Gut microbiota metabolic pathways: Key players in knee osteoarthritis development.

OBJECTIVE: To confirm the causality of gut microbiota pathway abundance and knee osteoarthritis (KOA). METHODS: Microbial metabolic pathways were taken as exposures, with data from the Dutch Microbiome Project (DMP). Data on KOA from the UK Biobank were utilized as endpoints. In addition, we extracted significant and independent single nucleotide polymorphisms as instrumental variables. Two-sample Mendelian randomization (MR) analysis was applied to explore the causal relationship between gut microbiota pathway abundance and KOA, and MR-Egger and weighted median were used as additional validation of the MR results. Meanwhile, Cochran Q, MR-Egger intercept, MR-PRESSO, and leave-one-out were used to perform sensitivity analyses on the MR results. RESULTS: MR results showed that enterobactin biosynthesis, diacylglycerol biosynthesis I, Clostridium acetobutylicum acidogenic fermentation, glyoxylate bypass and tricarboxylic acid cycle were the risk factors for KOA. (OR = 1.13,95%CI = 1.04-1.23;OR = 1.12,95%CI = 1.04-1.20;OR = 1.14,95%CI = 1.04-1.26; OR = 1.06,95%CI = 1.00-1.12) However, adenosylcobalamin salvage from cobinamide I, hexitol fermentation to lactate formate ethanol and acetate, purine nucleotides degradation II aerobic, L tryptophan biosynthesis and inosine 5 phosphate biosynthesis III pathway showed significant protection against KOA. (OR = 0.93,95%CI = 0.86-1.00;OR = 0.94,95%CI = 0.88-1.00;OR = 0.91,95%CI = 0.86-0.97;OR = 0.95,95%CI = 0.92-0.99; OR = 0.91, 95%CI = 0.85-0.98) Further multiplicity and sensitivity analyses demonstrated the robustness of the results. CONCLUSION: Our study identified specific metabolic pathways in gut microbiota that promote or inhibit KOA, which provides the most substantial evidence-based medical evidence for the pathogenesis and prevention of KOA.

Serum metabolomics analysis of malnutrition in patients with gastric cancer: a cross sectional study.

BACKGROUND: Although malnutrition is common in cancer patients, its molecular mechanisms has not been fully clarified. This study aims to identify significantly differential metabolites, match the corresponding metabolic pathways, and develop a predictive model of malnutrition in patients with gastric cancer. METHODS: In this cross-sectional study, we applied non-targeted metabolomics using liquid chromatography-mass spectrometry to explore the serum fingerprinting of malnutrition in patients with gastric cancer. Malnutrition-specific differential metabolites were identified by orthogonal partial least-squares discriminant analysis and t-test and matched with the Human Metabolome Database and the LIPID Metabolites and Pathways Strategy. We matched the corresponding metabolic pathways of malnutrition using pathway analysis at the MetaboAnalyst 5.0. We used random forest analyses to establish the predictive model. RESULTS: We recruited 220 malnourished and 198 non-malnourished patients with gastric cancer. The intensities of 25 annotated significantly differential metabolites were lower in patients with malnutrition than those without, while two others were higher in patients with malnutrition than those without, including newly identified significantly differential metabolites such as indoleacrylic acid and lysophosphatidylcholine(18:3/0:0). We matched eight metabolic pathways associated with malnutrition, including aminoacyl-tRNA biosynthesis, tryptophan metabolism, and glycerophospholipid metabolism. We established a predictive model with an area under the curve of 0.702 (95% CI: 0.651-0.768) based on four annotated significantly differential metabolites, namely indoleacrylic acid, lysophosphatidylcholine(18:3/0:0), L-tryptophan, and lysophosphatidylcholine(20:3/0:0). CONCLUSIONS: We identified 27 specific differential metabolites of malnutrition in malnourished compared to non-malnourished patients with gastric cancer. We also matched eight corresponding metabolic pathways and developed a predictive model. These findings provide supportive data to better understand molecular mechanisms of malnutrition in patients with gastric cancer and new strategies for the prediction, diagnosis, prevention, and treatment for those malnourished.

[Research progress in bioconversion of C1 gases into oleochemicals].

The utilization of C1 gases (CH4, CO2, and CO) for the production of oleochemicals applied in the energy and platform chemicals through microbial engineering has emerged as a promising approach to reduce greenhouse gas emissions and decrease dependence on fossil fuel. C1 gas-utilizing microorganisms, such as methanotrophs, microalgae, and acetogens, are capable of converting C1 gases as the sole substrates for cell growth and oleochemical synthesis with different carbon-chain lengths, garnering considerable attention from both scientific community and industry field for sustainable biomanufacturing. This paper comprehensively reviews recent advancements in the development of engineered cell factories utilizing C1 gases for the production of oleochemicals, elucidating the key metabolic pathways of biosynthesis. Furthermore, this paper highlights the research progress and prospects in optimizing gene expression, metabolic pathway reconstruction, and fermentation conditions for efficient oleochemical production from C1 gases. This review provides valuable insights and guidance for the efficient utilization of C1 gases and the development of carbon cycling-based bioeconomy.

Metabolic network connectivity disturbances in Parkinson's disease: a novel imaging biomarker.

The diagnosis of Parkinson's Disease (PD) presents ongoing challenges. Advances in imaging techniques like 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) have highlighted metabolic alterations in PD, yet the dynamic network interactions within the metabolic connectome remain elusive. To this end, we examined a dataset comprising 49 PD patients and 49 healthy controls. By employing a personalized metabolic connectome approach, we assessed both within- and between-network connectivities using Standard Uptake Value (SUV) and Jensen-Shannon Divergence Similarity Estimation (JSSE). A random forest algorithm was utilized to pinpoint key neuroimaging features differentiating PD from healthy states. Specifically, the results revealed heightened internetwork connectivity in PD, specifically within the somatomotor (SMN) and frontoparietal (FPN) networks, persisting after multiple comparison corrections (P < 0.05, Bonferroni adjusted for 10% and 20% sparsity). This altered connectivity effectively distinguished PD patients from healthy individuals. Notably, this study utilizes 18F-FDG PET imaging to map individual metabolic networks, revealing enhanced connectivity in the SMN and FPN among PD patients. This enhanced connectivity may serve as a promising imaging biomarker, offering a valuable asset for early PD detection.

Evolution of the biochemistry underpinning purine alkaloid metabolism in plants.

Purine alkaloids are naturally occurring nitrogenous methylated derivatives of purine nucleotide degradation products, having essential roles in medicine, food and various other aspects of our daily lives. They are generated through convergent evolution in different plant species. The pivotal reaction steps within the purine alkaloid metabolic pathways have been largely elucidated, and the convergent evolution of purine alkaloids has been substantiated through bioinformatic, biochemical and other research perspectives within S-adenosyl-ʟ-methionine-dependent N-methyltransferases. Currently, the biological and ecological roles of purine alkaloids, further refinement of the purine alkaloid metabolic pathways and the investigation of purine alkaloid adaptive evolutionary mechanisms continue to attract widespread research interest. The exploration of the purine alkaloid metabolic pathways also enhances our comprehension of the biochemical mechanism, providing insights into inter-species interactions and adaptive evolution and offering potential value in drug development and agricultural applications. Here, we review the progress of research in the distribution, metabolic pathway elucidation and regulation, evolutionary mechanism and ecological roles of purine alkaloids in plants. The opportunities and challenges involved in elucidating the biochemical basis and evolutionary mechanisms of the purine alkaloid metabolic pathways, as well as other research aspects, are also discussed. This article is part of the theme issue 'The evolution of plant meta-bolism'.

Evolution of plant metabolism: the state-of-the-art.

Immense chemical diversity is one of the hallmark features of plants. This chemo-diversity is mainly underpinned by a highly complex and biodiverse biochemical machinery. Plant metabolic enzymes originated and were inherited from their eukaryotic and prokaryotic ancestors and further diversified by the unprecedentedly high rates of gene duplication and functionalization experienced in land plants. Unlike prokaryotic microbes, which display frequent horizontal gene transfer events and multiple inputs of energy and organic carbon, land plants predominantly rely on organic carbon generated from CO2 and have experienced relatively few gene transfers during their recent evolutionary history. As such, plant metabolic networks have evolved in a stepwise manner using existing networks as a starting point and under various evolutionary constraints. That said, until recently, the evolution of only a handful of metabolic traits had been extensively investigated and as such, the evolution of metabolism has received a fraction of the attention of, the evolution of development, for example. Advances in metabolomics and next-generation sequencing have, however, recently led to a deeper understanding of how a wide range of plant primary and specialized (secondary) metabolic pathways have evolved both as a consequence of natural selection and of domestication and crop improvement processes. This article is part of the theme issue 'The evolution of plant metabolism'.

Applications of ancestral sequence reconstruction for understanding the evolution of plant specialized metabolism.

Studies of enzymes in modern-day plants have documented the diversity of metabolic activities retained by species today but only provide limited insight into how those properties evolved. Ancestral sequence reconstruction (ASR) is an approach that provides statistical estimates of ancient plant enzyme sequences which can then be resurrected to test hypotheses about the evolution of catalytic activities and pathway assembly. Here, I review the insights that have been obtained using ASR to study plant metabolism and highlight important methodological aspects. Overall, studies of resurrected plant enzymes show that (i) exaptation is widespread such that even low or undetectable levels of ancestral activity with a substrate can later become the apparent primary activity of descendant enzymes, (ii) intramolecular epistasis may or may not limit evolutionary paths towards catalytic or substrate preference switches, and (iii) ancient pathway flux often differs from modern-day metabolic networks. These and other insights gained from ASR would not have been possible using only modern-day sequences. Future ASR studies characterizing entire ancestral metabolic networks as well as those that link ancient structures with enzymatic properties should continue to provide novel insights into how the chemical diversity of plants evolved. This article is part of the theme issue 'The evolution of plant metabolism'.

Understanding metabolic diversification in plants: branchpoints in the evolution of specialized metabolism.

Plants are chemical engineers par excellence. Collectively they make a vast array of structurally diverse specialized metabolites. The raw materials for building new pathways (genes encoding biosynthetic enzymes) are commonly recruited directly or indirectly from primary metabolism. Little is known about how new metabolic pathways and networks evolve in plants, or what key nodes contribute to branches that lead to the biosynthesis of diverse chemicals. Here we review the molecular mechanisms underlying the generation of biosynthetic branchpoints. We also consider examples in which new metabolites are formed through the joining of precursor molecules arising from different biosynthetic routes, a scenario that greatly increases both the diversity and complexity of specialized metabolism. Given the emerging importance of metabolic gene clustering in helping to identify new enzymes and pathways, we further cover the significance of biosynthetic gene clusters in relation to metabolic networks and dedicated biosynthetic pathways. In conclusion, an improved understanding of the branchpoints between metabolic pathways will be key in order to be able to predict and illustrate the complex structure of metabolic networks and to better understand the plasticity of plant metabolism. This article is part of the theme issue 'The evolution of plant metabolism'.

Exploring the progression of drug dependence in a methamphetamine self-administration rat model through targeted and non-targeted metabolomics analyses.

Persistent neurochemical and biological disturbances resulting from repeated cycles of drug reward, withdrawal, and relapse contribute to drug dependence. Methamphetamine (MA) is a psychostimulant with substantial abuse potential and neurotoxic effects, primarily affecting monoamine neurotransmitter systems in the brain. In this study, we aimed to explore the progression of drug dependence in rat models of MA self-administration, extinction, and reinstatement through targeted and non-targeted metabolomics analyses. Metabolic profiles were examined in rat plasma during the following phases: after 16 days of MA self-administration (Group M); after 16 days of self-administration followed by 14 days of extinction (Group MS); and after self-administration and extinction followed by a reinstatement injection of MA (Group MSM). Each group of MA self-administration, extinction, and reinstatement induces distinct changes in the metabolic pathways, particularly those related to the TCA cycle, arginine and proline metabolism, and arginine biosynthesis. Additionally, the downregulation of glycerophospholipids and sphingomyelins in Group MSM suggests their potential role in MA reinstatement. These alterations may signify the progressive deterioration of these metabolic pathways, possibly contributing to drug dependence following repeated cycles of drug reward, withdrawal, and relapse. These results provide valuable insights into the metabolic changes associated with MA use at various stages, potentially facilitating the discovery of early diagnostic biomarkers and therapeutic targets for MA use disorders.

Pathway and Production Differences in Branched-Chain Hydroxy Acids as Bioactive Metabolites in Limosilactobacillus fermentum, Ligilactobacillus salivarius, and Latilactobacillus sakei.

Branched-chain hydroxy acids (BCHAs) as bioactive metabolites of Lactobacillaceae include 2-hydroxy isovaleric acid (HIVA), 2-hydroxy isocaproic acid (HICA), and 2-hydroxy-3-methyl isovaleric acid (HMVA). Combining targeted and untargeted metabolomics, this study elucidates differences in extracellular BCHA production in Limosilactobacillus fermentum, Ligilactobacillus salivarius, and Latilactobacillus sakei alongside comparing comprehensive metabolic changes. Through targeted metabolomics, BCHA production among 38 strains exhibited strain specificity, except for L. sakei, which showed significantly lower BCHA production. Explaining the lower production in L. sakei, which lacks the branched-chain amino acid (BCAA)-utilizing pathway, comparison of BCHA production by precursor reaction revealed that the pathway utilizing BCAAs is more dominant than the pathway utilizing pyruvate. Expanding upon the targeted approach, untargeted metabolomics revealed the effects of the reaction compound on other metabolic pathways besides BCHAs. Metabolism alterations induced by BCAA reactions varied among species. Significant differences were observed in glycine, serine, and threonine metabolism, pyruvate metabolism, butanoate metabolism, and galactose metabolism (p < 0.05). These results emphasize the importance of the synergy between fermentation strains and substrates in influencing nutritional components of fermented foods. By uncovering novel aspects of BCAA metabolism pathways, this study could inform the selection of fermentation strains and support the targeted production of BCHAs.

Sulfonation metabolism in the gut microbiota is the main metabolic pathway of cholesterol in hypercholesterolemic mice.

The interactions between dietary cholesterol and intestinal microbiota strongly affect host health. Sulfonation is a major conjugating pathway responsible for regulating the chemical and functional homeostasis of endogenous and exogenous molecules. However, the role of cholesterol sulfonation metabolism in the host remains unclear. This work was designed to profile cholesterol-specific host-microbe interaction and conversion focusing on cholesterol sulfonation metabolism. Results indicated that the serum and fecal cholesterol sulfate (CHS) levels were significantly higher than those of total bile acid (TBA) levels in hypercholesterolemic mice. Deletion of the gut microbiota by antibiotics could dramatically increase total cholesterol (TC) levels but it decreased CHS levels in a pseudo-germ-free (PGF) mouse host. 16S rRNA gene sequencing assay and correlation analysis between the abundance of various intestinal bacteria (phylum and class) and the CHS/TC ratio showed that the intestinal genera Bacteroides contributed essentially to cholesterol sulfonation metabolism. These results were further confirmed in an in situ and ex vivo mouse intestinal model, which indicated that the sulfonation metabolism rate of cholesterol could reach 42% under high cholesterol conditions. These findings provided new evidence that the sulfonation metabolic pathway dominated cholesterol metabolism in hypercholesterolemic mice and microbial conversion of cholesterol-to-CHS was of vital importance for cholesterol-lowering by Bacteroides. This suggested that the gut microbiota could regulate cholesterol metabolism and that it was feasible to reduce cholesterol levels by dietary interventions involving the gut microbiota.

[Construction and analysis of brain metabolic network in temporal lobe epilepsy patients based on 18F-FDG PET].

The establishment of brain metabolic network is based on 18fluoro-deoxyglucose positron emission computed tomography ( 18F-FDG PET) analysis, which reflect the brain functional network connectivity in normal physiological state or disease state. It is now applied to basic and clinical brain functional network research. In this paper, we constructed a metabolic network for the cerebral cortex firstly according to 18F-FDG PET image data from patients with temporal lobe epilepsy (TLE).Then, a statistical analysis to the network properties of patients with left or right TLE and controls was performed. It is shown that the connectivity of the brain metabolic network is weakened in patients with TLE, the topology of the network is changed and the transmission efficiency of the network is reduced, which means the brain metabolic network connectivity is extensively impaired in patients with TLE. It is confirmed that the brain metabolic network analysis based on 18F-FDG PET can provide a new perspective for the diagnose and therapy of epilepsy by utilizing PET images.

Individual-specific metabolic network based on 18F-FDG PET revealing multi-level aberrant metabolisms in Parkinson's disease.

Metabolic network analysis in Parkinson's disease (PD) based on 18F-FDG PET has revealed PD-related metabolic patterns. However, alterations at the systemic metabolic network level and at the connection level between different brain regions still remain unknown. This study aimed to explore metabolic network alterations at multiple network levels among PD patients using an individual-specific metabolic network (ISMN) approach. 18F-FDG-PET images of patients with PD (n = 34) and healthy subjects (n = 47) were collected. Healthy subjects were further separated into reference group (n = 28) and control group (n = 19) randomly. Standardized uptake value normalized by lean body mass ratio (SULr) maps was calculated from the PET images. ISMNs were constructed based on SULr maps for PD patients and controls with reference to the reference group. Comparisons of nodal and edge features were performed between PD and control groups. Correlation analysis was conducted between multilevel network properties and clinical scales in PD group. A linear classifier was trained based on nodal or edge features to distinguish PD from controls. The distance from each patient's ISMN to the group-level difference network showed a negative correlation with Hoehn and Yahr stage (r = -0.390, p = .023). Eight nodes from ISMN were identified which exhibited significantly increased nodal degree in PD patients compared to controls (p < .05). Eleven edges were observed which demonstrated significant distinctions in Z-score values in comparisons to the control group (p < .05). Furthermore, the nodal and edge features showed comparable performances in PD diagnosis compared to the traditional SULr values, with area under the receiver operating characteristic curve larger than 0.91. The proposed ISMN approach revealed systemic metabolic deviations, as well as nodal and edge distinctions in PD, which might be supplementary to the existing findings on PD-related metabolic patterns.

Metabolic Characteristics of Gut Microbiota and Insomnia: Evidence from a Mendelian Randomization Analysis.

Insomnia is a common sleep disorder that significantly impacts individuals' sleep quality and daily life. Recent studies have suggested that gut microbiota may influence sleep through various metabolic pathways. This study aims to explore the causal relationships between the abundance of gut microbiota metabolic pathways and insomnia using Mendelian randomization (MR) analysis. This two-sample MR study used genetic data from the OpenGWAS database (205 gut bacterial pathway abundance) and the FinnGen database (insomnia-related data). We identified single nucleotide polymorphisms (SNPs) associated with gut bacterial pathway abundance as instrumental variables (IVs) and ensured their validity through stringent selection criteria and quality control measures. The primary analysis employed the inverse variance-weighted (IVW) method, supplemented by other MR methods, to estimate causal effects. The MR analysis revealed significant positive causal effects of specific carbohydrate, amino acid, and nucleotide metabolism pathways on insomnia. Key pathways, such as gluconeogenesis pathway (GLUCONEO.PWY) and TCA cycle VII acetate producers (PWY.7254), showed positive associations with insomnia (B > 0, p < 0.05). Conversely, pathways like hexitol fermentation to lactate, formate, ethanol and acetate pathway (P461.PWY) exhibited negative causal effects (B < 0, p < 0.05). Multivariable MR analysis confirmed the independent causal effects of these pathways (p < 0.05). Sensitivity analyses indicated no significant pleiotropy or heterogeneity, ensuring the robustness of the results. This study identifies specific gut microbiota metabolic pathways that play critical roles in the development of insomnia. These findings provide new insights into the biological mechanisms underlying insomnia and suggest potential targets for therapeutic interventions. Future research should further validate these causal relationships and explore how modulating gut microbiota or its metabolic products can effectively improve insomnia symptoms, leading to more personalized and precise treatment strategies.

Untargeted metabolomics combined with chemometrics reveals distinct metabolic profiles across two sparkling cider fermentation stages.

This study aimed to examine the metabolic profiles of Saccharomyces cerevisiae yeasts (WLS21 and Y41) in two phases of sparkling cider making (normal and pressure fermentation) by combining untargeted metabolomic with chemometrics. The results showed that of the 634 nonvolatile metabolites identified using LC-MS and 83 volatile metabolites identified by GC-MS, the differential metabolites were 226 and 54, respectively. Metabolic pathway and correlation analyses showed that aspartic acid, phenylalanine and tyrosine, glutamic acid and purine metabolism were associated with flavor formation. The pressure fermentation process increased apigenin, naringenin, toxifolin, pyridoxine and thiamine contents in the final cider. These findings provide useful information and new research ideas for the formation of flavor in sparkling cider and the regulation of phenolic and vitamin production by microbial stress fermentation.

Understanding microbial biomineralization at the molecular level: recent advances.

Microbial biomineralization is a phenomenon involving deposition of inorganic minerals inside or around microbial cells as a direct consequence of biogeochemical cycling. The microbial metabolic processes often create environmental conditions conducive for the precipitation of silicate, carbonate or phosphate, ferrate forms of ubiquitous inorganic ions. Till date the fundamental mechanisms underpinning two of the major types of microbial biomineralization such as, microbially controlled and microbially induced remains poorly understood. While microbially-controlled mineralization (MCM) depends entirely on the genetic makeup of the cell, microbially-induced mineralization (MIM) is dependent on factors such as cell morphology, cell surface structures and extracellular polymeric substances (EPS). In recent years, the organic template-mediated nucleation of inorganic minerals has been considered as an underlying mechanism based on the principles of solid-state bioinorganic chemistry. The present review thus attempts to provide a comprehensive and critical overview on the recent progress in holistic understanding of both MCM and MIM, which involves, organic-inorganic biomolecular interactions that lead to template formation, biomineral nucleation and crystallization. Also, the operation of specific metabolic pathways and molecular operons in directing microbial biomineralization have been discussed. Unravelling these molecular mechanisms of biomineralization can help in the biomimetic synthesis of minerals for potential therapeutic applications, and facilitating the engineering of microorganisms for commercial production of biominerals.

Metabolic network rewiring and temperature-dependent regulation for enhanced 3-dehydroshikimate production in Escherichia coli.

The cyclohexane organic acid 3-dehydroshikimate (DHS) has potent antioxidant activity and is widely utilised in chemical and pharmaceutical industries. However, its production requires a long fermentation with a suboptimal yield and low productivity, and a disproportionate growth-to-production ratio impedes the upscaling of DHS synthesis in microbial cell factories. To overcome these limitations, competing and degradation pathways were knocked-out and key enzymes were balanced in an engineered Escherichia coli production strain, resulting in 12.2 g/L DHS. Furthermore, to achieve equilibrium between cell growth and DHS production, a CRISPRi-based temperature-responsive multi-component repressor system was developed to dynamically control the expression of critical genes (pykF and aroE), resulting in a 30-fold increase in DHS titer. After 33 h fermentation in 5 L bioreactor, the DHS titer, productivity and yield reached 94.2 g/L, 2.8 g/L/h and 55 % glucose conversion, respectively. The results provided valuable insight into the production of DHS and its derivatives.

Reconstruction and Analysis of a Genome-Scale Metabolic Model of Acinetobacter lwoffii.

Acinetobacter lwoffii is widely considered to be a harmful bacterium that is resistant to medicines and disinfectants. A. lwoffii NL1 degrades phenols efficiently and shows promise as an aromatic compound degrader in antibiotic-contaminated environments. To gain a comprehensive understanding of A. lwoffii, the first genome-scale metabolic model of A. lwoffii was constructed using semi-automated and manual methods. The iNX811 model, which includes 811 genes, 1071 metabolites, and 1155 reactions, was validated using 39 unique carbon and nitrogen sources. Genes and metabolites critical for cell growth were analyzed, and 12 essential metabolites (mainly in the biosynthesis and metabolism of glycan, lysine, and cofactors) were identified as antibacterial drug targets. Moreover, to explore the metabolic response to phenols, metabolic flux was simulated by integrating transcriptomics, and the significantly changed metabolism mainly included central carbon metabolism, along with some transport reactions. In addition, the addition of substances that effectively improved phenol degradation was predicted and validated using the model. Overall, the reconstruction and analysis of model iNX811 helped to study the antimicrobial systems and biodegradation behavior of A. lwoffii.

Metabolic Pathways Affected in Patients Undergoing Hemodialysis and Their Relationship with Inflammation.

Worldwide, 3.9 million individuals rely on kidney replacement therapy. They experience heightened susceptibility to cardiovascular diseases and mortality, alongside an increased risk of infections and malignancies, with inflammation being key to explaining this intensified risk. This study utilized semi-targeted metabolomics to explore novel metabolic pathways related to inflammation in this population. We collected pre- and post-session blood samples of patients who had already undergone one year of chronic hemodialysis and used liquid chromatography and high-resolution mass spectrometry to perform a metabolomic analysis. Afterwards, we employed both univariate (Mann-Whitney test) and multivariate (logistic regression with LASSO regularization) to identify metabolites associated with inflammation. In the univariate analysis, indole-3-acetaldehyde, 2-ketobutyric acid, and urocanic acid showed statistically significant decreases in median concentrations in the presence of inflammation. In the multivariate analysis, metabolites positively associated with inflammation included allantoin, taurodeoxycholic acid, norepinephrine, pyroglutamic acid, and L-hydroorotic acid. Conversely, metabolites showing negative associations with inflammation included benzoic acid, indole-3-acetaldehyde, methionine, citrulline, alphaketoglutarate, n-acetyl-ornithine, and 3-4-dihydroxibenzeneacetic acid. Non-inflamed patients exhibit preserved autophagy and reduced mitochondrial dysfunction. Understanding inflammation in this group hinges on the metabolism of arginine and the urea cycle. Additionally, the microbiota, particularly uricase-producing bacteria and those metabolizing tryptophan, play critical roles.

Gastric Cancer and Intestinal Metaplasia: Differential Metabolic Landscapes and New Pathways to Diagnosis.

Gastric cancer (GC) is the fifth most common cause of cancer-related death worldwide. Early detection is crucial for improving survival rates and treatment outcomes. However, accurate GC-specific biomarkers remain unknown. This study aimed to identify the metabolic differences between intestinal metaplasia (IM) and GC to determine the pathways involved in GC. A metabolic analysis of IM and tissue samples from 37 patients with GC was conducted using ultra-performance liquid chromatography with tandem mass spectrometry. Overall, 665 and 278 significant features were identified in the aqueous and 278 organic phases, respectively, using false discovery rate analysis, which controls the expected proportion of false positives among the significant results. sPLS-DA revealed a clear separation between IM and GC samples. Steroid hormone biosynthesis, tryptophan metabolism, purine metabolism, and arginine and proline metabolism were the most significantly altered pathways. The intensity of 11 metabolites, including N1, N2-diacetylspermine, creatine riboside, and N-formylkynurenine, showed significant elevation in more advanced GC. Based on pathway enrichment analysis and cancer stage-specific alterations, we identified six potential candidates as diagnostic biomarkers: aldosterone, N-formylkynurenine, guanosine triphosphate, arginine, S-adenosylmethioninamine, and creatine riboside. These metabolic differences between IM and GC provide valuable insights into gastric carcinogenesis. Further validation is needed to develop noninvasive diagnostic tools and targeted therapies to improve the outcomes of patients with GC.