Huilei Xu

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.

Oct4 Links Multiple Epigenetic Pathways to the Pluripotency Network

Oct4 is a well-known transcription factor that plays fundamental roles in stem cell self-renewal, pluripotency, and somatic cell reprogramming. However, limited information is available on Oct4-associated protein complexes and their intrinsic protein-protein interactions that dictate Oct4s critical regulatory activities. Here we employed an improved affinity purification approach combined with mass spectrometry to purify Oct4 protein complexes in mouse embryonic stem cells (mESCs), and discovered many novel Oct4 partners important for self-renewal and pluripotency of mESCs. Notably, we found that Oct4 is associated with multiple chromatin-modifying complexes with documented as well as newly proved functional significance in stem cell maintenance and somatic cell reprogramming. Our study establishes a solid biochemical basis for genetic and epigenetic regulation of stem cell pluripotency and provides a framework for exploring alternative factor-based reprogramming strategies.

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.

Construction and validation of a regulatory network for pluripotency and self-renewal of mouse embryonic stem cells.

A 30-node signed and directed network responsible for self-renewal and pluripotency of mouse embryonic stem cells (mESCs) was extracted from several ChIP-Seq and knockdown followed by expression prior studies. The underlying regulatory logic among network components was then learned using the initial network topology and single cell gene expression measurements from mESCs cultured in serum/LIF or serum-free 2i/LIF conditions. Comparing the learned network regulatory logic derived from cells cultured in serum/LIF vs. 2i/LIF revealed differential roles for Nanog, Oct4/Pou5f1, Sox2, Esrrb and Tcf3. Overall, gene expression in the serum/LIF condition was more variable than in the 2i/LIF but mostly consistent across the two conditions. Expression levels for most genes in single cells were bimodal across the entire population and this motivated a Boolean modeling approach. In silico predictions derived from removal of nodes from the Boolean dynamical model were validated with experimental single and combinatorial RNA interference (RNAi) knockdowns of selected network components. Quantitative post-RNAi expression level measurements of remaining network components showed good agreement with the in silico predictions. Computational removal of nodes from the Boolean network model was also used to predict lineage specification outcomes. In summary, data integration, modeling, and targeted experiments were used to improve our understanding of the regulatory topology that controls mESC fate decisions as well as to develop robust directed lineage specification protocols.

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

Toward a complete in silico, multi-layered embryonic stem cell regulatory network

Recent efforts in systematically profiling embryonic stem (ES) cells have yielded a wealth of high‐throughput data. Complementarily, emerging databases and computational tools facilitate ES cell studies and further pave the way toward the in silico reconstruction of regulatory networks encompassing multiple molecular layers. Here, we briefly survey databases, algorithms, and software tools used to organize and analyze high‐throughput experimental data collected to study mammalian cellular systems with a focus on ES cells. The vision of using heterogeneous data to reconstruct a complete multi‐layered ES cell regulatory network is discussed. This review also provides an accompanying manually extracted dataset of different types of regulatory interactions from low‐throughput experimental ES cell studies available at http://amp.pharm.mssm.edu/iscmid/literature. Copyright

Network- and attribute-based classifiers can prioritize genes and pathways for autism spectrum disorders and intellectual disability†‡

Autism spectrum disorders (ASD) are a group of related neurodevelopmental disorders with significant combined prevalence (∼1%) and high heritability. Dozens of individually rare genes and loci associated with high‐risk for ASD have been identified, which overlap extensively with genes for intellectual disability (ID). However, studies indicate that there may be hundreds of genes that remain to be identified. The advent of inexpensive massively parallel nucleotide sequencing can reveal the genetic underpinnings of heritable complex diseases, including ASD and ID. However, whole exome sequencing (WES) and whole genome sequencing (WGS) provides an embarrassment of riches, where many candidate variants emerge. It has been argued that genetic variation for ASD and ID will cluster in genes involved in distinct pathways and protein complexes. For this reason, computational methods that prioritize candidate genes based on additional functional information such as protein–protein interactions or association with specific canonical or empirical pathways, or other attributes, can be useful. In this study we applied several supervised learning approaches to prioritize ASD or ID disease gene candidates based on curated lists of known ASD and ID disease genes. We implemented two network‐based classifiers and one attribute‐based classifier to show that we can rank and classify known, and predict new, genes for these neurodevelopmental disorders. We also show that ID and ASD share common pathways that perturb an overlapping synaptic regulatory subnetwork. We also show that features relating to neuronal phenotypes in mouse knockouts can help in classifying neurodevelopmental genes. Our methods can be applied broadly to other diseases helping in prioritizing newly identified genetic variation that emerge from disease gene discovery based on WES and WGS.

SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells

BackgroundMouse embryonic stem cells (mESCs) are derived from the inner cell mass of a developing blastocyst and can be cultured indefinitely in-vitro. Their distinct features are their ability to self-renew and to differentiate to all adult cell types. Genes that maintain mESCs self-renewal and pluripotency identity are of interest to stem cell biologists. Although significant steps have been made toward the identification and characterization of such genes, the list is still incomplete and controversial. For example, the overlap among candidate self-renewal and pluripotency genes across different RNAi screens is surprisingly small. Meanwhile, machine learning approaches have been used to analyze multi-dimensional experimental data and integrate results from many studies, yet they have not been applied to specifically tackle the task of predicting and classifying self-renewal and pluripotency gene membership.ResultsFor this study we developed a classifier, a supervised machine learning framework for predicting self-renewal and pluripotency mESCs stemness membership genes (MSMG) using support vector machines (SVM). The data used to train the classifier was derived from mESCs-related studies using mRNA microarrays, measuring gene expression in various stages of early differentiation, as well as ChIP-seq studies applied to mESCs profiling genome-wide binding of key transcription factors, such as Nanog, Oct4, and Sox2, to the regulatory regions of other genes. Comparison to other classification methods using the leave-one-out cross-validation method was employed to evaluate the accuracy and generality of the classification. Finally, two sets of candidate genes from genome-wide RNA interference screens are used to test the generality and potential application of the classifier.ConclusionsOur results reveal that an SVM approach can be useful for prioritizing genes for functional validation experiments and complement the analyses of high-throughput profiling experimental data in stem cell research.

Activation of Alternate Prosurvival Pathways Accounts for Acquired Sunitinib Resistance in U87MG Glioma Xenografts

Acquired drug resistance represents a major obstacle to using sunitinib for the treatment of solid tumors. Here, we examined the cellular and molecular alterations in tumors that are associated with acquired brain tumor resistance to sunitinib by using an in vivo model. U87MG tumors obtained from nude mice that received sunitinib (40 mg/kg/day) for 30 days were classified into sunitinib-sensitive and -resistant groups based on tumor volume and underwent targeted gene microarray and protein array analyses. The expression of several angiogenesis-associated genes was significantly modulated in sunitinib-treated tumors compared with those in control tumors (p < 0.05), whereas no significant differences were observed between sunitinib-sensitive and -resistant tumors (p > 0.05). Tumor vasculature based on microvessel density, neurogenin 2 chondroitin sulfate proteoglycan density, and α-smooth muscle actin density was also similar in sunitinib-treatment groups (p > 0.05). The moderate increase in unbound sunitinib tumor-to-plasma area-under-the-curve ratio in sunitinib-resistant mice was accompanied by up-regulated ATP-binding cassette G2 expression in tumor. The most profound difference between the sunitinib-sensitive and -resistant groups was found in the expression of several phosphorylated proteins involved in intracellular signaling. In particular, phospholipase C-γ1 phosphorylation in sunitinib-resistant tumors was up-regulated by 2.6-fold compared with that in sunitinib-sensitive tumors (p < 0.05). In conclusion, acquired sunitinib resistance in U87MG tumors is not associated with revascularization in tumors, but rather with the activation of alternate prosurvival pathways involved in an escape mechanism facilitating tumor growth and possibly insufficient drug uptake in tumor cells caused by an up-regulated membrane efflux transporter.

Quantitative Approaches to Model Pluripotency and Differentiation in Stem Cells

Some of the most valuable information in the field of stem cell research still comes from empirical studies. Recently developed informatics and “omics” approaches facilitate these studies by pinpointing candidate genes that maintain stemness or induce differentiation. These genes and then tested empirically. However, biological models explaining the principles of stem cell renewal and differentiation currently remain in their infancy.