Classifying leukemia types with chromatin conformation data
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: JosM-CM-)e Dostie
PROVIDER: E-GEOD-55408 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:Background: Although genetic or epigenetic alterations have shown to affect the three-dimensional organization of genomes, the utility of chromatin conformation in the classification of human disease has never been addressed. Results: Here, we explore whether chromatin conformation can be used to classify human leukemia. We map the conformation of the HOXA gene cluster in a panel of cell lines with 5C chromosome conformation capture technology, and use the data to train and test a support vector machine classifier named 3D-SP. We show that 3D-SP is able to accurately distinguish leukemias expressing MLL-fusion proteins from those expressing only wild-type MLL, and that it can also classify leukemia subtypes according to MLL fusion partner, based solely on 5C data. Conclusions: Our study provides the first proof-of-principle demonstration that chromatin conformation contains the information value necessary for classification of leukemia subtypes. Analysis of 38 samples using 5C technology. All data normalized using a 'master' BAC consisting of 5C data from 6 samples.
2014-02-27 | E-GEOD-55406 | biostudies-arrayexpress
Project description:Background: Although genetic or epigenetic alterations have shown to affect the three-dimensional organization of genomes, the utility of chromatin conformation in the classification of human disease has never been addressed. Results: Here, we explore whether chromatin conformation can be used to classify human leukemia. We map the conformation of the HOXA gene cluster in a panel of cell lines with 5C chromosome conformation capture technology, and use the data to train and test a support vector machine classifier named 3D-SP. We show that 3D-SP is able to accurately distinguish leukemias expressing MLL-fusion proteins from those expressing only wild-type MLL, and that it can also classify leukemia subtypes according to MLL fusion partner, based solely on 5C data. Conclusions: Our study provides the first proof-of-principle demonstration that chromatin conformation contains the information value necessary for classification of leukemia subtypes. Examination of CTCF and RAD21 binding sites in THP-1 cell.
2014-02-27 | E-GEOD-55407 | biostudies-arrayexpress
Project description:Conventional reverse genetic approaches for study of Plasmodium malaria parasite gene function are limited, or not applicable. Hence, new inducible systems are needed. Here we describe a method to control P. falciparum gene expression in which target genes bearing a glmS ribozyme in the 3M-bM-^@M-2 untranslated region (3M-bM-^@M-2-UTR) are efficiently knocked down in transgenic P. falciparum parasites in response to exogenous glucosamine (GlcN) inducer. Using reporter genes, we show that the glmS ribozyme cleaves reporter mRNA in vivo leading to reduction in mRNA expression following GlcN treatment. GlcN-induced ribozyme activation also led to efficient reduction of reporter protein, which could be rapidly reversed by removing the inducer. The glmS ribozyme was validated as a reverse-genetic tool by integration into the essential gene and antifolate drug target dihydrofolate reductase-thymidylate synthase (PfDHFR-TS). GlcN treatment of transgenic parasites led to rapid and efficient knockdown of PfDHFR-TS mRNA and protein. PfDHFR-TS knockdown led to a growth/arrest mutant phenotype and hypersensitivity to pyrimethamine. The glmS ribozyme is thus an important tool for study of P. falciparum essential genes and anti-malarial drug discovery. mRNA profiles were generated from 3D7 wild-type and DHFR-TS-GFP_glmS integrant parasites in untreated and treated with 10 mM Glucosamine conditions in duplicate.
2013-09-03 | E-GEOD-43125 | biostudies-arrayexpress
Project description:Mitotic chromosomes are among the most recognizable structures in the cell, yet for over a century their internal organization remains largely unsolved. We applied chromosome conformation capture methods, 5C and Hi-C, across the cell cycle and revealed two alternative three-dimensional folding states of the human genome. We show that the highly compartmentalized and cell-type-specific organization described previously for non-synchronous cells is restricted to interphase. In metaphase, we identify a homogenous folding state, which is locus-independent, common to all chromosomes, and consistent among cell types, suggesting a general principle of metaphase chromosome organization. Using polymer simulations, we find that metaphase Hi-C data is inconsistent with classic hierarchical models, and is instead best described by a linearly-organized longitudinally compressed array of consecutive chromatin loops.
2013-11-05 | E-MTAB-1948 | biostudies-arrayexpress
Project description:Androgen receptor (AR) orchestrates an intricate transcriptional regulatory network that governs prostate cancer initiation, development and progression. To understand this network in detail, we generated genome-wide maps of AR occupancy by ChIP-seq in LNCaP cells. We found NKX3-1, an androgen-dependent homeobox protein well-characterized for its role in prostate development and differentiation, being recruited to AR binding sites (ARBS) in response to androgen signaling. We identified 6,359 NKX3-1 binding sites, most of which overlapped with AR. In addition to its novel collaborative transcriptional role at well-known prostate cancer model genes, our binding and knockdown studies further suggested that NKX3-1 potentially regulates AR in a feed-forward manner. Integrative analysis of Oncomine molecular concepts showed that these androgen-regulated AR and NKX3-1 associated genes are significantly overexpressed in prostate carcinoma as well as advanced and recurrent prostate tumors. From our transcriptomic profiling and Gene Ontology analysis, we observed that AR and NKX3-1 co-regulate genes involved in "protein trafficking" processes, which are mandatory events in the integration of oncogenic signaling pathways leading to prostate cancer development and progression. Interestingly, we found that AR and NKX3-1 co-regulate several members of the RAB GTPase family of secretory/trafficking proteins via the involvement of FoxA1 in a ternary complex and we believe that these AR/NKX3-1/FoxA1 co-regulated RAB genes could serve as expression signatures in prostate carcinogenesis. More specifically, through functional analyses, we showed that NKX3-1, together with AR and FoxA1, could promote prostate cancer cell survival through activation of RAB3B expression. Collectively, our study has provided important insights into the hierarchical transcriptional regulatory network established between AR and NKX3-1 and sought to elucidate the important genetic-molecular-phenotypic paradigm in androgen-dependent prostate cancer. Genome-wide binding analyses of AR, NKX3-1 and FoxA1 in LNCaP with or without DHT (5alpha-dihydrotestosterone) stimulation using ChIP-Seq.
2011-11-16 | E-GEOD-28264 | biostudies-arrayexpress
Project description:Transcription factors (TFs) do not function alone but work together with other TFs (called co-TFs) in a combinatorial fashion to precisely control the transcription of target genes. Mining co-TFs is thus important to understand the mechanism of transcriptional regulation. Although existing methods can identify co-TFs, their accuracy depends heavily on the chosen background model and other parameters such as the enrichment window size and the PWM score cut-off. In this study, we have developed a novel web-based co-motif scanning program called CENTDIST (http://compbio.ddns.comp.nus.edu.sg/~chipseq/centdist/). In comparison to current co-motif scanning programs, CENTDIST does not require the input of any user-specific parameters and background information. Instead, CENTDIST automatically determines the best set of parameters and ranks co-TF motifs based on their distribution around ChIP-seq peaks. We tested CENTDIST on 14 ChIP-seq datasets and found CENTDIST is more accurate than existing methods. In particular, we applied CENTDIST on an Androgen Receptor (AR) ChIP-seq dataset from a prostate cancer cell line and correctly predicted all known co-TFs (8 TFs) of AR in the top 20 hits as well as discovering AP4 as a novel co-TF of AR (which was missed by existing methods). Taken together, CENTDIST, which exploits the imbalanced nature of co-TF binding, is a user-friendly, parameter-less, and powerful predictive web-based program for understanding the mechanism of transcriptional co-regulation. Genome-wide binding analyses of AP4 in LNCaP with DHT (5alpha-dihydrotestosterone) stimulation using ChIP-Seq.
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Project description:When using cell lines to study cancer, phenotypic similarity to the original tumor is paramount. Yet, little has been done to characterize how closely Merkel cell carcinoma (MCC) cell lines model native tumors. To determine their similarity to MCC tumor samples, we characterized MCC cell lines via gene expression microarrays. Using whole transcriptome gene expression signatures and a computational bioinformatic approach, we identified significant differences between variant cell lines (UISO, MCC13, and MCC26) and fresh frozen MCC tumors. Conversely, the classic WaGa and Mkl-1 cell lines more closely represented the global transcriptome of MCC tumors. When compared to publicly available cancer lines, WaGa and Mkl-1 cells were similar to other neuroendocrine tumors, but the variant cell lines were not. WaGa and Mkl-1 cells grown as xenografts in mice had histological and immunophenotypical features consistent with MCC, while UISO xenograft tumors were atypical for MCC. Spectral karyotyping and short tandem repeat analysis of the UISO cells matched the original cell line's description, ruling out contamination. Our results validate the use of transcriptome analysis to assess the cancer cell line representativeness and indicate that UISO, MCC13, and MCC26 cell lines are not representative of MCC tumors, whereas WaGa and Mkl-1 more closely model MCC. RNA was extracted from MCC cell lines and MCC and SCLC tumor samples and hybridized to Affymetrix microarrays for transcriptome profiling.
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