Upon activation mast cells (MC) secrete numerous inflammatory compounds stored in their cytoplasmic secretory granules (SG) by a process called anaphylactic degranulation, which is responsible for type I hypersensitivity responses. Prestored mediators include histamine and mast cell proteases but also some cytokines and growth factors making them available within minutes for a maximal biological effect. Degranulation is followed by the de novo synthesis of lipid mediators such as prostaglandins and leukotrienes as well as a vast array of cytokines, chemokines and growth factors, which are responsible for late phase inflammatory responses. While lipid mediators diffuse freely out of the cell through lipid bilayers, both anaphylactic degranulation and secretion of cytokines, chemokines and growth factors depends on highly regulated vesicular trafficking steps that occur along the secretory pathway starting with the translocation of proteins to the ER. Vesicular trafficking in mast cells also intersects with endocytic routes, notably to form specialized cytoplasmic granules called secretory lysosomes. Some of the mediators like histamine reach granules via specific vesicular monoamine transporters directly from the cytoplasm. In this review, we try to summarize the available data on granule biogenesis and signaling events that coordinate the complex steps that lead to the release of the inflammatory mediators from the various vesicular carriers in mast cells.

The transforming growth factor-β (TGF-β) family plays major pleiotropic roles by regulating many physiological processes in development and tissue homeostasis. The TGF-β signaling pathway outcome relies on the control of the spatial and temporal expression of >500 genes, which depend on the functions of the Smad protein along with those of diverse modulators of this signaling pathway, such as transcriptional factors and cofactors. Ski (Sloan-Kettering Institute) and SnoN (Ski novel) are Smad-interacting proteins that negatively regulate the TGF-β signaling pathway by disrupting the formation of R-Smad/Smad4 complexes, as well as by inhibiting Smad association with the p300/CBP coactivators. The Ski and SnoN transcriptional cofactors recruit diverse corepressors and histone deacetylases to repress gene transcription. The TGF-β/Smad pathway and coregulators Ski and SnoN clearly regulate each other through several positive and negative feedback mechanisms. Thus, these cross-regulatory processes finely modify the TGF-β signaling outcome as they control the magnitude and duration of the TGF-β signals. As a result, any alteration in these regulatory mechanisms may lead to disease development. Therefore, the design of targeted therapies to exert tight control of the levels of negative modulators of the TGF-β pathway, such as Ski and SnoN, is critical to restore cell homeostasis under the specific pathological conditions in which these cofactors are deregulated, such as fibrosis and cancer.

The human SKI-like (SKIL) gene encodes the SMAD transcriptional corepressor SNON that antagonizes TGF-β signaling. SNON protein levels are tightly regulated by the TGF-β pathway: whereas a short stimulation with TGF-β decreases SNON levels by its degradation via the proteasome, longer TGF-β treatment increases SNON levels by inducing SKIL gene expression. Here, we investigated the molecular mechanisms involved in the self-regulation of SKIL gene expression by SNON. Bioinformatics analysis showed that the human SKIL gene proximal promoter contains a TGF-β response element (TRE) bearing four groups of SMAD-binding elements that are also conserved in mouse. Two regions of 408 and 648 bp of the human SKIL gene (∼2.4 kb upstream of the ATG initiation codon) containing the core promoter, transcription start site, and the TRE were cloned for functional analysis. Binding of SMAD and SNON proteins to the TRE region of the SKIL gene promoter after TGF-β treatment was demonstrated by ChIP and sequential ChIP assays. Interestingly, the SNON-SMAD4 complex negatively regulated basal SKIL gene expression through binding the promoter and recruiting histone deacetylases. In response to TGF-β signal, SNON is removed from the SKIL gene promoter, and then the activated SMAD complexes bind the promoter to induce SKIL gene expression. Subsequently, the up-regulated SNON protein in complex with SMAD4 represses its own expression as part of the negative feedback loop regulating the TGF-β pathway. Accordingly, when the SNON-SMAD4 complex is absent as in some cancer cells lacking SMAD4 the regulation of some TGF-β target genes is modified.

Following the general aim of recapitulating the native mechanical properties of tissues and organs in vitro, the field of materials science and engineering has benefited from recent progress in developing compliant substrates with physical and chemical properties similar to those of biological materials. In particular, in the field of mechanobiology, soft hydrogels can now reproduce the precise range of stiffnesses of healthy and pathological tissues to study the mechanisms behind cell responses to mechanics. However, it was shown that biological tissues are not only elastic but also relax at different timescales. Cells can, indeed, perceive this dissipation and actually need it because it is a critical signal integrated with other signals to define adhesion, spreading and even more complicated functions. The mechanical characterization of hydrogels used in mechanobiology is, however, commonly limited to the elastic stiffness (Young’s modulus) and this value is known to depend greatly on the measurement conditions that are rarely reported in great detail. Here, we report that a simple relaxation test performed under well-defined conditions can provide all the necessary information for characterizing soft materials mechanically, by fitting the dissipation behavior with a generalized Maxwell model (GMM). The simple method was validated using soft polyacrylamide hydrogels and proved to be very useful to readily unveil precise mechanical properties of gels that cells can sense and offer a set of characteristic values that can be compared with what is typically reported from microindentation tests.