PROMIS: Global Analysis of PROtein-Metabolite Interactions

Non-volatile metabolites mediate plant interactions with insect herbivores

Non-volatile metabolites constitute the bulk of plant biomass. From the perspective of plant-insect interactions, these structurally diverse compounds include nutritious core metabolites and defensive specialized metabolites. In this review, we synthesize the current literature on multiple scales of plant-insect interactions mediated by non-volatile metabolites. At the molecular level, functional genetics studies have revealed a large collection of receptors targeting plant non-volatile metabolites in model insect species and agricultural pests. By contrast, examples of plant receptors of insect-derived molecules remain sparse. For insect herbivores, plant non-volatile metabolites function beyond the dichotomy of core metabolites, classed as nutrients, and specialized metabolites, classed as defensive compounds. Insect feeding tends to elicit evolutionarily conserved changes in plant specialized metabolism, whereas its effect on plant core metabolism varies widely based the interacting species. Finally, several recent studies have demonstrated that non-volatile metabolites can mediate tripartite communication on the community scale, facilitated by physical connections established through direct root-to-root communication, parasitic plants, arbuscular mycorrhizae and the rhizosphere microbiome. Recent advances in both plant and insect molecular biology will facilitate further research on the role of non-volatile metabolites in mediating plant-insect interactions.

Metabolite interactions between host and microbiota during health and disease: Which feeds the other?

Metabolites produced by the host and microbiota play a crucial role in how human bodies develop and remain healthy. Most of these metabolites are produced by microbiota and hosts in the digestive tract. Metabolites in the gut have important roles in energy metabolism, cellular communication, and host immunity, among other physiological activities. Although numerous host metabolites, such as free fatty acids, amino acids, and vitamins, are found in the intestine, metabolites generated by gut microbiota are equally vital for intestinal homeostasis. Furthermore, microbiota in the gut is the sole source of some metabolites, including short-chain fatty acids (SCFAs). Metabolites produced by microbiota, such as neurotransmitters and hormones, may modulate and significantly affect host metabolism. The gut microbiota is becoming recognized as a second endocrine system. A variety of chronic inflammatory disorders have been linked to aberrant host-microbiota interplays, but the precise mechanisms underpinning these disturbances and how they might lead to diseases remain to be fully elucidated. Microbiome-modulated metabolites are promising targets for new drug discovery due to their endocrine function in various complex disorders. In humans, metabolotherapy for the prevention or treatment of various disorders will be possible if we better understand the metabolic preferences of bacteria and the host in specific tissues and organs. Better disease treatments may be possible with the help of novel complementary therapies that target host or bacterial metabolism. The metabolites, their physiological consequences, and functional mechanisms of the host-microbiota interplays will be highlighted, summarized, and discussed in this overview.

Don't let go: co-fractionation mass spectrometry for untargeted mapping of protein-metabolite interactomes

The chemical complexity of metabolomes goes hand in hand with their functional diversity. Small molecules have many essential roles, many of which are executed by binding and modulating the function of a protein partner. The complex and dynamic protein-metabolite interaction (PMI) network underlies most if not all biological processes, but remains under-characterized. Herein, we highlight how co-fractionation mass spectrometry (CF-MS), a well-established approach to map protein assemblies, can be used for proteome and metabolome identification of the PMIs. We will review recent CF-MS studies, discuss the main advantages and limitations, summarize the available CF-MS guidelines, and outline future challenges and opportunities.

Selective chemical probes can untangle the complexity of the plant cell endomembrane system

The endomembrane system is critical for plant growth and development and understanding its function and regulation is of great interest for plant biology research. Small-molecule targeting distinctive endomembrane components have proven powerful tools to dissect membrane trafficking in plant cells. However, unambiguous elucidation of the complex and dynamic trafficking processes requires chemical probes with enhanced precision. Determination of the mechanism of action of a compound, which is facilitated by various chemoproteomic approaches, opens new avenues for the improvement of its specificity. Moreover, rational molecule design and reverse chemical genetics with the aid of virtual screening and artificial intelligence will enable us to discover highly precise chemical probes more efficiently. The next decade will witness the emergence of more such accurate tools, which together with advanced live quantitative imaging techniques of subcellular phenotypes, will deepen our insights into the plant endomembrane system.

The diversity of quinoa morphological traits and seed metabolic composition

Quinoa (Chenopodium quinoa Willd.) is an herbaceous annual crop of the amaranth family (Amaranthaceae). It is increasingly cultivated for its nutritious grains, which are rich in protein and essential amino acids, lipids, and minerals. Quinoa exhibits a high tolerance towards various abiotic stresses including drought and salinity, which supports its agricultural cultivation under climate change conditions. The use of quinoa grains is compromised by anti-nutritional saponins, a terpenoid class of secondary metabolites deposited in the seed coat; their removal before consumption requires extensive washing, an economically and environmentally unfavorable process; or their accumulation can be reduced through breeding. In this study, we analyzed the seed metabolomes, including amino acids, fatty acids, and saponins, from 471 quinoa cultivars, including two related species, by liquid chromatography - mass spectrometry. Additionally, we determined a large number of agronomic traits including biomass, flowering time, and seed yield. The results revealed considerable diversity between genotypes and provide a knowledge base for future breeding or genome editing of quinoa.

Autophagy modulates the metabolism and growth of tomato fruit during development

Although autophagy is a conserved mechanism operating across eukaryotes, its effects on crops and especially their metabolism has received relatively little attention. Indeed, whilst a few recent studies have used systems biology tools to look at the consequences of lack of autophagy in maize these focused on leaf tissues rather than the kernels. Here we utilized RNA interference (RNAi) to generate tomato plants that were deficient in the autophagy-regulating protease ATG4. Plants displayed an early senescence phenotype yet relatively mild changes in the foliar metabolome and were characterized by a reduced fruit yield phenotype. Metabolite profiling indicated that metabolites of ATG4-RNAi tomato leaves just exhibited minor alterations while that of fruit displayed bigger difference compared to the WT. In detail, many primary metabolites exhibited decreases in the ATG4-RNAi lines, such as proline, tryptophan and phenylalanine, while the representative secondary metabolites (quinic acid and 3-trans-caffeoylquinic acid) were present at substantially higher levels in ATG4-RNAi green fruits than in WT. Moreover, transcriptome analysis indicated that the most prominent differences were in the significant upregulation of organelle degradation genes involved in the proteasome or chloroplast vesiculation pathways, which was further confirmed by the reduced levels of chloroplastic proteins in the proteomics data. Furthermore, integration analysis of the metabolome, transcriptome and proteome data indicated that ATG4 significantly affected the lipid metabolism, chlorophyll binding proteins and chloroplast biosynthesis. These data collectively lead us to propose a more sophisticated model to explain the cellular co-ordination of the process of autophagy.

Proteogenic Dipeptides Are Characterized by Diel Fluctuations and Target of Rapamycin Complex-Signaling Dependency in the Model Plant Arabidopsis thaliana

As autotrophic organisms, plants capture light energy to convert carbon dioxide into ATP, nicotinamide adenine dinucleotide phosphate (NADPH), and sugars, which are essential for the biosynthesis of building blocks, storage, and growth. At night, metabolism and growth can be sustained by mobilizing carbon (C) reserves. In response to changing environmental conditions, such as light-dark cycles, the small-molecule regulation of enzymatic activities is critical for reprogramming cellular metabolism. We have recently demonstrated that proteogenic dipeptides, protein degradation products, act as metabolic switches at the interface of proteostasis and central metabolism in both plants and yeast. Dipeptides accumulate in response to the environmental changes and act via direct binding and regulation of critical enzymatic activities, enabling C flux distribution. Here, we provide evidence pointing to the involvement of dipeptides in the metabolic rewiring characteristics for the day-night cycle in plants. Specifically, we measured the abundance of 13 amino acids and 179 dipeptides over short- (SD) and long-day (LD) diel cycles, each with different light intensities. Of the measured dipeptides, 38 and eight were characterized by day-night oscillation in SD and LD, respectively, reaching maximum accumulation at the end of the day and then gradually falling in the night. Not only the number of dipeptides, but also the amplitude of the oscillation was higher in SD compared with LD conditions. Notably, rhythmic dipeptides were enriched in the glucogenic amino acids that can be converted into glucose. Considering the known role of Target of Rapamycin (TOR) signaling in regulating both autophagy and metabolism, we subsequently investigated whether diurnal fluctuations of dipeptides levels are dependent on the TOR Complex (TORC). The Raptor1b mutant (raptor1b), known for the substantial reduction of TOR kinase activity, was characterized by the augmented accumulation of dipeptides, which is especially pronounced under LD conditions. We were particularly intrigued by the group of 16 dipeptides, which, based on their oscillation under SD conditions and accumulation in raptor1b, can be associated with limited C availability or photoperiod. By mining existing protein-metabolite interaction data, we delineated putative protein interactors for a representative dipeptide Pro-Gln. The obtained list included enzymes of C and amino acid metabolism, which are also linked to the TORC-mediated metabolic network. Based on the obtained results, we speculate that the diurnal accumulation of dipeptides contributes to its metabolic adaptation in response to changes in C availability. We hypothesize that dipeptides would act as alternative respiratory substrates and by directly modulating the activity of the focal enzymes.

PROMISed: A novel web-based tool to facilitate analysis and visualization of the molecular interaction networks from co-fractionation mass spectrometry (CF-MS) experiments

Co-fractionation mass spectrometry (CF-MS)-based approaches enable cell-wide identification of protein-protein and protein-metabolite complexes present in the cellular lysate. CF-MS combines biochemical separation of molecular complexes with an untargeted mass-spectrometry-based proteomics and/or metabolomics analysis of the obtained fractions, and is used to delineate putative interactors. CF-MS data are a treasure trove for biological discovery. To facilitate analysis and visualization of original or publically available CF-MS datasets, we designed PROMISed, a user-friendly tool available online via https://myshiny.mpimp-golm.mpg.de/PDP1/ or as a repository via https://github.com/DennisSchlossarek/PROMISed. Specifically, starting with raw fractionation profiles, PROMISed (i) contains activities for data pre-processing and normalization, (ii) deconvolutes complex fractionation profiles into single, distinct peaks, (iii) identifies co-eluting protein-protein or protein-metabolite pairs using user-defined correlation methods, and (iv) performs co-fractionation network analysis. Given multiple CF-MS datasets, for instance representing different environmental condition, PROMISed allows to select for proteins and metabolites that differ in their elution profile, which may indicate change in the interaction status. But it also enables the identification of protein-protein and protein-metabolite pairs that co-elute together across multiple datasets. PROMISed enables users to (i) easily adjust parameters at each step of the analysis, (ii) download partial and final results, and (iii) select among different data-visualization options. PROMISed renders CF-MS data accessible to a broad scientific audience, allowing users with no computational or statistical background to look for novel protein-protein and protein-metabolite complexes for further experimental validation.

Combination of network and molecule structure accurately predicts competitive inhibitory interactions

Mining of metabolite-protein interaction networks facilitates the identification of design principles underlying the regulation of different cellular processes. However, identification and characterization of the regulatory role that metabolites play in interactions with proteins on a genome-scale level remains a pressing task. Based on availability of high-quality metabolite-protein interaction networks and genome-scale metabolic networks, here we propose a supervised machine learning approach, called CIRI that determines whether or not a metabolite is involved in a competitive inhibitory regulatory interaction with an enzyme. First, we show that CIRI outperforms the naive approach based on a structural similarity threshold for a putative competitive inhibitor and the substrates of a metabolic reaction. We also validate the performance of CIRI on several unseen data sets and databases of metabolite-protein interactions not used in the training, and demonstrate that the classifier can be effectively used to predict competitive inhibitory interactions. Finally, we show that CIRI can be employed to refine predictions about metabolite-protein interactions from a recently proposed PROMIS approach that employs metabolomics and proteomics profiles from size exclusion chromatography in E. coli to predict metabolite-protein interactions. Altogether, CIRI fills a gap in cataloguing metabolite-protein interactions and can be used in directing future machine learning efforts to categorize the regulatory type of these interactions.

Multi-omics analysis of early leaf development in Arabidopsis thaliana

The growth of plant organs is driven by cell division and subsequent cell expansion. The transition from proliferation to expansion is critical for the final organ size and plant yield. Exit from proliferation and onset of expansion is accompanied by major metabolic reprogramming, and in leaves with the establishment of photosynthesis. To learn more about the molecular mechanisms underlying the developmental and metabolic transitions important for plant growth, we used untargeted proteomics and metabolomics analyses to profile young leaves of a model plant Arabidopsis thaliana representing proliferation, transition, and expansion stages. The dataset presented represents a unique resource comprising approximately 4,000 proteins and 300 annotated small-molecular compounds measured across 6 consecutive days of leaf growth. These can now be mined for novel developmental and metabolic regulators of plant growth and can act as a blueprint for studies aimed at better defining the interface of development and metabolism in other species.

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Untargeted metabolomics and proteomics characterization of early leaf growth

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Translation is the primary determiner of protein abundance during early leaf growth

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12-OPDA accumulation coincides with meristem arrest

Developmental and metabolic transitions occurring during plant growth are critical for crop yield. The multi-omics dataset presented here was generated to enable the identification of novel molecular players involved in the regulation of plant growth. It comprised approximately 4,000 proteins and 300 annotated small-molecular compounds, measured across early leaf development spanning major developmental transitions. As such, the work provides a blueprint for studies aimed at better defining the interface between metabolism and development, an appreciated yet understudied research frontier across all kingdoms of life.

Global mapping of protein-metabolite interactions in Saccharomyces cerevisiae reveals that Ser-Leu dipeptide regulates phosphoglycerate kinase activity

Protein-metabolite interactions are of crucial importance for all cellular processes but remain understudied. Here, we applied a biochemical approach named PROMIS, to address the complexity of the protein-small molecule interactome in the model yeast Saccharomyces cerevisiae. By doing so, we provide a unique dataset, which can be queried for interactions between 74 small molecules and 3982 proteins using a user-friendly interface available at https://promis.mpimp-golm.mpg.de/yeastpmi/ . By interpolating PROMIS with the list of predicted protein-metabolite interactions, we provided experimental validation for 225 binding events. Remarkably, of the 74 small molecules co-eluting with proteins, 36 were proteogenic dipeptides. Targeted analysis of a representative dipeptide, Ser-Leu, revealed numerous protein interactors comprising chaperones, proteasomal subunits, and metabolic enzymes. We could further demonstrate that Ser-Leu binding increases activity of a glycolytic enzyme phosphoglycerate kinase (Pgk1). Consistent with the binding analysis, Ser-Leu supplementation leads to the acute metabolic changes and delays timing of a diauxic shift. Supported by the dipeptide accumulation analysis our work attests to the role of Ser-Leu as a metabolic regulator at the interface of protein degradation and central metabolism.

Liquid Chromatography-Mass Spectrometry (LC-MS)-Based Analysis for Lipophilic Compound Profiling in Plants

Lipids are fascinating due to their chemical diversity, which is especially vast in the plant kingdom thanks to the high plasticity of the plant biosynthetic machinery. Lipidomic studies aim to simultaneously analyze a large number of lipid compounds of diverse classes in a given sample. The method presented here uses liquid chromatography-mass spectrometry (LC-MS)-based lipidomic profiling in a relatively fast, robust, and high-throughput manner for high-coverage quantification and annotation of lipophilic compounds. Protocols cover sample preparation, LC-MS-based measurement, and data extraction and annotation. An extensive lipid library for triacylglycerols, galactolipids, and phospholipids is provided. The extended profiling described here could be used in a range of applications and is suitable for integration with other omic datasets. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Sample preparation and metabolite extraction Basic Protocol 2: Liquid chromatography-mass spectrometry (LC-MS) analysis Basic Protocol 3: Data extraction, annotation, and quantification.