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. 2017 Sep 12;18(1):723.
doi: 10.1186/s12864-017-4111-x.

Regulatory Network Changes Between Cell Lines and Their Tissues of Origin

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Free PMC article

Regulatory Network Changes Between Cell Lines and Their Tissues of Origin

Camila M Lopes-Ramos et al. BMC Genomics. .
Free PMC article

Abstract

Background: Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. Although there are recognized important cellular and transcriptomic differences between cell lines and tissues, a systematic overview of the differences between the regulatory processes of a cell line and those of its tissue of origin has not been conducted. The RNA-Seq data generated by the GTEx project is the first available data resource in which it is possible to perform a large-scale transcriptional and regulatory network analysis comparing cell lines with their tissues of origin.

Results: We compared 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines (LCLs) and whole blood samples, and 244 paired primary fibroblast cell lines and skin samples. While gene expression analysis confirms that these cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, network analysis indicates that expression changes are the cumulative result of many previously unreported alterations in transcription factor (TF) regulation. More specifically, cell cycle genes are over-expressed in cell lines compared to primary tissues, and this alteration in expression is a result of less repressive TF targeting. We confirmed these regulatory changes for four TFs, including SMAD5, using independent ChIP-seq data from ENCODE.

Conclusions: Our results provide novel insights into the regulatory mechanisms controlling the expression differences between cell lines and tissues. The strong changes in TF regulation that we observe suggest that network changes, in addition to transcriptional levels, should be considered when using cell lines as models for tissues.

Keywords: Fibroblast cell lines; GTEx; Lymphoblastoid cell lines; Regulatory networks; Transcriptome.

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The authors declare that they have no competing interests.

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Figures

Fig. 1
Fig. 1
Pathways are differentially expressed between cell lines and their tissues of origin. a Number of differentially expressed genes (absolute log2 fold change >2 and FDR < 0.05) using voom on paired samples. b Results of GSEA reported based on the log10(FDR) significance scale, with one group in red and the other one in blue. The 15 pathways most significantly differentially expressed between each cell line and its tissue of origin. c Pathways enriched for at least two group comparisons (FDR < 0.05). The pathways differentially expressed between the tissues that are also differentially expressed between the cell lines (preserved pathways) are highlighted in red and blue. Pathways over-expressed in both cell lines compared to their tissues of origin are highlighted in yellow. Rows are ordered by hierarchical clustering of the enrichment significance values, log10(FDR). To represent the FDR significance in the heatmap, the color was saturated at 1.1 × 10−4. The exact reported FDR can be found in Additional file 2
Fig. 2
Fig. 2
Transcription factors differentially-targeting genes in cell lines and their tissues of origin. a Illustration of the TF out-degree difference between each cell line and its tissue of origin. Positive values indicate higher targeting in cell lines, and negative values indicate higher targeting in tissues. b Function of the TFs with the largest difference in out-degree comparing LCL-vs-blood; and fibroblast-vs-skin regulatory networks. The complete table with references and differential expression results is shown in Additional file 8
Fig. 3
Fig. 3
Cell cycle pathway genes are less strongly targeted by TFs in cell lines. a Group-specific gene regulatory networks were generated using PANDA. The illustrations represent subnetworks of the 1000 edges with the highest edge weight difference between a cell line and its tissue of origin around the cell cycle genes. The color indicates the edge weight strength between the TF and target gene (the edges shown have a weight greater than 2 in at least one network). b Illustration of the gene in-degree difference between each cell line and its tissue of origin. Positive values indicate higher targeting in cell lines, and negative values indicate higher targeting in tissues. c Boxplot of the gene in-degree differences for the genes in the KEGG cell cycle pathway and for genes not in this pathway (significance measured using a t-test). Reduction of gene in-degree difference indicates that the genes in the cell cycle pathway are less strongly targeted by TFs in the cell line compared to its tissue of origin
Fig. 4
Fig. 4
SMAD5 is differentially regulating cell cycle pathway genes. a Spearman correlation between the log2 fold change in gene expression (LCL-blood difference) of KEGG cell cycle pathway genes and the differential targeting they receive by the TF SMAD5. Red: evidence of SMAD5 ChIP-Seq binding in the promoter of the gene, black: no evidence of SMAD5 binding. The negative correlation observed indicates the cell cycle genes are more highly expressed but less targeted by SMAD5 in LCL compared to blood. b Boxplot of Spearman correlation coefficients between SMAD5 expression levels and expression levels of all genes, and between SMAD5 expression levels and the expression levels of cell cycle genes with SMAD5 ChIP-Seq binding evidence for LCL and blood samples. Difference in magnitude was tested using a Wilcoxon rank-sum test LCL-vs- blood comparison. c Visualization of the correlation between TF and cell cycle gene expression for interactions that have ChIP-Seq binding evidence. More positively-correlated associations are shown in red, more negatively correlated are blue, and correlations near zero are gray

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References

    1. Hu VW, Frank BC, Heine S, Lee NH, Quackenbush J. Gene expression profiling of lymphoblastoid cell lines from monozygotic twins discordant in severity of autism reveals differential regulation of neurologically relevant genes. BMC Genomics. 2006;7:118. doi: 10.1186/1471-2164-7-118. - DOI - PMC - PubMed
    1. Tan X-L, Moyer AM, Fridley BL, Schaid DJ, Niu N, Batzler AJ, et al. Genetic variation predicting cisplatin cytotoxicity associated with overall survival in lung cancer patients receiving platinum-based chemotherapy. Clin Cancer Res. 2011;17:5801–5811. doi: 10.1158/1078-0432.CCR-11-1133. - DOI - PMC - PubMed
    1. Ezer D, Moignard V, Göttgens B, Adryan B, Treutlein B, Rothenberg M. Determining physical mechanisms of gene expression regulation from single cell gene expression data. PLOS Comput. 2016;12:e1005072. doi: 10.1371/journal.pcbi.1005072. - DOI - PMC - PubMed
    1. Sandberg R, Ernberg I. The molecular portrait of in vitro growth by meta-analysis of gene-expression profiles. Genome Biol. 2005;6:R65. doi: 10.1186/gb-2005-6-8-r65. - DOI - PMC - PubMed
    1. Pan C, Kumar C, Bohl S, Klingmueller U, Mann M. Comparative proteomic phenotyping of cell lines and primary cells to assess preservation of cell type-specific functions. Mol Cell Proteomics. 2009;8:443–450. doi: 10.1074/mcp.M800258-MCP200. - DOI - PMC - PubMed

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