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Nielsen, P. Y.

Publications and source records attributed to Nielsen, P. Y..

2 recordsLinked to original sources

Tipping-point transition from transient to persistent inflammation in pancreatic islets

Type 2 diabetes (T2D) is associated with a systemic increase in the pro-inflammatory cytokine IL-1{beta}. While transient exposure to low IL-1{beta} concentrations improves insulin secretion and {beta}-cell proliferation in pancreatic islets, prolonged exposure leads to impaired insulin secretion and collective {beta}-cell death. IL-1 is secreted locally by islet-resident macrophages and {beta}-cells; however it is unknown if and how the two opposing modes may emerge at single islet level. We investigated the duality of IL-1{beta} with a quantitative in-silico model of the IL-1 regulatory network in pancreatic islets. We find that the network can produce either transient or persistent IL-1 responses, when induced by pro-inflammatory and metabolic cues. This suggests that the duality of IL-1 may be regulated at the single islet level. We use two core feedbacks in the IL-1 regulation to explain both modes: First, a fast positive feedback in which IL-1 induces its own production through IL-1R/IKK/NF-{kappa}B pathway. Second, a slow negative feedback where NF{kappa}-{kappa}B upregulates inhibitors acting at different levels along the IL-1R/IKK/NF-{kappa}B pathway - IL-1 receptor antagonist and A20 among others. A transient response ensues when the two feedbacks are balanced. When positive feedback is dominating over the negative islets transit into the persistent inflammation mode. Consistent with several observations, where the size of islets was implicated in its inflammatory state, we find that large islets and islets with high density of IL-1{beta} amplifying cells are more prone to transit into persistent IL-1{beta} mode. Our results are likely not limited to IL-1{beta} but general for the combined effect of multiple pro-inflammatory cytokines and chemokines. Generalizing complex regulations in terms of two feedbacks of opposing nature and acting on different time scales provides a number of testable predictions, which call for dynamic monitoring of pro-inflammatory cytokines at the single islet level. AUTHOR SUMMARYDifferent expression or activity dynamics of the same proteins or signaling molecules can lead to opposing fates in cells and tissues. While it is known that brief and prolonged exposure to pro-inflammatory cytokine IL-1{beta} have opposing effects on the functionality and viability of pancreatic {beta}-cells, it is unclear if and how these differences in dynamics may arise at the single islet level. We use a mathematical model of the core feedback loops in the IL-1{beta} regulatory network to show that transient and persistent responses are the two characteristic dynamic modes of the IL-1{beta} response. The likelihood of each mode depends on systemic inflammation and elevated glycaemia and free fatty acids levels. We find that large islets are more prone to transit into the persistent mode, which may provide an explanation for why large islets are underrepresented in type 2 diabetes patients.

biophysics↗

A Systems Level Explanation for Gompertzian Mortality Patterns is provided by the "Multiple and Inter-dependent Component Cause Model"

Understanding and facilitating healthy aging has become a major goal in medical research and it is becoming increasingly acknowledged that there is a need for understanding the aging phenotype as a whole rather than focusing on individual factors. Here, we provide a universal explanation for the emergence of Gompertzian mortality patterns using a systems approach to describe aging in complex organisms that consist of many inter-dependent subsystems. Our model relates to the Sufficient-Component Cause Model, widely used within the field of epidemiology, and we show that including inter-dependencies between subsystems and modeling the temporal evolution of subsystem failure results in Gompertizan mortality on the population level. Our model also provides temporal trajectories of mortality-risk for the individual. These results may give insight into understanding how biological age evolves stochastically within the individual, and how this in turn leads to a natural heterogeneity of biological age in a population.

systems biology↗