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Quax, R.

Publications and source records attributed to Quax, R..

2 recordsLinked to original sources

Compromised beta-cell identity in type 2 diabetes

Compromised beta-cell identity is emerging as an important contributor of beta-cell dysfunction in type 2 diabetes (T2D). Several studies suggest that hyperglycemia induces the inactivation of transcription factors involved in mature beta-cell identity. More specifically, chronic hyperglycemia leads to the downregulation of PDX1 and MAFA, two important beta-cell identity transcription factors and regulators of insulin promoter activity. Regulation of these transcription factors depends on interactions between multiple signaling cascades and processes driven by complex non-linear dynamics and taking place in different cellular compartments. To better understand these non-linear dynamics, we developed an integrated mathematical model of the underlying mechanisms regulating these key transcription factors. Our model was able to reproduce experimentally measured variations in the levels of PDX1, MAFA and insulin mRNA under different glucose concentrations. We used this model to simulate scenarios that could allow to restore PDX1 and MAFA levels and therefore insulin gene expression. From these simulations, FOXO1 emerged as an important target for the restoration of beta-cell identity. Author summaryGlucose regulation depends on the secretion of insulin by beta-cells and uptake of glucose by the peripheral cells mediated by the action of insulin. In type 2 diabetes both aspects can be compromised. Defective insulin secretion results from compromised beta-cell function. One of the reasons behind compromised beta-cell function is that beta-cells fail to express one or more of the genes involved in insulin production and secretion and thus maintenance of beta-cell identity. The processes involved in the regulation of insulin production and secretion are complex. In this work, we are particularly interested in the role and downregulation of transcription factors, PDX1 and MAFA, which are critical regulators of insulin production, in relation with compromised beta-cell identity and function in the presence of chronic hyperglycemia. To understand better these complex processes, we use mathematical modelling which enables to generate hypotheses and simulate scenarios to extend our understanding of the mechanisms leading to compromised beta-cell function in the presence of chronic hyperglycemia. Our model and similar models can serve to identify therapeutical targets in beta-cells in order to restore their function.

systems biology↗

Emergence of High-Order Functional Hubs in the Human Brain

Network theory is often based on pairwise relationships between nodes, which is not necessarily realistic for modeling complex systems. Importantly, it does not accurately capture non-pairwise interactions in the human brain, often considered one of the most complex systems. In this work, we develop a multivariate signal processing pipeline to build high-order networks from time series and apply it to resting-state functional magnetic resonance imaging (fMRI) signals to characterize high-order communication between brain regions. We also propose connectivity and signal processing rules for building uniform hypergraphs and argue that each multivariate interdependence metric could define weights in a hypergraph. As a proof of concept, we investigate the most relevant three-point interactions in the human brain by searching for high-order "hubs" in a cohort of 100 individuals from the Human Connectome Project. We find that, for each choice of multivariate interdependence, the high-order hubs are compatible with distinct systems in the brain. Additionally, the high-order functional brain networks exhibit simultaneous integration and segregation patterns qualitatively observable from their high-order hubs. Our work hereby introduces a promising heuristic route for hypergraph representation of brain activity and opens up exciting avenues for further research in high-order network neuroscience and complex systems.

neuroscience↗