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Integrated salivary proteomic and metabolomic analyses reveal molecular characterization and novel biomarker panels of chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder characterized by chronic inflammation, oxidative stress, and metabolic dysregulation. The lack of convenient and easily-accessible non-invasive diagnostic approaches remains a major clinical challenge. This study applied an integrated saliva-based proteomic and untargeted metabolomic strategy to identify potential biomarkers for COPD classification. Comprehensive multi-omics analyses identified 225 differentially abundant proteins and 60 differentially abundant metabolites between patients with COPD and healthy controls, including 24 biologically relevant endogenous metabolites. Functional enrichment analyses revealed pronounced dysregulation of mitochondrial energy metabolism, redox homeostasis, lipid remodeling, and inflammatory-related pathways in COPD. By integrating salivary proteomic and metabolomic biomarkers, a stepwise feature selection combined with LASSO logistic regression was used to construct diagnostic models, yielding an optimized biomarker panel consisting of 11 proteins and 2 endogenous metabolites. This integrated model achieved excellent diagnostic performance, with an area under the ROC curve of 0.96. Collectively, these findings demonstrate that integrated salivary proteomic and metabolomic profiling provides a robust, non-invasive approach for COPD classification and offers a promising foundation for the development of biosensor-based diagnostic platforms and early disease detection. SIGNIFICANCE: Chronic obstructive pulmonary disease (COPD) remains a major global health burden. Current diagnostic approaches rely largely on spirometry and clinical assessment, which are limited in sensitivity for early-stage disease and unsuitable for large-scale screening. This study employs an integrated saliva-based proteomic and metabolomic strategy to identify non-invasive biomarkers for COPD classification. Our findings reveal coordinated dysregulation of mitochondrial energy metabolism, redox homeostasis, and lipid remodeling in COPD, highlighting the interconnected roles of metabolic reprogramming, oxidative stress, and inflammation in disease pathophysiology. Notably, a robust diagnostic panel comprising 11 proteins and 2 endogenous metabolites was established, achieving excellent classification performance (AUC of 0.96). To our knowledge, the integrated application of salivary proteomics and metabolomics for COPD diagnosis remains largely unexplored, underscoring the significance and translational potential of our findings.

Humans↗

Integration of metabolomics and proteomics reveals the toxicological mechanisms of environmentally relevant concentrations of cadmium on juvenile rockfish (Sebastes schlegelii).

As a highly toxic heavy metal, cadmium (Cd) is widely distributed in the coastal environments of the Bohai Sea, posing significant ecological and health risks. This is of particular concern for Sebastes schlegelii, a rockfish species commonly found along the Bohai coast and consumed by local populations. In this study, juvenile S. schlegelii were randomly assigned to three groups (control, 5 and 50&#xa0;&#x3bc;g/L Cd) for a 14-day exposure period, followed by analysis of Cd bioaccumulation, as well as metabolomic and proteomic profiling. ICP-MS analysis indicated dose-dependent Cd bioaccumulation in the whole body, with 0.11&#xa0;&#xb1;&#xa0;0.07&#xa0;&#x3bc;g/g dry weight in the 5&#xa0;&#x3bc;g/L group and 0.38&#xa0;&#xb1;&#xa0;0.09&#xa0;&#x3bc;g/g dry weight in the 50&#xa0;&#x3bc;g/L group (9.5-fold higher than the control, p&#xa0;<&#xa0;0.05). An iTRAQ-based proteomic analysis determined 168 differentially expressed proteins, while 1H NMR-based metabolomic profiling identified 34 metabolites with significant alterations. Integrated analysis of the proteomic and metabolomic data provided insights into the molecular responses of juvenile rockfish to Cd exposure. Specifically, metabolomic results indicated significant alterations in key metabolites, including lactate, phosphocholine, adenosine triphosphate, alanine, and inosine in the Cd-treated groups. Proteomic analysis further suggested that Cd exposure was associated with immune and oxidative stress responses, neurotoxicity, cellular damage, and disruptions in critical metabolic pathways, such as glycolysis, the tricarboxylic acid cycle, amino acid and lipid metabolism. Overall, this study demonstrates the utility of integrating proteomics and metabolomics to characterize molecular responses to Cd stress in juvenile S. schlegelii.

Animals↗

Integrated landscape of salivary metagenome and multi-biofluid metabolome characterizes a microbial-metabolic axis in upper gastrointestinal cancer progression.

BACKGROUND: Upper gastrointestinal cancer (UGIC) imposes a major global health burden, yet the stage-specific molecular changes along the microbial-metabolic axis remain limited understood. We aimed to delineate this molecular landscape across UGIC progression and evaluate its potential as non-invasive methods for precision screening. RESULTS: Derived from a multi-center population-based UGIC screening program, we enrolled 420 individuals, stratified into normal, low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and UGIC (n&#x2009;=&#x2009;105 per group). Integrated salivary metagenomics and paired salivary/plasma metabolomics were performed to capture local and systemic dysregulation. We uncovered distinct stage-specific divergence during UGIC progression: profound remodeling of the salivary microbiota (104 differential species) and salivary metabolomics (80 differential metabolites) initiated early at the LGIN stage, whereas plasma metabolic dysregulation (40 differential metabolites) peaked significantly later at the HGIN stage. Integrative analysis revealed salivary microbiota related more closely with salivary metabolome than plasma metabolome. Moreover, statistical evidence suggested that dysbiotic salivary microbiota was associated with altered lysine- and tryptophan-related catabolic pathways converging on Acetyl-CoA-related metabolic nodes, supporting a potential metabolic mechanism in precancerous lesions. Finally, the discriminative model integrating metagenomic and metabolomic markers demonstrated promising diagnostic performance in distinguishing these precancerous lesions (LGIN: area under the curve [AUC]&#x2009;=&#x2009;0.83; HGIN: AUC&#x2009;=&#x2009;0.77) and UGIC (AUC&#x2009;=&#x2009;0.76) from normal. CONCLUSION: This study characterizes a stage-specific microbial-metabolic axis that facilitates the comprehensive understanding of UGIC pathogenesis. These multi-biofluid signatures offer a promising non-invasive triage strategy for detecting precancerous lesions and optimizing endoscopic resource allocation. Video Abstract.

Female↗

Plasma Exosome Metabolomics Reveal Stage-Specific Alterations in Elderly Women With Premetabolic and Metabolic Syndrome.

BACKGROUND: Metabolic syndrome (MetS) is a chronic disorder that poses a major threat to global health. Exosomes have emerged as promising biomarkers for diagnosing and monitoring chronic diseases. However, stage-specific alterations in the exosomal metabolome during MetS development remain poorly understood. This study aimed to characterize the plasma exosomal metabolome and explore candidate exosomal biomarkers in individuals with MetS. METHODS: This study included 20 patients with MetS, 23 individuals with pre-MetS, and 45 healthy controls. Plasma exosomes were isolated and analyzed using untargeted liquid chromatography-mass spectrometry-based metabolomics. Differential metabolites were defined by a dual-threshold, that is, p&#x2009;<&#x2009;0.05 from t-test and variable importance in projection >&#x2009;1 from partial least squares discriminant analysis, with fold change indicating their expression changes. Further, we employed machine learning algorithms to predict MetS status. RESULTS: We identified 27 differential metabolites between the pre-MetS and control groups, mainly enriched in histidine metabolism and the tricarboxylic acid cycle. Of these, 12 metabolites were upregulated, and 15 were downregulated, with 1-methylhistidine and isocitrate playing central regulatory roles. Comparison between the MetS and control groups revealed 45 differentially expressed metabolites, mainly enriched in thiamine metabolism, including 13 upregulated and 32 downregulated. In the pre-MetS group, cladribine showed the highest area under the curve (AUC) (0.743, p&#x2009;<&#x2009;0.05), whereas 3-methylxanthine yielded the largest AUC (0.714, p&#x2009;<&#x2009;0.05) in the MetS group. CONCLUSION: Our study characterized stage-dependent alterations in the plasma exosome-derived metabolome in MetS and suggests that exosomal metabolomics may provide complementary molecular information on early MetS metabolic perturbations.

exosomal features↗

Metabolomic differences in the Ophiura sarsii complex from the Yellow Sea Cold Water Mass and Bering Sea Cold Pool.

Metabolomics provides a functional readout of cellular physiology and can reveal metabolite-level differences associated with environmental and evolutionary contexts. Here, we used GC-MS- and LC-MS-based metabolomics to characterize metabolic profiles of the Ophiura sarsii complex from the Yellow Sea Cold Water Mass (YSCWM) and the Bering Sea Cold Pool (BSCP). This metabolomics analysis identified 398 LC-MS/MS and 87 GC-MS/MS differential metabolites (DEMs). Marked metabolic differences were observed between the two taxa, involving antioxidant-related metabolites, central carbon-related intermediates, osmolyte-associated compounds, and membrane lipid components. O. sarsii vadicola from the YSCWM showed higher levels of glutathione, glucose, citric acid, D-ribulose 5-phosphate, and unsaturated lipid-related metabolites, indicating differences in antioxidant-related and energy-associated metabolic profiles. By contrast, O. sarsii from the BSCP was characterized by higher levels of sugar alcohols, particularly myo-inositol, together with differences in membrane lipid-associated metabolites. These results provide metabolomics-based evidence for metabolite-level physiological differences between two members of the O. sarsii complex sampled from the Yellow Sea Cold Water Mass and the Bering Sea Cold Pool, while the relative contributions of lineage divergence and site-specific environmental variation remain to be tested experimentally.

Metabolomics↗

Mass spectrometry-based metabolomics.

This review presents an overview of the dynamically developing field of mass spectrometry-based metabolomics. Metabolomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. These numerous analytes have very diverse physico-chemical properties and occur at different abundance levels. Consequently, comprehensive metabolomics investigations are primarily a challenge for analytical chemistry and specifically mass spectrometry has vast potential as a tool for this type of investigation. Metabolomics require special approaches for sample preparation, separation, and mass spectrometric analysis. Current examples of those approaches are described in this review. It primarily focuses on metabolic fingerprinting, a technique that analyzes all detectable analytes in a given sample with subsequent classification of samples and identification of differentially expressed metabolites, which define the sample classes. To perform this complex task, data analysis tools, metabolite libraries, and databases are required. Therefore, recent advances in metabolomics bioinformatics are also discussed.

Databases, Protein↗

Metabolomics and its role in understanding cellular responses in plants.

A natural shift is taking place in the approaches being adopted by plant scientists in response to the accessibility of systems-based technology platforms. Metabolomics is one such field, which involves a comprehensive non-biased analysis of metabolites in a given cell at a specific time. This review briefly introduces the emerging field and a range of analytical techniques that are most useful in metabolomics when combined with computational approaches in data analyses. Using cases from Arabidopsis and other selected plant systems, this review highlights how information can be integrated from metabolomics and other functional genomics platforms to obtain a global picture of plant cellular responses. We discuss how metabolomics is enabling large-scale and parallel interrogation of cell states under different stages of development and defined environmental conditions to uncover novel interactions among various pathways. Finally, we discuss selected applications of metabolomics.

Biochemistry↗

Design of experiments: an efficient strategy to identify factors influencing extraction and derivatization of Arabidopsis thaliana samples in metabolomic studies with gas chromatography/mass spectrometry.

The usual aim in metabolomic studies is to quantify the entire metabolome of each of a series of biological samples. To do this for complex biological matrices, e.g., plant tissues, efficient and reproducible extraction protocols must be developed. However, derivatization protocols must also be developed if GC/MS (one of the mostly widely used analytical methods for metabolomics) is involved. The aim of this study was to investigate how different chemical and physical factors (extraction solvent, derivatization reagents, and temperature) affect the extraction and derivatization of the metabolome from leaves of the plant Arabidopsis thaliana. Using design of experiment procedures, variation was systematically introduced, and the effects of this variation were analyzed using regression models. The results show that this approach allows a reliable protocol for metabolomic analysis of Arabidopsis to be determined with a relatively limited number of experiments. Following two different investigations an extraction and derivatization protocol was chosen. Further, the reproducibility of the analysis of 66 endogenous compounds was investigated, and it was shown that both hydrophilic and lipophilic compounds were detected with high reproducibility.

Arabidopsis↗

Metabolomics technology and bioinformatics.

Metabolomics is the global analysis of all or a large number of cellular metabolites. Like other functional genomics research, metabolomics generates large amounts of data. Handling, processing and analysis of this data is a clear challenge and requires specialized mathematical, statistical and bioinformatics tools. Metabolomics needs for bioinformatics span through data and information management, raw analytical data processing, metabolomics standards and ontology, statistical analysis and data mining, data integration and mathematical modelling of metabolic networks within a framework of systems biology. The major approaches in metabolomics, along with the modern analytical tools used for data generation, are reviewed in the context of these specific bioinformatics needs.

Animals↗

Nutrient-gene interaction: tracer-based metabolomics.

Understanding nutrient-gene interaction requires tools for both the study of nutrigenomics and the characterization of phenotype. Metabolomics or metabolite profiling is a powerful tool for characterizing metabolic phenotype, and tracer-based metabolomics is a subset of metabolomics that focuses on metabolite distribution and flux determination using tracers. In this review, the characterizations of metabolic phenotype by metabolite profiling and by metabolic flux measurements are compared. The rationale and methodologies of tracer-based metabolomics are explained. Tracer-based metabolomics provides a relational database of metabolites linked by the relationship of shared metabolic pathways, common substrates, and cofactors. Such a collection of flux measurements provides precise and accurate information on the operation of the cellular metabolic network and its response to genetic and nutrient environment changes. Nutrient-gene interaction can be studied using the concept of constraint-based modeling, which states that the observed metabolic phenotype is a consequence of constraints from genetic factors and the nutrient environment. Thus, genetic inheritance (genomic constraints) confers a wide range of possible phenotypes whereas selection by metabolic (structural and pathway relationship) and environmental (physical environment and nutrient availability) constraints determines the final observed phenotype. The study of the contribution from nutrient and genetic factors to the survival advantage of cancer cells using flux measurements is a critical first step in our understanding of the relationship between nutrient intake and cancer risk.

Diet↗

Metabolomics in monitoring kidney transplants.

PURPOSE OF REVIEW: The success of any given kidney transplant is closely tied to the ability to monitor patients and responsively change their medications. Transplant monitoring is still, however, dependent on relatively old technologies: serum creatinine levels, urine output, blood pressure, blood glucose and histopathology of biopsy samples. These older technologies do not offer sufficient specificity, sensitivity, or accuracy to allow appropriate and timely interventions. Using the tools of genomics, proteomics and metabolomics new biomarkers are being found that may greatly improve transplant monitoring and significantly enhance graft survival. This review describes the basic principles of metabolomics and summarizes a number of recent developments in the use of metabolite biomarkers and metabolomics to monitor kidney transplants. RECENT FINDINGS: Changes in the concentration profiles of a number of small molecule metabolites found in either blood or urine can be used to localize organ damage, identify organs at risk of rejection, assess organs suffering from ischemia-repurfusion injury or identify organs that have been damaged by immunosuppressive drugs. SUMMARY: The application of metabolomics to kidney transplant monitoring is still very much in its infancy. Nevertheless, there are a number of easily measured metabolites in both urine and serum that can provide reliable indications of organ function, organ injury, and immunosuppressive drug toxicity. As the field matures, metabolomics may eventually lead to the development of rapid, inexpensive and noninvasive approaches to assist clinicians in monitoring kidney transplants.

Biomarkers↗

Nutritional effects on blood pressure.

PURPOSE OF REVIEW: There has not been a thorough recent evaluation of the nutritional effects on blood pressure. Apart from outstanding clinical trials like Dietary Approaches to Stop Hypertension, there have been controversial papers on a number of factors influencing blood pressure. This paper is a systematic review of the current literature as it relates to hypertension. RECENT FINDINGS: Results from many meta-analyses and well controlled clinical trials on the effects of a variety of nutritional factors are presented in this review. Evidence suggests that dietary sodium intake needs reduction. There is a seemingly inverse relationship between protein intake and blood pressure, but data are inconclusive. High monounsaturated fat and fish oil appear to be beneficial. Several studies on dietary fiber indicate that the strongest evidence for blood pressure lowering effects is in hypertensive as opposed to normotensive participants. Vegetarians seem to have lower levels of hypertension and cardiovascular disease risk. Low carbohydrate diets show short-term beneficial effects but are not sustained. High levels of potassium, magnesium, calcium and soy seem to have some benefit, but results remain inconclusive. Weight reduction positively impacts blood pressure. SUMMARY: More compelling research defining specific factors is needed to inform the public as to steps needed to reduce blood pressure and improve cardiovascular risk.

Blood Pressure↗

Metabolomics: the principles and potential applications to transplantation.

This review provides a summary of the applications and potential applications of metabolite profiling (i.e. metabolomics) in monitoring organ transplants. While the concept of metabolomics is relatively new to organ transplantation, the idea of measuring metabolites as a quick, noninvasive probe of organ function is not. Indeed, metabolite measurements of serum creatinine have long been used to assess pre- and post-operative organ function. Over the past 10 years, a number of lesser-known, organ-specific metabolites have also been shown to be good diagnostic indicators of both organ function and viability. In general, metabolomics offers a complementary picture to what can be revealed via techniques based on genomics, proteomics or histology. Because metabolic changes typically happen within seconds or minutes after an 'event', whereas some transcript, protein abundance or tissue changes may take place over days or weeks, metabolomic measurements may offer a particularly useful and inexpensive diagnostic tool to monitor donor organ viability or to detect organ rejection. The excitement associated with metabolomics, however, must be tempered by the fact that the technology for rapid metabolite identification is still in its infancy, and that metabolites are but one part of a very complex picture pertaining to organ function.

Biochemical Phenomena↗

Tryptophan-driven metabolomic shift in Acidobacteriaceae reveals phytohormones and antifungal metabolites.

UNLABELLED: Acidobacteriota is one of the most abundant phyla in soils and has recently attracted attention for its potential role in promoting phytosanitary benefits. The metabolomic capabilities of this phylum remain poorly characterized, with few experimentally confirmed metabolites described. To address these gaps, we combined untargeted metabolomic profiling with comparative genomic analyses to explore the functional potential of newly isolated Acidobacteriaceae strains. Genome mining across the Acidobacteriota phylum revealed the presence and taxon-specific enrichment of genes associated with plant-related traits, including phytohormone biosynthesis. In parallel, metabolomic analyses of OSMAC-derived extracts uncovered pronounced condition-dependent metabolic variation. Tryptophan supplementation was associated with marked metabolomic reprogramming, including changes in indole-derived metabolites, such as indole-3-acetic acid. Subsequent analyses linked these metabolic shifts to the suppression of phytopathogenic fungi and enabled the identification of malassezindoles and pityriacitrins as active compounds, confirmed by structure elucidation using NMR spectroscopy. Overall, these findings shed light on the previously unexplored metabolic potential of the Acidobacteriota phylum, emphasizing its ecological importance for phytosanitary applications. IMPORTANCE: Despite their ubiquity and genomic diversity, the functional metabolism of members of the Acidobacteriota has largely remained uncharacterized. This study links genomic predictions to experimentally verified metabolomic outputs of Acidobacteriaceae, demonstrating tryptophan-responsive metabolic shifts translating to phytohormones and metabolites suppressing fungal growth. Our work underscores the emerging role of Acidobacteriota as important contributors to soil ecosystem functioning and plant-microbe interactions.

Acidobacteriota↗

MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics.

BACKGROUND: The genome sequencing projects have shown our limited knowledge regarding gene function, e.g. S. cerevisiae has 5-6,000 genes of which nearly 1,000 have an uncertain function. Their gross influence on the behaviour of the cell can be observed using large-scale metabolomic studies. The metabolomic data produced need to be structured and annotated in a machine-usable form to facilitate the exploration of the hidden links between the genes and their functions. DESCRIPTION: MeMo is a formal model for representing metabolomic data and the associated metadata. Two predominant platforms (SQL and XML) are used to encode the model. MeMo has been implemented as a relational database using a hybrid approach combining the advantages of the two technologies. It represents a practical solution for handling the sheer volume and complexity of the metabolomic data effectively and efficiently. The MeMo model and the associated software are available at http://dbkgroup.org/memo/. CONCLUSION: The maturity of relational database technology is used to support efficient data processing. The scalability and self-descriptiveness of XML are used to simplify the relational schema and facilitate the extensibility of the model necessitated by the creation of new experimental techniques. Special consideration is given to data integration issues as part of the systems biology agenda. MeMo has been physically integrated and cross-linked to related metabolomic and genomic databases. Semantic integration with other relevant databases has been supported through ontological annotation. Compatibility with other data formats is supported by automatic conversion.

Computer Simulation↗

Pattern recognition and biomarker validation using quantitative 1H-NMR-based metabolomics.

The collection of global metabolic data and their interpretation (both spectral and biochemical) using modern spectroscopic techniques and appropriate statistical approaches, are known as 'metabolic profiling', 'metabonomics' or 'metabolomics'. This review addresses 1H-nuclear magnetic resonance (NMR)-based metabolomic principles and their application in biomedical science, with special emphasis on their potential in translational research in transplantation, oncology, and drug toxicity or discovery. Various steps in metabolomics analysis are described in order to illustrate the types of biological samples, their respective handling and preparation for 1H-NMR analysis; provide a rationale for using pattern-recognition techniques (spectral database concept) versus quantitative 1H-NMR-based metabolomics (metabolite database concept); and identify necessary technological and logistical future developments that will allow 1H-NMR-based metabolomics to become an established tool in biomedical research and patient care.

Animals↗

Proteomic and metabolomic profiling reveals dysregulation of immune states, mucin-type glycosylation and steroid metabolism in extramammary Paget's disease.

BACKGROUND: Extramammary Paget's disease is a rare cutaneous adenocarcinoma characterized by mucin-rich Paget cells and chronic inflammation, yet its molecular basis remains unclear. OBJECTIVE: To systematically characterize the proteomic and metabolomic landscape of EMPD, uncover immune heterogeneity, and identify molecular pathways underlying tumor progression and microenvironment remodeling. METHODS: We performed integrated proteomic and metabolomic analyses on 92 male tumor patients and 30 healthy controls, identifying 10,217 proteins and 1466 metabolites. RESULTS: Extramammary Paget's disease lesions exhibited broad activation of inflammatory pathways. Immune profiling further uncovered substantial inflammatory heterogeneity, delineating immune-cold and immune-hot subtypes, with the latter associated with stronger invasive potential. Aberrant mucin-type glycosylation was also prominent, featuring Tn-modified MUC1 and MUC5AC accompanied by elevated GALNT7, GALNT6, GALNT4, and ST6GAL1, which correlated with inflammatory intensity. Metabolomic data demonstrated elevated levels of testosterone, dehydroepiandrosterone, and related intermediates in tumor tissues, indicating an androgen-enriched metabolic profile in extramammary Paget's disease. CONCLUSION: These findings reveal immune, glycoproteomic, and metabolomic pathways in extramammary Paget's disease pathogenesis and provide novel insights for molecular classification and therapeutic targeting.

Humans↗

Stepping out of the dark: how metabolomics shed light on fungal biology.

Metabolomics, a critical tool for analyzing small-molecule metabolites, integrates with genomics, transcriptomics, and proteomics to provide a systems-level understanding of fungal biology. By mapping metabolic networks, it elucidates regulatory mechanisms driving physiological and ecological adaptations. In fungal pathogenesis, metabolomics reveals host-pathogen dynamics, identifying virulence factors like gliotoxin in Aspergillus fumigatus and metabolic shifts, such as glyoxylate cycle upregulation in Candida albicans. Ecologically, it highlights fungal responses to abiotic stressors, including osmolyte production like trehalose, enhancing survival in extreme environments. These insights highlight metabolomics' role in decoding fungal persistence and niche colonization. In drug discovery, it aids target identification by profiling biosynthetic pathways, supporting novel antifungal and nanostructured therapy development. Combined with multi-omics, metabolomics advances insights into fungal pathogenesis, ecological interactions, and therapeutic innovation, offering translational potential for addressing antifungal resistance and improving treatment outcomes for fungal infections. Its progress shed light on complex fungal molecular profiles, advancing discovery and innovation in fungal biology.

Metabolomics↗