Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Potential Role of Inflammation in SARS-CoV-2 Disease Pathology: A multi-omic study in Type II pneumocytes of A/J mice exposed to Lipopolysaccharide

ABSTRACT: Potential Role of Inflammation in SARS-CoV-2 Disease Pathology: A multi-omic study in Type II pneumocytes of A/J mice exposed to Lipopolysaccharide

PROVIDER: PRJNA868752 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR21017360_1.fastq.gz	Fastqsanger.gz
	SRR21017360_2.fastq.gz	Fastqsanger.gz
	SRR21017361_1.fastq.gz	Fastqsanger.gz
	SRR21017361_2.fastq.gz	Fastqsanger.gz
	SRR21017362_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 24

Similar Datasets

Potential Role of Inflammation in SARS-CoV-2 Disease Pathology: A multi-omic study in Type II pneumocytes of A/J mice exposed to Lipopolysaccharide

Project description:Multi-omic analyses of A/J Type II pneumocytes exposed to lipopolysaccharide were performed to ascertain the similarity of the inflammatory response under these conditions to the inflammatory response seen in SARS-CoV-2 infections.

2022-08-15 | GSE211061 | GEO

A multi-omics study of epigenetic changes in type II alveolar cells in A/J mice exposed to lipopolysaccharide and environmental tobacco smoke

Project description:A/J mice were exposed to environmental tobacco smoke, lipopolysaccharide, or clean air over the course of several weeks. Following exposure, subjects were sacrificed and their type II pneumocytes were isolated for multi-omic analyses.

2025-05-07 | PXD054404 | Pride

Multi-omics analysis of the epigenetic effects of lipopolysaccharide-induced inflammation in Type II Pneumocytes

Project description:Chronic inflammation plays a central role in the pathogenesis of many lung diseases including asthma, SARS-COVID19, obstructive pulmonary disease (COPD), and lung cancer. Lipopolysaccharide (LPS) is an inflammatory agent produced by most Gram-negative bacteria and found in cigarette smoke. In a mouse model of inflammation, inhalation exposure to LPS induced COPD and increased the size and the multiplicity of lung tumors induced by tobacco-specific nitrosamines. In the present work, we employed a comprehensive multi-omics approach that monitored changes in DNA methylation/hydroxymethylation, gene expression, and global protein abundances in Type II pneumocytes of A/J mice following sub-chronic exposure to LPS.

2025-06-04 | GSE275769 | GEO

Multi-omics analysis of the epigenetic effects of lipopolysaccharide-induced inflammation in Type II Pneumocytes

Project description:Multi-omics analysis of the epigenetic effects of lipopolysaccharide-induced inflammation in Type II Pneumocytes

| PRJNA1152971 | ENA

Comparison of KRas;Atg5fl/+ and KRas;Atg5fl/fl pneumocytes

Project description:Primary pneumocytes from KRas;Atg5fl/+ and KRas;Atg5fl/fl littermates were cultured for 48 hours and infected with AdCre-GFP to induce expression of the KrasG12D oncogene and concomitant Atg5 deletion. The transcriptional profile of those cells was determined by mRNA sequencing and uncovered differential expression in cellular movement, inflammatory response and oxidative stress response. Comparison of transcriptomes from KRas;Atg5fl/+ and KRas;Atg5fl/fl pneumocytes

2014-01-07 | E-GEOD-48387 | biostudies-arrayexpress

RNAseq of CD31-/CD45- pneumocytes after 4 weeks of KRasG12V activation by tamoxifen

Project description:We report the RNAseq data obtained from 50.000-100.000 CD31-/CD45- pneumocytes isolated by FACS from mice harboring a normal dose or one extra copy of the Sirt1 gene, and a tamoxifen-inducible oncogenic KI alelle of KRasG12V after 4 weeks of tamoxifen treatment. Pneumocytes with the activated form of the inducible KRasG12V oncogene sere selected making use of the reporter gene LacZ (located next to the oncogene in the same polycistronic mRNA), by loading CD31-/CD45- pneumocytes with the LacZ-activated fuorogenic molecule FDG prior to FACS sorting.

2018-06-25 | GSE115179 | GEO

Profiling of pancreatic adenocarcinoma using artificial intelligence-based integration of multi-omic and computational pathology features

Project description:Contemporary analyses focused on a limited number of clinical and molecular features have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). Here we describe a novel, conceptual approach and use it to analyze clinical, computational pathology, and molecular (DNA, RNA, protein, and lipid) analyte data from 74 patients with resectable PDAC. Multiple, independent, machine learning models were developed and tested on curated singleand multi-omic feature/analyte panels to determine their ability to predict clinical outcomes in patients. The multi-omic models predicted recurrence with an accuracy and positive predictive value (PPV) of 0.90, 0.91, and survival of 0.85, 0.87, respectively, outperforming every singleomic model. In predicting survival, we defined a parsimonious model with only 589 multi-omic analytes that had an accuracy and PPV of 0.85. Our approach enables discovery of parsimonious biomarker panels with similar predictive performance to that of larger and resource consuming panels and thereby has a significant potential to democratize precision cancer medicine worldwide.

2024-01-23 | PXD037038 | Pride

Comparison of KRas;Atg5fl/+ and KRas;Atg5fl/fl pneumocytes

2014-01-07 | GSE48387 | GEO

Profiling of pancreatic adenocarcinoma using artificial intelligence-based integration of multi-omic and computational pathology features: lipid

Project description:Contemporary analyses focused on a limited number of clinical and molecular features have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). Here we describe a novel, conceptual approach and use it to analyze clinical, computational pathology, and molecular (DNA, RNA, protein, and lipid) analyte data from 74 patients with resectable PDAC. Multiple, independent, machine learning models were developed and tested on curated single and multi-omic feature/analyte panels to determine their ability to predict clinical outcomes in patients. The multi-omic models predicted recurrence with an accuracy and positive predictive value (PPV) of 0.90, 0.91, and survival of 0.85, 0.87, respectively, outperforming every single-omic model. In predicting survival, we defined a parsimonious model with only 589 multi-omic analytes that had an accuracy and PPV of 0.85. Our approach enables discovery of parsimonious biomarker panels with similar predictive performance to that of larger and resource consuming panels and thereby has a significant potential to democratize precision cancer medicine worldwide.

2023-10-15 | MSV000093118 | MassIVE

RNAseq of CD31-/CD45- pneumocytes after 4 weeks of KRasG12V activation by tamoxifen and 2 weeks of chase

Project description:We report the RNAseq data obtained from 50.000-100.000 CD31-/CD45- pneumocytes isolated by FACS from mice harboring a normal dose or one extra copy of the Sirt1 gene, and a tamoxifen-inducible oncogenic KI alelle of KRasG12V after 4 weeks of tamoxifen treatment plus 2 weeks without tamoxifen. Pneumocytes with the activated form of the inducible KRasG12V oncogene sere selected making use of the fluorescent reporter gene Katushka (located at an independent locus), by detecting Katushka fluorescence.

2018-06-25 | GSE115186 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data