Oscillatory autophagy induction is enabled by an updated AMPK-ULK1 regulatory wiring.

Autophagy-dependent survival relies on a crucial oscillatory response during cellular stress. Although oscillatory behaviour is typically associated with processes like the cell cycle or circadian rhythm, emerging experimental and theoretical evidence suggests that such periodic dynamics may explain conflicting experimental results in autophagy research. In this study, we demonstrate that oscillatory behaviour in the regulation of the non-selective, stress-induced macroautophagy arises from a series of interlinked negative and positive feedback loops within the mTORC1-AMPK-ULK1 regulatory triangle.

AutophagyNet: high-resolution data source for the analysis of autophagy and its regulation.

Macroautophagy/autophagy is a highly-conserved catabolic procss eliminating dysfunctional cellular components and invading pathogens. Autophagy malfunction contributes to disorders such as cancer, neurodegenerative and inflammatory diseases. Understanding autophagy regulation in health and disease has been the focus of the last decades.

Oscillation of Autophagy Induction under Cellular Stress and What Lies behind It, a Systems Biology Study.

One of the main inducers of autophagy-dependent self-cannibalism, called ULK1, is tightly regulated by the two sensor molecules of nutrient conditions and energy status, known as mTOR and AMPK kinases, respectively. Recently, we developed a freely available mathematical model to explore the oscillatory characteristic of the AMPK-mTOR-ULK1 regulatory triangle. Here, we introduce a systems biology analysis to explain in detail the dynamical features of the essential negative and double-negative feedback loops and also the periodic repeat of autophagy induction upon cellular stress.

Mapping the epithelial-immune cell interactome upon infection in the gut and the upper airways.

Increasing evidence points towards the key role of the epithelium in the systemic and over-activated immune response to viral infection, including SARS-CoV-2 infection. Yet, how viral infection alters epithelial-immune cell interactions regulating inflammatory responses, is not well known. Available experimental approaches are insufficient to properly analyse this complex system, and computational predictions and targeted data integration are needed as an alternative approach.