http://www.jbc.org/article/S0021925822012819/pdf

Regulation and function of the mammalian tricarboxylic acid cycle

The nature of redox is electron transfer; in this way, energy metabolism brings redox stress. Lactate production is associated with NAD regeneration, which is now recognized to play a role in maintaining redox homeostasis. The cellular lactate/pyruvate ratio could be described as a proxy for the cytosolic NADH/NAD ratio, meaning lactate metabolism is the key to redox regulation. Here, we review the role of lactate dehydrogenases in cellular redox regulation, which play the role of the direct regulator of lactate–pyruvate transforming. Lactate dehydrogenases (LDHs) are found in almost all animal tissues; while LDHA catalyzed pyruvate to lactate, LDHB catalyzed the reverse reaction . LDH enzyme activity affects cell oxidative stress with NAD/NADH regulation, especially LDHA recently is also thought as an ROS sensor. We focus on the mutual regulation of LDHA and redox robustness. ROS accumulation regulates the transcription of LDHA. Conversely, diverse post-translational modifications of LDHA, such as phosphorylation and ubiquitination, play important roles in enzyme activity on ROS elimination, emphasizing the potential role of the ROS sensor and regulator of LDHA.

Mutual regulation of lactate dehydrogenase and redox robustness

Intracellular Ca2+ concentrations are strictly controlled by plasma membrane transporters, the endoplasmic reticulum, and mitochondria, in which Ca2+ uptake is mediated by the mitochondrial calcium uniporter complex (MCUc), while efflux occurs mainly through the mitochondrial Na+/Ca2+ exchanger (NCLX). RNAseq database repository searches led us to identify the Nclx transcript as highly enriched in astrocytes when compared to neurons. To assess the role of NCLX in mouse primary culture astrocytes, we inhibited its function both pharmacologically or genetically. This resulted in re-shaping of cytosolic Ca2+ signaling and a metabolic shift that increased glycolytic flux and lactate secretion in a Ca2+-dependent manner. Interestingly, in vivo genetic deletion of NCLX in hippocampal astrocytes improved cognitive performance in behavioral tasks, whereas hippocampal neuron-specific deletion of NCLX impaired cognitive performance. These results unveil a role for NCLX as a novel modulator of astrocytic glucose metabolism, impacting on cognition.

Mitochondrial sodium/calcium exchanger NCLX regulates glycolysis in astrocytes, impacting on cognitive performance

Targeting lactate-related cell cycle activities for cancer therapy.

What is cancer metabolism?

Saturation of the mitochondrial NADH shuttles drives aerobic glycolysis in proliferating cells.

Abstract 2406: Glucose oxidation is the preferred metabolic phenotype in proliferating cells


 Proliferating cancer cells exhibit an elevated rate of glucose consumption and lactate excretion, a phenomenon described as the “Warburg effect”. Although increased glycolytic flux in proliferating cells is well established, metabolic changes associated with mitochondria still remain elusive. Here, using various inducible quiescence models and mass spectrometry-based metabolomics, we directly compared mitochondrial respiration and TCA cycle activity from the same cells in the proliferative and quiescent states. We found that mitochondrial respiration and TCA cycle flux is significantly increased during proliferation in both transformed and non-transformed cell lines. This increase was supported by mitochondrial fusion rather than mitochondrial biogenesis. Notably, as cells transitioned from a quiescent state to a proliferative state, the elongation of mitochondria occurred as quickly as 3 hours. Our results reveal that the Warburg effect, which was originally associated with dysfunctional mitochondria, does not necessitate decreased mitochondrial respiration. Rather, in many proliferating cancer cells, mitochondrial respiration is actually elevated, underscoring the importance of mitochondrial respiration during proliferation.
 Citation Format: Yahui Wang, Conghui Yao, Ethan Stancliffe, Ronald Fowle-Grider, Rencheng Wang, Cheng Wang, Michaela Schwaiger-Haber, Leah P. Shriver, Jason Weber, Gary J. Patti. Mitochondrial fusion is required to support higher rates of respiration in proliferating cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3773.

Abstract 3773: Mitochondrial fusion is required to support higher rates of respiration in proliferating cancer cells

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.

MPI-VGAE: protein-metabolite enzymatic reaction link learning 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.

/pdf/genome-scale-enzymatic-reaction-prediction-by-variational-24eaz1o7.pdf

Cheng Wang

Papers

Saturation of the mitochondrial NADH shuttles drives aerobic glycolysis in proliferating cells.

Abstract 2406: Glucose oxidation is the preferred metabolic phenotype in proliferating cells

Abstract 3773: Mitochondrial fusion is required to support higher rates of respiration in proliferating cancer cells

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

Genome-scale enzymatic reaction prediction by variational graph autoencoders