Alexander Lachmann

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments.

MOTIVATION Experiments such as ChIP-chip, ChIP-seq, ChIP-PET and DamID (the four methods referred herein as ChIP-X) are used to profile the binding of transcription factors to DNA at a genome-wide scale. Such experiments provide hundreds to thousands of potential binding sites for a given transcription factor in proximity to gene coding regions. RESULTS In order to integrate data from such studies and utilize it for further biological discovery, we collected interactions from such experiments to construct a mammalian ChIP-X database. The database contains 189,933 interactions, manually extracted from 87 publications, describing the binding of 92 transcription factors to 31,932 target genes. We used the database to analyze mRNA expression data where we perform gene-list enrichment analysis using the ChIP-X database as the prior biological knowledge gene-list library. The system is delivered as a web-based interactive application called ChIP Enrichment Analysis (ChEA). With ChEA, users can input lists of mammalian gene symbols for which the program computes over-representation of transcription factor targets from the ChIP-X database. The ChEA database allowed us to reconstruct an initial network of transcription factors connected based on shared overlapping targets and binding site proximity. To demonstrate the utility of ChEA we present three case studies. We show how by combining the Connectivity Map (CMAP) with ChEA, we can rank pairs of compounds to be used to target specific transcription factor activity in cancer cells. AVAILABILITY The ChEA software and ChIP-X database is freely available online at: http://amp.pharm.mssm.edu/lib/chea.jsp.

Global phosphorylation analysis of β-arrestin–mediated signaling downstream of a seven transmembrane receptor (7TMR)

β-Arrestin–mediated signaling downstream of seven transmembrane receptors (7TMRs) is a relatively new paradigm for signaling by these receptors. We examined changes in protein phosphorylation occurring when HEK293 cells expressing the angiotensin II type 1A receptor (AT1aR) were stimulated with the β-arrestin–biased ligand Sar1, Ile4, Ile8-angiotensin (SII), a ligand previously found to signal through β-arrestin–dependent, G protein-independent mechanisms. Using a phospho-antibody array containing 46 antibodies against signaling molecules, we found that phosphorylation of 35 proteins increased upon SII stimulation. These SII-mediated phosphorylation events were abrogated after depletion of β-arrestin 2 through siRNA-mediated knockdown. We also performed an MS-based quantitative phosphoproteome analysis after SII stimulation using a strategy of stable isotope labeling of amino acids in cell culture (SILAC). We identified 1,555 phosphoproteins (4,552 unique phosphopeptides), of which 171 proteins (222 phosphopeptides) showed increased phosphorylation, and 53 (66 phosphopeptides) showed decreased phosphorylation upon SII stimulation of the AT1aR. This study identified 38 protein kinases and three phosphatases whose phosphorylation status changed upon SII treatment. Using computational approaches, we performed system-based analyses examining the β-arrestin–mediated phosphoproteome including construction of a kinase-substrate network for β-arrestin–mediated AT1aR signaling. Our analysis demonstrates that β-arrestin–dependent signaling processes are more diverse than previously appreciated. Notably, our analysis identifies an AT1aR-mediated cytoskeletal reorganization network whereby β-arrestin regulates phosphorylation of several key proteins, including cofilin and slingshot. This study provides a system-based view of β-arrestin–mediated phosphorylation events downstream of a 7TMR and opens avenues for research in a rapidly evolving area of 7TMR signaling.

KEA: kinase enrichment analysis

Motivation: Multivariate experiments applied to mammalian cells often produce lists of proteins/genes altered under treatment versus control conditions. Such lists can be projected onto prior knowledge of kinase–substrate interactions to infer the list of kinases associated with a specific protein list. By computing how the proportion of kinases, associated with a specific list of proteins/genes, deviates from an expected distribution, we can rank kinases and kinase families based on the likelihood that these kinases are functionally associated with regulating the cell under specific experimental conditions. Such analysis can assist in producing hypotheses that can explain how the kinome is involved in the maintenance of different cellular states and can be manipulated to modulate cells towards a desired phenotype. Summary: Kinase enrichment analysis (KEA) is a web-based tool with an underlying database providing users with the ability to link lists of mammalian proteins/genes with the kinases that phosphorylate them. The system draws from several available kinase–substrate databases to compute kinase enrichment probability based on the distribution of kinase–substrate proportions in the background kinase–substrate database compared with kinases found to be associated with an input list of genes/proteins. Availability: The KEA system is freely available at http://amp.pharm.mssm.edu/lib/kea.jsp Contact: avi.maayan@mssm.edu

Receptor Heteromerization Expands the Repertoire of Cannabinoid Signaling in Rodent Neurons

A fundamental question in G protein coupled receptor biology is how a single ligand acting at a specific receptor is able to induce a range of signaling that results in a variety of physiological responses. We focused on Type 1 cannabinoid receptor (CB1R) as a model GPCR involved in a variety of processes spanning from analgesia and euphoria to neuronal development, survival and differentiation. We examined receptor dimerization as a possible mechanism underlying expanded signaling responses by a single ligand and focused on interactions between CB1R and delta opioid receptor (DOR). Using co-immunoprecipitation assays as well as analysis of changes in receptor subcellular localization upon co-expression, we show that CB1R and DOR form receptor heteromers. We find that heteromerization affects receptor signaling since the potency of the CB1R ligand to stimulate G-protein activity is increased in the absence of DOR, suggesting that the decrease in CB1R activity in the presence of DOR could, at least in part, be due to heteromerization. We also find that the decrease in activity is associated with enhanced PLC-dependent recruitment of arrestin3 to the CB1R-DOR complex, suggesting that interaction with DOR enhances arrestin-mediated CB1R desensitization. Additionally, presence of DOR facilitates signaling via a new CB1R-mediated anti-apoptotic pathway leading to enhanced neuronal survival. Taken together, these results support a role for CB1R-DOR heteromerization in diversification of endocannabinoid signaling and highlight the importance of heteromer-directed signal trafficking in enhancing the repertoire of GPCR signaling.

Polycomb repressive complex 2 (PRC2) silences genes responsible for neurodegeneration

Normal brain function depends on the interaction between highly specialized neurons that operate within anatomically and functionally distinct brain regions. Neuronal specification is driven by transcriptional programs that are established during early neuronal development and remain in place in the adult brain. The fidelity of neuronal specification depends on the robustness of the transcriptional program that supports the neuron type-specific gene expression patterns. Here we show that polycomb repressive complex 2 (PRC2), which supports neuron specification during differentiation, contributes to the suppression of a transcriptional program that is detrimental to adult neuron function and survival. We show that PRC2 deficiency in striatal neurons leads to the de-repression of selected, predominantly bivalent PRC2 target genes that are dominated by self-regulating transcription factors normally suppressed in these neurons. The transcriptional changes in PRC2-deficient neurons lead to progressive and fatal neurodegeneration in mice. Our results point to a key role of PRC2 in protecting neurons against degeneration.

Lists2Networks: Integrated analysis of gene/protein lists

BackgroundSystems biologists are faced with the difficultly of analyzing results from large-scale studies that profile the activity of many genes, RNAs and proteins, applied in different experiments, under different conditions, and reported in different publications. To address this challenge it is desirable to compare the results from different related studies such as mRNA expression microarrays, genome-wide ChIP-X, RNAi screens, proteomics and phosphoproteomics experiments in a coherent global framework. In addition, linking high-content multilayered experimental results with prior biological knowledge can be useful for identifying functional themes and form novel hypotheses.ResultsWe present Lists2Networks, a web-based system that allows users to upload lists of mammalian genes/proteins onto a server-based program for integrated analysis. The system includes web-based tools to manipulate lists with different set operations, to expand lists using existing mammalian networks of protein-protein interactions, co-expression correlation, or background knowledge co-annotation correlation, as well as to apply gene-list enrichment analyses against many gene-list libraries of prior biological knowledge such as pathways, gene ontology terms, kinase-substrate, microRNA-mRAN, and protein-protein interactions, metabolites, and protein domains. Such analyses can be applied to several lists at once against many prior knowledge libraries of gene-lists associated with specific annotations. The system also contains features that allow users to export networks and share lists with other users of the system.ConclusionsLists2Networks is a user friendly web-based software system expected to significantly ease the computational analysis process for experimental systems biologists employing high-throughput experiments at multiple layers of regulation. The system is freely available at http://www.lists2networks.org.

GATE: Software for the Analysis and Visualization of High- Dimensional Time-series Expression Data

SUMMARY We present Grid Analysis of Time series Expression (GATE), an integrated computational software platform for the analysis and visualization of high-dimensional biomolecular time series. GATE uses a correlation-based clustering algorithm to arrange molecular time series on a two-dimensional hexagonal array and dynamically colors individual hexagons according to the expression level of the molecular component to which they are assigned, to create animated movies of systems-level molecular regulatory dynamics. In order to infer potential regulatory control mechanisms from patterns of correlation, GATE also allows interactive interroga-tion of movies against a wide variety of prior knowledge datasets. GATE movies can be paused and are interactive, allowing users to reconstruct networks and perform functional enrichment analyses. Movies created with GATE can be saved in Flash format and can be inserted directly into PDF manuscript files as interactive figures. AVAILABILITY GATE is available for download and is free for academic use from http://amp.pharm.mssm.edu/maayan-lab/gate.htm

FNV: light-weight flash-based network and pathway viewer

MOTIVATION Network diagrams are commonly used to visualize biochemical pathways by displaying the relationships between genes, proteins, mRNAs, microRNAs, metabolites, regulatory DNA elements, diseases, viruses and drugs. While there are several currently available web-based pathway viewers, there is still room for improvement. To this end, we have developed a flash-based network viewer (FNV) for the visualization of small to moderately sized biological networks and pathways. SUMMARY Written in Adobe ActionScript 3.0, the viewer accepts simple Extensible Markup Language (XML) formatted input files to display pathways in vector graphics on any web-page providing flexible layout options, interactivity with the user through tool tips, hyperlinks and the ability to rearrange nodes on the screen. FNV was utilized as a component in several web-based systems, namely Genes2Networks, Lists2Networks, KEA, ChEA and PathwayGenerator. In addition, FVN can be used to embed pathways inside pdf files for the communication of pathways in soft publication materials. AVAILABILITY FNV is available for use and download along with the supporting documentation and sample networks at http://www.maayanlab.net/FNV. CONTACT avi.maayan@mssm.edu.

L1000FWD: fireworks visualization of drug-induced transcriptomic signatures

Motivation: As part of the NIH Library of Integrated Network‐based Cellular Signatures program, hundreds of thousands of transcriptomic signatures were generated with the L1000 technology, profiling the response of human cell lines to over 20 000 small molecule compounds. This effort is a promising approach toward revealing the mechanisms‐of‐action (MOA) for marketed drugs and other less studied potential therapeutic compounds. Results: L1000 fireworks display (L1000FWD) is a web application that provides interactive visualization of over 16 000 drug and small‐molecule induced gene expression signatures. L1000FWD enables coloring of signatures by different attributes such as cell type, time point, concentration, as well as drug attributes such as MOA and clinical phase. Signature similarity search is implemented to enable the search for mimicking or opposing signatures given as input of up and down gene sets. Each point on the L1000FWD interactive map is linked to a signature landing page, which provides multifaceted knowledge from various sources about the signature and the drug. Notably such information includes most frequent diagnoses, co‐prescribed drugs and age distribution of prescriptions as extracted from the Mount Sinai Health System electronic medical records. Overall, L1000FWD serves as a platform for identifying functions for novel small molecules using unsupervised clustering, as well as for exploring drug MOA. Availability and implementation: L1000FWD is freely accessible at: http://amp.pharm.mssm.edu/L1000FWD. Supplementary information: Supplementary data are available at Bioinformatics online.