Cardiometabolic characteristics of people with metabolically healthy and unhealthy obesity.

There is considerable heterogeneity in the cardiometabolic abnormalities associated with obesity. We evaluated multi-organ system metabolic function in 20 adults with metabolically healthy obesity (MHO; normal fasting glucose and triglycerides, oral glucose tolerance, intrahepatic triglyceride content, and whole-body insulin sensitivity), 20 adults with metabolically unhealthy obesity (MUO; prediabetes, hepatic steatosis, and whole-body insulin resistance), and 15 adults who were metabolically healthy lean. Compared with MUO, people with MHO had (1) altered skeletal muscle biology (decreased ceramide content and increased expression of genes involved in BCAA catabolism and mitochondrial structure/function); (2) altered adipose tissue biology (decreased expression of genes involved in inflammation and extracellular matrix remodeling and increased expression of genes involved in lipogenesis); (3) lower 24-h plasma glucose, insulin, non-esterified fatty acids, and triglycerides; (4) higher plasma adiponectin and lower plasma PAI-1 concentrations; and (5) decreased oxidative stress. These findings provide a framework of potential mechanisms responsible for MHO and the metabolic heterogeneity of obesity. This study was registered at ClinicalTrials.gov (NCT02706262).

Metabolite Study and Structural Authentication for the First-in-Human Use Sphingosine-1-phosphate Receptor 1 Radiotracer.

The sphingosine-1-phosphate receptor 1 (S1PR1) radiotracer [11C]CS1P1 has shown promise in proof-of-concept PET imaging of neuroinflammation in multiple sclerosis (MS). Our HPLC radiometabolite analysis of human plasma samples collected during PET scans with [11C]CS1P1 detected a radiometabolite peak that is more lipophilic than [11C]CS1P1. Radiolabeled metabolites that cross the blood-brain barrier complicate quantitative modeling of neuroimaging tracers; thus, characterizing such radiometabolites is important. Here, we report our detailed investigation of the metabolite profile of [11C]CS1P1 in rats, nonhuman primates, and humans. CS1P1 is a fluorine-containing ligand that we labeled with C-11 or F-18 for preclinical studies; the brain uptake was similar for both radiotracers. The same lipophilic radiometabolite found in human studies also was observed in plasma samples of rats and NHPs for CS1P1 labeled with either C-11 or F-18. We characterized the metabolite in detail using rats after injection of the nonradioactive CS1P1. To authenticate the molecular structure of this radiometabolite, we injected rats with 8 mg/kg of CS1P1 to collect plasma for solvent extraction and HPLC injection, followed by LC/MS analysis of the same metabolite. The LC/MS data indicated in vivo mono-oxidation of CS1P1 produces the metabolite. Subsequently, we synthesized three different mono-oxidized derivatives of CS1P1 for further investigation. Comparing the retention times of the mono-oxidized derivatives with the metabolite observed in rats injected with CS1P1 identified the metabolite as N-oxide 1, also named TZ82121. The MS fragmentation pattern of N-oxide 1 also matched that of the major metabolite in rat plasma. To confirm that metabolite TZ82121 does not enter the brain, we radiosynthesized [18F]TZ82121 by the oxidation of [18F]FS1P1. Radio-HPLC analysis confirmed that [18F]TZ82121 matched the radiometabolite observed in rat plasma post injection of [18F]FS1P1. Furthermore, the acute biodistribution study in SD rats and PET brain imaging in a nonhuman primate showed that [18F]TZ82121 does not enter the rat or nonhuman primate brain. Consequently, we concluded that the major lipophilic radiometabolite N-oxide [11C]TZ82121, detected in human plasma post injection of [11C]CS1P1, does not enter the brain to confound quantitative PET data analysis. [11C]CS1P1 is a promising S1PR1 radiotracer for detecting S1PR1 expression in the CNS.

HTRA1 disaggregates α-synuclein amyloid fibrils and converts them into non-toxic and seeding incompetent species.

Parkinson's disease (PD) is closely linked to α-synuclein (α-syn) misfolding and accumulation in Lewy bodies. The PDZ serine protease HTRA1 degrades fibrillar tau, which is associated with Alzheimer's disease, and inactivating mutations to mitochondrial HTRA2 are implicated in PD. Here, we report that HTRA1 inhibits aggregation of α-syn as well as FUS and TDP-43, which are implicated in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia. The protease domain of HTRA1 is necessary and sufficient for inhibiting aggregation, yet this activity is proteolytically-independent. Further, HTRA1 disaggregates preformed α-syn fibrils, rendering them incapable of seeding aggregation of endogenous α-syn, while reducing HTRA1 expression promotes α-syn seeding. HTRA1 remodels α-syn fibrils by targeting the NAC domain, the key domain catalyzing α-syn amyloidogenesis. Finally, HTRA1 detoxifies α-syn fibrils and prevents formation of hyperphosphorylated α-syn accumulations in primary neurons. Our findings suggest that HTRA1 may be a therapeutic target for a range of neurodegenerative disorders.

13C-SpaceM: Spatial single-cell isotope tracing reveals heterogeneity of de novo fatty acid synthesis in cancer.

Metabolism has emerged as a key factor in homeostasis and disease including cancer. Yet, little is known about the heterogeneity of metabolic activity of cancer cells due to the lack of tools to directly probe it. Here, we present a novel method, 13C-SpaceM for spatial single-cell isotope tracing of glucose-dependent de novo lipogenesis. The method combines imaging mass spectrometry for spatially-resolved detection of 13C6-glucose-derived 13C label incorporated into esterified fatty acids with microscopy and computational methods for data integration and analysis. We validated 13C-SpaceM on a spatially-heterogeneous normoxia-hypoxia model of liver cancer cells. Investigating cultured cells, we revealed single-cell heterogeneity of lipogenic acetyl-CoA pool labelling degree upon ACLY knockdown that is hidden in the bulk analysis and its effect on synthesis of individual fatty acids. Next, we adapted 13C-SpaceM to analyze tissue sections of mice harboring isocitrate dehydrogenase (IDH)-mutant gliomas. We found a strong induction of de novo fatty acid synthesis in the tumor tissue compared to the surrounding brain. Comparison of fatty acid isotopologue patterns revealed elevated uptake of mono-unsaturated and essential fatty acids in the tumor. Furthermore, our analysis uncovered substantial spatial heterogeneity in the labelling of the lipogenic acetyl-CoA pool indicative of metabolic reprogramming during microenvironmental adaptation. Overall, 13C-SpaceM enables novel ways for spatial probing of metabolic activity at the single cell level. Additionally, this methodology provides unprecedented insight into fatty acid uptake, synthesis and modification in normal and cancerous tissues.

Investigating post-traumatic syringomyelia and local fluid osmoregulation via a rat model.

BACKGROUND: Syringomyelia (SM) is characterized by the development of fluid-filled cavities, referred to as syrinxes, within the spinal cord tissue. The molecular etiology of SM post-spinal cord injury (SCI) is not well understood and only invasive surgical based treatments are available to treat SM clinically. This study builds upon our previous omics studies and in vitro cellular investigations to further understand local fluid osmoregulation in post-traumatic SM (PTSM) to highlight important pathways for future molecular interventions. METHODS: A rat PTSM model consisting of a laminectomy at the C7 to T1 level followed by a parenchymal injection of 2 µL quisqualic acid (QA) and an injection of 5 µL kaolin in the subarachnoid space was utilized 6 weeks after initial surgery, parenchymal fluid and cerebrospinal fluid (CSF) were collected, and the osmolality of fluids were analyzed. Immunohistochemistry (IHC), metabolomics analysis using LC-MS, and mass spectrometry-based imaging (MSI) were performed on injured and laminectomy-only control spinal cords. RESULTS: We demonstrated that the osmolality of the local parenchymal fluid encompassing syrinxes was higher compared to control spinal cords after laminectomy, indicating a local osmotic imbalance due to SM injury. Moreover, we also found that parenchymal fluid is more hypertonic than CSF, indicating establishment of a local osmotic gradient in the PTSM injured spinal cord (syrinx site) forcing fluid into the spinal cord parenchyma to form and/or expand syrinxes. IHC results demonstrated upregulation of betaine, ions, water channels/transporters, and enzymes (BGT1, AQP1, AQP4, CHDH) at the syrinx site as compared to caudal and rostral sites to the injury, implying extensive local osmoregulation activities at the syrinx site. Further, metabolomics analysis corroborated alterations in osmolality at the syrinx site by upregulation of small molecule osmolytes including betaine, carnitine, glycerophosphocholine, arginine, creatine, guanidinoacetate, and spermidine. CONCLUSIONS: In summary, PTSM results in local osmotic disturbance that propagates at 6 weeks following initial injury. This coincides with and may contribute to syrinx formation/expansion.

Inducing vulnerability to InhA inhibition restores isoniazid susceptibility in drug-resistant Mycobacterium tuberculosis.

Of the approximately 10 million cases of Mycobacterium tuberculosis (Mtb) infections each year, over 10% are resistant to the frontline antibiotic isoniazid (INH). INH resistance is predominantly caused by mutations that decrease the activity of the bacterial enzyme KatG, which mediates the conversion of the pro-drug INH to its active form INH-NAD. We previously discovered an inhibitor of Mtb respiration, C10, that enhances the bactericidal activity of INH, prevents the emergence of INH-resistant mutants, and re-sensitizes a collection of INH-resistant mutants to INH through an unknown mechanism. To investigate the mechanism of action of C10, we exploited the toxicity of high concentrations of C10 to select for resistant mutants. We discovered two mutations that confer resistance to the disruption of energy metabolism and allow for the growth of Mtb in high C10 concentrations, indicating that growth inhibition by C10 is associated with inhibition of respiration. Using these mutants as well as direct inhibitors of the Mtb electron transport chain, we provide evidence that inhibition of energy metabolism by C10 is neither sufficient nor necessary to potentiate killing by INH. Instead, we find that C10 acts downstream of INH-NAD synthesis, causing Mtb to become particularly sensitive to inhibition of the INH-NAD target, InhA, without changing the concentration of INH-NAD or the activity of InhA, the two predominant mechanisms of potentiating INH. Our studies revealed that there exists a vulnerability in Mtb that can be exploited to render Mtb sensitive to otherwise subinhibitory concentrations of InhA inhibitor.IMPORTANCEIsoniazid (INH) is a critical frontline antibiotic to treat Mycobacterium tuberculosis (Mtb) infections. INH efficacy is limited by its suboptimal penetration of the Mtb-containing lesion and by the prevalence of clinical INH resistance. We previously discovered a compound, C10, that enhances the bactericidal activity of INH, prevents the emergence of INH-resistant mutants, and re-sensitizes a set of INH-resistant mutants to INH. Resistance is typically mediated by katG mutations that decrease the activation of INH, which is required for INH to inhibit the essential enzyme InhA. Our current work demonstrates that C10 re-sensitizes INH-resistant katG-hypomorphs without enhancing the activation of INH. We furthermore show that C10 causes Mtb to become particularly vulnerable to InhA inhibition without compromising InhA activity on its own. Therefore, C10 represents a novel strategy to curtail the development of INH resistance and to sensitize Mtb to sub-lethal doses of INH, such as those achieved at the infection site.

Loss of dihydroceramide desaturase drives neurodegeneration by disrupting endoplasmic reticulum and lipid droplet homeostasis in glial cells.

Dihydroceramide desaturases convert dihydroceramides to ceramides, the precursors of all complex sphingolipids. Reduction of DEGS1 dihydroceramide desaturase function causes pediatric neurodegenerative disorder hypomyelinating leukodystrophy-18 (HLD-18). We discovered that infertile crescent (ifc), the Drosophila DEGS1 homolog, is expressed primarily in glial cells to promote CNS development by guarding against neurodegeneration. Loss of ifc causes massive dihydroceramide accumulation and severe morphological defects in cortex glia, including endoplasmic reticulum (ER) expansion, failure of neuronal ensheathment, and lipid droplet depletion. RNAi knockdown of the upstream ceramide synthase schlank in glia of ifc mutants rescues ER expansion, suggesting dihydroceramide accumulation in the ER drives this phenotype. RNAi knockdown of ifc in glia but not neurons drives neuronal cell death, suggesting that ifc function in glia promotes neuronal survival. Our work identifies glia as the primary site of disease progression in HLD-18 and may inform on juvenile forms of ALS, which also feature elevated dihydroceramide levels.

Circadian regulation of MGMT expression and promoter methylation underlies daily rhythms in TMZ sensitivity in glioblastoma.

BACKGROUND: Glioblastoma (GBM) is the most common primary brain tumor in adults. Despite extensive research and clinical trials, median survival post-treatment remains at 15 months. Thus, all opportunities to optimize current treatments and improve patient outcomes should be considered. A recent retrospective clinical study found that taking TMZ in the morning compared to the evening was associated with a 6-month increase in median survival in patients with MGMT-methylated GBM. Here, we hypothesized that TMZ efficacy depends on time-of-day and O6-Methylguanine-DNA Methyltransferase (MGMT) activity in murine and human models of GBM. METHODS AND RESULTS: In vitro recordings using real-time bioluminescence reporters revealed that GBM cells have intrinsic circadian rhythms in the expression of the core circadian clock genes Bmal1 and Per2, as well as in the DNA repair enzyme, MGMT. Independent measures of MGMT transcript levels and promoter methylation also showed daily rhythms intrinsic to GBM cells. These cells were more susceptible to TMZ when delivered at the daily peak of Bmal1 transcription. We found that in vivo morning administration of TMZ also decreased tumor size and increased body weight compared to evening drug delivery in mice bearing GBM xenografts. Finally, inhibition of MGMT activity with O6-Benzylguanine abrogated the daily rhythm in sensitivity to TMZ in vitro by increasing sensitivity at both the peak and trough of Bmal1 expression. CONCLUSION: We conclude that chemotherapy with TMZ can be dramatically enhanced by delivering at the daily maximum of tumor Bmal1 expression and minimum of MGMT activity and that scoring MGMT methylation status requires controlling for time of day of biopsy.

DDR2-regulated arginase activity in ovarian cancer-associated fibroblasts promotes collagen production and tumor progression.

Ovarian cancer has poor survival outcomes particularly for advanced stage, metastatic disease. Metastasis is promoted by interactions of stromal cells, such as cancer-associated fibroblasts (CAFs) in the tumor microenvironment (TME), with tumor cells. CAFs play a key role in tumor progression by remodeling the TME and extracellular matrix (ECM) to result in a more permissive environment for tumor progression. It has been shown that fibroblasts, in particular myofibroblasts, utilize metabolism to support ECM remodeling. However, the intricate mechanisms by which CAFs support collagen production and tumor progression are poorly understood. In this study, we show that the fibrillar collagen receptor, Discoidin Domain Receptor 2 (DDR2), promotes collagen production in human and mouse omental CAFs through arginase activity. CAFs with high DDR2 or arginase promote tumor colonization in the omentum. In addition, DDR2-depleted CAFs had decreased ornithine levels leading to decreased collagen production and polyamine levels compared to WT control CAFs. Tumor cell invasion was decreased in the presence CAF conditioned media (CM) depleted of DDR2 or arginase-1, and this invasion defect was rescued in the presence of CM from DDR2-depleted CAFs that constitutively overexpressed arginase-1. Similarly, the addition of exogenous polyamines to CM from DDR2-depleted CAFs led to increased tumor cell invasion. We detected SNAI1 protein at the promoter region of the arginase-1 gene, and DDR2-depleted CAFs had decreased levels of SNAI1 protein at the arginase-1 promoter region. Furthermore, high stromal arginase-1 expression correlated with poor survival in ovarian cancer patients. These findings highlight how DDR2 regulates collagen production by CAFs in the tumor microenvironment by controlling the transcription of arginase-1, and CAFs are a major source of arginase activity and L-arginine metabolites in ovarian cancer models.

Effects of hepatic mitochondrial pyruvate carrier deficiency on de novo lipogenesis and gluconeogenesis in mice.

The liver coordinates the systemic response to nutrient deprivation and availability by producing glucose from gluconeogenesis during fasting and synthesizing lipids via de novo lipogenesis (DNL) when carbohydrates are abundant. Mitochondrial pyruvate metabolism is thought to play important roles in both gluconeogenesis and DNL. We examined the effects of hepatocyte-specific mitochondrial pyruvate carrier (MPC) deletion on the fasting-refeeding response. Rates of DNL during refeeding were impaired by hepatocyte MPC deletion, but this did not reduce intrahepatic lipid content. During fasting, glycerol is converted to glucose by two pathways; a direct cytosolic pathway and an indirect mitochondrial pathway requiring the MPC. Hepatocyte MPC deletion reduced the incorporation of 13C-glycerol into TCA cycle metabolites, but not into new glucose. Furthermore, suppression of glycerol and alanine metabolism did not affect glucose concentrations in fasted hepatocyte-specific MPC-deficient mice, suggesting multiple layers of redundancy in glycemic control in mice.

Circadian regulation of MGMT expression and promoter methylation underlies daily rhythms in TMZ sensitivity in glioblastoma.

Background: Glioblastoma (GBM) is the most common primary brain tumor in adults. Despite extensive research and clinical trials, median survival post-treatment remains at 15 months. Thus, all opportunities to optimize current treatments and improve patient outcomes should be considered. A recent retrospective clinical study found that taking TMZ in the morning compared to the evening was associated with a 6-month increase in median survival in patients with MGMT-methylated GBM. Here, we hypothesized that TMZ efficacy depends on time-of-day and O6-Methylguanine-DNA Methyltransferase (MGMT) activity in murine and human models of GBM. Methods and Results: In vitro recordings using real-time bioluminescence reporters revealed that GBM cells have intrinsic circadian rhythms in the expression of the core circadian clock genes Bmal1 and Per2, as well as in the DNA repair enzyme, MGMT. Independent measures of MGMT transcript levels and promoter methylation also showed daily rhythms intrinsic to GBM cells. These cells were more susceptible to TMZ when delivered at the daily peak of Bmal1 transcription. We found that in vivo morning administration of TMZ also decreased tumor size and increased body weight compared to evening drug delivery in mice bearing GBM xenografts. Finally, inhibition of MGMT activity with O6-Benzylguanine abrogated the daily rhythm in sensitivity to TMZ in vitro by increasing sensitivity at both the peak and trough of Bmal1 expression. Conclusion: We conclude that chemotherapy with TMZ can be dramatically enhanced by delivering at the daily maximum of tumor Bmal1 expression and minimum of MGMT activity.

BAP1 promotes osteoclast function by metabolic reprogramming.

Treatment of osteoporosis commonly diminishes osteoclast number which suppresses bone formation thus compromising fracture prevention. Bone formation is not suppressed, however, when bone degradation is reduced by retarding osteoclast functional resorptive capacity, rather than differentiation. We find deletion of deubiquitinase, BRCA1-associated protein 1 (Bap1), in myeloid cells (Bap1∆LysM), arrests osteoclast function but not formation. Bap1∆LysM osteoclasts fail to organize their cytoskeleton which is essential for bone degradation consequently increasing bone mass in both male and female mice. The deubiquitinase activity of BAP1 modifies osteoclast function by metabolic reprogramming. Bap1 deficient osteoclast upregulate the cystine transporter, Slc7a11, by enhanced H2Aub occupancy of its promoter. SLC7A11 controls cellular reactive oxygen species levels and redirects the mitochondrial metabolites away from the tricarboxylic acid cycle, both being necessary for osteoclast function. Thus, in osteoclasts BAP1 appears to regulate the epigenetic-metabolic axis and is a potential target to reduce bone degradation while maintaining osteogenesis in osteoporotic patients.

Stromal DDR2 Promotes Ovarian Cancer Metastasis through Regulation of Metabolism and Secretion of Extracellular Matrix Proteins.

Ovarian cancer is the leading cause of gynecologic cancer-related deaths. The propensity for metastasis within the peritoneal cavity is a driving factor for the poor outcomes associated with this disease, but there is currently no effective therapy targeting metastasis. In this study, we investigate the contribution of stromal cells to ovarian cancer metastasis and identify normal stromal cell expression of the collagen receptor, discoidin domain receptor 2 (DDR2), that acts to facilitate ovarian cancer metastasis. In vivo, global genetic inactivation of Ddr2 impairs the ability of Ddr2-expressing syngeneic ovarian cancer cells to spread throughout the peritoneal cavity. Specifically, DDR2 expression in mesothelial cells lining the peritoneal cavity facilitates tumor cell attachment and clearance. Subsequently, omentum fibroblast expression of DDR2 promotes tumor cell invasion. Mechanistically, we find DDR2-expressing fibroblasts are more energetically active, such that DDR2 regulates glycolysis through AKT/SNAI1 leading to suppressed fructose-1,6-bisphosphatase and increased hexokinase activity, a key glycolytic enzyme. Upon inhibition of DDR2, we find decreased protein synthesis and secretion. Consequently, when DDR2 is inhibited, there is reduction in secreted extracellular matrix proteins important for metastasis. Specifically, we find that fibroblast DDR2 inhibition leads to decreased secretion of the collagen crosslinker, LOXL2. Adding back LOXL2 to DDR2 deficient fibroblasts rescues the ability of tumor cells to invade. Overall, our results suggest that stromal cell expression of DDR2 is an important mediator of ovarian cancer metastasis. IMPLICATIONS: DDR2 is highly expressed by stromal cells in ovarian cancer that can mediate metastasis and is a potential therapeutic target in ovarian cancer.

PeakDetective: A Semisupervised Deep Learning-Based Approach for Peak Curation in Untargeted Metabolomics.

Peak-detection algorithms currently used to process untargeted metabolomics data were designed to maximize sensitivity at the sacrifice of selectively. Peak lists returned by conventional software tools therefore contain a high density of artifacts that do not represent real chemical analytes, which, in turn, hinder downstream analyses. Although some innovative approaches to remove artifacts have recently been introduced, they involve extensive user intervention due to the diversity of peak shapes present within and across metabolomics data sets. To address this bottleneck in metabolomics data processing, we developed a semisupervised deep learning-based approach, PeakDetective, for classification of detected peaks as artifacts or true peaks. Our approach utilizes two techniques for artifact removal. First, an unsupervised autoencoder is used to extract a low-dimensional, latent representation of each peak. Second, a classifier is trained with active learning to discriminate between artifacts and true peaks. Through active learning, the classifier is trained with less than 100 user-labeled peaks in a matter of minutes. Given the speed of its training, PeakDetective can be rapidly tailored to specific LC/MS methods and sample types to maximize performance on each type of data set. In addition to curation, the trained models can also be utilized for peak detection to immediately detect peaks with both high sensitivity and selectivity. We validated PeakDetective on five diverse LC/MS data sets, where PeakDetective showed greater accuracy compared to current approaches. When applied to a SARS-CoV-2 data set, PeakDetective enabled more statistically significant metabolites to be detected. PeakDetective is open source and available as a Python package at https://github.com/pattilab/PeakDetective.

Using mass spectrometry imaging to map fluxes quantitatively in the tumor ecosystem.

Tumors are comprised of a multitude of cell types spanning different microenvironments. Mass spectrometry imaging (MSI) has the potential to identify metabolic patterns within the tumor ecosystem and surrounding tissues, but conventional workflows have not yet fully integrated the breadth of experimental techniques in metabolomics. Here, we combine MSI, stable isotope labeling, and a spatial variant of Isotopologue Spectral Analysis to map distributions of metabolite abundances, nutrient contributions, and metabolic turnover fluxes across the brains of mice harboring GL261 glioma, a widely used model for glioblastoma. When integrated with MSI, the combination of ion mobility, desorption electrospray ionization, and matrix assisted laser desorption ionization reveals alterations in multiple anabolic pathways. De novo fatty acid synthesis flux is increased by approximately 3-fold in glioma relative to surrounding healthy tissue. Fatty acid elongation flux is elevated even higher at 8-fold relative to surrounding healthy tissue and highlights the importance of elongase activity in glioma.

Uridine-derived ribose fuels glucose-restricted pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDA) is a lethal disease notoriously resistant to therapy1,2. This is mediated in part by a complex tumour microenvironment3, low vascularity4, and metabolic aberrations5,6. Although altered metabolism drives tumour progression, the spectrum of metabolites used as nutrients by PDA remains largely unknown. Here we identified uridine as a fuel for PDA in glucose-deprived conditions by assessing how more than 175 metabolites impacted metabolic activity in 21 pancreatic cell lines under nutrient restriction. Uridine utilization strongly correlated with the expression of uridine phosphorylase 1 (UPP1), which we demonstrate liberates uridine-derived ribose to fuel central carbon metabolism and thereby support redox balance, survival and proliferation in glucose-restricted PDA cells. In PDA, UPP1 is regulated by KRAS-MAPK signalling and is augmented by nutrient restriction. Consistently, tumours expressed high UPP1 compared with non-tumoural tissues, and UPP1 expression correlated with poor survival in cohorts of patients with PDA. Uridine is available in the tumour microenvironment, and we demonstrated that uridine-derived ribose is actively catabolized in tumours. Finally, UPP1 deletion restricted the ability of PDA cells to use uridine and blunted tumour growth in immunocompetent mouse models. Our data identify uridine utilization as an important compensatory metabolic process in nutrient-deprived PDA cells, suggesting a novel metabolic axis for PDA therapy.

Mitochondrial pyruvate metabolism and glutaminolysis toggle steady-state and emergency myelopoiesis.

To define the metabolic requirements of hematopoiesis, we examined blood lineages in mice conditionally deficient in genes required for long-chain fatty acid oxidation (Cpt2), glutaminolysis (Gls), or mitochondrial pyruvate import (Mpc2). Genetic ablation of Cpt2 or Gls minimally impacted most blood lineages. In contrast, deletion of Mpc2 led to a sharp decline in mature myeloid cells and a slower reduction in T cells, whereas other hematopoietic lineages were unaffected. Yet MPC2-deficient monocytes and neutrophils rapidly recovered due to a transient and specific increase in myeloid progenitor proliferation. Competitive bone marrow chimera and stable isotope tracing experiments demonstrated that this proliferative burst was progenitor intrinsic and accompanied by a metabolic switch to glutaminolysis. Myeloid recovery after loss of MPC2 or cyclophosphamide treatment was delayed in the absence of GLS. Reciprocally, MPC2 was not required for myeloid recovery after cyclophosphamide treatment. Thus, mitochondrial pyruvate metabolism maintains myelopoiesis under steady-state conditions, while glutaminolysis in progenitors promotes emergency myelopoiesis.

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.

The Warburg effect: a signature of mitochondrial overload.

A long-standing question in cancer biology has been why oxygenated tumors ferment the majority of glucose they consume to lactate rather than oxidizing it in their mitochondria, a phenomenon known as the 'Warburg effect.' An abundance of evidence shows not only that most cancer cells have fully functional mitochondria but also that mitochondrial activity is important to proliferation. It is therefore difficult to rationalize the metabolic benefit of cancer cells switching from respiration to fermentation. An emerging perspective is that rather than mitochondrial metabolism being suppressed in tumors, as is often suggested, mitochondrial activity increases to the level of saturation. As such, the Warburg effect becomes a signature of excess glucose being released as lactate due to mitochondrial overload.

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.