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- Lövfors, William, 1991-, et al.
(author)
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A comprehensive mechanistic model of adipocyte signaling with layers of confidence
- 2023
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In: npj Systems Biology and Applications. - : Springer Nature. - 2056-7189. ; 9:1
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Journal article (peer-reviewed)abstract
- Adipocyte signaling, normally and in type 2 diabetes, is far from fully understood. We have earlier developed detailed dynamic mathematical models for several well-studied, partially overlapping, signaling pathways in adipocytes. Still, these models only cover a fraction of the total cellular response. For a broader coverage of the response, large-scale phosphoproteomic data and systems level knowledge on protein interactions are key. However, methods to combine detailed dynamic models with large-scale data, using information about the confidence of included interactions, are lacking. We have developed a method to first establish a core model by connecting existing models of adipocyte cellular signaling for: (1) lipolysis and fatty acid release, (2) glucose uptake, and (3) the release of adiponectin. Next, we use publicly available phosphoproteome data for the insulin response in adipocytes together with prior knowledge on protein interactions, to identify phosphosites downstream of the core model. In a parallel pairwise approach with low computation time, we test whether identified phosphosites can be added to the model. We iteratively collect accepted additions into layers and continue the search for phosphosites downstream of these added layers. For the first 30 layers with the highest confidence (311 added phosphosites), the model predicts independent data well (70–90% correct), and the predictive capability gradually decreases when we add layers of decreasing confidence. In total, 57 layers (3059 phosphosites) can be added to the model with predictive ability kept. Finally, our large-scale, layered model enables dynamic simulations of systems-wide alterations in adipocytes in type 2 diabetes.
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| 2. |
- Sandstedt, Mikael, 1990, et al.
(author)
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Regional transcriptomic profiling reveals immune system enrichment in nonfailing atria as well as all chambers of the failing human heart
- 2023
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In: American Journal of Physiology. Heart and Circulatory Physiology. - : American Physiological Society. - 0363-6135 .- 1522-1539. ; 325:6, s. H1430-H1445
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Journal article (peer-reviewed)abstract
- The different chambers of the human heart demonstrate regional physiological traits and may be differentially affected during pathologic remodeling, resulting in heart failure. Few previous studies have, however, characterized the different chambers at a transcriptomic level. We therefore conducted whole-tissue RNA sequencing and gene set enrichment analysis of biopsies collected from the four chambers of adult failing (n = 8) and nonfailing (n = 11) human hearts. Atria and ventricles demonstrated distinct transcriptional patterns. Compared to nonfailing ventricles, the transcriptional pattern of nonfailing atria was enriched for a large number of gene sets associated with cardiogenesis, the immune system and bone morphogenetic protein (BMP), transforming growth factor beta (TGF beta), MAPK/JNK and Wnt signaling. Differences between failing and nonfailing hearts were also determined. The transcriptional pattern of failing atria was distinct compared to that of nonfailing atria and enriched for gene sets associated with the innate and adaptive immune system, TGF beta/SMAD signaling, and changes in endothelial, smooth muscle cell and cardiomyocyte physiology. Failing ventricles were also enriched for gene sets associated with the immune system. Based on the transcriptomic patterns, upstream regulators associated with heart failure were identified. These included many immune response factors predicted to be similarly activated for all chambers of failing hearts. In summary, the heart chambers demonstrate distinct transcriptional patterns that differ between failing and nonfailing hearts. Immune system signaling may be a hallmark of all four heart chambers in failing hearts, and could constitute a novel therapeutic target.
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