R-catcher, a potent molecular tool to unveil the arginylome.

Protein arginylation is a critical regulator of a variety of biological processes. The ability to uncover the global arginylation pattern and its associated signaling pathways would enable us to identify novel disease targets. Here, we report the development of a tool able to capture the N-terminal arginylome. This tool, termed R-catcher, is based on the ZZ domain of p62, which was previously shown to bind N-terminally arginylated proteins. Mutating the ZZ domain enhanced its binding specificity and affinity for Nt-Arg. R-catcher pulldown coupled to LC-MS/MS led to the identification of 59 known and putative arginylated proteins. Among these were a subgroup of novel ATE1-dependent arginylated ER proteins that are linked to diverse biological pathways, including cellular senescence and vesicle-mediated transport as well as diseases, such as Amyotrophic Lateral Sclerosis and Alzheimer's disease. This study presents the first molecular tool that allows the unbiased identification of arginylated proteins, thereby unlocking the arginylome and provide a new path to disease biomarker discovery.

A condition metric for Eucalyptus woodland derived from expert evaluations.

The evaluation of ecosystem quality is important for land-management and land-use planning. Evaluation is unavoidably subjective, and robust metrics must be based on consensus and the structured use of observations. We devised a transparent and repeatable process for building and testing ecosystem metrics based on expert data. We gathered quantitative evaluation data on the quality of hypothetical grassy woodland sites from experts. We used these data to train a model (an ensemble of 30 bagged regression trees) capable of predicting the perceived quality of similar hypothetical woodlands based on a set of 13 site variables as inputs (e.g., cover of shrubs, richness of native forbs). These variables can be measured at any site and the model implemented in a spreadsheet as a metric of woodland quality. We also investigated the number of experts required to produce an opinion data set sufficient for the construction of a metric. The model produced evaluations similar to those provided by experts, as shown by assessing the model's quality scores of expert-evaluated test sites not used to train the model. We applied the metric to 13 woodland conservation reserves and asked managers of these sites to independently evaluate their quality. To assess metric performance, we compared the model's evaluation of site quality with the managers' evaluations through multidimensional scaling. The metric performed relatively well, plotting close to the center of the space defined by the evaluators. Given the method provides data-driven consensus and repeatability, which no single human evaluator can provide, we suggest it is a valuable tool for evaluating ecosystem quality in real-world contexts. We believe our approach is applicable to any ecosystem.