Edward Y. Chen

Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool

BackgroundSystem-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement.ResultsHere, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios.ConclusionsEnrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr.

LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures

For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites.

A systems approach identifies HIPK2 as a key regulator of kidney fibrosis.

Kidney fibrosis is a common process that leads to the progression of various types of kidney disease. We used an integrated computational and experimental systems biology approach to identify protein kinases that regulate gene expression changes in the kidneys of human immunodeficiency virus (HIV) transgenic mice (Tg26 mice), which have both tubulointerstitial fibrosis and glomerulosclerosis. We identified homeo-domain interacting protein kinase 2 (HIPK2) as a key regulator of kidney fibrosis. HIPK2 was upregulated in the kidneys of Tg26 mice and in those of patients with various kidney diseases. HIV infection increased the protein concentrations of HIPK2 by promoting oxidative stress, which inhibited the seven in absentia homolog 1 (SIAH1)-mediated proteasomal degradation of HIPK2. HIPK2 induced apoptosis and the expression of epithelial-to-mesenchymal transition markers in kidney epithelial cells by activating the p53, transforming growth factor β (TGF-β)–SMAD family member 3 (Smad3) and Wnt-Notch pathways. Knockout of HIPK2 improved renal function and attenuated proteinuria and kidney fibrosis in Tg26 mice, as well as in other murine models of kidney fibrosis. We therefore conclude that HIPK2 is a potential target for anti-fibrosis therapy.

Expression2Kinases: mRNA profiling linked to multiple upstream regulatory layers

MOTIVATION Genome-wide mRNA profiling provides a snapshot of the global state of cells under different conditions. However, mRNA levels do not provide direct understanding of upstream regulatory mechanisms. Here, we present a new approach called Expression2Kinases (X2K) to identify upstream regulators likely responsible for observed patterns in genome-wide gene expression. By integrating chromatin immuno-precipitation (ChIP)-seq/chip and position weight matrices (PWMs) data, protein-protein interactions and kinase-substrate phosphorylation reactions, we can better identify regulatory mechanisms upstream of genome-wide differences in gene expression. We validated X2K by applying it to recover drug targets of food and drug administration (FDA)-approved drugs from drug perturbations followed by mRNA expression profiling; to map the regulatory landscape of 44 stem cells and their differentiating progeny; to profile upstream regulatory mechanisms of 327 breast cancer tumors; and to detect pathways from profiled hepatic stellate cells and hippocampal neurons. The X2K approach can advance our understanding of cell signaling and unravel drugs mechanisms of action. AVAILABILITY The software and source code are freely available at: http://www.maayanlab.net/X2K. CONTACT avi.maayan@mssm.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

ESCAPE: database for integrating high-content published data collected from human and mouse embryonic stem cells.

High content studies that profile mouse and human embryonic stem cells (m/hESCs) using various genome-wide technologies such as transcriptomics and proteomics are constantly being published. However, efforts to integrate such data to obtain a global view of the molecular circuitry in m/hESCs are lagging behind. Here, we present an m/hESC-centered database called Embryonic Stem Cell Atlas from Pluripotency Evidence integrating data from many recent diverse high-throughput studies including chromatin immunoprecipitation followed by deep sequencing, genome-wide inhibitory RNA screens, gene expression microarrays or RNA-seq after knockdown (KD) or overexpression of critical factors, immunoprecipitation followed by mass spectrometry proteomics and phosphoproteomics. The database provides web-based interactive search and visualization tools that can be used to build subnetworks and to identify known and novel regulatory interactions across various regulatory layers. The web-interface also includes tools to predict the effects of combinatorial KDs by additive effects controlled by sliders, or through simulation software implemented in MATLAB. Overall, the Embryonic Stem Cell Atlas from Pluripotency Evidence database is a comprehensive resource for the stem cell systems biology community. Database URL: http://www.maayanlab.net/ESCAPE

The characteristic direction: a geometrical approach to identify differentially expressed genes

BackgroundIdentifying differentially expressed genes (DEG) is a fundamental step in studies that perform genome wide expression profiling. Typically, DEG are identified by univariate approaches such as Significance Analysis of Microarrays (SAM) or Linear Models for Microarray Data (LIMMA) for processing cDNA microarrays, and differential gene expression analysis based on the negative binomial distribution (DESeq) or Empirical analysis of Digital Gene Expression data in R (edgeR) for RNA-seq profiling.ResultsHere we present a new geometrical multivariate approach to identify DEG called the Characteristic Direction. We demonstrate that the Characteristic Direction method is significantly more sensitive than existing methods for identifying DEG in the context of transcription factor (TF) and drug perturbation responses over a large number of microarray experiments. We also benchmarked the Characteristic Direction method using synthetic data, as well as RNA-Seq data. A large collection of microarray expression data from TF perturbations (73 experiments) and drug perturbations (130 experiments) extracted from the Gene Expression Omnibus (GEO), as well as an RNA-Seq study that profiled genome-wide gene expression and STAT3 DNA binding in two subtypes of diffuse large B-cell Lymphoma, were used for benchmarking the method using real data. ChIP-Seq data identifying DNA binding sites of the perturbed TFs, as well as known drug targets of the perturbing drugs, were used as prior knowledge silver-standard for validation. In all cases the Characteristic Direction DEG calling method outperformed other methods. We find that when drugs are applied to cells in various contexts, the proteins that interact with the drug-targets are differentially expressed and more of the corresponding genes are discovered by the Characteristic Direction method. In addition, we show that the Characteristic Direction conceptualization can be used to perform improved gene set enrichment analyses when compared with the gene-set enrichment analysis (GSEA) and the hypergeometric test.ConclusionsThe application of the Characteristic Direction method may shed new light on relevant biological mechanisms that would have remained undiscovered by the current state-of-the-art DEG methods. The method is freely accessible via various open source code implementations using four popular programming languages: R, Python, MATLAB and Mathematica, all available at: http://www.maayanlab.net/CD.

Network2Canvas: network visualization on a canvas with enrichment analysis

MOTIVATION Networks are vital to computational systems biology research, but visualizing them is a challenge. For networks larger than ∼100 nodes and ∼200 links, ball-and-stick diagrams fail to convey much information. To address this, we developed Network2Canvas (N2C), a web application that provides an alternative way to view networks. N2C visualizes networks by placing nodes on a square toroidal canvas. The network nodes are clustered on the canvas using simulated annealing to maximize local connections where a nodes brightness is made proportional to its local fitness. The interactive canvas is implemented in HyperText Markup Language (HTML)5 with the JavaScript library Data-Driven Documents (D3). We applied N2C to visualize 30 canvases made from human and mouse gene-set libraries and 6 canvases made from the Food and Drug Administration (FDA)-approved drug-set libraries. Given lists of genes or drugs, enriched terms are highlighted on the canvases, and their degree of clustering is computed. Because N2C produces visual patterns of enriched terms on canvases, a trained eye can detect signatures instantly. In summary, N2C provides a new flexible method to visualize large networks and can be used to perform and visualize gene-set and drug-set enrichment analyses. AVAILABILITY N2C is freely available at http://www.maayanlab.net/N2C and is open source. CONTACT avi.maayan@mssm.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Krüppel-like factor 6 regulates mitochondrial function in the kidney

Maintenance of mitochondrial structure and function is critical for preventing podocyte apoptosis and eventual glomerulosclerosis in the kidney; however, the transcription factors that regulate mitochondrial function in podocyte injury remain to be identified. Here, we identified Krüppel-like factor 6 (KLF6), a zinc finger domain transcription factor, as an essential regulator of mitochondrial function in podocyte apoptosis. We observed that podocyte-specific deletion of Klf6 increased the susceptibility of a resistant mouse strain to adriamycin-induced (ADR-induced) focal segmental glomerulosclerosis (FSGS). KLF6 expression was induced early in response to ADR in mice and cultured human podocytes, and prevented mitochondrial dysfunction and activation of intrinsic apoptotic pathways in these podocytes. Promoter analysis and chromatin immunoprecipitation studies revealed that putative KLF6 transcriptional binding sites are present in the promoter of the mitochondrial cytochrome c oxidase assembly gene (SCO2), which is critical for preventing cytochrome c release and activation of the intrinsic apoptotic pathway. Additionally, KLF6 expression was reduced in podocytes from HIV-1 transgenic mice as well as in renal biopsies from patients with HIV-associated nephropathy (HIVAN) and FSGS. Together, these findings indicate that KLF6-dependent regulation of the cytochrome c oxidase assembly gene is critical for maintaining mitochondrial function and preventing podocyte apoptosis.

Renoprotective Effect of Combined Inhibition of Angiotensin-Converting Enzyme and Histone Deacetylase

The Connectivity Map database contains microarray signatures of gene expression derived from approximately 6000 experiments that examined the effects of approximately 1300 single drugs on several human cancer cell lines. We used these data to prioritize pairs of drugs expected to reverse the changes in gene expression observed in the kidneys of a mouse model of HIV-associated nephropathy (Tg26 mice). We predicted that the combination of an angiotensin-converting enzyme (ACE) inhibitor and a histone deacetylase inhibitor would maximally reverse the disease-associated expression of genes in the kidneys of these mice. Testing the combination of these inhibitors in Tg26 mice revealed an additive renoprotective effect, as suggested by reduction of proteinuria, improvement of renal function, and attenuation of kidney injury. Furthermore, we observed the predicted treatment-associated changes in the expression of selected genes and pathway components. In summary, these data suggest that the combination of an ACE inhibitor and a histone deacetylase inhibitor could have therapeutic potential for various kidney diseases. In addition, this study provides proof-of-concept that drug-induced expression signatures have potential use in predicting the effects of combination drug therapy.

Activation of tumor suppressor protein PP2A inhibits KRAS-driven tumor growth

Targeted cancer therapies, which act on specific cancer-associated molecular targets, are predominantly inhibitors of oncogenic kinases. While these drugs have achieved some clinical success, the inactivation of kinase signaling via stimulation of endogenous phosphatases has received minimal attention as an alternative targeted approach. Here, we have demonstrated that activation of the tumor suppressor protein phosphatase 2A (PP2A), a negative regulator of multiple oncogenic signaling proteins, is a promising therapeutic approach for the treatment of cancers. Our group previously developed a series of orally bioavailable small molecule activators of PP2A, termed SMAPs. We now report that SMAP treatment inhibited the growth of KRAS-mutant lung cancers in mouse xenografts and transgenic models. Mechanistically, we found that SMAPs act by binding to the PP2A A&agr; scaffold subunit to drive conformational changes in PP2A. These results show that PP2A can be activated in cancer cells to inhibit proliferation. Our strategy of reactivating endogenous PP2A may be applicable to the treatment of other diseases and represents an advancement toward the development of small molecule activators of tumor suppressor proteins.