The anti-cancer compound JTE-607 reveals hidden sequence specificity of the mRNA 3' processing machinery
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA906503 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR22443826.fastq.gz | Fastqsanger.gz | |||
| SRR22443827.fastq.gz | Fastqsanger.gz | |||
| SRR22443828.fastq.gz | Fastqsanger.gz | |||
| SRR22443829.fastq.gz | Fastqsanger.gz | |||
| SRR22443830.fastq.gz | Fastqsanger.gz |
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Project description:JTE-607 is a small molecule compound with anti-inflammation and anti-cancer activities. Upon entering the cell, it is hydrolyzed to Compound 2, which directly binds to and inhibits CPSF73, the endonuclease for the cleavage step in pre-mRNA 3' processing. Although CPSF73 is universally required for mRNA 3' end formation, we have unexpectedly found that Compound 2-mediated inhibition of pre-mRNA 3' processing is sequence-specific and that the sequences flanking the cleavage site (CS) are a major determinant for drug sensitivity. By using massively parallel in vitro assays, we have measured the Compound 2 sensitivities of over 260,000 sequence variants and identified key sequence features that determine drug sensitivity. A machine learning model trained on these data can predict the impact of JTE-607 on poly(A) site (PAS) selection and transcription termination genome-wide. We propose a biochemical model in which CPSF73 and other mRNA 3' processing factors bind to RNA of the CS region in a sequence-specific manner and the affinity of such interaction determines the Compound 2 sensitivity of a PAS. Together, our study not only characterized the mechanism of action of a compound with clinical implications, but also revealed a previously unknown sequence-specificity of the mRNA 3' processing machinery.
2023-07-22 | GSE218977 | GEO
Project description:Coordinated regulation of stress response pathways is crucial for cellular homeostasis. However, crosstalk between the different stress pathways and the physiological significance of this crosstalk remain poorly understood. In this study, using the model organism C. elegans, we discovered that suppression of the transcription factor LET-607/CREBH, a regulator of cellular defense and proteostatic responses, triggers adaptive induction of DAF-16-dependent stress responses. Suppression of LET-607 improves stress resistance and extends C. elegans lifespan in a DAF-16-dependent manner. We identified the sphingomyelin synthase SMS-5 to be a central mediator in the communication between LET-607 and DAF-16. SMS-5 reduces the contents of unsaturated phosphatidylcholine (PC), which activates DAF-16 through ITR-1-dependent calcium signaling and calcium-sensitive kinase PKC-2. Our data reveal the significance of crosstalk between different stress pathways in animal fitness and identify LET-607/CREBH and specific PC as regulators of DAF-16 and longevity.
2021-05-05 | GSE155935 | GEO
Project description:Blochmannia festinatus 607 genome sequencing and assembly
| PRJNA287679 | ENA
Project description:Motivation: Identification of eQTL, the genetic loci that contribute to heritable variation in gene expression, can be obstructed by factors that produce variation in expression profiles if these factors are unmeasured or hidden from direct analysis. Methods: We have developed a method for Hidden Expression Factor analysis (HEFT) that identifies individual and pleiotropic effects of eQTL in the presence of hidden factors. The HEFT model simultaneously accounts for the effects of genotypes while learning hidden factors, where we make use of the complete likelihood of a unified multivariate regression and factor analysis model to derive a ridge estimator for combined factor learning and detection of eQTL. HEFT requires no pre-estimation of hidden factor effects, no iterative model selection, it provides p-values, and is extremely fast, requiring just a few hours to complete an eQTL analysis of thousands of expression variables when analyzing hundreds of thousands of SNPs on a standard 8 core 2.6G desktop. Results: By analyzing simulated data, we demonstrate that HEFT can correct for an unknown number of hidden factors and outperforms related hidden factor methods for eQTL analysis, where the improved performance is particularly evident in the detection of eQTL with multivariate effects. To demonstrate a real-world application, we applied HEFT to identify eQTL affecting gene expression in human lung tissue for a study that included presumptive hidden factors. The analysis identified a number of eQTL with direct relevance to lung disease that could not be found without a hidden factor analysis, including cis-eQTL for GTF2H1 and MTRR, genes that have been independently associated with lung cancer. We have developed HEFT, a fast multivariate method that detect eQTLs by analyzing thousands of traits simultaneously in the presence of hidden factors. HEFT employs a combined regression factor analysis approach to analyze the gene expression and genotype data sets, looking for univariate or multivariate eQTLs that regulate gene expression, while simultaneously controlling for both orthogonal or non-orthogonal hidden factors. We show by extensive simulation that HEFT outperforms competing methods, and by applying HEFT to a study that included presumptive hidden factors, we identified a number of eQTL with direct relevance to lung disease that could not be found without a hidden factor analysis. HEFT analysis results file (includes all results, not just top hits) linked below as supplementary file.
2014-01-07 | E-GEOD-40364 | biostudies-arrayexpress
Project description:UI-607: IIa ST8 CT5004 Listeriosis outbreak in Germany
| PRJEB35042 | ENA
Project description:Motivation: Identification of eQTL, the genetic loci that contribute to heritable variation in gene expression, can be obstructed by factors that produce variation in expression profiles if these factors are unmeasured or hidden from direct analysis. Methods: We have developed a method for Hidden Expression Factor analysis (HEFT) that identifies individual and pleiotropic effects of eQTL in the presence of hidden factors. The HEFT model simultaneously accounts for the effects of genotypes while learning hidden factors, where we make use of the complete likelihood of a unified multivariate regression and factor analysis model to derive a ridge estimator for combined factor learning and detection of eQTL. HEFT requires no pre-estimation of hidden factor effects, no iterative model selection, it provides p-values, and is extremely fast, requiring just a few hours to complete an eQTL analysis of thousands of expression variables when analyzing hundreds of thousands of SNPs on a standard 8 core 2.6G desktop. Results: By analyzing simulated data, we demonstrate that HEFT can correct for an unknown number of hidden factors and outperforms related hidden factor methods for eQTL analysis, where the improved performance is particularly evident in the detection of eQTL with multivariate effects. To demonstrate a real-world application, we applied HEFT to identify eQTL affecting gene expression in human lung tissue for a study that included presumptive hidden factors. The analysis identified a number of eQTL with direct relevance to lung disease that could not be found without a hidden factor analysis, including cis-eQTL for GTF2H1 and MTRR, genes that have been independently associated with lung cancer.
2014-01-07 | GSE40364 | GEO
Project description:Gene expression regulated by let-607 RNAi in C. elegans
| PRJNA656126 | ENA
Project description:Cleavage and polyadenylation (CPA) defines the 3’ end of almost all eukaryotic mRNAs. CPA inhibition, or CPAi, leads to transcriptional readthrough. Here, we show that the CPSF-73 inhibitor JTE-607 globally perturbs gene expression, especially for those with a high GC content and located in high gene density regions. Based on regulated alternative polyadenylation (APA) events, we found that more frequently used CPA sites are inhibited by JTE-607 to a greater extent. Consistently, cells with elevated CPA activities, as indicated by preferential usage of proximal APA sites, display greater transcriptional readthrough and gene expression disturbance upon JTE-607 treatment. Remarkably, overexpression of the core CPA factor FIP1 enhances global CPA activity in the cell and leads to greater JTE-607 sensitivity. Taken together, our data indicate that CPAi selectively impacts genes based on their genomic features and the CPA activity of a cell is a key determinant of sensitivity to CPAi.
2023-06-07 | GSE218555 | GEO
Project description:IQG-607 is a metal complex previously reported as a promising anti-tuberculosis (TB) drug against isoniazid (INH)-resistant strains of Mycobacterium tuberculosis Unexpectedly, we found that INH-resistant clinical isolates were resistant to IQG-607. Spontaneous mutants resistant to IQG-607 were subjected to whole-genome sequencing, and all sequenced colonies carried alterations in the katG gene. The katG(S315T) mutation was sufficient to confer resistance to IQG-607 in both MIC assays and inside macrophages. Moreover, overexpression of the InhA(S94A) protein caused IQG-607's resistance.
| S-EPMC5786794 | biostudies-literature
Project description:Cleavage and polyadenylation (CPA) defines the 3’ end of almost all eukaryotic mRNAs. CPA inhibition, or CPAi, leads to transcriptional readthrough. Here, we show that the CPSF-73 inhibitor JTE-607 globally perturbs gene expression, especially for those with a high GC content and located in high gene density regions. Based on regulated alternative polyadenylation (APA) events, we found that more frequently used CPA sites are inhibited by JTE-607 to a greater extent. Consistently, cells with elevated CPA activities, as indicated by preferential usage of proximal APA sites, display greater transcriptional readthrough and gene expression disturbance upon JTE-607 treatment. Remarkably, overexpression of the core CPA factor FIP1 enhances global CPA activity in the cell and leads to greater JTE-607 sensitivity. Taken together, our data indicate that CPAi selectively impacts genes based on their genomic features and the CPA activity of a cell is a key determinant of sensitivity to CPAi.
2023-06-07 | GSE231527 | GEO