| 1. |
- Agudelo, Leandro Z., et al.
(författare)
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Metabolic resilience is encoded in genome plasticity
- 2021
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Metabolism plays a central role in evolution, as resource conservation is a selective pressure for fitness and survival.Resource-driven adaptations offer a good model to study evolutionary innovation more broadly. It remains unknown howresource-driven optimization of genome function integrates chromatin architecture with transcriptional phase transitions.Here we show that tuning of genome architecture and heterotypic transcriptional condensates mediate resilience tonutrient limitation. Network genomic integration of phenotypic, structural, and functional relationships reveals that fattissue promotes organismal adaptations through metabolic acceleration chromatin domains and heterotypic PGC1Acondensates. We find evolutionary adaptations in several dimensions; low conservation of amino acid residues withinprotein disorder regions, nonrandom chromatin location of metabolic acceleration domains, condensate-chromatin stabilitythrough cis-regulatory anchoring and encoding of genome plasticity in radial chromatin organization. We show thatenvironmental tuning of these adaptations leads to fasting endurance, through efficient nuclear compartmentalization oflipid metabolic regions, and, locally, human-specific burst kinetics of lipid cycling genes. This process reduces oxidativestress, and fatty-acid mediated cellular acidification, enabling endurance of condensate chromatin conformations.Comparative genomics of genetic and diet perturbations reveal mammalian convergence of phenotype and structuralrelationships, along with loss of transcriptional control by diet-induced obesity. Further, we find that radial transcriptionalorganization is encoded in functional divergence of metabolic disease variant-hubs, heterotypic condensate composition,and protein residues sensing metabolic variation. During fuel restriction, these features license the formation of largeheterotypic condensates that buffer proton excess, and shift viscoelasticity for condensate endurance. This mechanismmaintains physiological pH, reduces pH-resilient inflammatory gene programs, and enables genome plasticity throughtranscriptionally driven cell-specific chromatin contacts. In vivo manipulation of this circuit promotes fasting-likeadaptations with heterotypic nuclear compartments, metabolic and cell-specific homeostasis. In sum, we uncover here ageneral principle by which transcription uses environmental fluctuations for genome function, and demonstrate howresource conservation optimizes transcriptional self-organization through robust feedback integrators, highlighting obesityas an inhibitor of genome plasticity relevant for many diseases.
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| 2. |
- Kutlu, Burak, et al.
(författare)
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Detailed transcriptome atlas of the pancreatic beta cell
- 2009
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Ingår i: BMC Medical Genomics. - : Springer Science and Business Media LLC. - 1755-8794. ; 2, s. 3-
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Gene expression patterns provide a detailed view of cellular functions. Comparison of profiles in disease vs normal conditions provides insights into the processes underlying disease progression. However, availability and integration of public gene expression datasets remains a major challenge. The aim of the present study was to explore the transcriptome of pancreatic islets and, based on this information, to prepare a comprehensive and open access inventory of insulin-producing beta cell gene expression, the Beta Cell Gene Atlas (BCGA). METHODS: We performed Massively Parallel Signature Sequencing (MPSS) analysis of human pancreatic islet samples and microarray analyses of purified rat beta cells, alpha cells and INS-1 cells, and compared the information with available array data in the literature. RESULTS: MPSS analysis detected around 7600 mRNA transcripts, of which around a third were of low abundance. We identified 2000 and 1400 transcripts that are enriched/depleted in beta cells compared to alpha cells and INS-1 cells, respectively. Microarray analysis identified around 200 transcription factors that are differentially expressed in either beta or alpha cells. We reanalyzed publicly available gene expression data and integrated these results with the new data from this study to build the BCGA. The BCGA contains basal (untreated conditions) gene expression level estimates in beta cells as well as in different cell types in human, rat and mouse pancreas. Hierarchical clustering of expression profile estimates classify cell types based on species while beta cells were clustered together. CONCLUSION: Our gene atlas is a valuable source for detailed information on the gene expression distribution in beta cells and pancreatic islets along with insulin producing cell lines. The BCGA tool, as well as the data and code used to generate the Atlas are available at the T1Dbase website (T1DBase.org).
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| 3. |
- Lopes, Miguel, et al.
(författare)
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Temporal profiling of cytokine-induced genes in pancreatic beta-cells by meta-analysis and network inference
- 2014
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Ingår i: Genomics. - : Elsevier BV. - 0888-7543 .- 1089-8646. ; 103:4, s. 264-275
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Tidskriftsartikel (refereegranskat)abstract
- Type I Diabetes (T1D) is an autoimmune disease where local release of cytokines such as IL-1 beta and IFN-gamma contributes to beta-cell apoptosis. To identify relevant genes regulating this process we performed a meta-analysis of 8 datasets of beta-cell gene expression after exposure to IL-1 beta and IFN-gamma. Two of these datasets are novel and contain time-series expressions in human islet cells and rat INS-1E cells. Genes were ranked according to their differential expression within and after 24 h from exposure, and characterized by function and prior knowledge in the literature. A regulatory network was then inferred from the human time expression datasets, using a time-series extension of a network inference method. The two most differentially expressed genes previously unknown in T1D literature (RIPK2 and ELF3) were found to modulate cytokine-induced apoptosis. The inferred regulatory network is thus supported by the experimental validation, providing a proof-of-concept for the proposed statistical inference approach.
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