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PubMed2026

Metabolic pathways linked to sarcopenia in the Bushehr Elderly Health Program: kynurenine, nicotinamide, B-vitamins, and sulfur amino acids.

BACKGROUND: Sarcopenia, characterized by the loss of muscle mass and function, is a common condition in the elderly, associated with increased morbidity and mortality. Metabolic pathways, including the kynurenine, nicotinamide, B-vitamins, and sulfur amino acid pathways, may play a significant role in the development and progression of sarcopenia. This study investigates the relationship between metabolic pathways and sarcopenia, aiming to identify potential therapeutic targets. METHOD: Four hundred participants over 60 years were randomly selected from the second stage of the Bushehr Elderly Health Program (BEH). Frozen plasma samples were used to measure metabolomics. We used factor analysis and logistic regression analysis to determine the kynurenine-tryptophan metabolites associated with sarcopenia and its components. RESULT: Study participants included 89 sarcopenic subjects aged 72.92 ± 7.32 years and 307 non-sarcopenic subjects aged 68.12 ± 5.56 years. In full model adjustment, factor 3, which included methionine, tryptophan, 3-hydroxyanthranilic acid, picolinic acid, and xanthurenic acid, was associated with 38.3% lower risk of sarcopenia (OR = 0.617 [95%CI = 0.436–0.875]); Factor 6, which included methylmalonic acid and total homocysteine, was associated with a 33.7% increased risk of sarcopenia (OR = 1.337 [95%CI = 1.031–1.735]); and factor 7, consisting of nicotinamide, were related to a 25.2% lower risk of developing sarcopenia (OR = 0.748 [95%CI = 0.571–0.979]). Additionally, factor 1, which included quinolinic acid, kynurenine, 3-hydroxykynurenine, neopterin, kynurenic acid, anthranilic acid, cystathionine, and total cysteine, was linked to a 49.2% higher risk of low muscle strength, while factors 3 and 7 were associated with approximately a 24% decrease in risk of low muscle strength. Factors 5, consisting of serine and glycine, and factor 7 were related to 43% and 27.7% lower risk of low skeletal muscle index, respectively. While factor 6 was related to a 32.8% higher risk of low skeletal muscle index. Factor 1 was also related to a 32.9% higher risk of low walking speed, while factor 3 was related to a 28.5% lower risk of low walking speed. CONCLUSION: Specific metabolites from the kynurenine, nicotinamide, B-vitamin, and sulfur amino acid pathways are significantly associated with sarcopenia and its key parameters, such as muscle strength, skeletal muscle index, and walking speed. These findings suggest that metabolic profiling could offer valuable insights for early detection and targeted interventions for sarcopenia in elderly populations.

Humans↗

PubMed2025

Multi-Omics and Integrative Analytics in Natural Products Discovery.

Natural products (NPs) have long been an essential source of new bioactive compounds for drug discovery; however, traditional methods for screening and isolating these compounds can be slow and often yield diminishing returns. Fortunately, advanced multi-omics and computational approaches present powerful solutions to these challenges. This review highlights innovative methodologies that integrate metabolomics, genomics, transcriptomics, and proteomics with bioinformatics and analytical chemistry to accelerate NP discovery. For instance, untargeted metabolomics platforms like high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) and Global Natural Products Social (GNPS) molecular networking allow for comprehensive profiling of new compounds, while targeted isotope-labeling strategies enhance this process. Additionally, genome and metagenome mining tools such as antibiotics and secondary metabolite analysis shell (antiSMASH), Deep Biosynthetic Gene Cluster (DeepBGC), and Pipeline for Reconstructing Integrated Syntheses of Metabolites (PRISM) quickly identify biosynthetic gene clusters (BGCs) in both cultured and uncultured organisms, often using heterologous expression to validate products. Transcriptomic analyses, including RNA sequencing (RNA-seq), co-expression networks, and fluxomics, help clarify how pathways are regulated, while quantitative proteomics techniques like tandem mass tags/isobaric tags for relative and absolute quantitation (TMT/iTRAQ) and label-free methods, along with chemoproteomics approaches such as cellular thermal shift assay and thermal proteome profiling (TPP), uncover molecular targets and their mechanisms of action. This review also places significant emphasis on the role of artificial intelligence (AI) and machine learning (ML) in integrating multi-omics data, spanning activities from constructing gene-metabolite correlation networks to leveraging knowledge graphs and graph neural networks for data fusion and functional prediction. Finally, this review concludes by discussing the synergistic benefits of multi-omics for natural-product discovery, addressing current technical challenges, and exploring future directions toward high-throughput, intelligent data integration for next-generation NP research.

Biological Products↗

PubMed2006

Metabolomics: current technologies and future trends.

The ability to sequence whole genomes has taught us that our knowledge with respect to gene function is rather limited with typically 30-40% of open reading frames having no known function. Thus, within the life sciences there is a need for determination of the biological function of these so-called orphan genes, some of which may be molecular targets for therapeutic intervention. The search for specific mRNA, proteins, or metabolites that can serve as diagnostic markers has also increased, as has the fact that these biomarkers may be useful in following and predicting disease progression or response to therapy. Functional analyses have become increasingly popular. They include investigations at the level of gene expression (transcriptomics), protein translation (proteomics) and more recently the metabolite network (metabolomics). This article provides an overview of metabolomics and discusses its complementary role with transcriptomics and proteomics, and within system biology. It highlights how metabolome analyses are conducted and how the highly complex data that are generated are analysed. Non-invasive footprinting analysis is also discussed as this has many applications to in vitro cell systems. Finally, for studying biotic or abiotic stresses on animals, plants or microbes, we believe that metabolomics could very easily be applied to large populations, because this approach tends to be of higher throughput and generally lower cost than transcriptomics and proteomics, whilst also providing indications of which area of metabolism may be affected by external perturbation.

Biomarkers↗

PubMed2005

Towards quantitative biology: integration of biological information to elucidate disease pathways and to guide drug discovery.

Developing a new drug is a tedious and expensive undertaking. The recently developed high-throughput experimental technologies, summarised by the terms genomics, transcriptomics, proteomics and metabolomics provide for the first time ever the means to comprehensively monitor the molecular level of disease processes. The "-omics" technologies facilitate the systematic characterisation of a drug target's physiology, thereby helping to reduce the typically high attrition rates in discovery projects, and improving the overall efficiency of pharmaceutical research processes. Currently, the bottleneck for taking full advantage of the new experimental technologies are the rapidly growing volumes of automatically produced biological data. A lack of scalable database systems and computational tools for target discovery has been recognised as a major hurdle. In this review, an overview will be given on recent progress in computational biology that has an impact on drug discovery applications. The focus will be on novel in silico methods to reconstruct regulatory networks, signalling cascades, and metabolic pathways, with an emphasis on comparative genomics and microarray-based approaches. Promising methods, such as the mathematical simulation of pathway dynamics are discussed in the context of applications in discovery projects. The review concludes by exemplifying concrete data-driven studies in pharmaceutical research that demonstrate the value of integrated computational systems for drug target identification and validation, screening assay development, as well as drug candidate efficacy and toxicity evaluations.

Animals↗

PubMed2002

Metabolomics--the link between genotypes and phenotypes.

Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.

Arabidopsis↗

PubMed2026

Exploratory proteomic and metabolomic profiling of pleural effusions identifies histone H4 and alanine as promising complementary markers for pleural tuberculosis.

The diagnosis of pleural tuberculosis (Pl-TB) remains challenging. Histopathological analysis and pathogen detection in pleural biopsies are informative but limited. We investigated differentially expressed proteins and metabolites in pleural effusions from patients with Pl-TB, malignancies, and other pathologies. A proteomic analysis of pooled pleural effusions identified 45 proteins exclusively detected or upregulated in Pl-TB samples, many linked to infectious processes. Conversely, 18 proteins were uniquely found or upregulated in malignant pleural effusions, mainly associated with detoxification and hemostasis. To validate these findings, we employed targeted proteomics in individual samples. Eight proteins were validated: S100-A9, histone H4, insulin-like growth factor-binding protein 2, fibrinogen beta chain, ficolin-3, immunoglobulin heavy constant alpha 1, sulfhydryl oxidase 1, and histidine-rich glycoprotein. Additionally, NMR-based metabolomics identified 13 metabolites with differential abundance between Pl-TB and non-TB samples. Notably, N-acetyl-glycoprotein and the branched-chain amino acids, alanine and lysine differed between groups. Proteomic and metabolomic analyses revealed distinct molecular profiles between Pl-TB and non-TB patients, despite intra-group variability. To address this, we applied classification models. Histone H4 and alanine consistently emerged as discriminative features. Overall, this study provides novel insights into the molecular landscape of Pl-TB. The combined quantification of proteins and metabolites may improve differential diagnosis, although should be further validated in larger, independent cohorts before clinical application.

Humans↗

PubMed2026

Omics in hereditary optic neuropathies: A systematic review of clinical studies with an integrated point of view.

Hereditary optic neuropathies are characterized by bilateral visual loss due to the degeneration of retinal ganglion cells, resulting in optic nerve degeneration and atrophy. Although the genetic origin of the main isolated and syndromic hereditary optic neuropathies has been characterized, the clinical phenotypes exhibit significant and poorly understood variability in both penetrance and expressivity. Additionally, the genetic and environmental factors that influence the onset of these optic neuropathies remain poorly understood, with limited biomarkers to predict disease progression or as readouts for therapeutic trials. Data-driven omics strategies allow deep phenotyping to improve our understanding of pathophysiological mechanisms and to search for new biomarkers and therapeutic targets. We explore whether the omics strategies applied to patients with hereditary optic neuropathies have provided such new insights. MEDLINE, Web of Science and EMBASE databases were screened for studies with terms relating to hereditary optic neuropathies, transcriptomics, epigenomics, proteomics, metabolomics and lipidomics in clinical studies exploring patients' samples. Out of 1244 references identified, 22 articles were included after double-masked data curation. These articles focused only on the 3 main forms of hereditary optic neuropathies, namely, OPA1-related dominant optic atrophy (n = 4), Leber hereditary optic neuropathy (n = 13), and Wolfram syndrome (n = 5). While the methodological designs and results of these studies were highly heterogeneous, they revealed molecular alterations that we have attempted to discuss at the integrated multi-omics level. This data integration highlighted several common pathophysiological mechanisms such as energetic impairment, endoplasmic reticulum stress, proteotoxic and oxidative stresses, lipid remodeling and altered amino acid and purine metabolisms, while suggesting potential new biomarkers and therapeutic targets. These findings underscore the potential of integrated multi-omics approaches to deepen our understanding of the phenotypic complexity of hereditary optic neuropathies and to support the development of innovative diagnostic and therapeutic strategies.

Humans↗

PubMed2025

Integrated Multiomics Analyses of the Molecular Landscape of Sarcopenia in Alcohol-Related Liver Disease.

BACKGROUND: Skeletal muscle is a major target for ethanol-induced perturbations, leading to sarcopenia in alcohol-related liver disease (ALD). The complex interactions and pathways involved in adaptive and maladaptive responses to ethanol in skeletal muscle are not well understood. Unlike hypothesis-driven experiments, an integrated multiomics-experimental validation approach provides a comprehensive view of these interactions. METHODS: We performed multiomics analyses with experimental validation to identify novel regulatory mechanisms of sarcopenia in ALD. Studies were done in a comprehensive array of models including ethanol-treated (ET) murine and human-induced pluripotent stem cell-derived myotubes (hiPSCm), skeletal muscle from a mouse model of ALD (mALD) and human patients with alcohol-related cirrhosis and controls. We generated 13 untargeted datasets, including chromatin accessibility (assay for transposase accessible chromatin), RNA sequencing, proteomics, phosphoproteomics, acetylomics and metabolomics, and conducted integrated multiomics analyses using UpSet plots and feature extraction. Key findings were validated using immunoblots, redox measurements (NAD+/NADH ratio), imaging and senescence-associated molecular phenotype (SAMP) assays. Mechanistic studies included mitochondrial-targeted Lactobacillus brevis NADH oxidase (MitoLbNOX) to increase redox ratio and MitoTempo as a mitochondrial free radical scavenger. RESULTS: Multiomics analyses revealed enrichment in mitochondrial oxidative function, protein synthesis and senescence pathways consistent with the known effects of hypoxia-inducible factor 1α (HIF1α) during normoxia. Across preclinical and clinical models, HIF1α targets (n = 32 genes) and signalling genes (n > 100 genes) (n = 3 ATACseq, n = 65 phosphoproteomics, n = 10 acetylomics, n = 6 C2C12 proteomics, n = 106 C2C12 RNAseq, n = 64 hiPSC RNAseq, n = 30 hiPSC proteomics, n = 3 mouse proteomics, n = 25 mouse RNAseq, n = 8 human RNAseq, n = 3 human proteomics) were increased. Stabilization of HIF1α (C2C12, 6hEtOH 0.24 ± 0.09; p = 0.043; mALD 0.32 ± 0.074; p = 0.005; data shown as mean difference ± standard error mean) was accompanied by enrichment in the early transient and late change clusters, -log(p-value) = 1.5-3.8, of the HIF1α signalling pathway. Redox ratio was reduced in ET myotubes (C2C12: 15512 ± 872.1, p < 0.001) and mALD muscle, with decreased expression of electron transport chain components (CI-V, p < 0.05) and Sirt3 (C2C12: 0.067 ± 0.023, p = 0.025; mALD: 0.41 ± 0.12, p = 0.013). Acetylation of mitochondrial proteins was increased in both models (C2C12: 107364 ± 4558, p = 0.03; mALD: 40036 ± 18 987, p = 0.049). Ethanol-induced SAMP was observed across models (P16: C2C12: 0.2845 ± 0.1145, p < 0.05; hiPSCm: 0.2591, p = 0.041). MitoLbNOX treatment reversed redox imbalance, HIF1α stabilization, global acetylation and myostatin expression (p < 0.05). CONCLUSIONS: An integrated multiomics approach, combined with experimental validation, identifies HIF1α stabilization and accelerated post-mitotic senescence as novel mechanisms of sarcopenia in ALD. These findings show the complex molecular interactions leading to mitochondrial dysfunction and progressive sarcopenia in ALD.

Sarcopenia↗

PubMed2025

Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease.

INTRODUCTION: Gastrointestinal disorders (GIDs) affect nearly 40% of the global population, with gut microbiome-metabolome interactions playing a crucial role in gastric cancer (GC), colorectal cancer (CRC), and inflammatory bowel disease (IBD). This study aims to investigate how microbial and metabolic alterations contribute to disease development and assess whether biomarkers identified in one disease could potentially be used to predict another, highlighting cross-disease applicability. METHODS: Microbiome and metabolome datasets from Erawijantari et al. (GC: n = 42, Healthy: n = 54), Franzosa et al. (IBD: n = 164, Healthy: n = 56), and Yachida et al. (CRC: n = 150, Healthy: n = 127) were subjected to three machine learning algorithms, eXtreme gradient boosting (XGBoost), Random Forest, and Least Absolute Shrinkage and Selection Operator (LASSO). Feature selection identified microbial and metabolite biomarkers unique to each disease and shared across conditions. A microbial community (MICOM) model simulated gut microbial growth and metabolite fluxes, revealing metabolic differences between healthy and diseased states. Finally, network analysis uncovered metabolite clusters associated with disease traits. RESULTS: Combined machine learning models demonstrated strong predictive performance, with Random Forest achieving the highest Area Under the Curve(AUC) scores for GC(0.94[0.83-1.00]), CRC (0.75[0.62-0.86]), and IBD (0.93[0.86-0.98]). These models were then employed for cross-disease analysis, revealing that models trained on GC data successfully predicted IBD biomarkers, while CRC models predicted GC biomarkers with optimal performance scores. CONCLUSION: These findings emphasize the potential of microbial and metabolic profiling in cross-disease characterization particularly for GIDs, advancing biomarker discovery for improved diagnostics and targeted therapies.

Humans↗

PubMed2026

Integrated multi-omics analysis of fluoroquinolone tolerance mechanisms induced by enrofloxacin in Pasteurella multocida.

BACKGROUND: The global prevalence of multidrug-resistant bacteria has been rising at an alarming rate, posing a serious threat to both human and animal health. However, the mechanisms by which bacteria acquire antibiotic tolerance and subsequently develop resistance remain incompletely understood. METHODS: In this study, Pasteurella multocida, a common pathogen in the animal husbandry industry, was exposed to enrofloxacin, and genome resequencing, transcriptomic, and metabolomic analyses were performed to elucidate the adaptive mechanisms of P. multocida under fluoroquinolone-induced stress. RESULTS: Compared with the wild-type strain, the enrofloxacin-tolerant strain exhibited an extended lag phase, a prolonged logarithmic phase, reduced sensitivity to polymyxin B, reduced biofilm formation, and an elongated cellular morphology. Multi-omics analysis revealed a deletion in the dusB gene of the tolerant strain, resulting in a truncated non-functional protein. The deletion of dusB enhanced tolerance by prolonging the lag phase and reducing the growth rate. Moreover, the expression of genes in the CAMP pathway was up-regulated, and deletion of cpxR further promoted tolerance by modulating ribosome-associated genes. Integrated transcriptomic and metabolomic analyses indicated activation of the tricarboxylic acid (TCA) cycle during tolerance development. CONCLUSION: This study identified dusB and cpxR as key genes mediating enrofloxacin tolerance in P. multocida, elucidated the association between the antibiotic tolerance, growth, and gene expression, and may provide potential targets for future strategies aimed at limiting tolerance-associated resistance development.

Enrofloxacin↗

PubMed2026

Viral inhibition of the anaphase promoting complex enhances replication by elevating nucleotide pools.

The anaphase promoting complex/cyclosome (APC/C) is a large, ubiquitin ligase and a central regulator of cell cycle progression. By targeting key substrates for degradation during mitosis and G1 phase, the APC/C coordinates metabolic fluctuations that occur during the cell cycle. A diverse range of viruses have convergently evolved mechanisms to bind and inhibit the APC/C; however, a molecular understanding for these interactions has never been demonstrated. Here, we use chicken anemia virus (CAV), a small single-stranded DNA virus encoding only three proteins, to demonstrate the importance of viral APC/C inhibition during replication. We show that the Vp3 protein of CAV inhibits the APC/C, causing a dramatic mitotic arrest during infection. The mutant virus lacking Vp3 is defective for replication and can be rescued by APC/C inhibition. Metabolomic profiling during CAV infection revealed that Vp3 expression mediates a broad increase in nucleotide pools. Moreover, viral inhibition of the APC/C resulted in stabilization of enzymes required for nucleotide biosynthesis. These findings suggest that the APC/C is a general target of many viruses to elevate nucleotide levels and facilitate viral genome replication.

Anaphase-Promoting Complex-Cyclosome↗

PubMed2026

Multi-omics characterization of flavor profile differences in the Longissimus thoracis between Angus and Hereford cattle.

BACKGROUND: Angus and Hereford cattle are premier breeds widely used in genetic improvement and crossbreeding programs to enhance meat quality, yet the flavor differences between them remain poorly understood. RESULTS: In this study, we performed an integrated analysis using headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS)-based volatile metabolomics, lipidomics, and untargeted metabolomics to characterize the flavor profiles of the Longissimus thoracis (LT) muscle from both breeds and to identify potential precursor substances underlying flavor formation. In total, we identified 76 differential volatile organic compounds (VOCs) among the 493 candidate VOCs. By combing relative odor activity value (ROAV) and sensory attribute annotation, 2,3-butanedione which may contribute to the creamy aroma was revealed as the core differential VOC between the two breeds. This finding was robustly validated across SHAP (Shapley additive explanations) analysis, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment, and flavor annotation. Lipidomic analysis revealed 689 differential lipids primarily belonging to classes such as phosphatidylcholine, triglycerides, and phosphatidylethanolamine. Correlation analysis further linked these lipid profiles to flavor, showing that fatty acids (FAs) including FA(19:0), FA(18:2 + O), FA(14:1), FA(16:1), and FA(14:0) were significantly correlated with 2,3-butanedione. Notably, the unsaturated fatty acids (UFAs) in the Longissimus thoracis (LT) of Hereford cattle exhibited higher double bond content compared to Angus cattle, suggesting a greater potential for rich flavor development. Untargeted metabolomics revealed that nine of the 9474 metabolites were significantly correlated with both 2,3-butanedione and FAs, including norepinephrine, l-beta-aspartyl-l-leucine, and artemetin. CONCLUSIONS: Overall, our research has identified differential flavor compounds and potential precursor substances between Angus cattle and Hereford cattle, providing targeted guidance for breed improvement. © 2026 Society of Chemical Industry.

2,3‐butanedione↗

PubMed2025

Omics in Nonsteroidal Anti-Inflammatory Drugs-Exacerbated Respiratory Disease: Current Evidence From the Upper and Lower Airways.

Nonsteroidal anti-inflammatory drugs (NSAID)-exacerbated respiratory disease (N-ERD) is a mainly type 2 inflammatory condition that combines asthma, nasal polyps, and hypersensitivity to NSAIDs. Its pathogenesis involves both upper and lower airways, yet most studies to date have examined these compartments separately. It remains unclear whether the molecular mechanisms in the nose, sinuses, and lungs are distinct or overlapping-an important gap, given that clinical manifestations of N-ERD involve both sites. In this review, we summarize available omics studies-transcriptomics, proteomics, metabolomics, and epigenomics-performed on upper and lower airways in patients with N-ERD. While omics approaches have revealed new molecular insights, comparisons across studies are limited by heterogeneity in design, controls, and methodology. We emphasize the need for integrated multi-omics analyses and standardized frameworks to better characterize the disease across airways. Such efforts are essential for identifying robust biomarkers and therapeutic targets and for moving toward a systems-level understanding of N-ERD.

Humans↗

PubMed2026

Pathogenesis of psoriasis and psoriatic arthritis: Insights from animal models and single-cell and spatial transcriptomic analyses of skin, synovium and entheses.

Psoriasis (PsO) and psoriatic arthritis (PsA) are immune-mediated diseases characterized by chronic systemic inflammation, including inflammation of the skin and joints. Recent advances in animal models, single-cell transcriptomics, spatial transcriptomics, and proteomics have greatly enhanced our understanding of disease pathogenesis. Mouse models exhibit key features of skin and joint inflammation, facilitating analysis of molecular pathways, and identification of therapeutic targets. Single-cell and spatial transcriptomic analyses have revealed cell-type-specific contributions to inflammation, highlighting interactions between keratinocytes, T cells, fibroblasts, and dendritic cells that drive psoriatic pathology. In psoriatic synovium, type 17 tissue-resident memory T cells, monocytes, and fibroblasts contribute to local inflammation and joint damage, whereas the roles of B cells and plasma cells are less clear. Proteomic and metabolomic profiling in patients with PsA has identified circulating protein signatures and metabolites associated with disease progression, sex-specific differences, and response to therapy. The integration of these multiomic approaches provides a detailed map of immune-stromal-epithelial crosstalk across skin, synovium, and entheses, uncovering mechanisms that were previously inaccessible. These insights have implications for predicting disease progression, identifying novel therapeutic targets, and optimizing treatment strategies. Collectively, advances in animal models and multiomic profiling are reshaping our understanding of PsO and PsA, providing a framework for future research, disease monitoring, and therapeutic development.

Animals↗

PubMed2005

A metabolomic approach to ionotropic glutamate receptor subtype function: a nuclear magnetic resonance in vitro investigation.

A range of behaviours are elucidated via ionotropic glutamate receptors (iGluR). In this work, we examined the acute activation of iGluRs by a range of receptor ligands and effectors to see whether distinguishable metabolic sequelae were elucidated by the activity. We used a guinea-pig brain cortical tissue slice model using targeted receptor ligands ((RS)-(tetrazol-5-yl)glycine (TZG), (5S,10R)-(+)-5-methyl-10,11-dihydro-5H-dibenzo[a,d]cyclohepten-5,10-imine maleate (MK-801, dizocilpine), cis-4-[phosphomethyl]-piperidine-2-carboxylic acid (CGS 19755), (RS)-alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid, (2S, 3S, 4S)-2-carboxy-4-(1-methylethenyl)-3-pyrrolidineacetic acid (kainate) and D-serine (D-Ser), as well as compounds (quinolinic acid and kynurenic acid (KynA)) involved in some neuroinflammatory responses. The data were derived using 13C and 1H NMR spectroscopy, and analysed by metabolomic approaches and multivariate statistics. The metabolic effects of agonists at the three major classes of iGluR were easily separated from each other using this method. The classical N-methyl-D-aspartate receptor agonist TZG and the antagonist CGS 19755 produced excitatory and inhibitory metabolic responses, respectively, while the blocker MK-801 resulted in a significant decrease in net metabolism and produced the largest decrease in all metabolite pool sizes seen by any glutamatergic ligand we have studied. Quinolinic acid and KynA produced similar acute metabolic responses, which were unlike those to TZG or CGS 19755, but similar to that of D-Ser. D-Ser was highly stimulatory of net flux into the Krebs cycle. These data show that the metabolic response to iGluR perturbation in vitro is a sensitive discriminator of function.

Animals↗

PubMed2026

Multi-omics reveals that burdock seed aglycone alleviates renal fibrosis by restoring mitochondrial oxidative phosphorylation function.

Renal fibrosis (RF), a common pathological process driving chronic kidney disease (CKD) progression to end-stage renal failure, is closely associated with oxidative phosphorylation (OXPHOS). Arctigenin (ATG), the main active component of burdock seed, exhibits anti-inflammatory and anti-fibrotic activities, but its mechanisms in RF treatment remain unclear. Here, we performed integrated transcriptomic and proteomic analyses to identify key targets and pathways of ATG in a unilateral ureteral obstruction-induced rat RF model. Multi-omics enrichment analysis revealed that NDUFS8 and NDUFS2 were the core targets of ATG, with the OXPHOS pathway as the central intersecting pathway. Our results suggest that ATG exerts anti-renal fibrosis effects by targeting the OXPHOS pathway to inhibit excessive reactive oxygen species production and oxidative stress. SIGNIFICANCE: Chronic kidney disease (CKD) continues to impose an escalating global health and socioeconomic burden, while renal fibrosis (RF), as the convergent pathological endpoint of virtually all progressive nephropathies, remains the principal determinant of irreversible renal failure and adverse clinical outcomes. Despite extensive efforts to develop antifibrotic therapies, effective clinical interventions remain elusive, largely due to the complex and multifactorial nature of RF pathogenesis. In this study, we employed an integrated multi-omics framework encompassing transcriptomics, proteomics, and metabolomics to systematically decipher the antifibrotic mechanism of arctigenin (ATG), a bioactive natural compound derived from traditional Chinese medicine. Our findings identify mitochondrial oxidative phosphorylation as the pivotal regulatory axis underlying the renoprotective effects of ATG and further establish key catalytic subunits of mitochondrial complex I as its direct molecular targets. Mechanistically, ATG not only restores complex I activity and reprograms mitochondrial energy metabolism but also preserves the intracellular stability and localization of these subunits, thereby preventing their aberrant release-mediated inflammatory activation and disrupting the self-perpetuating cycle linking metabolic dysfunction, inflammation, and fibrosis progression. Beyond revealing a previously unrecognized dual mechanism integrating metabolic and inflammatory regulation, this study provides compelling evidence that mitochondrial dysfunction is not merely a secondary consequence of tissue injury but a fundamental driver of fibrotic remodeling. Importantly, our work highlights the translational potential of natural product-based mitochondrial interventions for CKD treatment and supports a broader conceptual shift toward metabolism-centered therapeutic strategies for chronic fibrotic diseases. Given the central role of mitochondrial dysfunction across multiple organs, these findings may also have far-reaching implications for the treatment of systemic fibrosis-related disorders beyond the kidney.

Animals↗

PubMed2026

Pseudomonas aeruginosa adaptation and persistence in the aspergilloma microbiome revealed by integrated multi-omics.

Chronic pulmonary aspergillosis involves the formation of a fungal ball (aspergilloma) in lung cavities. Pseudomonas aeruginosa commonly co-colonizes these lesions; however, the in vivo mechanisms underlying its persistence are unknown. Using a multi-omics approach on resected aspergillomas, we defined the genomic, transcriptional, and metabolic adaptations of P. aeruginosa within this polymicrobial niche. We reconstructed high-quality P. aeruginosa genomes and identified a conserved core genome, along with accessory genes for secondary metabolism, virulence, and antimicrobial resistance. Phylogenomics revealed heterogeneous evolutionary paths among co-colonizing strains. Metatranscriptomics showed stark physiological heterogeneity, from metabolically aggressive to stress-adapted states. High expression of phenazine, quorum-sensing (PQS), siderophore, and secretion-system operons was corroborated by metabolomic detection of phenazine-1-carboxylic acid and 2-heptylquinolin-4(1H)-one, confirming active bacterial antagonism in vivo. Concurrent Aspergillus fumigatus transcriptomics revealed the activation of oxidative stress responses, secondary metabolism (eg fumagillin), and iron scavenging, demonstrating reciprocal competition. Host transcriptomics revealed patient-specific immune signatures that correlated with the metabolic activity of the co-colonizers. This work provides an integrated systems-level analysis of the tri-kingdom aspergilloma ecosystem. P. aeruginosa persistence is driven by genomic plasticity and context-dependent expression of competitive pathways, shaped within a chronic inflammatory environment. These findings redefine aspergillomas as active polymicrobial consortia, establishing a framework for targeting resilient microbial communities in chronic lung disease.

Multiomics↗

PubMed2005

An integrative metabolism approach identifies stearoyl-CoA desaturase as a target for an arachidonate-enriched diet.

Epidemiological studies have correlated diets containing higher intakes of PUFA with lower rates of chronic metabolic diseases. The molecular mechanisms regulated by the consumption of PUFA were examined by using an integrative metabolism approach assaying the liver transcriptome and lipid-metabolome of mice fed a control diet, an arachidonate (AA)-enriched fungal oil, an eicosapentaenoic (EPA)/docosahexaenoic (DHA)-enriched fish oil, or a combination of the two oils. Hepatic gene transcription and fatty acid (FA) metabolism were significantly altered by diets enriched with AA, as revealed by global error assessment and singular value decomposition (SVD) analysis, respectively. SVD analysis of the lipid data, reinforced with transcriptomics, suggests that the chronic feeding of AA modulates molecular endpoints similar to those previously reported in the obesity-resistant SCD1-/- mouse, namely, genes involved in lipid oxidation/synthesis and the significant changes in FA metabolism stemming from a repressed SCD1 activity. Specifically, the total levels and FA composition of several phospholipid (PL) species were significantly changed, with phosphatidylcholine (PC) demonstrating the greatest alterations. Reduced PC levels were linked to decreased expression of enzymes in PC biosynthesis (choline kinase, -2.2-fold; glycerol-3-phosphate acyltransferase, -2.0-fold). Alterations in PL-FA composition were related to decreased expression of FA biosynthetic genes [fatty acid synthetase, -3.7-fold; stearoyl-CoA desaturase-1 (SCD1), -1.8-fold]. Lower hepatic SCD1 gene expression levels were reflected in various aspects of FA metabolism through increased concentrations of palmitic (fungal oil, +45%; combination, +106%) and stearic acids (fungal oil, +60%; combination, +63%) in PC. Importantly, an integrated approach showed that these effects were not attenuated by the addition of an EPA/DHA-enriched fish oil, thereby identifying a previously unrecognized and distinct role for AA in the regulation of hepatic lipid metabolism.

Animals↗