Qualitative modeling identifies IL-11 as a novel regulator in maintaining self-renewal in human pluripotent stem cells
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE51159 | GEO | 2013/09/26
SECONDARY ACCESSION(S): PRJNA221368
REPOSITORIES: GEO
Similar Datasets
Project description:A systems biology approach in which qualitative modeling based on combining boolean networks and in silico perturbation experiments were employed to identify 15 pluripotency regulating cytokines or cytokine related genes. IL-11 was validated as a novel factor capable of maintaing the undifferentiated state of human embryonic stem cells in the absence of exogenously added bFGF to the culture acting via a different mechanims than bFGF. Transcriptomic microarray data of eight overexpression and knock-down experiments were used for qualitative modeling based on boolean networks to predict novel factors - mainly cytokines - that maintain pluripotent human embryonic stem cells in the absence of bFGF in culture. The culture was maintained for at least 9 passages using and stained positive for alcaline phosphatase staining, OCT3/4, SOX2, NANOG, TRA1-60. Microarray based gene expression profiling showed that the IL-11 treatment occupies an intermediate state, between bFGF treatment and the negative control which is no cytokine treatment as judged by hierarchical clustering. Moreover, KEGG pathway analysis indicates common and additional distinct mechanisms of bFGF and IL-11 dependant pluripotency dependant mechanisms.
2013-09-26 | E-GEOD-51159 | biostudies-arrayexpress
Project description:Pluripotency in human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) is regulated by three transcription factors-OCT3/4, SOX2, and NANOG. To fully exploit the therapeutic potential of these cells it is essential to have a good mechanistic understanding of the maintenance of self-renewal and pluripotency. In this study, we demonstrate a powerful systems biology approach in which we first expand literature-based network encompassing the core regulators of pluripotency by assessing the behavior of genes targeted by perturbation experiments. We focused our attention on highly regulated genes encoding cell surface and secreted proteins as these can be more easily manipulated by the use of inhibitors or recombinant proteins. Qualitative modeling based on combining boolean networks and in silico perturbation experiments were employed to identify novel pluripotency-regulating genes. We validated Interleukin-11 (IL-11) and demonstrate that this cytokine is a novel pluripotency-associated factor capable of supporting self-renewal in the absence of exogenously added bFGF in culture. To date, the various protocols for hESCs maintenance require supplementation with bFGF to activate the Activin/Nodal branch of the TGF? signaling pathway. Additional evidence supporting our findings is that IL-11 belongs to the same protein family as LIF, which is known to be necessary for maintaining pluripotency in mouse but not in human ESCs. These cytokines operate through the same gp130 receptor which interacts with Janus kinases. Our finding might explain why mESCs are in a more naïve cell state compared to hESCs and how to convert primed hESCs back to the naïve state. Taken together, our integrative modeling approach has identified novel genes as putative candidates to be incorporated into the expansion of the current gene regulatory network responsible for inducing and maintaining pluripotency.
| S-EPMC3809568 | biostudies-literature
Project description:Analysis of musashi2 contribution towards maintaing myelodysplastic phenotype in stem cells. We find that musashi2 plays an integral role in maintaining the myelodysplastic phenotype
2016-02-16 | GSE76840 | GEO
Project description:Analysis of musashi2 contribution towards maintaing myelodysplastic phenotype in stem cells. We find that musashi2 plays an integral role in maintaining the myelodysplastic phenotype Control, NUP98-HOXD13; NHD13, NHD13/MSI2 bone marrow was transplated allowed to engraft into lethally irradiated congenic CD45.1 animals. Mice were then fed doxycycline to induce MSI2 overexpression. Mice were induced for 3 months and then CD45.2 Lineage lo Sca1+ and Kit+ cells were sorted and then assessed for gene expression.
2016-02-16 | E-GEOD-76840 | biostudies-arrayexpress
Project description:Pathological examination of gastroscopy biopsy specimens will make false diagnosis for gastric cancer (GC) due to inaccurate sampling locations and/or insufficient sampling amount. We extracted a robust qualitative transcriptional signature, based on the within-sample relative expression orderings (REOs) of gene pairs, to discriminate both GC tissues and adjacent-normal tissues from non-GC gastritis and normal gastric tissues.The qualitative transcriptional signature can be robustly applied at the individual level to aid the diagnosis of early GC.
2018-07-10 | GSE116782 | GEO
Project description:Histological classification by routine pathology assessment with hematoxylin-eosin staining and immunostaining for poorly differentiated tumors, particularly those from small biopsies, is still challenging. In this study, using gene expression profiles of pathologically-determined lung squamous cell carcinomas and adenocarcinomas, denoted as pSCC and pADC respectively, we developed a qualitative transcriptional signature, based on the within-sample relative gene expression orderings (REOs) of gene pairs, to individually distinguish ADC from SCC. The signature was validated in the frozen tissues, FFPE materials, mixed tumors, small biopsy specimens and poorly differentiated samples.In summary, the qualitative signature, independent of the subjective diagnosis of pathologists, would be an effective auxiliary tool in distinguishing ADC from SCC.
2018-10-11 | GSE121090 | GEO
Project description:Numerous gene expression datasets from diverse human tissue samples have been already deposited in the public domain. There have been several attempts to do large scale meta-analyses of all of these datasets. Most of these analyses summarize pairwise gene expression relationships using correlation, or identify differentially expressed genes in two conditions. We propose here a new large scale meta-analysis of all of the publicly available human datasets to identify Boolean logical relationships between genes. Boolean logic is a branch of mathematics that deals with two possible values. In the context of gene expression datasets we use qualitative high and low expression values. A strong logical relationship between genes emerges if at least one of the quadrants is sparsely populated.
2018-08-29 | GSE119087 | GEO
Project description:Numerous gene expression datasets from diverse mouse tissue samples have been already deposited in the public domain. There have been several attempts to do large scale meta-analyses of all of these datasets. Most of these analyses summarize pairwise gene expression relationships using correlation, or identify differentially expressed genes in two conditions. We propose here a new large scale meta-analysis of all of the publicly available mouse datasets to identify Boolean logical relationships between genes. Boolean logic is a branch of mathematics that deals with two possible values. In the context of gene expression datasets we use qualitative high and low expression values. A strong logical relationship between genes emerges if at least one of the quadrants is sparsely populated.
2018-08-29 | GSE119085 | GEO
Project description:Numerous gene expression datasets from diverse fly tissue samples have been already deposited in the public domain. There have been several attempts to do large scale meta-analyses of all of these datasets. Most of these analyses summarize pairwise gene expression relationships using correlation, or identify differentially expressed genes in two conditions. We propose here a new large scale meta-analysis of all of the publicly available fly datasets to identify Boolean logical relationships between genes. Boolean logic is a branch of mathematics that deals with two possible values. In the context of gene expression datasets we use qualitative high and low expression values. A strong logical relationship between genes emerges if at least one of the quadrants is sparsely populated.
2018-08-29 | GSE119084 | GEO
Project description:Numerous gene expression datasets from diverse plant tissue samples have been already deposited in the public domain. There have been several attempts to do large scale meta-analyses of all of these datasets. Most of these analyses summarize pairwise gene expression relationships using correlation, or identify differentially expressed genes in two conditions. We propose here a new large scale meta-analysis of all of the publicly available plant datasets to identify Boolean logical relationships between genes. Boolean logic is a branch of mathematics that deals with two possible values. In the context of gene expression datasets we use qualitative high and low expression values. A strong logical relationship between genes emerges if at least one of the quadrants is sparsely populated.
2018-08-29 | GSE119083 | GEO