A Map of Protein-Metabolite Interactions Reveals Principles of Chemical Communication

Integrated Protein Solubility Shift Assays for Comprehensive Drug Target Identification on a Proteome-Wide Scale

Target proteins are often stabilized after binding with a ligand and thereby typically become more resistant to denaturation. Based on this phenomenon, several methods without the need to covalently modify the ligand have been developed to identify target proteins for a specific ligand. These methods usually employ complicated workflows with high cost and limited throughput. Here, we develop an iso-pH shift assay (ipHSA) method, a proteome-wide target identification method that detects ligand-induced protein solubility shifts by precipitating proteins with a single concentration of acidic agent followed by protein quantification via data-independent acquisition (DIA). Using a pan-kinase inhibitor, staurosporine, we demonstrated that ipHSA increased throughput compared to the previously developed pH-dependent protein precipitation (pHDPP) method. ipHSA was found to have high complementarity in staurosporine target identification compared with the improved isothermal shift assay (iTSA) and isosolvent shift assay (iSSA) using DIA instead of tandem mass tags (TMTs) for quantification. To further improve target identification sensitivity, we developed an integrated protein solubility shift assay (IPSSA) by pooling the supernatants yielded from ipHSA, iTSA, and iSSA methods. IPSSA exhibited increased sensitivity in screening staurosporine targets by 38, 29, and 38% compared to individual methods. Increasing the number of replicate experiments further enhanced the sensitivity of target identification. Meanwhile, IPSSA also improved the throughput and reduced the cost compared with previous methods. As a fast and efficient tool for drug target identification, IPSSA is expected to have broad applications in the study of the mechanism of action.

Metabolite interactions in the bacterial Calvin cycle and implications for flux regulation

Metabolite-level regulation of enzyme activity is important for microbes to cope with environmental shifts. Knowledge of such regulations can also guide strain engineering for biotechnology. Here we apply limited proteolysis-small molecule mapping (LiP-SMap) to identify and compare metabolite-protein interactions in the proteomes of two cyanobacteria and two lithoautotrophic bacteria that fix CO2 using the Calvin cycle. Clustering analysis of the hundreds of detected interactions shows that some metabolites interact in a species-specific manner. We estimate that approximately 35% of interacting metabolites affect enzyme activity in vitro, and the effect is often minor. Using LiP-SMap data as a guide, we find that the Calvin cycle intermediate glyceraldehyde-3-phosphate enhances activity of fructose-1,6/sedoheptulose-1,7-bisphosphatase (F/SBPase) from Synechocystis sp. PCC 6803 and Cupriavidus necator in reducing conditions, suggesting a convergent feed-forward activation of the cycle. In oxidizing conditions, glyceraldehyde-3-phosphate inhibits Synechocystis F/SBPase by promoting enzyme aggregation. In contrast, the glycolytic intermediate glucose-6-phosphate activates F/SBPase from Cupriavidus necator but not F/SBPase from Synechocystis. Thus, metabolite-level regulation of the Calvin cycle is more prevalent than previously appreciated.

Local flux coordination and global gene expression regulation in metabolic modeling

Genome-scale metabolic networks (GSMs) are fundamental systems biology representations of a cell's entire set of stoichiometrically balanced reactions. However, such static GSMs do not incorporate the functional organization of metabolic genes and their dynamic regulation (e.g., operons and regulons). Specifically, there are numerous topologically coupled local reactions through which fluxes are coordinated; the global growth state often dynamically regulates many gene expression of metabolic reactions via global transcription factor regulators. Here, we develop a GSM reconstruction method, Decrem, by integrating locally coupled reactions and global transcriptional regulation of metabolism by cell state. Decrem produces predictions of flux and growth rates, which are highly correlated with those experimentally measured in both wild-type and mutants of three model microorganisms Escherichia coli, Saccharomyces cerevisiae, and Bacillus subtilis under various conditions. More importantly, Decrem can also explain the observed growth rates by capturing the experimentally measured flux changes between wild-types and mutants. Overall, by identifying and incorporating locally organized and regulated functional modules into GSMs, Decrem achieves accurate predictions of phenotypes and has broad applications in bioengineering, synthetic biology, and microbial pathology.

Fluctuating pH for efficient photomixotrophic succinate production

Cyanobacteria are attracting increasing attention as a photosynthetic chassis organism for diverse biochemical production, however, photoautotrophic production remains inefficient. Photomixotrophy, a method where sugar is used to supplement baseline autotrophic metabolism in photosynthetic hosts, is becoming increasingly popular for enhancing sustainable bioproduction with multiple input energy streams. In this study, the commercially relevant diacid, succinate, was produced photomixotrophically. Succinate is an important industrial chemical that can be used for the production of a wide array of products, from pharmaceuticals to biopolymers. In this system, the substrate, glucose, is transported by a proton symporter and the product, succinate, is hypothesized to be transported by another proton symporter, but in the opposite direction. Thus, low pH is required for the import of glucose and high pH is required for the export of succinate. Succinate production was initiated in a pH 7 medium containing bicarbonate. Glucose was efficiently imported at around neutral pH. Utilization of bicarbonate by CO2 fixation raised the pH of the medium. As succinate, a diacid, was produced, the pH of the medium dropped. By repeating this cycle with additional pH adjustment, those contradictory requirements for transport were overcome. pH affects a variety of biological factors and by cycling from high pH to neutral pH processes such as CO2 fixation rates and CO2 solubility can vary. In this study the engineered strains produced succinate during fluctuating pH conditions, achieving a titer of 5.0 g L-1 after 10 days under shake flask conditions. These results demonstrate the potential for photomixotrophic production as a viable option for the large-scale production of succinate.

Biochemical evidence that the whole compartment activity behavior of GAPDH differs between the cytoplasm and nucleus

Some metabolic enzymes normally occur in the nucleus and cytoplasm. These compartments differ in molecular composition. Since post-translational modification and interaction with allosteric effectors can tune enzyme activity, it follows that the behavior of an enzyme as a catalyst may differ between the cytoplasm and nucleus. We explored this possibility for the glycolytic enzyme glyceraldehyde 3-phosphate dehydrogenase (GAPDH). Homogenates of pristine nuclei and cytoplasms isolated from Xenopus laevis oocytes were used for whole compartment activity profiling in a near-physiological buffer. Titrations of NAD+ revealed similar whole compartment activity profiles for GAPDH in nuclear and cytoplasmic homogenates. Surprisingly however GAPDH in these compartments did not have the same behavior in assays of the dependence of initial velocity (v0) on G3P concentration. First, the peak v0 for nuclear GAPDH was up to 2.5-fold higher than the peak for cytoplasmic GAPDH. Second, while Michaelis Menten-like behavior was observed in all assays of cytoplasm, the v0 versus [G3P] plots for nuclear GAPDH typically exhibited a non-Michaelis Menten (sigmoidal) profile. Apparent Km and Vmax (G3P) values for nuclear GAPDH activity were highly variable, even between replicates of the same sample. Possible sources of this variability include in vitro processing of a metabolite that allosterically regulates GAPDH, turnover of a post-translational modification of the enzyme, and fluctuation of the state of interaction of GAPDH with other proteins. Collectively these findings are consistent with the hypothesis that the environment of the nucleus is distinct from the environment of the cytoplasm with regard to GAPDH activity and its modulation. This finding warrants further comparison of the regulation of nuclear and cytoplasmic GAPDH, as well as whole compartment activity profiling of other enzymes of metabolism with cytosolic and nuclear pools.

Targeting phenylpyruvate restrains excessive NLRP3 inflammasome activation and pathological inflammation in diabetic wound healing

Moderate inflammation is essential for standard wound healing. In pathological conditions, such as diabetes, protracted and refractory wounds are associated with excessive inflammation, manifested by persistent proinflammatory macrophage states. However, the mechanisms are still unclear. Herein, we perform a metabolomic profile and find a significant phenylpyruvate accumulation in diabetic foot ulcers. Increased phenylpyruvate impairs wound healing and augments inflammatory responses, whereas reducing phenylpyruvate via dietary phenylalanine restriction relieves uncontrolled inflammation and benefits diabetic wounds. Mechanistically, phenylpyruvate is ingested into macrophages in a scavenger receptor CD36-dependent manner, binds to PPT1, and inhibits depalmitoylase activity, thus increasing palmitoylation of the NLRP3 protein. Increased NLRP3 palmitoylation is found to enhance NLRP3 protein stability, decrease lysosome degradation, and promote NLRP3 inflammasome activation and the release of inflammatory factors, such as interleukin (IL)-1β, finally triggering the proinflammatory macrophage phenotype. Our study suggests a potential strategy of targeting phenylpyruvate to prevent excessive inflammation in diabetic wounds.

Preparation of poly (methacrylic acid)/graphene oxide aerogel as solid-phase extraction adsorbent for extraction and determination of dopamine and tyrosine in urine of patients with depression

Dopamine (DA) and l-tyrosine (l-Tyr) are neurotransmitters involved in various neuropsychiatric disorders. Therefore, it is important to monitor their levels for diagnosis and treatment. In this study, we synthesized poly (methacrylic acid)/graphene oxide aerogels (p(MAA)/GOA) by in situ polymerization and freeze-drying using graphene oxide and methacrylic acid as substrates. Then, the p(MAA)/GOA were applied as solid-phase extraction adsorbents to extract DA and l-Tyr from urine samples, followed by quantification using high performance liquid chromatography (HPLC). The p(MAA)/GOA showed better adsorption performance for DA and l-Tyr than commercial adsorbents, likely as a result of the strong adsorption of the target analytes via π-π and hydrogen bonding interactions. Further, the developed method had good linearity (r > 0.9990) at concentrations of DA and l-Tyr of 0.075-2.0 and 0.75-20.0 μg mL^-1, respectively, as well as a limit of detection of 0.018-0.048 μg mL^-1, limit of quantitation of 0.059-0.161 μg mL^-1, spiked recovery of 91.1-104.0%, and interday precision of 3.58-7.30%.The method was successfully applied to determine DA and l-Tyr in the urine samples of patients suffering from depression, demonstrating its potential for clinical applications.

Targeting 14-3-3ζ by a small-molecule compound AI-34 maintains epithelial barrier integrity and alleviates colitis in mice via stabilizing β-catenin

Aberrant intestinal epithelial barrier function is the primary pathology of Ulcerative colitis (UC), making it a desirable drug target. In this study, our small-molecule compound AI-34 exerted a significant protective effect in an LPS-induced epithelial barrier injury model. In vitro, AI-34 treatment significantly decreased cell permeability, increased transmembrane resistance, and maintained the junctional protein (ZO-1 and E-cadherin) levels in monolayer cells. Using the LiP-small molecule mapping approach (LiP-SMap), we demonstrated that AI-34 binds to 14-3-3ζ. AI-34 promoted the interaction between 14-3-3ζ and β-catenin, decreasing the ubiquitination of β-catenin and thus maintaining intestinal epithelial barrier function. Finally, AI-34 triggered the stabilization of β-catenin mediated by 14-3-3ζ, provoking a significant improvement in the DSS-induced colitis model. Our findings suggest that AI-34 may be a promising candidate for UC treatment.

MPI-VGAE: protein-metabolite enzymatic reaction link learning by variational graph autoencoders

Enzymatic reactions are crucial to explore the mechanistic function of metabolites and proteins in cellular processes and to understand the etiology of diseases. The increasing number of interconnected metabolic reactions allows the development of in silico deep learning-based methods to discover new enzymatic reaction links between metabolites and proteins to further expand the landscape of existing metabolite-protein interactome. Computational approaches to predict the enzymatic reaction link by metabolite-protein interaction (MPI) prediction are still very limited. In this study, we developed a Variational Graph Autoencoders (VGAE)-based framework to predict MPI in genome-scale heterogeneous enzymatic reaction networks across ten organisms. By incorporating molecular features of metabolites and proteins as well as neighboring information in the MPI networks, our MPI-VGAE predictor achieved the best predictive performance compared to other machine learning methods. Moreover, when applying the MPI-VGAE framework to reconstruct hundreds of metabolic pathways, functional enzymatic reaction networks and a metabolite-metabolite interaction network, our method showed the most robust performance among all scenarios. To the best of our knowledge, this is the first MPI predictor by VGAE for enzymatic reaction link prediction. Furthermore, we implemented the MPI-VGAE framework to reconstruct the disease-specific MPI network based on the disrupted metabolites and proteins in Alzheimer's disease and colorectal cancer, respectively. A substantial number of novel enzymatic reaction links were identified. We further validated and explored the interactions of these enzymatic reactions using molecular docking. These results highlight the potential of the MPI-VGAE framework for the discovery of novel disease-related enzymatic reactions and facilitate the study of the disrupted metabolisms in diseases.

Mapping Peptide-Protein Interactions by Amine-Reactive Cleavable Photoaffinity Reagents

Photoaffinity labeling followed by tandem mass spectrometry is an often used strategy to identify protein targets of small-molecule drugs or drug candidates, which, under ideal conditions, enables the identification of the actual drug binding site. In the case of bioactive peptides, however, identifying the distinct binding site is hampered because of complex fragmentation patterns during tandem mass spectrometry. We here report the development and use of small cleavable photoaffinity reagents that allow functionalization of bioactive peptides for light-induced covalent binding to their protein targets. Upon cleavage of the covalently linked peptide drug, a chemical remnant of a defined mass remains on the bound amino acid, which is then used to unambiguously identify the drug binding site. Applying our approach to known peptide-drug/protein pairs with reported crystal structures, such as the calmodulin-melittin interaction, we were able to validate the identified binding sites based on structural models. Overall, our cleavable photoaffinity labeling strategy represents a powerful tool to enable the identification of protein targets and specific binding sites of a wide variety of bioactive peptides in the future.

Prediction of metabolite-protein interactions based on integration of machine learning and constraint-based modeling

Motivation

Metabolite-protein interactions play an important role in regulating protein functions and metabolism. Yet, predictions of metabolite-protein interactions using genome-scale metabolic networks are lacking. Here, we fill this gap by presenting a computational framework, termed SARTRE, that employs features corresponding to shadow prices determined in the context of flux variability analysis to predict metabolite-protein interactions using supervised machine learning.

Results

By using gold standards for metabolite-protein interactomes and well-curated genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae, we found that the implementation of SARTRE with random forest classifiers accurately predicts metabolite-protein interactions, supported by an average area under the receiver operating curve of 0.86 and 0.85, respectively. Ranking of features based on their importance for classification demonstrated the key role of shadow prices in predicting metabolite-protein interactions. The quality of predictions is further supported by the excellent agreement of the organism-specific classifiers on unseen interactions shared between the two model organisms. Further, predictions from SARTRE are highly competitive against those obtained from a recent deep-learning approach relying on a variety of protein and metabolite features. Together, these findings show that features extracted from constraint-based analyses of metabolic networks pave the way for understanding the functional roles of the interactions between proteins and small molecules.

Availability and implementation

https://github.com/fayazsoleymani/SARTRE.

The Introduction of Detergents in Thermal Proteome Profiling Requires Lowering the Applied Temperatures for Efficient Target Protein Identification

Although the use of detergents in thermal proteome profiling (TPP) has become a common practice to identify membrane protein targets in complex biological samples, surprisingly, there is no proteome-wide investigation into the impacts of detergent introduction on the target identification performance of TPP. In this study, we assessed the target identification performance of TPP in the presence of a commonly used non-ionic detergent or a zwitterionic detergent using a pan-kinase inhibitor staurosporine, our results showed that the addition of either of these detergents significantly impaired the identification performance of TPP at the optimal temperature for soluble target protein identification. Further investigation showed that detergents destabilized the proteome and increased protein precipitation. By lowering the applied temperature point, the target identification performance of TPP with detergents is significantly improved and is comparable to that in the absence of detergents. Our findings provide valuable insight into how to select the appropriate temperature range when detergents are used in TPP. In addition, our results also suggest that the combination of detergent and heat may serve as a novel precipitation-inducing force that can be applied for target protein identification.

AzidoTMT Enables Direct Enrichment and Highly Multiplexed Quantitation of Proteome-Wide Functional Residues

Recent advances in targeted covalent inhibitors have aroused significant interest for their potential in drug development for difficult therapeutic targets. Proteome-wide profiling of functional residues is an integral step of covalent drug discovery aimed at defining actionable sites and evaluating compound selectivity in cells. A classical workflow for this purpose is called IsoTOP-ABPP, which employs an activity-based probe and two isotopically labeled azide-TEV-biotin tags to mark, enrich, and quantify proteome from two samples. Here we report a novel isobaric 11plex-AzidoTMT reagent and a new workflow, named AT-MAPP, that significantly expands multiplexing power as compared to the original isoTOP-ABPP. We demonstrate its application in identifying cysteine on- and off-targets using a KRAS G12C covalent inhibitor ARS-1620. However, changes in some of these hits can be explained by modulation at the protein and post-translational levels. Thus, it would be crucial to interrogate site-level bona fide changes in concurrence to proteome-level changes for corroboration. In addition, we perform a multiplexed covalent fragment screening using four acrylamide-based compounds as a proof-of-concept. This study identifies a diverse set of liganded cysteine residues in a compound-dependent manner with an average hit rate of 0.07% in intact cell. Lastly, we screened 20 sulfonyl fluoride-based compounds to demonstrate that the AT-MAPP assay is flexible for noncysteine functional residues such as tyrosine and lysine. Overall, we envision that 11plex-AzidoTMT will be a useful addition to the current toolbox for activity-based protein profiling and covalent drug development.

Conditional lethality profiling reveals anticancer mechanisms of action and drug-nutrient interactions

Chemical screening studies have identified drug sensitivities across hundreds of cancer cell lines but most putative therapeutics fail to translate. Discovery and development of drug candidates in models that more accurately reflect nutrient availability in human biofluids may help in addressing this major challenge. Here we performed high-throughput screens in conventional versus Human Plasma-Like Medium (HPLM). Sets of conditional anticancer compounds span phases of clinical development and include non-oncology drugs. Among these, we characterize a unique dual-mechanism of action for brivudine, an agent otherwise approved for antiviral treatment. Using an integrative approach, we find that brivudine affects two independent targets in folate metabolism. We also traced conditional phenotypes for several drugs to the availability of nucleotide salvage pathway substrates and verified others for compounds that seemingly elicit off-target anticancer effects. Our findings establish generalizable strategies for exploiting conditional lethality in HPLM to reveal therapeutic candidates and mechanisms of action.

Plant Extracts as a Source of Natural Products with Potential Antimalarial Effects: An Update from 2018 to 2022

Malaria kills more than 500,000 people yearly, mainly affecting Africa and Southeast Asia. The disease is caused by the protozoan parasite from the genus Plasmodium, with Plasmodium vivax and Plasmodium falciparum being the main species that cause the disease in humans. Although substantial progress has been observed in malaria research in the last years, the threat of the spread of Plasmodium parasites persists. Artemisinin-resistant strains of this parasite have been reported mainly in Southeast Asia, highlighting the urgent need to develop more effective and safe antimalarial drugs. In this context, natural sources, mainly from flora, remain underexplored antimalarial spaces. The present mini-review explores this space focusing on plant extracts and some of their isolated natural products with at least in vitro antiplasmodial effects reported in the literature comprising the last five years (2018-2022).

Engineering the plant metabolic system by exploiting metabolic regulation

Plants are the most sophisticated biofactories and sources of food and biofuels present in nature. By engineering plant metabolism, the production of desired compounds can be increased and the nutritional or commercial value of the plant species can be improved. However, this can be challenging because of the complexity of the regulation of multiple genes and the involvement of different protein interactions. To improve metabolic engineering (ME) capabilities, different tools and strategies for rerouting the metabolic pathways have been developed, including genome editing and transcriptional regulation approaches. In addition, cutting-edge technologies have provided new methods for understanding uncharacterized biosynthetic pathways, protein degradation mechanisms, protein-protein interactions, or allosteric feedback, enabling the design of novel ME approaches.

Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations

Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.

An automated workflow for multi-omics screening of microbial model organisms

Multi-omics datasets are becoming of key importance to drive discovery in fundamental research as much as generating knowledge for applied biotechnology. However, the construction of such large datasets is usually time-consuming and expensive. Automation might enable to overcome these issues by streamlining workflows from sample generation to data analysis. Here, we describe the construction of a complex workflow for the generation of high-throughput microbial multi-omics datasets. The workflow comprises a custom-built platform for automated cultivation and sampling of microbes, sample preparation protocols, analytical methods for sample analysis and automated scripts for raw data processing. We demonstrate possibilities and limitations of such workflow in generating data for three biotechnologically relevant model organisms, namely Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida.

Mapping metabolite change in the mouse brain after esketamine injection by ambient mass spectrometry imaging and metabolomics

Ketamine is a new, fast, and effective antidepression treatment method; however, the possible dissociation effects, sensory changes, abuse risk, and the inability to accurately identify whether patients have a significant response to ketamine limit its clinical use. Further exploration of the antidepressant mechanisms of ketamine will contribute to its safe and practical application. Metabolites, the products of upstream gene expression and protein regulatory networks, play an essential role in various physiological and pathophysiological processes. In traditional metabonomics it is difficult to achieve the spatial localization of metabolites, which limits the further analysis of brain metabonomics by researchers. Here, we used a metabolic network mapping method called ambient air flow-assisted desorption electrospray ionization (AFADESI)-mass spectrometry imaging (MSI). We found the main changes in glycerophospholipid metabolism around the brain and sphingolipid metabolism changed mainly in the globus pallidus, which showed the most significant metabolite change after esketamine injection. The spatial distribution of metabolic changes was evaluated in the whole brain, and the potential mechanism of esketamine's antidepressant effect was explored in this research.

Co-evolution-based prediction of metal-binding sites in proteomes by machine learning

Metal ions have various important biological roles in proteins, including structural maintenance, molecular recognition and catalysis. Previous methods of predicting metal-binding sites in proteomes were based on either sequence or structural motifs. Here we developed a co-evolution-based pipeline named 'MetalNet' to systematically predict metal-binding sites in proteomes. We applied MetalNet to proteomes of four representative prokaryotic species and predicted 4,849 potential metalloproteins, which substantially expands the currently annotated metalloproteomes. We biochemically and structurally validated previously unannotated metal-binding sites in several proteins, including apo-citrate lyase phosphoribosyl-dephospho-CoA transferase citX, an Escherichia coli enzyme lacking structural or sequence homology to any known metalloprotein (Protein Data Bank (PDB) codes: 7DCM and 7DCN ). MetalNet also successfully recapitulated all known zinc-binding sites from the human spliceosome complex. The pipeline of MetalNet provides a unique and enabling tool for interrogating the hidden metalloproteome and studying metal biology.

Imaging the metabolic reprograming of fatty acid synthesis pathway enables new diagnostic and therapeutic opportunity for breast cancer

BACKGROUND

Reprogrammed metabolic network is a key hallmark of cancer. Profiling cancer metabolic alterations with spatial signatures not only provides clues for understanding cancer biochemical heterogeneity, but also helps to decipher the possible roles of metabolic reprogramming in cancer development.

METHODS

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) technique was used to characterize the expressions of fatty acids in breast cancer tissues. Specific immunofluorescence staining was further carried out to investigate the expressions of fatty acid synthesis-related enzymes.

RESULTS

The distributions of 23 fatty acids in breast cancer tissues have been mapped, and the levels of most fatty acids in cancer tissues are significantly higher than those in adjacent normal tissues. Two metabolic enzymes, fatty acid synthase (FASN) and acetyl CoA carboxylase (ACC), which being involved in the de novo synthesis of fatty acid were found to be up-regulated in breast cancer. Targeting the up-regulation of FASN and ACC is an effective approach to limiting the growth, proliferation, and metastasis of breast cancer cells.

CONCLUSIONS

These spatially resolved findings enhance our understanding of cancer metabolic reprogramming and give an insight into the exploration of metabolic vulnerabilities for better cancer treatment.

Dynamic fluctuations in a bacterial metabolic network

The operation of the central metabolism is typically assumed to be deterministic, but dynamics and high connectivity of the metabolic network make it potentially prone to generating fluctuations. However, time-resolved measurements of metabolite levels in individual cells that are required to characterize such fluctuations remained a challenge, particularly in small bacterial cells. Here we use single-cell metabolite measurements based on Förster resonance energy transfer, combined with computer simulations, to explore the real-time dynamics of the metabolic network of Escherichia coli. We observe that steplike exposure of starved E. coli to glycolytic carbon sources elicits large periodic fluctuations in the intracellular concentration of pyruvate in individual cells. These fluctuations are consistent with predicted oscillatory dynamics of E. coli metabolic network, and they are primarily controlled by biochemical reactions around the pyruvate node. Our results further indicate that fluctuations in glycolysis propagate to other cellular processes, possibly leading to temporal heterogeneity of cellular states within a population.

Network medicine: an approach to complex kidney disease phenotypes

Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.

Farrerol directly activates the deubiqutinase UCHL3 to promote DNA repair and reprogramming when mediated by somatic cell nuclear transfer

Farrerol, a natural flavanone, promotes homologous recombination (HR) repair to improve genome-editing efficiency, but the specific protein that farrerol directly targets to regulate HR repair and the underlying molecular mechanisms have not been determined. Here, we find that the deubiquitinase UCHL3 is the direct target of farrerol. Mechanistically, farrerol enhanced the deubiquitinase activity of UCHL3 to promote RAD51 deubiquitination, thereby improving HR repair. Importantly, we find that embryos of somatic cell nuclear transfer (SCNT) exhibited defective HR repair, increased genomic instability and aneuploidy, and that the farrerol treatment post nuclear transfer enhances HR repair, restores transcriptional and epigenetic network, and promotes SCNT embryo development. Ablating UCHL3 significantly attenuates farrerol-mediated stimulation in HR and SCNT embryo development. In summary, we identify farrerol as an activator of the deubiquitinase UCHL3, highlighted the importance of HR and epigenetic changes in SCNT reprogramming and provide a feasible method to promote SCNT efficiency.

PrePCI: A structure- and chemical similarity-informed database of predicted protein compound interactions

We describe the Predicting Protein-Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between 6.8 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence- and structural similarity-based metrics are established between template proteins, T, in the Protein Data Bank that bind compounds, C, and query proteins in the model database, Q. When the metrics exceed threshold values, it is assumed that C also binds to Q with a likelihood ratio (LR) derived from machine learning. If the relationship is based on structural similarity, the LR is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT-scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto coefficient identifies other small molecules that may bind to Q. An overall LR for the binding of C to Q is obtained from Naive Bayesian statistics. The PrePCI database can be queried by entering a UniProt ID or gene name for a protein to obtain a list of compounds predicted to bind to it along with associated LRs. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database to lead discovery, elucidation of drug mechanism of action, and biological function annotation are described.

Mass spectrometric exploration of phytohormone profiles and signaling networks

Phytohormones have crucial roles in plant growth, development, and acclimation to environmental stress; however, measuring phytohormone levels and unraveling their complex signaling networks and interactions remains challenging. Mass spectrometry (MS) has revolutionized the study of complex biological systems, enabling the comprehensive identification and quantification of phytohormones and their related targets. Here, we review recent advances in MS technologies and highlight studies that have used MS to discover and analyze phytohormone-mediated molecular events. In particular, we focus on the application of MS for profiling phytohormones, elucidating phosphorylation signaling, and mapping protein interactions in plants.

Plasma Protein-Mediated Uptake and Contradictions to the Free Drug Hypothesis: A Critical Review

According to the free drug hypothesis (FDH), only free, unbound drug is available to interact with biological targets. This hypothesis is the fundamental principle that continues to explain the vast majority of all pharmacokinetic and pharmacodynamic processes. Under the FDH, the free drug concentration at the target site is considered the driver of pharmacodynamic activity and pharmacokinetic processes. However, deviations from the FDH are observed in hepatic uptake and clearance predictions, where observed unbound intrinsic hepatic clearance (CL_int,u) is larger than expected. Such deviations are commonly observed when plasma proteins are present and form the basis of the so-called plasma protein-mediated uptake effect (PMUE). This review will discuss the basis of plasma protein binding as it pertains to hepatic clearance based on the FDH, as well as several hypotheses that may explain the underlying mechanisms of PMUE. Notably, some, but not all, potential mechanisms remained aligned with the FDH. Finally, we will outline possible experimental strategies to elucidate PMUE mechanisms. Understanding the mechanisms of PMUE and its potential contribution to clearance underprediction is vital to improving the drug development process.

Metabolic sensing and control in mitochondria

Mitochondria are membrane-enclosed organelles with endosymbiotic origins, harboring independent genomes and a unique biochemical reaction network. To perform their critical functions, mitochondria must maintain a distinct biochemical environment and coordinate with the cytosolic metabolic networks of the host cell. This coordination requires them to sense and control metabolites and respond to metabolic stresses. Indeed, mitochondria adopt feedback or feedforward control strategies to restrain metabolic toxicity, enable metabolic conservation, ensure stable levels of key metabolites, allow metabolic plasticity, and prevent futile cycles. A diverse panel of metabolic sensors mediates these regulatory circuits whose malfunctioning leads to inborn errors of metabolism with mild to severe clinical manifestations. In this review, we discuss the logic and molecular basis of metabolic sensing and control in mitochondria. The past research outlined recurring patterns in mitochondrial metabolic sensing and control and highlighted key knowledge gaps in this organelle that are potentially addressable with emerging technological breakthroughs.

Glutamate ionotropic receptor NMDA type subunit 1: A novel potential protein target of dapagliflozin against renal interstitial fibrosis

Renal interstitial fibrosis (RIF) is the final pathway for chronic kidney diseases (CKD) to end-stage renal disease, with no ideal therapy at present. Previous studies indicated that sodium glucose co-transporter-2 inhibitor (SGLT2i) dapagliflozin had the effect of anti-RIF, but the mechanism remains elusive and the renal protective effect could not be fully explained by singly targeting SGLT2. In this study, we aimed to explore the mechanism of dapagliflozin against RIF and identify novel potential targets. Firstly, dapagliflozin treatment improved pro-fibrotic indicators in unilateral ureteral obstruction mice and transforming growth factor beta 1 induced human proximal tubular epithelial cells. Then, transcriptomics and bioinformatics analysis were performed, and results revealed that dapagliflozin against RIF by regulating inflammation and oxidative stress related signals. Subsequently, targets prediction and analysis demonstrated that glutamate ionotropic receptor NMDA type subunit 1 (GRIN1) was a novel potential target of dapagliflozin, which was related to inflammation and oxidative stress related signals. Moreover, molecular dynamics simulation revealed that dapagliflozin could stably bind to GRIN1 protein and change its spatial conformation. Furthermore, human renal samples and Nephroseq data were used for GRIN1 expression evaluation, and the results showed that GRIN1 expression were increased in renal tissues of CKD and RIF patients than controls. Additionally, further studies demonstrated that dapagliflozin could reduce intracellular calcium influx in renal tubular cells, which depended on regulating GRIN1 protein but not gene. In conclusion, GRIN1 is probably a novel target of dapagliflozin against RIF.

A complete allosteric map of a GTPase switch in its native cellular network

Allosteric regulation is central to protein function in cellular networks. A fundamental open question is whether cellular regulation of allosteric proteins occurs only at a few defined positions or at many sites distributed throughout the structure. Here, we probe the regulation of GTPases-protein switches that control signaling through regulated conformational cycling-at residue-level resolution by deep mutagenesis in the native biological network. For the GTPase Gsp1/Ran, we find that 28% of the 4,315 assayed mutations show pronounced gain-of-function responses. Twenty of the sixty positions enriched for gain-of-function mutations are outside the canonical GTPase active site switch regions. Kinetic analysis shows that these distal sites are allosterically coupled to the active site. We conclude that the GTPase switch mechanism is broadly sensitive to cellular allosteric regulation. Our systematic discovery of new regulatory sites provides a functional map to interrogate and target GTPases controlling many essential biological processes.

Genome-scale enzymatic reaction prediction by variational graph autoencoders

Background

Enzymatic reaction networks are crucial to explore the mechanistic function of metabolites and proteins in biological systems and understanding the etiology of diseases and potential target for drug discovery. The increasing number of metabolic reactions allows the development of deep learning-based methods to discover new enzymatic reactions, which will expand the landscape of existing enzymatic reaction networks to investigate the disrupted metabolisms in diseases.

Results

In this study, we propose the MPI-VGAE framework to predict metabolite-protein interactions (MPI) in a genome-scale heterogeneous enzymatic reaction network across ten organisms with thousands of enzymatic reactions. We improved the Variational Graph Autoencoders (VGAE) model to incorporate both molecular features of metabolites and proteins as well as neighboring features to achieve the best predictive performance of MPI. The MPI-VGAE framework showed robust performance in the reconstruction of hundreds of metabolic pathways and five functional enzymatic reaction networks. The MPI-VGAE framework was also applied to a homogenous metabolic reaction network and achieved as high performance as other state-of-art methods. Furthermore, the MPI-VGAE framework could be implemented to reconstruct the disease-specific MPI network based on hundreds of disrupted metabolites and proteins in Alzheimer's disease and colorectal cancer, respectively. A substantial number of new potential enzymatic reactions were predicted and validated by molecular docking. These results highlight the potential of the MPI-VGAE framework for the discovery of novel disease-related enzymatic reactions and drug targets in real-world applications.

Data availability and implementation

The MPI-VGAE framework and datasets are publicly accessible on GitHub https://github.com/mmetalab/mpi-vgae .

Author Biographies

Cheng Wang received his Ph.D. in Chemistry from The Ohio State Univesity, USA. He is currently a Assistant Professor in School of Public Health at Shandong University, China. His research interests include bioinformatics, machine learning-based approach with applications to biomedical networks. Chuang Yuan is a research assistant at Shandong University. He obtained the MS degree in Biology at the University of Science and Technology of China. His research interests include biochemistry & molecular biology, cell biology, biomedicine, bioinformatics, and computational biology. Yahui Wang is a PhD student in Department of Chemistry at Washington University in St. Louis. Her research interests include biochemistry, mass spectrometry-based metabolomics, and cancer metabolism. Ranran Chen is a master graduate student in School of Public Health at University of Shandong, China. Yuying Shi is a master graduate student in School of Public Health at University of Shandong, China. Gary J. Patti is the Michael and Tana Powell Professor at Washington University in St. Louis, where he holds appointments in the Department of Chemisrty and the Department of Medicine. He is also the Senior Director of the Center for Metabolomics and Isotope Tracing at Washington University. His research interests include metabolomics, bioinformatics, high-throughput mass spectrometry, environmental health, cancer, and aging. Leyi Wei received his Ph.D. in Computer Science from Xiamen University, China. He is currently a Professor in School of Software at Shandong University, China. His research interests include machine learning and its applications to bioinformatics. Qingzhen Hou received his Ph.D. in the Centre for Integrative Bioinformatics VU (IBIVU) from Vrije Universiteit Amsterdam, the Netherlands. Since 2020, He has serveved as the head of Bioinformatics Center in National Institute of Health Data Science of China and Assistant Professor in School of Public Health, Shandong University, China. His areas of research are bioinformatics and computational biophysics.

Key points

Genome-scale heterogeneous networks of metabolite-protein interaction (MPI) based on thousands of enzymatic reactions across ten organisms were constructed semi-automatically.An enzymatic reaction prediction method called Metabolite-Protein Interaction Variational Graph Autoencoders (MPI-VGAE) was developed and optimized to achieve higher performance compared with existing machine learning methods by using both molecular features of metabolites and proteins.MPI-VGAE is broadly useful for applications involving the reconstruction of metabolic pathways, functional enzymatic reaction networks, and homogenous networks (e.g., metabolic reaction networks).By implementing MPI-VGAE to Alzheimer's disease and colorectal cancer, we obtained several novel disease-related protein-metabolite reactions with biological meanings. Moreover, we further investigated the reasonable binding details of protein-metabolite interactions using molecular docking approaches which provided useful information for disease mechanism and drug design.

The utility of proteases in proteomics, from sequence profiling to structure and function analysis

In mass spectrometry (MS)-based bottom-up proteomics, protease digestion plays an essential role in profiling both proteome sequences and post-translational modifications (PTMs). Trypsin is the gold standard in digesting intact proteins into small-size peptides, which are more suitable for high-performance liquid chromatography (HPLC) separation and tandem MS (MS/MS) characterization. However, protein sequences lacking Lys and Arg cannot be cleaved by trypsin and may be missed in conventional proteomic analysis. Proteases with cleavage sites complementary to trypsin are widely applied in proteomic analysis to greatly improve the coverage of proteome sequences and PTM sites. In this review, we survey the common and newly emerging proteases used in proteomics analysis mainly in the last 5 years, focusing on their unique cleavage features and specific proteomics applications such as missing protein characterization, new PTM discovery, and de novo sequencing. In addition, we summarize the applications of proteases in structural proteomics and protein function analysis in recent years. Finally, we discuss the future development directions of new proteases and applications in proteomics.

Proteome-wide structural changes measured with limited proteolysis-mass spectrometry: an advanced protocol for high-throughput applications

Proteins regulate biological processes by changing their structure or abundance to accomplish a specific function. In response to a perturbation, protein structure may be altered by various molecular events, such as post-translational modifications, protein-protein interactions, aggregation, allostery or binding to other molecules. The ability to probe these structural changes in thousands of proteins simultaneously in cells or tissues can provide valuable information about the functional state of biological processes and pathways. Here, we present an updated protocol for LiP-MS, a proteomics technique combining limited proteolysis with mass spectrometry, to detect protein structural alterations in complex backgrounds and on a proteome-wide scale. In LiP-MS, proteins undergo a brief proteolysis in native conditions followed by complete digestion in denaturing conditions, to generate structurally informative proteolytic fragments that are analyzed by mass spectrometry. We describe advances in the throughput and robustness of the LiP-MS workflow and implementation of data-independent acquisition-based mass spectrometry, which together achieve high reproducibility and sensitivity, even on large sample sizes. We introduce MSstatsLiP, an R package dedicated to the analysis of LiP-MS data for the identification of structurally altered peptides and differentially abundant proteins. The experimental procedures take 3 d, mass spectrometric measurement time and data processing depend on sample number and statistical analysis typically requires ~1 d. These improvements expand the adaptability of LiP-MS and enable wide use in functional proteomics and translational applications.

The Knowns and Unknowns in Protein-Metabolite Interactions

Increasing attention has been focused on the study of protein-metabolite interactions (PMI), which play a key role in regulating protein functions and directing an orchestra of cellular processes. The investigation of PMIs is complicated by the fact that many such interactions are extremely short-lived, which requires very high resolution in order to detect them. As in the case of protein-protein interactions, protein-metabolite interactions are still not clearly defined. Existing assays for detecting protein-metabolite interactions have an additional limitation in the form of a limited capacity to identify interacting metabolites. Thus, although recent advances in mass spectrometry allow the routine identification and quantification of thousands of proteins and metabolites today, they still need to be improved to provide a complete inventory of biological molecules, as well as all interactions between them. Multiomic studies aimed at deciphering the implementation of genetic information often end with the analysis of changes in metabolic pathways, as they constitute one of the most informative phenotypic layers. In this approach, the quantity and quality of knowledge about PMIs become vital to establishing the full scope of crosstalk between the proteome and the metabolome in a biological object of interest. In this review, we analyze the current state of investigation into the detection and annotation of protein-metabolite interactions, describe the recent progress in developing associated research methods, and attempt to deconstruct the very term "interaction" to advance the field of interactomics further.

Out With a Bang: Celebrating Global Chemical Biology

On November 8-10, 2022, 163 participants from all over the world gathered at the Campus Biotech in Geneva, Switzerland to share in the latest research in chemical biology. The fourth international symposium of the Swiss National Centres of Competence in Research (NCCR) Chemical Biology coincided with the end of this successful research consortium, and as such this event marked a celebration of the past 12 years of chemical biology research in Switzerland. The inspiring talks delivered by the 15 well-known scientists, balanced in gender, expertise, and geographic location, as well as the numerous poster presentations by junior scientists showcased the breadth of global chemical biology and the bright future ahead.

Evaluation of a Pooling Chemoproteomics Strategy with an FDA-Approved Drug Library

Chemoproteomics is a key platform for characterizing the mode of action for compounds, especially for targeted protein degraders such as proteolysis targeting chimeras (PROTACs) and molecular glues. With deep proteome coverage, multiplexed tandem mass tag-mass spectrometry (TMT-MS) can tackle up to 18 samples in a single experiment. Here, we present a pooling strategy for further enhancing the throughput and apply the strategy to an FDA-approved drug library (95 best-in-class compounds). The TMT-MS-based pooling strategy was evaluated in the following steps. First, we demonstrated the capability of TMT-MS by analyzing more than 15 000 unique proteins (> 12 000 gene products) in HEK293 cells treated with five PROTACs (two BRD/BET degraders and three degraders for FAK, ALK, and BTK kinases). We then introduced a rationalized pooling strategy to separate structurally similar compounds in different pools and identified the proteomic response to 14 pools from the drug library. Finally, we validated the proteomic response from one pool by reprofiling the cells via treatment with individual drugs with sufficient replicates. Interestingly, numerous proteins were found to change upon drug treatment, including AMD1, ODC1, PRKX, PRKY, EXO1, AEN, and LRRC58 with 7-hydroxystaurosporine; C6orf64, HMGCR, and RRM2 with Sorafenib; SYS1 and ALAS1 with Venetoclax; and ATF3, CLK1, and CLK4 with Palbocilib. Thus, pooling chemoproteomics screening provides an efficient method for dissecting the molecular targets of compound libraries.

The potential biomarker TIFA regulates pyroptosis in sepsis-induced acute kidney injury

Sepsis is the leading cause of acute kidney injury (AKI), and specific treatment options for septic AKI are very limited. Here, we used bulk RNA sequencing of a septic model of AKI to characterize the mRNA profile during AKI. The differentially expressed genes (DEGs) mainly participate in the inflammatory response and metabolic processes. Analysis of comprehensive mRNA-seq datasets revealed sepsis-induced AKI-specific cohorts of expressed genes, and six DEGs were tested in urine from septic patients with/without AKI. TRAF-interacting protein with forkhead-associated domain (TIFA) and fatty acid synthase (FASN) were differentially expressed in the urine from the sepsis-induced AKI group. Furthermore, we found that TIFA expression was significantly upregulated in mouse kidney tissue following cecal ligation and puncture (CLP). We sought to investigate its role in lipopolysaccharide (LPS) (TLR4 ligand)- and oligodeoxynucleotides (ODN) (TLR9 ligand)-treated human kidney cells and mouse. TIFA was located in Lotus tetragonolobus lectin (LTL) positive renal cells in kidney tissue, which was stained by immunofluorescence. Exposure of HK-2 cells to LPS and ODN caused disruption of the mitochondrial transmembrane potential. The results of transmission electron microscope (TEM) showed that mitochondrial damages were improved in TIFA-knockdown group. Moreover, knockdown of TIFA resulted in a decrease in the percentage of annexin V-positive and PI-negative cells after ODN treatment. The protein of NLRP3, Caspase-1 and GSDMD were also decreased when si-TIFA was transferred into HK-2 cells following LPS and ODN treatment. Activation of TIFA enhanced the expression of IL-1β and IL18. These results indicated that TIFA induced pyroptosis by activating the mitochondrial damage. Our study provides a detailed transcriptomic description of the renal cellular responses after sepsis. Our study suggest that TIFA is involved in pyroptosis by activating the mitochondrial damage and may be a therapeutic target to treat sepsis-induced kidney injury.

Self- and cross-attention accurately predicts metabolite-protein interactions

Metabolites regulate activity of proteins and thereby affect cellular processes in all organisms. Despite extensive efforts to catalogue the metabolite-protein interactome in different organisms by employing experimental and computational approaches, the coverage of such interactions remains fragmented, particularly for eukaryotes. Here, we make use of two most comprehensive collections, BioSnap and STITCH, of metabolite-protein interactions from seven eukaryotes as gold standards to train a deep learning model that relies on self- and cross-attention over protein sequences. This innovative protein-centric approach results in interaction-specific features derived from protein sequence alone. In addition, we designed and assessed a first double-blind evaluation protocol for metabolite-protein interactions, demonstrating the generalizability of the model. Our results indicated that the excellent performance of the proposed model over simpler alternatives and randomized baselines is due to the local and global features generated by the attention mechanisms. As a results, the predictions from the deep learning model provide a valuable resource for studying metabolite-protein interactions in eukaryotes.

Blockade of NMT1 enzymatic activity inhibits N-myristoylation of VILIP3 protein and suppresses liver cancer progression

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. Identification of the underlying mechanism of HCC progression and exploration of new therapeutic drugs are urgently needed. Here, a compound library consisting of 419 FDA-approved drugs was taken to screen potential anticancer drugs. A series of functional assays showed that desloratadine, an antiallergic drug, can repress proliferation in HCC cell lines, cell-derived xenograft (CDX), patient-derived organoid (PDO) and patient-derived xenograft (PDX) models. N-myristoyl transferase 1 (NMT1) was identified as a target protein of desloratadine by drug affinity responsive target stability (DARTS) and surface plasmon resonance (SPR) assays. Upregulation of NMT1 expression enhanced but NMT1 knockdown suppressed tumor growth in vitro and in vivo. Metabolic labeling and mass spectrometry analyses revealed that Visinin-like protein 3 (VILIP3) was a new substrate of NMT1 in protein N-myristoylation modification, and high NMT1 or VILIP3 expression was associated with advanced stages and poor survival in HCC. Mechanistically, desloratadine binds to Asn-246 in NMT1 and inhibits its enzymatic activity, disrupting the NMT1-mediated myristoylation of the VILIP3 protein and subsequent NFκB/Bcl-2 signaling. Conclusively, this study demonstrates that desloratadine may be a novel anticancer drug and that NMT1-mediated myristoylation contributes to HCC progression and is a potential biomarker and therapeutic target in HCC.

Protein-Metabolite Interactions Shape Cellular Metabolism and Physiology

Protein-metabolite interactions regulate many important cellular processes but still remain understudied. Recent technological advancements are gradually uncovering the complexity of the protein-metabolite interactome. Here, we highlight some classic and recent examples of how protein metabolite interactions regulate metabolism, both locally and globally, and how this contributes to cellular physiology. We also discuss the importance of these interactions in diseases such as cancer.

Cellular Thermal Shift Assay for the Detection of Small Molecule-Target Interactions in Arabidopsis Cells

Chemical genetics takes advantage of small molecule-protein interactions to explore various biological processes. Although an attractive alternative to classical genetics in plants, the identification of small-molecule targets remains a challenge and limits the broad use of the compounds. The cellular thermal shift assay (CETSA), based on the principle that binding of small molecules could affect the thermal stability of proteins, has been applied for target validation in plant cells. Combined with high-resolution mass spectrometry, CETSA can detect small-molecule targets by monitoring the changes in the protein thermal stability caused by the interactions with small molecules at the proteome level. Here we describe the small-molecule target validation as well as the target identification with mass spectrometry by means of CETSA.

Databases and Tools to Investigate Protein-Metabolite Interactions

Protein-metabolite interactions (PMIs) are directly responsible for the regulation of numerous processes. From the direct regulation of enzymes to complex developmental processes intermediated by hormones, PMIs are central to understanding the molecular mechanisms of important physiological phenomena. Still, proving such interactions experimentally has proven an arduous task. We discuss here some of the current technologies contributing to expand our knowledge on PMIs, with particular emphasis on platforms and databases to explore the highly heterogenous nature of characterized PMIs, which is likely to be an essential resource on the development of new computational approaches to predict and validate interactions based on large-scale PMI screenings.

Metabolomics: Going Deeper, Going Broader, Going Further

Metabolomics is a continuously dynamic field of research that is driven by demanding research questions and technological advances alike. In this review we highlight selected recent and ongoing developments in the area of mass spectrometry-based metabolomics. The field of view that can be seen through the metabolomics lens can be broadened by adoption of separation techniques such as hydrophilic interaction chromatography and ion mobility mass spectrometry (going broader). For a given biospecimen, deeper metabolomic analysis can be achieved by resolving smaller entities such as rare cell populations or even single cells using nano-LC and spatially resolved metabolomics or by extracting more useful information through improved metabolite identification in untargeted metabolomic experiments (going deeper). Integration of metabolomics with other (omics) data allows researchers to further advance in the understanding of the complex metabolic and regulatory networks in cells and model organisms (going further). Taken together, diverse fields of research from mechanistic studies to clinics to biotechnology applications profit from these technological developments.

Identification of Plant Protein-Metabolite Interactions by Limited Proteolysis-Coupled Mass Spectrometry (LiP-MS)

The interactions between metabolites and proteins constitute crucial events in cell signaling and metabolism. In recent years, large-scale proteomics techniques have emerged to identify and characterize protein-metabolite interactions. However, their implementation in plants is generally lagging behind, preventing a complete understanding of the regulatory mechanisms governing plant physiology. Recently, a novel approach to identify metabolite-binding proteins, namely, limited proteolysis-coupled mass spectrometry (LiP-MS), was developed originally for microbial proteomes. Here, we present an adapted and accessible version of the LiP-MS protocol for use in plants. Plant proteomes are extracted and incubated with the metabolite of interest or control treatment, followed by a limited digestion by a nonspecific/promiscuous protease. Subsequently, a conventional shotgun proteomics sample preparation is performed including a complete digestion with the sequence-specific protease trypsin. Finally, label-free proteomics analysis is applied to identify structure-dependent proteolytic patterns corresponding to protein targets of the specific metabolite and their binding sites. Given its amenability to relatively high throughput, the LiP-MS approach may open a potent avenue for the discovery of novel regulatory mechanisms in plant species.

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.

Limited Proteolysis-Mass Spectrometry to Identify Metabolite-Protein Interactions

Metabolite-protein interactions regulate diverse cellular processes, prompting the development of methods to investigate the metabolite-protein interactome at a global scale. One such method is our previously developed structural proteomics approach, limited proteolysis-mass spectrometry (LiP-MS), which detects proteome-wide metabolite-protein and drug-protein interactions in native bacterial, yeast, and mammalian systems, and allows identification of binding sites without chemical modification. Here we describe a detailed experimental and analytical workflow for conducting a LiP-MS experiment to detect small molecule-protein interactions, either in a single-dose (LiP-SMap) or a multiple-dose (LiP-Quant) format. LiP-Quant analysis combines the peptide-level resolution of LiP-MS with a machine learning-based framework to prioritize true protein targets of a small molecule of interest. We provide an updated R script for LiP-Quant analysis via a GitHub repository accessible at https://github.com/RolandBruderer/MiMB-LiP-Quant .

Messenger RNA in lipid nanoparticles rescues HEK 293 cells from lipid-induced mitochondrial dysfunction as studied by real time pulse chase NMR, RTPC-NMR, spectroscopy

Analytical tools to study cell physiology are critical for optimizing drug-host interactions. Real time pulse chase NMR spectroscopy, RTPC-NMR, was introduced to monitor the kinetics of metabolite production in HEK 293T cells treated with COVID-19 vaccine-like lipid nanoparticles, LNPs, with and without mRNA. Kinetic flux parameters were resolved for the incorporation of isotopic label into metabolites and clearance of labeled metabolites from the cells. Changes in the characteristic times for alanine production implicated mitochondrial dysfunction as a consequence of treating the cells with lipid nanoparticles, LNPs. Mitochondrial dysfunction was largely abated by inclusion of mRNA in the LNPs, the presence of which increased the size and uniformity of the LNPs. The methodology is applicable to all cultured cells.