Effect of the chemical environment on the degradation of nucleotide triphosphates
Ontology highlight
ABSTRACT:
ORGANISM(S): None
TISSUE(S): Synthetic
PROVIDER: ST000473 | MetabolomicsWorkbench |
REPOSITORIES: MetabolomicsWorkbench
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| mwtab | Other |
Items per page: 5 1 - 1 of 1 |
Similar Datasets
Project description: Not available
| ST000472 | MetabolomicsWorkbench
Project description: Not available
| ST000471 | MetabolomicsWorkbench
Project description: Not available
| ST000470 | MetabolomicsWorkbench
Project description:Analysis of CGTH-W-1 follicular thyroid carcinoma cells transcriptome following 48 hrs siRNA-mediated depletion of PROX1. PROX1 is a homeobox transcription factor. PROX1 depletion decreases migratory ability, motility and invasivness and induces profound cytoskeleton changes of CGTH-W-1 cells. Results provide insight into the role of PROX1 in the thyroid cancer. Three biological replicates for a given condition
2016-07-31 | E-GEOD-80135 | biostudies-arrayexpress
Project description:Evaluation of change in gene expression upon degradation of OGT by siRNA
2024-08-31 | E-MTAB-14313 | biostudies-arrayexpress
Project description:Interactions among genes and the environment are a common source of phenotypic variation. To characterize the interplay between genetics and the environment at single nucleotide resolution, we quantified the genetic and environmental interactions of four quantitative trait nucleotides (QTN) that govern yeast sporulation efficiency. We first constructed a panel of strains that together carry all 32 possible combinations of the 4 QTN genotypes in 2 distinct genetic backgrounds. We then measured the sporulation efficiencies of these 32 strains across 8 controlled environments. This dataset shows that variation in sporulation efficiency is shaped largely by genetic and environmental interactions. We find clear examples of QTN:environment, QTN: background, and environment:background interactions. However, we find no QTN:QTN interactions that occur consistently across the entire dataset. Instead, interactions between QTN only occur under specific combinations of environment and genetic background. Thus, what might appear to be a QTN:QTN interaction in one background and environment becomes a more complex QTN:QTN:environment:background interaction when we consider the entire dataset as a whole. As a result, the phenotypic impact of a set of QTN alleles cannot be predicted from genotype alone. Our results instead demonstrate that the effects of QTN and their interactions are inextricably linked both to genetic background and to environmental variation.
| S-EPMC2947989 | biostudies-literature
Project description:MicroRNAs are important negative regulators of protein coding gene expression, and have been studied intensively over the last few years. To this purpose, different measurement platforms to determine their RNA abundance levels in biological samples have been developed. In this study, we have systematically compared 12 commercially available microRNA expression platforms by measuring an identical set of 20 standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples, and synthetic spikes from homologous microRNA family members. We developed novel quality metrics in order to objectively assess platform performance of very different technologies such as small RNA sequencing, RT-qPCR and (microarray) hybridization. We assessed reproducibility, sensitivity, quantitative performance, and specificity. The results indicate that each method has its strengths and weaknesses, which helps guiding informed selection of a quantitative microRNA gene expression platform in function of particular study goals.
2013-09-01 | E-MTAB-1815 | biostudies-arrayexpress
Project description:Moving toward species-relevant chemical safety assessments and away from animal testing requires access to reliable data to develop and build confidence in new approaches. The Integrated Chemical Environment (ICE) provides tools and curated data centered around chemical safety assessment. This article describes updates to ICE, including improved accessibility and interpretability of in vitro data via mechanistic target mapping and enhanced interactive tools for in vitro to in vivo extrapolation (IVIVE). Mapping of in vitro assay targets to toxicity endpoints of regulatory importance uses literature-based mode-of-action information and controlled terminology from existing knowledge organization systems to support data interoperability with external resources. The most recent ICE update includes Tox21 high-throughput screening data curated using analytical chemistry data and assay-specific parameters to eliminate potential artifacts or unreliable activity. Also included are physicochemical/ADME parameters for over 800,000 chemicals predicted by quantitative structure-activity relationship models. These parameters are used by the new ICE IVIVE tool in combination with the U.S. Environmental Protection Agency's httk R package to estimate in vivo exposures corresponding to in vitro bioactivity concentrations from stored or user-defined assay data. These new ICE features allow users to explore the applications of an expanded data space and facilitate building confidence in non-animal approaches.
| S-EPMC7393692 | biostudies-literature
Project description:The anion exchange membrane (AEM) is a main component for AEM fuel cells. Recently, a series of electrolytes based on covalent organic frameworks (COFs) functionalized with quaternary ammonium (QA) of showed extraordinary ionic conductivities thanks to the intrinsic porosity of the COF structures, which also provide a robust backbone for good mechanical strength. However, the chemical stability of the COF-based AEMs in alkaline conditions is yet to be understood. Here we systematically investigate the chemical degradation of the COF-based structures tethered with alkyl spacers by combining molecular dynamics (MD) simulations and density functional theory (DFT) calculations. We find that the water environment protects the cationic groups from chemical degradation in terms of both physical and chemical effects, which play a synergistic role. Moreover, we introduce the effective density of water as an order parameter to quantitatively characterize the level of degradation of the COF-based systems with similar design of architecture. The results provide guidance for estimation of the chemical stability of COF-based AEMs.
| S-EPMC9248038 | biostudies-literature
Project description:The human gut harbors a dynamic microbial community whose composition bears great importance for the health of the host. Here, we investigate how colonic physiology impacts bacterial growth, which ultimately dictates microbiota composition. Combining measurements of bacterial physiology with analysis of published data on human physiology into a quantitative, comprehensive modeling framework, we show how water flow in the colon, in concert with other physiological factors, determine the abundances of the major bacterial phyla. Mechanistically, our model shows that local pH values in the lumen, which differentially affect the growth of different bacteria, drive changes in microbiota composition. It identifies key factors influencing the delicate regulation of colonic pH, including epithelial water absorption, nutrient inflow, and luminal buffering capacity, and generates testable predictions on their effects. Our findings show that a predictive and mechanistic understanding of microbial ecology in the gut is possible. Such predictive understanding is needed for the rational design of intervention strategies to actively control the microbiota.
| S-EPMC5488924 | biostudies-literature