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.

Rewiring of the protein-protein-metabolite interactome during the diauxic shift in yeast.

In budding yeast Saccharomyces cerevisiae, the switch from aerobic fermentation to respiratory growth is separated by a period of growth arrest, known as the diauxic shift, accompanied by a significant metabolic rewiring, including the derepression of gluconeogenesis and the establishment of mitochondrial respiration. Previous studies reported hundreds of proteins and tens of metabolites accumulating differentially across the diauxic shift transition. To assess the differences in the protein-protein (PPIs) and protein-metabolite interactions (PMIs) yeast samples harvested in the glucose-utilizing, fermentative phase, ethanol-utilizing and early stationary respiratory phases were analysed using isothermal shift assay (iTSA) and a co-fractionation mass spectrometry approach, PROMIS. Whereas iTSA monitors changes in protein stability and is informative towards protein interaction status, PROMIS uses co-elution to delineate putative PPIs and PMIs. The resulting dataset comprises 1627 proteins and 247 metabolites, hundreds of proteins and tens of metabolites characterized by differential thermal stability and/or fractionation profile, constituting a novel resource to be mined for the regulatory PPIs and PMIs. The examples discussed here include (i) dissociation of the core and regulatory particle of the proteasome in the early stationary phase, (ii) the differential binding of a co-factor pyridoxal phosphate to the enzymes of amino acid metabolism and (iii) the putative, phase-specific interactions between proline-containing dipeptides and enzymes of central carbon metabolism.

PROMIS, global analysis of PROtein-metabolite interactions using size separation in Arabidopsis thaliana.

Small molecules not only represent cellular building blocks and metabolic intermediates, but also regulatory ligands and signaling molecules that interact with proteins. Although these interactions affect cellular metabolism, growth, and development, they have been largely understudied. Herein, we describe a method, which we named PROtein-Metabolite Interactions using Size separation (PROMIS), that allows simultaneous, global analysis of endogenous protein-small molecule and of protein-protein complexes. To this end, a cell-free native lysate from Arabidopsis thaliana cell cultures was fractionated by size-exclusion chromatography, followed by quantitative metabolomic and proteomic analyses. Proteins and small molecules showing similar elution behavior, across protein-containing fractions, constituted putative interactors. Applying PROMIS to an A. thaliana extract, we ascertained known protein-protein (PPIs) and protein-metabolite (PMIs) interactions and reproduced binding between small-molecule protease inhibitors and their respective proteases. More importantly, we present examples of two experimental strategies that exploit the PROMIS dataset to identify novel PMIs. By looking for similar elution behavior of metabolites and enzymes belonging to the same biochemical pathways, we identified putative feedback and feed-forward regulations in pantothenate biosynthesis and the methionine salvage cycle, respectively. By combining PROMIS with an orthogonal affinity purification approach, we identified an interaction between the dipeptide Tyr-Asp and the glycolytic enzyme glyceraldehyde-3-phosphate dehydrogenase. In summary, we present proof of concept for a powerful experimental tool that enables system-wide analysis of PMIs and PPIs across all biological systems. The dataset obtained here comprises nearly 140 metabolites and 5000 proteins, which can be mined for putative interactors.

Protein and metabolite composition of Arabidopsis stress granules.

Stress granules (SGs) are evolutionary conserved aggregates of proteins and untranslated mRNAs formed in response to stress. Despite their importance for stress adaptation, no complete proteome composition has been reported for plant SGs. In this study, we addressed the existing gap. Importantly, we also provide evidence for metabolite sequestration within the SGs. To isolate SGs we used Arabidopsis seedlings expressing green fluorescent protein (GFP) fusion of the SGs marker protein, Rbp47b, and an experimental protocol combining differential centrifugation with affinity purification (AP). SGs isolates were analysed using mass spectrometry-based proteomics and metabolomics. A quarter of the identified proteins constituted known or predicted SG components. Intriguingly, the remaining proteins were enriched in key enzymes and regulators, such as cyclin-dependent kinase A (CDKA), that mediate plant responses to stress. In addition to proteins, nucleotides, amino acids and phospholipids also accumulated in SGs. Taken together, our results indicated the presence of a preexisting SG protein interaction network; an evolutionary conservation of the proteins involved in SG assembly and dynamics; an important role for SGs in moderation of stress responses by selective storage of proteins and metabolites.

PROMIS: Co-fractionation Mass Spectrometry for Analysis of Protein-Metabolite Interactions.

The roles of small molecules in every aspect of life have been gaining increased recognition. Many are known to exert their effect by binding proteins-but a comprehensive overview of protein-metabolite interactions (PMIs) is missing. Recently we devised a non-targeted method for detecting PMIs using size-exclusion chromatography followed by proteomic and metabolomic analysis: PROMIS. Under test this method was able to identify known PMIs such as enzyme-cofactor complexes as well as novel ones.

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.

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.

A Multi-OMICs Approach Sheds Light on the Higher Yield Phenotype and Enhanced Abiotic Stress Tolerance in Tobacco Lines Expressing the Carrot lycopene ß-cyclase1 Gene.

Recently, we published a set of tobacco lines expressing the Daucus carota (carrot) DcLCYB1 gene with accelerated development, increased carotenoid content, photosynthetic efficiency, and yield. Because of this development, DcLCYB1 expression might be of general interest in crop species as a strategy to accelerate development and increase biomass production under field conditions. However, to follow this path, a better understanding of the molecular basis of this phenotype is essential. Here, we combine OMICs (RNAseq, proteomics, and metabolomics) approaches to advance our understanding of the broader effect of LCYB expression on the tobacco transcriptome and metabolism. Upon DcLCYB1 expression, the tobacco transcriptome (~2,000 genes), proteome (~700 proteins), and metabolome (26 metabolites) showed a high number of changes in the genes involved in metabolic processes related to cell wall, lipids, glycolysis, and secondary metabolism. Gene and protein networks revealed clusters of interacting genes and proteins mainly involved in ribosome and RNA metabolism and translation. In addition, abiotic stress-related genes and proteins were mainly upregulated in the transgenic lines. This was well in line with an enhanced stress (high light, salt, and H2O2) tolerance response in all the transgenic lines compared with the wild type. Altogether, our results show an extended and coordinated response beyond the chloroplast (nucleus and cytosol) at the transcriptome, proteome, and metabolome levels, supporting enhanced plant growth under normal and stress conditions. This final evidence completes the set of benefits conferred by the expression of the DcLCYB1 gene, making it a very promising bioengineering tool to generate super crops.

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.

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.

PROMIS: Global Analysis of PROtein-Metabolite Interactions.

Small molecules are not only intermediates of metabolism, but also play important roles in signaling and in controlling cellular metabolism, growth, and development. Although a few systematic studies have been conducted, the true extent of protein-small molecule interactions in biological systems remains unknown. PROtein-metabolite interactions using size separation (PROMIS) is a method for studying protein-small molecule interactions in a non-targeted, proteome- and metabolome-wide manner. This approach uses size-exclusion chromatography followed by proteomics and metabolomics liquid chromatography-mass spectrometry analysis of the collected fractions. Assuming that small molecules bound to proteins would co-fractionate together, we found numerous small molecules co-eluting with proteins, strongly suggesting the formation of stable complexes. Using PROMIS, we identified known small molecule-protein complexes, such as between enzymes and cofactors, and also found novel interactions. © 2019 The Authors. Basic Protocol 1: Preparation of native cell lysate from plant material Support Protocol: Bradford assay to determine protein concentration Basic Protocol 2: Separation of molecular complexes using size-exclusion chromatography Basic Protocol 3: Simultaneous extraction of proteins and metabolites using single-step extraction protocol Basic Protocol 4: Metabolomics analysis Basic Protocol 5: Proteomics analysis.