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Vai, M.

Publications and source records attributed to Vai, M..

2 recordsLinked to original sources

Mechanoreceptors initiate innate immunity in response to microbial infections

How mammals mount an effective immune response against infectious agents remains an unresolved fundamental issue in biology. Here, we discovered an unforeseen two-tier mechanism of neutrophil recruitment during infections, in which mechanosensing is key to initiating innate immunity. Leveraging a skin infection model and pathogenic bacteria and fungi, we demonstrate that the early recruitment of neutrophils is mainly danger-driven and partly reminiscent of sterile inflammation. Mechanistically, neutrophil recruitment is initiated by a mechanosensor-dependent pathway, involving the activation of PIEZO1 channels. This leads to LTB4 production, which, along with IL-1, induces the release of CXCL1, promoting neutrophil arrival to the site of infection. In contrast, later neutrophil recruitment is TLR- and CXCL2-dependent, highlighting a shift towards a pathogen-driven response to sustain inflammation. These findings advance our understanding of innate immunity by uncovering that mechanical and biochemical signals integrate into a circuit that initiates innate immune responses to microbial infections.

immunology↗

A modular model integrating metabolism, growth, and cell cycle predicts that fermentation is required to modulate cell size in yeast populations

For unicellular organisms, the reproduction rate and growth are crucial determinants of fitness and, therefore, essential functional manifestations of the organism genotype. Using the budding yeast Saccharomyces cerevisiae as a model organism, we integrated metabolism, which provides energy and building blocks for growth, with cell mass growth and cell cycle progression into a low-granularity, multiscale (from cell to population) computational model. This model predicted that cells with constitutive respiration do not modulate cell size according to the growth conditions. We experimentally validated the model predictions using mutants with defects in the upper part of glycolysis or glucose transport. Plugging in molecular details of cellular subsystems allowed us to refine predictions from the cellular to the molecular level. Our hybrid multiscale modeling approach provides a framework for structuring molecular knowledge and predicting cell phenotypes under various genetic and environmental conditions.

systems biology↗