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Cross-linking of proteins by mammalian transglutaminases (TGs) plays important roles in physiological phenomena such as blood coagulation and skin formation. We show that Drosophila TG suppressed innate immune signaling in the gut. RNA interference (RNAi) directed against TG reduced the life span of flies reared under conventional nonsterile conditions but not of those raised under germ-free conditions. In conventionally reared flies, TG RNAi enhanced the expression of genes encoding antimicrobial peptides in the immune deficiency (IMD) pathway. Wild-type flies that ingested gut lysates prepared from conventionally reared TG RNAi-treated flies had shorter life spans. In conventionally reared flies, TG RNAi triggered apoptosis in the gut and induced the nuclear translocation of Relish, the NF-κB (nuclear factor κB)-like transcription factor of the IMD pathway. Wild-type flies that ingested synthetic amine donors, which inhibit the TG-catalyzed protein-protein cross-linking reaction, showed nuclear translocation of Relish and enhanced expression of genes encoding IMD-controlled antimicrobial peptide genes in the gut. We conclude that TG-catalyzed Relish cross-linking suppressed the IMD signaling pathway to enable immune tolerance against commensal microbes.

Corrosion science has been based mainly upon deterministic approaches, particularly the electrochemical theory of corrosion. Localized corrosion, however, cannot be explained without statistical and stochastic points of view because of the large scatter in data common in the laboratory and the field. Toshio Shibata was the 1996 recipient of the W.R. Whitney Award sponsored by NACE International. In his award lecture at CORROSION/96, Shibata reviewed successful applications of statistical approaches to localized corrosion in engineering data and presented a stochastic theory of pitting corrosion based upon sensitivity analysis of parameters in the stochastic model that rationally could explain statistical distributions of pitting potential (E pit ) and induction time for pit formation. The most successful application in the statistical analysis was found in the extreme-value analysis using the Gumbel distribution to estimate the maximum pit depth that will be found in a largearea installation by using a small number of samples with a small area. A birth and death stochastic process model was developed to explain the statistical distribution of E pit and induction time for pit generation. Effects of potential sweep velocity (v), surface area (S), alloying elements, and solution flow velocity on E pit were explained. A Monte Carlo simulation using the birth and death model simulated the observed distribution. Image analysis of stainless steels (SS) exposed to an atmospheric environment was discussed as a means to characterize rust spot distribution.