Are the metabolomic responses to folivory of closely related plant species linked to macroevolutionary and plant-folivore coevolutionary processes?

Implications of mistletoe parasitism for the host metabolome: A new plant identity in the forest canopy

Mistletoe-host systems exemplify an intimate and chronic relationship where mistletoes represent protracted stress for hosts, causing long-lasting impact. Although host changes in morphological and reproductive traits due to parasitism are well known, shifts in their physiological system, altering metabolite concentrations, are less known due to the difficulty of quantification. Here, we use ecometabolomic techniques in the plant-plant interaction, comparing the complete metabolome of the leaves from mistletoe (Viscum album) and needles from their host (Pinus nigra), both parasitized and unparasitized, to elucidate host responses to plant parasitism. Our results show that mistletoe acquires metabolites basically from the primary metabolism of its host and synthesizes its own defence compounds. In response to mistletoe parasitism, pines modify a quarter of their metabolome over the year, making the pine canopy metabolome more homogeneous by reducing the seasonal shifts in top-down stratification. Overall, host pines increase antioxidant metabolites, suggesting oxidative stress, and also increase part of the metabolites required by mistletoe, which act as a permanent sink of host resources. In conclusion, by exerting biotic stress and thereby causing permanent systemic change, mistletoe parasitism generates a new host-plant metabolic identity available in forest canopy, which could have notable ecological consequences in the forest ecosystem.

Atmo-ecometabolomics: a novel atmospheric particle chemical characterization methodology for ecological research

Aerosol particles play important roles in processes controlling the composition of the atmosphere and function of ecosystems. A better understanding of the composition of aerosol particles is beginning to be recognized as critical for ecological research to further comprehend the link between aerosols and ecosystems. While chemical characterization of aerosols has been practiced in the atmospheric science community, detailed methodology tailored to the needs of ecological research does not exist yet. In this study, we describe an efficient methodology (atmo-ecometabolomics), in step-by-step details, from the sampling to the data analyses, to characterize the chemical composition of aerosol particles, namely atmo-metabolome. This method employs mass spectrometry platforms such as liquid and gas chromatography mass spectrometries (MS) and Fourier transform ion cyclotron resonance MS (FT-ICR-MS). For methodology evaluation, we analyzed aerosol particles collected during two different seasons (spring and summer) in a low-biological-activity ecosystem. Additionally, to further validate our methodology, we analyzed aerosol particles collected in a more biologically active ecosystem during the pollination peaks of three different representative tree species. Our statistical results showed that our sampling and extraction methods are suitable for characterizing the atmo-ecometabolomes in these two distinct ecosystems with any of the analytical platforms. Datasets obtained from each mass spectrometry instrument showed overall significant differences of the atmo-ecometabolomes between spring and summer as well as between the three pollination peak periods. Furthermore, we have identified several metabolites that can be attributed to pollen and other plant-related aerosol particles. We additionally provide a basic guide of the potential use ecometabolomic techniques on different mass spectrometry platforms to accurately analyze the atmo-ecometabolomes for ecological studies. Our method represents an advanced novel approach for future studies in the impact of aerosol particle chemical compositions on ecosystem structure and function and biogeochemistry.

We Are What We Eat: A Stoichiometric and Ecometabolomic Study of Caterpillars Feeding on Two Pine Subspecies of Pinus sylvestris

Many studies have addressed several plant-insect interaction topics at nutritional, molecular, physiological, and evolutionary levels. However, it is still unknown how flexible the metabolism and the nutritional content of specialist insect herbivores feeding on different closely related plants can be. We performed elemental, stoichiometric, and metabolomics analyses on leaves of two coexisting Pinus sylvestris subspecies and on their main insect herbivore; the caterpillar of the processionary moth (Thaumetopoea pityocampa). Caterpillars feeding on different pine subspecies had distinct overall metabolome structure, accounting for over 10% of the total variability. Although plants and insects have very divergent metabolomes, caterpillars showed certain resemblance to their plant-host metabolome. In addition, few plant-related secondary metabolites were found accumulated in caterpillar tissues which could potentially be used for self-defense. Caterpillars feeding on N and P richer needles had lower N and P tissue concentration and higher C:N and C:P ratios, suggesting that nutrient transfer is not necessarily linear through trophic levels and other plant-metabolic factors could be interfering. This exploratory study showed that little chemical differences between plant food sources can impact the overall metabolome of specialist insect herbivores. Significant nutritional shifts in herbivore tissues could lead to larger changes of the trophic web structure.

Metabolic discrimination of pine resins using multiple analytical platforms

Resins are one of the first sites of interaction between plants and biotic and abiotic factors. Despite their evident morphological and chemical differentiation from other plant organs, the detailed correlation between resins and biological or environmental factors is not yet clear. In this study, ¹H nuclear magnetic resonance (NMR), gas chromatography coupled with mass spectrometry (GC-MS) and high-performance thin-layer chromatography (HPTLC)-based profiling techniques were applied to the metabolic characterisation of plant resins of different species and season of collection, using samples from five different species that were collected during early and late spring. The ¹H NMR analysis confirmed the main metabolic groups in the resins to be terpenoids and further GC-MS analysis revealed a notable chemical variation between the species and collection periods. Abies grandis displayed a significant differentiation from the other species, showing a higher number of monoterpenes. The HPTLC-based profiling method hyphenated with multivariate data analysis (MVDA) also showed a clear separation confirming the GC-MS terpenoidal profiling results. Additionally, the unknown compounds were obtained by preparative TLC for identification. Based on the results of the three analytical platforms, it was concluded that the major difference in chemical composition of pine species was between species rather than the collection period. Nonetheless, the chemical profiles of resins from different species and collection periods can be well discriminated and correlated to mono- and sesquiterpenes in the case of species and diterpenes for the collection periods.

Seasonal variation of secondary metabolites in nine different bryophytes

Bryophytes occur in almost all land ecosystems and contribute to global biogeochemical cycles, ecosystem functioning, and influence vegetation dynamics. As growth and biochemistry of bryophytes are strongly dependent on the season, we analyzed metabolic variation across seasons with regard to ecological characteristics and phylogeny. Using bioinformatics methods, we present an integrative and reproducible approach to connect ecology with biochemistry. Nine different bryophyte species were collected in three composite samples in four seasons. Untargeted liquid chromatography coupled with mass spectrometry (LC/MS) was performed to obtain metabolite profiles. Redundancy analysis, Pearson's correlation, Shannon diversity, and hierarchical clustering were used to determine relationships among species, seasons, ecological characteristics, and hierarchical clustering. Metabolite profiles of Marchantia polymorpha and Fissidens taxifolius which are species with ruderal life strategy (R-selected) showed low seasonal variability, while the profiles of the pleurocarpous mosses and Grimmia pulvinata which have characteristics of a competitive strategy (C-selected) were more variable. Polytrichum strictum and Plagiomnium undulatum had intermediary life strategies. Our study revealed strong species-specific differences in metabolite profiles between the seasons. Life strategies, growth forms, and indicator values for light and soil were among the most important ecological predictors. We demonstrate that untargeted Eco-Metabolomics provide useful biochemical insight that improves our understanding of fundamental ecological strategies.

Computational workflow to study the seasonal variation of secondary metabolites in nine different bryophytes

In Eco-Metabolomics interactions are studied of non-model organisms in their natural environment and relations are made between biochemistry and ecological function. Current challenges when processing such metabolomics data involve complex experiment designs which are often carried out in large field campaigns involving multiple study factors, peak detection parameter settings, the high variation of metabolite profiles and the analysis of non-model species with scarcely characterised metabolomes. Here, we present a dataset generated from 108 samples of nine bryophyte species obtained in four seasons using an untargeted liquid chromatography coupled with mass spectrometry acquisition method (LC/MS). Using this dataset we address the current challenges when processing Eco-Metabolomics data. Here, we also present a reproducible and reusable computational workflow implemented in Galaxy focusing on standard formats, data import, technical validation, feature detection, diversity analysis and multivariate statistics. We expect that the representative dataset and the reusable processing pipeline will facilitate future studies in the research field of Eco-Metabolomics.

Current Challenges in Plant Eco-Metabolomics

The relatively new research discipline of Eco-Metabolomics is the application of metabolomics techniques to ecology with the aim to characterise biochemical interactions of organisms across different spatial and temporal scales. Metabolomics is an untargeted biochemical approach to measure many thousands of metabolites in different species, including plants and animals. Changes in metabolite concentrations can provide mechanistic evidence for biochemical processes that are relevant at ecological scales. These include physiological, phenotypic and morphological responses of plants and communities to environmental changes and also interactions with other organisms. Traditionally, research in biochemistry and ecology comes from two different directions and is performed at distinct spatiotemporal scales. Biochemical studies most often focus on intrinsic processes in individuals at physiological and cellular scales. Generally, they take a bottom-up approach scaling up cellular processes from spatiotemporally fine to coarser scales. Ecological studies usually focus on extrinsic processes acting upon organisms at population and community scales and typically study top-down and bottom-up processes in combination. Eco-Metabolomics is a transdisciplinary research discipline that links biochemistry and ecology and connects the distinct spatiotemporal scales. In this review, we focus on approaches to study chemical and biochemical interactions of plants at various ecological levels, mainly plant⁻organismal interactions, and discuss related examples from other domains. We present recent developments and highlight advancements in Eco-Metabolomics over the last decade from various angles. We further address the five key challenges: (1) complex experimental designs and large variation of metabolite profiles; (2) feature extraction; (3) metabolite identification; (4) statistical analyses; and (5) bioinformatics software tools and workflows. The presented solutions to these challenges will advance connecting the distinct spatiotemporal scales and bridging biochemistry and ecology.

Moving beyond the van Krevelen Diagram: A New Stoichiometric Approach for Compound Classification in Organisms

van Krevelen diagrams (O/C vs H/C ratios of elemental formulas) have been widely used in studies to obtain an estimation of the main compound categories present in environmental samples. However, the limits defining a specific compound category based solely on O/C and H/C ratios of elemental formulas have never been accurately listed or proposed to classify metabolites in biological samples. Furthermore, while O/C vs H/C ratios of elemental formulas can provide an overview of the compound categories, such classification is inefficient because of the large overlap among different compound categories along both axes. We propose a more accurate compound classification for biological samples analyzed by high-resolution mass spectrometry based on an assessment of the C/H/O/N/P stoichiometric ratios of over 130 000 elemental formulas of compounds classified in 6 main categories: lipids, peptides, amino sugars, carbohydrates, nucleotides, and phytochemical compounds (oxy-aromatic compounds). Our multidimensional stoichiometric compound classification (MSCC) constraints showed a highly accurate categorization of elemental formulas to the main compound categories in biological samples with over 98% of accuracy representing a substantial improvement over any classification based on the classic van Krevelen diagram. This method represents a signficant step forward in environmental research, especially ecological stoichiometry and eco-metabolomics studies, by providing a novel and robust tool to improve our understanding of the ecosystem structure and function through the chemical characterization of biological samples.

Close and distant: Contrasting the metabolism of two closely related subspecies of Scots pine under the effects of folivory and summer drought

Metabolomes, as chemical phenotypes of organisms, are likely not only shaped by the environment but also by common ancestry. If this is the case, we expect that closely related species of pines will tend to reach similar metabolomic solutions to the same environmental stressors. We examined the metabolomes of two sympatric subspecies of Pinus sylvestris in Sierra Nevada (southern Iberian Peninsula), in summer and winter and exposed to folivory by the pine processionary moth. The overall metabolomes differed between the subspecies but both tended to respond more similarly to folivory. The metabolomes of the subspecies were more dissimilar in summer than in winter, and iberica trees had higher concentrations of metabolites directly related to drought stress. Our results are consistent with the notion that certain plant metabolic responses associated with folivory have been phylogenetically conserved. The larger divergence between subspecies metabolomes in summer is likely due to the warmer and drier conditions that the northern iberica subspecies experience in Sierra Nevada. Our results provide crucial insights into how iberica populations would respond to the predicted conditions of climate change under an increased defoliation in the Mediterranean Basin.