Protein-metabolite Interactions Research Articles

Thermal proteome profiling reveals fructose-1,6-bisphosphate as a phosphate donor to activate phosphoglycerate mutase 1

Deep understanding of sugar metabolite-protein interactions should provide implications on sugar metabolic reprogramming in human physiopathology. Although tremendous efforts have been made for determining individual event, global profiling of such interactome remains challenging. Here we describe thermal proteome profiling of glycolytic metabolite fructose-1,6-bisphosphate (FBP)-interacting proteins. Our results reveal a chemical signaling role of FBP which acts as a phosphate donor to activate phosphoglycerate mutase 1 (PGAM1) and contribute an intrapathway feedback for glycolysis and cell proliferation. At molecular level, FBP donates either C1-O-phosphate or C6-O-phosphate to the catalytic histidine of PGAM1 to form 3-phosphate histidine (3-pHis) modification. Importantly, structure-activity relationship studies facilitate the discovery of PGAM1 orthostatic inhibitors which can potentially restrain cancer cell proliferation. Collectively we have profiled a spectrum of FBP interactome, and discovered a unique covalent signaling function of FBP that supports Warburg effect via histidine phosphorylation which inspires the development of pharmacological tools targeting sugar metabolism.

Semi-supervised meta-learning elucidates understudied molecular interactions

Many biological problems are understudied due to experimental limitations and human biases. Although deep learning is promising in accelerating scientific discovery, its power compromises when applied to problems with scarcely labeled data and data distribution shifts. We develop a deep learning framework—Meta Model Agnostic Pseudo Label Learning (MMAPLE)—to address these challenges by effectively exploring out-of-distribution (OOD) unlabeled data when conventional transfer learning fails. The uniqueness of MMAPLE is to integrate the concept of meta-learning, transfer learning and semi-supervised learning into a unified framework. The power of MMAPLE is demonstrated in three applications in an OOD setting where chemicals or proteins in unseen data are dramatically different from those in training data: predicting drug-target interactions, hidden human metabolite-enzyme interactions, and understudied interspecies microbiome metabolite-human receptor interactions. MMAPLE achieves 11% to 242% improvement in the prediction-recall on multiple OOD benchmarks over various base models. Using MMAPLE, we reveal novel interspecies metabolite-protein interactions that are validated by activity assays and fill in missing links in microbiome-human interactions. MMAPLE is a general framework to explore previously unrecognized biological domains beyond the reach of present experimental and computational techniques.

Using protein–metabolite interactions to identify novel plant metabolite receptors

AbstractAn increasing interest in plant protein–metabolite interactions has occurred in the past decade due to advancements in technology and methodology. Metabolite receptors especially are of great importance in plant research due to their role in cell signaling for the regulation of growth and development and environmental sensing. Energy, carbon, and nitrogen signaling through AMPK/SNF1/SnRK1, TOR, and PII receptors are core components conserved across Kingdoms of Life and what is known in plants often came first from study in non-plant systems. In contrast, known phytohormone receptors are relatively distinct to plants and identified within a plant system. Therefore, this review will give an update on known plant receptors for energy, carbon, and nitrogen signaling as well as phytohormones, focusing on the detection methods used to provide our current understanding of their function in the plant. Finally, it will address emerging strategies for identifying protein–metabolite interactions to discover novel plant receptors.

The role of GZMA as a target of cysteine and biomarker in Alzheimer's disease, pelvic organ prolapse, and tumor progression.

Objective: This study aims to investigate how changes in peripheral blood metabolites in Alzheimer's Disease (AD) patients affect the development of Pelvic Organ Prolapse (POP) using a multi-omics approach. We specifically explore the interactions of signaling pathways, gene expression, and protein-metabolite interactions, with a focus on GZMA and cysteine in age-related diseases. Methods: This study utilized multi-omics analysis, including metabolomics and transcriptomics, to evaluate the perturbations in peripheral blood metabolites and their effect on POP in AD patients. Additionally, a comprehensive pan-cancer and immune infiltration analysis was performed on the core targets of AD combined with POP, exploring their potential roles in tumor progression and elucidating their pharmacological relevance to solid tumors. Results: We identified 47 differential metabolites linked to 9 significant signaling pathways, such as unsaturated fatty acid biosynthesis and amino acid metabolism. A thorough gene expression analysis revealed numerous differentially expressed genes (DEGs), with Gene Set Enrichment Analysis (GSEA) showing significant changes in gene profiles of AD and POP. Network topology analysis highlighted central nodes in the AD-POP co-expressed genes network. Functional analyses indicated involvement in critical biological processes and pathways. Molecular docking studies showed strong interactions between cysteine and proteins PTGS2 and GZMA, and molecular dynamics simulations confirmed the stability of these complexes. In vitro validation demonstrated that cysteine reduced ROS levels and protected cell viability. GZMA was widely expressed in various cancers, associated with immune cells, and correlated with patient survival prognosis. Conclusion: Multi-omics analysis revealed the role of peripheral blood metabolites in the molecular dynamics of AD and their interactions with POP. This study identified potential biomarkers and therapeutic targets, emphasizing the effectiveness of integrative approaches in treating AD and POP concurrently. The findings highlight the need for in-depth research on novel targets and biomarkers to advance therapeutic strategies.

Unraveling Protein-Metabolite Interactions in Precision Nutrition: A Case Study of Blueberry-Derived Metabolites Using Advanced Computational Methods.

Metabolomics, the study of small-molecule metabolites within biological systems, has become a potent instrument for understanding cellular processes. Despite its profound insights into health, disease, and drug development, identifying the protein partners for metabolites, especially dietary phytochemicals, remains challenging. In the present study, we introduced an innovative in silico, structure-based target prediction approach to efficiently predict protein targets for metabolites. We analyzed 27 blood serum metabolites from nutrition intervention studies' blueberry-rich diets, known for their health benefits, yet with elusive mechanisms of action. Our findings reveal that blueberry-derived metabolites predominantly interact with Carbonic Anhydrase (CA) family proteins, which are crucial in acid-base regulation, respiration, fluid balance, bone metabolism, neurotransmission, and specific aspects of cellular metabolism. Molecular docking showed that these metabolites bind to a common pocket on CA proteins, with binding energies ranging from -5.0 kcal/mol to -9.0 kcal/mol. Further molecular dynamics (MD) simulations confirmed the stable binding of metabolites near the Zn binding site, consistent with known compound interactions. These results highlight the potential health benefits of blueberry metabolites through interaction with CA proteins.

HNCGAT: a method for predicting plant metabolite-protein interaction using heterogeneous neighbor contrastive graph attention network.

The prediction of metabolite-protein interactions (MPIs) plays an important role in plant basic life functions. Compared with the traditional experimental methods and the high-throughput genomics methods using statistical correlation, applying heterogeneous graph neural networks to the prediction of MPIs in plants can reduce the cost of manpower, resources, and time. However, to the best of our knowledge, applying heterogeneous graph neural networks to the prediction of MPIs in plants still remains under-explored. In this work, we propose a novel model named heterogeneous neighbor contrastive graph attention network (HNCGAT), for the prediction of MPIs in Arabidopsis. The HNCGAT employs the type-specific attention-based neighborhood aggregation mechanism to learn node embeddings of proteins, metabolites, and functional-annotations, and designs a novel heterogeneous neighbor contrastive learning framework to preserve heterogeneous network topological structures. Extensive experimental results and ablation study demonstrate the effectiveness of the HNCGAT model for MPI prediction. In addition, a case study on our MPI prediction results supports that the HNCGAT model can effectively predict the potential MPIs in plant.

Machine learning of metabolite-protein interactions from model-derived metabolic phenotypes.

Unraveling metabolite-protein interactions is key to identifying the mechanisms by which metabolism affects the function of other cellular layers. Despite extensive experimental and computational efforts to identify the regulatory roles of metabolites in interaction with proteins, it remains challenging to achieve a genome-scale coverage of these interactions. Here, we leverage established gold standards for metabolite-protein interactions to train supervised classifiers using features derived from genome-scale metabolic models and matched data on protein abundance and reaction fluxes to distinguish interacting from non-interacting pairs. Through a comprehensive comparative study, we explore the impact of different features and assess the effect of gold standards for non-interacting pairs on the performance of the classifiers. Using data sets from Escherichia coli and Saccharomyces cerevisiae, we demonstrate that the features constructed by integrating fluxomic and proteomic data with metabolic phenotypes predicted from genome-scale metabolic models can be effectively used to train classifiers, accurately predicting metabolite-protein interactions in the context of metabolism. Our results reveal that the high performance of classifiers trained on these features is unaffected by the method used to generate gold standards for non-interacting pairs. Overall, our study introduces valuable features that improve the performance of identifying metabolite-protein interactions in the context of metabolism.

MetalinksDB: a flexible and contextualizable resource of metabolite-protein interactions.

From the catalytic breakdown of nutrients to signaling, interactions between metabolites and proteins play an essential role in cellular function. An important case is cell-cell communication, where metabolites, secreted into the microenvironment, initiate signaling cascades by binding to intra- or extracellular receptors of neighboring cells. Protein-protein cell-cell communication interactions are routinely predicted from transcriptomic data. However, inferring metabolite-mediated intercellular signaling remains challenging, partially due to the limited size of intercellular prior knowledge resources focused on metabolites. Here, we leverage knowledge-graph infrastructure to integrate generalistic metabolite-protein with curated metabolite-receptor resources to create MetalinksDB. MetalinksDB is an order of magnitude larger than existing metabolite-receptor resources and can be tailored to specific biological contexts, such as diseases, pathways, or tissue/cellular locations. We demonstrate MetalinksDB's utility in identifying deregulated processes in renal cancer using multi-omics bulk data. Furthermore, we infer metabolite-driven intercellular signaling in acute kidney injury using spatial transcriptomics data. MetalinksDB is a comprehensive and customizable database of intercellular metabolite-protein interactions, accessible via a web interface (https://metalinks.omnipathdb.org/) and programmatically as a knowledge graph (https://github.com/biocypher/metalinks). We anticipate that by enabling diverse analyses tailored to specific biological contexts, MetalinksDB will facilitate the discovery of disease-relevant metabolite-mediated intercellular signaling processes.

Unveiling Actin Cytoskeleton Role in Mediating Chikungunya-Associated Arthritis: An Integrative Proteome-Metabolome Study.

Background: Chikungunya is a zoonotic disease caused by the Chikungunya virus (CHIKV), primarily transmitted to humans through infected Aedes mosquitoes. The infection is characterized by symptoms such as high fever, musculoskeletal pain, polyarthritis, and a rash, which can lead to severe complications such as encephalitis, meningitis, and even fatalities. While many disease manifestations resemble those of other viral infections, chronic arthritis caused by CHIKV is unique, and its molecular mechanisms remain ill-defined. Materials and Methods: Proteomics data from both cellular and patient levels of CHIKV infection were curated from PubMed and screened using inclusion and exclusion criteria. Patient serum proteomics data obtained from P RIDE underwent reanalysis using Proteome Discoverer 2.2. Enrichment and protein-protein interaction network analysis were conducted on differentially expressed proteins from both serum and cellular datasets. Metabolite data from CHIKV-infected patients were further retrieved, and their protein binding partners were identified using BindingDB. The protein-metabolite interaction pathway was further developed using MetaboAnalyst. Results: The proteomics data analysis revealed differential expression of proteins involved in critical host mechanisms, such as cholesterol metabolism and mRNA splicing, during CHIKV infection. Consistent upregulation of two actin cytoskeleton proteins, TAGLN2 and PFN1, was noted in both serum and cellular datasets, and their upregulations are associated with arthritis. Furthermore, alterations in purine metabolism were observed in the integrative proteome-metabolome analysis, correlating with cytoskeletal remodelling. Conclusion: Collectively, this integrative view sheds light on the involvement of actin cytoskeleton remodeling proteins and purine metabolic pathways in the development of arthritis during CHIKV infection.

Abstract 913: Automated reasoning artificial intelligence (AI) to model the cell-cell biochemical and cellular interactions in the tumor microenvironment (TME) of oral cancer (OC)

Abstract Our goal was to study the spatial complexity of HPV negative OC by integrating scRNAseq and spatial transcriptomics from 11 samples including 5 from never smokers never drinkers (NSND). We used Turing Biosystems proprietary graph database engine and automated reasoning AI to analyze and model the cell-cell biochemical and cellular interactions in the TME. The tumors H&E images, Visium 10X, and single cell 10X data were pre-processed using standard methods. QC-based genes and cells filtering, gene expression normalization, dimensionality reduction, clustering of Visium spots, and identification of spatially variable genes were performed. We generated multiple networks from tumor data and prior biological knowledge to integrate different layers of information: single-cell and spatial transcriptomics data, cell-cell interactions networks, cell neighboring, spatial correlation networks. We first used a novel network-based technique to identify the spatial distribution of any cell subtypes (e.g. CAF, T cells), by integrating differentially expressed genes from scRNAseq, prior knowledge and prior data (e.g. TCGA). We used a multilayer network approach to integrate the different networks of data and an automated reasoning AI analysis which allows to interpret the results based on a reasoning on biological and clinical knowledge in opposition to machine learning methods based on statistical patterns. We identified CAFs/T cells/IFN-g to be strongly correlated within the tumor islets interacting with the stroma in NSND while they were more correlated within the stromal regions in smokers. We also identified 2 or 3 subtypes of tumor islets areas based on their functional states (e.g., immune pathways activated or metabolic differences), allowing us to unravel a more detailed map of the intratumoral heterogeneity when compared to histopathology annotations. In order to get some insights into the functional impact of the spatial distribution of cells, the tumors were then represented as multilayer networks to identify all possible cell-cell interactions in the TME. This was followed by a spatial simulation of these interactions using logic (e.g., inhibition, activation) and correlative (e.g., gene expression, imaging features correlations) interactions in the networks to identify spatial interactions. From that we analysed all the possible known protein-protein and metabolite-protein interactions between the cell types analysed and mapped the tumors with the gene expressions corresponding to the pairs of interacting proteins (or enzymes linked to metabolites). We found an interaction between CAF and Tregs via CXCL12 (CAFs) and CXCR4 (Tregs). In conclusion, automated reasoning AI integrating scRNAseq and spatial transcriptomics allows an in-depth analysis of cells spatial distribution and its functional impact in OC. Citation Format: Karène Mahtouk, Maxime Vincent, Yannick Le Meitour, Béatrice Vanbervliet-Defrance, Sonia Canjura, Cyril Degletagne, Laurie Tonon, Lucas Michon, Jebrane Bouaoud, Aude Excoffier, Philippe Zrounba, Rémy Boutonnet, Adam Amara, Pierre Saintigny. Automated reasoning artificial intelligence (AI) to model the cell-cell biochemical and cellular interactions in the tumor microenvironment (TME) of oral cancer (OC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 913.

Phytohormones in a universe of regulatory metabolites: lessons from jasmonate.

Small-molecule phytohormones exert control over plant growth, development, and stress responses by coordinating the patterns of gene expression within and between cells. Increasing evidence indicates that currently recognized plant hormones are part of a larger group of regulatory metabolites that have acquired signaling properties during the evolution of land plants. This rich assortment of chemical signals reflects the tremendous diversity of plant secondary metabolism, which offers evolutionary solutions to the daunting challenges of sessility and other unique aspects of plant biology. A major gap in our current understanding of plant regulatory metabolites is the lack of insight into the direct targets of these compounds. Here, we illustrate the blurred distinction between classical phytohormones and other bioactive metabolites by highlighting the major scientific advances that transformed the view of jasmonate from an interesting floral scent to a potent transcriptional regulator. Lessons from jasmonate research generally apply to other phytohormones and thus may help provide a broad understanding of regulatory metabolite-protein interactions. In providing a framework that links small-molecule diversity to transcriptional plasticity, we hope to stimulate future research to explore the evolution, functions, and mechanisms of perception of a broad range of plant regulatory metabolites.

MiRNA-protein-metabolite interaction network reveals the regulatory network and players of pregnancy regulation in dairy cows.

Pregnancy is a complex process involving complex molecular interaction networks, such as between miRNA-protein, protein-protein, metabolite-metabolite, and protein-metabolite interactions. Advances in technology have led to the identification of many pregnancy-associated microRNA (miRNA), protein, and metabolite fingerprints in dairy cows. An array of miRNA, protein, and metabolite fingerprints produced during the early pregnancy of dairy cows were described. We have found the in silico interaction networks between miRNA-protein, protein-protein, metabolite-metabolite, and protein-metabolite. We have manually constructed miRNA-protein-metabolite interaction networks such as bta-miR-423-3p-IGFBP2-PGF2α interactomes. This interactome is obtained by manually combining the interaction network formed between bta-miR-423-3p-IGFBP2 and the interaction network between IGFBP2-PGF2α with IGFBP2 as a common interactor with bta-miR-423-3p and PGF2α with the provided sources of evidence. The interaction between bta-miR-423-3p and IGFBP2 has many sources of evidence including a high miRanda score of 169, minimum free energy (MFE) score of -25.14, binding probability (p) of 1, and energy of -25.5. The interaction between IGFBP2 and PGF2α occurs at high confidence scores (≥0.7 or 70%). Interestingly, PGF2α is also found to interact with different metabolites, such as PGF2α-PGD2, PGF2α-thromboxane B2, PGF2α-PGE2, and PGF2α-6-keto-PGF1α at high confidence scores (≥0.7 or 70%). Furthermore, the interactions between C3-PGE2, C3-PGD2, PGE2-PGD2, PGD2-thromboxane B2, PGE2-thromboxane B2, 6-keto-PGF1α-thromboxane B2, and PGE2-6-keto-PGF1α were also obtained at high confidence scores (≥0.7 or 70%). Therefore, we propose that miRNA-protein-metabolite interactomes involving miRNA, protein, and metabolite fingerprints of early pregnancy of dairy cows such as bta-miR-423-3p, IGFBP2, PGF2α, PGD2, C3, PGE2, 6-keto-PGF1 alpha, and thromboxane B2 may form the key regulatory networks and players of pregnancy regulation in dairy cows. This is the first study involving miRNA-protein-metabolite interactomes obtained in the early pregnancy stage of dairy cows.

Abstract B075: Identifying functional allosteric binding sites using a systematic and scalable AMPS screening platform for drug discovery

Abstract Endogenous cellular metabolites have been shown to bind and regulate proteins involved in tumorigenesis. These low-affinity metabolite-protein interactions can modulate protein function through binding at allosteric sites. As an example, in normal cells, the ABL1 protein is regulated by N-terminal myristylation, which binds to a hydrophobic pocket in the C-terminal lobe of the kinase domain autoinhibiting its activity. Fusion of BCR to ABL1 leads to deletion of the N-terminal myristylation site activating BCR-ABL oncoprotein leading to chronic myeloid leukemia (CML). Similarly, autopalmitoylation of TEAD proteins regulates transcriptional output of Hippo signaling and tumorigenesis. Allosteric metabolite binding sites on these targets have been successfully used to generate new medicines (e.g.: Asciminib for BCR-ABL) and investigational cancer therapies (e.g.: IK-930, VT3989 for TEAD). However, there are limited approaches that can be broadly deployed to discover functional regulatory sites on precision oncology targets. Atavistik Bio has developed the AMPS (Atavistik Metabolite Protein Screening) platform that enables systematic and scalable screening of metabolites against proteins or protein complexes of interest. The AMPS platform technology integrates equilibrium dialysis with sensitive mass-spectrometry to screen a proprietary metabolite library. A state-of-the-art computational workflow coupled with deep mechanistic structure-function characterization enables us to discover novel functional allosteric modulators. To validate the platform, we have screened several precision oncology targets and characterized the AMPS metabolite hits with functional assays. For ABL1 tyrosine kinase, we have applied AMPS on distinct enzyme forms to probe the role of metabolite binding in the context of phosphorylation states and drug resistant T315I mutant forms. In addition, AMPS allows us to investigate novel metabolite sensitization of ABL1 in the presence of existing drugs Imatinib and Asciminib. Results from AMPS and follow up experiments highlight phosphorylation state dependency on myristate binding to ABL1. In conclusion, we demonstrate the AMPS platform can identify highly conserved allosteric protein-metabolite interactions to advance small molecule drug discovery efforts for new precision medicines. Citation Format: Anil K Padyana, Anna Schinzel, Gordon Murray, Joseph H LaPointe, Kaitlyn Selmer, Truc Pham, Melissa J Buskes, Shomit Sengupta, Maria-Jesus Blanco, Thomas Roddy, Marion Dorsch. Identifying functional allosteric binding sites using a systematic and scalable AMPS screening platform for drug discovery [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr B075.

Metabolomics and mitochondrial dysfunction in cardiometabolic disease

Circulating metabolites are indicators of systemic metabolic dysfunction and can be detected through contemporary techniques in metabolomics. These metabolites are involved in numerous mitochondrial metabolic processes including glycolysis, fatty acid β-oxidation, and amino acid catabolism, and changes in the abundance of these metabolites is implicated in the pathogenesis of cardiometabolic diseases (CMDs). Epigenetic regulation and direct metabolite-protein interactions modulate metabolism, both within cells and in the circulation. Dysfunction of multiple mitochondrial components stemming from mitochondrial DNA mutations are implicated in disease pathogenesis. This review will summarize the current state of knowledge regarding: i) the interactions between metabolites found within the mitochondrial environment during CMDs, ii) various metabolites' effects on cellular and systemic function, iii) how harnessing the power of metabolomic analyses represents the next frontier of precision medicine, and iv) how these concepts integrate to expand the clinical potential for translational cardiometabolic medicine.

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.

Clinical prognosis and related molecular features of hepatitis B-associated adolescent and young adult hepatocellular carcinoma

BackgroundInattention has been given to the pathogenesis of adolescent and young adult (AYA) hepatocellular carcinoma (HCC). Due to the more advanced tumor progression and poorer prognosis of AYA-HCC, together with a better tolerance ability, noncirrhotic background, and a stronger willingness to treat AYA-HCC, clinical and molecular biology studies are urgent and necessary, especially for those with hepatitis B infection.MethodsFor clinical aspects, the overall survival, the recurrence-free survival, and the Cox analyses were performed. Then, functional analysis, gene clustering, metabolic-related analysis, immune infiltration and competing endogenous RNA (ceRNA) construction were carried out using whole transcriptome sequencing technique.ResultsBased on the clinical information of our HCC cohort, the overall survival and recurrence-free survival rates were worse in the AYA group than in the elderly group as previously described. According to our whole transcriptome sequencing results, functional analysis revealed that metabolism-related pathways as well as protein translation and endoplasmic reticulum processing were enriched. Then the hub metabolism-related genes were screened by metabolite–protein interactions (MPIs) and protein–protein interactions (PPIs). Fatty acid metabolism is a crucial component of metabolic pathways, abnormalities of which may be the reason for the worse prognosis of HBV-AYA HCC. Finally, the relationship of disrupted expression of metabolism-related genes with immune infiltration was also analyzed, and the lncRNA‒miRNA‒mRNA-related ceRNA network for HBV-AYA HCC was constructed, which may provide new cues for HBV-AHA HCC prevention.ConclusionThe worse prognosis and recurrence rate of HBV-AYA HCC may be related to abnormalities in metabolism-related pathways, especially disorders of fatty acid metabolism.

Protein-Metabolite Interactions: Discovery and Significance.

Metabolites orchestrate cellular processes as either substrates, co-enzymes, inhibitors, or activators of cellular proteins such as enzymes and receptors. Although traditional biochemical and structural biology-based approaches have been successfully employed for the discovery of protein-metabolite interactions, they often fail to detect transient and low-affinity biomolecular relationships. Another limitation of these approaches is that they are performed under in vitro conditions lacking the physiological context. Recently developed mass spectrometry-based methodologies overcome both these shortcomings, and have resulted in the discovery of global protein-metabolite cellular interaction networks. Herein, we describe traditional and modern approaches for the discovery of protein-metabolite interactions, and discuss the impact of these discoveries on our understanding of cellular physiology and on drug development.

Metabolite signaling in the heart.

The heart is the most metabolically active organ in the body, sustaining a continuous and high flux of nutrient catabolism via oxidative phosphorylation. The nature and relative contribution of these fuels have been studied extensively for decades. By contrast, less attention has been placed on how intermediate metabolites generated from this catabolism affect intracellular signaling. Numerous metabolites, including intermediates of glycolysis and the tricarboxylic acid (TCA) cycle, nucleotides, amino acids, fatty acids and ketones, are increasingly appreciated to affect signaling in the heart, via various mechanisms ranging from protein-metabolite interactions to modifying epigenetic marks. We review here the current state of knowledge of intermediate metabolite signaling in the heart.

The involvement of allosteric effectors and post-translational modifications in the control of plant central carbon metabolism.

Plant metabolism is finely orchestrated to allow the occurrence of complementary and sometimes opposite metabolic pathways. In part this is achieved by the allosteric regulation of enzymes, which has been a cornerstone of plant research for many decades. The completion of the Arabidopsis genome and the development of the associated toolkits for Arabidopsis research moved the focus of many researchers to other fields. This is reflected by the increasing number of high-throughput proteomic studies, mainly focused on post-translational modifications. However, follow-up 'classical' biochemical studies to assess the functions and upstream signaling pathways responsible for such modifications have been scarce. In this work, we review the basic concepts of allosteric regulation of enzymes involved in plant carbon metabolism, comprising photosynthesis and photorespiration, starch and sucrose synthesis, glycolysis and gluconeogenesis, the oxidative pentose phosphate pathway and the tricarboxylic acid cycle. Additionally, we revisit the latest results on the allosteric control of the enzymes involved in these pathways. To conclude, we elaborate on the current methods for studying protein-metabolite interactions, which we consider will become crucial for discoveries in the future.

NMR proven albumin interaction with metabolites in complex mixtures

One critical step in new drugs development is the investigation of the interactions between drug candidate and target protein. Nuclear Magnetic Resonance Spectroscopy (NMR) is a well-established technique for studding these interactions. Due to its availability and structural similarities to human albumin, bovine serum albumin (BSA) is widely accepted as a model for investigating the binding of small molecules to serum albumin. We report here on the evaluation of binding interactions between BSA and 18 metabolites using saturation transfer difference (STD) NMR experiments. Positive STD signals that indicate metabolite-protein interactions were obtained for leucine, pyruvic acid, valine, threonine, alanine, 4-aminohippuric acid and tryptophan.