SAS WP5 metaT
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB64858 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR11806815_1.fastq.gz | Fastqsanger.gz | |||
| ERR11806815_2.fastq.gz | Fastqsanger.gz | |||
| ERR11806816_1.fastq.gz | Fastqsanger.gz | |||
| ERR11806816_2.fastq.gz | Fastqsanger.gz | |||
| ERR11806817_1.fastq.gz | Fastqsanger.gz |
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Project description:The specificity of action of a pharmaceutical compound to a particular target cell type is desirable to mitigate undesired effects in off target cell types. However, drug screening strategies necessarily employ cell lines that aim to represent but are potentially poor surrogates for the target cell phenotype. In this study, we addressed whether transcriptomic similarity between cell phenotpyes is predictive of their drug response. Thus, we differentiated hiPSC from three individuals into twelve different distinct phenotypes (cardiomyocytes, smooth muscle cells, hepatocytes, renal proximal tubular cells, podocytes, cortical neurons, dopaminergic neurons, neural progenitor cells, nociceptors, astrocytes, macrophages and endothelial cells). We exposed these cells and the MCF-7 cancer cell line with 12 compounds (Taxol (Paclitaxel) (0.04 µM), 17-AAG (Tanespimycin) (0.2 µM), Bardoxolone methyl (CDDO-Me) (0.2 µM), Rapamycin (Sirolimus) (0.2 µM), Tunicamycin (0.2 µM), Vorinostat (0.2 µM), A-366 (1 µM), A-971432 (1 µM), Compound 81 (1 µM), Rosiglitazone (1 µM), Thiamet-G (1 µM), Lithium chloride (5000 µM)) for 7 hours. Analysis of the RNAseq profiles revealed that the impact of drug treatment varies significantly among different cell types. Despite some compounds showing consistent effects across various cell types, there was no clear correlation between transcriptomic similarity and the magnitude of response to the same drug. These findings underscore that accurate predictions of drug responses necessitate employing the most representative models of the target cell types involved.
2025-04-30 | GSE295447 | GEO
Project description:The specificity of action of a pharmaceutical compound to a particular target cell type is desirable to mitigate undesired effects in off target cell types. However, drug screening strategies necessarily employ cell lines that aim to represent but are potentially poor surrogates for the target cell phenotype. In this study, we addressed whether transcriptomic similarity between cell phenotpyes is predictive of their drug response. Thus, we differentiated hiPSC from three individuals into twelve different distinct phenotypes (cardiomyocytes, smooth muscle cells, hepatocytes, renal proximal tubular cells, podocytes, cortical neurons, dopaminergic neurons, neural progenitor cells, nociceptors, astrocytes, macrophages and endothelial cells). We exposed these cells and the MCF-7 cancer cell line with 12 compounds (Taxol (Paclitaxel) (0.04 µM), 17-AAG (Tanespimycin) (0.2 µM), Bardoxolone methyl (CDDO-Me) (0.2 µM), Rapamycin (Sirolimus) (0.2 µM), Tunicamycin (0.2 µM), Vorinostat (0.2 µM), A-366 (1 µM), A-971432 (1 µM), Compound 81 (1 µM), Rosiglitazone (1 µM), Thiamet-G (1 µM), Lithium chloride (5000 µM)) for 7 hours. Analysis of the RNAseq profiles revealed that the impact of drug treatment varies significantly among different cell types. Despite some compounds showing consistent effects across various cell types, there was no clear correlation between transcriptomic similarity and the magnitude of response to the same drug. These findings underscore that accurate predictions of drug responses necessitate employing the most representative models of the target cell types involved.
2025-04-30 | GSE295444 | GEO
Project description:Kristineberg station metaT depth profile July and Sep
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Project description:Ammonia-oxidizing archaea (AOA) have been reported at high abundance in much of the global ocean, even in environments such as pelagic oxygen minimum zones (OMZs), where conditions seem unlikely to support aerobic ammonium oxidation. Due to the lack of information on any potential alternative metabolism of AOA, the AOA community composition might be expected to differ between oxic and anoxic environments, indicating some difference in ecology and/or physiology of the AOA assemblage. This hypothesis was tested by evaluating AOA community composition using a functional gene microarray that targets the ammonia monooxygenase gene subunit A (amoA). The relationship between environmental parameters and the biogeography of the Arabian Sea and the Eastern Tropical South Pacific (ETSP) AOA assemblages was investigated using principal component analysis (PCA) and redundancy analysis (RDA). In both the Arabian Sea and the ETSP, AOA communities within the core of the OMZ were not significantly different from those inhabiting the oxygenated surface waters above the OMZ. The AOA communities in the Arabian Sea were significantly different from those in the ETSP. In both oceans, the abundance of archaeal amoA gene in the core of the OMZ was higher than that in the surface waters. Our results indicate that AOA communities are distinguished by their geographic origin. RDA suggested that temperature was the main factor that correlated with the differences between the AOA communities from the Arabian Sea and those from the ETSP. Physicochemical properties that characterized the different environments of the OMZ and surface waters played a less important role than did geography in shaping the AOA community composition.
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Project description:Transcriptional profiling of R. sphaeroides Δlov compared to control R. sphaeroides 2.4.1 under singlet oxygen stress, aerobic conditions Two-strain experiment with overnight cultures of the wild type 2.4.1 and the Δlov mutant. Both were diluted to an OD660 of 0.2 and gassed to adjust aerobic conditions (≈ 170 µM O2). 0.2 µM of methylene blue was added to the cultures to act as photosensitizer. After one doubling time of growth in the darkness cells were illuminated with 800 W m-2 of white light for 7 min.
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