Ontology highlight
ABSTRACT:
PROVIDER: PRJNA774158 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR16559773.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 1 of 1 |
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Project description:This study involves a forward genetic screen to identify common insertion sites in drug resistant clones. We will be utilising piggybac transposon systems in order to generate multiple drug resistant clones in a range of human cancer cell lines.
| EGAD00001000980 | EGA
Project description:This study involves a forward genetic screen to identify common insertion sites in drug resistant clones. We will be utilising piggybac transposon systems in order to generate multiple drug resistant clones in a range of human cancer cell lines.
| EGAS00001000468 | EGA
Project description:With the widespread of drug-resistant Mycobacterium tuberculosis (Mtb), anti-TB drugs with novel structures and targets are urgently needed to prevent the prevalence of drug-resistant strains. For the past few decades, many Mtb CYPs been structurally and functionally characterized, and some of them were proved to be potential drug targets. CYP138 belongs to the Mtb CYPs whose structures and functions are still unclear. In our study, to discover differentially expressed proteins, a cyp138-knockout strain was built, and the function of CYP138 was speculated by the comparison between cyp138-knockout and wild-type strains through 6-plex TMT-labeling-based quantitative proteomic approach.
2024-03-10 | PXD045505 | Pride
Project description:Introduction: Glioblastomas utilize malignant gene expression pathways to drive growth. Many of these gene pathways are not directly accessible with molecularly targeted pharmacological agents. Chromatin-modifying compounds can alter gene expression and target glioblastoma growth pathways. In this study, we utilize a systematic screen of chromatin-modifying compounds on a panel of patient-derived glioblastoma lines to identify promising compounds and their associated gene targets. Methods: Five glioblastoma cell lines were subjected to a drug screen of 106 chromatin-modifying compounds representing 36 unique drug classes to determine the twelve most promising drug classes and the best candidate inhibitors in each class. These twelve drugs were then tested with a panel of twelve patient-derived gliomasphere lines to identify growth inhibition and corresponding gene expression patterns. Overlap analysis and weighted co-expression network analysis (WCGNA) were utilized to determine potential target genes and gene pathways. Results: The initial drug screen identified twelve candidate pharmacologic agents for further testing. Drug sensitivity testing indicated an overall high degree of variability between gliomasphere lines. However, CPI203 was the most consistently effective compound, and the BET inhibitor class was the most consistently effective class of compounds across the gliomasphere panel. Correspondingly, most of the compounds tested had highly variable effects on gene expression between gliomasphere lines. CPI203 stood out as the only compound to induce a consistent effect on gene expression across different gliomasphere lines, specifically down-regulation of DNA-synthesis genes. Amongst the twelve tested cell lines, high expression of CDKN2A and CDKN2B distinguished more drug sensitive from more drug resistant lines. WCGNA identified two oncogenic gene modules (FBXO5 and MELK) that were effectively downregulated by CPI203 (FBXO5) and ML228 (FBXO5 and MELK). Conclusions: The bromodomain inhibitor CPI203 induced relatively consistent effects on gene expression and growth across a variety of glioblastoma lines, specifically down-regulating genes associated with DNA replication. We propose that clinically effective BET inhibitors have the potential to induce consistent beneficial effects across a spectrum of glioblastomas.
2025-03-06 | GSE290922 | GEO
Project description:To understand the mechanisms of drug resistance to AC220, we undertook an unbiased approach with a novel CRISPR pooled library to screen new genes whose loss of function confers resistance to AC220. In our screen, we identified SPRY3, an intracellular inhibitor of FGF signaling, and GSK3, a canonical Wnt signaling antagonist, and demonstrated that re-activation of downstream FGF/Ras/ERK and Wnt signaling as major mechanisms of resistance to the FLT3 inhibitor. We also confirmed our findings in primary AML patient samples. We demonstrated that the expression level of SPRY3 and GSK3A is dramatically reduced in AC220 resistant AML samples and SPRY3 deleted primary AML cells are resistant to AC220. Intriguingly, we found that expression of SPRY3 is greatly reduced in GSK3 knockout AML cells, which positioned SPRY3 downstream of GSK3 in the resistance pathway. Taken together, our study identified novel genes whose loss of function confers resistance to a selective FLT3 inhibitor and revealed the underlying mechanism, thereby providing new insight into signaling pathways that contribute to the acquired resistance in AML.
2017-05-06 | GSE98612 | GEO
Project description:N39 APOA1-tdTomato drug screen
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Project description:To identify miRNAs involved in drug resistance of human breast cancer, a miRNA microarray was performed on 5 cases of drug resistant tissues and 5 cases of drug sensitive tissues.The expression levels of totally 2019 miRNAs in 5 pairs of matched, drug resistant and drug sensitive tissues were examined by microarray. There were 27 differentially expressed miRNAs between drug resistant and drug sensitive tissues were identified of which there were 11 significantly up-regulated while the other 16 were down-regulated in drug resistant tissues compared to drug sensitive tissues. It was found that miR-489 was one of the most downregulated miRNAs in drug resistant tissues.
2017-08-01 | GSE71142 | GEO
Project description:The authors use a large dataset (>30k) to train an explainable graph-based model to identify potential antibiotics with low cytotoxicity. The model uses a substructure-based approach to explore the chemical space. Using this method, they were able to screen 283 compounds and identify a candidate active against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci.
Model Type: Predictive machine learning model.
Model Relevance: The model predicts the probability of growth inhibition.
Model Encoded by: Sarima Chiorlu (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam
Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos18ie
2024-05-08 | MODEL2405080002 | BioModels
Project description:The authors use a large dataset (>30k) to train an explainable graph-based model to identify potential antibiotics with low cytotoxicity. The model uses a substructure-based approach to explore the chemical space. Using this method, they were able to screen 283 compounds and identify a candidate active against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci.
Model Type: Predictive machine learning model.
Model Relevance: Prediction of Human cytotoxicity endpoints.
Model Encoded by: Sarima Chiorlu (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam
Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos42ez
2024-05-13 | MODEL2405130002 | BioModels
Project description:Tuberculosis (TB) is one of the deadliest infectious disorders in the world. To effectively TB manage, an essential step is to gain insight into the lineage of Mycobacterium tuberculosis (MTB) strains and the distribution of drug resistance. Although the Campania region is declared a cluster area for the infection, to contribute to the effort to understand TB evolution and transmission, still poorly known, we have generated a dataset of 159 genomes of MTB strains, from Campania region collected during 2018-2021, obtained from the analysis of whole genome sequence data. The results show that the most frequent MTB lineage is the 4 according for 129 strains (81.11%). Regarding drug resistance, 139 strains (87.4%) were classified as multi susceptible, while the remaining 20 (12.58%) showed drug resistance. Among the drug-resistance strains, 8 were isoniazid-resistant MTB (HR-MTB), 7 were resistant only to one antibiotic (3 were resistant only to ethambutol and 3 isolate to streptomycin while one isolate showed resistance to fluoroquinolones), 4 multidrug-resistant MTB, while only one was classified as pre-extensively drug-resistant MTB (pre-XDR). This dataset expands the existing available knowledge on drug resistance and evolution of MTB, contributing to further TB-related genomics studies to improve the management of TB infection.
2023-11-17 | E-MTAB-13058 | biostudies-arrayexpress