XbCteSpea
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB85219 | ENA |
REPOSITORIES: ENA
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Project description:Co-fractionation mass spectrometry (CF-MS) is a technique with potential to characterise endogenous and unmanipulated protein complexes on an unprecedented scale. However this potential has been offset by a lack of guidelines for best-practice CF-MS data collection and analysis. To obtain such guidelines, this study exploits very high proteome coverage libraries of gold standard Saccharomyces cerevisiae complexes to thoroughly evaluate novel and published yeast CF-MS datasets. A new method for identifying gold standard complexes in CF-MS data, Reference Complex Profiling, and the Extending ‘Guilt-by-Association’ by Degree (EGAD) R package are used for these evaluations, which are reinforced with concurrent analyses of published human data. By evaluating data collection designs, which involve fractionation of cell lysates, it is found that near-maximum recall of complexes can be achieved with fewer samples than published studies. Distributing sample collection across orthogonal fractionation methods, rather than a single high resolution dataset, leads to particularly efficient recall. By evaluating 17 different similarity scoring metrics, which are central to CF-MS data analysis, it is found that two metrics rarely used in past CF-MS studies – Spearman and Kendall correlations – and the recently introduced Co-apex metric frequently maximise recall, while a popular metric – Euclidean distance – delivers poor recall. The common practice of integrating external genomic data into CF-MS data analysis is also evaluated, revealing that this practice may improve the precision and recall of known complexes but is generally unsuitable for predicting novel complexes in model organisms. If studying non-model organisms using orthologous genomic data, it is found that particular subsets of fractionation profiles (e.g. the lowest abundance quartile) should be excluded to minimise false discovery. Together these guidelines identify avenues for precise, sensitive and efficient CF-MS studies of known complexes, and effective predictions of novel complexes for orthogonal experimental validation.
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Project description:Early life viral infections are responsible for pulmonary exacerbations that can contribute to disease progression in young children with CF. The most common respiratory viruses detected in the CF airway are human rhinoviruses (RV) and susceptibility to infection has been attributed to dysregulated airway epithelial responses, although evidence has been conflicting. Here, we exposed airway epithelial cells from children with and without CF to RV in vitro. Using RNA-Seq, we profiled the transcriptomic differences of CF and non-CF airway epithelial cells at baseline and in response to RV. There were only modest differences between CF and non-CF cells at baseline. In response to RV there were 1442 and 896 differentially expressed genes in CF and non-CF airway epithelial cells respectively. The core antiviral responses in CF and non-CF airway epithelial cells were mediated through interferon signaling although type 1 and 3 interferon proteins were reduced in CF airway epithelial cells following viral challenge consistent with previous reports. The transcriptional responses in CF airway epithelial cells were more complex than in non-CF airway epithelial cells with more over-represented biological pathways ranging from cytokine signaling to metabolic and biosynthetic pathways. Network analysis highlighted that the differentially expressed genes of CF airway epithelial cells transcriptional responses were highly interconnected and formed a more complex network than observed in non-CF airway epithelial cells. These data provide novel insights to the CF airway epithelial cells responses to RV infection and highlight potential pathways that could be targeted to improve antiviral and anti-inflammatory responses in CF.
2020-07-03 | GSE138167 | GEO