Transglutaminase 2 is involved in autophagosome maturation

Autophagy is a highly conserved cellular process responsible for the degradation of long-lived proteins and organelles. Autophagy occurs at low levels under normal conditions, but it is enhanced in response to stress, e.g. nutrient deprivation, hypoxia, mitochondrial dysfunction and infection. "Tissue" transglutaminase (TG2) accumulates, both in vivo and in vitro, to high levels in cells under stressful conditions. Therefore, in this study, we investigated whether TG2 could also play a role in the autophagic process. To this end, we used TG2 knockout mice and cell lines in which the enzyme was either absent or overexpressed. The ablation of TG2 protein both in vivo and in vitro, resulted in an evident accumulation of microtubule-associated protein 1 light chain 3 cleaved isoform II (LC3 II) on pre-autophagic vesicles, suggesting a marked induction of autophagy. By contrast, the formation of the acidic vesicular organelles in the same cells was very limited, indicating an impairment of the final maturation of autophagolysosomes. In fact, the treatment of TG2 proficient cells with NH4Cl, to inhibit lysosomal activity, led to a marked accumulation of LC3 II and damaged mitochondria similar to what we observed in TG2-deficient cells. These data indicate a role for TG2-mediated post-translational modifications of proteins in the maturation of autophagosomes accompanied by the accumulation of many damaged mitochondria.

Regrouping metric-space search index for search engine size adaptation

This work contributes to the development of search engines that self-adapt their size in response to fluctuations in workload. Deploying a search engine in an Infrastructure as a Service (IaaS) cloud facilitates allocating or deallocating computational resources to or from the engine. In this paper, we focus on the problem of regrouping the metric-space search index when the number of virtual machines used to run the search engine is modified to reflect changes in workload. We propose an algorithm for incrementally adjusting the index to fit the varying number of virtual machines. We tested its performance using a custom-build prototype search engine deployed in the Amazon EC2 cloud, while calibrating the results to compensate for the performance fluctuations of the platform. Our experiments show that, when compared with computing the index from scratch, the incremental algorithm speeds up the index computation 2–10 times while maintaining a similar search performance.