Similar Datasets
Project description:In order to identify direct gene targets and pathways regulated by MEIS1 in prostate cells and identify mechanisms of observed tumor suppression with MEIS1 expression, we performed Chromatin Immunoprecipitation and sequencing (ChIP-seq) of MEIS1 in the CWR22Rv1-LV-MEIS1 line. Also, given the dependence of HOXB13, and to enable determination of HOXB13-dependent vs. HOXB13-independent MEIS1 DNA binding, we performed parallel MEIS1 ChIP-seq in the CWR22Rv1-HOXB13ko-LV-MEIS1 line. LV-MEIS1 denotes cells with exogenous lentiviral expression of MEIS1, and they still express endogenous HOXB13. Knockout of HOXB13 was achieved by CRISPR and validated with western blotting. HOXB13ko-LV-MEIS1 denotes cells where the same lentiviral expression of MEIS1 was infected into the HOXB13ko cells, so these cells are positive for MEIS1 expression and negative for HOXB13.
2020-07-15 | GSE132716 | GEO
Project description:We conducted RNA-sequencing to determine MEIS1-mediated gene regulation in prostate cancer cells because phenotypic data has demonstrated that expression of MEIS1 is sufficient to slow tumor growth and metastatic colonization in vitro and in vivo. These analyses compared global gene expression of CWR22Rv1-Control and CWR22Rv1-LV-MEIS1, as well as the HOXB13ko and HOXB13ko-LV-MEIS1 cells to precisely delineate MEIS1 and HOXB13-regulated genes. Importantly, inclusion of HOXB13ko lines enabled determination of HOXB13-associated gene regulation, as well as identification of significant changes between LV-MEIS1 vs. Control that are HOXB13-independent and thus unrelated to tumor suppression. Are samples are from CWR-22Rv1 cells. Control denotes cells with constitutive expression of Cas9, but with no gRNA provided. Control cells have nearly undetectable levels of endogenous MEIS1 expression, but do express HOXB13. LV-MEIS1 denotes cells with exogenous lentiviral expression of MEIS1, and they still express endogenous HOXB13. HOXB13ko denotes cells where HOXB13 was knocked out using CRISPR, 6 cell clones were isolated with confirmed HOXB13 knockout by western blot and pooled together into one line, so these cells lack both MEIS1 and HOXB13. HOXB13ko-LV-MEIS1 denotes cells where the same lentiviral expression of MEIS1 was infected into the HOXB13ko cell pool, so these cells are positive for MEIS1 expression and negative for HOXB13.
2020-07-15 | GSE132694 | GEO
Project description:Deciphering molecular mechanisms of MEIS1-mediated prostate tumor suppression through RNAseq and ChIPseq
| PRJNA548792 | ENA
Project description:Deciphering molecular mechanisms of MEIS1-mediated prostate tumor suppression through RNAseq and ChIPseq - ChIPseq data
| PRJNA548800 | ENA
Project description:Deciphering molecular mechanisms of MEIS1-mediated prostate tumor suppression through RNAseq and ChIPseq - RNAseq data
| PRJNA548764 | ENA
Project description:SummaryShort reads sequencing technology has been used for more than a decade now. However, the analysis of RNAseq and ChIPseq data is still computational demanding and the simple access to raw data does not guarantee results reproducibility between laboratories. To address these two aspects, we developed SeqBox, a cheap, efficient and reproducible RNAseq/ChIPseq hardware/software solution based on NUC6I7KYK mini-PC (an Intel consumer game computer with a fast processor and a high performance SSD disk), and Docker container platform. In SeqBox the analysis of RNAseq and ChIPseq data is supported by a friendly GUI. This allows access to fast and reproducible analysis also to scientists with/without scripting experience.Availability and implementationDocker container images, docker4seq package and the GUI are available at http://www.bioinformatica.unito.it/reproducibile.bioinformatics.html.Contactbeccuti@di.unito.it.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6030956 | biostudies-literature
Project description:Bordetella pertussis regulates the production of its virulence factors by the two-component system BvgAS. In the virulence phase, BvgS phosphorylates BvgA, which then activates the transcription of virulence-activated genes (vags). In the avirulence phase, such as during growth in the presence of MgSO4, BvgA is not phosphorylated and the vags are not expressed. Instead, a set of virulence-repressed genes (vrgs) is expressed. Here, we performed transcriptome sequencing (RNAseq) analyses on B. pertussis cultivated with or without MgSO4 and on a BvgA-deficient Tohama I derivative. We observed that 146 genes were less expressed under modulating conditions or in the BvgA-deficient strain than under the nonmodulating condition, while 130 genes were more expressed. Some of the genes code for proteins with regulatory functions, suggesting a BvgA/S regulation cascade. To determine which genes are directly regulated by BvgA, we performed chromatin immunoprecipitation sequencing (ChIPseq) analyses. We identified 148 BvgA-binding sites, 91 within putative promoter regions, 52 within open reading frames, and 5 in noncoding regions. Among the former, 32 are in BvgA-regulated putative promoter regions. Some vags, such as dnt and fhaL, contain no BvgA-binding site, suggesting indirect BvgA regulation. Unexpectedly, BvgA also bound to some vrg putative promoter regions. Together, these observations indicate an unrecognized complexity of BvgA/S biology.IMPORTANCEBordetella pertussis, the etiological agent of whooping cough, remains a major global health problem. Despite the global usage of whole-cell vaccines since the 1950s and of acellular vaccines in the 1990s, it still is one of the most prevalent vaccine-preventable diseases in industrialized countries. Virulence of B. pertussis is controlled by BvgA/S, a two-component system responsible for upregulation of virulence-activated genes (vags) and downregulation of virulence-repressed genes (vrgs). By transcriptome sequencing (RNAseq) analyses, we identified more than 270 vags or vrgs, and chromatin immunoprecipitation sequencing (ChIPseq) analyses revealed 148 BvgA-binding sites, 91 within putative promoter regions, 52 within open reading frames, and 5 in noncoding regions. Some vags, such as dnt and fhaL, do not contain a BvgA-binding site, suggesting indirect regulation. In contrast, several vrgs and some genes not identified by RNAseq analyses under laboratory conditions contain strong BvgA-binding sites, indicating previously unappreciated complexities of BvgA/S biology.
| S-EPMC7253368 | biostudies-literature
Project description:In vitro derivation of human neurons in the autonomic nervous system (ANS) is an important technology, given its regulatory roles in maintaining homeostasis in the human body. Although several induction protocols for autonomic lineages have been reported, the regulatory machinery remains largely undefined, primarily due to the absence of a comprehensive understanding of the molecular mechanism regulating human autonomic induction in vitro. In this study, our objective was to pinpoint key regulatory components using integrated bioinformatics analysis. A protein-protein interaction network construction for the proteins encoded by the differentially expressed genes from our RNA sequencing data, and conducting subsequent module analysis, we identified distinct gene clusters and hub genes involved in the induction of autonomic lineages. Moreover, we analyzed the impact of transcription factor (TF) activity on target gene expression, revealing enhanced autonomic TF activity that could lead to the induction of autonomic lineages. The accuracy of this bioinformatics analysis was corroborated by employing calcium imaging to observe specific responses to certain ANS agonists. This investigation offers novel insights into the regulatory machinery in the generation of neurons in the ANS, which would be valuable for further understanding and precise regulation of autonomic induction and differentiation.
| S-EPMC10219521 | biostudies-literature
Project description:Hox homeobox transcription factors drive leukemogenesis efficiently only in the presence of Meis or Pbx proteins. Here we show that Pbx3 and Meis1 need to dimerize to support Hox-induced leukemia and we analyze the molecular details of this cooperation. In the absence of Pbx3, Meis1 was highly unstable. As shown by a deletion analysis Meis1 degradation was contingent on a motif coinciding with the Pbx-binding domain. Either deletion of this sequence or binding to Pbx3 prolonged the half-life of Meis1 by preventing its ubiquitination. Meis1 break-down could also be blocked by inhibition of the ubiquitin proteasome system, indicating tight post-transcriptional control. In addition, Meis1 and Pbx3 cooperated genetically as overexpression of Pbx3 induced endogenous Meis1 transcription. These functional interactions translated into in vivo activity. Blocking Meis1/Pbx3 dimerization abrogated the ability to enhance proliferation and colony-forming cell numbers in primary cells transformed by Hoxa9. Furthermore, expression of Meis1 target genes Flt3 and Trib2 was dependent on Pbx3/Meis1 dimerization. This correlated with the requirement of Meis1 to bind Pbx3 in order to form high affinity DNA/Hoxa9/Meis1/Pbx3 complexes in vitro. Finally, kinetics and severity of disease in transplantation assays indicated that Pbx3/Meis1 dimers are rate-limiting factors for Hoxa9-induced leukemia.
| S-EPMC4486225 | biostudies-literature
Project description:Drug resistance threatens many critical therapeutics through mutations in the drug target. The molecular mechanisms by which combinations of mutations, especially those remote from the active site, alter drug binding to confer resistance are poorly understood and thus difficult to counteract. A machine learning strategy was developed that coupled parallel molecular dynamics simulations with experimental potency to identify specific conserved mechanisms underlying resistance. Physical features were extracted from the simulations, analyzed, and integrated into one consistent and interpretable elastic network model. To rigorously test this strategy, HIV-1 protease variants with diverse mutations were used, with potencies ranging from picomolar to micromolar to the drug darunavir. Feature reduction resulted in a model with four specific features that predicts for both the training and test sets inhibitor binding free energy within 1 kcal/mol of the experimental value over this entire range of potency. These predictive features are physically interpretable, as they vary specifically with affinity and diagonally transverse across the protease homodimer. This physics-based strategy of parallel molecular dynamics and machine learning captures mechanisms by which complex combinations of mutations confer resistance and identify critical features that serve as bellwethers of affinity, which will be critical in future drug design.
| S-EPMC8164521 | biostudies-literature