Person: Kuijjer, Marieke
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Kuijjer
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Marieke
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Kuijjer, Marieke
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Publication Regulatory network changes between cell lines and their tissues of origin(BioMed Central, 2017) Lopes-Ramos, Camila; Paulson, Joseph; Chen, Cho-Yi; Kuijjer, Marieke; Fagny, Maud; Platig, John; Sonawane, Abhijeet; Demeo, Dawn; Quackenbush, John; Glass, KimberlyBackground: 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. Electronic supplementary material The online version of this article (10.1186/s12864-017-4111-x) contains supplementary material, which is available to authorized users.Publication Kinome and mRNA expression profiling of high-grade osteosarcoma cell lines implies Akt signaling as possible target for therapy(BioMed Central, 2014) Kuijjer, Marieke; van den Akker, Brendy EWM; Hilhorst, Riet; Mommersteeg, Monique; Buddingh, Emilie P; Serra, Massimo; Bürger, Horst; Hogendoorn, Pancras CW; Cleton-Jansen, Anne-MarieBackground: High-grade osteosarcoma is a primary malignant bone tumor mostly occurring in adolescents and young adults, with a second peak at middle age. Overall survival is approximately 60%, and has not significantly increased since the introduction of neoadjuvant chemotherapy in the 1970s. The genomic profile of high-grade osteosarcoma is complex and heterogeneous. Integration of different types of genome-wide data may be advantageous in extracting relevant information from the large number of aberrations detected in this tumor. Methods: We analyzed genome-wide gene expression data of osteosarcoma cell lines and integrated these data with a kinome screen. Data were analyzed in statistical language R, using LIMMA for detection of differential expression/phosphorylation. We subsequently used Ingenuity Pathways Analysis to determine deregulated pathways in both data types. Results: Gene set enrichment indicated that pathways important in genomic stability are highly deregulated in these tumors, with many genes showing upregulation, which could be used as a prognostic marker, and with kinases phosphorylating peptides in these pathways. Akt and AMPK signaling were identified as active and inactive, respectively. As these pathways have an opposite role on mTORC1 signaling, we set out to inhibit Akt kinases with the allosteric Akt inhibitor MK-2206. This resulted in inhibition of proliferation of osteosarcoma cell lines U-2 OS and HOS, but not of 143B, which harbors a KRAS oncogenic transformation. Conclusions: We identified both overexpression and hyperphosphorylation in pathways playing a role in genomic stability. Kinome profiling identified active Akt signaling, which could inhibit proliferation in 2/3 osteosarcoma cell lines. Inhibition of PI3K/Akt/mTORC1 signaling may be effective in osteosarcoma, but further studies are required to determine whether this pathway is active in a substantial subgroup of this heterogeneous tumor.Publication MEK inhibition induces apoptosis in osteosarcoma cells with constitutive ERK1/2 phosphorylation(Impact Journals LLC, 2015) Baranski, Zuzanna; Booij, Tijmen H.; Kuijjer, Marieke; de Jong, Yvonne; Cleton-Jansen, Anne-Marie; Price, Leo S.; van de Water, Bob; Bovée, Judith V. M. G.; Hogendoorn, Pancras C.W.; Danen, Erik H.J.Conventional high-grade osteosarcoma is the most common primary bone cancer with relatively high incidence in young people. Recurrent and metastatic tumors are difficult to treat. We performed a kinase inhibitor screen in two osteosarcoma cell lines, which identified MEK1/2 inhibitors. These inhibitors were further validated in a panel of six osteosarcoma cell lines. Western blot analysis was performed to assess ERK activity and efficacy of MEK inhibition. A 3D culture system was used to validate results from 2D monolayer cultures. Gene expression analysis was performed to identify differentially expressed gene signatures in sensitive and resistant cell lines. Activation of the AKT signaling network was explored using Western blot and pharmacological inhibition. In the screen, Trametinib, AZD8330 and TAK-733 decreased cell viability by more than 50%. Validation in six osteosarcoma cell lines identified three cell lines as resistant and three as sensitive to the inhibitors. Western blot analysis of ERK activity revealed that sensitive lines had high constitutive ERK activity. Treatment with the three MEK inhibitors in a 3D culture system validated efficacy in inhibition of osteosarcoma viability. MEK1/2 inhibition represents a candidate treatment strategy for osteosarcomas displaying high MEK activity as determined by ERK phosphorylation status.Publication PyPanda: a Python package for gene regulatory network reconstruction(Oxford University Press, 2016) van IJzendoorn, David G.P.; Glass, Kimberly; Quackenbush, John; Kuijjer, MariekeSummary: PANDA (Passing Attributes between Networks for Data Assimilation) is a gene regulatory network inference method that uses message-passing to integrate multiple sources of ‘omics data. PANDA was originally coded in C ++. In this application note we describe PyPanda, the Python version of PANDA. PyPanda runs considerably faster than the C ++ version and includes additional features for network analysis. Availability and implementation: The open source PyPanda Python package is freely available at http://github.com/davidvi/pypanda. Contact: mkuijjer@jimmy.harvard.edu or d.g.p.van_ijzendoorn@lumc.nlPublication Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data(BioMed Central, 2017) Paulson, Joseph; Chen, Cho-Yi; Lopes-Ramos, Camila; Kuijjer, Marieke; Platig, John; Sonawane, Abhijeet; Fagny, Maud; Glass, Kimberly; Quackenbush, JohnBackground: Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data – critical first steps for any subsequent analysis. Results: We find that analysis of large RNA-Seq data sets requires both careful quality control and the need to account for sparsity due to the heterogeneity intrinsic in multi-group studies. We developed Yet Another RNA Normalization software pipeline (YARN), that includes quality control and preprocessing, gene filtering, and normalization steps designed to facilitate downstream analysis of large, heterogeneous RNA-Seq data sets and we demonstrate its use with data from the Genotype-Tissue Expression (GTEx) project. Conclusions: An R package instantiating YARN is available at http://bioconductor.org/packages/yarn. Electronic supplementary material The online version of this article (10.1186/s12859-017-1847-x) contains supplementary material, which is available to authorized users.