This study demonstrates simulated microgravity effects on E. coli K 12 MG1655 when grown on LB medium supplemented with glycerol. The results imply that E. coli readily reprograms itself to combat the multiple stresses imposed due to microgravity. Under these conditions it survives by upregulating oxidative stress protecting genes and simultaneously down regulating the membrane transporters and synthases to maintain cell homeostasis.
Effect of simulated microgravity on E. coli K12 MG1655 growth and gene expression.
No sample metadata fields
View SamplesDysregulation of professional APC has been postulated as a major mechanism underlying Ag-specific T cell hyporesponsiveness in patients with patent filarial infection. To address the nature of this dysregulation, dendritic cells (DC) and macrophages generated from elutriated monocytes were exposed to live microfilariae (mf), the parasite stage that circulates in blood and is responsible for most immune dysregulation in filarial infections. DC exposed to mf for 2496 h showed a marked increase in cell death and caspase-positive cells compared with unexposed DC, while mf exposure did not induce apoptosis in macrophages. Interestingly, 48 h exposure of DC to mf induced mRNA expression of the pro-apoptotic gene TRAIL and both mRNA and protein expression of TNF-alpha. mAb to TRAIL-R2, TNF-R1, or TNF-alpha partially reversed mf-induced cell death in DC, as did knocking down the receptor for TRAIL-R2 using small interfering RNA. Mf also induced gene expression of BH3-interacting domain death agonist (Bid) and protein expression of cytochrome c in DC; mf-induced cleavage of Bid could be shown to induce release of cytochrome c, leading to activation of caspase 9. Our data suggest that mf induce DC apoptosis in a TRAIL- and TNF-alpha-dependent fashion.
Induction of TRAIL- and TNF-alpha-dependent apoptosis in human monocyte-derived dendritic cells by microfilariae of Brugia malayi.
Sex, Treatment, Race
View SamplesSingle cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers
Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.
No sample metadata fields
View SamplesSingle cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers
Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.
No sample metadata fields
View SamplesSingle cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers
Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.
No sample metadata fields
View SamplesSingle cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers
Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.
No sample metadata fields
View SamplesSingle cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers
Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.
No sample metadata fields
View SamplesSingle cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers
Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.
Specimen part, Cell line, Subject
View SamplesSingle cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers
Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.
No sample metadata fields
View SamplesSingle cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers
Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.
No sample metadata fields
View Samples