Corrado, G. | bioRxiv Science

bioRxiv2021

Tomato Prosystemin is much more than a simple Systemin precursor

Systemin (Sys) is an octadecapeptide which, upon wounding, is released from the carboxy terminus of its precursor, prosystemin(ProSys) to promote plant defenses. Recent findings on the disordered structure of ProSysprompted us to investigate a putative biological role of the whole precursor deprived of Sys peptide. We produced transgenic tomato plants expressing a truncated ProSys gene in which the exon coding for Sys was removed and compared their defense response with that induced by the exogenous application of the recombinant deleted ProSys[ProSys(1-178)].By combining protein structure analyses, transcriptomic analysis, gene expression profiling and bioassays with different pests we demonstrate that the truncated ProSys, that does not induce the endogenous ProSys gene, promotes defense barriers in tomato plants through a hormone independent defense pathway, likely associated with the production of oligogalacturonides (OGs). Both transgenic and plants treated with the recombinant protein showed the modulation of the expression of genes linked with defense responses and resulted protected against the lepidopteran pest Spodoptera littoralis and the fungus Botrytis cinerea. Our results suggest that the overall function of the wild type prosystemin is more complex than previously shown as it might activate at least two tomato defense pathways: the well-known Sys-dependent pathway connected with the induction of JA biosynthesis and the successive activation of a set of defense-related genes and the ProSys(1-178)-dependent pathway associated with OGs production leading to the OGs mediate plant immunity.

genetics↗

bioRxiv2017

Learning Fast And Slow: Deviations From The Matching Law Can Reflect An Optimal Strategy Under Uncertainty

Behavior which deviates from our normative expectations often appears irrational. A classic example concerns the question of how choice should be distributed among multiple alternatives. The so-called matching law predicts that the fraction of choices made to any option should match the fraction of total rewards earned from the option. This choice strategy can maximize reward in a stationary reward schedule. Empirically, however, behavior often deviates from this ideal. While such deviations have often been interpreted as reflecting noisy, suboptimal, decision-making, here we instead suggest that they reflect a strategy which is adaptive in nonstationary and uncertain environments. We analyze the results of a dynamic foraging task. Animals exhibited significant deviations from matching, and animals turned out to be able to collect more rewards when deviation was larger. We show that this behavior can be understood if one considers that animals had incomplete information about the environments dynamics. In particular, using computational models, we show that in such nonstationary environments, learning on both fast and slow timescales is beneficial. Learning on fast timescales means that an animal can react to sudden changes in the environment, though this inevitably introduces large fluctuations (variance) in value estimates. Concurrently, learning on slow timescales reduces the amplitude of these fluctuations at the price of introducing a bias that causes systematic deviations. We confirm this prediction in data - monkeys indeed solved the bias-variance tradeoff by combining learning on both fast and slow timescales. Our work suggests that multi-timescale learning could be a biologically plausible mechanism for optimizing decisions under uncertainty.

neuroscience↗