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Stress-Induced Changes in Gene Interactions in Human Cells Renuka R

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Stress-Induced Changes in Gene Interactions in Human Cells Renuka R Nucleic Acids Research Advance Access published October 28, 2013 Nucleic Acids Research, 2013, 1–15 doi:10.1093/nar/gkt999 Stress-induced changes in gene interactions in human cells Renuka R. Nayak1, William E. Bernal2, Jessica W. Lee3, Michael J. Kearns4,5 and Vivian G. Cheung5,6,7,* 1Medical Scientist Training Program, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA, 2Division of Rheumatology, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA, 3HHMI Medical Research Fellows Program, University of Pennsylvania, Philadelphia, PA 19104, USA, 4Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA, 5 6 Department of Pediatrics, University of Pennsylvania, Philadelphia, PA 19104, USA, Department of Genetics, Downloaded from University of Pennsylvania, Philadelphia, PA 19104, USA and 7Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA Received May 26, 2013; Revised September 12, 2013; Accepted October 4, 2013 http://nar.oxfordjournals.org/ ABSTRACT INTRODUCTION Cells respond to variable environments by changing Human cells exist in dynamic environments where proper gene expression and gene interactions. To study function depends on coordinated responses to stresses. how human cells response to stress, we analyzed These responses include changes in gene expression and the expression of >5000 genes in cultured B cells gene interactions. Ineffective responses can lead to the de- velopment of diseases. Knowing how genes are induced from nearly 100 normal individuals following endo- and how they interact is important for modulating stress at University of Pennsylvania Library on October 30, 2013 plasmic reticulum stress and exposure to ionizing response for disease prevention and treatment. In radiation. We identified thousands of genes that addition, effective therapies rely on knowing which part are induced or repressed. Then, we constructed of the response network to target and how that interven- coexpression networks and inferred interactions tion will be propagated through gene networks. among genes. We used coexpression and machine In different cellular states, genes change how they learning analyses to study how genes interact with interact with each other. Although studies have identified each other in response to stress. The results genes that constitute stress responses, changes in gene showed that for most genes, their interactions with interaction in response to stress are less well known. In each other are the same at baseline and in response particular, there is a paucity of information on the extent to different stresses; however, a small set of genes to which gene interactions are specific to various stimuli. acquired new interacting partners to engage in In this study, we focused on the response of human cells to stress-specific responses. These genes with two different stresses: endoplasmic reticulum (ER) stress altered interacting partners are associated with and exposure to ionizing radiation (IR). We examined changes in gene expression and gene interactions in diseases in which endoplasmic reticulum stress human cultured B cells from 100 normal individuals. response or sensitivity to radiation has been Previous studies examined changes in the expression implicated. Thus, our findings showed that to under- level in response to stressful stimuli (1–3), but not in stand disease-specific pathways, it is important to gene interactions. Here, we took advantage of individual identify not only genes that change expression differences in human gene expression to infer interactions levels but also those that alter interactions with among thousands of genes at baseline and in response to other genes. these two stresses (4). We compared the results to *To whom correspondence should be addressed. Tel: +1 734 763 2484; Email: [email protected] Present addresses: Renuka R. Nayak, Department of Medicine, University of California, San Francisco, CA 94143, USA. William E. Bernal, Department of Medicine, University of California, San Francisco, CA 94143, USA. Jessica W. Lee, Life Sciences Institute, University of Michigan, Ann Arbor, MI 48109, USA. Vivian G. Cheung, Life Sciences Institute, University of Michigan, Ann Arbor, MI 48109, USA. ß The Author(s) 2013. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. 2 Nucleic Acids Research, 2013 determine changes in gene interactions and to identify the IR treatment changes that are generalized and those that are stress- Cells were harvested before radiation and at 2 and 6 h specific. To our knowledge, these analyses are the first following exposure to IR (10 Gy in a 137Cs irradiator). genome-wide examination of gene interactions in human cells before and after stress. We examined cellular responses to two different Gene correlation and construction of stresses: ER stress and IR. The ER is the organelle in coexpression networks which secreted or transmembrane proteins are folded. There were 12 660 unique genes that were represented on When the demand for protein-folding machinery exceeds both the Human Genome U133 Plus 2.0 and Affymetrix capacity, cells activate a complex set of gene pathways in a Human U133A 2.0 Arrays. Of these, 6775 genes were called process known as ER stress response. These processes ‘present’ by the MAS 5.0 algorithm in at least 80% of include attenuation of global translation and increased samples in either the DMSO- or tunicamycin-treated expression of protein-folding machinery. Second, we samples. There were 5975 genes called ‘present’ in at least examined cellular response to IR, which is present in the 80% of samples at 0, 2 or 6 h after radiation treatment. We environment and commonly used in medicine for diagnos- considered these genes as ‘expressed’ and focused on these tic and therapeutic purposes. Radiation induces DNA for all analyses. For all possible pairs of genes, we double-strand breaks and damages cellular structures calculated the Pearson correlation of expression levels (5,6). In response, cells repair the damages or undergo across individuals. This calculation was done separately cell death. Failure to respond properly to radiation for each treatment or time point. The distributions of cor- exposure or protein load has been implicated in many relations were normal. Fisher’s test of homogeneity was human diseases from cancers to neurodegenerative used to identify correlations that significantly differed diseases (7) and immunodeficiencies (8); thus, a better (Bonferroni corrected P < 0.05) between treatments or understanding of stress response should allow for more among time points. Correlated gene pairs were connected precise approaches to disease prevention and treatment. to construct a coexpression network. We constructed multiple networks using different thresholds and measured topological properties of the resulting networks. MATERIALS AND METHODS Correlations and topological properties of the network Cells and gene expression experiments were analyzed using MATLAB (The MathWorks, Inc, Cultured B cells were obtained from Coriell Cell Natick, MA, USA). Networks were represented as adja- Repositories. Cell lines were treated with dimethyl sulfox- cency matrices in MATLAB, and standard MATLAB ide (DMSO)/tunicamycin (N = 131) or IR (N = 95). functions were used to calculate the number of genes, the There were 56 individuals who were studied in both number of connections and the distribution of connections experiments. Gene expression was measured as described in each network. MATLAB functions for determining the previously. Briefly, cells were grown in RPMI 1640 with clustering coefficient (10), gamma (11) and scale-free topology criteria (12) were implemented as previously 15% fetal bovine serum, 2 mM L-glutamine and 100 U/ml penicillin–streptomycin. RNA was extracted using the described. Code will be provided on request. RNeasy Mini-Kit (Qiagen), amplified, labeled and A similar series of analyses was done examining fold hybridized as per the manufacturer’s instructions. changes across individuals, so that changes in expression Expression levels of genes were measured using Human levels are normalized against each person’s baseline. This Genome U133 Plus 2.0 or Affymetrix Human U133A type of analysis reduces the number of genes included in 2.0 Arrays (Affymetrix, CA, USA). Gene expression the network: only those genes showing changes in expres- signals were normalized using the MAS 5.0 algorithm sion levels are included when examining correlation in fold (Affymetrix, CA, USA). Expression intensity was scaled changes. This method also eliminates the baseline or 0-h time point, which is used for normalization, as a basis for to 500 and log2-transformed. Some genes are represented multiple times on the Human Genome U133 Plus 2.0 comparison. Nonetheless, we found similar findings using Arrays (ER dataset) and Affymetrix Human U133A 2.0 these smaller networks and present the results of the more Arrays (IR dataset). If a gene was represented more than inclusive analysis here. once, only one of the probesets was selected (randomly) to include in our analysis. Probesets annotated as ‘x_at’ were Random gene pairs and networks excluded from this analysis. Random gene pairs were genes
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