scholarly journals Transcription factor RFX7 governs a tumor suppressor network in response to p53 and stress

2021 ◽  
Author(s):  
Luis Coronel ◽  
Konstantin Riege ◽  
Katjana Schwab ◽  
Silke Förste ◽  
David Häckes ◽  
...  
Keyword(s):  
Tumor Suppressor ◽  
Target Gene ◽  
Target Genes ◽  
Cell Types ◽  
Factor X ◽  
Tumor Suppressor P53 ◽  
Established Tumor ◽  
Distinct Cell ◽  
Signaling Axis ◽  
Cancer Types

AbstractDespite its prominence, the mechanisms through which the tumor suppressor p53 regulates most genes remain unclear. Recently, the regulatory factor X 7 (RFX7) emerged as a suppressor of lymphoid neoplasms, but its regulation and target genes mediating tumor suppression remain unknown. Here, we identify a novel p53-RFX7 signaling axis. Integrative analysis of the RFX7 DNA binding landscape and the RFX7-regulated transcriptome in three distinct cell systems reveals that RFX7 directly controls multiple established tumor suppressors, including PDCD4, PIK3IP1, MXD4, and PNRC1, across cell types and is the missing link for their activation in response to p53 and stress. RFX7 target gene expression correlates with cell differentiation and better prognosis in numerous cancer types. Interestingly, we find that RFX7 sensitizes cells to Doxorubicin by promoting apoptosis. Together, our work establishes RFX7’s role as a ubiquitous regulator of cell growth and fate determination and a key node in the p53 transcriptional program.

scholarly journals Annotation of chromatin states in 66 complete mouse epigenomes during development

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Arjan van der Velde ◽  
Kaili Fan ◽  
Junko Tsuji ◽  
Jill E. Moore ◽  
Michael J. Purcaro ◽  
...  
Keyword(s):  
Target Genes ◽  
Cell Types ◽  
Developmental Trajectory ◽  
Phase Iii ◽  
Chromatin State ◽  
Mammalian Development ◽  
Chromatin States ◽  
Transcription Start Sites ◽  
Repressive Mark ◽  
Distinct Cell

AbstractThe morphologically and functionally distinct cell types of a multicellular organism are maintained by their unique epigenomes and gene expression programs. Phase III of the ENCODE Project profiled 66 mouse epigenomes across twelve tissues at daily intervals from embryonic day 11.5 to birth. Applying the ChromHMM algorithm to these epigenomes, we annotated eighteen chromatin states with characteristics of promoters, enhancers, transcribed regions, repressed regions, and quiescent regions. Our integrative analyses delineate the tissue specificity and developmental trajectory of the loci in these chromatin states. Approximately 0.3% of each epigenome is assigned to a bivalent chromatin state, which harbors both active marks and the repressive mark H3K27me3. Highly evolutionarily conserved, these loci are enriched in silencers bound by polycomb repressive complex proteins, and the transcription start sites of their silenced target genes. This collection of chromatin state assignments provides a useful resource for studying mammalian development.

p22/PACAP Response Gene 1 (PRG1): A Putative Target Gene for the Tumor Suppressor p53

1998 ◽  
Vol 865 (1 VIP, PACAP, A) ◽  
pp. 27-36 ◽  
Author(s):  
HEINER SCHAFER ◽  
ANNA TRAUZOLD ◽  
THORSTEN SEBENS ◽  
WOLFGANG DEPPERT ◽  
ULRICH R. FOLSCH ◽  
...  
Keyword(s):  
Tumor Suppressor ◽  
Target Gene ◽  
Tumor Suppressor P53 ◽  
Putative Target Gene ◽  
Response Gene ◽  
Putative Target

scholarly journals Integrated Analysis of MicroRNA (miRNA) and mRNA Profiles Reveals Reduced Correlation between MicroRNA and Target Gene in Cancer

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xingsong Li ◽  
Xiaokang Yu ◽  
Yuting He ◽  
Yuhuan Meng ◽  
Jinsheng Liang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Mirna Target ◽  
Target Gene ◽  
Target Genes ◽  
Integrated Analysis ◽  
Cancer Type ◽  
Gene Pairs ◽  
Cancer Types

Background. Accumulating evidences demonstrated that microRNA-target gene pairs were closely related to tumorigenesis and development. However, the correlation between miRNA and target gene was insufficiently understood, especially its changes between tumor and normal tissues. Objectives. The aim of this study was to evaluate the changes of correlation of miRNAs-target pairs between normal and tumor. Materials and Methods. 5680 mRNA and 5740 miRNA expression profiles of 11 major human cancers were downloaded from the Cancer Genome Atlas (TCGA). The 11 cancer types were bladder urothelial carcinoma, breast invasive carcinoma, head and neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, stomach adenocarcinoma, and thyroid carcinoma. For each cancer type, we firstly obtained differentially expressed miRNAs (DEMs) and genes (DEGs) in tumor and then acquired critical miRNA-target gene pairs by combining DEMs, DEGs and two experimentally validated miRNA-target interaction databases, miRTarBase and miRecords. We collected samples with both miRNA and mRNA expression values and performed a correlation analysis by Pearson method for miRNA-target pairs in normal and tumor, respectively. Results. We totally got 4743 critical miRNA-target pairs across 11 cancer types, and 4572 of them showed weaker correlation in tumor than in normal. The average correlation coefficients of miRNA-target pairs were different greatly between normal (-0.38 ~ -0.61) and tumor (-0.04 ~ -0.26) for 11 cancer type. The pan-cancer network, which consisted of 108 edges connecting 35 miRNAs and 89 target genes, showed the interactions of pairs appeared in multicancers. Conclusions. This comprehensive analysis revealed that correlation between miRNAs and target genes was greatly reduced in tumor and these critical pairs we got were involved in cellular adhesion, proliferation, and migration. Our research could provide opportunities for investigating cancer molecular regulatory mechanism and seeking therapeutic targets.

The complex interactions of p53 with target DNA: we learn as we go

2003 ◽  
Vol 81 (3) ◽  
pp. 141-150 ◽  
Author(s):  
Ella Kim ◽  
Wolfgang Deppert
Keyword(s):  
Tumor Suppressor ◽  
Dna Binding ◽  
Target Genes ◽  
Dna Structure ◽  
Structural Features ◽  
Binding Activity ◽  
Tumor Suppressor P53 ◽  
Dna Binding Activity ◽  
Complex Interactions ◽  
Target Dna

The most import biological function of the tumor suppressor p53 is that of a sequence-specific transactivator. In response to a variety of cellular stress stimuli, p53 induces the transcription of an ever-increasing number of target genes, leading to growth arrest and repair, or to apoptosis. Long considered as a "latent" DNA binder that requires prior activation by C-terminal modification, recent data provide strong evidence that the DNA binding activity of p53 is strongly dependent on structural features within the target DNA and is latent only if the target DNA lacks a certain structural signal code. In this review we discuss evidence for complex interactions of p53 with DNA, which are strongly dependent on the dynamics of DNA structure, especially in the context of chromatin. We provide a model of how this complexity may serve to achieve selectivity of target gene regulation by p53 and how DNA structure in the context of chromatin may serve to modulate p53 functions.Key words: tumor suppressor p53, sequence-specific DNA binding, DNA conformation, chromatin, chromatin remodeling.

ICSBP Activates Transcription of the Gene Encoding Neurofibromin 1 in Differentiating Myeloid Cells.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 122-122 ◽  
Author(s):  
Weiqi Huang ◽  
Gurveen Saberwal ◽  
Chunliu Zhu ◽  
Elizabeth A. Eklund
Keyword(s):  
Tumor Suppressor ◽  
Target Gene ◽  
Target Genes ◽  
Consensus Sequence ◽  
Myeloid Cells ◽  
Interferon Regulatory Factor ◽  
Regulatory Factor ◽  
Gene Encoding ◽  
Nf1 Gene ◽  
Neurofibromin 1

Abstract Previous studies in murine models and human myeloid malignancies indicate that the interferon consensus sequence binding protein (ICSBP) functions as a leukemia tumor suppressor. Previously identified ICSBP target genes include genes encoding gp91phox and p67phox (components of the phagocyte respiratory burst oxidase), the Toll like receptor 4, and other genes involved in mediating the phagocyte inflammatory response. Transcription of these target genes involves interaction of ICSBP with PU.1 and/or interferon regulatory factor 1 (IRF1), and is restricted to mature phagocytic cells. These results predict that ICSBP-deficiency would be associated with terminal differentiation block and immune dysfunction. However, the phenotype of ICSBP −/− mice is dominated by development of a myeloproliferative disorder which evolves to acute myeloid leukemia. Therefore, we used the chromatin immuno-precipitation technique to identify novel ICSBP target genes which might be involved in the ICSBP tumor suppressor effect. We identified the gene encoding neurofibromin 1 (the NF1 gene) as an ICSBP-target-gene. We found that ICSBP activates NF1 transcription during cytokine induced differentiation of myeloid progenitor cells. Because Nf1 protein has ras-GAP activity, increased Nf1-expression down regulates cytokine-induced ras activity, resulting in proliferation arrest during myeloid cell differentiation. In the current studies, we investigate the mechanism by which ICSBP activates NF1-transcription. We find that ICSBP is part of a multi protein complex that binds to and activates a cis element in the NF1 gene. This complex also includes the ets protein PU.1 and interferon regulatory factor 2 (IRF2). We find that tyrosine phosphorylation of ICSBP in response to hematopoietic cytokines is required for assembly of this transcriptional activation complex and therefore NF1-transcription. Identification of NF1 as an ICSBP target gene represents a mechanism for ICSBP-leukemia-suppression. These studies also provide the first indication of the importance of NF1-transcriptional regulation to the control of cytokine-induced proliferation in differentiating myeloid cells.

Comparison of miRNA Expression Profiles in Leukemia-Derived Microvesicles and Corresponding Leukemia Cells and Analysis of Their Roles in Leukemia

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 1388-1388
Author(s):  
Xiaomei Chen ◽  
Fang Liu ◽  
Wei Xiong ◽  
Xiangjun Chen ◽  
Cong Lu ◽  
...  
Keyword(s):  
Tumor Suppressor ◽  
Mirna Expression ◽  
Tumor Suppressors ◽  
Target Genes ◽  
Expression Profiles ◽  
K562 Cells ◽  
Cell Types ◽  
Leukemic Cells ◽  
Mirna Expression Profiles ◽  
Tumor Suppressor Mirnas

Abstract Abstract 1388 Microvesicles(MVs) are small exosomes of endocytic origin released by normal healthy or damaged cell types, including leukemic cells. MVs have been considered as cell dust, however, recent data bring evidences that MVs generated during cell activation or apoptosis can transfer biologic messages between different cell types. MicroRNAs (miRNAs) have been demonstrated to be aberrantly expressed in leukemia and the overall miRNA expression could differentiate normal versus leukemia. The MVs expressing miRNAs were found in the primary tumors. However it is currently unknown whether miRNA content changes in MVs derived from leukemic cells. Here we compared the miRNA expression in leukemia-derived MVs to corresponding leukemia cells and analysed their roles in leukemia. K562 cells were cultured and collected. MVs derived from K562 cells were also isolated. The presence and levels of specific miRNAs from both MVs derived from K562 cells and K562 cells were determined by Agilent miRNA microarray analysis probing for 888 miRNAs. Some selected miRNAs were verified by real time qRT-PCR. Bioinformatic software tools were used to predict the target genes of identified miRNAs and define their function. Our results showed that 77 and 122 miRNAs were only expressed in MVs and K562 cells, respectively. There were significant differences in miRNA expression profiles between MVs and K562 cells. We also found that 112 miRNAs were co-expressed in MVs and K562 cells. This observaton may suggest that compartmentalization of miRNAs from cells into to MVs, for at least some miRNAs, is an active (selective) process. Among those abnormally expressed miRNAs, some have been proposed oncomiRNAs or tumor suppressors. For example, miR-155, has been proposed as oncomiRNA, was abnormally expressed only in MVs in our study, suggesting that oncomiRNA was present in MVs. Further analysis revealed that 39 potential target genes regulated by miR-155. Among them, 4 genes involed in oncogenes and the signal genes. OncomiRNAs such as miR-27a and miR-21 expressed in both MVs and corresponding cells, indicating that MVs bear miRNA characteristic of the cell origin. MVs, released into the leukemia microenvironment, play an important role in leukemia. In contrast to oncomiRNAs, if miRNA is associated with tumor suppressive activity, it is regarded as a tumor suppressor (oncosuppressor). The aberrantly expressed miR-125a-3p, miR-125-5p,miR-27b, which have implicated as tumor suppressors, appear in both cellular and MVs of leukemia in our study. MiR-125a-3p, miR-125-5p and miR-27b regulated 308 potential target genes. To our knowledge, 10 of them are tumor suppression genes. It is possible that these aberrantly expressed tumor suppressor miRNAs decreased or lost their roles of tumor suppression, which led to decrease or loss their roles of regulating their target genes including oncogenes, consequently resulted in leukemia. Since K562 cells presented t(9;22), we further examined the predicted function of the 6 expressed miRNAs located in chrosome 9 (hsa-miR-188-5p,hsa-miR-602)and 22(hsa-let-7b,hsa-miR-1249,hsa-miR-130b,hsa-miR-185), which expressed both in the MVs and K562 cells. Using the TargetScan, we found 442 predicted targets regulated by 6 miRNAs. Those miRNAs may play roles in leukemia via these 422 genes. This study is the first to identify and define miRNA expression between K562 cells presented t(9;22), derived from K562 cells and their corresponding cells. We found significant differences in miRNA expression between MVs and corresponding leukemia. K562 cells released MVs riched in miRNAs including oncomiRNAs or tumor suppressor miRNAs into leukemia microenvironment, which play a role in leukemia via regulating their targer genes including oncogenes, consequently resulted in leukemia. Disclosures: No relevant conflicts of interest to declare.

scholarly journals EAGLE: an algorithm that utilizes a small number of genomic features to predict tissue/cell type-specific enhancer-gene interactions

2019 ◽  
Author(s):  
Tianshun Gao ◽  
Jiang Qian
Keyword(s):  
Target Gene ◽  
Target Genes ◽  
Cross Validation ◽  
Cell Types ◽  
Gene Interactions ◽  
Tissue Cell ◽  
Cell Type ◽  
Genomic Features ◽  
Link Type ◽  
Cell Type Specific

AbstractLong-range regulation by distal enhancers is crucial for many biological processes. The existing methods for enhancer-target gene prediction often require many genomic features. This makes them difficult to be applied to many cell types, in which the relevant datasets are not always available. Here, we design a tool EAGLE, an enhancer and gene learning ensemble method for identification of Enhancer-Gene (EG) interactions. Unlike existing tools, EAGLE used only six features derived from the genomic features of enhancers and gene expression datasets. Cross-validation revealed that EAGLE outperformed other existing methods. Enrichment analyses on special transcriptional factors, epigenetic modifications, and eQTLs demonstrated that EAGLE could distinguish the interacting pairs from non- interacting ones. Finally, EAGLE was applied to mouse and human genomes and identified 7,680,203 and 7,437,255 EG interactions involving 31,375 and 43,724 genes, 138,547 and 177,062 enhancers across 89 and 110 tissue/cell types in mouse and human, respectively. The obtained interactions are accessible through an interactive database enhanceratlas.org. The EAGLE method is available at https://github.com/EvansGao/EAGLE and the predicted datasets are available in http://www.enhanceratlas.org/.Author summaryEnhancers are DNA sequences that interact with promoters and activate target genes. Since enhancers often located far from the target genes and the nearest genes are not always the targets of the enhancers, the prediction of enhancer-target gene relationships is a big challenge. Although a few computational tools are designed for the prediction of enhancer-target genes, it’s difficult to apply them in most tissue/cell types due to a lack of enough genomic datasets. Here we proposed a new method, EAGLE, which utilizes a small number of genomic features to predict tissue/cell type-specific enhancer-gene interactions. Comparing with other existing tools, EAGLE displayed a better performance in the 10-fold cross-validation and cross-sample test. Moreover, the predictions by EAGLE were validated by other independent evidence such as the enrichment of relevant transcriptional factors, epigenetic modifications, and eQTLs.Finally, we integrated the enhancer-target relationships obtained from human and mouse genomes into an interactive database EnhancerAtlas, http://www.enhanceratlas.org/.

scholarly journals Capturing large genomic contexts for accurately predicting enhancer-promoter interactions

2021 ◽  
Author(s):  
Ken Chen ◽  
Huiying Zhao ◽  
Yuedong Yang
Keyword(s):  
Target Gene ◽  
Target Genes ◽  
Cell Types ◽  
Training Data ◽  
Independent Test ◽  
Distant Target ◽  
Comparable Performance ◽  
Precision Recall Curve ◽  
Different Cell Types ◽  
The Impact

AbstractAccurately identifying enhancer-promoter interactions (EPIs) is challenging because enhancers usually act on the promoters of distant target genes. Although a variety of machine learning and deep learning models have been developed, many of them are not designed to or could not be well applied to predict EPIs in cell types different from the training data. In this study, we develop the TransEPI model for EPI prediction based on datasets derived from Hi-C and ChIA-PET data. TransEPI compiles genomic features from large intervals harboring the enhancer-promoter pair and adopts a Transformer-based architecture to capture the long-range dependencies. Thus, TransEPI could achieve more accurate prediction by addressing the impact of other genomic loci that may competitively interact with the enhancer-promoter pair. We evaluate TransEPI in a challenging scenario, where the independent test samples are predicted by models trained on the data from different cell types and chromosomes. TransEPI robustly predicts cross-cell-type EPI prediction by achieving comparable performance in cross-validation and independent test. More importantly, TransEPI significantly outperforms the state-of-the-art EPI models on the independent test datasets, with the Area Under Precision-Recall Curve (auPRC) score increasing by 48.84 % on average. Hence, TransEPI is applicable for accurate EPI prediction in cell types without chromatin structure data. Moreover, we find the TransEPI framework could also be extended to identify the target gene of non-coding mutations, which may facilitate studying pathogenic non-coding mutations.

scholarly journals EnhancerAtlas 2.0: an updated resource with enhancer annotation in 586 tissue/cell types across nine species

2019 ◽  
Author(s):  
Tianshun Gao ◽  
Jiang Qian
Keyword(s):  
Large Scale ◽  
Target Gene ◽  
Target Genes ◽  
Cell Types ◽  
Regulatory Elements ◽  
Tissue Cell ◽  
Normal Tissues ◽  
Genome Wide ◽  
Wide Range ◽  
User Friendly

Abstract Enhancers are distal cis-regulatory elements that activate the transcription of their target genes. They regulate a wide range of important biological functions and processes, including embryogenesis, development, and homeostasis. As more and more large-scale technologies were developed for enhancer identification, a comprehensive database is highly desirable for enhancer annotation based on various genome-wide profiling datasets across different species. Here, we present an updated database EnhancerAtlas 2.0 (http://www.enhanceratlas.org/indexv2.php), covering 586 tissue/cell types that include a large number of normal tissues, cancer cell lines, and cells at different development stages across nine species. Overall, the database contains 13 494 603 enhancers, which were obtained from 16 055 datasets using 12 high-throughput experiment methods (e.g. H3K4me1/H3K27ac, DNase-seq/ATAC-seq, P300, POLR2A, CAGE, ChIA-PET, GRO-seq, STARR-seq and MPRA). The updated version is a huge expansion of the first version, which only contains the enhancers in human cells. In addition, we predicted enhancer–target gene relationships in human, mouse and fly. Finally, the users can search enhancers and enhancer–target gene relationships through five user-friendly, interactive modules. We believe the new annotation of enhancers in EnhancerAtlas 2.0 will facilitate users to perform useful functional analysis of enhancers in various genomes.

scholarly journals p19ARF Is Dispensable for Oncogenic Stress-Induced p53-Mediated Apoptosis and Tumor Suppression In Vivo

2002 ◽  
Vol 22 (1) ◽  
pp. 370-377 ◽  
Author(s):  
Dawn Tolbert ◽  
Xiangdong Lu ◽  
Chaoying Yin ◽  
Mathew Tantama ◽  
Terry Van Dyke
Keyword(s):  
Tumor Suppressor ◽  
Tumor Suppression ◽  
Tumor Model ◽  
Cell Types ◽  
Negative Regulator ◽  
Suppressor Activity ◽  
Distinct Cell ◽  
Tumor Suppressor Activity ◽  
Oncogenic Stress

ABSTRACT Recent studies have shown the p19ARF tumor suppressor to be involved in the response to oncogenic stress by regulating the activity of p53. This response is mediated by antagonizing the function of Mdm2, a negative regulator of p53, indicating a pathway for tumor suppression that involves numerous genes altered in human tumors. We previously described a transgenic mouse brain tumor model in which oncogenic stress, provided by cell-specific inactivation of the pRb pathway, triggers a p53-dependent apoptotic response. This response suppresses the growth of developing tumors and thus represents a bona fide in vivo tumor suppressor activity. We further showed that E2F1, a transcription factor known to induce p19ARF expression, was required for the response. Here, we use a genetic approach to test whether p19ARF functions to transduce the signal from E2F1 to p53 in this tumor suppression pathway. Contrary to the currently accepted hypothesis, we show that a deficiency in p19ARF has no impact on p53-mediated apoptosis or tumor suppression in this system. All measures of p53 function, including the level of apoptosis induced by pRb inactivation, the expression of p21 (a p53-responsive gene), and the rate of tumor growth, were comparable in mice with and without a functional p19ARF gene. Thus, although p19ARF is required in some cell types to transmit an oncogenic response signal to p53, it is dispensable for this function in an in vivo epithelial system. These results underscore the complexity of p53 tumor suppression and further indicate the existence of distinct cell-specific pathways that respond to similar stimuli.

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