The Choice between MapMan and Gene Ontology for Automated Gene Function Prediction in Plant Science

Since the introduction of the Gene Ontology (GO), the analysis of high-throughput data has become tightly coupled with the use of ontologies to establish associations between knowledge and data in an automated fashion. Ontologies provide a systematic description of knowledge by a controlled vocabulary of defined structure in which ontological concepts are connected by pre-defined relationships. In plant science, MapMan and GO offer two alternatives for ontology-driven analyses. Unlike GO, initially developed to characterize microbial systems, MapMan was specifically designed to cover plant-specific pathways and processes. While the dependencies between concepts in MapMan are modeled as a tree, in GO these are captured in a directed acyclic graph. Therefore, the difference in ontologies may cause discrepancies in data reduction, visualization, and hypothesis generation. Here provide the first systematic comparative analysis of GO and MapMan for the case of the model plant species Arabidopsis thaliana (Arabidopsis) with respect to their structural properties and difference in distributions of information content. In addition, we investigate the effect of the two ontologies on the specificity and sensitivity of automated gene function prediction via the coupling of co-expression networks and the guilt-by-association principle. Automated gene function prediction is particularly needed for the model plant Arabidopsis in which only half of genes have been functionally annotated based on sequence similarity to known genes. The results highlight the need for structured representation of species-specific biological knowledge, and warrants caution in the design principles employed in future ontologies

Additive Manufacturing and Vulcanization of Carbon Black Filled Natural Rubber Based Components

Additive manufacturing of thermoplastics or metals is a well-approved sustainable process for obtaining rapidly precise and individual technical components. Except for crosslinked silicone rubber or thermoplastic elastomers, there is no method of additive manufacturing of elastomers. Based on the development of the additive manufacturing of elastomers (AME) process, the material group of rubber-based cured elastomers may gain first access to the process field of three-dimensional (3D) printing. Printing and crosslinking of rubber is separated into two steps. In the first step, printing is realized by extrusion of the rubber by using a twin-screw extruder, which works according to the derived fused-filament-fabrication principle. In the second step, the component is vulcanized in a high-pressure hot-air autoclave. Because of the plastic flow behavior of non–crosslinked rubber materials, a thermoplastic shell is probably needed to maintain the geometry and position of the additively manufactured rubber. In this way, one layer of thermoplastic and one layer of rubber are printed alternatingly until the component is finished. Afterward, the manufactured binary component is placed in an autoclave to obtain the elastomer after vulcanization under a hot-air and high-pressure atmosphere. Then, the thermoplastic shell is removed from the elastomer and can subsequently be recycled. As compared with conventional thermoplastics, the high viscosity of rubber during processing and its instable shape after extrusion are challenging factors in the development of the AME. This contribution will show a modified 3D printer; explain the printing process from the designed component, via shell generation, to the vulcanized component; and show first printed components

Analysis of the Compartmentalized Metabolome – A Validation of the Non-Aqueous Fractionation Technique

With the development of high-throughput metabolic technologies, a plethora of primary and secondary compounds have been detected in the plant cell. However, there are still major gaps in our understanding of the plant metabolome. This is especially true with regards to the compartmental localization of these identified metabolites. Non-aqueous fractionation (NAF) is a powerful technique for the determination of subcellular metabolite distributions in eukaryotic cells, and it has become the method of choice to analyze the distribution of a large number of metabolites concurrently. However, the NAF technique produces a continuous gradient of metabolite distributions, not discrete assignments. Resolution of these distributions requires computational analyses based on marker molecules to resolve compartmental localizations. In this article we focus on expanding the computational analysis of data derived from NAF. Along with an experimental workflow, we describe the critical steps in NAF experiments and how computational approaches can aid in assessing the quality and robustness of the derived data. For this, we have developed and provide a new version (v1.2) of the BestFit command line tool for calculation and evaluation of subcellular metabolite distributions. Furthermore, using both simulated and experimental data we show the influence on estimated subcellular distributions by modulating important parameters, such as the number of fractions taken or which marker molecule is selected. Finally, we discuss caveats and benefits of NAF analysis in the context of the compartmentalized metabolome

Conserved changes in dynamics of metabolic processes during fruit development and ripening across species

Computational analyses of molecular phenotypes traditionally aim at identifying biochemical components that exhibit differential expression under various scenarios (e.g. environmental and internal perturbations) in a single species. High-throughput metabolomics technologies allow the quantification of (relative) metabolite levels across developmental stages in different tissues, organs, and species. Novel methods for analyzing the resulting multiple data tables could reveal preserved dynamics of metabolic processes across species. The problem we address in this study is 2-fold. (1) We derive a single data table, referred to as a compromise, which captures information common to the investigated set of multiple tables containing data on different fruit development and ripening stages in three climacteric (i.e. peach [Prunus persica] and two tomato [Solanum lycopersicum] cultivars, Ailsa Craig and M82) and two nonclimacteric (i.e. strawberry [Fragaria × ananassa] and pepper [Capsicum chilense]) fruits; in addition, we demonstrate the power of the method to discern similarities and differences between multiple tables by analyzing publicly available metabolomics data from three tomato ripening mutants together with two tomato cultivars. (2) We identify the conserved dynamics of metabolic processes, reflected in the data profiles of the corresponding metabolites that contribute most to the determined compromise. Our analysis is based on an extension to principal component analysis, called STATIS, in combination with pathway overenrichment analysis. Based on publicly available metabolic profiles for the investigated species, we demonstrate that STATIS can be used to identify the metabolic processes whose behavior is similarly affected during fruit development and ripening. These findings ultimately provide insights into the pathways that are essential during fruit development and ripening across species.Fil: Klie, Sebastian. Max Planck Institute of Molecular Plant Physiology; AlemaniaFil: Osorio, Sonia. Consejo Superior de Investigaciones Cientificas. Instituto de Hortofruticultura Subtropical y Mediterránea; EspañaFil: Tohge, Takayuki. Max Planck Institute of Molecular Plant Physiology; AlemaniaFil: Drincovich, Maria Fabiana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (i); ArgentinaFil: Fait, Aaron. Ben-Gurion University of the Negrev; IsraelFil: Giovannoni, Federico. Cornell University; Estados UnidosFil: Fernie, Alisdair R.. Max Planck Institute of Molecular Plant Physiology; AlemaniaFil: Nikoloski, Zoran. Max Planck Institute of Molecular Plant Physiology; Alemani

Glucocorticoid (dexamethasone)-induced metabolome changes in healthy males suggest prediction of response and side effects

Glucocorticoids are indispensable anti-inflammatory and decongestant drugs with high prevalence of use at (similar to)0.9% of the adult population. Better holistic insights into glucocorticoid-induced changes are crucial for effective use as concurrent medication and management of adverse effects. The profiles of 214 metabolites from plasma of 20 male healthy volunteers were recorded prior to and after ingestion of a single dose of 4 mg dexamethasone (+20 mg pantoprazole). Samples were drawn at three predefined time points per day: seven untreated (day 1 midday - day 3 midday) and four treated (day 3 evening - day 4 evening) per volunteer. Statistical analysis revealed tremendous impact of dexamethasone on the metabolome with 150 of 214 metabolites being significantly deregulated on at least one time point after treatment (ANOVA, Benjamini-Hochberg corrected, q < 0.05). Inter-person variability was high and remained uninfluenced by treatment. The clearly visible circadian rhythm prior to treatment was almost completely suppressed and deregulated by dexamethasone. The results draw a holistic picture of the severe metabolic deregulation induced by single-dose, short-term glucocorticoid application. The observed metabolic changes suggest a potential for early detection of severe side effects, raising hope for personalized early countermeasures increasing quality of life and reducing health care costs

Tailoring the Curing Kinetics of NBR-Based Rubber Compounds for Additive Manufacturing of Rod Seals

The additive manufacturing (AM) of elastomeric parts based on high-viscosity reinforced rubbers has increasingly become a topic of scientific research in recent years. In addition to the viscosity, which is several decades higher during processing than the viscosities of thermoplastics, the flowability of the compound after the printing process and the necessary chemical crosslinking of the printed component play a decisive role in producing an elastic, high-quality, and geometrically stable part. After the first technological achievements using the so-called additive manufacturing of elastomers (AME) process, the knowledge gained has to be transferred first to concrete industrial parts. Therefore, in this study, the cure kinetics of a conventional rubber compound are tailored to match the specific requirements for scorch safety in the additive manufacturing of an industrial 2-component rod seal based on an acrylonitrile butadiene rubber O-ring in combination with a thermoplastic polyurethane as the base body. Experimental tests on a test rig for rod seals demonstrate the functionality of this additively manufactured 2-component rod seal

Metabolomic and transcriptomic stress response of Escherichia coli

GC-MS-based analysis of the metabolic response of Escherichia coli exposed to four different stress conditions reveals reduction of energy expensive pathways.Time-resolved response of E. coli to changing environmental conditions is more specific on the metabolite as compared with the transcript level.Cease of growth during stress response as compared with stationary phase response invokes similar transcript but dissimilar metabolite responses.Condition-dependent associations between metabolites and transcripts are revealed applying co-clustering and canonical correlation analysis

Concurrent Conditional Clustering of Multiple Networks: COCONETS

<div><p>The accumulation of high-throughput data from different experiments has facilitated the extraction of condition-specific networks over the same set of biological entities. Comparing and contrasting of such multiple biological networks is in the center of differential network biology, aiming at determining general and condition-specific responses captured in the network structure (<i>i.e.</i>, included associations between the network components). We provide a novel way for comparison of multiple networks based on determining network clustering (<i>i.e.</i>, partition into communities) which is optimal across the set of networks with respect to a given cluster quality measure. To this end, we formulate the optimization-based problem of concurrent conditional clustering of multiple networks, termed COCONETS, based on the modularity. The solution to this problem is a clustering which depends on all considered networks and pinpoints their preserved substructures. We present theoretical results for special classes of networks to demonstrate the implications of conditionality captured by the COCONETS formulation. As the problem can be shown to be intractable, we extend an existing efficient greedy heuristic and applied it to determine concurrent conditional clusters on coexpression networks extracted from publically available time-resolved transcriptomics data of <i>Escherichia coli</i> under five stresses as well as on metabolite correlation networks from metabolomics data set from <i>Arabidopsis thaliana</i> exposed to eight environmental conditions. We demonstrate that the investigation of the differences between the clustering based on all networks with that obtained from a subset of networks can be used to quantify the specificity of biological responses. While a comparison of the <i>Escherichia coli</i> coexpression networks based on seminal properties does not pinpoint biologically relevant differences, the common network substructures extracted by COCONETS are supported by existing experimental evidence. Therefore, the comparison of multiple networks based on concurrent conditional clustering offers a novel venue for detection and investigation of preserved network substructures.</p></div