Genome-Wide Analyses of Aspartic Proteases on Potato Genome (Solanum tuberosum): Generating New Tools to Improve the Resistance of Plants to Abiotic Stress.

Aspartic proteases are proteolytic enzymes widely distributed in living organisms and viruses. Although they have been extensively studied in many plant species, they are poorly described in potatoes. The present study aimed to identify and characterize S. tuberosum aspartic proteases. Gene structure, chromosome and protein domain organization, phylogeny, and subcellular predicted localization were analyzed and integrated with RNAseq data from different tissues, organs, and conditions focused on abiotic stress. Sixty-two aspartic protease genes were retrieved from the potato genome, distributed in 12 chromosomes. A high number of intronless genes and segmental and tandem duplications were detected. Phylogenetic analysis revealed eight StAP groups, named from StAPI to StAPVIII, that were differentiated into typical (StAPI), nucellin-like (StAPIIIa), and atypical aspartic proteases (StAPII, StAPIIIb to StAPVIII). RNAseq data analyses showed that gene expression was consistent with the presence of cis-acting regulatory elements on StAP promoter regions related to water deficit. The study presents the first identification and characterization of 62 aspartic protease genes and proteins on the potato genome and provides the baseline material for functional gene determinations and potato breeding programs, including gene editing mediated by CRISPR.

Soil microbial communities and glyphosate decay in soils with different herbicide application history.

This study evaluates the glyphosate dissipation under field conditions in three types of soil, and aims to determine the importance of the following factors in the environmental persistence of herbicide: i) soil bacterial communities, ii) soil physicochemical properties, iii) previous exposure to the herbicide. A soil without previous record of GP application (P0) and two agricultural soils, with 5 and >10years of GP exposure (A5 and A10) were subjected to the application of glyphosate at doses of 3mg·kg-1. The concentration of GP and AMPA was determined over time and the dynamics of soil bacterial communities was evaluated using 16S ARN ribosomal gene amplicon-sequencing. The GP exposure history affected the rate but not the extent of GP biodegradation. The herbicide was degraded rapidly, but P0 soil showed a dissipation rate significantly lower than soils with agricultural history. In P0 soil, a significant increase in the relative abundance of Bacteroidetes was observed in response to herbicide application. More generally, all soils displayed shifts in bacterial community structure, which nevertheless could not be clearly associated to glyphosate dissipation, suggesting the presence of redundant bacteria populations of potential degraders. Yet the application of the herbicide prompted a partial disruption of the bacterial association network of unexposed soil. On the other hand, higher values of linear (Kd) and nonlinear (Kf) sorption coefficient in P0 point to the relevance of cation exchange capacity (CEC), clay and organic matter to the capacity of soil to adsorb the herbicide, suggesting that bioavailability was a key factor for the persistence of GP and AMPA. These results contribute to understand the relationship between bacterial taxa exposed to the herbicide, and the importance of soil properties as predictors of the possible rate of degradation and persistence of glyphosate in soil.

Developing integrated crop knowledge networks to advance candidate gene discovery.

The chances of raising crop productivity to enhance global food security would be greatly improved if we had a complete understanding of all the biological mechanisms that underpinned traits such as crop yield, disease resistance or nutrient and water use efficiency. With more crop genomes emerging all the time, we are nearer having the basic information, at the gene-level, to begin assembling crop gene catalogues and using data from other plant species to understand how the genes function and how their interactions govern crop development and physiology. Unfortunately, the task of creating such a complete knowledge base of gene functions, interaction networks and trait biology is technically challenging because the relevant data are dispersed in myriad databases in a variety of data formats with variable quality and coverage. In this paper we present a general approach for building genome-scale knowledge networks that provide a unified representation of heterogeneous but interconnected datasets to enable effective knowledge mining and gene discovery. We describe the datasets and outline the methods, workflows and tools that we have developed for creating and visualising these networks for the major crop species, wheat and barley. We present the global characteristics of such knowledge networks and with an example linking a seed size phenotype to a barley WRKY transcription factor orthologous to TTG2 from Arabidopsis, we illustrate the value of integrated data in biological knowledge discovery. The software we have developed (www.ondex.org) and the knowledge resources (http://knetminer.rothamsted.ac.uk) we have created are all open-source and provide a first step towards systematic and evidence-based gene discovery in order to facilitate crop improvement.