ABSTRACT: Noncoding variants play a central role in the genetics of complex traits, but we still lack a full description of the main molecular pathways through which they act. Here we used molecular data to quantify the contribution of cis-acting genetic effects at each major stage of gene regulation from chromatin to proteins, within a population sample of Yoruba lymphoblastoid cell lines (LCLs). We performed 4sU metabolic labeled transcripts in 65 YRI LCLs to identify genetic variants that affect transcription rates. As expected, we found an important contribution of genetic variation via chromatin, contributing ∼65% of eQTLs (expression Quantitative Trait Loci). The remaining eQTLs, which are not asso- ciated with chromatin-level variation, are highly enriched in transcribed regions, and hence may affect expression through co- or post-transcriptional processes. International HapMap lymphoblastoid cell lines (LCLs) derived from YRI (Yoruba in Ibadan, Nigeria); We adapted the 4sU labelling method from (PMID 21516085). Briefly, cell cultures were grown to log phase in volumes sufficient to yield about 300 ng of 4sU-labeled RNA. Cells were incubated with 4sU for the required length of time (0, 30, or 60 minutes), then washed, pelleted, and frozen. Total RNA was extracted, and 4sU-labeled RNA was separated from total RNA using a bead-based biotin-streptavidin purification protocol. We sequenced metabolic labeled transcripts in 65 YRI LCLs 30 minutes and 60 minutes after incubation.
Project description:TGF-b1-stimulation induces an epithelial dedifferentiation-process, throughout which epithelial cell sheets disintegrate and gradually switch into fibroblastic-appearing cells (EMT-like transition). The purpose of these profiles was to identify differentially expressed genes that are regulated transcriptionally. Standard microarry-based gene expression profiles measure steady-state RNA but do not provide insight into underlying regulatory principles. NIAC-NTR-based gene expression profiling (Kenzelmann et al., PNAS, 2007) essentially enables the dissection of transcriptionally versus non-transcriptionally regulated genes within respective analysed time-frames. Briefly, NIAC-NTR relies on incorporation of 4sU (thio-uridine) into nascent RNA, which can subsequently be specifically isolated by custom-made columns. Total- and enriched (4sU-labeled) are then further processed for microarray gene expression profiling by standard procedures. This dataset complements previously released data of NIAC-NTR-based gene expression profiling of cells treated with TGF-b1 and 4sU for 2hrs [GSE23833]. The present data and files represent the outcome of NIAC-NTR-based gene expression profiling of cells treated with TGF-b1 for 24hrs and incubation with 4sU for the last 2hrs (22hrs-24hrs of TGF-b1-stimulation). NIAC-NTR: Non Invasive Application and Capture of Newly Transcribed RNA NMuMG cells were seeded 24hrs prior to treatment. Cells were stimulated with 5ng/ml TGF-b1 for a total of 24hrs. After 22hrs of stimulation 200µM 4sU thio-uridine was added to the cultures and further incubated for another 2hrs. Total RNA was extracted using RNeasy Mini Kits (Qiagen). 4sU-labeled RNA was further extracted from total RNA using mercury-based custom-made columns. The experiments were performed as independent biological triplicate. Details about isolation of 4sU-labeled RNA can be found in Kenzelmann et al. PNAS, 2007.
Project description:Baseline microRNA (miRNA) expression was evaluated in 107 HapMap lymphoblastoid cell lines (LCLs; 53 CEU and 54 YRI) using Exiqon miRCURY LNA arrays v10.0 (Exiqon array). Total RNA from each of the 107 HapMap sample was labeled and hybridized onto an Exiqon array
Project description:We evaluated changes in mRNA stability and transcription using 4sU metabolic pulse labeling across a four hour time course following activation of Jurkat T cells with PMA and PHA Measurement of total mRNA (T) and 4sU labeled mRNA (IP) in three biological replicates at five time points: prior to activation (U) and the first four hours after activation (1-4)
Project description:Transcript abundance results from the balance between transcription and mRNA decay, and varies pervasively in humans. We have examined the effect of DNA variation on mRNA half-life differences by conducting a genome-wide survey of mRNA stability in seven human HapMap lymphoblastoid cell lines (LCLs). We determined the mRNA half-life for each gene from the ratio of 4-thio-uridine (4sU)-labeled nascent RNAs to total RNAs. 5,145 (46%) of 11,132 analyzed genes showed inter-individual mRNA half-life differences at a false discovery rate, FDR<0.05. As previously reported, we found transcription to be the main factor influencing transcript abundance. Although mRNA half-life explained only ~6% of transcript abundance on average, it explained ~16% for the subset of genes (~10%) showing inter-individual mRNA half-life differences (P<0.001). We confirmed previously reported correlations of mRNA half-life with transcript length, 3’-UTR length, and number of exon-junctions per kb of transcript. The number of miRNA targets in 3’-UTRs was negatively correlated with half-life (P=2.2×10-16), a new observation that is consistent with the role of miRNA in inducing mRNA degradation. Notably, coding GC and GC3 content showed positive correlations with mRNA half-life in genes with inter-individual mRNA half-life differences, implying a role of mRNA stability in shaping synonymous codon usage bias. Consistently, G or C alleles of coding SNPs were found associated with longer mRNA half-life (P=0.021). As expected, we also found that nonsense SNPs were associated with shorter mRNA half-life (P=0.009). Our results strongly suggest that inter-individual mRNA stability differences are widespread and affected by DNA sequence and composition variation. A total of 7 HapMap LCLs were used to measure mRNA half-life. Total RNAs and the 4sU-labeled-newly synthesized RNAs (nascent RNAs) were isolated from the same cell culture and were assayed simultaneously with human Exon array. For 3 LCLs, we included 3 biological replicates (i.e., independent cell cultures) and for 1 LCL we also included technical duplicates. mRNA half-life was calculated from the ratio of nascent RNAs/total RNAs. We used ANOVA to test inter-individual difference of mRNA half-life between 3 subjects who have biological replicates and technical duplicates. We examined the Spearman rank correlation of mRNA half-life with a number of gene features, including transcript length, intron length, 5'-UTR length and folding energy, 3'-UTR length and folding energy, microRNA target sites, GC and GC3 contents, etc. We also performed linear regression to test the effects of specific type of sequence variants (nonsense SNPs, SNPs within miRNA target sites, and coding synonymous and nonsynonymous SNPs) on mRNA half-life across 3 subjects that have whole genome sequencing data available (1000 genome project June 2011 release).
Project description:RNA was labeled in BL41 cells by culturing cells for 60 min in media containing 100µM 4sU. Tc-RNA was separated into nt- and p-RNA. All three RNA subsets were subjected to microarray analysis. Only probe sets providing present calls in all RNA samples/subsets were included into the analysis; We used microarrays to determine half-lives of mRNAs expressed in BL41 cells Experiment Overall Design: RNA was labeled in BL41 cells by culturing cells for 60 min in media containing 100µM 4sU. Tc-RNA was separated into nt- and p-RNA. All three RNA subsets were subjected to microarray analysis.
Project description:Protein-RNA interactions are fundamental to core biological processes, such as mRNA splicing, localization, degradation and translation. We developed a photoreactive nucleotide-enhanced UV crosslinking and oligo(dT) purification approach to identify the mRNA-bound proteome using quantitative proteomics and to display the protein occupancy on mRNA transcripts by next-generation sequencing. Application to a human embryonic kidney cell line identified close to 800 proteins. Close to one third of these proteins, were neither previously annotated nor could be functionally predicted to bind RNA. Protein occupancy profiling provides a transcriptome-wide catalog of potential cis-regulatory regions on mammalian mRNAs and showed that large stretches in 3' UTRs can be contacted by the mRNA-bound proteome, with numerous putative binding sites in regions harboring disease-associated nucleotide polymorphisms. Our observations indicate the presence of a large number of unexpected mRNA-binders with novel molecular functions participating in combinatorial post-transcriptional gene-expression networks. To obtain a more detailed picture of the RNA present in the pooled precipitates of four consecutive oligo(dT)-purifications, we constructed a cDNA library by random priming of 4-thiouridine (4SU)- and 6-thioguanosine (6SG)-labeled RNA derived from UV-irradiated (365 nm)and non-irradiated cells. Digital gene expression analysis of the cDNA library of non-irradiated cells, labeled with 4SU and 6SG, was performed. To monitor the incorporation of photoreactive nucleotides into mRNA, we isolated 4SU- and 6SG-labeled RNA from the oligo(dT) precipitate of non-crosslinked cells by biotinylation and streptavidin purification (Dolken et al., 2008).
Project description:The Forkhead family of transcription factors comprises numerous members and is implicated in various cellular functions, including cell growth, apoptosis, migration and differentiation.In this study we identified the Forkhead factor FoxQ1 as increased in expression during TGF-beta1 induced changes in epithelial differentiation, suggesting functional roles of FoxQ1 for epithelial plasticity.The repression of FoxQ1 in mammary epithelial cells led to a change in cell morphology characterized by an increase in cell size, pronounced cell-cell contacts and an increased expression of several junction proteins (e.g. E-cadherin). In addition, FoxQ1 knock-down cells revealed rearrangements in the actin-cytoskeleton and slowed down cell cycle G1-phase progression.Furthermore, repression of FoxQ1 enhanced the migratory capacity of coherent mammary epithelial cells.Gene expression profiling of NM18 cells indicated that FoxQ1 is a relevant downstream mediator of TGF-beta1 induced gene expression changes. This included the differential expression of transcription factors involved in epithelial plasticity, e.g. Ets-1, Zeb1 and Zeb2.In summary, this study has elucidated the functional impact of FoxQ1 on epithelial differentiation Cells treated with 200µM 4sU for 2h, biological replicate 1:2h_co._total1, Cells treated with 200µM 4sU for 2h, biological replicate 2:2h_co._total2, Cells treated with 200µM 4sU for 2h, biological replicate 3 :2h_co._total3, Cells treated with 200µM 4sU for 2h and enriched for 4sU labelled RNA, biological replicate 1: 2h_co._enriched1, Cells treated with 200µM 4sU for 2h and enriched for 4sU labelled RNA, biological replicate 2: 2h_co._enriched2, Cells treated with 200µM 4sU for 2h and enriched for 4sU labelled RNA, biological replicate 3:2h_co._enriched3, Cells treated with 5 ng/ml TGF-b1 and 200µM 4sU for 2h, biological replicate 1:2h_TGFbeta_total1, Cells treated with 5 ng/ml TGF-b1 and 200µM 4sU for 2h, biological replicate 2:2h_TGFbeta_total2, Cells treated with 5 ng/ml TGF-b1 and 200µM 4sU for 2h, biological replicate 3:2h_TGFbeta_total3, Cells treated with 5 ng/ml TGF-b1 and 200µM 4sU for 2h and enriched for 4sU labelled RNA, biological replicate 1:2h_TGFbeta_enriched1, Cells treated with 5 ng/ml TGF-b1 and 200µM 4sU for 2h and enriched for 4sU labelled RNA, biological replicate 2:2h_TGFbeta_enriched2, Cells treated with 5 ng/ml TGF-b1 and 200µM 4sU for 2h and enriched for 4sU labelled RNA, biological replicate 3:2h_TGFbeta_enriched3, FoxQ1 dataset Cells transfected with 25nm scrambled siRNA for 48hrs, biological replicate 1:ns-siRNA 48hrs 1, Cells transfected with 25nm scrambled siRNA for 48hrs, biological replicate 2:ns-siRNA 48hrs 2, Cells transfected with 25nm scrambled siRNA for 48hrs, biological replicate 3:ns-siRNA 48hrs 3, Cells transfected with 25nm scrambled siRNA for 48hrs and treated with TGF-b1 for 40hrs biological replicate 1:ns-siRNA 48hrs TGF-b1 40hrs 1, Cells transfected with 25nm scrambled siRNA for 48hrs and treated with TGF-b1 for 40hrs biological replicate 2:ns-siRNA 48hrs TGF-b1 40hrs 2, Cells transfected with 25nm scrambled siRNA for 48hrs and treated with TGF-b1 for 40hrs biological replicate 3:ns-siRNA 48hrs TGF-b1 40hrs 3, Cells transfected with 25nm FoxQ1 siRNA for 48hrs and treated with TGF-b1 for 40hrs biological replicate 1:FoxQ1 siRNA 48hrs TGF-b1 40hrs 1, Cells transfected with 25nm FoxQ1 siRNA for 48hrs and treated with TGF-b1 for 40hrs biological replicate 2:FoxQ1 siRNA 48hrs TGF-b1 40hrs 2 Cells transfected with 25nm FoxQ1 siRNA for 48hrs and treated with TGF-b1 for 40hrs biological replicate 3:FoxQ1 siRNA 48hrs TGF-b1 40hrs 3
Project description:MYCN overexpression in the doxycline MYCN inducible cell line SH-SY5Y/6TR(EU)/pTrex-Dest-30/MYCN (SY5Y-MYCN) using the dynamic transcriptome analysis (DTA) protocol. Samples at different time-points after MYCN over-expression and uninduced controls (0h) were sequenced in duplicates. The experimental setup consists of [A] total mRNA at 0h, 1h, 4h, 24h, which corresponds to the standard mRNA-seq protocol, [B] 4-thioUridine (4sU) labelled mRNA 30 min before extraction at time-points 0h, 1h, 4h, which is the freshly transcribed mRNA within the last 30 min before the time-point and [C] the counter-part, 4sU unlabelled mRNA 30 min before extraction which corresponds to the mRNA from before 30 min of extraction. The samples were sequenced on an Illumina GA IIx using the Illumina protocols.
Project description:The mRNA m6A reader YTHDF2 is overexpressed in a broad spectrum of human acute myeloid leukemias (AML). To study the role of YTHDF2 on mRNA decay rates in leukemia, c-Kit+ cells from foetal livers of Ythdf2fl/fl; Vav-iCre (Ythdf2CKO) and Ythdf2fl/fl (Ythdf2CTL) 14.5 dpc embryos were transduced with Meis1 and Hoxa9 oncogenes and serially re-plated to generate pre-leukemic cells. Medium with 4SU was used for pre-leukemic cells labelling for 12 hours and was later replaced with 4SU-free medium (time 0). Cells were collected immediately after medium change and at 1, 3 and 9 hours for library generation. RNA from Ythdf2CKO (n=3 biological replicates) and Ythdf2CTL (n=3 biological replicates) pre-leukemic cells were used for SLAM-seq library generation.