This study focuses on the specific problems of protein extraction from recalcitrant plant tissues and evaluates several methods to bypass them. Sample preparation is a critical step in a two-dimensional gel electrophoresis proteome approach and is absolutely essential for good results. We evaluated four methods: the classical trichloroacetic acid (TCA)/acetone precipitation, TCA/acetone precipitation and fractionation, an alternative based on fractionation and without precipitation, and phenol extraction methanol/ammonium acetate precipitation. We optimized the phenol extraction protocol for small amounts of tissue, which is essential when the study material is limited. The protocol was optimized for banana (Musa spp.) and was subsequently applied to two other plant species: apple (Malus domestica L.) and potato (Solanum tuberosum L.). Banana (Musa spp.) is a good representative of a "difficult" plant species since it contains many interfering metabolites. Only classical TCA/acetone precipitation and phenol extraction methods proved useful as standard methods. Both methods are associated with a minor but reproducible loss of proteins. Every extraction method and the subsequent analytical procedure have their physicochemical limitations; both methods should be investigated before selecting an appropriate protocol. The study, which is presented in this paper, is useful for guiding the experimental setup of many other nonmodel species, containing various interfering elements.

Aggregation is a sequence-specific process, nucleated by short aggregation-prone regions (APRs) that can be exploited to induce aggregation of proteins containing the same APR. Here, we find that most APRs are unique within a proteome, but that a small minority of APRs occur in many proteins. When aggregation is nucleated in bacteria by such frequently occurring APRs, it leads to massive and lethal inclusion body formation containing a large number of proteins. Buildup of bacterial resistance against these peptides is slow. In addition, the approach is effective against drug-resistant clinical isolates of Escherichia coli and Acinetobacter baumannii, reducing bacterial load in a murine bladder infection model. Our results indicate that redundant APRs are weak points of bacterial protein homeostasis and that targeting these may be an attractive antibacterial strategy.

Introduction of microbial trehalose biosynthesis enzymes has been reported to enhance abiotic stress resistance in plants but also resulted in undesirable traits. Here, we present an approach for engineering drought stress tolerance by modifying the endogenous trehalase activity in Arabidopsis (Arabidopsis thaliana). AtTRE1 encodes the Arabidopsis trehalase, the only enzyme known in this species to specifically hydrolyze trehalose into glucose. AtTRE1-overexpressing and Attre1 mutant lines were constructed and tested for their performance in drought stress assays. AtTRE1-overexpressing plants had decreased trehalose levels and recovered better after drought stress, whereas Attre1 mutants had elevated trehalose contents and exhibited a drought-susceptible phenotype. Leaf detachment assays showed that Attre1 mutants lose water faster than wild-type plants, whereas AtTRE1-overexpressing plants have a better water-retaining capacity. In vitro studies revealed that abscisic acidmediated closure of stomata is impaired in Attre1 lines, whereas the AtTRE1 overexpressors are more sensitive toward abscisic acid-dependent stomatal closure. This observation is further supported by the altered leaf temperatures seen in trehalase-modified plantlets during in vivo drought stress studies. Our results show that overexpression of plant trehalase improves drought stress tolerance in Arabidopsis and that trehalase plays a role in the regulation of stomatal closure in the plant drought stress response.

Stomata are microscopic pores found on the surfaces of leaves that act to control CO 2 uptake and water loss. By integrating information derived from endogenous signals with cues from the surrounding environment, the guard cells, which surround the pore, 'set' the stomatal aperture to suit the prevailing conditions. Much research has concentrated on understanding the rapid intracellular changes that result in immediate changes to the stomatal aperture. In this study, we look instead at how stomata acclimate to longer timescale variations in their environment. We show that the closure-inducing signals abscisic acid (ABA), increased CO 2 , decreased relative air humidity and darkness each access a unique gene network made up of clusters (or modules) of common cellular processes. However, within these networks some gene clusters are shared amongst all four stimuli. All stimuli modulate the expression of members of the PYR/PYL/RCAR family of ABA receptors. However, they are modulated differentially in a stimulus-specific manner. Of the six members of the PYR/PYL/RCAR family expressed in guard cells, PYL2 is sufficient for guard cell ABA-induced responses, whereas in the responses to CO 2 , PYL4 and PYL5 are essential. Overall, our work shows the importance of ABA as a central regulator and integrator of long-term changes in stomatal behaviour, including sensitivity, elicited by external signals. Understanding this architecture may aid in breeding crops with improved water and nutrient efficiency.

BackgroundThe purpose of this manuscript is to provide, based on an extensive analysis of a proteomic data set, suggestions for proper statistical analysis for the discovery of sets of clinically relevant biomarkers. As tractable example we define the measurable proteomic differences between apparently healthy adult males and females. We choose urine as body-fluid of interest and CE-MS, a thoroughly validated platform technology, allowing for routine analysis of a large number of samples. The second urine of the morning was collected from apparently healthy male and female volunteers (aged 21-40) in the course of the routine medical check-up before recruitment at the Hannover Medical School.ResultsWe found that the Wilcoxon-test is best suited for the definition of potential biomarkers. Adjustment for multiple testing is necessary. Sample size estimation can be performed based on a small number of observations via resampling from pilot data. Machine learning algorithms appear ideally suited to generate classifiers. Assessment of any results in an independent test-set is essential.ConclusionsValid proteomic biomarkers for diagnosis and prognosis only can be defined by applying proper statistical data mining procedures. In particular, a justification of the sample size should be part of the study design.