SUMMARYAbscisic acid (ABA) is a major phytohormone involved in important stress-related and developmental plant processes. Recent phosphoproteomic analyses revealed a large set of ABA-triggered phosphoproteins as putative mitogen-activated protein kinase (MAPK) targets, although the evidence for MAPKs involved in ABA signalling is still scarce. Here, we identified and reconstituted in vivo a complete ABA-activated MAPK cascade, composed of the MAP3Ks MAP3K17/18, the MAP2K MKK3 and the four C group MAPKs MPK1/2/7/ 14. In planta, we show that ABA activation of MPK7 is blocked in mkk3-1 and map3k17mapk3k18 plants. Coherently, both mutants exhibit hypersensitivity to ABA and altered expression of a set of ABA-dependent genes. A genetic analysis further reveals that this MAPK cascade is activated by the PYR/PYL/RCAR-SnRK2-PP2C ABA core signalling module through protein synthesis of the MAP3Ks, unveiling an atypical mechanism for MAPK activation in eukaryotes. Our work provides evidence for a role of an ABA-induced MAPK pathway in plant stress signalling.

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MotivationMost computational approaches for the analysis of omics data in the context of interaction networks have very long running times, provide single or partial, often heuristic, solutions and/or contain user-tuneable parameters.ResultsWe introduce local enrichment analysis (LEAN) for the identification of dysregulated subnetworks from genome-wide omics datasets. By substituting the common subnetwork model with a simpler local subnetwork model, LEAN allows exact, parameter-free, efficient and exhaustive identification of local subnetworks that are statistically dysregulated, and directly implicates single genes for follow-up experiments.Evaluation on simulated and biological data suggests that LEAN generally detects dysregulated subnetworks better, and reflects biological similarity between experiments more clearly than standard approaches. A strong signal for the local subnetwork around Von Willebrand Factor (VWF), a gene which showed no change on the mRNA level, was identified by LEAN in transcriptome data in the context of the genetic disease Cerebral Cavernous Malformations (CCM). This signal was experimentally found to correspond to an unexpected strong cellular effect on the VWF protein. LEAN can be used to pinpoint statistically significant local subnetworks in any genome-scale dataset.Availability and ImplementationThe R-package LEANR implementing LEAN is supplied as supplementary material and available on CRAN (https://cran.r-project.org).Supplementary information Supplementary data are available at Bioinformatics online.

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Human biomonitoring (HBM) depends on high-quality human samples to identify status and trends in exposure and ensure comparability of results. In this context, much effort has been put into the development of standardized processes and quality assurance for sampling and chemical analysis, while effects of sample storage and shipment on sample quality have been less thoroughly addressed. To characterize the currently applied storage and shipment procedures within the consortium of the European Human Biomonitoring Initiative (HBM4EU), which aims at harmonization of HBM in Europe, a requirement analysis based on data from an online survey was conducted. In addition, the online survey was addressed to professionals in clinical biobanking represented by members of the European, Middle Eastern and African Society for Biopreservation and Biobanking (ESBB) to identify the current state-of-the-art in terms of sample storage and shipment. Results of this survey conducted in these two networks were compared to detect processes with potential for optimization and harmonization. In general, many similarities exist in sample storage and shipment procedures applied by ESBB members and HBM4EU partners and many requirements for ensuring sample quality are already met also by HBM4EU partners. Nevertheless, a need for improvement was identified for individual steps in sample storage, shipment, and related data management with potential impact on sample and data quality for HBM purposes. Based on these findings, recommendations for crucial first steps to further strengthen sample quality, and thus foster advancement in HBM on a pan-European level are given.

In this study, we developed a novel computational approach based on protein-protein interaction (PPI) networks to identify a list of proteins that might have remained undetected in differential proteomic profiling experiments. We tested our computational approach on two sets of human smooth muscle cell (SMC) protein extracts which were affected differently by DNase I treatment. Differential proteomic analysis by saturation DIGE resulted in the identification of 41 human proteins. The application of our approach to these 41 input proteins consisted of four steps: 1) Compilation of a human PPI network from public databases, 2) Calculation of interaction scores based on functional similarity, 3) Determination of a set of candidate proteins that are needed to efficiently and confidently connect the 41 input proteins, and 4) Ranking of the resulting 25 candidate proteins. Two of the three highest-ranked proteins, beta-arrestin 1 and beta-arrestin 2, were experimentally tested, revealing that their abundance levels in human SMC samples were indeed affected by DNase I treatment. These proteins had not been detected during the experimental proteomic analysis. Our study suggests that our computational approach may represent a simple, universal, and cost-effective means to identify additional proteins that remain elusive for current 2D gel-based proteomic profiling techniques.