Respiratory disease complex
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ABSTRACT:
PROVIDER: PRJEB18792 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Whole genome shotgun metagenomics for Respiratory Disease Complex in Poultry
| PRJEB18958 | ENA
Project description:Respiratory disease complex in poultry
| PRJNA397641 | ENA
Project description:Bovine respiratory epithelial cells have different susceptibility to bovine
respiratory syncytial virus infection. The cells derived from the lower
respiratory tract were significantly more susceptible to the virus than those
derived from the upper respiratory tract. Pre-infection with virus of lower
respiratory tract with increased adherence of P. multocida; this was not the
case for upper tract. However, the molecular mechanisms of enhanced
bacterial adherence are not completely understood. To investigate whether
virus infection regulates the cellular adherence receptor on bovine trachea-,
bronchus- and lung-epithelial cells, we performed proteomic analyses.
2020-07-28 | PXD019509 | JPOST Repository
Project description:Data-dependent and -independent acquisition of tryptic peptides from Mycobacterium smegmatis respiratory Complex I preparation.
2022-08-30 | MSV000090235 | MassIVE
Project description:Primary mitochondrial respiratory chain (RC) diseases are heterogeneous in etiology and manifestations but collectively impair cellular energy metabolism. To identify a common cellular response to RC disease, systems biology level transcriptome investigations were performed in human RC disease skeletal muscle and fibroblasts. Global transcriptional and post-transcriptional dysregulation in a tissue-specific fashion was identified across diverse RC complex and genetic etiologies. RC disease muscle was characterized by decreased transcription of cytosolic ribosomal proteins to reduce energy-intensive anabolic processes, increased transcription of mitochondrial ribosomal proteins, shortened 5'-UTRs to improve translational efficiency, and stabilization of 3'-UTRs containing AU-rich elements. These same modifications in a reversed direction typified RC disease fibroblasts. RC disease also dysregulated transcriptional networks related to basic nutrient-sensing signaling pathways, which collectively mediate many aspects of tissue-specific cellular responses to primary RC disease. These findings support the utility of a systems biology approach to improve mechanistic understanding of mitochondrial RC disease. To identify a common cellular response to primary RC that might improve mechanistic understanding and lead to targeted therapies for human RC disease, we performed collective transcriptome profiling in skeletal muscle biopsy specimens and fibroblast cell lines (FCLs) of a diverse cohort of human mitochondrial disease subjects relative to controls. Systems biology investigations of common cellular responses to primary RC disease revealed a collective pattern of transcriptional, post-transcriptional and translational dysregulation occurring in a highly tissue-specific fashion. Affymetrix Human Exon 1.0ST microarray analysis was performed on 29 skeletal muscle samples and Fibroblast cell lines from mitochondrial disease patients and age- and gender-matched controls.
2013-07-01 | E-GEOD-42986 | biostudies-arrayexpress
Project description:RSC is a growth essential ATP-dependent chromatin-remodeling complex of Saccharomyces cerevisiae. Nps1/Sth1 is the ATPase subunit of the complex. A temperature-sensitive mutant allele of NPS1, nps1-13 and the null mutation of the RSC2 or RSC7 gene showed growth defect on a medium containing non fermentable carbon source, such as lactate or ethanol-glycerol (YPEG), suggested a possibility that RSC plays a role on mitochondria function. We used microarrays to compare the global gene expression profiles between the wild type and nps1-13 mutant under respiratory condition. WT (BY4743) and nps1-13 strains pre-grown in YPD medium were inoculated in YPEG medium for RNA extraction and hybridization on Affymetrix microarrays. In order to obtain more global insights into the role of RSC under respiratory conditions, we perfomed the genome-wide expression analysis of nps1-13 in YPEG medium.
2015-03-10 | E-GEOD-66685 | biostudies-arrayexpress
Project description:Alternative assembly of respiratory complex II connects energy stress to metabolic checkpoints
2018-10-18 | PXD009656 | Pride
Project description:Fatal enhanced respiratory syncytial virus disease in toddlers
| PRJNA760778 | ENA
Project description:BACKGROUND. Lower respiratory tract infection (LRTI) is a leading cause of death in children worldwide. LRTI diagnosis is challenging since non-infectious respiratory illnesses appear clinically similar and existing microbiologic tests are often falsely negative or detect incidentally-carried microbes common in children. These challenges result in antimicrobial overuse and adverse patient outcomes. Lower airway metagenomics has the potential to detect host and microbial signatures of LRTI. Whether it can be applied at scale and in a pediatric population to enable improved diagnosis and precision treatment remains unclear. METHODS. We used tracheal aspirate RNA-sequencing to profile host gene expression and respiratory microbiota in 261 children with acute respiratory failure. We developed a random forest gene expression classifier for LRTI by training on patients with an established diagnosis of LRTI (n=117) or of non-infectious respiratory failure (n=50). We then developed a classifier that integrates the: i) host LRTI probability, ii) abundance of respiratory viruses, and iii) dominance in the lung microbiome of bacteria/fungi considered pathogenic by a rules-based algorithm. RESULTS. The host classifier achieved a median AUC of 0.967 by 5-fold cross-validation, driven by activation markers of T cells, alveolar macrophages and the interferon response. The integrated classifier achieved a median AUC of 0.986 and significantly increased the confidence of patient classifications. When applied to patients with an uncertain diagnosis (n=94), the integrated classifier indicated LRTI in 52% of cases and nominated likely causal pathogens in 98% of those. CONCLUSIONS. Lower airway metagenomics enables accurate LRTI diagnosis and pathogen identification in a heterogeneous cohort of critically ill children through integration of host, pathogen, and microbiome features.
2022-12-06 | GSE212532 | GEO