A systems approach to delineate functions of YAP family members
Ontology highlight
ABSTRACT:
ORGANISM(S): Saccharomyces cerevisiae
PROVIDER: GSE10146 | GEO | 2008/02/04
SECONDARY ACCESSION(S): PRJNA108695
REPOSITORIES: GEO
Similar Datasets
Project description:Duplication of genes encoding transcription factors plays an essential role in driving phenotypic variation. Because regulation can occur at multiple levels, it is often difficult to discern how each duplicated factor achieves its regulatory specificity. In these cases, a systems approach may distinguish the role of each factor by integrating complementary large-scale measurements of the regulatory network. To explore such an approach, we integrate growth phenotypes, promoter binding profiles, and gene expression patterns to model the DNA damage response network controlled by the Yeast-specific AP-1 (YAP) family of transcription factors. This analysis reveals that YAP regulatory specificity is achieved by at least three mechanisms: (a) Divergence of DNA-binding sequences into two subfamilies; (b) Condition-specific combinatorial regulation by multiple Yap factors; and (c) Interactions of Yap 1, 4, and 6 with chromatin remodeling proteins. Additional microarray experiments establish that Yap4 and 6 regulate gene expression through interactions with the histone deacetylase, Hda1. The data further highlight differences among Yap paralogs in terms of their regulatory mode of action (activation vs. repression). This study suggests how other large TF families might be disentangled in the future. Keywords: gene expression microarray and ChIP-chip Gene expression of wild type and single YAP deletion (YAP1/2/4/5/6) strains were profiled in media supplemented with either MMS or CDDP at two different concentrations. ChIP-chip of five YAPs (YAP1/2/4/6/6) were done in duplicate in both drug-treated (MMS or CDDP) or untreated (SC media) conditions.
2008-02-03 | E-GEOD-10146 | biostudies-arrayexpress
Project description:Hepatitis B virus (HBV) infection promotes liver cancer initiation by inducing inflammation and cellular stress, but its impact in established tumors is not well understood. We used affinity purification mass spectrometry to comprehensively map a network of 145 physical interactions between HBV and human proteins in hepatocellular carcinoma (HCC). A subset of the host factors targeted by HBV proteins are preferentially mutated in non-HB¬V-associated HCC suggesting that their interaction with HBV influences HCC biology. These include proteins involved in mRNA splicing, mitogenic signaling and DNA repair, with the latter set interacting with the HBV oncoprotein X (HBx). We show that HBx remodels the PP2A phosphatase complex by excluding striatin regulatory subunits from the PP2A holoenzyme. We find that HBx effects on PP2A cause Hippo kinase activation, particularly in cells with high striatin levels. In parallel, HBx activates mTOR complex 2 (mTORC2) to prevent YAP degradation. mTORC2 effects on YAP can be observed in human HCC specimens and mouse HCC models and can be targeted with mTOR kinase inhibitors. Thus, HBV interaction with host proteins rewires HCC signaling rather than directly activating mitogenic pathways. These findings provide a new paradigm for the cellular effects of a tumor promoting virus and support a model where HBV may have therapeutic actionable effects on HCC biology.
2024-11-23 | PXD055366 | Pride
Project description:Uncontrolled Transforming growth factor-beta (TGFβ) signaling promotes aggressive metastatic properties in late-stage breast cancers. However, how TGFβ-mediated cues are directed to induce late-stage tumorigenic events is poorly understood, particularly given that TGFβ has clear tumor suppressing activity in other contexts. Here we demonstrate that the transcriptional regulators TAZ and YAP (TAZ/YAP), key effectors of the Hippo pathway, are necessary to promote and maintain TGFβ-induced tumorigenic phenotypes in breast cancer cells. Interactions between TAZ/YAP, TGFβ-activated SMAD2/3, and TEAD transcription factors reveal convergent roles for these factors in the nucleus. Genome-wide expression analyses indicate that TAZ/YAP, TEADs and TGFβ-induced signals coordinate a specific pro-tumorigenic transcriptional program. Importantly, genes cooperatively regulated by TAZ/YAP, TEAD, and TGFβ, such as the novel targets NEGR1 and UCA1, are necessary for maintaining tumorigenic activity in metastatic breast cancer cells. Nuclear TAZ/YAP also cooperate with TGFβ signaling to promote phenotypic and transcriptional changes in non-tumorigenic cells to overcome TGFβ repressive effects. Our work thus identifies crosstalk between nuclear TAZ/YAP and TGFβ signaling in breast cancer cells, revealing novel insight into late-stage disease-driving mechanisms. Expression profiling was conducted following the repression of the transcriptional regulators TAZ and YAP (TAZ/YAP), the TEAD family of transcription factors (TEAD1/2/3/4), or the TGFb signaling pathway (with SB-431542, an inhibitor of the TBRI recpeptor) in human MDA-MB-231-LM2 breast cancer cells treated with TGFβ1. Human MDA-MB-231-LM2-4 breast cancer cells were transfected with control siRNA, or siRNAs targeting TAZ/YAP or all four TEADs and were treated 24 hours later with 500pM TGFβ1 or 5mM SB-431542 for an additional 24 hours. Total RNA was isolated and twelve microarrays in total were performed, with each condition carried out three times on separate days. The Boston University Microarray Core generated the data using the Affymetrix Human Gene 1.0 St Array.
2014-04-02 | E-GEOD-56445 | biostudies-arrayexpress
Project description:Dynamic and specificity of CUC transcription factors during leaf development : Towards a high-resolution Gene Regulatory Network model
| PRJNA293163 | ENA
Project description:In the related work, proteins were computationally designed and experimentally optimized to bind with high affinity and specificity to each human pro-survival BCL2 homolog. Site-directed saturation mutagenesis libraries were generated based on partially-specific computational designs 2CDP06 (for Bcl-2-targeting Computationally Designed Protein), XCDP07 (Bcl-xL), WCDP03 (Bcl-w), and FCDP01 (Bfl-1), and BCDP01 (Bcl-B) which were then transformed into yeast for surface display, as described in Chao et al (Nat Protoc. 2006). Binding to labeled target homolog was detected via FACS after co-incubation with unlabeled competitors to favor specific interactions. Similarly, an SSM library was generated based on partially-specific Mcl-1-targeting design MCDP02 and was sorted for specific interactions toward each Bcl-2 proteins (data labeled MCDP02_[homolog]). DNA was harvested from naïve libraries and sorted populations and deep sequenced. The frequency of each mutation in a sorted population was compared to its frequency in the naïve population to determine enrichment ratios for all possible single amino acid substitutions (frequency calculations based on counts, which can be found in analyzed data file along with enrichment ratios). The most enriching mutations were then combined in combinatorial libraries, sorted as described above, and variants present after four or five rounds of sorting exhibited excellent specificity. Please see our corresponding publication for additional detail.
2016-05-09 | GSE80194 | GEO
Project description:In vivo protein-DNA interactions connect each transcription factor with its direct targets to form a gene network scaffold. To map these protein-DNA interactions comprehensively across entire mammalian genomes, we developed a large-scale chromatin immunoprecipitation assay (ChIPSeq) based on direct ultrahigh-throughput DNA sequencing. This sequence census method was then used to map in vivo binding of the neuron-restrictive silencer factor (NRSF; also known as REST, for repressor element–1 silencing transcription factor) to 1946 locations in the human genome. The data display sharp resolution of binding position [±50 base pairs (bp)], which facilitated our finding motifs and allowed us to identify noncanonical NRSF-binding motifs. These ChIPSeq data also have high sensitivity and specificity [ROC (receiver operator characteristic) area ≥ 0.96] and statistical confidence (P < 10−4), properties that were important for inferring new candidate interactions. These include key transcription factors in the gene network that regulates pancreatic islet cell development. Experiment Overall Design: Exam NRSF binding in Jurkat T cell line
2009-08-29 | E-GEOD-13047 | biostudies-arrayexpress
Project description:We conducted a network-based analysis of the seven transcription factors belonging to the Yap (Yeast AP1) family in Candida glabrata.
2016-12-01 | GSE77904 | GEO
Project description:The identification of deregulated miRNA in multiple myeloma (MM) has progressively added a further level of complexity to MM biology. In the present study, we take virtue of in silico integrative genomics analysis to generate an unprecedented global view of the transcriptional regulatory networks modulated in MM to define microRNAs impacting in regulatory circuits with potential functional and clinical relevance. miRNA and gene expression profiles in two large representative MM datasets, available from retrospective and prospective clinical trials and encompassing a total of 249 patients at diagnosis, were analyzed by means of two robust computational procedure to identify (i) relevant miRNA/transcription factors/target gene circuits in the disease and (ii) highly modulated miRNA-gene networks in those pathways enriched with miRNA-target gene interactions in specific MM subgroups. The analysis reinforced the pivotal role the miRNA cluster miR-99b/let-7e/miR-125a, specifically deregulated in MM patients with t(4;14) translocation, and disentangled its major relationships with transcriptional relevance. Integrated pathway analyses performed on the expression data of the MM patients stratified according to t(4;14) further allowed to define the pathway composed by the interactions that mainly characterize this MM subset and unravel connected pathways with putative role in the tumor biology. miRNA and transcripts expression data were analyzed using MAGIA2, to identify mixed circuits (triplets) involving miRNA/gene/transcription factor (TF; http://gencomp.bio.unipd.it/magia2/), as previously described [Bisognin A et al, 2012, Nucl Acid Res]. Specifically, Targetscan was used as target prediction algorithm, and Pearson coefficient was used to measure relationships between microRNA and target mRNA expression profiles. Only the most variable 75% genes according to the coefficient of variation were considered. Lower threshold for absolute correlation coefficients within circuits was set to 0.2; 0.4 was used for miRNA/target binary relationships. Micrographite pipeline allows integrating pathway topologies with predicted and validated miRNAâtarget interactions, to perform integrated analyses of miRNA and gene expression profiles, for the identification of modulated regulatory circuits involved in the disease in terms of both expression variations and differential strength of inferred interactions [Calura E et al, 2014, Nucl Acid Res]. Micrographite has two steps: i) the extension of pathway annotation using miRNA-target interaction and ii) recursive topological pathway analysis on these networks. We considered network topologies derived from KEGG database by Graphite package [Sales G et al, 2012, BMC Bioinformatics] and miRNA-target gene interactions identified by the above-described MAGIA2 analysis. Specifically, a miRNA was added to a pathway-derived network only if one (or more) of its validated or predicted target genes is a pathway component. Then, a modified recursive version of CliPPER topological pathway analysis [Martini P et al, 2013; Nucl Acid Res] was applied to the composite network, as previously described [Calura E et al, 2014, Nucl Acid Res] in order to identify the most important and non-redundant circuit modulated across groups. Briefly, (i) in the first step, the most significant pathways were selected using P<0.1 as cut-off value for significance; (ii) for each dataset, the upper-scored 10th percentile of the portion of these previously selected pathways (i.e. âpathsâ, calculated over a 10,000-permutation step) mostly associated with phenotype were selected; and (iii) for each dataset a meta-pathway was assembled using the paths extracted from previous step and finally re-analyzed. GSE70254 and GSE70319 Samples with the same patient number represent the same sample, profiled using two different Platforms.
2016-03-23 | E-GEOD-70319 | biostudies-arrayexpress
Project description:The identification of deregulated miRNA in multiple myeloma (MM) has progressively added a further level of complexity to MM biology. In the present study, we take virtue of in silico integrative genomics analysis to generate an unprecedented global view of the transcriptional regulatory networks modulated in MM to define microRNAs impacting in regulatory circuits with potential functional and clinical relevance. miRNA and gene expression profiles in two large representative MM datasets, available from retrospective and prospective clinical trials and encompassing a total of 249 patients at diagnosis, were analyzed by means of two robust computational procedure to identify (i) relevant miRNA/transcription factors/target gene circuits in the disease and (ii) highly modulated miRNA-gene networks in those pathways enriched with miRNA-target gene interactions in specific MM subgroups. The analysis reinforced the pivotal role the miRNA cluster miR-99b/let-7e/miR-125a, specifically deregulated in MM patients with t(4;14) translocation, and disentangled its major relationships with transcriptional relevance. Integrated pathway analyses performed on the expression data of the MM patients stratified according to t(4;14) further allowed to define the pathway composed by the interactions that mainly characterize this MM subset and unravel connected pathways with putative role in the tumor biology. miRNA and transcripts expression data were analyzed using MAGIA2, to identify mixed circuits (triplets) involving miRNA/gene/transcription factor (TF; http://gencomp.bio.unipd.it/magia2/), as previously described [Bisognin A et al, 2012, Nucl Acid Res]. Specifically, Targetscan was used as target prediction algorithm, and Pearson coefficient was used to measure relationships between microRNA and target mRNA expression profiles. Only the most variable 75% genes according to the coefficient of variation were considered. Lower threshold for absolute correlation coefficients within circuits was set to 0.2; 0.4 was used for miRNA/target binary relationships. Micrographite pipeline allows integrating pathway topologies with predicted and validated miRNAâtarget interactions, to perform integrated analyses of miRNA and gene expression profiles, for the identification of modulated regulatory circuits involved in the disease in terms of both expression variations and differential strength of inferred interactions [Calura E et al, 2014, Nucl Acid Res]. Micrographite has two steps: i) the extension of pathway annotation using miRNA-target interaction and ii) recursive topological pathway analysis on these networks. We considered network topologies derived from KEGG database by Graphite package [Sales G et al, 2012, BMC Bioinformatics] and miRNA-target gene interactions identified by the above-described MAGIA2 analysis. Specifically, a miRNA was added to a pathway-derived network only if one (or more) of its validated or predicted target genes is a pathway component. Then, a modified recursive version of CliPPER topological pathway analysis [Martini P et al, 2013; Nucl Acid Res] was applied to the composite network, as previously described [Calura E et al, 2014, Nucl Acid Res] in order to identify the most important and non-redundant circuit modulated across groups. Briefly, (i) in the first step, the most significant pathways were selected using P<0.1 as cut-off value for significance; (ii) for each dataset, the upper-scored 10th percentile of the portion of these previously selected pathways (i.e. âpathsâ, calculated over a 10,000-permutation step) mostly associated with phenotype were selected; and (iii) for each dataset a meta-pathway was assembled using the paths extracted from previous step and finally re-analyzed GSE70254 and GSE70319 Samples with the same patient number represent the same sample, profiled using two different Platforms.
2015-12-08 | E-GEOD-70254 | biostudies-arrayexpress
Project description:The identification of deregulated miRNA in multiple myeloma (MM) has progressively added a further level of complexity to MM biology. In the present study, we take virtue of in silico integrative genomics analysis to generate an unprecedented global view of the transcriptional regulatory networks modulated in MM to define microRNAs impacting in regulatory circuits with potential functional and clinical relevance. miRNA and gene expression profiles in two large representative MM datasets, available from retrospective and prospective clinical trials and encompassing a total of 249 patients at diagnosis, were analyzed by means of two robust computational procedure to identify (i) relevant miRNA/transcription factors/target gene circuits in the disease and (ii) highly modulated miRNA-gene networks in those pathways enriched with miRNA-target gene interactions in specific MM subgroups. The analysis reinforced the pivotal role the miRNA cluster miR-99b/let-7e/miR-125a, specifically deregulated in MM patients with t(4;14) translocation, and disentangled its major relationships with transcriptional relevance. Integrated pathway analyses performed on the expression data of the MM patients stratified according to t(4;14) further allowed to define the pathway composed by the interactions that mainly characterize this MM subset and unravel connected pathways with putative role in the tumor biology. miRNA and transcripts expression data were analyzed using MAGIA2, to identify mixed circuits (triplets) involving miRNA/gene/transcription factor (TF; http://gencomp.bio.unipd.it/magia2/), as previously described [Bisognin A et al., 2012, Nucl Acid Res]. Specifically, Targetscan was used as target prediction algorithm, and Pearson coefficient was used to measure relationships between microRNA and target mRNA expression profiles. Only the most variable 75% genes according to the coefficient of variation were considered. Lower threshold for absolute correlation coefficients within circuits was set to 0.2; 0.4 was used for miRNA/target binary relationships. Micrographite pipeline allows integrating pathway topologies with predicted and validated miRNA-target interactions, to perform integrated analyses of miRNA and gene expression profiles, for the identification of modulated regulatory circuits involved in the disease in terms of both expression variations and differential strength of inferred interactions [Calura E et al., 2014, Nucl Acid Res]. Micrographite has two steps: i) the extension of pathway annotation using miRNA-target interaction and ii) recursive topological pathway analysis on these networks. We considered network topologies derived from KEGG database by Graphite package [Sales G et al., 2012, BMC Bioinformatics] and miRNA-target gene interactions identified by the above-described MAGIA2 analysis. Specifically, a miRNA was added to a pathway-derived network only if one (or more) of its validated or predicted target genes is a pathway component. Then, a modified recursive version of CliPPER topological pathway analysis [Martini P et al., 2013; Nucl Acid Res] was applied to the composite network, as previously described [Calura E et al., 2014, Nucl Acid Res] in order to identify the most important and non-redundant circuit modulated across groups. Briefly, (i) in the first step, the most significant pathways were selected using P<0.1 as cut-off value for significance; (ii) for each dataset, the upper-scored 10th percentile of the portion of these previously selected pathways (i.e. paths, calculated over a 10,000-permutation step) mostly associated with phenotype were selected; and (iii) for each dataset a meta-pathway was assembled using the paths extracted from previous step and finally re-analyzed. GSE73452 and GSE73454 Samples with the same patient number represent the same sample, profiled using two different Platforms.
2015-12-08 | E-GEOD-73452 | biostudies-arrayexpress