A validated gene regulatory network and GWAS identifies early regulators of T-cell associated diseases (exon array)
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ABSTRACT: In this work we present an analytical strategy to systematically identify early regulators by combining gene regulatory networks (GRN) with GWAS. We hypothesized that early regulators in T-cell associated diseases could be found by defining upstream transcription factors (TFs) in T-cell differentiation. Time series expression and DNA methylation profiling of T-cell differentiation identified several upstream TFs, of which TFs involved in Th1/2 differentiation were most enriched for disease associated SNPs identified by GWAS. Peripheral blood mononuclear cells (PBMCs) were prepared from fresh blood from 10 patients with seasonal allergic rhinitis and 10 healthy controls using Lymphoprep (Axis-Shield PoC, Oslo, Norway) according to the manufacturerâs protocol. PBMCs were stimulated with allergen extract (ALK-Abelló A/S; 100 μg/ml) or diluent (PBS) in RPMI 1640 supplemented with 2 mM L-glutamine (PAA Laboratories, Linz, Austria), 5% human AB serum (Lonza, Switzerland), 5 µM beta-mercaptoethanol (Sigma-Aldrich, St. Louis, Missouri, USA) and 50 µg/mL gentamicin (Sigma-Aldrich, St. Louis, Missouri, USA). After 17 hours of incubation, total CD4+ T cells were enriched from PBMCs by MACS negative sorting. Total RNA was extracted using a miRneasy Mini Kit (Qiagen, Valencia, CA, USA). The cRNA was prepared using a Low Input QuickAmp Labeling Kit. The expression microarray analyses were performed using Agilent SurePrint G3 Human Exon 4x180K Microarrays according to the manufacturer's instructions. Complementary microRNA data have been deposited in ArrayExpress under accession number E-MTAB-4900 ( http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-4900/ ).
Project description:In this work we present an analytical strategy to systematically identify early regulators by combining gene regulatory networks (GRN) with GWAS. We hypothesized that early regulators in T-cell associated diseases could be found by defining upstream transcription factors (TFs) in T-cell differentiation. Time series expression and DNA methylation profiling of T-cell differentiation identified several upstream TFs, of which TFs involved in Th1/2 differentiation were most enriched for disease associated SNPs identified by GWAS. Naïve CD4+ T cells were isolated from buffy coat of four healthy donors using a naïve CD4+ T cell isolation kit (Miltenyi, Bergisch-Gladbach, Germany). Naïve CD4+ T cells were stimulated with TGF-β (1 ng/mL), IL-1β (10 ng/mL), IL-6 (25 ng/mL), IL-21 (25 ng/mL) and IL-23 (25 ng/mL) for Th17, and TGF-β (10 ng/mL) and IL-2 (10 ng/mL) for Treg. Cells were cultured for six days in Iscoveâs modified Dulbecco medium (IMDM) supplemented with 2 mM L-glutamine (PAA Laboratories, Linz, Austria), 10% heat-inactivated FCS (PAA Laboratories, Linz, Austria), 5 µM βâmercaptoethanol (Sigma-Aldrich, St. Louis, Missouri, USA) and 50 ug/mL gentamicin (Sigma-Aldrich, St. Louis, Missouri, USA). Cells were cultured for 6 days and then re-stimulated with plate-bound anti-CD3 and soluble anti-CD28 in the presence of corresponding polarizing cytokines and antibodies for another 2 days (Zhang et al. 2013). RNA was extracted using a miRneasy Mini Kit (Qiagen). The RNA concentrations were analysed with NanoDrop ND-1000 Spectrophotometer (NanoDrop Technologies). For the gene expression microarray analysis, The cRNA was prepared using a Low Input QuickAmp Labeling Kit. For Th17 and Treg cells the gene expression microarray analysis was performed using SurePrint G3 Human Gene Expression 8x60K v2 microarray kit, according to the manufactureâs instruction (Agilent Technologies).
Project description:In this work we present an analytical strategy to systematically identify early regulators by combining gene regulatory networks (GRN) with GWAS. We hypothesized that early regulators in T-cell associated diseases could be found by defining upstream transcription factors (TFs) in T-cell differentiation. Time series expression and DNA methylation profiling of T-cell differentiation identified several upstream TFs, of which TFs involved in Th1/2 differentiation were most enriched for disease associated SNPs identified by GWAS. Naïve CD4+ T cells were isolated from buffy coat of four healthy donors using a naïve CD4+ T cell isolation kit (Miltenyi, Bergisch-Gladbach, Germany). Naïve CD4+ T cells were stimulated with plate-bound anti-CD3 (500 ng/mL), soluble anti-CD28 (500 ng/mL), in the presence of IL-12 (5 ng/mL), IL-2 (10 ng/mL) and antiâ??IL-4 (5 µg/mL) for Th1, IL-4 (10 ng/mL), IL-2 (10 ng/mL) and anti-IL-12 (5 µg/mL) and antiâ??IFN-g (5 µg/mL) for Th2. Cells were cultured for six days in Iscoveâ??s modified Dulbecco medium (IMDM) supplemented with 2 mM L-glutamine (PAA Laboratories, Linz, Austria), 10% heat-inactivated FCS (PAA Laboratories, Linz, Austria), 5 µM βâ??mercaptoethanol (Sigma-Aldrich, St. Louis, Missouri, USA) and 50 ug/mL gentamicin (Sigma-Aldrich, St. Louis, Missouri, USA). Cells were cultured for 6 days and then re-stimulated with plate-bound anti-CD3 and soluble anti-CD28 in the presence of corresponding polarizing cytokines and antibodies for another 2 days (Zhang et al. 2013). RNA was extracted using a miRneasy Mini Kit (Qiagen). The RNA concentrations were analysed with NanoDrop ND-1000 Spectrophotometer (NanoDrop Technologies). For the gene expression microarray analysis, The cRNA was prepared using a Low Input QuickAmp Labeling Kit. For Th1 and Th2 cells the gene expression microarray analysis was performed using SurePrint G3 Human Gene Expression 8x60K microarray kit, according to the manufactureâ??s instruction (Agilent Technologies).
Project description:In this work we present an analytical strategy to systematically identify early regulators by combining gene regulatory networks (GRN) with GWAS. We hypothesized that early regulators in T-cell associated diseases could be found by defining upstream transcription factors (TFs) in T-cell differentiation. Time series expression and DNA methylation profiling of T-cell differentiation identified several upstream TFs, of which TFs involved in Th1/2 differentiation were most enriched for disease associated SNPs identified by GWAS. Naïve CD4+ T cells were isolated from buffy coat of four healthy donors using a naïve CD4+ T cell isolation kit (Miltenyi, Bergisch-Gladbach, Germany). Naïve CD4+ T cells were stimulated with TGF-β (1 ng/mL), IL-1β (10 ng/mL), IL-6 (25 ng/mL), IL-21 (25 ng/mL) and IL-23 (25 ng/mL) for Th17, and TGF-β (10 ng/mL) and IL-2 (10 ng/mL) for Treg. Cells were cultured for six days in Iscoveâs modified Dulbecco medium (IMDM) supplemented with 2 mM L-glutamine (PAA Laboratories, Linz, Austria), 10% heat-inactivated FCS (PAA Laboratories, Linz, Austria), 5 µM βâmercaptoethanol (Sigma-Aldrich, St. Louis, Missouri, USA) and 50 ug/mL gentamicin (Sigma-Aldrich, St. Louis, Missouri, USA). Cells were cultured for 6 days and then re-stimulated with plate-bound anti-CD3 and soluble anti-CD28 in the presence of corresponding polarizing cytokines and antibodies for another 2 days (Zhang et al. 2013). RNA was extracted using a miRneasy Mini Kit (Qiagen). The RNA concentrations were analysed with NanoDrop ND-1000 Spectrophotometer (NanoDrop Technologies). For the gene expression microarray analysis, The cRNA was prepared using a Low Input QuickAmp Labeling Kit. For Th17 and Treg cells the gene expression microarray analysis was performed using SurePrint G3 Human Gene Expression 8x60K v2 microarray kit, according to the manufactureâs instruction (Agilent Technologies).
Project description:Purpose: We aim to identify transciptional changes of human CD4+/CD8+ T cells due to high fat low carbohydrate ketogenic diet (KD) in vivo. Method: Healthy volunteers conducted a 21 days ketogenic diet, limiting carbohydrate intake to <30g/day. Before the start (T0) and at the end (T1) of the diet, blood samples were taken and PBMC were isolated. PBMCs were obtained by density centrifugation (Histopaque 1077, Sigma-Aldrich, St. Louis, MO, USA). A ViCell analyzer (Beckman Coulter, Fullerton, CA, USA) was used to evaluate the cell count and viability. Only samples exceeding a cell viability of 90% were processed further. PBMCs were subjected to cell cultivation in RPMI 1640 (Invitrogen, Carlsbad, CA, USA) containing 10% heat-inactivated fetal calf serum (Biochrom, Berlin, Germany), 1% HEPES (Sigma-Aldrich, St. Louis, MO) and 1% L-glutamine (Life Technologies, Carlsbad, CA, USA). T cells were stimulated via the addition of CD3/CD28 Dynabeads (Thermo Fisher Scientific, Waltham, MA, USA) with a bead-to-cell ratio of 1:8 for a duration 24 hours. After stimulation, CD3/CD28 Dynabeads were magnetically removed. Pan T cell-, CD4+- and CD8+-cell-isolation was performed by magnetic cell separation (Pan T Cell Isolation Kit, # 130-096-535 | human CD4 MicroBeads, # 130-045-101 | human CD8 MicroBeads, # 130-045-201, Miltenyi Biotec, Bergisch Gladbach, Germany) using an AutoMACS Pro Separator ( # 130-092-545, Miltenyi Biotec, Bergisch-Gladbach, Germany) according to the manufacturer’s instructions. Results: 11.545 expressed genes were identified, for CD4/CD8 T cells, we detected 5.667/ 5.799 up-regulated genes and 5.878/5.746 down-regulated genes. 294/346 genes and 325/252 genes were significantly up/down-regulated for CD4+/CD8+ T cells (p-val. <0.05). Gene set enrichment analysis revealed 117/17 and 22/6 significantly up/down-regulated pathways for CD4+/CD8+ T cells (p-val. <0.05). Genes and Gene sets differentially regulated were relevant for T cell immune response and metabolic function. Conclusion: KD resulted in immunometabolic reprogramming of human CD4+/CD8+ T cells.
Project description:As part of cross-platform comparisons of microarray and RNA-seq, the current experiment using the Illumina NextSeq 500 and MGI DNBSEQ-G400RS aimed to quantify gene-level expression in subjects administered recombinant human erythropoietin over a 10-week protocol for the identification of gene signatures of blood doping. These results were compared to results obtained from other gene expression quantification platforms using the same experimental cohort, including the Illumina HumanHT-12v4 Expression BeadChips (archived in ArrayExpress; E-MTAB-2874) and Affymetrix Human Transcriptome Array 2.0 (archived in ArrayExpress; E-MTAB-11080).
Project description:The aim of this study is to survey global gene expression across a range of mouse tissues. Biotinylated cRNA was synthesized from total RNA, then fragmented and hybridized to Affymetrix Mouse Genome 430 2.0 GeneChip arrays at the Siteman Cancer Center Gene Chip Core Facility (Washington University, St Louis, Missouri) according to manufacturer's protocols. Image files were generated using MicroArray Suite 5.0 (Affymetrix). Keywords: other
Project description:This dataset represents woody plants recorded in 16 1-ha forest plots in an elevational gradient in Madidi National Park, Bolivia, ranging from lowland Amazonian moist forest and lowland dry forest to the treeline of the Andean Altiplano. This work was carried out by David Henderson and Jonathan Myers (Washington University in St. Louis), Sebastian Tello (Missouri Botanical Garden and University of Missouri, St. Louis), and Brian Sedio (University of Texas at Austin and Smithsonian Tropical Research Institute).
Project description:PLC/PRF/5 cells expressing 3xFlag-SPOP were lysed and incubated with ANTI-FLAG M2 Affinity Gel (Sigma-Aldrich, St. Louis, MO, USA) at 4 °C overnight. The immunoprecipitated complexes were separated by SDS-PAGE and stained with Coomassie Blue. Mass spectrometry assays were performed at Shanghai Applied Protein Technology Co., Ltd.
Project description:The expression data for this study can be found here:
http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-1088/
and its SNP6 data can be found here:
http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-1087/