Computational discovery of pathway-level genetic vulnerabilities in non-small-cell lung cancer

Young, Jonathan H.,Peyton, Michael,Seok Kim, Hyun,McMillan, Elizabeth,Minna, John D.,White, Michael A.,Marcotte, Edward M. Oxford University Press 2016 Bioinformatics Vol.32 No.9

<P><B>Motivation:</B> Novel approaches are needed for discovery of targeted therapies for non-small-cell lung cancer (NSCLC) that are specific to certain patients. Whole genome RNAi screening of lung cancer cell lines provides an ideal source for determining candidate drug targets.</P><P><B>Results:</B> Unsupervised learning algorithms uncovered patterns of differential vulnerability across lung cancer cell lines to loss of functionally related genes. Such genetic vulnerabilities represent candidate targets for therapy and are found to be involved in splicing, translation and protein folding. In particular, many NSCLC cell lines were especially sensitive to the loss of components of the LSm2-8 protein complex or the CCT/TRiC chaperonin. Different vulnerabilities were also found for different cell line subgroups. Furthermore, the predicted vulnerability of a single adenocarcinoma cell line to loss of the Wnt pathway was experimentally validated with screening of small-molecule Wnt inhibitors against an extensive cell line panel.</P><P><B>Availability and implementation:</B> The clustering algorithm is implemented in Python and is freely available at https://bitbucket.org/youngjh/nsclc_paper.</P><P><B>Contact:</B>marcotte@icmb.utexas.edu or jon.young@utexas.edu</P><P><B>Supplementary information:</B>Supplementary data are available at <I>Bioinformatics</I> online.</P>

Automated Screen of a Preliminary Lead-Drug for Chitosanase Drug Design

Kim,Youngsoo,Marcotte,Edward,Robertus,Jon D.,Park,Yonghyun 이화여자대학교 환경문제연구소 1997 이화환경연구 Vol.1 No.-

Chitosan is a naturally occurring component of certain bacterial and fungal cell walls. If some groups of medically and agriculturally significant fungi contain chitosan, chitosan metabolism repersents attractive drug targets specific to those fungal systems. Recently, structure-based drug design emerges as a powerful technique in drug screening. The process initially requires three dimensional structure of a target molecule. Because the bacterial Streptomyces lividans N174 chitosanase is only one chitosanase whose X-ray structure has been solved, we begin the process of structure-based drug design with the bacterial enzyme but it should be extended to a fungal one. In order to initiate the process, a preliminary leaddrug was screened by automated computer search from chemical database. The 5-nitro-isatin showed an inhibitory effect by 50% at 1.5mM on the Streptomyces lividans N174 chitosanase.

미국 연방정부의 에너지관리프로그램

에릭 J. 마르코드,J. Marcotte Eric 에너지절약전문기업협회 2003 esco Vol.23 No.-

Characterising and Predicting Haploinsufficiency in the Human Genome

Huang, Ni,Lee, Insuk,Marcotte, Edward M.,Hurles, Matthew E. Public Library of Science 2010 PLoS genetics Vol.6 No.10

<▼1><P>Haploinsufficiency, wherein a single functional copy of a gene is insufficient to maintain normal function, is a major cause of dominant disease. Human disease studies have identified several hundred haploinsufficient (HI) genes. We have compiled a map of 1,079 haplosufficient (HS) genes by systematic identification of genes unambiguously and repeatedly compromised by copy number variation among 8,458 apparently healthy individuals and contrasted the genomic, evolutionary, functional, and network properties between these HS genes and known HI genes. We found that HI genes are typically longer and have more conserved coding sequences and promoters than HS genes. HI genes exhibit higher levels of expression during early development and greater tissue specificity. Moreover, within a probabilistic human functional interaction network HI genes have more interaction partners and greater network proximity to other known HI genes. We built a predictive model on the basis of these differences and annotated 12,443 genes with their predicted probability of being haploinsufficient. We validated these predictions of haploinsufficiency by demonstrating that genes with a high predicted probability of exhibiting haploinsufficiency are enriched among genes implicated in human dominant diseases and among genes causing abnormal phenotypes in heterozygous knockout mice. We have transformed these gene-based haploinsufficiency predictions into haploinsufficiency scores for genic deletions, which we demonstrate to better discriminate between pathogenic and benign deletions than consideration of the deletion size or numbers of genes deleted. These robust predictions of haploinsufficiency support clinical interpretation of novel loss-of-function variants and prioritization of variants and genes for follow-up studies.</P></▼1><▼2><P><B>Author Summary</B></P><P>Humans, like most complex organisms, have two copies of most genes in their genome, one from the mother and one from the father. This redundancy provides a back-up copy for most genes, should one copy be lost through mutation. For a minority of genes, one functional copy is not enough to sustain normal human function, and mutations causing the loss of function of one of the copies of such genes are a major cause of childhood developmental diseases. Over the past 20 years medical geneticists have identified over 300 such genes, but it is not known how many of the 22,000 genes in our genome may also be sensitive to gene loss. By comparing these ∼300 genes known to be sensitive to gene loss with over 1,000 genes where loss of a single copy does not result in disease, we have identified some key evolutionary and functional similarities between genes sensitive to loss of a single copy. We have used these similarities to predict for most genes in the genome, whether loss of a single copy is likely to result in disease. These predictions will help in the interpretation of mutations seen in patients.</P></▼2>

MORPHIN: a web tool for human disease research by projecting model organism biology onto a human integrated gene network

Hwang, Sohyun,Kim, Eiru,Yang, Sunmo,Marcotte, Edward M.,Lee, Insuk Oxford University Press 2014 Nucleic acids research Vol.42 No.w1

<P>Despite recent advances in human genetics, model organisms are indispensable for human disease research. Most human disease pathways are evolutionally conserved among other species, where they may phenocopy the human condition or be associated with seemingly unrelated phenotypes. Much of the known gene-to-phenotype association information is distributed across diverse databases, growing rapidly due to new experimental techniques. Accessible bioinformatics tools will therefore facilitate translation of discoveries from model organisms into human disease biology. Here, we present a web-based discovery tool for human disease studies, MORPHIN (<U>m</U>odel <U>or</U>ganisms <U>p</U>rojected on a <U>h</U>uman <U>i</U>ntegrated gene <U>n</U>etwork), which prioritizes the most relevant human diseases for a given set of model organism genes, potentially highlighting new model systems for human diseases and providing context to model organism studies. Conceptually, MORPHIN investigates human diseases by an orthology-based projection of a set of model organism genes onto a genome-scale human gene network. MORPHIN then prioritizes human diseases by relevance to the projected model organism genes using two distinct methods: a conventional overlap-based gene set enrichment analysis and a network-based measure of closeness between the query and disease gene sets capable of detecting associations undetectable by the conventional overlap-based methods. MORPHIN is freely accessible at http://www.inetbio.org/morphin.</P>

Identification of Disease Specific Protein Interactions between the Gastric Cancer Causing Pathogen, H. pylori, and Human Hosts using Protein Network Modeling and Gene Chip Analysis

김완규,김규완,이은정,Edward M. Marcotte,김형하,서정근 한국바이오칩학회 2007 BioChip Journal Vol.1 No.3

Proteins are traditionally known as the building blocks or functional units that make up the cellular physiology of living organisms. In the post-genomic view of a protein, however, it can function as an element within a protein network and its role can then be evaluated by protein-protein interaction analysis. The role of proteins within such a network can be defined by their cellular function within the functional modules of the network as well as their individual activity. In this study, we used a proteinprotein interaction modeling system to identify the functional modules and proteins involved in the pathogenic interaction between the gastric pathogen, <I>H. pylori</I>, and humans. We analyzed 1,590 <I>H. pylori</I> proteins against 10,257 human entries expressed in human gastric tissues and identified 4,349 potential protein-protein interactions between 159 <I>H. pylori</I> proteins and 108 human proteins. We then investigated the association of gastric cancer with the 108 human proteins found to have an interaction with the <I>H. pylori</I> proteins using a GeneChip database that we generated. Among the 108 human proteins, 93 (86%) were shown to be associated with gastric cancer, 91 of which were up-regulated and 2 of which were down-regulated by at least 4 fold in gastric cancer tissues. Additionally, 32 of the proteins were found to be gastric cancer-specific, whereas the remaining proteins were found to be associated with several other forms of cancer. Taken together, these results suggest that protein network modeling in conjunction with GeneChip technology can be a useful tool for the analysis of the complex relationship between human pathogens and their hosts.

Systematic prediction of gene function in Arabidopsis thaliana using a probabilistic functional gene network

Hwang, Sohyun,Rhee, Seung Y,Marcotte, Edward M,Lee, Insuk Nature Publishing Group, a division of Macmillan P 2011 NATURE PROTOCOLS -ELECTRONIC EDITION- Vol.6 No.9

AraNet is a functional gene network for the reference plant Arabidopsis and has been constructed in order to identify new genes associated with plant traits. It is highly predictive for diverse biological pathways and can be used to prioritize genes for functional screens. Moreover, AraNet provides a web-based tool with which plant biologists can efficiently discover novel functions of Arabidopsis genes (http://www.functionalnet.org/aranet/). This protocol explains how to conduct network-based prediction of gene functions using AraNet and how to interpret the prediction results. Functional discovery in plant biology is facilitated by combining candidate prioritization by AraNet with focused experimental tests.

Rational association of genes with traits using a genome-scale gene network for Arabidopsis thaliana

Lee, Insuk,Ambaru, Bindu,Thakkar, Pranjali,Marcotte, Edward M,Rhee, Seung Y Nature Publishing Group 2010 Nature biotechnology Vol.28 No.2

We introduce a rational approach for associating genes with plant traits by combined use of a genome-scale functional network and targeted reverse genetic screening. We present a probabilistic network (AraNet) of functional associations among 19,647 (73%) genes of the reference flowering plant Arabidopsis thaliana. AraNet associations are predictive for diverse biological pathways, and outperform predictions derived only from literature-based protein interactions, achieving 21% precision for 55% of genes. AraNet prioritizes genes for limited-scale functional screening, resulting in a hit-rate tenfold greater than screens of random insertional mutants, when applied to early seedling development as a test case. By interrogating network neighborhoods, we identify AT1G80710 (now DROUGHT SENSITIVE 1; DRS1) and AT3G05090 (now LATERAL ROOT STIMULATOR 1; LRS1) as regulators of drought sensitivity and lateral root development, respectively. AraNet (http://www.functionalnet.org/aranet/) provides a resource for plant gene function identification and genetic dissection of plant traits.

HumanNet v2: human gene networks for disease research

Hwang, Sohyun,Kim, Chan Yeong,Yang, Sunmo,Kim, Eiru,Hart, Traver,Marcotte, Edward M,Lee, Insuk Oxford University Press 2019 Nucleic acids research Vol.47 No.d1

<P><B>Abstract</B></P><P>Human gene networks have proven useful in many aspects of disease research, with numerous network-based strategies developed for generating hypotheses about gene-disease-drug associations. The ability to predict and organize genes most relevant to a specific disease has proven especially important. We previously developed a human functional gene network, HumanNet, by integrating diverse types of omics data using Bayesian statistics framework and demonstrated its ability to retrieve disease genes. Here, we present HumanNet v2 (http://www.inetbio.org/humannet), a database of human gene networks, which was updated by incorporating new data types, extending data sources and improving network inference algorithms. HumanNet now comprises a hierarchy of human gene networks, allowing for more flexible incorporation of network information into studies. HumanNet performs well in ranking disease-linked gene sets with minimal literature-dependent biases. We observe that incorporating model organisms’ protein–protein interactions does not markedly improve disease gene predictions, suggesting that many of the disease gene associations are now captured directly in human-derived datasets. With an improved interactive user interface for disease network analysis, we expect HumanNet will be a useful resource for network medicine.</P>

Predicting genetic modifier loci using functional gene networks

Lee, I.,Lehner, B.,Vavouri, T.,Shin, J.,Fraser, A.G.,Marcotte, E.M. COLD SPRING HARBOR LABORATORY PRESS 2010 Genome research Vol.20 No.8