Characterization of poplar metabotypes via mass difference enrichment analysis

Automatization of metabolite extraction for high-throughput metabolomics: case study on transgenic isoprene-emitting birch

Metabolomics studies are becoming increasingly common for understanding how plant metabolism responds to changes in environmental conditions, genetic manipulations and treatments. Despite the recent advances in metabolomics workflow, the sample preparation process still limits the high-throughput analysis in large-scale studies. Here, we present a highly flexible robotic system that integrates liquid handling, sonication, centrifugation, solvent evaporation and sample transfer processed in 96-well plates to automatize the metabolite extraction from leaf samples. We transferred an established manual extraction protocol performed to a robotic system, and with this, we show the optimization steps required to improve reproducibility and obtain comparable results in terms of extraction efficiency and accuracy. We then tested the robotic system to analyze the metabolomes of wild-type and four transgenic silver birch (Betula pendula Roth) lines under unstressed conditions. Birch trees were engineered to overexpress the poplar (Populus × canescens) isoprene synthase and to emit various amounts of isoprene. By fitting the different isoprene emission capacities of the transgenic trees with their leaf metabolomes, we observed an isoprene-dependent upregulation of some flavonoids and other secondary metabolites as well as carbohydrates, amino acid and lipid metabolites. By contrast, the disaccharide sucrose was found to be strongly negatively correlated to isoprene emission. The presented study illustrates the power of integrating robotics to increase the sample throughput, reduce human errors and labor time, and to ensure a fully controlled, monitored and standardized sample preparation procedure. Due to its modular and flexible structure, the robotic system can be easily adapted to other extraction protocols for the analysis of various tissues or plant species to achieve high-throughput metabolomics in plant research.

FT-ICR-MS reveals the molecular imprints of the brewing process

The study of fermentation and brewing has a long history of pioneering discoveries that continue to influence modern industrial food production. Since then, numerous research endeavors have yielded conventional criteria that guide contemporary brewing practices. However, the intricate open challenges faced today necessitate a more exhaustive understanding of the process at the molecular scale. We have developed an ultra-high-resolution mass spectrometric analysis (FT-ICR-MS) of the brewing process that can rapidly and comprehensively resolve thousands of molecules. This approach allows us to track molecular fluctuation during brewing at the level of chemical compositions. Employing biological triplicates, our investigation of two brewing lines that are otherwise identical except for the malt used revealed over 8,000 molecular descriptors of the brewing process. Metabolite imprints of both the similarities and differences arising from deviating malting temperatures were visualized. Additionally, we translated traditional brewing attributes such as the EBC-value, free amino nitrogen, pH-value, and concentration curves of specific molecules, into highly correlative molecular patterns consisting of hundreds of metabolites. These in-depth molecular imprints provide a better understanding of the molecular circumstances leading to various changes throughout the brewing process. Such chemical maps go beyond the observation of traditional brewing attributes and are of great significance in the investigation strategies of current open challenges in brewing research. The molecular base of knowledge, along with advancements in technological and data integration schemes, can facilitate the efficient monitoring of brewing and other productions processes.

Blue-green fluorescence during hypersensitive cell death arises from phenylpropanoid deydrodimers

Infection of Arabidopsis with avirulent Pseudomonas syringae and exposure to nitrogen dioxide (NO2) both trigger hypersensitive cell death (HCD) that is characterized by the emission of bright blue-green (BG) autofluorescence under UV illumination. The aim of our current work was to identify the BG fluorescent molecules and scrutinize their biosynthesis, localization, and functions during the HCD. Compared with wild-type (WT) plants, the phenylpropanoid-deficient mutant fah1 developed normal HCD except for the absence of BG fluorescence. Ultrahigh resolution metabolomics combined with mass difference network analysis revealed that WT but not fah1 plants rapidly accumulate dehydrodimers of sinapic acid, sinapoylmalate, 5-hydroxyferulic acid, and 5-hydroxyferuloylmalate during the HCD. FAH1-dependent BG fluorescence appeared exclusively within dying cells of the upper epidermis as detected by microscopy. Saponification released dehydrodimers from cell wall polymers of WT but not fah1 plants. Collectively, our data suggest that HCD induction leads to the formation of free BG fluorescent dehydrodimers from monomeric sinapates and 5-hydroxyferulates. The formed dehydrodimers move from upper epidermis cells into the apoplast where they esterify cell wall polymers. Possible functions of phenylpropanoid dehydrodimers are discussed.

Concealed by darkness: Combination of NMR and HRMS reveal the molecular nature of dissolved organic matter in fractured-rock groundwater and connected surface waters

Detailed molecular composition of solid phase extracted dissolved organic matter (SPEDOM) collected from fractured-rock groundwater was compared to connected surface river water at two different watersheds in the unconfined chalk aquifer of Champagne in France using full scan ultrahigh resolution electrospray and photoionization Fourier transform ion cyclotron mass spectrometry (FT-ICR MS), Orbitrap tandem MS (MS/MS) and 1H magnetic resonance spectroscopy (NMR). 1H NMR spectroscopy indicated that groundwater SPEDOM carried a higher contribution of aliphatic compounds while surface river waters SPEDOM were enriched in carboxyl-rich alicyclic molecules (CRAM), acetate derivatives and oxygenated units. Furthermore, we show here that use of photoionization (APPI(+)) in aquifer studies is key, ionizing about eight times more compounds than ESI in surface river water samples, specifically targeting the dissolved organic nitrogen pool, accounting for more than 50% of the total molecular space, as well as a non-polar, more aromatic fraction; with little overlap with compounds detected by ESI(-) FT-ICR MS. On the other hand, groundwater SPEDOM samples did not show similar selectivity as less molecular diversity was observed in APPI compared to ESI. Mass-difference transformation networks (MDiNs) applied to ESI(-) and APPI(+) FT-ICR MS datasets provided an overview of the biogeochemical relationships within the aquifer, revealing chemical diversity and microbial/abiotic reactions. Finally, the combination of ESI(-) FT-ICR MS and detailed Orbitrap MS/MS analysis revealed a pool of polar, anthropogenic sulfur-containing surfactants in the groundwaters, likely originating from agricultural runoff. Overall, our study shows that in this aquifer, groundwater SPEDOM contains a significantly reduced pool of organic compounds compared to surface river waters, possibly related to a combination of lack of sunlight and adsorption of high O/C formulas to mineral surfaces.

Influences of chemotype and parental genotype on metabolic fingerprints of tansy plants uncovered by predictive metabolomics

Intraspecific plant chemodiversity shapes plant-environment interactions. Within species, chemotypes can be defined according to variation in dominant specialised metabolites belonging to certain classes. Different ecological functions could be assigned to these distinct chemotypes. However, the roles of other metabolic variation and the parental origin (or genotype) of the chemotypes remain poorly explored. Here, we first compared the capacity of terpenoid profiles and metabolic fingerprints to distinguish five chemotypes of common tansy (Tanacetum vulgare) and depict metabolic differences. Metabolic fingerprints captured higher variation in metabolites while preserving the ability to define chemotypes. These differences might influence plant performance and interactions with the environment. Next, to characterise the influence of the maternal origin on chemodiversity, we performed variation partitioning and generalised linear modelling. Our findings revealed that maternal origin was a higher source of chemical variation than chemotype. Predictive metabolomics unveiled 184 markers predicting maternal origin with 89% accuracy. These markers included, among others, phenolics, whose functions in plant-environment interactions are well established. Hence, these findings place parental genotype at the forefront of intraspecific chemodiversity. We recommend considering this factor when comparing the ecology of various chemotypes. Additionally, the combined inclusion of inherited variation in main terpenoids and other metabolites in computational models may help connect chemodiversity and evolutionary principles.

Formulae Differences Commence a Database for Interlaboratory Studies of Natural Organic Matter

Direct comparison of high-resolution mass spectrometry (HRMS) data acquired with different instrumentation or parameters remains problematic as the derived lists of molecular species via HRMS, even for the same sample, appear distinct. This inconsistency is caused by inherent inaccuracies associated with instrumental limitations and sample conditions. Hence, experimental data may not reflect a corresponding sample. We propose a method that classifies HRMS data based on the differences in the number of elements between each pair of molecular formulae within the formulae list to preserve the essence of the given sample. The novel metric, formulae difference chains expected length (FDCEL), allowed for comparing and classifying samples measured by different instruments. We also demonstrate a web application and a prototype for a uniform database for HRMS data serving as a benchmark for future biogeochemical and environmental applications. FDCEL metric was successfully employed for both spectrum quality control and examination of samples of various nature.

A Strategy for Uncovering the Serum Metabolome by Direct-Infusion High-Resolution Mass Spectrometry

Direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) is a promising tool for high-throughput metabolomics analysis. However, metabolite assignment is limited by the inadequate mass accuracy and chemical space of the metabolome database. Here, a serum metabolome characterization method was proposed to make full use of the potential of DI-nESI-HRMS. Different from the widely used database search approach, unambiguous formula assignments were achieved by a reaction network combined with mass accuracy and isotopic patterns filter. To provide enough initial known nodes, an initial network was directly constructed by known metabolite formulas. Then experimental formula candidates were screened by the predefined reaction with the network. The effects of sources and scales of networks on assignment performance were investigated. Further, a scoring rule for filtering unambiguous formula candidates was proposed. The developed approach was validated by a pooled serum sample spiked with reference standards. The coverage and accuracy rates for the spiked standards were 98.9% and 93.6%, respectively. A total of 1958 monoisotopic features were assigned with unique formula candidates for the pooled serum, which is twice more than the database search. Finally, a case study of serum metabolomics in diabetes was carried out using the developed method.

Real-Time Monitoring of Miniaturized Thermal Food Processing by Advanced Mass Spectrometric Techniques

Mass spectrometry is a popular and powerful analytical tool to study the effects of food processing. Industrial sampling, real-life sampling, or challenging academic research on process-related volatile and aerosol research often demand flexible, time-sensitive data acquisition by state-of-the-art mass analyzers. Here, we show a laboratory-scaled, miniaturized, and highly controllable setup for the online monitoring of aerosols and volatiles from thermal food processing based on dielectric barrier discharge ionization (DBDI) mass spectrometry (MS). We demonstrate the opportunities offered by the setup from a foodomics perspective to study emissions from the thermal processing of wheat bread rolls at 210 °C by Fourier transformation ion cyclotron resonance MS. As DBDI is an emerging technology, we compared its ionization selectivity to established atmospheric pressure ionization tools: we found DBDI preferably ionizes saturated, nitrogenous compounds. We likewise identified a sustainable overlap in the selectivity of detected analytes with APCI and electrospray ionization (ESI). Further, we dynamically recorded chemical fingerprints throughout the thermal process. Unsupervised classification of temporal response patterns was used to describe the dynamic nature of the reaction system. Compared to established tools for real-time MS, our setup permits one to monitor chemical changes during thermal food processing at ultrahigh resolution, establishing an advanced perspective for real-time mass spectrometric analysis of food processing.

Untargeted metabolomic analysis by ultra-high-resolution mass spectrometry for the profiling of new Italian wine varieties

The chemical composition of wine samples comprises numerous bioactive compounds responsible for unique flavor and health-promoting properties. Thus, it's important to have a complete overview of the metabolic profile of new wine products in order to obtain peculiar information in terms of their phytochemical composition, quality, and traceability. To achieve this aim, in this work, a mass spectrometry-based phytochemical screening was performed on seven new wine products from Villa D'Agri in the Basilicata region (Italy), i.e., Aglianico Bianco, Plavina, Guisana, Giosana, Malvasia ad acino piccolo, Colata Murro and Santa Sofia. Ultra-high-resolution mass spectrometry data were processed into absorption mode FT-ICR mass spectra, in order to remove artifacts and achieve a higher resolution and lower levels of noise. Accurate mass-to-charge ratio (m/z) values were converted into putative elemental formulas. Therefore, 2D van Krevelen diagrams were used as a tool to obtain molecular formula maps useful to perform a rapid and more comprehensive analysis of the wine sample metabolome. The presence of important metabolite classes, i.e., fatty acid derivatives, amino acids and peptides, carbohydrates and phenolic derivatives, was assessed. Moreover, the comparison of obtained metabolomic maps revealed some differences among profiles, suggesting their employment as metabolic fingerprints. This study shed some light on the metabolic composition of seven new Italian wine varieties, improving their value in terms of related bioactive compound content. Moreover, different metabolomic fingerprints were obtained for each of them, suggesting the use of molecular maps as innovative tool to ascertain their unique metabolic profile.

Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics

Untargeted metabolomics seeks to identify and quantify most metabolites in a biological system. In general, metabolomics results are represented by numerical matrices containing data that represent the intensities of the detected variables. These matrices are subsequently analyzed by methods that seek to extract significant biological information from the data. In mass spectrometry-based metabolomics, if mass is detected with sufficient accuracy, below 1 ppm, it is possible to derive mass-difference networks, which have spectral features as nodes and chemical changes as edges. These networks have previously been used as means to assist formula annotation and to rank the importance of chemical transformations. In this work, we propose a novel role for such networks in untargeted metabolomics data analysis: we demonstrate that their properties as graphs can also be used as signatures for metabolic profiling and class discrimination. For several benchmark examples, we computed six graph properties and we found that the degree profile was consistently the property that allowed for the best performance of several clustering and classification methods, reaching levels that are competitive with the performance using intensity data matrices and traditional pretreatment procedures. Furthermore, we propose two new metrics for the ranking of chemical transformations derived from network properties, which can be applied to sample comparison or clustering. These metrics illustrate how the graph properties of mass-difference networks can highlight the aspects of the information contained in data that are complementary to the information extracted from intensity-based data analysis.

FT-ICR/MS deciphers formation of unknown macromolecular disinfection byproducts from algal organic matters after plasma oxidation

Algal organic matter (AOM) is a potential precursor of disinfection byproducts (DBPs) in water treatment. It is a major challenge to identify macromolecular DBPs due to the diversity of AOM. In this study, Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR/MS) was applied to diagnose the AOM diversity after algae removal by plasma oxidation and to recognize the macromolecular DBPs in subsequent chlorination. Significant removal of AOM released by M. aeruginosa, C. raciborskii, and A. spiroies was achieved by plasma oxidation, accompanied by decrease in the proportion of CHNO formulas and increase in CHO formulas. Without plasma treatment, chlorination generated approximately 2486 macromolecular carbonaceous DBPs (C-DBPs) and 1984 nitrogenous DBPs (N-DBPs), with C₁₁H_nO_mCl_x and C₁₈H_nN_mO_zCl_x as the most abundant DBPs. The numbers of C-DBPs and N-DBPs decreased by 63.3% and 62.9%, respectively, if plasma treatment was applied prior to chlorination. Network computational analysis revealed that Cl substitution was the main formation pathway of AOM-derived DBP formation rather than HOCl addition. The precursors of macromolecular DBPs contained a characteristic atomic number of C and O (7 ≤ C ≤ 18; 3 ≤ O ≤ 11). This study firstly disclosed the relationship between AOM diversity and novel macromolecular DBPs during algae-laden water treatment.

Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation

Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.

Gfi1 Loss Protects against Two Models of Induced Diabetes

Background: Although several approaches have revealed much about individual factors that regulate pancreatic development, we have yet to fully understand their complicated interplay during pancreas morphogenesis. Gfi1 is transcription factor specifically expressed in pancreatic acinar cells, whose role in pancreas cells fate identity and specification is still elusive. Methods: In order to gain further insight into the function of this factor in the pancreas, we generated animals deficient for Gfi1 specifically in the pancreas. Gfi1 conditional knockout animals were phenotypically characterized by immunohistochemistry, RT-qPCR, and RNA scope. To assess the role of Gfi1 in the pathogenesis of diabetes, we challenged Gfi1-deficient mice with two models of induced hyperglycemia: long-term high-fat/high-sugar feeding and streptozotocin injections. Results: Interestingly, mutant mice did not show any obvious deleterious phenotype. However, in depth analyses demonstrated a significant decrease in pancreatic amylase expression, leading to a diminution in intestinal carbohydrates processing and thus glucose absorption. In fact, Gfi1-deficient mice were found resistant to diet-induced hyperglycemia, appearing normoglycemic even after long-term high-fat/high-sugar diet. Another feature observed in mutant acinar cells was the misexpression of ghrelin, a hormone previously suggested to exhibit anti-apoptotic effects on β-cells in vitro. Impressively, Gfi1 mutant mice were found to be resistant to the cytotoxic and diabetogenic effects of high-dose streptozotocin administrations, displaying a negligible loss of β-cells and an imperturbable normoglycemia. Conclusions: Together, these results demonstrate that Gfi1 could turn to be extremely valuable for the development of new therapies and could thus open new research avenues in the context of diabetes research.

Ecological theory applied to environmental metabolomes reveals compositional divergence despite conserved molecular properties

Stream and river systems transport and process substantial amounts of dissolved organic matter (DOM) from terrestrial and aquatic sources to the ocean, with global biogeochemical implications. However, the underlying mechanisms affecting the spatiotemporal organization of DOM composition are under-investigated. To understand the principles governing DOM composition, we leverage the recently proposed synthesis of metacommunity ecology and metabolomics, termed 'meta-metabolome ecology.' Applying this novel approach to a freshwater ecosystem, we demonstrated that despite similar molecular properties across metabolomes, metabolite identity significantly diverged due to environmental filtering and variations in putative biochemical transformations. We refer to this phenomenon as 'thermodynamic redundancy,' which is analogous to the ecological concept of functional redundancy. We suggest that under thermodynamic redundancy, divergent metabolomes can support equivalent biogeochemical function just as divergent ecological communities can support equivalent ecosystem function. As these analyses are performed in additional ecosystems, potentially generalizable concepts, like thermodynamic redundancy, can be revealed and provide insight into DOM dynamics.

Linking the FTO obesity rs1421085 variant circuitry to cellular, metabolic, and organismal phenotypes in vivo

Variants in FTO have the strongest association with obesity; however, it is still unclear how those noncoding variants mechanistically affect whole-body physiology. We engineered a deletion of the rs1421085 conserved cis-regulatory module (CRM) in mice and confirmed in vivo that the CRM modulates Irx3 and Irx5 gene expression and mitochondrial function in adipocytes. The CRM affects molecular and cellular phenotypes in an adipose depot-dependent manner and affects organismal phenotypes that are relevant for obesity, including decreased high-fat diet-induced weight gain, decreased whole-body fat mass, and decreased skin fat thickness. Last, we connected the CRM to a genetically determined effect on steroid patterns in males that was dependent on nutritional challenge and conserved across mice and humans. Together, our data establish cross-species conservation of the rs1421085 regulatory circuitry at the molecular, cellular, metabolic, and organismal level, revealing previously unknown contextual dependence of the variant's action.

Using Community Science to Reveal the Global Chemogeography of River Metabolomes

River corridor metabolomes reflect organic matter (OM) processing that drives aquatic biogeochemical cycles. Recent work highlights the power of ultrahigh-resolution mass spectrometry for understanding metabolome composition and river corridor metabolism. However, there have been no studies on the global chemogeography of surface water and sediment metabolomes using ultrahigh-resolution techniques. Here, we describe a community science effort from the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium to characterize global metabolomes in surface water and sediment that span multiple stream orders and biomes. We describe the distribution of key aspects of metabolomes including elemental groups, chemical classes, indices, and inferred biochemical transformations. We show that metabolomes significantly differ across surface water and sediment and that surface water metabolomes are more rich and variable. We also use inferred biochemical transformations to identify core metabolic processes shared among surface water and sediment. Finally, we observe significant spatial variation in sediment metabolites between rivers in the eastern and western portions of the contiguous United States. Our work not only provides a basis for understanding global patterns in river corridor biogeochemical cycles but also demonstrates that community science endeavors can enable global research projects that are unfeasible with traditional research models.

Using metacommunity ecology to understand environmental metabolomes

Environmental metabolomes are fundamentally coupled to microbially-linked biogeochemical processes within ecosystems. However, significant gaps exist in our understanding of their spatiotemporal organization, limiting our ability to uncover transferrable principles and predict ecosystem function. We propose that a theoretical paradigm, which integrates concepts from metacommunity ecology, is necessary to reveal underlying mechanisms governing metabolomes. We call this synthesis between ecology and metabolomics ‘meta-metabolome ecology’ and demonstrate its utility using a mass spectrometry dataset. We developed three relational metabolite dendrograms using molecular properties and putative biochemical transformations and performed ecological null modeling. Based upon null modeling results, we show that stochastic processes drove molecular properties while biochemical transformations were structured deterministically. We further suggest that potentially biochemically active metabolites were more deterministically assembled than less active metabolites. Understanding variation in the influences of stochasticity and determinism provides a way to focus attention on which meta-metabolomes and which parts of meta-metabolomes are most likely to be important to consider in mechanistic models. We propose that this paradigm will allow researchers to study the connections between ecological systems and their molecular processes in previously inaccessible detail.

Despite growing interest in environmental metabolomics, we lack conceptual frameworks for considering how metabolites vary across space and time in ecological systems. Here, the authors apply (species) community assembly concepts to metabolomics data, offering a way forward in understanding the assembly of metabolite assemblages.

Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks

Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed.

Root isoprene formation alters lateral root development

Isoprene is a C5 volatile organic compound, which can protect aboveground plant tissue from abiotic stress such as short-term high temperatures and accumulation of reactive oxygen species (ROS). Here, we uncover new roles for isoprene in the plant belowground tissues. By analysing Populus x canescens isoprene synthase (PcISPS) promoter reporter plants, we discovered PcISPS promoter activity in certain regions of the roots including the vascular tissue, the differentiation zone and the root cap. Treatment of roots with auxin or salt increased PcISPS promoter activity at these sites, especially in the developing lateral roots (LR). Transgenic, isoprene non-emitting poplar roots revealed an accumulation of O₂^- in the same root regions where PcISPS promoter activity was localized. Absence of isoprene emission, moreover, increased the formation of LRs. Inhibition of NAD(P)H oxidase activity suppressed LR development, suggesting the involvement of ROS in this process. The analysis of the fine root proteome revealed a constitutive shift in the amount of several redox balance, signalling and development related proteins, such as superoxide dismutase, various peroxidases and linoleate 9S-lipoxygenase, in isoprene non-emitting poplar roots. Together our results indicate for isoprene a ROS-related function, eventually co-regulating the plant-internal signalling network and development processes in root tissue.

Ecology and Evolution of Intraspecific Chemodiversity of Plants

An extraordinarily high intraspecific chemical diversity, i.e. chemodiversity, has been found in several plant species, of which some are of major ecological or economic relevance. Moreover, even within an individual plant there is substantial chemodiversity among tissues and across seasons. This chemodiversity likely has pronounced ecological effects on plant mutualists and antagonists, associated foodwebs and, ultimately, biodiversity. Surprisingly, studies on interactions between plants and their herbivores or pollinators often neglect plant chemistry as a level of diversity and phenotypic variation. The main aim of this Research Unit (RU) is to understand the emergence and maintenance of intraspecific chemodiversity in plants. We address the following central questions:

1) How does plant chemodiversity vary across levels, i.e., within individuals, among individuals within populations, and among populations?

2) What are the ecological consequences of intraspecific plant chemodiversity?

3) How is plant chemodiversity genetically determined and maintained?

By combining field and laboratory studies with metabolomics, transcriptomics, genetic tools, statistical data analysis and modelling, we aim to understand causes and consequences of plant chemodiversity and elucidate its impacts on the interactions of plants with their biotic environment. Furthermore, we want to identify general principles, which hold across different species, and develop meaningful measures to describe the fascinating diversity of defence chemicals in plants. These tasks require integrated scientific collaboration of experts in experimental and theoretical ecology, including chemical and molecular ecology, (bio)chemistry and evolution.

Molecular change of dissolved organic matter and patterns of bacterial activity in a stream along a land-use gradient

Fluvial networks are globally relevant for the processing of dissolved organic matter (DOM). To investigate the change in molecular DOM diversity along the river course, high-field FTICR mass spectrometry and NMR spectroscopy of riverine DOM as well as bacterial abundance and activity were measured in a third order stream along a land-use gradient from pristine, agricultural to urban landscapes. DOM composition showed a clear evolution along the river course with an initial decrease of average oxidation and unsaturation followed by an increased relative abundance of CHNO and CHOS compounds introduced by agriculture and waste water, respectively. DOM composition was dominated by rather unsaturated CHO compounds (H/C ≤ 1) in headwaters and by more aliphatic molecules at downstream sites. Oxygenated functional groups shifted from aromatic ethers and hydroxyl groups to aliphatic carboxylic acids and aliphatic hydroxyl groups. This massive dislocation of oxygen significantly increased the diversity of atomic environments in branched aliphatic groups from headwater to downstream DOM. Mass spectra of DOM enabled the detection of compositional relationships to bacterial abundance and activity which was positively related to more aliphatic components (H/C > 1) and negatively related to unsaturated components. FTICR mass and NMR spectra corroborated the initial decline in DOM molecular diversity predicted by the River Continuum Concept (RCC) but demonstrated an anthropogenic increase in the molecular diversity of DOM further downstream. While the high DOM molecular diversity in first order headwater streams was the result of small scale ecosystem plurality, agriculture and waste water treatment introduced many components in the lower reaches. These anthropogenic influences together with massive bacterial oxidation of DOM contributed to a growth of molecular diversity of downstream DOM whose composition and structure differed entirely from those found in pristine headwaters.

Simulated Sunlight Selectively Modifies Maillard Reaction Products in a Wide Array of Chemical Reactions

The photochemical transformation of Maillard reaction products (MRPs) under simulated sunlight into mostly unexplored photoproducts is reported herein. Non-enzymatic glycation of amino acids leads to a heterogeneous class of intermediates with extreme chemical diversity, which is of particular relevance in processed and stored food products as well as in diabetic and age-related protein damage. Here, three amino acids (lysine, arginine, and histidine) were reacted with ribose at 100 °C in water for ten hours. Exposing these model systems to simulated sunlight led to a fast decay of MRPs. The photodegradation of MRPs and the formation of new compounds have been studied by fluorescence spectroscopy and nontargeted (ultra)high-resolution mass spectrometry. Photoreactions showed strong selectivity towards the degradation of electron-rich aromatic heterocycles, such as pyrroles and pyrimidines. The data show that oxidative cleavage mechanisms dominate the formation of photoproducts. The photochemical transformations differed fundamentally from "traditional" thermal Maillard reactions and indicated a high amino acid specificity.

Milk-Derived Amadori Products in Feces of Formula-Fed Infants

Food processing of infant formula alters chemical structures, including the formation of Maillard reaction products between proteins and sugars. We detected early Maillard reaction products, so-called Amadori products, in stool samples of formula-fed infants. In total, four Amadori products (N-deoxylactulosyllysine, N-deoxyfructosyllysine, N-deoxylactulosylleucylisoleucine, N-deoxyfructosylleucylisoleucine) were identified by a combination of complementary nontargeted and targeted metabolomics approaches. Chemical structures were confirmed by preparation and isolation of reference compounds, LC-MS/MS, and NMR. The leucylisoleucine Amadori compounds, which most likely originate from β-lactoglobulin, were excreted throughout the first year of life in feces of formula-fed infants but were absent in feces of breastfed infants. Despite high inter- and intraindividual differences of Amadori products in the infants' stool, solid food introduction resulted in a continuous decrease, proving infant formula as the major source of the excreted Amadori products.

Systems chemical analytics: introduction to the challenges of chemical complexity analysis

We present concepts of complexity, and complex chemistry in systems subjected to biotic and abiotic transformations, and introduce analytical possibilities to disentangle chemical complexity into its elementary parts as a global integrated approach termed systems chemical analytics.

Metabolomic investigations in cerebrospinal fluid of Parkinson's disease

The underlying mechanisms of Parkinson´s disease are not completely revealed. Especially, early diagnostic biomarkers are lacking. To characterize early pathophysiological events, research is focusing on metabolomics. In this case-control study we investigated the metabolic profile of 31 Parkinson´s disease-patients in comparison to 95 neurologically healthy controls. The investigation of metabolites in CSF was performed by a 12 Tesla SolariX Fourier transform-ion cyclotron resonance-mass spectrometer (FT-ICR-MS). Multivariate statistical analysis sorted the most important biomarkers in relation to their ability to differentiate Parkinson versus control. The affected metabolites, their connection and their conversion pathways are described by means of network analysis. The metabolic profiling by FT-ICR-MS in CSF yielded in a good group separation, giving insights into the disease mechanisms. A total number of 243 metabolites showed an affected intensity in Parkinson´s disease, whereas 15 of these metabolites seem to be the main biological contributors. The network analysis showed a connection to the tricarboxylic cycle (TCA cycle) and therefore to mitochondrial dysfunction and increased oxidative stress within mitochondria. The metabolomic analysis of CSF in Parkinson´s disease showed an association to pathways which are involved in lipid/ fatty acid metabolism, energy metabolism, glutathione metabolism and mitochondrial dysfunction.

Metabotype variation in a field population of tansy plants influences aphid host selection

It is well known that plant volatiles influence herbivores in their selection of a host plant; however, less is known about how the nonvolatile metabolome affects herbivore host selection. Metabolic diversity between intraspecific plants can be characterized using non-targeted mass spectrometry that gives us a snapshot overview of all metabolic processes occurring within a plant at a particular time. Here, we show that non-targeted metabolomics can be used to reveal links between intraspecific chemical diversity and ecological processes in tansy (Tanacetum vulgare). First, we show that tansy plants can be categorized into five subgroups based up on their metabolic profiles, and that these "metabotypes" influenced natural aphid colonization in the field. Second, this grouping was not due to induced metabolomic changes within the plant due to aphid feeding but rather resulted from constitutive differences in chemical diversity between plants. These findings highlight the importance of intraspecific chemical diversity within one plant population and provide the first report of a non-targeted metabolomic field study in chemical ecology.

Multi 'omics comparison reveals metabolome biochemistry, not microbiome composition or gene expression, corresponds to elevated biogeochemical function in the hyporheic zone

Biogeochemical hotspots are pervasive at terrestrial-aquatic interfaces, particularly within groundwater-surface water mixing zones (hyporheic zones), and they are critical to understanding spatiotemporal variation in biogeochemical cycling. Here, we use multi 'omic comparisons of hotspots to low-activity sediments to gain mechanistic insight into hyporheic zone organic matter processing. We hypothesized that microbiome structure and function, as described by metagenomics and metaproteomics, would distinguish hotspots from low-activity sediments by shifting metabolism towards carbohydrate-utilizing pathways and elucidate discrete mechanisms governing organic matter processing in each location. We also expected these differences to be reflected in the metabolome, whereby hotspot carbon (C) pools and metabolite transformations therein would be enriched in sugar-associated compounds. In contrast to expectations, we found pronounced phenotypic plasticity in the hyporheic zone microbiome that was denoted by similar microbiome structure, functional potential, and expression across sediments with dissimilar metabolic rates. Instead, diverse nitrogenous metabolites and biochemical transformations characterized hotspots. Metabolomes also corresponded more strongly to aerobic metabolism than bulk C or N content only (explaining 67% vs. 42% and 37% of variation respectively), and bulk C and N did not improve statistical models based on metabolome composition alone. These results point to organic nitrogen as a significant regulatory factor influencing hyporheic zone organic matter processing. Based on our findings, we propose incorporating knowledge of metabolic pathways associated with different chemical fractions of C pools into ecosystem models will enhance prediction accuracy.

Data-Driven Astrochemistry: One Step Further within the Origin of Life Puzzle

Astrochemistry, meteoritics and chemical analytics represent a manifold scientific field, including various disciplines. In this review, clarifications on astrochemistry, comet chemistry, laboratory astrophysics and meteoritic research with respect to organic and metalorganic chemistry will be given. The seemingly large number of observed astrochemical molecules necessarily requires explanations on molecular complexity and chemical evolution, which will be discussed. Special emphasis should be placed on data-driven analytical methods including ultrahigh-resolving instruments and their interplay with quantum chemical computations. These methods enable remarkable insights into the complex chemical spaces that exist in meteorites and maximize the level of information on the huge astrochemical molecular diversity. In addition, they allow one to study even yet undescribed chemistry as the one involving organomagnesium compounds in meteorites. Both targeted and non-targeted analytical strategies will be explained and may touch upon epistemological problems. In addition, implications of (metal)organic matter toward prebiotic chemistry leading to the emergence of life will be discussed. The precise description of astrochemical organic and metalorganic matter as seeds for life and their interactions within various astrophysical environments may appear essential to further study questions regarding the emergence of life on a most fundamental level that is within the molecular world and its self-organization properties.

From correlation to causation: analysis of metabolomics data using systems biology approaches

INTRODUCTION

Metabolomics is a well-established tool in systems biology, especially in the top-down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.

OBJECTIVES

This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.

METHODS

We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.

RESULTS

We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.

CONCLUSIONS

Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.

Alterations in Grapevine Leaf Metabolism Occur Prior to Esca Apoplexy Appearance

Esca disease is one of the major grapevine trunk diseases in Europe and the etiology is complex, since several inhabiting fungi are identified to be associated with this disease. Among the foliar symptom expressions, the apoplectic form may be distinguished and characterized by sudden dieback of shoots, leaf drop, and shriveling of grape clusters in a few days that can ultimately induce the plant death. To further understand this drastic event, we conducted transcriptomic and metabolomic analyses to characterize responses of leaves during the period preceding symptom appearance (20 and 7 days before foliar symptom expression) and at the day of apoplexy expression. Transcriptomic and metabolomic analyses provide signatures for the apoplectic leaves and most changes concerning the metabolism of carbohydrates, amino acids, and phenylpropanoids. In deciphering glutathione-S-transferase (GST), its preferential location in phloem, correlated with the upregulation of GST genes and a decrease of the glutathione level, offers further support to the putative role of glutathione during apoplexy expression.