Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat Commun. 2014;5:3083. [PMID: 24419221 DOI: 10.1038/ncomms4083]

Amplifying hepatic L-aspartate levels suppresses CCl4-induced liver fibrosis by reversing glucocorticoid receptor β-mediated mitochondrial malfunction

Liver fibrosis is a determinant-stage process of many chronic liver diseases and affected over 7.9 billion populations worldwide with increasing demands of ideal therapeutic agents. Discovery of active molecules with anti-hepatic fibrosis efficacies presents the most attacking filed. Here, we revealed that hepatic L-aspartate levels were decreased in CCl4-induced fibrotic mice. Instead, supplementation of L-aspartate orally alleviated typical manifestations of liver injury and fibrosis. These therapeutic efficacies were alongside improvements of mitochondrial adaptive oxidation. Notably, treatment with L-aspartate rebalanced hepatic cholesterol-steroid metabolism and reduced the levels of liver-impairing metabolites, including corticosterone (CORT). Mechanistically, L-aspartate treatment efficiently reversed CORT-mediated glucocorticoid receptor β (GRβ) signaling activation and subsequent transcriptional suppression of the mitochondrial genome by directly binding to the mitochondrial genome. Knockout of GRβ ameliorated corticosterone-mediated mitochondrial dysfunction and hepatocyte damage which also weakened the improvements of L-aspartate in suppressing GRβ signaling. These data suggest that L-aspartate ameliorates hepatic fibrosis by suppressing GRβ signaling via rebalancing cholesterol-steroid metabolism, would be an ideal candidate for clinical liver fibrosis treatment.

Adding metabolic tasks to human GEM models to improve the study of gene targets and their associated toxicities

Genetic minimal cut sets (gMCS) are genes that must be deactivated simultaneously to avoid unwanted states in a metabolic model. The concept of gMCS can be applied to two different scenarios. First, it can be used to identify potential gene toxicities in generic or healthy cell models. Second, it can be used to develop genetic strategies to target cancer cells and prevent their proliferation. Up to now, gMCS have been evaluated using the traditional procedure of preventing biomass production. This paper proposes an additional way: using essential metabolic tasks, which any human cell should perform, to enlarge the set of unwanted states. Including this addition can significantly improve the study of toxicities and reveal targets that can be used to treat unhealthy cells. Excluding metabolic tasks can cause important information to be overlooked, which could impact the study's success. Regarding toxicities, using the generic Human model, the number of detected generic toxicities with metabolic tasks increases from 106 to 281 (136 gMCSs of length 1 and 49 of length 2). We have used the following context-specific models to evaluate specific toxicities in different healthy tissues: blood, pancreas, liver, heart, and kidney. Again, considering metabolic tasks, we have found new toxicities (lengths 1 and 2) whose inactivation could damage these healthy tissues.Our research strategy has been applied to identify new cancer drug targets in two myeloma cell lines. We obtained new therapeutic targets of lengths 1 and 2 for each cell line. After analyzing the data, we conclude that incorporating metabolic tasks into cancer models can reveal important therapeutic targets previously disregarded by the conventional method of inhibiting biomass production. This approach also improves the evaluation of potential drug toxicities.

Host cell CRISPR genomics and modelling reveal shared metabolic vulnerabilities in the intracellular development of Plasmodium falciparum and related hemoparasites

Parasitic diseases, particularly malaria (caused by Plasmodium falciparum) and theileriosis (caused by Theileria spp.), profoundly impact global health and the socioeconomic well-being of lower-income countries. Despite recent advances, identifying host metabolic proteins essential for these auxotrophic pathogens remains challenging. Here, we generate a novel metabolic model of human hepatocytes infected with P. falciparum and integrate it with a genome-wide CRISPR knockout screen targeting Theileria-infected cells to pinpoint shared vulnerabilities. We identify key host metabolic enzymes critical for the intracellular survival of both of these lethal hemoparasites. Remarkably, among the metabolic proteins identified by our synergistic approach, we find that host purine and heme biosynthetic enzymes are essential for the intracellular survival of P. falciparum and Theileria, while other host enzymes are only essential under certain metabolic conditions, highlighting P. falciparum's adaptability and ability to scavenge nutrients selectively. Unexpectedly, host porphyrins emerge as being essential for both parasites. The shared vulnerabilities open new avenues for developing more effective therapies against these debilitating diseases, with the potential for broader applicability in combating apicomplexan infections.

Genome-scale metabolic network of human carotid plaque reveals the pivotal role of glutamine/glutamate metabolism in macrophage modulating plaque inflammation and vulnerability

BACKGROUND

Metabolism is increasingly recognized as a key regulator of the function and phenotype of the primary cellular constituents of the atherosclerotic vascular wall, including endothelial cells, smooth muscle cells, and inflammatory cells. However, a comprehensive analysis of metabolic changes associated with the transition of plaque from a stable to a hemorrhaged phenotype is lacking.

METHODS

In this study, we integrated two large mRNA expression and protein abundance datasets (BIKE, n = 126; MaasHPS, n = 43) from human atherosclerotic carotid artery plaque to reconstruct a genome-scale metabolic network (GEM). Next, the GEM findings were linked to metabolomics data from MaasHPS, providing a comprehensive overview of metabolic changes in human plaque.

RESULTS

Our study identified significant changes in lipid, cholesterol, and inositol metabolism, along with altered lysosomal lytic activity and increased inflammatory activity, in unstable plaques with intraplaque hemorrhage (IPH+) compared to non-hemorrhaged (IPH-) plaques. Moreover, topological analysis of this network model revealed that the conversion of glutamine to glutamate and their flux between the cytoplasm and mitochondria were notably compromised in hemorrhaged plaques, with a significant reduction in overall glutamate levels in IPH+ plaques. Additionally, reduced glutamate availability was associated with an increased presence of macrophages and a pro-inflammatory phenotype in IPH+ plaques, suggesting an inflammation-prone microenvironment.

CONCLUSIONS

This study is the first to establish a robust and comprehensive GEM for atherosclerotic plaque, providing a valuable resource for understanding plaque metabolism. The utility of this GEM was illustrated by its ability to reliably predict dysregulation in the cholesterol hydroxylation, inositol metabolism, and the glutamine/glutamate pathway in rupture-prone hemorrhaged plaques, a finding that may pave the way to new diagnostic or therapeutic measures.

Pan-Cancer, Genome-Scale Metabolic Network Analysis of over 10,000 Patients Elucidates Relationship between Metabolism and Survival

Despite the high variability in cancer biology, cancers nevertheless exhibit cohesive hallmarks across multiple cancer types, notably dysregulated metabolism. Metabolism plays a central role in cancer biology, and shifts in metabolic pathways have been linked to tumor aggressiveness and likelihood of response to therapy. We therefore sought to interrogate metabolism across cancer types and understand how intrinsic modes of metabolism vary within and across indications and how they relate to patient prognosis. We used context specific genome-scale metabolic modeling to simulate metabolism across 10,915 patients from 34 cancer types from The Cancer Genome Atlas and the MMRF-COMMPASS study. We found that cancer metabolism clustered into modes characterized by differential glycolysis, oxidative phosphorylation, and growth rate. We also found that the simulated activities of metabolic pathways are intrinsically prognostic across cancer types, especially tumor growth rate, fatty acid biosynthesis, folate metabolism, oxidative phosphorylation, steroid metabolism, and glutathione metabolism. This work shows the prognostic power of individual patient metabolic modeling across multiple cancer types. Additionally, it shows that analyzing large-scale models of cancer metabolism with survival information provides unique insights into underlying relationships across cancer types and suggests how therapies designed for one cancer type may be repurposed for use in others.

A unified framework for prediction of liver steatosis dynamics in response to different diet and drug interventions

BACKGROUND & AIMS

Non-alcoholic fatty liver disease (NAFLD) is a common metabolic disorder, characterized by the accumulation of excess fat in the liver, and is a driving factor for various severe liver diseases. These multi-factorial and multi-timescale changes are observed in different clinical studies, but these studies have not been integrated into a unified framework. In this study, we aim to present such a unified framework in the form of a dynamic mathematical model.

METHODS

For model training and validation, we collected data for dietary or drug-induced interventions aimed at reducing or increasing liver fat. The model was formulated using ordinary differential equations (ODEs) and the mathematical analysis, model simulation, model formulation and the model parameter estimation were all performed in MATLAB.

RESULTS

Our mathematical model describes accumulation of fat in the liver and predicts changes in lipid fluxes induced by both dietary and drug interventions. The model is validated using data from a wide range of drug and dietary intervention studies and can predict both short-term (days) and long-term (weeks) changes in liver fat. Importantly, the model computes the contribution of each individual lipid flux to the total liver fat dynamics. Furthermore, the model can be combined with an established bodyweight model, to simulate even longer scenarios (years), also including the effects of insulin resistance and body weight. To help prepare for corresponding eHealth applications, we also present a way to visualize the simulated changes, using dynamically changing lipid droplets, seen in images of liver biopsies.

CONCLUSION

In conclusion, we believe that the minimal model presented herein might be a useful tool for future applications, and to further integrate and understand data regarding changes in dietary and drug induced changes in ectopic TAG in the liver. With further development and validation, the minimal model could be used as a disease progression model for steatosis.

Temporal multiscale modeling of biochemical regulatory networks: Calcium-regulated hepatocyte lipid and glucose metabolism

Hepatocyte lipid and glucose metabolism is regulated not only by major hormones like insulin and glucagon but also by many other factors, including calcium ions. Recently, mitochondria-associated membrane (MAM) dysfunction combined with incorrect IP3-receptor regulation has been shown to result in abnormal calcium signaling in hepatocytes. This dysfunction could further lead to hepatic metabolism pathology. However, the exact contribution of MAM dysfunction, incorrect IP3-receptor regulation and insulin resistance to the calcium-insulin-glucagon interplay is not understood yet. In this work, we analyze the role of abnormal calcium signaling and insulin dysfunction in hepatocytes by proposing a model of hepatocyte metabolic regulatory network with a detailed focus on the model construction details besides the biological aspect. In this work, we analyze the role of abnormal calcium signaling and insulin dysfunction in hepatocytes by proposing a model of hepatocyte metabolic regulatory network. We focus on the model construction details, model validation, and predictions. We describe the dynamic regulation of signaling processes by sigmoid Hill function. In particular, we study the effect of both the Hill function slope and the distance between Hill function extremes on metabolic processes in hepatocytes as a model of nonspecific insulin dysfunction. We also address the significant time difference between characteristic time of glucose hepatic processing and a typical calcium oscillation period in hepatocytes. Our modeling results show that calcium signaling dysfunction results in an abnormal increase in postprandial glucose levels, an abnormal glucose decrease in fasting, and a decreased amount of stored glycogen. An insulin dysfunction of glucose phosphorylation, glucose dephosphorylation, and glycogen breakdown also cause a noticeable effect. We also get some insight into the so-called hepatic insulin resistance paradox, confirming the hypothesis regarding indirect insulin action on hepatocytes via dysfunctional adipocyte lipolysis.

Logistic PCA explains differences between genome-scale metabolic models in terms of metabolic pathways

Genome-scale metabolic models (GSMMs) offer a holistic view of biochemical reaction networks, enabling in-depth analyses of metabolism across species and tissues in multiple conditions. However, comparing GSMMs Against each other poses challenges as current dimensionality reduction algorithms or clustering methods lack mechanistic interpretability, and often rely on subjective assumptions. Here, we propose a new approach utilizing logisitic principal component analysis (LPCA) that efficiently clusters GSMMs while singling out mechanistic differences in terms of reactions and pathways that drive the categorization. We applied LPCA to multiple diverse datasets, including GSMMs of 222 Escherichia-strains, 343 budding yeasts (Saccharomycotina), 80 human tissues, and 2943 Firmicutes strains. Our findings demonstrate LPCA's effectiveness in preserving microbial phylogenetic relationships and discerning human tissue-specific metabolic profiles, exhibiting comparable performance to traditional methods like t-distributed stochastic neighborhood embedding (t-SNE) and Jaccard coefficients. Moreover, the subsystems and associated reactions identified by LPCA align with existing knowledge, underscoring its reliability in dissecting GSMMs and uncovering the underlying drivers of separation.

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

Hepatic glucose metabolism in the steatotic liver

The liver is central in regulating glucose homeostasis, being the major contributor to endogenous glucose production and the greatest reserve of glucose as glycogen. It is both a target and regulator of the action of glucoregulatory hormones. Hepatic metabolic functions are altered in and contribute to the highly prevalent steatotic liver disease (SLD), including metabolic dysfunction-associated SLD (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH). In this Review, we describe the dysregulation of hepatic glucose metabolism in MASLD and MASH and associated metabolic comorbidities, and how advances in techniques and models for the assessment of hepatic glucose fluxes in vivo have led to the identification of the mechanisms related to the alterations in glucose metabolism in MASLD and comorbidities. These fluxes can ultimately increase hepatic glucose production concomitantly with fat accumulation and alterations in the secretion and action of glucoregulatory hormones. No pharmacological treatment has yet been approved for MASLD or MASH, but some antihyperglycaemic drugs approved for treating type 2 diabetes have shown positive effects on hepatic glucose metabolism and hepatosteatosis. A deep understanding of how MASLD affects glucose metabolic fluxes and glucoregulatory hormones might assist in the early identification of at-risk individuals and the use or development of targeted therapies.

Remodeling Intestinal Microbiota Alleviates Severe Combined Hyperlipidemia-Induced Nonalcoholic Steatohepatitis and Atherosclerosis in LDLR-/- Hamsters

Combined hyperlipidemia (CHL) manifests as elevated cholesterol and triglycerides, associated with fatty liver and cardiovascular diseases. Emerging evidence underscores the crucial role of the intestinal microbiota in metabolic disorders. However, the potential therapeutic viability of remodeling the intestinal microbiota in CHL remains uncertain. In this study, CHL was induced in low-density lipoprotein receptor-deficient (LDLR-/-) hamsters through an 8-week high-fat and high-cholesterol (HFHC) diet or a 4-month high-cholesterol (HC) diet. Placebo or antibiotics were administered through separate or cohousing approaches. Analysis through 16S rDNA sequencing revealed that intermittent antibiotic treatment and the cohousing approach effectively modulated the gut microbiota community without impacting its overall abundance in LDLR-/- hamsters exhibiting severe CHL. Antibiotic treatment mitigated HFHC diet-induced obesity, hyperglycemia, and hyperlipidemia, enhancing thermogenesis and alleviating nonalcoholic steatohepatitis (NASH), concurrently reducing atherosclerotic lesions in LDLR-/- hamsters. Metabolomic analysis revealed a favorable liver lipid metabolism profile. Increased levels of microbiota-derived metabolites, notably butyrate and glycylglycine, also ameliorated NASH and atherosclerosis in HFHC diet-fed LDLR-/- hamsters. Notably, antibiotics, butyrate, and glycylglycine treatment exhibited protective effects in LDLR-/- hamsters on an HC diet, aligning with outcomes observed in the HFHC diet scenario. Our findings highlight the efficacy of remodeling gut microbiota through antibiotic treatment and cohousing in improving obesity, NASH, and atherosclerosis associated with refractory CHL. Increased levels of beneficial microbiota-derived metabolites suggest a potential avenue for microbiome-mediated therapies in addressing CHL-associated diseases.

Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning

Graft injury affects over 50% of liver transplant (LT) recipients, but non-invasive biomarkers to diagnose and guide treatment are currently limited. We aimed to develop a biomarker of graft injury by integrating serum metabolomic profiles with clinical variables. Serum from 55 LT recipients with biopsy confirmed metabolic dysfunction-associated steatohepatitis (MASH), T-cell mediated rejection (TCMR) and biliary complications was collected and processed using a combination of LC-MS/MS assay. The metabolomic profiles were integrated with clinical information using a multi-class Machine Learning (ML) classifier. The model's efficacy was assessed through the Out-of-Bag (OOB) error estimate evaluation. Our ML model yielded an overall accuracy of 79.66% with an OOB estimate of the error rate at 19.75%. The model exhibited a maximum ability to distinguish MASH, with an OOB error estimate of 7.4% compared to 22.2% for biliary and 29.6% for TCMR. The metabolites serine and serotonin emerged as the topmost predictors. When predicting binary outcomes using three models: Biliary (biliary vs. rest), MASH (MASH vs. rest) and TCMR (TCMR vs. rest); the AUCs were 0.882, 0.972 and 0.896, respectively. Our ML tool integrating serum metabolites with clinical variables shows promise as a non-invasive, multi-class serum biomarker of graft pathology.

Combined Metabolic Activators with Different NAD+ Precursors Improve Metabolic Functions in the Animal Models of Neurodegenerative Diseases

BACKGROUND

Mitochondrial dysfunction and metabolic abnormalities are acknowledged as significant factors in the onset of neurodegenerative disorders such as Parkinson's disease (PD) and Alzheimer's disease (AD). Our research has demonstrated that the use of combined metabolic activators (CMA) may alleviate metabolic dysfunctions and stimulate mitochondrial metabolism. Therefore, the use of CMA could potentially be an effective therapeutic strategy to slow down or halt the progression of PD and AD. CMAs include substances such as the glutathione precursors (L-serine and N-acetyl cysteine), the NAD+ precursor (nicotinamide riboside), and L-carnitine tartrate.

METHODS

Here, we tested the effect of two different formulations, including CMA1 (nicotinamide riboside, L-serine, N-acetyl cysteine, L-carnitine tartrate), and CMA2 (nicotinamide, L-serine, N-acetyl cysteine, L-carnitine tartrate), as well as their individual components, on the animal models of AD and PD. We assessed the brain and liver tissues for pathological changes and immunohistochemical markers. Additionally, in the case of PD, we performed behavioral tests and measured responses to apomorphine-induced rotations.

FINDINGS

Histological analysis showed that the administration of both CMA1 and CMA2 formulations led to improvements in hyperemia, degeneration, and necrosis in neurons for both AD and PD models. Moreover, the administration of CMA2 showed a superior effect compared to CMA1. This was further corroborated by immunohistochemical data, which indicated a reduction in immunoreactivity in the neurons. Additionally, notable metabolic enhancements in liver tissues were observed using both formulations. In PD rat models, the administration of both formulations positively influenced the behavioral functions of the animals.

INTERPRETATION

Our findings suggest that the administration of both CMA1 and CMA2 markedly enhanced metabolic and behavioral outcomes, aligning with neuro-histological observations. These findings underscore the promise of CMA2 administration as an effective therapeutic strategy for enhancing metabolic parameters and cognitive function in AD and PD patients.

Liver-innervating vagal sensory neurons play an indispensable role in the development of hepatic steatosis and anxiety-like behavior in mice fed a high-fat diet

Background and Aims

The visceral organ-brain axis, mediated by vagal sensory neurons in the vagal nerve ganglion, is essential for maintaining various physiological functions. In this study, we investigated the impact of liver-projecting vagal sensory neurons on energy balance, hepatic steatosis, and anxiety-like behavior in mice under obesogenic conditions.

Methods

We performed single-nucleus RNA sequencing of vagal sensory neurons innervating the liver. Based on our snRNA-Seq results, we used the Avil CreERT2 strain to identify vagal sensory neurons that innervate the liver.

Results

A small subset of polymodal sensory neurons innervating the liver was located in the left and right ganglia, projecting centrally to the nucleus of the tractus solitarius, area postrema, and dorsal motor nucleus of the vagus, and peripherally to the periportal areas in the liver. Male and female control mice developed diet-induced obesity (DIO) during high-fat diet feeding. Deleting liver-projecting advillin-positive vagal sensory neurons prevented DIO in male and female mice, and these outcomes are associated with increased energy expenditure. Although males and females exhibited improved glucose homeostasis following disruption of liver-projecting vagal sensory neurons, only male mice displayed increased insulin sensitivity. The loss of liver-projecting vagal sensory neurons limited the progression of hepatic steatosis in male and female mice fed a steatogenic diet. Finally, mice lacking liver-innervating vagal sensory neurons exhibited less anxiety-like behavior compared to the control mice.

Conclusions

The liver-brain axis contributes to the regulation of energy balance, glucose tolerance, hepatic steatosis, and anxiety-like behavior depending on the nutrient status in healthy and obesogenic conditions.

Uncovering the Effect of SARS-CoV-2 on Liver Metabolism via Genome-Scale Metabolic Modeling for Reprogramming and Therapeutic Strategies

Genome-scale metabolic models (GEMs) are promising computational tools that contribute to elucidating host-virus interactions at the system level and developing therapeutic strategies against viral infection. In this study, the effect of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on liver metabolism was investigated using integrated GEMs of human hepatocytes and SARS-CoV-2. They were generated for uninfected and infected hepatocytes using transcriptome data. Reporter metabolite analysis resulted in significant transcriptional changes around several metabolites involved in xenobiotics, drugs, arachidonic acid, and leukotriene metabolisms due to SARS-CoV-2 infection. Flux balance analysis and minimization of metabolic adjustment approaches unraveled possible virus-induced hepatocellular reprogramming in fatty acid, glycerophospholipid, sphingolipid cholesterol, and folate metabolisms, bile acid biosynthesis, and carnitine shuttle among others. Reaction knockout analysis provided critical reactions in glycolysis, oxidative phosphorylation, purine metabolism, and reactive oxygen species detoxification subsystems. Computational analysis also showed that administration of dopamine, glucosamine, D-xylose, cysteine, and (R)-3-hydroxybutanoate contributes to alleviating viral infection. In essence, the reconstructed host-virus GEM helps us understand metabolic programming and develop therapeutic strategies to battle SARS-CoV-2.

Prioritization of genes for translation: a computational approach

INTRODUCTION

Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation.

AREAS COVERED

In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes.

EXPERT OPINION

Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.

Amino Acid Metabolism and Atherosclerotic Cardiovascular Disease

Despite significant advances in medical treatments and drug development, atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of death worldwide. Dysregulated lipid metabolism is a well-established driver of ASCVD. Unfortunately, even with potent lipid-lowering therapies, ASCVD-related deaths have continued to increase over the past decade, highlighting an incomplete understanding of the underlying risk factors and mechanisms of ASCVD. Accumulating evidence over the past decades indicates a correlation between amino acids and disease state. This review explores the emerging role of amino acid metabolism in ASCVD, uncovering novel potential biomarkers, causative factors, and therapeutic targets. Specifically, the significance of arginine and its related metabolites, homoarginine and polyamines, branched-chain amino acids, glycine, and aromatic amino acids, in ASCVD are discussed. These amino acids and their metabolites have been implicated in various processes characteristic of ASCVD, including impaired lipid metabolism, endothelial dysfunction, increased inflammatory response, and necrotic core development. Understanding the complex interplay between dysregulated amino acid metabolism and ASCVD provides new insights that may lead to the development of novel diagnostic and therapeutic approaches. Although further research is needed to uncover the precise mechanisms involved, it is evident that amino acid metabolism plays a role in ASCVD.

Hedan tablet ameliorated non-alcoholic steatohepatitis by moderating NF-κB and lipid metabolism-related pathways via regulating hepatic metabolites

Non-alcoholic steatohepatitis (NASH) is a severe form of fatty liver disease. If not treated, it can lead to liver damage, cirrhosis and even liver cancer. However, advances in treatment have remained relatively slow, and there is thus an urgent need to develop appropriate treatments. Hedan tablet (HDP) is used to treat metabolic syndrome. However, scientific understanding of the therapeutic effect of HDP on NASH remains limited. We used HDP to treat a methionine/choline-deficient diet-induced model of NASH in rats to elucidate the therapeutic effects of HDP on liver injury. In addition, we used untargeted metabolomics to investigate the effects of HDP on metabolites in liver of NASH rats, and further validated its effects on inflammation and lipid metabolism following screening for potential target pathways. HDP had considerable therapeutic, anti-oxidant, and anti-inflammatory effects on NASH. HDP could also alter the hepatic metabolites changed by NASH. Moreover, HDP considerable moderated NF-κB and lipid metabolism-related pathways. The present study found that HDP had remarkable therapeutic effects in NASH rats. The therapeutic efficacy of HDP in NASH mainly associated with regulation of NF-κB and lipid metabolism-related pathways via arachidonic acid metabolism, glycine-serine-threonine metabolism, as well as steroid hormone biosynthesis.

Transcriptomics-driven metabolic pathway analysis reveals similar alterations in lipid metabolism in mouse MASH model and human

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent chronic liver disease worldwide, and can rapidly progress to metabolic dysfunction-associated steatohepatitis (MASH). Accurate preclinical models and methodologies are needed to understand underlying metabolic mechanisms and develop treatment strategies. Through meta-analysis of currently proposed mouse models, we hypothesized that a diet- and chemical-induced MASH model closely resembles the observed lipid metabolism alterations in humans.

METHODS

We developed transcriptomics-driven metabolic pathway analysis (TDMPA), a method to aid in the evaluation of metabolic resemblance. TDMPA uses genome-scale metabolic models to calculate enzymatic reaction perturbations from gene expression data. We performed TDMPA to score and compare metabolic pathway alterations in MASH mouse models to human MASH signatures. We used an already-established WD+CCl4-induced MASH model and performed functional assays and lipidomics to confirm TDMPA findings.

RESULTS

Both human MASH and mouse models exhibit numerous altered metabolic pathways, including triglyceride biosynthesis, fatty acid beta-oxidation, bile acid biosynthesis, cholesterol metabolism, and oxidative phosphorylation. We confirm a significant reduction in mitochondrial functions and bioenergetics, as well as in acylcarnitines for the mouse model. We identify a wide range of lipid species within the most perturbed pathways predicted by TDMPA. Triglycerides, phospholipids, and bile acids are increased significantly in mouse MASH liver, confirming our initial observations.

CONCLUSIONS

We introduce TDMPA, a methodology for evaluating metabolic pathway alterations in metabolic disorders. By comparing metabolic signatures that typify human MASH, we show a good metabolic resemblance of the WD+CCl4 mouse model. Our presented approach provides a valuable tool for defining metabolic space to aid experimental design for assessing metabolism.

Reconstruction of cell-specific models capturing the influence of metabolism on DNA methylation in cancer

The imbalance of epigenetic regulatory mechanisms such as DNA methylation, which can promote aberrant gene expression profiles without affecting the DNA sequence, may cause the deregulation of signaling, regulatory, and metabolic processes, contributing to a cancerous phenotype. Since some metabolites are substrates and cofactors of epigenetic regulators, their availability can be affected by characteristic cancer cell metabolic shifts, feeding cancer onset and progression through epigenetic deregulation. Hence, there is a need to study the influence of cancer metabolic reprogramming in DNA methylation to design new effective treatments. In this study, a generic Genome-Scale Metabolic Model (GSMM) of a human cell, integrating DNA methylation or demethylation reactions, was obtained and used for the reconstruction of Genome-Scale Metabolic Models enhanced with Enzymatic Constraints using Kinetic and Omics data (GECKOs) of 31 cancer cell lines. Furthermore, cell-line-specific DNA methylation levels were included in the models, as coefficients of a DNA composition pseudo-reaction, to depict the influence of metabolism over global DNA methylation in each of the cancer cell lines. Flux simulations demonstrated the ability of these models to provide simulated fluxes of exchange reactions similar to the equivalent experimentally measured uptake/secretion rates and to make good functional predictions. In addition, simulations found metabolic pathways, reactions and enzymes directly or inversely associated with the gene promoter methylation. Two potential candidates for targeted cancer epigenetic therapy were identified.

Validation of a screening panel for pediatric metabolic dysfunction-associated steatotic liver disease using metabolomics

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as NAFLD, is the most common liver disease in children. Liver biopsy remains the gold standard for diagnosis, although more efficient screening methods are needed. We previously developed a novel NAFLD screening panel in youth using machine learning applied to high-resolution metabolomics and clinical phenotype data. Our objective was to validate this panel in a separate cohort, which consisted of a combined cross-sectional sample of 161 children with stored frozen samples (75% male, 12.8±2.6 years of age, body mass index 31.0±7.0 kg/m2, 81% with MASLD, 58% Hispanic race/ethnicity).

METHODS

Clinical data were collected from all children, and high-resolution metabolomics was performed using their fasting serum samples. MASLD was assessed by MRI-proton density fat fraction or liver biopsy and cardiometabolic factors. Our previously developed panel included waist circumference, triglycerides, whole-body insulin sensitivity index, 3 amino acids, 2 phospholipids, dihydrothymine, and 2 unknowns. To improve feasibility, a simplified version without the unknowns was utilized in the present study. Since the panel was modified, the data were split into training (67%) and test (33%) sets to assess the validity of the panel.

RESULTS

Our present highest-performing modified model, with 4 clinical variables and 8 metabolomics features, achieved an AUROC of 0.92, 95% sensitivity, and 80% specificity for detecting MASLD in the test set.

CONCLUSIONS

Therefore, this panel has promising potential for use as a screening tool for MASLD in youth.

SHMT2 reduces fatty liver but is necessary for liver inflammation and fibrosis in mice

Non-alcoholic fatty liver disease is associated with an irregular serine metabolism. Serine hydroxymethyltransferase 2 (SHMT2) is a liver enzyme that breaks down serine into glycine and one-carbon (1C) units critical for liver methylation reactions and overall health. However, the contribution of SHMT2 to hepatic 1C homeostasis and biological functions has yet to be defined in genetically modified animal models. We created a mouse strain with targeted SHMT2 knockout in hepatocytes to investigate this. The absence of SHMT2 increased serine and glycine levels in circulation, decreased liver methylation potential, and increased susceptibility to fatty liver disease. Interestingly, SHMT2-deficient mice developed simultaneous fatty liver, but when fed a diet high in fat, fructose, and cholesterol, they had significantly less inflammation and fibrosis. This study highlights the critical role of SHMT2 in maintaining hepatic 1C homeostasis and its stage-specific functions in the pathogenesis of NAFLD.

Multi-omics reveals goose fatty liver formation from metabolic reprogramming

To comprehensively provide insight into goose fatty liver formation, we performed an integrative analysis of the liver transcriptome, lipidome, and amino acid metabolome, as well as peripheral adipose tissue transcriptome analysis using samples collected from the overfed geese and normally fed geese. Transcriptome analysis showed that liver metabolism pathways were mainly enriched in glucolipid metabolism, amino acid metabolism, inflammation response, and cell cycle; peripheral adipose tissue and the liver cooperatively regulated liver lipid accumulation during overfeeding. Liver lipidome patterns obviously changed after overfeeding, and 157 different lipids were yielded. In the liver amino acid metabolome, the level of Lys increased after overfeeding. In summary, this is the first study describing goose fatty liver formation from an integrative analysis of transcriptome, lipidome, and amino acid metabolome, which will provide a whole new dimension to understanding the mechanism of goose fatty liver formation.

Inferring secretory and metabolic pathway activity from omic data with secCellFie

Understanding protein secretion has considerable importance in biotechnology and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer properties of protein secretion from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can help predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.

NME4 mediates metabolic reprogramming and promotes nonalcoholic fatty liver disease progression

Nonalcoholic fatty liver disease (NAFLD) is mainly characterized by excessive fat accumulation in the liver, and it is associated with liver-related complications and adverse systemic diseases. NAFLD has become the most prevalent liver disease; however, effective therapeutic agents for NAFLD are still lacking. We combined clinical data with proteomics and metabolomics data, and found that the mitochondrial nucleoside diphosphate kinase NME4 plays a central role in mitochondrial lipid metabolism. Nme4 is markedly upregulated in mice fed with high-fat diet, and its expression is positively correlated with the level of steatosis. Hepatic deletion of Nme4 suppresses the progression of hepatic steatosis. Further studies demonstrated that NME4 interacts with several key enzymes in coenzyme A (CoA) metabolism and increases the level of acetyl-CoA and malonyl-CoA, which are the major lipid components of the liver in NAFLD. Increased level of acetyl-CoA and malonyl-CoA  lead to increased triglyceride levels and lipid accumulation in the liver. Taken together, these findings reveal that NME4 is a critical regulator of NAFLD progression and a potential therapeutic target for NAFLD.

Immune cell-specific and common molecular signatures in rheumatoid arthritis through molecular network approaches

Rheumatoid arthritis (RA) is an autoimmune disorder and common symptom of RA is chronic synovial inflammation. The pathogenesis of RA is not fully understood. Therefore, we aimed to identify underlying common and distinct molecular signatures and pathways among ten types of tissue and cells obtained from patients with RA. In this study, transcriptomic data including synovial tissues, macrophages, blood, T cells, CD4+T cells, CD8+T cells, natural killer T (NKT), cells natural killer (NK) cells, neutrophils, and monocyte cells were analyzed with an integrative and comparative network biology perspective. Each dataset yielded a list of differentially expressed genes as well as a reconstruction of the tissue-specific protein-protein interaction (PPI) network. Molecular signatures were identified by a statistical test using the hypergeometric probability density function by employing the interactions of transcriptional regulators and PPI. Reporter metabolites of each dataset were determined by using genome-scale metabolic networks. It was defined as the common hub proteins, novel molecular signatures, and metabolites in two or more tissue types while immune cell-specific molecular signatures were identified, too. Importantly, miR-155-5p is found as a common miRNA in all tissues. Moreover, NCOA3, PRKDC and miR-3160 might be novel molecular signatures for RA. Our results establish a novel approach for identifying immune cell-specific molecular signatures of RA and provide insights into the role of common tissue-specific genes, miRNAs, TFs, receptors, and reporter metabolites. Experimental research should be used to validate the corresponding genes, miRNAs, and metabolites.

Metabolic reprogramming and its clinical implication for liver cancer

Cancer cells often encounter hypoxic and hypo-nutrient conditions, which force them to make adaptive changes to meet their high demands for energy and various biomaterials for biomass synthesis. As a result, enhanced catabolism (breakdown of macromolecules for energy production) and anabolism (macromolecule synthesis from bio-precursors) are induced in cancer. This phenomenon is called "metabolic reprogramming," a cancer hallmark contributing to cancer development, metastasis, and drug resistance. HCC and cholangiocarcinoma (CCA) are 2 different liver cancers with high intertumoral heterogeneity in terms of etiologies, mutational landscapes, transcriptomes, and histological representations. In agreement, metabolism in HCC or CCA is remarkably heterogeneous, although changes in the glycolytic pathways and an increase in the generation of lactate (the Warburg effect) have been frequently detected in those tumors. For example, HCC tumors with activated β-catenin are addicted to fatty acid catabolism, whereas HCC tumors derived from fatty liver avoid using fatty acids. In this review, we describe common metabolic alterations in HCC and CCA as well as metabolic features unique for their subsets. We discuss metabolism of NAFLD as well, because NAFLD will likely become a leading etiology of liver cancer in the coming years due to the obesity epidemic in the Western world. Furthermore, we outline the clinical implication of liver cancer metabolism and highlight the computation and systems biology approaches, such as genome-wide metabolic models, as a valuable tool allowing us to identify therapeutic targets and develop personalized treatments for liver cancer patients.

Cardinality optimization in constraint-based modelling: application to human metabolism

MOTIVATION

Several applications in constraint-based modelling can be mathematically formulated as cardinality optimization problems involving the minimization or maximization of the number of nonzeros in a vector. These problems include testing for stoichiometric consistency, testing for flux consistency, testing for thermodynamic flux consistency, computing sparse solutions to flux balance analysis problems and computing the minimum number of constraints to relax to render an infeasible flux balance analysis problem feasible. Such cardinality optimization problems are computationally complex, with no known polynomial time algorithms capable of returning an exact and globally optimal solution.

RESULTS

By approximating the zero-norm with nonconvex continuous functions, we reformulate a set of cardinality optimization problems in constraint-based modelling into a difference of convex functions. We implemented and numerically tested novel algorithms that approximately solve the reformulated problems using a sequence of convex programs. We applied these algorithms to various biochemical networks and demonstrate that our algorithms match or outperform existing related approaches. In particular, we illustrate the efficiency and practical utility of our algorithms for cardinality optimization problems that arise when extracting a model ready for thermodynamic flux balance analysis given a human metabolic reconstruction.

AVAILABILITY AND IMPLEMENTATION

Open source scripts to reproduce the results are here https://github.com/opencobra/COBRA.papers/2023_cardOpt with general purpose functions integrated within the COnstraint-Based Reconstruction and Analysis toolbox: https://github.com/opencobra/cobratoolbox.

Hepatic and ovarian effects of perfluorooctanoic acid exposure differ in lean and obese adult female mice

Obesity and overweight cause poor oocyte quality, miscarriage, infertility, polycystic ovarian syndrome, and offspring birth defects and affects 40% and 20% of US women and girls, respectively. Perfluorooctanoic acid (PFOA), a per- and poly-fluoroalkyl substance (PFAS), is environmentally persistent and has negative female reproductive effects including endocrine disruption, oxidative stress, altered menstrual cyclicity, and decreased fertility in humans and animal models. PFAS exposure is associated with non-alcoholic fatty liver disease which affects ∼24-26% of the US population. This study investigated the hypothesis that PFOA exposure impacts hepatic and ovarian chemical biotransformation and alters the serum metabolome. At 7 weeks of age, female lean, wild type (KK.Cg-a/a) or obese (KK.Cg-Ay/J) mice received saline (C) or PFOA (2.5 mg/Kg) per os for 15 d. Hepatic weight was increased by PFOA exposure in both lean and obese mice (P < 0.05) and obesity also increased liver weight (P < 0.05) compared to lean mice. The serum metabolome was also altered (P < 0.05) by PFOA exposure and differed between lean and obese mice. Exposure to PFOA altered (P < 0.05) the abundance of ovarian proteins with roles in xenobiotic biotransformation (lean - 6; obese - 17), metabolism of fatty acids (lean - 3; obese - 9), cholesterol (lean - 8; obese - 11), amino acids (lean - 18; obese - 19), glucose (lean - 7; obese - 10), apoptosis (lean - 18; obese - 13), and oxidative stress (lean - 3; obese - 2). Use of qRT-PCR determined that exposure to PFOA increased (P < 0.05) hepatic Ces1 and Chst1 in lean but Ephx1 and Gstm3 in obese mice. Also, obesity basally increased (P < 0.05) Nat2, Gpi and Hsd17b2 mRNA levels. These data identify molecular changes resultant from PFOA exposure that may cause liver injury and ovotoxicity in females. In addition, differences in toxicity induced by PFOA exposure occurs in lean and obese mice.

Integrative analysis of mitochondrial metabolic reprogramming in early-stage colon and liver cancer

Gastrointestinal malignancies, including colon adenocarcinoma (COAD) and liver hepatocellular carcinoma (LIHC), remain leading causes of cancer-related deaths worldwide. To better understand the underlying mechanisms of these cancers and identify potential therapeutic targets, we analyzed publicly accessible Cancer Genome Atlas datasets of COAD and LIHC. Our analysis revealed that differentially expressed genes (DEGs) during early tumorigenesis were associated with cell cycle regulation. Additionally, genes related to lipid metabolism were significantly enriched in both COAD and LIHC, suggesting a crucial role for dysregulated lipid metabolism in their development and progression. We also identified a subset of DEGs associated with mitochondrial function and structure, including upregulated genes involved in mitochondrial protein import and respiratory complex assembly. Further, we identified mitochondrial 3-hydroxy-3-methylglutaryl-CoA synthase (HMGCS2) as a crucial regulator of cancer cell metabolism. Using a genome-scale metabolic model, we demonstrated that HMGCS2 suppression increased glycolysis, lipid biosynthesis, and elongation while decreasing fatty acid oxidation in colon cancer cells. Our study highlights the potential contribution of dysregulated lipid metabolism, including ketogenesis, to COAD and LIHC development and progression and identifies potential therapeutic targets for these malignancies.

Mitochondrial heterogeneity in diseases

As key organelles involved in cellular metabolism, mitochondria frequently undergo adaptive changes in morphology, components and functions in response to various environmental stresses and cellular demands. Previous studies of mitochondria research have gradually evolved, from focusing on morphological change analysis to systematic multiomics, thereby revealing the mitochondrial variation between cells or within the mitochondrial population within a single cell. The phenomenon of mitochondrial variation features is defined as mitochondrial heterogeneity. Moreover, mitochondrial heterogeneity has been reported to influence a variety of physiological processes, including tissue homeostasis, tissue repair, immunoregulation, and tumor progression. Here, we comprehensively review the mitochondrial heterogeneity in different tissues under pathological states, involving variant features of mitochondrial DNA, RNA, protein and lipid components. Then, the mechanisms that contribute to mitochondrial heterogeneity are also summarized, such as the mutation of the mitochondrial genome and the import of mitochondrial proteins that result in the heterogeneity of mitochondrial DNA and protein components. Additionally, multiple perspectives are investigated to better comprehend the mysteries of mitochondrial heterogeneity between cells. Finally, we summarize the prospective mitochondrial heterogeneity-targeting therapies in terms of alleviating mitochondrial oxidative damage, reducing mitochondrial carbon stress and enhancing mitochondrial biogenesis to relieve various pathological conditions. The possibility of recent technological advances in targeted mitochondrial gene editing is also discussed.

Sources and Sinks of Serine in Nutrition, Health, and Disease

Amino acid dysregulation has emerged as an important driver of disease progression in various contexts. l-Serine lies at a central node of metabolism, linking carbohydrate metabolism, transamination, glycine, and folate-mediated one-carbon metabolism to protein synthesis and various downstream bioenergetic and biosynthetic pathways. l-Serine is produced locally in the brain but is sourced predominantly from glycine and one-carbon metabolism in peripheral tissues via liver and kidney metabolism. Compromised regulation or activity of l-serine synthesis and disposal occurs in the context of genetic diseases as well as chronic disease states, leading to low circulating l-serine levels and pathogenesis in the nervous system, retina, heart, and aging muscle. Dietary interventions in preclinical models modulate sensory neuropathy, retinopathy, tumor growth, and muscle regeneration. A serine tolerance test may provide a quantitative readout of l-serine homeostasis that identifies patients who may be susceptible to neuropathy or responsive to therapy.

The acute effect of different NAD+ precursors included in the combined metabolic activators

NAD+ and glutathione precursors are currently used as metabolic modulators for improving the metabolic conditions associated with various human diseases, including non-alcoholic fatty liver disease, neurodegenerative diseases, mitochondrial myopathy, and age-induced diabetes. Here, we performed a one-day double blinded, placebo-controlled human clinical study to assess the safety and acute effects of six different Combined Metabolic Activators (CMAs) with 1 g of different NAD+ precursors based on global metabolomics analysis. Our integrative analysis showed that the NAD+ salvage pathway is the main source for boosting the NAD+ levels with the administration of CMAs without NAD+ precursors. We observed that incorporation of nicotinamide (Nam) in the CMAs can boost the NAD+ products, followed by niacin (NA), nicotinamide riboside (NR) and nicotinamide mononucleotide (NMN), but not flush free niacin (FFN). In addition, the NA administration led to a flushing reaction, accompanied by decreased phospholipids and increased bilirubin and bilirubin derivatives, which could be potentially risky. In conclusion, this study provided a plasma metabolomic landscape of different CMA formulations, and proposed that CMAs with Nam, NMN as well as NR can be administered for boosting NAD+ levels to improve altered metabolic conditions.

Plasma Amino Acids in NAFLD Patients with Obesity Are Associated with Steatosis and Fibrosis: Results from the MAST4HEALTH Study

Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) have been linked to changes in amino acid (AA) levels. The objective of the current study was to examine the relationship between MRI parameters that reflect inflammation and fibrosis and plasma AA concentrations in NAFLD patients. Plasma AA levels of 97 NAFLD patients from the MAST4HEALTH study were quantified with liquid chromatography. Medical, anthropometric and lifestyle characteristics were collected and biochemical parameters, as well as inflammatory and oxidative stress biomarkers, were measured. In total, subjects with a higher MRI-proton density fat fraction (MRI-PDFF) exhibited higher plasma AA levels compared to subjects with lower PDFF. The concentrations of BCAAs (p-Value: 0.03), AAAs (p-Value: 0.039), L-valine (p-Value: 0.029), L-tyrosine (p-Value: 0.039) and L-isoleucine (p-Value: 0.032) were found to be significantly higher in the higher PDFF group compared to lower group. Plasma AA levels varied according to MRI-PDFF. Significant associations were also demonstrated between AAs and MRI-PDFF and MRI-cT1, showing the potential utility of circulating AAs as diagnostic markers of NAFLD.

Multi-Modality, Multi-Dimensional Characterization of Pediatric Non-Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease is a multifaceted disease that progresses through multiple phases; it involves metabolic as well as structural changes. These alterations can be measured directly or indirectly through blood, non-invasive imaging, and/or tissue analyses. While some studies have evaluated the correlations between two sets of measurements (e.g., histopathology with cross-sectional imaging or blood biomarkers), the interrelationships, if any, among histopathology, clinical blood profiles, cross-sectional imaging, and metabolomics in a pediatric cohort remain unknown. We created a multiparametric clinical MRI-histopathologic NMR network map of pediatric NAFLD through multimodal correlation networks, in order to gain insight into how these different sets of measurements are related. We found that leptin and other blood markers were correlated with many other measurements; however, upon filtering out the blood biomarkers, the network was decomposed into three independent hubs centered around histopathological features, each with associated MRI and plasma metabolites. These multi-modality maps could serve as a framework for characterizing disease status and progression and could potentially guide medical interventions.

A new metabolic model of Drosophila melanogaster and the integrative analysis of Parkinson's disease

High conservation of the disease-associated genes between flies and humans facilitates the common use of Drosophila melanogaster to study metabolic disorders under controlled laboratory conditions. However, metabolic modeling studies are highly limited for this organism. We here report a comprehensively curated genome-scale metabolic network model of Drosophila using an orthology-based approach. The gene coverage and metabolic information of the draft model derived from a reference human model were expanded via Drosophila-specific KEGG and MetaCyc databases, with several curation steps to avoid metabolic redundancy and stoichiometric inconsistency. Furthermore, we performed literature-based curations to improve gene-reaction associations, subcellular metabolite locations, and various metabolic pathways. The performance of the resulting Drosophila model (8,230 reactions, 6,990 metabolites, and 2,388 genes), iDrosophila1 (https://github.com/SysBioGTU/iDrosophila), was assessed using flux balance analysis in comparison with the other currently available fly models leading to superior or comparable results. We also evaluated the transcriptome-based prediction capacity of iDrosophila1, where differential metabolic pathways during Parkinson's disease could be successfully elucidated. Overall, iDrosophila1 is promising to investigate system-level metabolic alterations in response to genetic and environmental perturbations.

Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine

Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.

Ketohexokinase-C regulates global protein acetylation to decrease carnitine palmitoyltransferase 1a-mediated fatty acid oxidation

BACKGROUND & AIMS

The consumption of sugar and a high-fat diet (HFD) promotes the development of obesity and metabolic dysfunction. Despite their well-known synergy, the mechanisms by which sugar worsens the outcomes associated with a HFD are largely elusive.

METHODS

Six-week-old, male, C57Bl/6 J mice were fed either chow or a HFD and were provided with regular, fructose- or glucose-sweetened water. Moreover, cultured AML12 hepatocytes were engineered to overexpress ketohexokinase-C (KHK-C) using a lentivirus vector, while CRISPR-Cas9 was used to knockdown CPT1α. The cell culture experiments were complemented with in vivo studies using mice with hepatic overexpression of KHK-C and in mice with liver-specific CPT1α knockout. We used comprehensive metabolomics, electron microscopy, mitochondrial substrate phenotyping, proteomics and acetylome analysis to investigate underlying mechanisms.

RESULTS

Fructose supplementation in mice fed normal chow and fructose or glucose supplementation in mice fed a HFD increase KHK-C, an enzyme that catalyzes the first step of fructolysis. Elevated KHK-C is associated with an increase in lipogenic proteins, such as ACLY, without affecting their mRNA expression. An increase in KHK-C also correlates with acetylation of CPT1α at K508, and lower CPT1α protein in vivo. In vitro, KHK-C overexpression lowers CPT1α and increases triglyceride accumulation. The effects of KHK-C are, in part, replicated by a knockdown of CPT1α. An increase in KHK-C correlates negatively with CPT1α protein levels in mice fed sugar and a HFD, but also in genetically obese db/db and lipodystrophic FIRKO mice. Mechanistically, overexpression of KHK-C in vitro increases global protein acetylation and decreases levels of the major cytoplasmic deacetylase, SIRT2.

CONCLUSIONS

KHK-C-induced acetylation is a novel mechanism by which dietary fructose augments lipogenesis and decreases fatty acid oxidation to promote the development of metabolic complications.

IMPACT AND IMPLICATIONS

Fructose is a highly lipogenic nutrient whose negative consequences have been largely attributed to increased de novo lipogenesis. Herein, we show that fructose upregulates ketohexokinase, which in turn modifies global protein acetylation, including acetylation of CPT1a, to decrease fatty acid oxidation. Our findings broaden the impact of dietary sugar beyond its lipogenic role and have implications on drug development aimed at reducing the harmful effects attributed to sugar metabolism.

Aqueous extract of Polygala japonica Houtt. ameliorated nonalcoholic steatohepatitis in mice through restoring the gut microbiota disorders and affecting the metabolites in feces and liver

BACKGROUND

Polygala japonica Houtt. (PJ) has been demonstrated with several biological potentials such as lipid-lowering and anti-inflammatory effects. However, the effects and mechanisms of PJ on nonalcoholic steatohepatitis (NASH) remain unclear.

PURPOSE

The aim of this study was to evaluate the effects of PJ on NASH and illustrate the mechanism based on modulating gut microbiota and host metabolism.

MATERIALS AND METHODS

NASH mouse model was induced using methionine and choline deficient (MCD) diet and orally treated with PJ. The therapeutic, anti-inflammatory, and anti-oxidative effects of PJ on mice with NASH were firstly assessed. Then, the gut microbiota of mice was analyzed using 16S rRNA sequencing to assess the changes. Finally, the effects of PJ on the metabolites in liver and feces were explored by untargeted metabolomics.

RESULTS

The results indicated that PJ could improve hepatic steatosis, liver injury, inflammatory response, and oxidative stress in NASH mice. PJ treatment also affected the diversity of gut microbiota and changed the relative abundances of Faecalibaculum. Lactobacillus, Muribaculaceae, Dubosiella, Akkermansia, Lachnospiraceae_NK4A136_group, and Turicibacter in NASH mice. In addition, PJ treatment modulated 59 metabolites both in liver and feces. Metabolites involved in histidine, and tryptophan metabolism pathways were identified as the key metabolites according to the correlation analysis between differential gut microbiota and metabolites.

CONCLUSION

Our study demonstrated the therapeutic, anti-inflammatory and anti-oxidative potentials of PJ on NASH. The mechanisms of PJ treatment were related to the improvement of gut microbiota dysbiosis and the regulation of histidine and tryptophan metabolism.

A holistic view of muscle metabolic reprogramming through personalized metabolic modeling in newly diagnosed diabetic patients

Type 2 diabetes mellitus (T2DM) is a challenging and progressive metabolic disease caused by insulin resistance. Skeletal muscle is the major insulin-sensitive tissue that plays a pivotal role in blood sugar homeostasis. Dysfunction of muscle metabolism is implicated in the disturbance of glucose homeostasis, the development of insulin resistance, and T2DM. Understanding metabolism reprogramming in newly diagnosed patients provides opportunities for early diagnosis and treatment of T2DM as a challenging disease to manage. Here, we applied a system biology approach to investigate metabolic dysregulations associated with the early stage of T2DM. We first reconstructed a human muscle-specific metabolic model. The model was applied for personalized metabolic modeling and analyses in newly diagnosed patients. We found that several pathways and metabolites, mainly implicating in amino acids and lipids metabolisms, were dysregulated. Our results indicated the significance of perturbation of pathways implicated in building membrane and extracellular matrix (ECM). Dysfunctional metabolism in these pathways possibly interrupts the signaling process and develops insulin resistance. We also applied a machine learning method to predict potential metabolite markers of insulin resistance in skeletal muscle. 13 exchange metabolites were predicted as the potential markers. The efficiency of these markers in discriminating insulin-resistant muscle was successfully validated.

Longitudinal metabolomics analysis reveals the acute effect of cysteine and NAC included in the combined metabolic activators

Growing evidence suggests that the depletion of plasma NAD⁺ and glutathione (GSH) may play an important role in the development of metabolic disorders. The administration of Combined Metabolic Activators (CMA), consisting of GSH and NAD⁺ precursors, has been explored as a promising therapeutic strategy to target multiple altered pathways associated with the pathogenesis of the diseases. Although studies have examined the therapeutic effect of CMA that contains N-acetyl-l-cysteine (NAC) as a metabolic activator, a system-wide comparison of the metabolic response to the administration of CMA with NAC and cysteine remains lacking. In this placebo-controlled study, we studied the acute effect of the CMA administration with different metabolic activators, including NAC or cysteine with/without nicotinamide or flush free niacin, and performed longitudinal untargeted-metabolomics profiling of plasma obtained from 70 well-characterized healthy volunteers. The time-series metabolomics data revealed the metabolic pathways affected after the administration of CMAs showed high similarity between CMA containing nicotinamide and NAC or cysteine as metabolic activators. Our analysis also showed that CMA with cysteine is well-tolerated and safe in healthy individuals throughout the study. Last, our study systematically provided insights into a complex and dynamics landscape involved in amino acid, lipid and nicotinamide metabolism, reflecting the metabolic responses to CMA administration containing different metabolic activators.

A Protocol for the Automatic Construction of Highly Curated Genome-Scale Models of Human Metabolism

Genome-scale metabolic models (GEMs) have emerged as a tool to understand human metabolism from a holistic perspective with high relevance in the study of many diseases and in the metabolic engineering of human cell lines. GEM building relies on either automated processes that lack manual refinement and result in inaccurate models or manual curation, which is a time-consuming process that limits the continuous update of reliable GEMs. Here, we present a novel algorithm-aided protocol that overcomes these limitations and facilitates the continuous updating of highly curated GEMs. The algorithm enables the automatic curation and/or expansion of existing GEMs or generates a highly curated metabolic network based on current information retrieved from multiple databases in real time. This tool was applied to the latest reconstruction of human metabolism (Human1), generating a series of the human GEMs that improve and expand the reference model and generating the most extensive and comprehensive general reconstruction of human metabolism to date. The tool presented here goes beyond the current state of the art and paves the way for the automatic reconstruction of a highly curated, up-to-date GEM with high potential in computational biology as well as in multiple fields of biological science where metabolism is relevant.

Inferring secretory and metabolic pathway activity from omic data with secCellFie

Understanding protein secretion has considerable importance in the biotechnology industry and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer protein secretion capabilities from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can be used to predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.

Nobiletin Ameliorates Nonalcoholic Fatty Liver Disease by Regulating Gut Microbiota and Myristoleic Acid Metabolism

Disturbance of the gut microbiota plays a critical role in the development of nonalcoholic fatty liver disease (NAFLD). Increasing evidence supports that natural products may serve as prebiotics to regulate the gut microbiota in the treatment of NAFLD. In the present study, the effect of nobiletin, a naturally occurring polymethoxyflavone, on NAFLD was evaluated, and metabolomics, 16S rRNA gene sequencing, and transcriptomics analysis were performed to determine the underlying mechanism of nobiletin, and the key bacteria and metabolites screened were confirmed by in vivo experiment. Nobiletin treatment could significantly reduce lipid accumulation in high-fat/high-sucrose diet-fed mice. 16S rRNA analysis demonstrated that nobiletin could reverse the dysbiosis of gut microbiota in NAFLD mice and nobiletin could regulate myristoleic acid metabolism, as revealed by untargeted metabolomics analysis. Treatment with the bacteria Allobaculum stercoricanis, Lactobacillus casei, or the metabolite myristoleic acid displayed a protective effect on liver lipid accumulation under metabolic stress. These results indicated that nobiletin might target gut microbiota and myristoleic acid metabolism to ameliorate NAFLD.

Metabolic role of the hepatic valine/3-hydroxyisobutyrate (3-HIB) pathway in fatty liver disease

BACKGROUND

The valine (branched-chain amino acid) metabolite 3-hydroxyisobutyrate (3-HIB), produced by 3-Hydroxyisobutyryl-CoA Hydrolase (HIBCH), is associated with insulin resistance and type 2 diabetes, but implicated tissues and cellular mechanisms are poorly understood. We hypothesized that HIBCH and 3-HIB regulate hepatic lipid accumulation.

METHODS

HIBCH mRNA in human liver biopsies ("Liver cohort") and plasma 3-HIB ("CARBFUNC" cohort) were correlated with fatty liver and metabolic markers. Human Huh7 hepatocytes were supplemented with fatty acids (FAs) to induce lipid accumulation. Following HIBCH overexpression, siRNA knockdown, inhibition of PDK4 (a marker of FA β-oxidation) or 3-HIB supplementation, we performed RNA-seq, Western blotting, targeted metabolite analyses and functional assays.

FINDINGS

We identify a regulatory feedback loop between the valine/3-HIB pathway and PDK4 that shapes hepatic FA metabolism and metabolic health and responds to 3-HIB treatment of hepatocytes. HIBCH overexpression increased 3-HIB release and FA uptake, while knockdown increased cellular respiration and decreased reactive oxygen species (ROS) associated with metabolic shifts via PDK4 upregulation. Treatment with PDK4 inhibitor lowered 3-HIB release and increased FA uptake, while increasing HIBCH mRNA. Implicating this regulatory loop in fatty liver, human cohorts show positive correlations of liver fat with hepatic HIBCH and PDK4 expression (Liver cohort) and plasma 3-HIB (CARBFUNC cohort). Hepatocyte 3-HIB supplementation lowered HIBCH expression and FA uptake and increased cellular respiration and ROS.

INTERPRETATION

These data implicate the hepatic valine/3-HIB pathway in mechanisms of fatty liver, reflected in increased plasma 3-HIB concentrations, and present possible targets for therapeutic intervention.

FUNDING

Funding was provided by the Research Council of Norway (263124/F20), the University of Bergen, the Western Norway Health Authorities, Novo Nordisk Scandinavia AS, the Trond Mohn Foundation and the Norwegian Diabetes Association.

A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.

DT-109 ameliorates nonalcoholic steatohepatitis in nonhuman primates

Nonalcoholic steatohepatitis (NASH) prevalence is rising with no pharmacotherapy approved. A major hurdle in NASH drug development is the poor translatability of preclinical studies to safe/effective clinical outcomes, and recent failures highlight a need to identify new targetable pathways. Dysregulated glycine metabolism has emerged as a causative factor and therapeutic target in NASH. Here, we report that the tripeptide DT-109 (Gly-Gly-Leu) dose-dependently attenuates steatohepatitis and fibrosis in mice. To enhance the probability of successful translation, we developed a nonhuman primate model that histologically and transcriptionally mimics human NASH. Applying a multiomics approach combining transcriptomics, proteomics, metabolomics, and metagenomics, we found that DT-109 reverses hepatic steatosis and prevents fibrosis progression in nonhuman primates, not only by stimulating fatty acid degradation and glutathione formation, as found in mice, but also by modulating microbial bile acid metabolism. Our studies describe a highly translatable NASH model and highlight the need for clinical evaluation of DT-109.

Hepatic and serum branched-chain fatty acid profile in patients with nonalcoholic fatty liver disease: A case-control study

OBJECTIVE

Alterations in the hepatic lipidome are a crucial factor involved in the pathophysiology of nonalcoholic fatty liver disease (NAFLD). The aim of this study was to evaluate the serum and hepatic profile of branched-chain fatty acids (BCFAs) in patients with different stages of NAFLD.

METHODS

This was a case-control study performed in 27 patients without NAFLD, 49 patients with nonalcoholic fatty liver, and 17 patients with nonalcoholic steatohepatitis, defined by liver biopsies. Serum and hepatic levels of BCFAs were analyzed by gas chromatography-mass spectrometry. The hepatic expression of genes involved in the endogenous synthesis of BCFAs was analyzed by real-time quantitative polymerase chain reaction (RT-qPCR).

RESULTS

A significant increase in hepatic BCFAs was found in subjects with NAFLD compared with those without NAFLD; no differences were observed in serum BCFAs between study groups. Trimethyl BCFAs, iso-BCFAs, and anteiso-BCFAs were increased in subjects with NAFLD (either nonalcoholic fatty liver or nonalcoholic steatohepatitis) compared with those without NAFLD. Correlation analysis showed a relationship between hepatic BCFAs and the histopathological diagnosis of NAFLD, as well as other histological and biochemical parameters related to this disease. Gene expression analysis in liver showed that the mRNA levels of BCAT1, BCAT2, and BCKDHA were upregulated in patients with NAFLD.

CONCLUSIONS

These results suggest that the increased production of liver BCFAs might be related to NAFLD development and progression.

Loss of the Kidney Urate Transporter, Urat1, Leads to Disrupted Redox Homeostasis in Mice

High uric acid is associated with gout, hypertension, metabolic syndrome, cardiovascular disease, and kidney disease. URAT1 (SLC22A12), originally discovered in mice as Rst, is generally considered a very selective uric acid transporter compared to other closely-related kidney uric acid transporters such as OAT1 (SLC22A6, NKT) and OAT3 (SLC22A8). While the role of URAT1 in regulating human uric acid is well-established, in recent studies the gene has been linked to redox regulation in flies as well as progression of renal cell carcinoma. We have now identified over twenty metabolites in the Urat1 knockout that are generally distinct from metabolites accumulating in the Oat1 and Oat3 knockout mice, with distinct molecular properties as revealed by chemoinformatics and machine learning analysis. These metabolites are involved in seemingly disparate aspects of cellular metabolism, including pyrimidine, fatty acid, and amino acid metabolism. However, through integrative systems metabolic analysis of the transcriptomic and metabolomic data using a human metabolic reconstruction to build metabolic genome-scale models (GEMs), the cellular response to loss of Urat1/Rst revealed compensatory processes related to reactive oxygen species handling and maintaining redox state balances via Vitamin C metabolism and cofactor charging reactions. These observations are consistent with the increasingly appreciated role of the antioxidant properties of uric acid. Collectively, the results highlight the role of Urat1/Rst as a transporter strongly tied to maintaining redox homeostasis, with implications for metabolic side effects from drugs that block its function.