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BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: O18 Oral presentation 10th Benelux Bioinformatics Conference bbc 2015 O18. SPATIAL CO-EXPRESSION ANALYSIS OF STEROID RECEPTORS IN THE MOUSE BRAIN IDENTIFIES REGION-SPECIFIC REGULATION MECHANISMS Ahmed Mahfouz 1,2* , Boudewijn P.F. Lelieveldt 1,2 , Aldo Grefhorst 3 , Isabel M. Mol 4 , Hetty C.M. Sips 4 , José K. van den Heuvel 4 , Jenny A. Visser 3 , Marcel J.T. Reinders 2 , & Onno C. Meijer 4 . Department of Radiology, Leiden University Medical Center 1 ; Delft Bioinformatics Lab, Delft University of Technology 2 ; Department of Internal Medicine, Erasmus University Medical Center 3 ; Department of Internal Medicine, Leiden University Medical Center 4 . * a.mahfouz@lumc.nl Steroid hormones coordinate the activity of many brain regions by binding to nuclear receptors that act as transcription factors. This study uses genome wide correlation of gene expression in the mouse brain to discover 1) brain regions that respond in a similar manner to particular steroids, 2) signaling pathways that are used in a steroid receptor and brain region-specific manner, and 3) potential target genes and relationships between groups of target genes. The data constitute a rich repository for the research community to support new insights in neuroendocrine relationships, and to develop novel ways to manipulate brain activity in research of clinical settings. INTRODUCTION Steroid receptors are pleiotropic transcription factors that coordinate adaptation to different physiological states. An important target organ is the brain, but its complexity hampers the understanding of their modulation. METHODS We used the Allen Brain Atlas (ABA) (Lein et al., 2007), the most comprehensive repository of in situ hybridization-based gene expression in the adult mouse brain, to identify genes that have three dimensional (3D) spatial gene expression profiles similar to steroid receptors. To validate the functional relevance of this approach, we analyzed the co-expression relationship of the glucocorticoid receptor (Gr) and estrogen receptor alpha (Esr1) and their known transcriptional targets in their brain regions of action. Next, we studied the regionspecific co-expression of nuclear receptors and their coregulators to identify potential partners mediating the hormonal effects on dopaminergic transmission. Finally, to illustrate the potential of using spatial co-expression to predict region-specific steroid receptor targets in the brain, we identified and validated gene which responded to changes in estrogen in the arcuate nucleus and medial preoptic area of the mouse hypothalamus. RESULTS & DISCUSSION For each steroid receptor, we ranked genes based on their spatial co-expression across the whole brain as well as in each of the aforementioned 12 brain structures separately. For each steroid receptor, strongly co-expressed genes within a brain region are likely related to the localized functional role of the receptor. For example, out of the top 10 genes co-expressed with Esr1 across the whole brain, 4 were previously shown to be regulated by Esr1 and/or estrogens in various tissues (Gpr101, Calcr, Ngb, and Gpx3) We assessed the extent of co-expression of glucocorticoid (GC)-responsive genes (Datson et al., 2012) with Gr in the whole brain, the hippocampus and its substructures the dentate gyrus (DG) and the different subregions of the cornu ammonis (CA). GC-responsive genes were significantly co-expressed with Gr in the DG, but interestingly also in the whole brain and in the CA3 region (FDR-corrected p < 1.8×10 -3 ; Mann-Whitney U-Test). Similarly, A Mann-Whitney U-test showed that a set of 15 genes that are sensitive to gonadal steroids (Xu et al., 2012) is significantly correlated to Esr1 across the whole brain (FDR-corrected p = 8.69 ×10 -14 ), as well as in the hypothalamus (p = 3.85×10 -10 ) , the brain region responsible for the sexual behavior in animals. In order to identify putative region-dependent coregulators of steroid receptors, we analyzed the coexpression relationships of the each steroid receptor and a set of 62 nuclear receptor co-regulators as present on a peptide array (Nwachukwu et al., 2014). We focused our analysis on well-established target regions of steroid hormone action, dopaminergic brain regions (ventral tegmental area; VTA & substantia nigra; SN). We found three significantly co-expressed co-regulators with androgen receptor (Ar): Pnrc2, Pak6 and Trerf1, suggesting that these receptors may be involved in mediating Ar effects on dopaminergic transmission. In order to validate the predictive value of high correlated expression with a steroid receptor, we analyzed the response of top 10 genes that are strongly co-expressed with Esr1 in the hypothalamus to the estrogen diethylstilbesterol (DES) in castrated male mice using qPCR. We performed quantitative double in situ hybridization (dISH) for Esr1 and the six mRNAs (Irs4, Magel2, Adck4, Unc5, Ngb, and Gdpd2) that showed more than 1.3 fold enrichment in qPCR. We found Irs4 and Magel2 mRNA were both significantly upregulated by DES treatment (1.9 and 2.4-fold, respectively). REFERENCES Lein E. et al. Nature 445, 168–76 (2007). Datson N. et al. Hippocampus 22, 359–71 (2012). Xu X. et al., Cell 3, 596–607 (2012). Nwachukwu J. et al. eLife 3, e02057 (2014). 38
BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: O19 Oral presentation 10th Benelux Bioinformatics Conference bbc 2015 O19. A SYSTEMS BIOLOGY COMPENDIUM FOR LEISHMANIA DONOVANI Bart Cuypers 1,2,3* , Pieter Meysman 1,2 , Manu Vanaerschot 3 , Maya Berg 3 , Malgorzata Domagalska 3 , Jean-Claude Dujardin 3,4# & Kris Laukens 1,2# . Advanced Database Research and Modeling (ADReM), University of Antwerp 1 ; Biomedical informatics research center Antwerpen (biomina) 2 ; Molecular Parasitology Unit, Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp 3 ; 4 Department of Biomedical Sciences, University of Antwerp 4 . * bart.cuypers@uantwerpen.be # shared senior authors Leishmania donovani is the cause of visceral leishmaniasis in the Indian subcontinent and poses a threat to public health due to increasing drug resistance. Only little is known about its very peculiar molecular biology and there has been little ‘omics integration effort so far. Here we present an integratory database or ‘omics compendium that contains all genomics, transcriptomics proteomics and metabolomics experiments that are currently publically available for Leishmania donovani. Additionally the user interface contains analysis tools for new datasets that uses smart data mining strategies like frequent itemset mining to link results from different ‘omics layers. INTRODUCTION The protozoan parasite Leishmania donovani causes visceral leishmaniasis (VL), a life threatening disease which affects 500 000 people each year. With only four drugs available and rapidly emerging drug resistance, knowledge about the parasite’s resistance mechanisms is essential to boost the development of new drugs. However, only little is known about the gene regulation of Leishmania and the few findings indicate major differences to known gene expression systems. Indeed, no polymerase II promotors have ever been found in Leishmania 1 . Genes are constitutively transcribed in large polycistronic units and subsequently spliced into individual mRNAs (trans-splicing) 1 . A modified thymine, Base J, marks the end of transcription units and functions as a stop signal for the RNA polymerase 2 . Gene expression is then assumed to be regulated at the posttranscriptional level (mRNA stability, translation efficiency, epigenetic factors, etc…) but evidence to support this is scarce 1 . Integration of different ‘omics could shed light on these gene regulatory mechanisms, but there has been little integration effort so far. METHODS We developed an easy to use tool, able to import and connect all existing L. donovani –omics experiments. Genomics, epigenomics, transcriptomics, proteomics, metabolomics and phenotypic data was collected and added to a MySQL database compendium, further complemented with publicly available data. Relations between different ‘omics layers were explicitly defined and provided with a level of confidence. Python scripts were developed to preprocess, analyse and import the data. To allow comparability between different experiments, platforms and labs the three integration principles of the COLOMBOS bacterial expression compendium were adapted 3 . 1) Use the same data-analysis pipeline for all data. 2) Work with contrasts to a control condition instead of expression values. 3) Annotate these contrasts in a unified and structured manner. Next to this vast data source a set of integrative dataanalysis tools was developed based on data mining strategies. For example: One tool uses frequent itemset mining algorithms to detect which proteins and metabolites frequently exhibit the same behaviour under different conditions. Another tool converts several –omics layers to a network format that can be opened in Cytoscape and can thus be the basis for network analysis. The Django and Twitter Bootstrap frameworks were used to create a web portal to make the tools accessible to any Leishmania researcher. RESULTS & DISCUSSION Excellent public gene, protein, metabolite annotation databases for Leishmania and related species are already available (e.g. TriTrypDB and GeneDB). However, the strength of our tool is that it links these annotation data to ‘omics experiments that are either provided by the user, or that are publically available. New experiments can quickly be preprocessed, analysed and integrated in the database via its python back end. The compendium is therefore not only a look-up tool (e.g. under which conditions is this gene or metabolite upregulated?), but has tools available to also analyse the user-provided data with intelligent data mining tools (e.g. which metabolites/genes are typically upregulated in drug-resistant strains?). These new experiments provide additional confidence and information about the biological entities in the database. Unlike many other databases, the compendium has an elaborate quality control system. Every result provided by the tools can be traced back to the experimental data, which contains the necessary quality control plots to support the experiment’s validity. Additionally, it contains all relevant information about the extractions and the origin of the biological material. Using the compendium and its tools, we characterized the development and drug-resistance in a system biology context of Leishmania donovani. The genomes of more than 200 strains were examined for associations with phenotypical features and a subset was linked to transcriptomics, proteomics and metabolomics results. The compendium and its scripts were designed to be generic and can therefore be used for other organisms with only minor changes. REFERENCES 1. Donelson, J. (1999) PNAS. 96, 2579–258. 2. Van Luenen, H. G. a M. et al. (2012) Cell. 150, 909–21. 3. Meysman. et al. (2014) Nucleic acids research. 42, D649- D653. 39
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