Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Dendritic cell stimulation: 5 ligands, 9 time points

ABSTRACT: mouse primary BMDCs were stimulated with tlr ligands and gene expression changes were profiled on Affymetrix arrays BMDC were stimulated with 5 tlr ligands (LPS, pIC ,PAM, CpG, GRD) across 9 time points (.5, 1, 2, 4, 6, 8, 12, 16, 24 hours). Unstimulated cells were used as controls.

ORGANISM(S): Mus musculus

SUBMITTER: Tal Shay

PROVIDER: E-GEOD-17721 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses.

Amit Ido I Garber Manuel M Chevrier Nicolas N Leite Ana Paula AP Donner Yoni Y Eisenhaure Thomas T Guttman Mitchell M Grenier Jennifer K JK Li Weibo W Zuk Or O Schubert Lisa A LA Birditt Brian B Shay Tal T Goren Alon A Zhang Xiaolan X Smith Zachary Z Deering Raquel R McDonald Rebecca C RC Cabili Moran M Bernstein Bradley E BE Rinn John L JL Meissner Alex A Root David E DE Hacohen Nir N Regev Aviv A

Science (New York, N.Y.) 20090903 5950

Models of mammalian regulatory networks controlling gene expression have been inferred from genomic data but have largely not been validated. We present an unbiased strategy to systematically perturb candidate regulators and monitor cellular transcriptional responses. We applied this approach to derive regulatory networks that control the transcriptional response of mouse primary dendritic cells to pathogens. Our approach revealed the regulatory functions of 125 transcription factors, chromatin ...[more]

PMID: 19729616

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Project description:Individual genetic variation affects gene expression and cell phenotype by acting within complex molecular circuits, but this relationship is still largely unknown. Here, we combine genomic and meso-scale profiling with novel computational methods to detect genetic variants that affect the responsiveness of gene expression to stimulus (responsiveness QTLs) and position them in circuit diagrams. We apply this approach to study individual variation in transcriptional responsiveness to three different pathogen components in the model response of primary bone marrow dendritic cells (DCs) from recombinant inbred mice strains. We show that reQTLs are common both in cis (affecting a single target gene) and in trans (pleiotropically affecting co-regulated gene modules) and are specific to some stimuli but not others. Leveraging the stimulus-specific activity of reQTLs and the differential responsiveness of their associated targets, we show how to position reQTLs within the context of known pathways in this regulatory circuit. For example, we find that a pleiotropic trans-acting genetic factor in chr1:129-165Mb affects the responsiveness of 35 anti-viral genes only during an anti-viral like stimulus. Using RNAi we uncover RGS16 the likely causal gene in this interval, and an activator of the antiviral response. Our approach charts an experimental and analytic path to decipher the mechanisms underlying genetic variation in other complex circuits in primary mammalian cells. The transcriptional response of DCs to pathogen components in the context of the mouse BXD recombinant inbred lines. We isolated DCs from individual age-synchronized female mice of different BXD and parental strains. We measured 30 global transcription profiles in resting and stimulated DCs from two parental strains (B6 and D2) and six BXD strains. We used bone marrow derived primary DCs from a panel of recombinant inbred (RI) BXD mice derived from a cross of the parental C57BL/6J (B6) and DBA/2J (D2) strains. We measured transcriptional responses in DCs from each of two parental strains and 6 BXD strains stimulated by three ligands of Toll-like receptors: LPS (TLR4 ligand), Pam3CSK ('PAM', TLR2 ligand) and poly IC (TLR3/RIG-I ligand).

Project description:The innate immune system is vital to rapidly responding to pathogens and Toll-like receptors (TLRs) are a critical component of this response. Nanovesicular exosomes play a role in immunity, but to date their exact contribution to the dissemination of the TLR response is unknown. To understand the effect of exosomal cargo released from locally stimulated cells on distal cell expression, we collected exosomes from local ovarian adenocarcinoma (HEY) cells that were either unstimulated (control-exosomes), stimulated with pIC (pIC-exosomes), or lipopolysaccharide (LPS-exosomes) for 48 hours. The three groups of exosomes were added to naÃ¯ve (distal) cells and the gene expression profiles were compared between local TLR stimulation (for 6 hours) and distal stimulation mediated by exosomes at the 48-hour time point The goal of the study was to delineate the differential effector function of TLR-exosomes based on the innate immune activation state (control-, LPS-, pIC-stimulation) of the cell of origin in vitro. We used microarrays to understand the changes in gene expression in 2 samples of local hey cells either unstimulated(control) or stimulated with poly(I:C) or LPS each. We also profiled 2 samples of distal naÃ¯ve cells that were exposed to either control, LPS or poly(I:C) exosomes from local cells. Here we show that exosomes from TLR stimulated cells (TLR-exosomes) can largely recapitulate TLR activation in distal cells in vitro. We can abrogate the action-at-a-distance signaling of exosomes by UV irradiation, demonstrating that RNA is crucial for their effector function. This work definitively establishes the differential effector function for TLR-exosomes in communicating the activation state of the cell of origin. HEY cells were either unstimulated (Local control) or stimulated with LPS or poly (I:C) for 6 hours and the RNA from these cells was profiled using a microarray. Exosomes were collected from local cells that were unstimulated or stimulated for 48 hours and added to distal (naive) HEY cells that were exposed to control exosomes, LPS exosomes or pIC exosomes and then the RNA was collected for analysis by microarray. mRNA expression was captured on Affymetrix U133 Plus 2 chips. mRNA microarray data were analyzed using the Expression Console software (Affymetrix) and Bioconductor tools written in the R statistical programming language. Pre-processing of raw signal intensities and normalization was performed using GCRMA (R). Linear modelling of the transformed data was determined by using Limma in R with the Benjamini and Hochberg correction. Differentially expressed probesets were identified using a threshold 5% FDR correction and a fold change â¥ 1.4 was applied

Dataset Information

Dendritic cell stimulation: 5 ligands, 9 time points

Publications

Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses.

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