The granzyme B-Serpinb9 axis controls the fate of lymphocytes after lysosomal stress

Cytotoxic lymphocytes (CLs) contain lysosome-related organelles (LROs) that perform the normal degradative functions of the lysosome, in addition to storage and release of powerful cytotoxins employed to kill virally infected or abnormal cells. Among these cytotoxins is granzyme B (GrB), a protease that has also been implicated in activation (restimulation)-induced cell death of natural killer (NK) and T cells, but the underlying mechanism and its regulation are unclear. Here we show that restimulation of previously activated human or mouse lymphocytes induces lysosomal membrane permeabilisation (LMP), followed by GrB release from LROs into the CL cytosol. The model lysosomal stressors sphingosine and Leu-Leu-methyl-ester, and CLs from gene-targeted mice were used to show that LMP releases GrB in both a time- and concentration-dependent manner, and that the liberated GrB is responsible for cell death. The endogenous GrB inhibitor Serpinb9 (Sb9) protects CLs against LMP-induced death but is decreasingly effective as the extent of LMP increases. We also used these model stressors to show that GrB is the major effector of LMP-mediated death in T cells, but that in NK cells additional effectors are released, making GrB redundant. We found that limited LMP and GrB release occurs constitutively in proliferating lymphocytes and in NK cells engaged with targets in vitro. In Ectromelia virus-infected lymph nodes, working NK cells lacking Sb9 are more susceptible to GrB-mediated death. Taken together, these data show that a basal level of LMP occurs in proliferating and activated lymphocytes, and is increased on restimulation. LMP releases GrB from LROs into the lymphocyte cytoplasm and its ensuing interaction with Sb9 dictates whether or not the cell survives. The GrB-Sb9 nexus may therefore represent an additional mechanism of limiting lymphocyte lifespan and populations

Investigation into the Effects of Textural Properties on Cuttability Performance of a Chisel Tool

The main objective of this study is to investigate the effect of textural properties of stones on cutting performance of a standard chisel tool. Therewithal, the relationships between textural properties and cutting performance parameters and physical and mechanical properties were statistically analyzed. For this purpose, physical and mechanical property tests and mineralogical and petrographic analyses were carried out on eighteen natural stone samples, which can be grouped into three fundamentally different geological origins, i.e., metamorphic, igneous, and sedimentary. Then, texture coefficient analyses were performed on the samples. To determine the cuttability of the stones; the samples were cut with a portable linear cutting machine using a standard chisel tool at different depths of cut in unrelieved (non-interactive) cutting mode. The average and maximum forces (normal and cutting) and specific energy were measured, and the obtained values were correlated with texture coefficient, packing weighting, and grain size. With reference to the relation between depth of cut and cutting performance of the chisel tool for three types of natural stone groups, specific energy decreases with increasing depth of cut, and cutting forces increase in proportion to the depth of cut. The same is observed for the relationship between packing weighting and both of specific energy and cutter forces. On the other hand, specific energy and the forces decrease while grain size increases. Based on the findings of the present study, texture coefficient has strong correlation with specific energy. Generally, the lower depth of cut values in cutting tests shows higher and more reliable correlations with texture coefficient than the increased depth of cut. The results of cutting tests show also that, at a lower depth of cut (less than 1.5 mm), even stronger correlations can be observed between texture coefficient and cutting performance. Experimental studies indicate that cutting performance of chisel tools can be predicted based on texture coefficients of the natural stones