Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

CD14+ Bovine Milk Macrophage Challenge with Streptococcus uberis

ABSTRACT: CD14+ Macrophage were challenged with Wildtype (WT), or S1154-deletion mutant (Del), and sequenced at timepoints zero hours (T0) and twenty-four hours (T24). To generate DEGs T0 and T24 timepoints were compared, a multifactor DESEQ2 run comparing T0 and T24 in both Wildtype and Deletion strains, and a direct comparison of T24 timepoints in Mutant and Deletion were generated.

ORGANISM(S): Bos taurus

PROVIDER: GSE138793 | GEO | 2020/11/30

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Emergence of a developmental stage-dependent human liver disease signature demonstrated by directed differentiation of alpha-1 antitrypsin deficient iPS cells [HumanMethylation450]

2015-04-15 | GSE66077 | GEO

Emergence of a developmental stage-dependent human liver disease signature demonstrated by directed differentiation of alpha-1 antitrypsin deficient iPS cells [HuGene-1_0-st]

2015-04-15 | GSE66076 | GEO

Emergence of a developmental stage-dependent human liver disease signature demonstrated by directed differentiation of alpha-1 antitrypsin deficient iPS cells [miRNA-2_0]

2015-04-15 | GSE66075 | GEO

The Extracellular RNA Quality Control (exRNAQC) study: Small RNA sequencing of blood collection tube study (exRNAQC013)

Project description:To evaluate the impact of blood collection tubes on extracellular RNA (exRNA) sequencing, 10 different blood collection tubes were compared by applying Small RNA sequencing (Illumina) to exRNA from human healthy donor plasma or serum. Three time spans between blood draw and downstream processing were evaluated for each of the tubes. Preservation tubes were processed immediately upon blood collection (T0), after 24 hours (T24), or after 72 hours (T72). Non-preservation plasma and serum tubes were processed immediately upon blood collection (T0), after 4 hours (T4), or after 16 hours (T16). Due to donor privacy concerns the raw data for this study have been submitted to the controlled-access archive EGA under the accession EGAS00001005263.

2021-06-10 | E-MTAB-10506 | biostudies-arrayexpress

The Extracellular RNA Quality Control (exRNAQC) study: RNA Exome sequencing of blood collection tube study (exRNAQC005)

Project description:To evaluate the impact of blood collection tubes on extracellular RNA (exRNA) sequencing, 10 different blood collection tubes were compared by applying RNA Exome sequencing (Illumina) to exRNA from human healthy donor plasma or serum. Three time spans between blood draw and downstream processing were evaluated for each of the tubes. Preservation tubes were processed immediately upon blood collection (T0), after 24 hours (T24), or after 72 hours (T72). Non-preservation plasma and serum tubes were processed immediately upon blood collection (T0), after 4 hours (T4), or after 16 hours (T16). Due to donor privacy concerns the raw data for this study have been submitted to the controlled-access archive EGA under the accession EGAS00001005263.

2021-06-10 | E-MTAB-10507 | biostudies-arrayexpress

Emergence of a developmental stage-dependent human liver disease signature demonstrated by directed differentiation of alpha-1 antitrypsin deficient iPS cells [miRNA-2_0]

Project description:We monitored 9 pluripotent stem cell lines across three time points of hepatic directed differentiation, representing 3 developmental stages: undifferentiated (T0), definitive endoderm (T5), and early hepatocyte (T24). ESCs (n=3) and patient-derived normal (n=3) or PiZZ (n=3) iPSCs were analyzed in the undifferentiated state (T0), after differentiation to definitive endoderm (T5), and upon reaching hepatic stage (T24) for a total of 27 samples. We sought to test the hypothesis that a single transgene-free iPSC clone from each donor could be used to detect disease-specific differences between the normal cohort and the PiZZ cohort, anticipating that this difference would emerge only at a developmental stage in which the mutant AAT gene is expressed. Cells were sorted before analysis at T0 and T5 after antibody staining for TRA1-80+/SSEA3+ (T0) or C-kit+/CXCR4+ (T5) cells. ESCs (n=3) and patient-derived normal (n=3) or PiZZ (n=3) iPSCs were analyzed in the undifferentiated state (T0), after differentiation to definitive endoderm (T5), and upon reaching hepatic stage (T24) for a total of 27 samples. Cells were sorted before analysis at T0 and T5 after antibody staining for TRA1-80+/SSEA3+ (T0) or C-kit+/CXCR4+ (T5) cells. RNA was extracted at each stage, biotin labeled, and hybridized to either Affymetrix GeneChip Human Gene 1.0 ST or miRNA 2.0 arrays. For methylation analysis, gDNA was sorted from the same 27 samples before undergoing bisulfite conversion. Bisulfite converted DNA was then amplified and purified prior to overnight hybridization to Illumina’s Infinium HumanMethylation 450 BeadChips. Next day staining of hybridized arrays produced methylation-dependent differential fluorescence that was detected via an Illumina iScan array scanner.

2015-04-15 | E-GEOD-66075 | biostudies-arrayexpress

Emergence of a developmental stage-dependent human liver disease signature demonstrated by directed differentiation of alpha-1 antitrypsin deficient iPS cells [HumanMethylation450]

2015-04-15 | E-GEOD-66077 | biostudies-arrayexpress

Emergence of a developmental stage-dependent human liver disease signature demonstrated by directed differentiation of alpha-1 antitrypsin deficient iPS cells [HuGene-1_0-st]

2015-04-15 | E-GEOD-66076 | biostudies-arrayexpress

RF003-V3: Glucocorticoid-responsiveness correlates with an Interferon signature in Rheumatoid Arthritis Patients

Project description:Background: Pathogenesis of Rheumatoid arthritis (RA) is driven by monocytes and T-cells, which are heavily influenced by Glucocorticoids (GC). Although a cornerstone of RA-therapy, one third of RA-patients do not respond adequately to GC and the mechanisms of resistance have not yet been clarified. Objective: To find differences in GC-working mechanism in RA-patients responding versus GC-resistant patients by gene expression profiling. Methods: Patients were treated with 3x 1000mg Methylprednisolone. Before start (T0) and 24 hours (T24) after first treatment, CD14+ and CD4+cells were MACS-isolated At day 5, response was determined using the DAS28. Labeled cRNA from 5 GC-Responders and 5 Non-Responders was hybridized to Agilent 4x44K microarray chips. Differentially expressed genes between T0 and T24 were identified using MAANOVA, FWER (family wise error rate) correction and a >1.5 ratio cut-off. Gene Ontology was used for pathway analysis; Transcription-factors were identified via TRANSFAC. Selected genes were validated by qPCR. Results: After 24 hours, 48 genes were exclusively changed in GC-Responders’ monocytes (CD4+cells: 19), 253 exclusively in Non-Responders (CD4+cells: 104) and 104 genes in both (CD4+: 18). In both cell-types a more pronounced down-regulation of interferon related genes was seen in Non-Responders, which was validated by qPCR in CD4+cells. At T24 higher expression of ERAP2 and FAM26F was seen in GC-Responders compared to Non-Responders. Several relevant transcription-factors were identified, among which LEF1in both cell types. Conclusion: GC treatment resulted in stronger suppression of the interferon signature in Non-Responders. This might be disease-specific, but should prompt further investigation of GC-mechanism in several auto-immune diseases known for an interferon signature.

2020-01-30 | GSE53221 | GEO

Time course infection of HeLa cells by Chlamydia tracomatis

Project description:Human Hela cells were infected with Chtrac L2 at timepoints t2hrs, t4hrs, t10hrs, t24hrs and t43hrs including controls without infection at t0 and t24hrs.

2018-01-08 | PXD001457 | Pride

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