Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA627874 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Genetically identical individuals in bacterial populations can display significant phenotypic variability. This variability can be functional, for example by allowing a fraction of stress prepared cells to survive an otherwise lethal stress. The optimal fraction of stress prepared cells depends on environmental conditions. However, how bacterial populations modulate their level of phenotypic variability remains unclear. Here we show that the alternative sigma factor σV circuit in B. subtilis generates functional phenotypic variability that can be tuned by stress level, environmental history, and genetic perturbations. Using single-cell time-lapse microscopy and microfluidics, we find the fraction of cells that immediately activate σV under lysozyme stress depends on stress level and on a transcriptional memory of previous stress. Iteration between model and experiment reveals that this tunability can be explained by the autoregulatory feedback structure of the sigV operon. As predicted by the model, genetic perturbations to the operon also modulate the response variability. The conserved sigma-anti-sigma autoregulation motif is thus a simple mechanism for bacterial populations to modulate their heterogeneity based on their environment.
2021-04-10 | GSE171761 | GEO
Project description:Genetically identical inbred mice exhibit substantial stable individual variability in exploratory behavior. We used microarrays to look at gene expression differences in the hippocampus in female mice separated by stable differences in exploratory behavior
2008-10-08 | GSE11680 | GEO
Project description:Gene expression differences between high and low exploratory genetically identical mice
| PRJNA106083 | ENA
Project description:In differentiated cells, aging is associated with hypermethylation of DNA regions enriched in repressive histone posttranslational modifications. However, the chromatin marks associated with changes in DNA methylation in adult stem cells during lifetime are still largely unknown. Here, DNA methylation profiling of mesenchymal stem cells obtained from individuals aged 2 to 92 identified 18735 hypermethylated and 45407 hypomethylated CpG sites associated with aging. As in differentiated cells, hypermethylated sequences were enriched in chromatin repressive marks. Most importantly, hypomethylated CpG sites were strongly enriched in the active chromatin mark H3K4me1 in stem and differentiated cells, suggesting this is a cell type-independent chromatin signature of DNA hypomethylation during aging. Analysis of scedasticity showed that interindividual variability of DNA methylation increased during aging in MSCs and differentiated cells, providing a new avenue for the identification of DNA methylation changes over time. DNA methylation profiling of genetically identical individuals showed that both the tendency of DNA methylation changes and scedasticity depended on non-genetic as well as genetic factors. Our results indicate that the dynamics of DNA methylation during aging depend on a complex mixture of factors that include the DNA sequence, cell type and chromatin context involved, and that, depending on the locus, the changes can be modulated by genetic and/or external factors. Total DNA isolated by standard procedures from human adult mesenchymal stem cells (MSCs) obtained from 34 individuals aged 2 to 92
2014-09-25 | E-GEOD-52112 | biostudies-arrayexpress
Project description:The experimental goals of this study were to determine differences in adipose tissue gene expression in genetically identical mice that have variability in their susceptibility towards diet-induced obesity following 4 weeks feeding a high saturated fat diet. Keywords: comparative gene expression analysis
2006-04-28 | GSE4692 | GEO
Project description:BACKGROUND: The transcript levels of many genes exhibit significant variation in tissue samples from inbred laboratory mice. A microarray experiment was designed to separate transcript abundance variation across samples from adipose, heart, kidney, and liver tissues of C57BL/6J mice into within-mouse and between-mouse components. Within-mouse variance captures variation due to heterogeneity of gene expression within tissues, RNA-extraction, and array processing. Between-mouse variance reflects differences in transcript levels between these genetically identical mice. Many biological sources can contribute to heterogeneous transcript levels within a tissue sample including inherent stochasticity of biochemical processes such as intrinsic and extrinsic noise within cells and differences in cell-type composition which can result from heterogeneity of stem and progenitor cell populations. Differences in global signaling patterns between individuals and micro-environmental influences such as interactions with pathogens and cage mates can also contribute to variation, but are likely to contribute more to the between-mouse variance component. RESULTS: The nature and extent of transcript abundance variation differs across tissues. Adipose has the largest total variance and the largest within-mouse variance. Liver has the smallest total variance, but it has the most between-mouse variation. Genes with high variability can be classified into groups with correlated patterns of expression that are enriched for specific biological functions. Variation between mice is associated with circadian rhythm, growth hormone signaling, immune response, androgen regulation, lipid metabolism, and the extracellular matrix. Genes showing correlated patterns of within mouse variation were also associated with biological functions, spatial connectivity of stochastic variation and heterogeneity of cell types within tissues. CONCLUSIONS: Genetically identical mice are individuals and they can experience different outcomes for medically important traits. This is reflected in the stochastic variation in gene expression observed between genetically identical mice. Much of the stochasticity has organismal, tissue, or spatial connectivity. Prior knowledge of the genes and functional classes of genes that are likely to vary in the absence of experimental perturbations, whether these are genetic or environmental, can inform experimental design decisions and the interpretation of gene expression data. Variation in gene expression in genetically identical mice sheds light on the impact of stochastic and micro-environmental factors and their phenotypic consequences.
2010-10-01 | GSE20121 | GEO
Project description:Variability of gene expression profiles in human blood and lymphoblastoid cell lines
| PRJNA146313 | ENA
Project description:Stochastic differences among clonal cells can initiate cell fate decisions in development or cause cell-to-cell differences in the responses to drugs or extracellular ligands. We hypothesize that some of this phenotypic variability is caused by stochastic fluctuations in the activities of transcription factors. We tested this hypothesis in NIH3T3-CG cells using the response to Hedgehog signaling as a model cellular response. Here we present evidence for the existence of distinct fast and slow responding substates of NIH3T3-CG cells. These two substates have distinct expression profiles, and fluctuations in the activity of the Prrx1 transcription factor (TF) underlie some of the differences in expression and responsiveness between fast and slow cells. We speculate that similar variability in other TFs may underlie other phenotypic differences among genetically identical cells.
2022-05-19 | GSE203134 | GEO
Project description:Stochastic differences among clonal cells can initiate cell fate decisions in development or cause cell-to-cell differences in the responses to drugs or extracellular ligands. We hypothesize that some of this phenotypic variability is caused by stochastic fluctuations in the activities of transcription factors. We tested this hypothesis in NIH3T3-CG cells using the response to Hedgehog signaling as a model cellular response. Here we present evidence for the existence of distinct fast and slow responding substates of NIH3T3-CG cells. These two substates have distinct expression profiles, and fluctuations in the activity of the Prrx1 transcription factor (TF) underlie some of the differences in expression and responsiveness between fast and slow cells. We speculate that similar variability in other TFs may underlie other phenotypic differences among genetically identical cells.
2022-06-18 | GSE206154 | GEO
Project description:The experimental goals of this study were to determine the differences in hypothalamus gene expression in genetically identical mice that have variability in their susceptibility towards diet-induced obesity following 6 weeks feeding a high fat diet, 2 weeks low fat diet and 6 weeks high fat diet. Keywords: Comparative gene expression analysis
2006-04-28 | GSE4697 | GEO