Dissecting the dynamics of dysregulation of cellular processes in mouse mammary gland tumor

Co-Expression Networks Unveiled Long Non-Coding RNAs as Molecular Targets of Drugs Used to Treat Bipolar Disorder

Long non-coding RNAs (lncRNAs) may play a role in psychiatric diseases including bipolar disorder (BD). We investigated mRNA-lncRNA co-expression patterns in neuronal-like cells treated with widely prescribed BD medications. The aim was to unveil insights into the complex mechanisms of BD medications and highlight potential targets for new drug development. Human neuronal-like (NT2-N) cells were treated with either lamotrigine, lithium, quetiapine, valproate or vehicle for 24 h. Genome-wide mRNA expression was quantified for weighted gene co-expression network analysis (WGCNA) to correlate the expression levels of mRNAs with lncRNAs. Functional enrichment analysis and hub lncRNA identification was conducted on key co-expressed modules associated with the drug response. We constructed lncRNA-mRNA co-expression networks and identified key modules underlying these treatments, as well as their enriched biological functions. Processes enriched in key modules included synaptic vesicle cycle, endoplasmic reticulum-related functions and neurodevelopment. Several lncRNAs such as GAS6-AS1 and MIR100HG were highlighted as driver genes of key modules. Our study demonstrates the key role of lncRNAs in the mechanism(s) of action of BD drugs. Several lncRNAs have been suggested as major regulators of medication effects and are worthy of further investigation as novel drug targets to treat BD.

Conditional transcriptional relationships may serve as cancer prognostic markers

BACKGROUND

While most differential coexpression (DC) methods are bound to quantify a single correlation value for a gene pair across multiple samples, a newly devised approach under the name Correlation by Individual Level Product (CILP) revolutionarily projects the summary correlation value to individual product correlation values for separate samples. CILP greatly widened DC analysis opportunities by allowing integration of non-compromised statistical methods.

METHODS

Here, we performed a study to verify our hypothesis that conditional relationships, i.e., gene pairs of remarkable differential coexpression, may be sought as quantitative prognostic markers for human cancers. Alongside the seeking of prognostic gene links in a pan-cancer setting, we also examined whether a trend of global expression correlation loss appeared in a wide panel of cancer types and revisited the controversial subject of mutual relationship between the DE approach and the DC approach.

RESULTS

By integrating CILP with classical univariate survival analysis, we identified up to 244 conditional gene links as potential prognostic markers in five cancer types. In particular, five prognostic gene links for kidney renal papillary cell carcinoma tended to condense around cancer gene ESPL1, and the transcriptional synchrony between ESPL1 and PTTG1 tended to be elevated in patients of adverse prognosis. In addition, we extended the observation of global trend of correlation loss in more than ten cancer types and empirically proved DC analysis results were independent of gene differential expression in five cancer types.

CONCLUSIONS

Combining the power of CILP and the classical survival analysis, we successfully fetched conditional transcriptional relationships that conferred prognosis power for five cancer types. Despite a general trend of global correlation loss in tumor transcriptomes, most of these prognosis conditional links demonstrated stronger expression correlation in tumors, and their stronger coexpression was associated with poor survival.

Fundamental gene network rewiring at the second order within and across mammalian systems

MOTIVATION

Genetic or epigenetic events can rewire molecular networks to induce extraordinary phenotypical divergences. Among the many network rewiring approaches, no model-free statistical methods can differentiate gene-gene pattern changes not attributed to marginal changes. This may obscure fundamental rewiring from superficial changes.

RESULTS

Here we introduce a model-free Sharma-Song test to determine if patterns differ in the second order, meaning that the deviation of the joint distribution from the product of marginal distributions is unequal across conditions. We prove an asymptotic chi-squared null distribution for the test statistic. Simulation studies demonstrate its advantage over alternative methods in detecting second-order differential patterns. Applying the test on three independent mammalian developmental transcriptome datasets, we report a lower frequency of co-expression network rewiring between human and mouse for the same tissue group than the frequency of rewiring between tissue groups within the same species. We also find secondorder differential patterns between microRNA promoters and genes contrasting cerebellum and liver development in mice. These patterns are enriched in the spliceosome pathway regulating tissue specificity. Complementary to previous mammalian comparative studies mostly driven by first-order effects, our findings contribute an understanding of system-wide second-order gene network rewiring within and across mammalian systems. Second-order differential patterns constitute evidence for fundamentally rewired biological circuitry due to evolution, environment, or disease.

AVAILABILITY

The generic Sharma-Song test is available from the R package 'DiffXTables' at https://cran.rproject.org/package=DiffXTables. Other code and data are described in Methods.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Expression correlation attenuates within and between key signaling pathways in chronic kidney disease

BACKGROUND

Compared to the conventional differential expression approach, differential coexpression analysis represents a different yet complementary perspective into diseased transcriptomes. In particular, global loss of transcriptome correlation was previously observed in aging mice, and a most recent study found genetic and environmental perturbations on human subjects tended to cause universal attenuation of transcriptome coherence. While methodological progresses surrounding differential coexpression have helped with research on several human diseases, there has not been an investigation of coexpression disruptions in chronic kidney disease (CKD) yet.

METHODS

RNA-seq was performed on total RNAs of kidney tissue samples from 140 CKD patients. A combination of differential coexpression methods were employed to analyze the transcriptome transition in CKD from the early, mild phase to the late, severe kidney damage phase.

RESULTS

We discovered a global expression correlation attenuation in CKD progression, with pathway Regulation of nuclear SMAD2/3 signaling demonstrating the most remarkable intra-pathway correlation rewiring. Moreover, the pathway Signaling events mediated by focal adhesion kinase displayed significantly weakened crosstalk with seven pathways, including Regulation of nuclear SMAD2/3 signaling. Well-known relevant genes, such as ACTN4, were characterized with widespread correlation disassociation with partners from a wide array of signaling pathways.

CONCLUSIONS

Altogether, our analysis reported a global expression correlation attenuation within and between key signaling pathways in chronic kidney disease, and presented a list of vanishing hub genes and disrupted correlations within and between key signaling pathways, illuminating on the pathophysiological mechanisms of CKD progression.

Differential coexpression in human tissues and the confounding effect of mean expression levels

Motivation

Differential coexpression-the alteration of gene coexpression patterns observed in different biological conditions-has been proposed to be a mechanism for revealing rewiring of transcription regulatory networks. Despite wide use of methods for differential coexpression analysis, the phenomenon has not been well-studied. In particular, in many applications, differential coexpression is confounded with differential expression, that is, changes in average levels of expression across conditions. This confounding, despite affecting the interpretation of the differential coexpression, has rarely been studied.

Results

We constructed high-quality coexpression networks for five human tissues and identified coexpression links (gene pairs) that were specific to each tissue. Between 3 and 32% of coexpression links were tissue-specific (differentially coexpressed) and this specificity is reproducible in an external dataset. However, we show that up to 75% of the observed differential coexpression is substantially explained by average expression levels of the genes. 'Pure' differential coexpression independent from differential expression is a minority and is less reproducible in external datasets. We also investigated the functional relevance of pure differential coexpression. Our conclusion is that to a large extent, differential coexpression is more parsimoniously explained by changes in average expression levels and pure links have little impact on network-based functional analysis.

Supplementary information

Supplementary data are available at Bioinformatics online.

Genetic and environmental perturbations lead to regulatory decoherence

Correlation among traits is a fundamental feature of biological systems that remains difficult to study. To address this problem, we developed a flexible approach that allows us to identify factors associated with inter-individual variation in correlation. We use data from three human cohorts to study the effects of genetic and environmental variation on correlations among mRNA transcripts and among NMR metabolites. We first show that environmental exposures (infection and disease) lead to a systematic loss of correlation, which we define as 'decoherence'. Using longitudinal data, we show that decoherent metabolites are better predictors of whether someone will develop metabolic syndrome than metabolites commonly used as biomarkers of this disease. Finally, we demonstrate that correlation itself is under genetic control by mapping hundreds of 'correlation quantitative trait loci (QTLs)'. Together, this work furthers our understanding of how and why coordinated biological processes break down, and points to a potential role for decoherence in disease.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

KEDDY: a knowledge-based statistical gene set test method to detect differential functional protein-protein interactions

MOTIVATION

Identifying differential patterns between conditions is a popular approach to understanding the discrepancy between different biological contexts. Although many statistical tests were proposed for identifying gene sets with differential patterns based on different definitions of differentiality, few methods were suggested to identify gene sets with differential functional protein networks due to computational complexity.

RESULTS

We propose a method of Knowledge-based Evaluation of Dependency DifferentialitY (KEDDY), which is a statistical test for differential functional protein networks of a set of genes between two conditions with utilizing known functional protein-protein interaction information. Unlike other approaches focused on differential expressions of individual genes or differentiality of individual interactions, KEDDY compares two conditions by evaluating the probability distributions of functional protein networks based on known functional protein-protein interactions. The method has been evaluated and compared with previous methods through simulation studies, where KEDDY achieves significantly improved performance in accuracy and speed than the previous method that does not use prior knowledge and better performance in identifying gene sets with differential interactions than other methods evaluating changes in gene expressions. Applications to cancer data sets show that KEDDY identifies alternative cancer subtype-related differential gene sets compared to other differential expression-based methods, and the results also provide detailed gene regulatory information that drives the differentiality of the gene sets.

AVAILABILITY AND IMPLEMENTATION

The Java implementation of KEDDY is freely available to non-commercial users at https://sites.google.com/site/sjunggsm/keddy.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification and functional analysis of a core gene module associated with hepatitis C virus-induced human hepatocellular carcinoma progression

Hepatitis C virus (HCV)-induced human hepatocellular carcinoma (HCC) progression may be due to a complex multi-step processes. The developmental mechanism of these processes is worth investigating for the prevention, diagnosis and therapy of HCC. The aim of the present study was to investigate the molecular mechanism underlying the progression of HCV-induced hepatocarcinogenesis. First, the dynamic gene module, consisting of key genes associated with progression between the normal stage and HCC, was identified using the Weighted Gene Co-expression Network Analysis tool from R language. By defining those genes in the module as seeds, the change of co-expression in differentially expressed gene sets in two consecutive stages of pathological progression was examined. Finally, interaction pairs of HCV viral proteins and their directly targeted proteins in the identified module were extracted from the literature and a comprehensive interaction dataset from yeast two-hybrid experiments. By combining the interactions between HCV and their targets, and protein-protein interactions in the Search Tool for the Retrieval of Interacting Genes database (STRING), the HCV-key genes interaction network was constructed and visualized using Cytoscape software 3.2. As a result, a module containing 44 key genes was identified to be associated with HCC progression, due to the dynamic features and functions of those genes in the module. Several important differentially co-expressed gene pairs were identified between non-HCC and HCC stages. In the key genes, cyclin dependent kinase 1 (CDK1), NDC80, cyclin A2 (CCNA2) and rac GTPase activating protein 1 (RACGAP1) were shown to be targeted by the HCV nonstructural proteins NS5A, NS3 and NS5B, respectively. The four genes perform an intermediary role between the HCV viral proteins and the dysfunctional module in the HCV key genes interaction network. These findings provided valuable information for understanding the mechanism of HCV-induced HCC progression and for seeking drug targets for the therapy and prevention of HCC.

Identifying a combined biomarker for bisphosphonate-related osteonecrosis of the jaw

BACKGROUND

For this study, the aim was to identify combined biomarkers associated with bisphosphonate-related osteonecrosis of the jaw (BRONJ).

MATERIALS AND METHODS

Microarray data for GSE7116 were downloaded from the Gene Expression Omnibus database, which contains 26 samples, including without ONJ, and 5 healthy volunteers. The combined biomarkers were identified using principal component analysis, and the pathway enrichment analyses were performed using the DAVID online tool.

RESULTS

Two hundred differently expressed genes between groups were detected according to the significances. From functional annotation, Y-box binding protein 1 and heterogeneous nuclear ribonucleoprotein C were found to be included in the most significant 10 pathways. Ten combined gene sets were identified that were effective in classifying multiple myeloma (MM) with ONJ and MM without ONJ.

CONCLUSION

Identifying combined gene expression profiles is expected to contribute to more personalized management of BRONJ and to improve existing therapies, and it will be helpful in finding new therapies by identifying more predictive biomarkers.

DIFFERENTIAL PATHWAY DEPENDENCY DISCOVERY ASSOCIATED WITH DRUG RESPONSE ACROSS CANCER CELL LINES

The effort to personalize treatment plans for cancer patients involves the identification of drug treatments that can effectively target the disease while minimizing the likelihood of adverse reactions. In this study, the gene-expression profile of 810 cancer cell lines and their response data to 368 small molecules from the Cancer Therapeutics Research Portal (CTRP) are analyzed to identify pathways with significant rewiring between genes, or differential gene dependency, between sensitive and non-sensitive cell lines. Identified pathways and their corresponding differential dependency networks are further analyzed to discover essentiality and specificity mediators of cell line response to drugs/compounds. For analysis we use the previously published method EDDY (Evaluation of Differential DependencY). EDDY first constructs likelihood distributions of gene-dependency networks, aided by known genegene interaction, for two given conditions, for example, sensitive cell lines vs. non-sensitive cell lines. These sets of networks yield a divergence value between two distributions of network likelihoods that can be assessed for significance using permutation tests. Resulting differential dependency networks are then further analyzed to identify genes, termed mediators, which may play important roles in biological signaling in certain cell lines that are sensitive or non-sensitive to the drugs. Establishing statistical correspondence between compounds and mediators can improve understanding of known gene dependencies associated with drug response while also discovering new dependencies. Millions of compute hours resulted in thousands of these statistical discoveries. EDDY identified 8,811 statistically significant pathways leading to 26,822 compound-pathway-mediator triplets. By incorporating STITCH and STRING databases, we could construct evidence networks for 14,415 compound-pathway-mediator triplets for support. The results of this analysis are presented in a searchable website to aid researchers in studying potential molecular mechanisms underlying cells' drug response as well as in designing experiments for the purpose of personalized treatment regimens.

KNOWLEDGE-ASSISTED APPROACH TO IDENTIFY PATHWAYS WITH DIFFERENTIAL DEPENDENCIES

We have previously developed a statistical method to identify gene sets enriched with condition-specific genetic dependencies. The method constructs gene dependency networks from bootstrapped samples in one condition and computes the divergence between distributions of network likelihood scores from different conditions. It was shown to be capable of sensitive and specific identification of pathways with phenotype-specific dysregulation, i.e., rewiring of dependencies between genes in different conditions. We now present an extension of the method by incorporating prior knowledge into the inference of networks. The degree of prior knowledge incorporation has substantial effect on the sensitivity of the method, as the data is the source of condition specificity while prior knowledge incorporation can provide additional support for dependencies that are only partially supported by the data. Use of prior knowledge also significantly improved the interpretability of the results. Further analysis of topological characteristics of gene differential dependency networks provides a new approach to identify genes that could play important roles in biological signaling in a specific condition, hence, promising targets customized to a specific condition. Through analysis of TCGA glioblastoma multiforme data, we demonstrate the method can identify not only potentially promising targets but also underlying biology for new targets.

Discovery of Intermediary Genes between Pathways Using Sparse Regression

The use of pathways and gene interaction networks for the analysis of differential expression experiments has allowed us to highlight the differences in gene expression profiles between samples in a systems biology perspective. The usefulness and accuracy of pathway analysis critically depend on our understanding of how genes interact with one another. That knowledge is continuously improving due to advances in next generation sequencing technologies and in computational methods. While most approaches treat each of them as independent entities, pathways actually coordinate to perform essential functions in a cell. In this work, we propose a methodology based on a sparse regression approach to find genes that act as intermediary to and interact with two pathways. We model each gene in a pathway using a set of predictor genes, and a connection is formed between the pathway gene and a predictor gene if the sparse regression coefficient corresponding to the predictor gene is non-zero. A predictor gene is a shared neighbor gene of two pathways if it is connected to at least one gene in each pathway. We compare the sparse regression approach to Weighted Correlation Network Analysis and a correlation distance based approach using time-course RNA-Seq data for dendritic cell from wild type, MyD88-knockout, and TRIF-knockout mice, and a set of RNA-Seq data from 60 Caucasian individuals. For the sparse regression approach, we found overrepresented functions for shared neighbor genes between TLR-signaling pathway and antigen processing and presentation, apoptosis, and Jak-Stat pathways that are supported by prior research, and compares favorably to Weighted Correlation Network Analysis in cases where the gene association signals are weak.

DECODE: an integrated differential co-expression and differential expression analysis of gene expression data

BACKGROUND

Both differential expression (DE) and differential co-expression (DC) analyses are appreciated as useful tools in understanding gene regulation related to complex diseases. The performance of integrating DE and DC, however, remains unexplored.

RESULTS

In this study, we proposed a novel analytical approach called DECODE (Differential Co-expression and Differential Expression) to integrate DC and DE analyses of gene expression data. DECODE allows one to study the combined features of DC and DE of each transcript between two conditions. By incorporating information of the dependency between DC and DE variables, two optimal thresholds for defining substantial change in expression and co-expression are systematically defined for each gene based on chi-square maximization. By using these thresholds, genes can be categorized into four groups with either high or low DC and DE characteristics. In this study, DECODE was applied to a large breast cancer microarray data set consisted of two thousand tumor samples. By identifying genes with high DE and high DC, we demonstrated that DECODE could improve the detection of some functional gene sets such as those related to immune system, metastasis, lipid and glucose metabolism. Further investigation on the identified genes and the associated functional pathways would provide an additional level of understanding of complex disease mechanism.

CONCLUSIONS

By complementing the recent DC and the traditional DE analyses, DECODE is a valuable methodology for investigating biological functions of genes exhibiting disease-associated DE and DC combined characteristics, which may not be easily revealed through DC or DE approach alone. DECODE is available at the Comprehensive R Archive Network (CRAN): http://cran.r-project.org/web/packages/decode/index.html .

Gene co-expression modules as clinically relevant hallmarks of breast cancer diversity

Co-expression modules are groups of genes with highly correlated expression patterns. In cancer, differences in module activity potentially represent the heterogeneity of phenotypes important in carcinogenesis, progression, or treatment response. To find gene expression modules active in breast cancer subpopulations, we assembled 72 breast cancer-related gene expression datasets containing ∼5,700 samples altogether. Per dataset, we identified genes with bimodal expression and used mixture-model clustering to ultimately define 11 modules of genes that are consistently co-regulated across multiple datasets. Functionally, these modules reflected estrogen signaling, development/differentiation, immune signaling, histone modification, ERBB2 signaling, the extracellular matrix (ECM) and stroma, and cell proliferation. The Tcell/Bcell immune modules appeared tumor-extrinsic, with coherent expression in tumors but not cell lines; whereas most other modules, interferon and ECM included, appeared intrinsic. Only four of the eleven modules were represented in the PAM50 intrinsic subtype classifier and other well-established prognostic signatures; although the immune modules were highly correlated to previously published immune signatures. As expected, the proliferation module was highly associated with decreased recurrence-free survival (RFS). Interestingly, the immune modules appeared associated with RFS even after adjustment for receptor subtype and proliferation; and in a multivariate analysis, the combination of Tcell/Bcell immune module down-regulation and proliferation module upregulation strongly associated with decreased RFS. Immune modules are unusual in that their upregulation is associated with a good prognosis without chemotherapy and a good response to chemotherapy, suggesting the paradox of high immune patients who respond to chemotherapy but would do well without it. Other findings concern the ECM/stromal modules, which despite common themes were associated with different sites of metastasis, possibly relating to the “seed and soil” hypothesis of cancer dissemination. Overall, co-expression modules provide a high-level functional view of breast cancer that complements the “cancer hallmarks” and may form the basis for improved predictors and treatments.

EDDY: a novel statistical gene set test method to detect differential genetic dependencies

Identifying differential features between conditions is a popular approach to understanding molecular features and their mechanisms underlying a biological process of particular interest. Although many tests for identifying differential expression of gene or gene sets have been proposed, there was limited success in developing methods for differential interactions of genes between conditions because of its computational complexity. We present a method for Evaluation of Dependency DifferentialitY (EDDY), which is a statistical test for differential dependencies of a set of genes between two conditions. Unlike previous methods focused on differential expression of individual genes or correlation changes of individual gene–gene interactions, EDDY compares two conditions by evaluating the probability distributions of dependency networks from genes. The method has been evaluated and compared with other methods through simulation studies, and application to glioblastoma multiforme data resulted in informative cancer and glioblastoma multiforme subtype-related findings. The comparison with Gene Set Enrichment Analysis, a differential expression-based method, revealed that EDDY identifies the gene sets that are complementary to those identified by Gene Set Enrichment Analysis. EDDY also showed much lower false positives than Gene Set Co-expression Analysis, a method based on correlation changes of individual gene–gene interactions, thus providing more informative results. The Java implementation of the algorithm is freely available to noncommercial users. Download from: http://biocomputing.tgen.org/software/EDDY.

Constructing module maps for integrated analysis of heterogeneous biological networks

Improved methods for integrated analysis of heterogeneous large-scale omic data are direly needed. Here, we take a network-based approach to this challenge. Given two networks, representing different types of gene interactions, we construct a map of linked modules, where modules are genes strongly connected in the first network and links represent strong inter-module connections in the second. We develop novel algorithms that considerably outperform prior art on simulated and real data from three distinct domains. First, by analyzing protein-protein interactions and negative genetic interactions in yeast, we discover epistatic relations among protein complexes. Second, we analyze protein-protein interactions and DNA damage-specific positive genetic interactions in yeast and reveal functional rewiring among protein complexes, suggesting novel mechanisms of DNA damage response. Finally, using transcriptomes of non-small-cell lung cancer patients, we analyze networks of global co-expression and disease-dependent differential co-expression and identify a sharp drop in correlation between two modules of immune activation processes, with possible microRNA control. Our study demonstrates that module maps are a powerful tool for deeper analysis of heterogeneous high-throughput omic data.

Dissection of regulatory networks that are altered in disease via differential co-expression

Comparing the gene-expression profiles of sick and healthy individuals can help in understanding disease. Such differential expression analysis is a well-established way to find gene sets whose expression is altered in the disease. Recent approaches to gene-expression analysis go a step further and seek differential co-expression patterns, wherein the level of co-expression of a set of genes differs markedly between disease and control samples. Such patterns can arise from a disease-related change in the regulatory mechanism governing that set of genes, and pinpoint dysfunctional regulatory networks.

Here we present DICER, a new method for detecting differentially co-expressed gene sets using a novel probabilistic score for differential correlation. DICER goes beyond standard differential co-expression and detects pairs of modules showing differential co-expression. The expression profiles of genes within each module of the pair are correlated across all samples. The correlation between the two modules, however, differs markedly between the disease and normal samples.

We show that DICER outperforms the state of the art in terms of significance and interpretability of the detected gene sets. Moreover, the gene sets discovered by DICER manifest regulation by disease-specific microRNA families. In a case study on Alzheimer's disease, DICER dissected biological processes and protein complexes into functional subunits that are differentially co-expressed, thereby revealing inner structures in disease regulatory networks.

The most fundamental and popular gene-expression experiments measure genome-wide transcription levels in two populations: perturbed and wild type, or cases and controls. The genes that show significantly different expression between the two populations (the differentially expressed genes) are useful for understanding the biology underlying the phenotype difference, and can sometimes also serve as biomarkers for classification. In contrast, genes that have similar expression to each other across all profiles (co-expressed genes) can yield clues about the functional commonality of the two populations. Differential co-expression has recently been proposed as a way to combine the benefits of these two approaches: it seeks gene groups that are co-expressed in one phenotype much more than in the other. Here we develop a new method for detecting differential co-expression and test it on case-control expression profiles of several diseases. Our algorithm improves upon the state of the art in the strength of the detected patterns and in agreement with current biological knowledge. We show that our method can predict gene regulators that are associated with the disease of interest and demonstrate that it can dissect known biological pathways into subcomponents that are not detected using standard analyses.

Causal co-expression method with module analysis to screen drugs with specific target

The considerable increase of investment in research and development by the pharmaceutical industry over the past three decades has not added the number of approved new drugs. An important issue ignored by drug discovery practice is the multi-dimensional interaction network between drugs and their targets. Thus, it is essential to view drug actions through the lens of network biology. In the current study, based on the co-expression network of transcription factors and their downstream genes, we proposed a novel approach, called causal co-expression method with module analysis, to screen drugs with specific target and fewer side effects. We presented a causal co-expression method with module analysis and it could be used in analyzing the microarray data of different drug candidates. At first, the differential wiring value (DW) was calculated to find some causal transcription factors (TFs) by combining with differential expression genes in the regulated networks. After the discovery of the causal TFs, co-expression module analysis method was applied to mine molecular pharmacology pathways around these causal TFs at molecular level. We applied our methods to two drug candidates, Argyrin A and Bortezomib, both with anti-cancer activities. We first obtained some differentially expressed transcription factors of cells treated with Argyrin A or Bortezomib. Nearly all these transcription factors are associated with the tumor suppressor protein p27kip1. Furthermore, module analysis showed that Bortezomib inhibited tumor growth not specifically by cell cycle and cell proliferation pathway, but through many basic metabolic processes which result in cell toxicity. In contrast, Argyrin A had influence on cell cycle, and was involved in DNA damage repair at the same time, showing that Argyrin A was a more suitable drug for anti-cancer treatment. Our study revealed that the causal co-expression method with module analysis was effective and can be used as a tool to evaluate drug candidates.

An empirical Bayesian approach for identifying differential coexpression in high-throughput experiments

A common goal of microarray and related high-throughput genomic experiments is to identify genes that vary across biological condition. Most often this is accomplished by identifying genes with changes in mean expression level, so called differentially expressed (DE) genes, and a number of effective methods for identifying DE genes have been developed. Although useful, these approaches do not accommodate other types of differential regulation. An important example concerns differential coexpression (DC). Investigations of this class of genes are hampered by the large cardinality of the space to be interrogated as well as by influential outliers. As a result, existing DC approaches are often underpowered, exceedingly prone to false discoveries, and/or computationally intractable for even a moderately large number of pairs. To address this, an empirical Bayesian approach for identifying DC gene pairs is developed. The approach provides a false discovery rate controlled list of significant DC gene pairs without sacrificing power. It is applicable within a single study as well as across multiple studies. Computations are greatly facilitated by a modification to the expectation-maximization algorithm and a procedural heuristic. Simulations suggest that the proposed approach outperforms existing methods in far less computational time; and case study results suggest that the approach will likely prove to be a useful complement to current DE methods in high-throughput genomic studies.

Conserved and differential gene interactions in dynamical biological systems

MOTIVATION

While biological systems operated from a common genome can be conserved in various ways, they can also manifest highly diverse dynamics and functions. This is because the same set of genes can interact differentially across specific molecular contexts. For example, differential gene interactions give rise to various stages of morphogenesis during cerebellar development. However, after over a decade of efforts toward reverse engineering biological networks from high-throughput omic data, gene networks of most organisms remain sketchy. This hindrance has motivated us to develop comparative modeling to highlight conserved and differential gene interactions across experimental conditions, without reconstructing complete gene networks first.

RESULTS

We established a comparative dynamical system modeling (CDSM) approach to identify conserved and differential interactions across molecular contexts. In CDSM, interactions are represented by ordinary differential equations and compared across conditions through statistical heterogeneity and homogeneity tests. CDSM demonstrated a consistent superiority over differential correlation and reconstruct-then-compare in simulation studies. We exploited CDSM to elucidate gene interactions important for cellular processes poorly understood during mouse cerebellar development. We generated hypotheses on 66 differential genetic interactions involved in expansion of the external granule layer. These interactions are implicated in cell cycle, differentiation, apoptosis and morphogenesis. Additional 1639 differential interactions among gene clusters were also identified when we compared gene interactions during the presence of Rhombic lip versus the presence of distinct internal granule layer. Moreover, compared with differential correlation and reconstruct-then-compare, CDSM makes fewer assumptions on data and thus is applicable to a wider range of biological assays.

AVAILABILITY

Source code in C++ and R is available for non-commercial organizations upon request from the corresponding author. The cerebellum gene expression dataset used in this article is available upon request from the Goldowitz lab (dang@cmmt.ubc.ca, http://grits.dglab.org/).

CONTACT

joemsong@cs.nmsu.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Link-based quantitative methods to identify differentially coexpressed genes and gene pairs

Background

Differential coexpression analysis (DCEA) is increasingly used for investigating the global transcriptional mechanisms underlying phenotypic changes. Current DCEA methods mostly adopt a gene connectivity-based strategy to estimate differential coexpression, which is characterized by comparing the numbers of gene neighbors in different coexpression networks. Although it simplifies the calculation, this strategy mixes up the identities of different coexpression neighbors of a gene, and fails to differentiate significant differential coexpression changes from those trivial ones. Especially, the correlation-reversal is easily missed although it probably indicates remarkable biological significance.

Results

We developed two link-based quantitative methods, DCp and DCe, to identify differentially coexpressed genes and gene pairs (links). Bearing the uniqueness of exploiting the quantitative coexpression change of each gene pair in the coexpression networks, both methods proved to be superior to currently popular methods in simulation studies. Re-mining of a publicly available type 2 diabetes (T2D) expression dataset from the perspective of differential coexpression analysis led to additional discoveries than those from differential expression analysis.

Conclusions

This work pointed out the critical weakness of current popular DCEA methods, and proposed two link-based DCEA algorithms that will make contribution to the development of DCEA and help extend it to a broader spectrum.

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.

From 'differential expression' to 'differential networking' - identification of dysfunctional regulatory networks in diseases

Understanding diseases requires identifying the differences between healthy and affected tissues. Gene expression data have revolutionized the study of diseases by making it possible to simultaneously consider thousands of genes. The identification of disease-associated genes requires studying the genes in the context of the regulatory systems they are involved in. A major goal is to identify specific regulatory networks that are dysfunctional in a given disease state. Although we still have not reached a stage where the elucidation of differential regulatory networks is commonly feasible, recent advances have described the first steps towards this goal - the identification of differential coexpression networks. This review describes the shift from differential gene expression to differential networking and outlines how this shift will affect the study of the genetic basis of disease.