Unlocking Human Brain Metabolism by Genome-Scale and Multiomics Metabolic Models: Relevance for Neurology Research, Health, and Disease

Exploring Human Brain Metabolism via Genome-Scale Metabolic Modeling with Highlights on Multiple Sclerosis

Cerebral dysfunctions give rise to a wide range of neurological diseases due to the structural and functional complexity of the human brain stemming from the interactive cellular metabolism of its specific cells, including neurons and glial cells. In parallel with advances in isolation and measurement technologies, genome-scale metabolic models (GEMs) have become a powerful tool in the studies of systems biology to provide critical insights into the understanding of sophisticated eukaryotic systems. In this study, brain cell-specific GEMs were reconstructed for neurons, astrocytes, microglia, oligodendrocytes, and oligodendrocyte precursor cells by integrating single-cell RNA-seq data and global Human1 via a task-driven integrative network inference for tissues (tINIT) algorithm. Then, intercellular reactions among neurons, astrocytes, microglia, and oligodendrocytes were added to generate a combined brain model, iHumanBrain2690. This brain network was used in the prediction of metabolic alterations in glucose, ketone bodies, oxygen change, and reporter metabolites. Glucose supplementation increased the subsystems' activities in glycolysis, and ketone bodies elevated those in the TCA cycle and oxidative phosphorylation. Reporter metabolite analysis identified L-carnitine and arachidonate as the top reporter metabolites in gray and white matter microglia in multiple sclerosis (MS), respectively. Carbamoyl-phosphate was found to be the top reporter metabolite in primary progressive MS. Taken together, single and integrated iHumanBrain2690 metabolic networks help us elucidate complex metabolism in brain physiology and homeostasis in health and disease.

Multiscale metabolomics techniques: Insights into neuroscience research

The field of metabolomics examines the overall composition and dynamic patterns of metabolites in living organisms. The primary methods used in metabolomics include liquid chromatography (LC), nuclear magnetic resonance (NMR), and mass spectrometry (MS) analysis. These methods enable the identification and examination of metabolite types and contents within organisms, as well as modifications to metabolic pathways and their connection to the emergence of diseases. Research in metabolomics has extensive value in basic and applied sciences. The field of metabolomics is growing quickly, with the majority of studies concentrating on biomedicine, particularly early disease diagnosis, therapeutic management of human diseases, and mechanistic knowledge of biochemical processes. Multiscale metabolomics is an approach that integrates metabolomics techniques at various scales, including the holistic, tissue, cellular, and organelle scales, to enable more thorough and in-depth studies of metabolic processes in organisms. Multiscale metabolomics can be combined with methods from systems biology and bioinformatics. In recent years, multiscale metabolomics approaches have become increasingly important in neuroscience research due to the nervous system's high metabolic demands. Multiscale metabolomics can offer novel concepts and approaches for the diagnosis, treatment, and development of medication for neurological illnesses in addition to a more thorough understanding of brain metabolism and nervous system function. In this review, we summarize the use of multiscale metabolomics techniques in neuroscience, address the promise and constraints of these techniques, and provide an overview of the metabolome and its applications in neuroscience.

A comprehensive exploration of astrocytes in migraine: a bibliometric and visual analysis

BACKGROUND

Migraine, as a prevalent neurologic disorder, involves intricate and yet incompletely elucidated pathophysiological mechanisms. A plethora of research findings underscores the pivotal role played by astrocytes in the progression of migraines. In order to elucidate the current advances and directions in research pertaining to astrocytes in migraines, we conducted bibliometric analysis of relevant literature and visualized the results. Subsequently, we expound upon these findings to contribute to the evolving understanding of the role of astrocytes in migraine pathophysiology.

METHODS

On November 21, 2023, we conducted a search on Web of Science (WOS), restricting the document type to articles or reviews and language to English. Following a meticulous selection process involving three researchers, we identified the literature to be included in our analysis. Subsequently, we employed Microsoft Office Excel programs, R, VOSviewer, Scimago Graphica, and CiteSpace software to conduct visualization analysis of basic information and trends regarding journals, countries/regions, and influential authors, institutions, keywords, and papers.

RESULTS

As of November 21, 2023, relevant literature has been published in 71 journals across 27 countries/regions. This corpus comprises contributions from 576 authors affiliated with 220 institutions, encompassing 865 keywords and referencing 6065 scholarly articles. CEPHALALGIA stands out as the most influential journal in this field, while authors PIETROBON D and DALKARA T have significant impact. The United States is highly influential, with CNR and UNIV PADUA emerging as highly influential institutions. The predominant category is Neurosciences.

CONCLUSIONS

Future investigators may continue to focus on migraines with aura, familial hemiplegic migraine (FHM), and the crucial calcitonin gene-related peptide (CGRP) system. Employing advanced observational techniques, such as imaging, researchers should pay attention to cellular and tissue structures, such as microglia and the trigeminal ganglion, as well as mechanisms involving inflammation and central sensitization. Moreover, animal models are paramount in obtaining high-quality evidence.

A comprehensive exploration of astrocytes in migraine: a bibliometric and visual analysis

BACKGROUND

Migraine, as a prevalent neurologic disorder, involves intricate and yet incompletely elucidated pathophysiological mechanisms. A plethora of research findings underscores the pivotal role played by astrocytes in the progression of migraines. In order to elucidate the current advances and directions in research pertaining to astrocytes in migraines, we conducted bibliometric analysis of relevant literature and visualized the results. Subsequently, we expound upon these findings to contribute to the evolving understanding of the role of astrocytes in migraine pathophysiology.

METHODS

On November 21, 2023, we conducted a search on Web of Science (WOS), restricting the document type to articles or reviews and language to English. Following a meticulous selection process involving three researchers, we identified the literature to be included in our analysis. Subsequently, we employed Microsoft Office Excel programs, R, VOSviewer, Scimago Graphica, and CiteSpace software to conduct visualization analysis of basic information and trends regarding journals, countries/regions, and influential authors, institutions, keywords, and papers.

RESULTS

As of November 21, 2023, relevant literature has been published in 71 journals across 27 countries/regions. This corpus comprises contributions from 576 authors affiliated with 220 institutions, encompassing 865 keywords and referencing 6065 scholarly articles. CEPHALALGIA stands out as the most influential journal in this field, while authors PIETROBON D and DALKARA T have significant impact. The United States is highly influential, with CNR and UNIV PADUA emerging as highly influential institutions. The predominant category is Neurosciences.

CONCLUSIONS

Future investigators may continue to focus on migraines with aura, familial hemiplegic migraine (FHM), and the crucial calcitonin gene-related peptide (CGRP) system. Employing advanced observational techniques, such as imaging, researchers should pay attention to cellular and tissue structures, such as microglia and the trigeminal ganglion, as well as mechanisms involving inflammation and central sensitization. Moreover, animal models are paramount in obtaining high-quality evidence.

Brain energy metabolism: A roadmap for future research

Although we have learned much about how the brain fuels its functions over the last decades, there remains much still to discover in an organ that is so complex. This article lays out major gaps in our knowledge of interrelationships between brain metabolism and brain function, including biochemical, cellular, and subcellular aspects of functional metabolism and its imaging in adult brain, as well as during development, aging, and disease. The focus is on unknowns in metabolism of major brain substrates and associated transporters, the roles of insulin and of lipid droplets, the emerging role of metabolism in microglia, mysteries about the major brain cofactor and signaling molecule NAD+, as well as unsolved problems underlying brain metabolism in pathologies such as traumatic brain injury, epilepsy, and metabolic downregulation during hibernation. It describes our current level of understanding of these facets of brain energy metabolism as well as a roadmap for future research.

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.

Uncovering ferroptosis in Parkinson's disease via bioinformatics and machine learning, and reversed deducing potential therapeutic natural products

Objective: Ferroptosis, a novel form of cell death, is closely associated with excessive iron accumulated within the substantia nigra in Parkinson's disease (PD). Despite extensive research, the underlying molecular mechanisms driving ferroptosis in PD remain elusive. Here, we employed a bioinformatics and machine learning approach to predict the genes associated with ferroptosis in PD and investigate the interactions between natural products and their active ingredients with these genes. Methods: We comprehensively analyzed differentially expressed genes (DEGs) for ferroptosis associated with PD (PDFerDEGs) by pairing 3 datasets (GSE7621, GSE20146, and GSE202665) from the NCBI GEO database and the FerrDb V2 database. A machine learning approach was then used to screen PDFerDEGs for signature genes. We mined the interacted natural product components based on screened signature genes. Finally, we mapped a network combined with ingredients and signature genes, then carried out molecular docking validation of core ingredients and targets to uncover potential therapeutic targets and ingredients for PD. Results: We identified 109 PDFerDEGs that were significantly enriched in biological processes and KEGG pathways associated with ferroptosis (including iron ion homeostasis, iron ion transport and ferroptosis, etc.). We obtained 29 overlapping genes and identified 6 hub genes (TLR4, IL6, ADIPOQ, PTGS2, ATG7, and FADS2) by screening with two machine learning algorithms. Based on this, we screened 263 natural product components and subsequently mapped the "Overlapping Genes-Ingredients" network. According to the network, top 5 core active ingredients (quercetin, 17-beta-estradiol, glycerin, trans-resveratrol, and tocopherol) were molecularly docked to hub genes to reveal their potential role in the treatment of ferroptosis in PD. Conclusion: Our findings suggested that PDFerDEGs are associated with ferroptosis and play a role in the progression of PD. Taken together, core ingredients (quercetin, 17-beta-estradiol, glycerin, trans-resveratrol, and tocopherol) bind well to hub genes (TLR4, IL6, ADIPOQ, PTGS2, ATG7, and FADS2), highlighting novel biomarkers for PD.

A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling

Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to as multimodal clinical data. In order to accommodate the volume and heterogeneity of such diverse data types and aid in their interpretation when they are combined with a multi-scale predictive model, machine learning is a useful tool that can be wielded to deconstruct biological complexity and extract relevant outputs. Additionally, genome-scale metabolic models (GSMMs) are one of the main frameworks striving to bridge the gap between genotype and phenotype by incorporating prior biological knowledge into mechanistic models. Consequently, the utilization of GSMMs as a foundation for the integration of multi-omic data originating from different domains is a valuable pursuit towards refining predictions. In this chapter, we show how cancer multi-omic data can be analyzed via multimodal machine learning and metabolic modeling. Firstly, we focus on the merits of adopting an integrative systems biology led approach to biomedical data mining. Following this, we propose how constraint-based metabolic models can provide a stable yet adaptable foundation for the integration of multimodal data with machine learning. Finally, we provide a step-by-step tutorial for the combination of machine learning and GSMMs, which includes: (i) tissue-specific constraint-based modeling; (ii) survival analysis using time-to-event prediction for cancer; and (iii) classification and regression approaches for multimodal machine learning. The code associated with the tutorial can be found at https://github.com/Angione-Lab/Tutorials_Combining_ML_and_GSMM .

Bioinformatics-Based Analysis of lncRNA-mRNA Interaction Network of Mild Hepatic Encephalopathy in Cirrhosis

Backgrounds

Serum long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) interaction network was discovered to exert an important role in liver cirrhosis while little is known in mild hepatic encephalopathy (MHE). Therefore, we aim to systematically evaluate the serum lncRNA-mRNA network and its regulatory mechanism in MHE.

Methods

The data of serum mRNAs and lncRNAs were derived from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were calculated between 11 cirrhotic patients with and without MHE. Next, the biological functions and underlined pathways of DEGs were determined through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Finally, an interactive network between lncRNAs and mRNAs was built, and hub genes were identified, respectively.

Results

A total of 64 differentially expressed lncRNAs (dif-lncRNAs) were found between patients with and without MHE, including 30 up- and 34 downregulated genes. 187 differentially expressed mRNAs (dif-mRNAs) were identified, including 84 up- and 103 downregulated genes. Functional enrichment analysis suggested that the regulatory pathways involved in MHE mainly consisted of a series of immune and inflammatory responses. Several hub mRNAs involved in regulatory network were identified, including CCL5, CCR5, CXCR3, CD274, STAT1, CXCR6, and EOMES. In addition, lnc-FAM84B-8 and lnc-SAMD3-1 were found to regulate these above hub genes through building a lncRNA-mRNA network.

Conclusion

This is the first study to construct the serum lncRNA-mRNA network in MHE, demonstrating the critical role of lncRNAs in regulating inflammatory and immunological profiles in the developing of MHE, suggesting a latent mechanism in this pathophysiological process.

Age-Related Neurometabolomic Signature of Mouse Brain

Neurometabolites are the ultimate gene products in the brain and the most precise biomolecular indicators of brain endophenotypes. Metabolomics is the only "omics" that provides a moment-to-moment "snapshot" of brain circuits' biochemical activities in response to external stimuli within the context of specific genetic variations. Although the expression levels of neurometabolites are highly dynamic, the underlying metabolic processes are tightly regulated during brain development, maturation, and aging. Therefore, this study aimed to identify mouse brain metabolic profiles in neonatal and adult stages and reconstruct both the active metabolic network and the metabolic pathway functioning. Using high-throughput metabolomics and bioinformatics analyses, we show that the neonatal mouse brain has its distinct metabolomic signature, which differs from the adult brain. Furthermore, lipid metabolites showed the most profound changes between the neonatal and adult brain, with some lipid species reaching 1000-fold changes. There were trends of age-dependent increases and decreases among lipids and non-lipid metabolites, respectively. A few lipid metabolites such as HexCers and SHexCers were almost absent in neonatal brains, whereas other non-lipid metabolites such as homoarginine were absent in the adult brains. Several molecules that act as neurotransmitters/neuromodulators showed age-dependent levels, with adenosine and GABA exhibiting around 100- and 10-fold increases in the adult compared with the neonatal brain. Of particular interest is the observation that purine and pyrimidines nucleobases exhibited opposite age-dependent changes. Bioinformatics analysis revealed an enrichment of lipid biosynthesis pathways in metabolites, whose levels increased in adult brains. In contrast, pathways involved in the metabolism of amino acids, nucleobases, glucose (glycolysis), tricarboxylic acid cycle (TCA) were enriched in metabolites whose levels were higher in the neonatal brains. Many of these pathways are associated with pathological conditions, which can be predicted as early as the neonatal stage. Our study provides an initial age-related biochemical directory of the mouse brain and warrants further studies to identify temporal brain metabolome across the lifespan, particularly during adolescence and aging. Such neurometabolomic data may provide important insight about the onset and progression of neurological/psychiatric disorders and may ultimately lead to the development of precise diagnostic biomarkers and more effective preventive/therapeutic strategies.

Multi-omic analysis unveils biological pathways in peripheral immune system associated to minimal hepatic encephalopathy appearance in cirrhotic patients

Patients with liver cirrhosis may develop minimal hepatic encephalopathy (MHE) which affects their quality of life and life span. It has been proposed that a shift in peripheral inflammation triggers the appearance of MHE. However, the mechanisms involved in this immune system shift remain unknown. In this work we studied the broad molecular changes involved in the induction of MHE with the goal of identifying (1) altered genes and pathways in peripheral blood cells associated to the appearance of MHE, (2) serum metabolites and cytokines with modified levels in MHE patients and (3) MHE-regulated immune response processes related to changes in specific serum molecules. We adopted a multi-omic approach to profile the transcriptome, metabolome and a panel of cytokines of blood samples taken from cirrhotic patients with or without MHE. Transcriptomic analysis supports the hypothesis of alternations in the Th1/Th2 and Th17 lymphocytes cell populations as major drivers of MHE. Cluster analysis of serum molecules resulted in six groups of chemically similar compounds, suggesting that functional modules operate during the induction of MHE. Finally, the multi-omic integrative analysis suggested a relationship between cytokines CCL20, CX3CL1, CXCL13, IL-15, IL-22 and IL-6 with alteration in chemotaxis, as well as a link between long-chain unsaturated phospholipids and the increased fatty acid transport and prostaglandin production. We found altered immune pathways that may collectively contribute to the mild cognitive impairment phenotype in MHE. Our approach is able to combine extracellular and intracellular information, opening new insights to the understanding of the disease.

Advances in Astrocyte Computational Models: From Metabolic Reconstructions to Multi-omic Approaches

The growing importance of astrocytes in the field of neuroscience has led to a greater number of computational models devoted to the study of astrocytic functions and their metabolic interactions with neurons. The modeling of these interactions demands a combined understanding of brain physiology and the development of computational frameworks based on genomic-scale reconstructions, system biology, and dynamic models. These computational approaches have helped to highlight the neuroprotective mechanisms triggered by astrocytes and other glial cells, both under normal conditions and during neurodegenerative processes. In the present review, we evaluate some of the most relevant models of astrocyte metabolism, including genome-scale reconstructions and astrocyte-neuron interactions developed in the last few years. Additionally, we discuss novel strategies from the multi-omics perspective and computational models of other glial cell types that will increase our knowledge in brain metabolism and its association with neurodegenerative diseases.

Systems Biology Approaches to Understand the Host-Microbiome Interactions in Neurodegenerative Diseases

Neurodegenerative diseases (NDDs) comprise a broad range of progressive neurological disorders with multifactorial etiology contributing to disease pathophysiology. Evidence of the microbiome involvement in the gut-brain axis urges the interest in understanding metabolic interactions between the microbiota and host physiology in NDDs. Systems Biology offers a holistic integrative approach to study the interplay between the different biologic systems as part of a whole, and may elucidate the host–microbiome interactions in NDDs. We reviewed direct and indirect pathways through which the microbiota can modulate the bidirectional communication of the gut-brain axis, and explored the evidence of microbial dysbiosis in Alzheimer’s and Parkinson’s diseases. As the gut microbiota being strongly affected by diet, the potential approaches to targeting the human microbiota through diet for the stimulation of neuroprotective microbial-metabolites secretion were described. We explored the potential of Genome-scale metabolic models (GEMs) to infer microbe-microbe and host-microbe interactions and to identify the microbiome contribution to disease development or prevention. Finally, a systemic approach based on GEMs and ‘omics integration, that would allow the design of sustainable personalized anti-inflammatory diets in NDDs prevention, through the modulation of gut microbiota was described.

Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke

Despite many years of research, no biomarkers for stroke are available to use in clinical practice. Progress in high-throughput technologies has provided new opportunities to understand the pathophysiology of this complex disease, and these studies have generated large amounts of data and information at different molecular levels. The integration of these multi-omics data means that thousands of proteins (proteomics), genes (genomics), RNAs (transcriptomics) and metabolites (metabolomics) can be studied simultaneously, revealing interaction networks between the molecular levels. Integrated analysis of multi-omics data will provide useful insight into stroke pathogenesis, identification of therapeutic targets and biomarker discovery. In this Review, we detail current knowledge on the pathology of stroke and the current status of biomarker research in stroke. We summarize how proteomics, metabolomics, transcriptomics and genomics are all contributing to the identification of new candidate biomarkers that could be developed and used in clinical stroke management.