Structural annotation of electro- and photochemically generated transformation products of moxidectin using high-resolution mass spectrometry

Moxidectin (MOX) is a widely used anthelmintic drug for the treatment of internal and external parasites in food-producing and companion animals. Transformation products (TPs) of MOX, formed through metabolic degradation or acid hydrolysis, may pose a potential environmental risk, but only few were identified so far. In this study, we therefore systematically characterized electro- and photochemically generated MOX TPs using high-resolution mass spectrometry (HRMS). Oxidative electrochemical (EC) TPs were generated in an electrochemical reactor and photochemical (PC) TPs by irradiation with UV-C light. Subsequent HRMS measurements were performed to identify accurate masses and deduce occurring modification reactions of derived TPs in a suspected target analysis. In total, 26 EC TPs and 59 PC TPs were found. The main modification reactions were hydroxylation, (de-)hydration, and derivative formation with methanol for EC experiments and isomeric changes, (de-)hydration, and changes at the methoxime moiety for PC experiments. In addition, several combinations of different modification reactions were identified. For 17 TPs, we could predict chemical structures through interpretation of acquired MS/MS data. Most modifications could be linked to two specific regions of MOX. Some previously described metabolic reactions like hydroxylation or O-demethylation were confirmed in our EC and PC experiments as reaction type, but the corresponding TPs were not identical to known metabolites or degradation products. The obtained knowledge regarding novel TPs and reactions will aid to elucidate the degradation pathway of MOX which is currently unknown

QTL analysis of early stage heterosis for biomass in Arabidopsis

The main objective of this study was to identify genomic regions involved in biomass heterosis using QTL, generation means, and mode-of-inheritance classification analyses. In a modified North Carolina Design III we backcrossed 429 recombinant inbred line and 140 introgression line populations to the two parental accessions, C24 and Col-0, whose F1 hybrid exhibited 44% heterosis for biomass. Mid-parent heterosis in the RILs ranged from −31 to 99% for dry weight and from −58 to 143% for leaf area. We detected ten genomic positions involved in biomass heterosis at an early developmental stage, individually explaining between 2.4 and 15.7% of the phenotypic variation. While overdominant gene action was prevalent in heterotic QTL, our results suggest that a combination of dominance, overdominance and epistasis is involved in biomass heterosis in this Arabidopsis cross

Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers

A recombinant inbred line (RIL) population, derived from two Arabidopsis thaliana accessions, and the corresponding testcrosses with these two original accessions were used for the development and validation of machine learning models to predict the biomass of hybrids. Genetic and metabolic information of the RILs served as predictors. Feature selection reduced the number of variables (genetic and metabolic markers) in the models by more than 80% without impairing the predictive power. Thus, potential biomarkers have been revealed. Metabolites were shown to bear information on inherited macroscopic phenotypes. This proof of concept could be interesting for breeders. The example population exhibits substantial mid-parent biomass heterosis. The results of feature selection could therefore be used to shed light on the origin of heterosis. In this respect, mainly dominance effects were detected

Improved Heterosis Prediction by Combining Information on DNA- and Metabolic Markers

Background: Hybrids represent a cornerstone in the success story of breeding programs. The fundamental principle underlying this success is the phenomenon of hybrid vigour, or heterosis. It describes an advantage of the offspring as compared to the two parental lines with respect to parameters such as growth and resistance against abiotic or biotic stress. Dominance, overdominance or epistasis based models are commonly used explanations. Conclusion/Significance: The heterosis level is clearly a function of the combination of the parents used for offspring production. This results in a major challenge for plant breeders, as usually several thousand combinations of parents have to be tested for identifying the best combinations. Thus, any approach to reliably predict heterosis levels based on properties of the parental lines would be highly beneficial for plant breeding. Methodology/Principal Findings: Recently, genetic data have been used to predict heterosis. Here we show that a combination of parental genetic and metabolic markers, identified via feature selection and minimum-description-length based regression methods, significantly improves the prediction of biomass heterosis in resulting offspring. These findings will help furthering our understanding of the molecular basis of heterosis, revealing, for instance, the presence of nonlinear genotype-phenotype relationships. In addition, we describe a possible approach for accelerated selection in plant breeding

Decision tree supported substructure prediction of metabolites from GC-MS profiles

Gas chromatography coupled to mass spectrometry (GC-MS) is one of the most widespread routine technologies applied to the large scale screening and discovery of novel metabolic biomarkers. However, currently the majority of mass spectral tags (MSTs) remains unidentified due to the lack of authenticated pure reference substances required for compound identification by GC-MS. Here, we accessed the information on reference compounds stored in the Golm Metabolome Database (GMD) to apply supervised machine learning approaches to the classification and identification of unidentified MSTs without relying on library searches. Non-annotated MSTs with mass spectral and retention index (RI) information together with data of already identified metabolites and reference substances have been archived in the GMD. Structural feature extraction was applied to sub-divide the metabolite space contained in the GMD and to define the prediction target classes. Decision tree (DT)-based prediction of the most frequent substructures based on mass spectral features and RI information is demonstrated to result in highly sensitive and specific detections of sub-structures contained in the compounds. The underlying set of DTs can be inspected by the user and are made available for batch processing via SOAP (Simple Object Access Protocol)-based web services. The GMD mass spectral library with the integrated DTs is freely accessible for non-commercial use at http://gmd.mpimp-golm.mpg.de/. All matching and structure search functionalities are available as SOAP-based web services. A XML + HTTP interface, which follows Representational State Transfer (REST) principles, facilitates read-only access to data base entities

Translatome and metabolome effects triggered by gibberellins during rosette growth in Arabidopsis

Although gibberellins (GAs) are well known for their growth control function, little is known about their effects on primary metabolism. Here the modulation of gene expression and metabolic adjustment in response to changes in plant (Arabidopsis thaliana) growth imposed on varying the gibberellin regime were evaluated. Polysomal mRNA populations were profiled following treatment of plants with paclobutrazol (PAC), an inhibitor of GA biosynthesis, and gibberellic acid (GA3) to monitor translational regulation of mRNAs globally. Gibberellin levels did not affect levels of carbohydrates in plants treated with PAC and/or GA3. However, the tricarboxylic acid cycle intermediates malate and fumarate, two alternative carbon storage molecules, accumulated upon PAC treatment. Moreover, an increase in nitrate and in the levels of the amino acids was observed in plants grown under a low GA regime. Only minor changes in amino acid levels were detected in plants treated with GA3 alone, or PAC plus GA3. Comparison of the molecular changes at the transcript and metabolite levels demonstrated that a low GA level mainly affects growth by uncoupling growth from carbon availability. These observations, together with the translatome changes, reveal an interaction between energy metabolism and GA-mediated control of growth to coordinate cell wall extension, secondary metabolism, and lipid metabolism

XIII. International Conference on Logistics in Agriculture 2019

The 13th International Conference on Logistics in Agriculture, which has been organized by the Municipality of Sevnica, Grm Novo mesto - Biotechnology and Tourism Center, Faculty of Logistics, University of Maribor, Landscape Governance College GRM, Cooperative Union of Slovenia has this year's central theme the role and importance of human resources in logistics in agriculture. The conference has become traditional and paves the way for a different view of logistics in connection with agriculture. That's why we have invited lecturers on the topic Electric vehicles in agriculture. 13. mednarodna konferenca Logistika v kmetijstvu, ki jo organiziramo ObÄina Sevnica, Grm Novo mesto â center biotehnike in turizma, Fakulteta za logistiko Univerze v Mariboru, Visoka Å¡ola za upravljanje podeželja GRM Novo mesto in Zadružna zveza Slovenije ima letoÅ¡njo osrednjo temo vloga in pomen ÄloveÅ¡kih virov pri logistiki v kmetijstvu. Konferenca je postala že tradicionalna in utira pot razliÄnim pogledom logistike v povezavi s kmetijstvom. Ravno zato imamo vabljenega predavatelja na temo ElektriÄna vozila v kmetijstvu

Towards Unbiased Evaluation of Ionization Performance in LC-HRMS Metabolomics Method Development

As metabolomics increasingly finds its way from basic science into applied and regulatory environments, analytical demands on nontargeted mass spectrometric detection methods continue to rise. In addition to improved chemical comprehensiveness, current developments aim at enhanced robustness and repeatability to allow long-term, inter-study, and meta-analyses. Comprehensive metabolomics relies on electrospray ionization (ESI) as the most versatile ionization technique, and recent liquid chromatography-high resolution mass spectrometry (LC-HRMS) instrumentation continues to overcome technical limitations that have hindered the adoption of ESI for applications in the past. Still, developing and standardizing nontargeted ESI methods and instrumental setups remains costly in terms of time and required chemicals, as large panels of metabolite standards are needed to reflect biochemical diversity. In this paper, we investigated in how far a nontargeted pilot experiment, consisting only of a few measurements of a test sample dilution series and comprehensive statistical analysis, can replace conventional targeted evaluation procedures. To examine this potential, two instrumental ESI ion source setups were compared, reflecting a common scenario in practical method development. Two types of feature evaluations were performed, (a) summary statistics solely involving feature intensity values, and (b) analyses additionally including chemical interpretation. Results were compared in detail to a targeted evaluation of a large metabolite standard panel. We reflect on the advantages and shortcomings of both strategies in the context of current harmonization initiatives in the metabolomics field

Matapax: An Online High-Throughput Genome-Wide Association Study Pipeline[C][W][OA]

High-throughput sequencing and genotyping methods are dramatically increasing the number of observable genetic intraspecies differences that can be exploited as genetic markers. In addition, automated phenotyping platforms and “omics” profiling technologies further enlarge the set of quantifiable macroscopic and molecular traits at an ever-increasing pace. Combined, both lines of technological advances create unparalleled opportunities to identify candidate gene regions and, ideally, even single genes responsible for observed variations in a particular trait via association studies. However, as of yet, this new potential is not sufficiently matched by enabling software solutions to easily exploit this wealth of genotype/phenotype information. We have developed Matapax, a Web-based platform to address this need. Initially, we built the infrastructure to support association studies in Arabidopsis (Arabidopsis thaliana) based on several genotyping efforts covering up to 1,375 Arabidopsis accessions. Based on the user-supplied trait information, associated single-nucleotide polymorphism markers and single-nucleotide polymorphism-harboring or -neighboring genes are identified using both the GAPIT and EMMA libraries developed for R. Additional interrogation is facilitated by displaying candidate regions and genes in a genome browser and by providing relevant annotation information. In the future, we plan to broaden the scope of organisms to other plant species as more genotype/phenotype information becomes available. Matapax is freely available at http://matapax.mpimp-golm.mpg.de and can be accessed using any internet browser

Lipid metabolism in cancer cells under metabolic stress

Cancer cells are often exposed to a metabolically challenging environment with scarce availability of oxygen and nutrients. This metabolic stress leads to changes in the balance between the endogenous synthesis and exogenous uptake of fatty acids, which are needed by cells for membrane biogenesis, energy production and protein modification. Alterations in lipid metabolism and, consequently, lipid composition have important therapeutic implications, as they affect the survival, membrane dynamics and therapy response of cancer cells. In this article, we provide an overview of recent insights into the regulation of lipid metabolism in cancer cells under metabolic stress and discuss how this metabolic adaptation helps cancer cells thrive in a harsh tumour microenvironment.status: publishe