Identification and functional annotation of long intergenic non-coding RNAs in Brassicaceae.

Long intergenic noncoding RNAs (lincRNAs) are a large yet enigmatic class of eukaryotic transcripts that can have critical biological functions. The wealth of RNA-sequencing (RNA-seq) data available for plants provides the opportunity to implement a harmonized identification and annotation effort for lincRNAs that enables cross-species functional and genomic comparisons as well as prioritization of functional candidates. In this study, we processed >24 Tera base pairs of RNA-seq data from >16,000 experiments to identify ∼130,000 lincRNAs in four Brassicaceae: Arabidopsis thaliana, Camelina sativa, Brassica rapa, and Eutrema salsugineum. We used nanopore RNA-seq, transcriptome-wide structural information, peptide data, and epigenomic data to characterize these lincRNAs and identify conserved motifs. We then used comparative genomic and transcriptomic approaches to highlight lincRNAs in our data set with sequence or transcriptional conservation. Finally, we used guilt-by-association analyses to assign putative functions to lincRNAs within our data set. We tested this approach on a subset of lincRNAs associated with germination and seed development, observing germination defects for Arabidopsis lines harboring T-DNA insertions at these loci. LincRNAs with Brassicaceae-conserved putative miRNA binding motifs, small open reading frames, or abiotic-stress modulated expression are a few of the annotations that will guide functional analyses into this cryptic portion of the transcriptome.

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.

Functional characterization of proton antiport regulation in the thylakoid membrane.

During photosynthesis, energy is transiently stored as an electrochemical proton gradient across the thylakoid membrane. The resulting proton motive force (pmf) is composed of a membrane potential (ΔΨ) and a proton concentration gradient (ΔpH) and powers the synthesis of ATP. Light energy availability for photosynthesis can change very rapidly and frequently in nature. Thylakoid ion transport proteins buffer the effects that light fluctuations have on photosynthesis by adjusting pmf and its composition. Ion channel activities dissipate ΔΨ, thereby reducing charge recombinations within photosystem II. The dissipation of ΔΨ allows for increased accumulation of protons in the thylakoid lumen, generating the signal that activates feedback downregulation of photosynthesis. Proton export from the lumen via the thylakoid K+ exchange antiporter 3 (KEA3), instead, decreases the ΔpH fraction of the pmf and thereby reduces the regulatory feedback signal. Here, we reveal that the Arabidopsis (Arabidopsis thaliana) KEA3 protein homo-dimerizes via its C-terminal domain. This C-terminus has a regulatory function, which responds to light intensity transients. Plants carrying a C-terminus-less KEA3 variant show reduced feed-back downregulation of photosynthesis and suffer from increased photosystem damage under long-term high light stress. However, during photosynthetic induction in high light, KEA3 deregulation leads to an increase in carbon fixation rates. Together, the data reveal a trade-off between long-term photoprotection and a short-term boost in carbon fixation rates, which is under the control of the KEA3 C-terminus.

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.

Affinity purification with metabolomic and proteomic analysis unravels diverse roles of nucleoside diphosphate kinases.

Interactions between metabolites and proteins play an integral role in all cellular functions. Here we describe an affinity purification (AP) approach in combination with LC/MS-based metabolomics and proteomics that allows, to our knowledge for the first time, analysis of protein-metabolite and protein-protein interactions simultaneously in plant systems. More specifically, we examined protein and small-molecule partners of the three (of five) nucleoside diphosphate kinases present in the Arabidopsis genome (NDPK1-NDPK3). The bona fide role of NDPKs is the exchange of terminal phosphate groups between nucleoside diphosphates (NDPs) and triphosphates (NTPs). However, other functions have been reported, which probably depend on both the proteins and small molecules specifically interacting with the NDPK. Using our approach we identified 23, 17, and 8 novel protein partners of NDPK1, NDPK2, and NDPK3, respectively, with nucleotide-dependent proteins such as actin and adenosine kinase 2 being enriched. Particularly interesting, however, was the co-elution of glutathione S-transferases (GSTs) and reduced glutathione (GSH) with the affinity-purified NDPK1 complexes. Following up on this finding, we could demonstrate that NDPK1 undergoes glutathionylation, opening a new paradigm of NDPK regulation in plants. The described results extend our knowledge of NDPKs, the key enzymes regulating NDP/NTP homeostasis.

TusA influences Fe-S cluster assembly and iron homeostasis in E. coli by reducing the translation efficiency of Fur.

All sulfur transfer pathways have generally a l-cysteine desulfurase as an initial sulfur-mobilizing enzyme in common, which serves as a sulfur donor for the biosynthesis of numerous sulfur-containing biomolecules in the cell. In Escherichia coli, the housekeeping l-cysteine desulfurase IscS has several interaction partners, which bind at different sites of the protein. So far, the interaction sites of IscU, Fdx, CyaY, and IscX involved in iron-sulfur (Fe-S) cluster assembly have been mapped, in addition to TusA, which is required for molybdenum cofactor biosynthesis and mnm5s2U34 tRNA modifications, and ThiI, which is involved in thiamine biosynthesis and s4U8 tRNA modifications. Previous studies predicted that the sulfur acceptor proteins bind to IscS one at a time. E. coli TusA has, however, been suggested to be involved in Fe-S cluster assembly, as fewer Fe-S clusters were detected in a ∆tusA mutant. The basis for this reduction in Fe-S cluster content is unknown. In this work, we investigated the role of TusA in iron-sulfur cluster assembly and iron homeostasis. We show that the absence of TusA reduces the translation of fur, thereby leading to pleiotropic cellular effects, which we dissect in detail in this study.IMPORTANCEIron-sulfur clusters are evolutionarily ancient prosthetic groups. The ferric uptake regulator plays a major role in controlling the expression of iron homeostasis genes in bacteria. We show that a ∆tusA mutant is impaired in the assembly of Fe-S clusters and accumulates iron. TusA, therefore, reduces fur mRNA translation leading to pleiotropic cellular effects.

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.

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.

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.

Heteromeric HSFA2/HSFA3 complexes drive transcriptional memory after heat stress in Arabidopsis.

Adaptive plasticity in stress responses is a key element of plant survival strategies. For instance, moderate heat stress (HS) primes a plant to acquire thermotolerance, which allows subsequent survival of more severe HS conditions. Acquired thermotolerance is actively maintained over several days (HS memory) and involves the sustained induction of memory-related genes. Here we show that FORGETTER3/ HEAT SHOCK TRANSCRIPTION FACTOR A3 (FGT3/HSFA3) is specifically required for physiological HS memory and maintaining high memory-gene expression during the days following a HS exposure. HSFA3 mediates HS memory by direct transcriptional activation of memory-related genes after return to normal growth temperatures. HSFA3 binds HSFA2, and in vivo both proteins form heteromeric complexes with additional HSFs. Our results indicate that only complexes containing both HSFA2 and HSFA3 efficiently promote transcriptional memory by positively influencing histone H3 lysine 4 (H3K4) hyper-methylation. In summary, our work defines the major HSF complex controlling transcriptional memory and elucidates the in vivo dynamics of HSF complexes during somatic stress memory.

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.

Towards a functional understanding of the plant metabolome.

Plants are true organic chemists-the chemical diversity of plant metabolomes goes hand in hand with functional diversity. New, often unexpected roles are being reported for both evolutionarily conserved and well-characterised central metabolites such as amino acids, nucleotides, and sugars, and for specialized/secondary metabolites such as carotenoids, glucosinolates, and terpenoids. Our review aims to highlight recent studies reporting novel roles of metabolites and to emphasize the importance of cell-wide identification of metabolite-protein complexes for the comprehensive, functional understanding of the plant metabolome.

Identification and Characterization of the Heat-Induced Plastidial Stress Granules Reveal New Insight Into Arabidopsis Stress Response.

Plants exhibit different physiological and molecular responses to adverse changes in their environment. One such molecular response is the sequestration of proteins, RNAs, and metabolites into cytoplasmic bodies called stress granules (cSGs). Here we report that, in addition to cSGs, heat stress also induces the formation of SG-like foci (cGs) in the chloroplasts of the model plant Arabidopsis thaliana. Similarly to the cSGs, (i) cpSG assemble rapidly in response to stress and disappear when the stress ceases, (ii) cpSG formation is inhibited by treatment with a translation inhibitor (lincomycin), and (iii) cpSG are composed of a stable core and a fluid outer shell. A previously published protocol for cSG extraction was successfully adapted to isolate cpSG, followed by protein, metabolite, and RNA analysis. Analogously to the cSGs, cpSG sequester proteins essential for SG formation, dynamics, and function, also including RNA-binding proteins with prion-like domain, ATPases and chaperones, and the amino acids proline and glutamic acid. However, the most intriguing observation relates to the cpSG localization of proteins, such as a complete magnesium chelatase complex, which is involved in photosynthetic acclimation to stress. These data suggest that cpSG have a role in plant stress tolerance.

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.