BUSCADOR RECOLECTA

Raw microscopy data for Fig5E (.lif files) in a single zip file., Resources available on the publisher's site: https://doi.org/10.25452/figshare.plus.18348926.v1, Marie Skłodowska-Curie grant agreement No. 754510 Ministry of Science, Innovation and Universities PGC2018-095616-B-I00 Ministry of Science, Innovation and Universities PGC2018-099562-B-I00, Peer reviewed

Resources available on the publisher's site: https://doi.org/10.5281/zenodo.5554758, Streptococcal pyrogenic exotoxin B (SpeB) is a cysteine protease expressed during group A streptococcal infection that represents a major virulence factor. Although subject to several studies, its role during infection is still under debate, and its proteolytic properties remain insufficiently characterized. Here, we revisited this protease through a set of complementary approaches relying on state of-the-art HPLC-MS methods. After conceiving an efficient protocol to recombinantly express SpeB, the zymogen of the protease and its activation were characterized. Employing proteome-derived peptide libraries, a strong preference for hydrophobic and aromatic residues at P2 alongside negatively charged amino acids at P3′ to P6′ was revealed. To identify relevant in vivo substrates, native proteins were obtained from monocytic secretome and plasma to assess their cleavage under physiological conditions. Besides corroborating our findings concerning specificity, more than 200 cleaved proteins were identified, including proteins of the extracellular matrix, proteins of the immune system, and proteins involved in inflammation. Finally, the cleavage of IgG subclasses was studied in detail. This study precisely depicts the proteolytic properties of SpeB and provides a library of potential host substrates, including their exact cleavage positions, as a valuable source for further research to unravel the role of SpeB during streptococcal infection., Peer reviewed

Additional file 1: Table ST1. Treatments comparison by outcome in male patients. Table ST2. Treatments comparison by outcome in female patients. Table ST3. WHO classification of disease outcome. Table ST4. Panels and antibodies used for immunophenotyping. Figure SF1. Patients distribution by outcome, age, and comorbidities. Figure SF2. Distribution of male patients with comorbidities according to age and testosterone levels. Figure SF3. Longitudinal analysis of serum levels of IL-6, C-reactive protein (CRP), ferritin and lactate dehydrogenase (LDH) in male patients. Figure SF4. Bioavailable testosterone serum levels and correlation between age and sex-hormone binding globulin (SHBG). Figure SF5. Flow cytometry analysis of circulating immune subpopulations in three illustrative cases with moderate, severe survivor and severe deceased outcomes., Ministerio de Ciencia, Innovación y Universidades Consejo Superior de Investigaciones Científicas Agència de Gestió d’Ajuts Universitaris i de Recerca Conselleria d'Educació, Investigació, Cultura i Esport Instituto de Salud Carlos III, Peer reviewed

Additional file 2 Figure S1. Percentage of G-Actin in filaments. Percentage of G-Actin in filaments of different size for the three characteristic regimes., Ministerio de Ciencia, Innovación y Universidades Ministerio de Ciencia, Innovación y Universidades, Peer reviewed

Additional file 3 Figure S2. Dependence of speed of treadmiling with F-actin length. Plot of the dependence of the instantaneous speed on the filament size. Short filaments move faster than average. Long filaments move at the same speed in average., Ministerio de Ciencia, Innovación y Universidades, Peer reviewed

Additional file 6: Movie S3. Time-lapse movie of the system showing oscillations. Time-lapse movie of the system showing periodic assembly and disassembly of the actomyosin cortex (G-actin labeled in green, ACs labeled in red, Myosin labeled in red)., Ministerio de Ciencia, Innovación y Universidades, Peer reviewed

Additional file 7 Figure S4. Oscillations for different levels of Myosin. Oscillations in the number of G-actin in the cortex for conditions of (A) low and (B) high concentration of Myosin in the system. The corresponding Fourier transform for each oscillation is also shown., Ministerio de Ciencia, Innovación y Universidades, Peer reviewed

Additional file 8: Movie S4. Oscillations in D. Melanogaster basal follicle cells. Control conditions. Cells are stained with Lifeact-GFP., Ministerio de Ciencia, Innovación y Universidades, Peer reviewed

Additional file 12 Figure S7. Scheme of the effect of Myosin over F-actin. (1), Myosin movement in parallel f-Actin. (2) F-actin sliding. (3) Tension building in the filaments. (4) Release from cortex after threshold tension is reached. After release, tension (illustrated in red) is redistributed to other F-actin in the network., Ministerio de Ciencia, Innovación y Universidades, Peer reviewed

Autonomous oscillatory dynamics are ubiquitous at every level in Biology. At the cellular level, one of the most relevant and well characterized examples of periodic behavior is the cyclic assembly and disassembly of actomyosin networks. In Drosophila, these oscillations induce the robust contraction and expansion of individual cells required for correct dorsal closure, while in the follicular epithelium that surrounds the germline, periodic contractions of the basal actomyosin network are required for proper elongation of the egg chamber. While some studies suggest that actomyosin oscillations are driven by upstream signaling or mechanochemical features, we have recently proposed that they arise as a systems property from the competition between two well characterized features of the actomyosin machinery: 1) cooperative assembly of actin networks mediated by Actin crosslinker proteins and 2) tension-induced disassembly of actin networks mediated by myosin motors. Here, we perform experiments in amnioserosa and in the follicle cells of drosophila and simulations using a coarse-grained model of the actomyosin cortex to characterize the properties of the oscillations and how they depend on different features of the system. We also compare model and experiments to study the dynamics of actomyosin flows and the effect of mechanical coupling between cells in the tissue. In conclusion, our model is a powerful tool to study key features of actomyosin oscillations, from the effect of the individual components to network properties and finally supra-cellular organization of the oscillations at the tissue level., Peer reviewed