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- Broholm, Christa, et al.
(författare)
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Epigenetic programming of adipose-derived stem cells in low birthweight individuals
- 2016
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Ingår i: Diabetologia. - : Springer Science and Business Media LLC. - 0012-186X .- 1432-0428. ; 59:12, s. 2664-2673
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Tidskriftsartikel (refereegranskat)abstract
- Aims/hypothesis: Low birthweight (LBW) is associated with dysfunctions of adipose tissue and metabolic disease in adult life. We hypothesised that altered epigenetic and transcriptional regulation of adipose-derived stem cells (ADSCs) could play a role in programming adipose tissue dysfunction in LBW individuals. Methods: ADSCs were isolated from the subcutaneous adipose tissue of 13 normal birthweight (NBW) and 13 LBW adult men. The adipocytes were cultured in vitro, and genome-wide differences in RNA expression and DNA methylation profiles were analysed in ADSCs and differentiated adipocytes. Results: We demonstrated that ADSCs from LBW individuals exhibit multiple expression changes as well as genome-wide alterations in methylation pattern. Reduced expression of the transcription factor cyclin T2 encoded by CCNT2 may play a key role in orchestrating several of the gene expression changes in ADSCs from LBW individuals. Indeed, silencing of CCNT2 in human adipocytes decreased leptin secretion as well as the mRNA expression of several genes involved in adipogenesis, including MGLL, LIPE, PPARG, LEP and ADIPOQ. Only subtle genome-wide mRNA expression and DNA methylation changes were seen in mature cultured adipocytes from LBW individuals. Conclusions/interpretation: Epigenetic and transcriptional changes in LBW individuals are most pronounced in immature ADSCs that in turn may programme physiological characteristics of the mature adipocytes that influence the risk of metabolic diseases. Reduced expression of CCNT2 may play a key role in the developmental programming of adipose tissue.
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| 2. |
- Mansouri, Kamel, et al.
(författare)
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CERAPP : Collaborative Estrogen Receptor Activity Prediction Project
- 2016
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Ingår i: Journal of Environmental Health Perspectives. - : Environmental Health Perspectives. - 0091-6765 .- 1552-9924. ; 124:7, s. 1023-1033
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. OBJECTIVES: We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing. METHODS: CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies. RESULTS: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing.CONCLUSION: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other end points.
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