Changes and Significance of Central Carbon Metabolism before and after Metformin Treatment of Type 2 Diabetes Based on Mass Spectrometry.

BACKGROUND: An imbalance in energy metabolism serves as a causal factor for type 2 diabetes (T2D). Although metformin has been known to ameliorate the overall energy metabolism imbalance, but the direct correlation between metformin and central carbon metabolism (CCM) has not been thoroughly investigated. In this study, we employed a high-performance ion chromatography-tandem mass spectrometry (HPIC-MS/MS) technique to examine the alterations and significance of CCM both before and after metformin treatment for T2D. METHODS: We recruited 29 participants, comprising 10 individuals recently diagnosed with T2D (T2D group). Among these, 10 patients underwent a 4-6-week treatment with metformin (MET group). Additionally, we included 9 healthy subjects (CON group). Employing HPIC-MS/MS, we quantitatively analyzed 56 metabolites across 18 biologically relevant metabolic pathways associated with CCM. Univariate and multivariate statistical analyses were utilized to identify differential metabolites. Subsequently, correlation analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted on the identified differential metabolites. RESULTS: We identified seven distinct metabolites in individuals with T2D (p < 0.05). Notably, cyclic 3',5'-Adenosine MonoPhosphate (AMP), Glucose 6-phosphate, L-lactic acid, Maleic acid, and Malic acid exhibited a reversal to normal levels following metformin treatment. Furthermore, Malic acid demonstrated a positive correlation with L-lactic acid (r = 0.94, p < 0.05), as did succinic acid with malic acid (r = 0.81, p < 0.05), L-lactic acid with succinic acid (r = 0.78, p < 0.05), and L-lactic acid with glucose-6-phosphate (r = 0.72, p < 0.05). These metabolites were notably enriched in pyruvate metabolism (p = 0.005), tricarboxylic acid cycle (TCA) (p = 0.007), propanoate metabolism (p = 0.007), and glycolysis or gluconeogenesis (p = 0.009), respectively. CONCLUSIONS: We employed HPIC-MS/MS to uncover alterations in CCM among individuals recently diagnosed with T2D before and after metformin treatment. The findings suggest that metformin may ameliorate the energy metabolism imbalance in T2D by reducing intermediates within the CCM pathway.

The Fur-like regulatory protein MAP3773c modulates key metabolic pathways in Mycobacterium avium subsp. paratuberculosis under in-vitro iron starvation.

Johne's disease (JD) is a chronic enteric infection of dairy cattle worldwide. Mycobacterium avium subsp. paratuberculosis (MAP), the causative agent of JD, is fastidious often requiring eight to sixteen weeks to produce colonies in culture-a major hurdle in the diagnosis and therefore in implementation of optimal JD control measures. A significant gap in knowledge is the comprehensive understanding of the metabolic networks deployed by MAP to regulate iron both in-vitro and in-vivo. The genome of MAP carries MAP3773c, a putative metal regulator, which is absent in all other mycobacteria. The role of MAP3773c in intracellular iron regulation is poorly understood. In the current study, a field isolate (K-10) and an in-frame MAP3773c deletion mutant (ΔMAP3773c) derived from K-10, were exposed to iron starvation for 5, 30, 60, and 90 min and RNA-Seq was performed. A comparison of transcriptional profiles between K-10 and ΔMAP3773c showed 425 differentially expressed genes (DEGs) at 30 min time post-iron restriction. Functional analysis of DEGs in ΔMAP3773c revealed that pantothenate (Pan) biosynthesis, polysaccharide biosynthesis and sugar metabolism genes were downregulated at 30 min post-iron starvation whereas ATP-binding cassette (ABC) type metal transporters, putative siderophore biosynthesis, PPE and PE family genes were upregulated. Pathway analysis revealed that the MAP3773c knockout has an impairment in Pan and Coenzyme A (CoA) biosynthesis pathways suggesting that the absence of those pathways likely affect overall metabolic processes and cellular functions, which have consequences on MAP survival and pathogenesis.

Development and applications of genome-scale metabolic network models.

The genome-scale metabolic network model is an effective tool for characterizing the gene-protein-response relationship in the entire metabolic pathway of an organism. By combining various algorithms, the genome-scale metabolic network model can effectively simulate the influence of a specific environment on the physiological state of cells, optimize the culture conditions of strains, and predict the targets of genetic modification to achieve targeted modification of strains. In this review, we summarize the whole process of model building, sort out the various tools that may be involved in the model building process, and explain the role of various algorithms in model analysis. In addition, we also summarized the application of GSMM in network characteristics, cell phenotypes, metabolic engineering, etc. Finally, we discuss the current challenges facing GSMM.

EgMADS3 directly regulates EgLPAAT to mediate medium-chain fatty acids (MCFA) anabolism in the mesocarp of oil palm.

KEY MESSAGE: EgMADS3, a pivotal transcription factor, positively regulates MCFA accumulation via binding to the EgLPAAT promoter, advancing lipid content in mesocarp of oil palm. Lipids function as the structural components of cell membranes, which serve as permeable barriers to the external environment of cells. The medium-chain fatty acid in the stored lipids of plants is an important renewable energy. Most research on MCFA production in plant lipid synthesis is based on biochemical methods, and the importance of transcriptional regulation in MCFA synthesis and its incorporation into TAGs needs further research. Oil palm is the most productive oil crop in the world and has the highest productivity among the main oil crops. In this study, the MADS transcription factor (EgMADS3) in the mesocarp of oil palm was characterized. Through the VIGS-virus induced gene silencing, it was determined that the potential target gene of EgMADS3 was related to the biosynthesis of medium-chain fatty acid (MCFA). Transient transformation in protoplasts and qRT-PCR analysis showed that EgMADS3 positively regulated the expression of EgLPAAT. The results of the yeast one-hybrid assays and EMSA indicated the interaction between EgMADS3 and EgLPAAT promoter. Through genetic transformation and fatty acid analysis, it is concluded that EgMADS3 directly regulates the mid-chain fatty acid synthesis pathway of the potential target gene EgLPAAT, thus promotes the accumulation of MCFA and improves the total lipid content. This study is innovative in the functional analysis of the MADS family transcription factor in the metabolism of medium-chain fatty acids (MCFA) of oil palm, provides a certain research basis for improving the metabolic pathway of chain fatty acids in oil palm, and improves the synthesis of MCFA in plants. Our results will provide a reference direction for further research on improving the oil quality through biotechnology of oil palm.

Metabolic Pathway Coupled with Fermentation Process Optimization for High-Level Production of Retinol in Yarrowia lipolytica.

Retinol is a lipid-soluble form of vitamin A that is crucial for human visual and immune functions. The production of retinol through microbial fermentation has been the focus of recent exploration. However, the obtained titer remains limited and the product is often a mixture of retinal, retinol, and retinoic acid, necessitating purification. To achieve efficient biosynthesis of retinol in Yarrowia lipolytica, we improved the metabolic flux of ß-carotene to provide sufficient precursors for retinol in this study. Coupled with the optimization of the expression level of ß-carotene 15,15'-dioxygenase, de novo production of retinol was achieved. Furthermore, Tween 80 was used as an extractant and butylated hydroxytoluene as an antioxidant to extract intracellular retinol and prevent retinol oxidation, respectively. This strategy significantly increased the level of retinol production. By optimizing the enzymes converting retinal to retinol, the proportion of extracellular retinol in the produced retinoids reached 100%, totaling 1042.3 mg/L. Finally, total retinol production reached 5.4 g/L through fed-batch fermentation in a 5 L bioreactor, comprising 4.2 g/L extracellular retinol and 1.2 g/L intracellular retinol. This achievement represents the highest reported titer so far and advances the industrial production of retinol.

Logic programming-based Minimal Cut Sets reveal consortium-level therapeutic targets for chronic wound infections.

Minimal Cut Sets (MCSs) identify sets of reactions which, when removed from a metabolic network, disable certain cellular functions. The traditional search for MCSs within genome-scale metabolic models (GSMMs) targets cellular growth, identifies reaction sets resulting in a lethal phenotype if disrupted, and retrieves a list of corresponding gene, mRNA, or enzyme targets. Using the dual link between MCSs and Elementary Flux Modes (EFMs), our logic programming-based tool aspefm was able to compute MCSs of any size from GSMMs in acceptable run times. The tool demonstrated better performance when computing large-sized MCSs than the mixed-integer linear programming methods. We applied the new MCSs methodology to a medically-relevant consortium model of two cross-feeding bacteria, Staphylococcus aureus and Pseudomonas aeruginosa. aspefm constraints were used to bias the computation of MCSs toward exchanged metabolites that could complement lethal phenotypes in individual species. We found that interspecies metabolite exchanges could play an essential role in rescuing single-species growth, for instance inosine could complement lethal reaction knock-outs in the purine synthesis, glycolysis, and pentose phosphate pathways of both bacteria. Finally, MCSs were used to derive a list of promising enzyme targets for consortium-level therapeutic applications that cannot be circumvented via interspecies metabolite exchange.

Enhanced protein-metabolite correlation analysis: To investigate the association between Staphylococcus aureus mastitis and metabolic immune pathways.

Mastitis is a disease characterized by congestion, swelling, and inflammation of the mammary gland and usually caused by infection with pathogenic microorganisms. Furthermore, the development of mastitis is closely linked to the exogenous pathway of the gastrointestinal tract. However, the regulatory mechanisms governing the gut-metabolism-mammary axis remain incompletely understood. The present study revealed alterations in the gut microbiota of mastitis rats characterized by an increased abundance of the Proteobacteria phylum. Plasma analysis revealed significantly higher levels of L-isoleucine and cholic acid along with 7-ketodeoxycholic acid. Mammary tissue showed elevated levels of arachidonic acid metabolites and norlithocholic acid. Proteomic analysis showed increased levels of IFIH1, Tnfaip8l2, IRGM, and IRF5 in mastitis rats, which suggests that mastitis triggers an inflammatory response and immune stress. Follistatin (Fst) and progesterone receptor (Pgr) were significantly downregulated, raising the risk of breast cancer. Extracellular matrix (ECM) receptors and focal adhesion signaling pathways were downregulated, while blood-milk barrier integrity was disrupted. Analysis of protein-metabolic network regulation revealed that necroptosis, protein digestion and absorption, and arachidonic acid metabolism were the principal regulatory pathways involved in the development of mastitis. In short, the onset of mastitis leads to changes in the microbiota and alterations in the metabolic profiles of various biological samples, including colonic contents, plasma, and mammary tissue. Key manifestations include disturbances in bile acid metabolism, amino acid metabolism, and arachidonic acid metabolism. At the same time, the integrity of the blood-milk barrier is compromised while inflammation is promoted, thereby reducing cell adhesion in the mammary glands. These findings contribute to a more comprehensive understanding of the metabolic status of mastitis and provide new insights into its impact on the immune system.

Prioritization of therapeutic targets for cancers using integrative multi-omics analysis.

BACKGROUND: The integration of transcriptomic, proteomic, druggable genetic and metabolomic association studies facilitated a comprehensive investigation of molecular features and shared pathways for cancers' development and progression. METHODS: Comprehensive approaches consisting of transcriptome-wide association studies (TWAS), proteome-wide association studies (PWAS), summary-data-based Mendelian randomization (SMR) and MR were performed to identify genes significantly associated with cancers. The results identified in above analyzes were subsequently involved in phenotype scanning and enrichment analyzes to explore the possible health effects and shared pathways. Additionally, we also conducted MR analysis   to investigate metabolic pathways related to cancers. RESULTS: Totally 24 genes (18 transcriptomic, 1 proteomic and 5 druggable genetic) showed significant associations with cancers risk. All genes identified in multiple methods were mainly enriched in nuclear factor erythroid 2-related factor 2 (NRF2) pathway. Additionally, biosynthesis of ubiquinol and urate were found to play an important role in gastrointestinal tumors. CONCLUSIONS: A set of putatively causal genes and pathways relevant to cancers were identified in this study, shedding light on the shared biological processes for tumorigenesis and providing compelling genetic evidence to prioritize anti-cancer drugs development.

Metabolic modeling of Halomonas campaniensis improves polyhydroxybutyrate production under nitrogen limitation.

Poly-hydroxybutyrate (PHB) is an environmentally friendly alternative for conventional fossil fuel-based plastics that is produced by various microorganisms. Large-scale PHB production is challenging due to the comparatively higher biomanufacturing costs. A PHB overproducer is the haloalkaliphilic bacterium Halomonas campaniensis, which has low nutritional requirements and can grow in cultures with high salt concentrations, rendering it resistant to contamination. Despite its virtues, the metabolic capabilities of H. campaniensis as well as the limitations hindering higher PHB production remain poorly studied. To address this limitation, we present HaloGEM, the first high-quality genome-scale metabolic network reconstruction, which encompasses 888 genes, 1528 reactions (1257 gene-associated), and 1274 metabolites. HaloGEM not only displays excellent agreement with previous growth data and experiments from this study, but it also revealed nitrogen as a limiting nutrient when growing aerobically under high salt concentrations using glucose as carbon source. Among different nitrogen source mixtures for optimal growth, HaloGEM predicted glutamate and arginine as a promising mixture producing increases of 54.2% and 153.4% in the biomass yield and PHB titer, respectively. Furthermore, the model was used to predict genetic interventions for increasing PHB yield, which were consistent with the rationale of previously reported strategies. Overall, the presented reconstruction advances our understanding of the metabolic capabilities of H. campaniensis for rationally engineering this next-generation industrial biotechnology platform. KEY POINTS: A comprehensive genome-scale metabolic reconstruction of H. campaniensis was developed. Experiments and simulations predict N limitation in minimal media under aerobiosis. In silico media design increased experimental biomass yield and PHB titer.

Microalgae enhanced co-metabolism of sulfamethoxazole using aquacultural feedstuff components: Co-metabolic pathways and enzymatic mechanisms.

The application of antibiotics in freshwater aquaculture leads to increased contamination of aquatic environments. However, limited information is available on the co-metabolic biodegradation of antibiotics by microalgae in aquaculture. Feedstuffs provide multiple organic substrates for microalgae-mediated co-metabolism. Herein, we investigated the co-metabolism of sulfamethoxazole (SMX) by Chlorella pyrenoidosa when adding main components of feedstuff (glucose and lysine). Results showed that lysine had an approximately 1.5-fold stronger enhancement on microalgae-mediated co-metabolism of SMX than glucose, with the highest removal rate (68.77% ± 0.50%) observed in the 9-mM-Lys co-metabolic system. Furthermore, we incorporated reactive sites predicted by density functional theory calculations, 14 co-metabolites identified by mass spectrometry, and the roles of 18 significantly activated enzymes to reveal the catalytic reaction mechanisms underlying the microalgae-mediated co-metabolism of SMX. In lysine- and glucose-treated groups, five similar co-metabolic pathways were proposed, including bond breaking on the nucleophilic sulfur atom, ring cleavage and hydroxylation at multiple free radical reaction sites, together with acylation and glutamyl conjugation on electrophilic nitrogen atoms. Cytochrome P450, serine hydrolase, and peroxidase play crucial roles in catalyzing hydroxylation, bond breaking, and ring cleavage of SMX. These findings provide theoretical support for better utilization of microalgae-driven co-metabolism to reduce sulfonamide antibiotic residues in aquaculture.

Targeting nucleotide metabolic pathways in colorectal cancer by integrating scRNA-seq, spatial transcriptome, and bulk RNA-seq data.

BACKGROUND: Colorectal cancer is a malignant tumor of the digestive system originating from abnormal cell proliferation in the colon or rectum, often leading to gastrointestinal symptoms and severe health issues. Nucleotide metabolism, which encompasses the synthesis of DNA and RNA, is a pivotal cellular biochemical process that significantly impacts both the progression and therapeutic strategies of colorectal cancer METHODS: For single-cell RNA sequencing (scRNA-seq), five functions were employed to calculate scores related to nucleotide metabolism. Cell developmental trajectory analysis and intercellular interaction analysis were utilized to explore the metabolic characteristics and communication patterns of different epithelial cells. These findings were further validated using spatial transcriptome RNA sequencing (stRNA-seq). A risk model was constructed using expression profile data from TCGA and GEO cohorts to optimize clinical decision-making. Key nucleotide metabolism-related genes (NMRGs) were functionally validated by further in vitro experiments. RESULTS: In both scRNA-seq and stRNA-seq, colorectal cancer (CRC) exhibited unique cellular heterogeneity, with myeloid cells and epithelial cells in tumor samples displaying higher nucleotide metabolism scores. Analysis of intercellular communication revealed enhanced signaling pathways and ligand-receptor interactions between epithelial cells with high nucleotide metabolism and fibroblasts. Spatial transcriptome sequencing confirmed elevated nucleotide metabolism states in the core region of tumor tissue. After identifying differentially expressed NMRGs in epithelial cells, a risk prognostic model based on four genes effectively predicted overall survival and immunotherapy outcomes in patients. High-risk group patients exhibited an immunosuppressive microenvironment and relatively poorer prognosis and responses to chemotherapy and immunotherapy. Finally, based on data analysis and a series of cellular functional experiments, ACOX1 and CPT2 were identified as novel therapeutic targets for CRC. CONCLUSION: In this study, a comprehensive analysis of NMRGs in CRC was conducted using a combination of single-cell sequencing, spatial transcriptome sequencing, and high-throughput data. The prognostic model constructed with NMRGs shows potential as a standalone prognostic marker for colorectal cancer patients and may significantly influence the development of personalized treatment approaches for CRC.

Genomic factors shape carbon and nitrogen metabolic niche breadth across Saccharomycotina yeasts.

Organisms exhibit extensive variation in ecological niche breadth, from very narrow (specialists) to very broad (generalists). Two general paradigms have been proposed to explain this variation: (i) trade-offs between performance efficiency and breadth and (ii) the joint influence of extrinsic (environmental) and intrinsic (genomic) factors. We assembled genomic, metabolic, and ecological data from nearly all known species of the ancient fungal subphylum Saccharomycotina (1154 yeast strains from 1051 species), grown in 24 different environmental conditions, to examine niche breadth evolution. We found that large differences in the breadth of carbon utilization traits between yeasts stem from intrinsic differences in genes encoding specific metabolic pathways, but we found limited evidence for trade-offs. These comprehensive data argue that intrinsic factors shape niche breadth variation in microbes.

Melosira nummuloides Ethanol Extract Ameliorates Alcohol-Induced Liver Injury by Affecting Metabolic Pathways.

Melosira nummuloides is a microalga with a nutritionally favorable polyunsaturated fatty acid profile. In the present study, M. nummuloides ethanol extract (MNE) was administered to chronic-binge alcohol-fed mice and alcohol-treated HepG2 cells, and its hepatoprotective effects and underlying mechanisms were investigated. MNE administration reduced triglyceride (TG), total cholesterol (T-CHO), and liver injury markers, including aspartate transaminase (AST) and alanine transaminase (ALT), in the serum of chronic-binge alcohol-fed mice. However, MNE administration increased the levels of phosphorylated adenosine monophosphate-activated protein kinase (P-AMPK/AMPK) and PPARα, which was accompanied by a decrease in SREBP-1; this indicates that MNE can inhibit adipogenesis and improve fatty acid oxidation. Moreover, MNE administration upregulated the expression of antioxidant enzymes, including SOD, NAD(P)H quinone dehydrogenase 1, and GPX, and ameliorated alcohol-induced inflammation by repressing the Akt/NFκB/COX-2 pathway. Metabolomic analysis revealed that MNE treatment modulated many lipid metabolites in alcohol-treated HepG2 cells. Our study findings provide evidence for the efficacy and mechanisms of MNE in ameliorating alcohol-induced liver injury.

[Research Progress on Tumor Metabolic Biomarkers in Liquid Biopsy of Lung Cancer].

Liquid biopsy is gradually being applied in the clinical diagnosis and treatment of lung cancer. At present, with the development of metabolomics, more and more metabolic biomarkers are considered as potential sensitive markers reflecting the occurrence and development of tumors. This article summarizes the changes in the main metabolic pathways of lung cancer, including glucose metabolism, amino acid metabolism, lipid metabolism, sphingolipid metabolism, glycerophospholipid metabolism, and purine metabolism. Meanwhile, this article reviews the role of metabolic biomarkers in the early diagnosis of lung cancer, predicting disease progression, and evaluating the efficacy of chemotherapy and immunotherapy, aiming to provide effective biomarkers for tumor diagnosis and treatment.
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A Machine Learning Approach for Predicting Essentiality of Metabolic Genes.

The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that convert feedstocks into valuable products. Assessment of gene essentiality at a genome scale requires large and costly growth assays of knockout strains. Here we describe a strategy to predict the essentiality of metabolic genes using binary classification algorithms. The approach combines elements from genome-scale metabolic models, directed graphs, and machine learning into a predictive model that can be trained on small knockout data. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli and various machine learning algorithms for binary classification.

Mitochondrial metabolism sustains CD8+ T cell migration for an efficient infiltration into solid tumors.

The ability of CD8+ T cells to infiltrate solid tumors and reach cancer cells is associated with improved patient survival and responses to immunotherapy. Thus, identifying the factors controlling T cell migration in tumors is critical, so that strategies to intervene on these targets can be developed. Although interstitial motility is a highly energy-demanding process, the metabolic requirements of CD8+ T cells migrating in a 3D environment remain unclear. Here, we demonstrate that the tricarboxylic acid (TCA) cycle is the main metabolic pathway sustaining human CD8+ T cell motility in 3D collagen gels and tumor slices while glycolysis plays a more minor role. Using pharmacological and genetic approaches, we report that CD8+ T cell migration depends on the mitochondrial oxidation of glucose and glutamine, but not fatty acids, and both ATP and ROS produced by mitochondria are required for T cells to migrate. Pharmacological interventions to increase mitochondrial activity improve CD8+ T cell intratumoral migration and CAR T cell recruitment into tumor islets leading to better control of tumor growth in human xenograft models. Our study highlights the rationale of targeting mitochondrial metabolism to enhance the migration and antitumor efficacy of CAR T cells in treating solid tumors.

A Practical Guide to the Representation of Protein Regulation in the Web Application ChloroKB.

ChloroKB ( http://chlorokb.fr ) is a knowledge base providing synoptic representations of the metabolism of the model plant Arabidopsis thaliana and its regulation. Initially focused on plastid metabolism, ChloroKB now accounts for the metabolism throughout the cell. ChloroKB is based on the CellDesigner formalism. CellDesigner supports graphical notation and listing of the corresponding symbols based on the Systems Biology Graphical Notation. Thus, this formalism allows biologists to represent detailed biochemical processes in a way that can be easily understood and shared, facilitating communication between researchers. In this chapter, we will focus on a specificity of ChloroKB, the representation of multilayered regulation of protein activity. Information on regulation of protein activity is indeed central to understanding the plant response to fluctuating environmental conditions. However, the intrinsic diversity of the regulatory modes and the abundance of detail may hamper comprehension of the regulatory processes described in ChloroKB. With this chapter, ChloroKB users will be guided through the representation of these sophisticated biological processes of prime importance to understanding metabolism or for applied purposes. The descriptions provided, which summarize years of work and a broad bibliography in a few pages, can help speed up the integration of regulatory processes in kinetic models of plant metabolism.

Discovery of a Gut Bacterial Metabolic Pathway that Drives α-Synuclein Aggregation.

Parkinson's disease (PD) etiology is associated with aggregation and accumulation of α-synuclein (α-syn) proteins in midbrain dopaminergic neurons. Emerging evidence suggests that in certain subtypes of PD, α-syn aggregates originate in the gut and subsequently spread to the brain. However, mechanisms that instigate α-syn aggregation in the gut have remained elusive. In the brain, the aggregation of α-syn is induced by oxidized dopamine. Such a mechanism has not been explored in the context of the gastrointestinal tract, a niche harboring 46% of the body's dopamine reservoirs. Here, we report that Enterobacteriaceae, a bacterial family prevalent in human gut microbiotas, induce α-syn aggregation. More specifically, our in vitro data indicate that respiration of nitrate by Escherichia coli K-12, which results in production of nitrite that mediates oxidation of Fe2+ to Fe3+, creates an oxidizing redox potential. These oxidizing conditions enabled the formation of dopamine-derived quinones and α-syn aggregates. Exposing nitrite, but not nitrate, to enteroendocrine STC-1 cells induced aggregation of α-syn that is natively expressed in these cells, which line the intestinal tract. Taken together, our findings indicate that bacterial nitrate reduction may be critical for initiating intestinal α-syn aggregation.

Genome-wide characterization of circulating metabolic biomarkers.

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.

Unlocking the potential of T-cell metabolism reprogramming: Advancing single-cell approaches for precision immunotherapy in tumour immunity.

As single-cell RNA sequencing enables the detailed clustering of T-cell subpopulations and facilitates the analysis of T-cell metabolic states and metabolite dynamics, it has gained prominence as the preferred tool for understanding heterogeneous cellular metabolism. Furthermore, the synergistic or inhibitory effects of various metabolic pathways within T cells in the tumour microenvironment are coordinated, and increased activity of specific metabolic pathways generally corresponds to increased functional activity, leading to diverse T-cell behaviours related to the effects of tumour immune cells, which shows the potential of tumour-specific T cells to induce persistent immune responses. A holistic understanding of how metabolic heterogeneity governs the immune function of specific T-cell subsets is key to obtaining field-level insights into immunometabolism. Therefore, exploring the mechanisms underlying the interplay between T-cell metabolism and immune functions will pave the way for precise immunotherapy approaches in the future, which will empower us to explore new methods for combating tumours with enhanced efficacy.