Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Dynamic Transformations of Genome-wide Epigenetic Marking and Transcriptional Control Establish T Cell Identity

ABSTRACT: This SuperSeries is composed of the following subset Series: GSE31233: Dynamic transformations of epigenetic marking and genome-wide transcriptional regulation that establish T cell identity [ChIP-Seq] GSE31234: Dynamic transformations of epigenetic marking and genome-wide transcriptional regulation that establish T cell identity [RNA-Seq] Refer to individual Series

ORGANISM(S): Mus musculus

SUBMITTER: Jingli Zhang

PROVIDER: E-GEOD-31235 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Dynamic transformations of genome-wide epigenetic marking and transcriptional control establish T cell identity.

Zhang Jingli A JA Mortazavi Ali A Williams Brian A BA Wold Barbara J BJ Rothenberg Ellen V EV

Cell 20120401 2

T cell development comprises a stepwise process of commitment from a multipotent precursor. To define molecular mechanisms controlling this progression, we probed five stages spanning the commitment process using RNA-seq and ChIP-seq to track genome-wide shifts in transcription, cohorts of active transcription factor genes, histone modifications at diverse classes of cis-regulatory elements, and binding repertoire of GATA-3 and PU.1, transcription factors with complementary roles in T cell devel ...[more]

PMID: 22500808

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Project description:Breast cancers contain a minority population of cancer cells characterized by CD44 expression but low or undetectable levels of CD24 (CD44+CD24-/low) that have higher tumorigenic capacity than other subtypes of cancer cells. METHODS: We compared the gene-expression profile of CD44+CD24-/low tumorigenic breast-cancer cells with that of normal breast epithelium. Differentially expressed genes were used to generate a 186-gene invasiveness gene signature (IGS), which was evaluated for its association with overall survival and metastasis-free survival in patients with breast cancer or other types of cancer. RESULTS: There was a significant association between the IGS and both overall and metastasis-free survival (P<0.001, for both) in patients with breast cancer, which was independent of established clinical and pathological variables. When combined with the prognostic criteria of the National Institutes of Health, the IGS was used to stratify patients with high-risk early breast cancer into prognostic categories (good or poor); among patients with a good prognosis, the 10-year rate of metastasis-free survival was 81%, and among those with a poor prognosis, it was 57%. The IGS was also associated with the prognosis in medulloblastoma (P=0.004), lung cancer (P=0.03), and prostate cancer (P=0.01). The prognostic power of the IGS was increased when combined with the wound-response (WR) signature. CONCLUSIONS: The IGS is strongly associated with metastasis-free survival and overall survival for four different types of tumors. This genetic signature of tumorigenic breast-cancer cells was even more strongly associated with clinical outcomes when combined with the WR signature in breast cancer. Expression profling was performed on 6 tumorigenic, 3 non tumorigenic samples of breast tumors and 3 normal breast samples on two different platforms GPL96 and GPL97. A gene signature was derived by comparing the gene expressions of 6 tumorigenic samples with 3 normal breast samples.

Project description:4C-Seq has proven to be a powerful technique to identify genome-wide interactions with a single locus of interest (or "bait") that can be important for gene regulation. However, analysis of 4C-Seq data is complicated by the many biases inherent to the technique. An important consideration when dealing with 4C-Seq data is the differences in resolution of signal across the genome that result from differences in 3D distance separation from the bait. This leads to the highest signal in the region immediately surrounding the bait and increasingly lower signals in far-cis and trans. Another important aspect of 4C-Seq experiments is the resolution, which is greatly influenced by the choice of restriction enzyme and the frequency at which it can cut the genome. Thus, it is important that a 4C-Seq analysis method is flexible enough to analyze data generated using different enzymes and to identify interactions across the entire genome. Current methods for 4C-Seq analysis only identify interactions in regions near the bait or in regions located in far-cis and trans, but no method comprehensively analyzes 4C signals of different length scales. In addition, some methods also fail in experiments where chromatin fragments are generated using frequent cutter restriction enzymes. Here, we describe 4C-ker, a Hidden-Markov Model based pipeline that identifies regions throughout the genome that interact with the 4C bait locus. In addition, we incorporate methods for the identification of differential interactions in multiple 4C-seq datasets collected from different genotypes or experimental conditions. Adaptive window sizes are used to correct for differences in signal coverage in near-bait regions, far-cis and trans chromosomes. Using several datasets, we demonstrate that 4C-ker outperforms all existing 4C-Seq pipelines in its ability to reproducibly identify interaction domains at all genomic ranges with different resolution enzymes. 4C-Seq experiments from Igh and Cd83 bait in activated B cells and Tcrb (Eb) bait in double negative T cells and immature B cells. RNA-Seq and ATAC-Seq experiments in DN and Immature B cells.

Dataset Information

Dynamic Transformations of Genome-wide Epigenetic Marking and Transcriptional Control Establish T Cell Identity

Publications

Dynamic transformations of genome-wide epigenetic marking and transcriptional control establish T cell identity.

Similar Datasets

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