Kou Y.,Mount Sinai School of Medicine |
Betancur C.,Mount Sinai School of Medicine |
Xu H.,Mount Sinai School of Medicine |
Xu H.,University Pierre and Marie Curie |
And 3 more authors.
American Journal of Medical Genetics, Part C: Seminars in Medical Genetics | Year: 2012
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. © 2012 Wiley Periodicals, Inc.
Chen E.Y.,Systems Biology Center New York |
Xu H.,Systems Biology Center New York |
Gordonov S.,Systems Biology Center New York |
Lim M.P.,Systems Biology Center New York |
And 2 more authors.
Bioinformatics | Year: 2012
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. © The Author 2011. Published by Oxford University Press. All rights reserved.
Hansen J.,Systems Biology Center New York |
Iyengar R.,Systems Biology Center New York
Clinical Pharmacology and Therapeutics | Year: 2013
Over the past 50 years, like molecular cell biology, medicine and pharmacology have been driven by a reductionist approach. The focus on individual genes and cellular components as disease loci and drug targets has been a necessary step in understanding the basic mechanisms underlying tissue/organ physiology and drug action. Recent progress in genomics and proteomics, as well as advances in other technologies that enable large-scale data gathering and computational approaches, is providing new knowledge of both normal and disease states. Systems-biology approaches enable integration of knowledge from different types of data for precision medicine and systems therapeutics. In this review, we describe recent studies that contribute to these emerging fields and discuss how together these fields can lead to a mechanism-based therapy for individual patients.
Jin Y.,Mount Sinai School of Medicine |
Jin Y.,Shanghai JiaoTong University |
Ratnam K.,Mount Sinai School of Medicine |
Chuang P.Y.,Mount Sinai School of Medicine |
And 15 more authors.
Nature Medicine | Year: 2012
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. © 2012 Nature America, Inc. All rights reserved.
MacArthur B.D.,Systems Biology Center New York |
Lachmann A.,Systems Biology Center New York |
Lemischka I.R.,Systems Biology Center New York |
Ma'ayan A.,Systems Biology Center New York
Bioinformatics (Oxford, England) | Year: 2010
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