better methods to predict prognosis for each breast cancer patient. With the advent of large genetic databases and the reduction in cost for the experiments, researchers are faced with choosing from a large pool of potential prognostic markers from numerous breast cancer gene expression profile studies.

METHODS

Five microarray datasets related to breast cancer were examined using gene set analysis and the cancers were categorized into different subtypes using a scoring system based on genetic pathway activity.

RESULTS

We have observed that significant genes in the individual studies show little reproducibility across the datasets. From our comparative analysis, using gene pathways with clinical variables is more reliable across studies and shows promise in assessing a patient's prognosis.

CONCLUSIONS

This study concludes that, in light of clinical variables, there are significant gene pathways in common across the datasets. Specifically, several pathways can further significantly stratify patients for survival. These candidate pathways should help to develop a panel of significant biomarkers for the prognosis of breast cancer patients in a clinical setting.

MYCN/c-MYC-induced microRNAs repress coding gene networks associated with poor outcome in MYCN/c-MYC-activated tumors

Increased activity of MYC protein-family members is a common feature in many cancers. Using neuroblastoma as a tumor model, we established a microRNA (miRNA) signature for activated MYCN/c-MYC signaling in two independent primary neuroblastoma tumor cohorts and provide evidence that c-MYC and MYCN have overlapping functions. On the basis of an integrated approach including miRNA and messenger RNA (mRNA) gene expression data we show that miRNA activation contributes to widespread mRNA repression, both in c-MYC- and MYCN-activated tumors. c-MYC/MYCN-induced miRNA activation was shown to be dependent on c-MYC/MYCN promoter binding as evidenced by chromatin immunoprecipitation. Finally, we show that pathways, repressed through c-MYC/MYCN miRNA activation, are highly correlated to tumor aggressiveness and are conserved across different tumor entities suggesting that c-MYC/MYCN activate a core set of miRNAs for cooperative repression of common transcriptional programs related to disease aggressiveness. Our results uncover a widespread correlation between miRNA activation and c-MYC/MYCN-mediated coding gene expression modulation and further substantiate the overlapping functions of c-MYC and MYCN in the process of tumorigenesis.

Evolutionary forces act on promoter length: identification of enriched cis-regulatory elements

Transcription factors govern gene expression by binding to short DNA sequences called cis-regulatory elements. These sequences are typically located in promoters, which are regions of variable length upstream of the open reading frames of genes. Here, we report that promoter length and gene function are related in yeast, fungi, and plants. In particular, the promoters for stress-responsive genes are in general longer than those of other genes. Essential genes have, on the other hand, relatively short promoters. We utilize these findings in a novel method for identifying relevant cis-regulatory elements in a set of coexpressed genes. The method is shown to generate more accurate results and fewer false positives compared with other common procedures. Our results suggest that genes with complex transcriptional regulation tend to have longer promoters than genes responding to few signals. This phenomenon is present in all investigated species, indicating that evolution adjust promoter length according to gene function. Identification of cis-regulatory elements in Saccharomyces cerevisiae can be done with the web service located at http://enricher.zool.gu.se.

High Myc pathway activity and low stage of neuronal differentiation associate with poor outcome in neuroblastoma

The childhood cancer neuroblastoma arises in the developing sympathetic nervous system and is a genotypically and phenotypically heterogeneous disease. Prognostic markers of poor survival probability include amplification of the MYCN oncogene and an undifferentiated morphology. Whereas these features discriminate high- from low-risk patients with precision, identification of poor outcome low- and intermediate-risk patients is more challenging. In this study, we analyze two large neuroblastoma microarray datasets using a priori-defined gene expression signatures. We show that differential overexpression of Myc transcriptional targets and low expression of genes involved in sympathetic neuronal differentiation predicts relapse and death from disease. This was evident not only for high-risk patients but was also robust in identifying groups of poor prognosis patients who were otherwise judged to be at low- or intermediate-risk for adverse outcome. These data suggest that pathway-specific gene expression profiling might be useful in the clinic to adjust treatment strategies for children with neuroblastoma.