Metabolites最新文献

Background: Diet is a key modifiable factor that can either support renal health or accelerate the onset and progression of chronic kidney disease (CKD). Recent advances in multiomics, particularly proteomics and metabolomics, significantly enhanced our understanding of the molecular mechanisms linking diet to CKD risk. Proteomics offers a comprehensive analysis of protein expression, structure, and interactions, revealing how dietary components regulate cellular processes and signaling pathways. Meanwhile, metabolomics provides a detailed profile of low-molecular-weight compounds, including endogenous metabolites and diet-derived molecules, offering insights into the metabolic states that influence kidney function. Methods: We have conducted a narrative review of key papers from databases such as PubMed, Scopus, and Web of Science to explore the potential of proteomic and metabolomic analysis in identifying molecular signatures associated with diet in human and animal biological samples, such as blood plasma, urine, and in kidney tissues. These signatures help elucidate how specific foods, food groups, and overall dietary patterns may either contribute to or mitigate CKD risk. Results: Recent studies the impact of high-fat diets on protein expression involved in energy metabolism, inflammation, and fibrosis, identifying early biomarkers of kidney injury. Metabolic, including disruptions in in fatty acid metabolism, glucose regulation, and amino acid pathways, have been recognized as key indicators of CKD risk. Additionally, several studies explore specific metabolites found in biological fluids and renal tissue in response to protein-rich foods, assessing their potential roles in a progressive loss of kidney function. Emerging evidence also suggests that dietary interventions targeting the gut microbiota may help alleviate inflammation, oxidative stress, and toxin accumulation in chronic kidney disease. Notably, recent findings highlight metabolomic signatures linked to beneficial shifts in gut microbial metabolism, particularly in the context of prebiotic supplementation. Conclusions: By integrating proteomics and metabolomics, future research can refine precision nutrition strategies, helping mitigate CKD progression. Expanding large-scale studies and clinical trials will be essential in translating these molecular insights into actionable dietary guidelines.

Background/Objectives: Mixotrophic cultivation of microalgae using agro-industrial by-products as supplements offers a sustainable strategy to enhance biomass production and bioactive compound synthesis. This study aimed to evaluate the effects of different agro-industrial by-products-orange peel extract, Cladophora glomerata macroalgal hydrolysate, and solid-state fungal fermentation hydrolysate-on the growth and bioactivity of Chlorella sorokiniana. Methods: Microalgae were cultivated under mixotrophic conditions with different agro-industrial by-products as organic carbon sources. Biomass accumulation was monitored through dry weight measurements. Lipid extraction was carried out using dimethyl carbonate. The antimicrobial activity of the extracted compounds was assessed against Escherichia coli, Bacillus megaterium, and Bacillus subtilis by determining the minimal inhibitconcentrations. Results: Orange peel extract supplementation resulted in the highest biomass production. It increased dry weight by 13.86-fold compared to autotrophic conditions. Cladophora glomerata macroalgal hydrolysate followed with a 5.79-fold increase, and solid-state fungal fermentation hydrolysate showed a 4.14-fold increase. The lipophilic fraction extracted from microalgal biomass showed high yields. Orange peel extract supplementation achieved the highest extraction yield (274.36 mg/g DW). Antimicrobial activity varied based on the supplement used: biomass cultivated with orange peel extract exhibited superior activity against E. coli, whereas Cladophora glomerata macroalgal hydrolysate biomass demonstrated potent activity against B. subtilis (MIC: 5.67 g/mL). Conclusions: These findings underscore the potential of agro-industrial by-products for enhancing microalgal biomass and metabolite production. The observed antimicrobial properties highlight the application of microalgal-derived compounds in sustainable bioprocesses, supporting their use in pharmaceutical and biotechnological applications.

Tryptophan is an essential amino acid critical for human health. It plays a pivotal role in numerous physiological and biochemical processes through its metabolism. The kynurenine (KYN) pathway serves as the principal metabolic route for tryptophan, producing bioactive metabolites, including KYN, quinolinic acid, and 3-hydroxykynurenine. Numerous studies are actively investigating the relationship between tryptophan metabolism and physiological functions. These studies are highlighting the interactions among metabolites that may exert synergistic or antagonistic effects, such as neuroprotective or neurotoxic, and pro-oxidative or antioxidant activities. Minor disruptions in the homeostasis of these metabolites can result in immune dysregulation, contributing to a spectrum of diseases. These diseases include neurological disorders, mental illnesses, cardiovascular conditions, autoimmune diseases, and chronic kidney disease. Therefore, understanding the physiological roles of the KYN pathway metabolites is essential for elucidating the contribution of tryptophan metabolism to health regulation. The present review emphasizes the physiological roles of KYN pathway metabolites and their mechanisms in disease development, aiming to establish a theoretical basis for leveraging dietary nutrients to enhance human health.

Objectives: This study investigated the long-term effects of maternal undernutrition on overall muscle metabolism, growth performance, and muscle characteristics in postnatal offspring of Wagyu (Japanese Black) cattle. Methods: Wagyu cows were divided into nutrient-adequate (control, CNT; n = 4, 120% of requirements) and nutrient-restricted groups (NR; n = 4; 60% of requirements), and treated from day 35 of gestation until parturition. Diets were delivered on the basis of crude protein requirements, meeting 100% and 80% of dry matter requirements in CNT and NR groups, respectively. All offspring were provided with the same diet from birth to 300 days of age (d). Longissimus thoracis muscle (LM) samples were collected from the postnatal offspring. Results: The NR offspring had lower birth body weight, but their body weight caught up before weaning. These offspring showed enhanced efficiency in nutrient utilization during the post-weaning growth period. Comprehensive analyses of metabolites and transcripts revealed the accumulation of proteinogenic amino acid, asparagine, in NR offspring LM at 300 d, while the abundance of nicotinamide adenine dinucleotide (NADH) and succinate were reduced. These changes were accompanied by decreased gene expression of nicotinamide phosphoribosyltransferase (NAMPT), NADH: ubiquinone oxidoreductase subunit A12 (NDUFA12), and NADH dehydrogenase subunit 5 (ND5), which are essential for mitochondrial energy production. Additionally, NR offspring LM exhibited decreased abundance of neurotransmitter, along with a higher proportion of slow-oxidative myofibers and a lower proportion of fast-oxidative myofibers at 300 d. Conclusions: Offspring from nutrient-restricted cows might suppress muscle energy production, primarily in the mitochondria, and conserve energy expenditure for muscle protein synthesis. These findings suggest that maternal undernutrition programs a thrifty metabolism in offspring muscle, with long-term effects.

Psoriatic arthritis (PsA) is a chronic inflammatory disease that extends beyond musculoskeletal and dermatologic involvement to elevate cardiometabolic risk. Emerging evidence highlights the critical role of systemic inflammation in metabolic dysregulation, accelerating insulin resistance, dyslipidemia, and oxidative stress, all of which contribute to the increased burden of cardiovascular disease in PsA. This review explores the intricate interplay between inflammatory mediators-such as tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and interleukin-17 (IL-17),-adipokine imbalances, and lipid metabolism abnormalities, all of which foster endothelial dysfunction and atherosclerosis. The dysregulation of adipokines, including leptin, adiponectin, and resistin, further perpetuates inflammatory cascades, exacerbating cardiovascular risk. Additionally, the metabolic alterations seen in PsA, particularly insulin resistance and lipid dysfunction, not only contribute to cardiovascular comorbidities but also impact disease severity and therapeutic response. Understanding these mechanistic links is imperative for refining risk stratification strategies and tailoring interventions. By integrating targeted immunomodulatory therapies with metabolic and cardiovascular risk management, a more comprehensive approach to PsA treatment can be achieved. Future research must focus on elucidating shared inflammatory and metabolic pathways, enabling the development of innovative therapeutic strategies to mitigate both systemic inflammation and cardiometabolic complications in PsA.

Changes in the level of metabolites, small molecules that are intermediates produced by metabolism or catabolism, are associated with developing diseases. Metabolite signatures in body fluids such as plasma, cerebrospinal fluid, urine, and saliva are associated with Parkinson's disease. Here, we discuss alteration of metabolites in the TCA cycle, pentose phosphate pathway, kynurenic network, and redox system. We also summarize the efforts of many research groups to differentiate between metabolite profiles that characterize PD motor progression and dyskinesia, gait and balance, and non-motor symptoms such as depression and cognitive decline. Understanding how changes in metabolites lead to progression in PD may allow for the identification of individuals at the earliest stage of the disease and the development of new therapeutic strategies.

Background: The reprogramming of lipid metabolism, especially glycerolipid metabolism (GLM), plays a key role in cancer progression and response to therapy. However, the role and molecular characterization of GLM in pancreatic cancer (PC) remain unclear. Methods: A pan-cancer analysis of glycerolipid metabolism-related genes (GMRGs) was first conducted to assess copy-number variants, single-nucleotide variations, methylation, and mRNA expression. Subsequently, GLM in PC was characterized using lipidomics, single-cell RNA sequencing (scRNA-seq), and spatial transcriptomic analysis. A cluster analysis based on bulk RNA sequencing data from 930 PC samples identified GLM-associated subtypes, which were then analyzed for differences in prognosis, biological function, immune microenvironment, and drug sensitivity. To prioritize prognostically relevant GMRGs in PC, we employed a random forest (RF) algorithm to rank their importance across 930 PC samples. Finally, the key biomarker of PC was validated using PCR and immunohistochemistry. Results: Pan-cancer analysis identified molecular features of GMRGs in cancers, while scRNA-seq, spatial transcriptomics, and lipidomics highlighted GLM heterogeneity in PC. Two GLM-associated subtypes with significant prognostic, biofunctional, immune microenvironmental, and drug sensitivity differences were identified in 930 PC samples. Finally, ALDH2 was identified as a novel prognostic biomarker in PC and validated in a large number of datasets and clinical samples. Conclusions: This study highlights the crucial role of GLM in PC and defines a new PC subtype and prognostic biomarker. These findings establish a novel avenue for studying prognostic prediction and precision medicine in PC patients.

Background/objectives: Early diagnosis and treatment of rheumatoid arthritis (RA) are essential to reducing disability. However, the diagnostic criteria remain unclear, relying on clinical symptoms and blood markers.

Methods: Using high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS) targeted detection, we evaluated 76 carnitine indicators (55 carnitines and 21 corresponding ratios) in the serum of patients with RA to investigate the role of carnitine in RA. A total of 359 patients (207 patients with RA and 152 healthy controls) were included in the study. Screening involved three methods and integrated 76 carnitine indicators and 128 clinical indicators to identify candidate markers to establish a theoretical basis for RA diagnosis and new therapeutic targets. The diagnostic model derived from the screened markers was validated using three machine learning algorithms.

Results: The model was refined using eight candidate indicators (C0, C10:1, LYMPH, platelet distribution width, anti-keratin antibody, glucose, urobilinogen, and erythrocyte sedimentation rate (ESR)). The receiver operating characteristic curve, sensitivity, specificity, and accuracy of the V8 model obtained from the training set were >0.948, 79.46%, 92.99%, and 89.18%, whereas those of the test set were >0.925, 78.89%, 89.22%, and 85.87%, respectively. Twenty-four carnitines were identified as risk factors of RA, with three significantly correlating with ESR, four with anti-cyclic citrullinated peptide antibody activity, two with C-reactive protein, five with immunoglobulin-G, eight with immunoglobulin-A levels, and eleven with immunoglobulin-M levels.

Conclusions: Carnitine is integral in the progression of RA. The diagnostic model developed shows excellent diagnostic capacity, improving early detection and enabling timely intervention to minimize disability associated with RA.

Background: Timely and accurate identification of bloodstream infections (BSIs) in intensive care unit (ICU) patients remains a key challenge, particularly in COVID-19 settings, where immune dysregulation can obscure early clinical signs. Methods: Cytokine profiling was evaluated to discriminate between ICU patients with and without BSIs, and, among those with confirmed BSIs, to further stratify bacterial infections by Gram type. Serum samples from 45 ICU COVID-19 patients were analyzed using a 21-cytokine panel, with feature selection applied to identify candidate markers. Results: A machine learning workflow identified key features, achieving robust performance metrics with AUC values up to 0.97 for BSI classification and 0.98 for Gram typing. Conclusions: In contrast to traditional approaches that focus on individual cytokines or simple ratios, the present analysis employed programmatically generated ratios between pro-inflammatory and anti-inflammatory cytokines, refined through feature selection. Although further validation in larger and more diverse cohorts is warranted, these findings underscore the potential of advanced cytokine-based diagnostics to enhance precision medicine in infection management.

Ovarian cancer (OC) is a global health problem that frequently presents at advanced stages, is predisposed to recurrence, readily develops resistance to platinum-based drugs, and has a low survival rate. Predictive, preventive, and personalized medicine (PPPM/3PM) offers an integrated solution with the use of genetic, proteomic, and metabolic biomarkers to identify high-risk individuals for early detection. Metabolic reprogramming is one of the key strategies employed by tumor cells to adapt to the microenvironment and support unlimited proliferation. Pyruvate kinases M1 and M2 (PKM1/2) are encoded by the PKM gene, a pivotal enzyme in the last step of the glycolytic pathway, which is at the crossroads of aerobic oxidation and the Warburg effect to serve as a potential regulator of glucose metabolism and influence cellular energy production and metabolic reprogramming. Commonly, the ratio of PKM1-to-PKM2 is changed in tumors compared to normal controls, and PKM2 is highly expressed in OC to induce a high glycolysis rate and participate in the malignant invasion and metastatic characteristics of cancer cells with epithelial/mesenchymal transition (EMT). PKM2 inhibitors suppress the migration and growth of OC cells by interfering with the Warburg effect. Proteoforms are the final structural and functional forms of a gene/protein, and the canonical protein PKM contains all proteoforms encoded by the same PKM gene. The complexity of PKM can be elucidated by proteoformics. The OC-specific PKM proteoform might represent a specific target for therapeutic interventions against OC. In the framework of PPPM/3PM, the OC-specific PKM proteoform might be the early warning and prognosis biomarker. It is important to clarify the molecular mechanisms of PKM proteoforms in cancer metabolism. This review analyzes the expression, function, and molecular mechanisms of PKM proteoforms in OC, which help identify specific biomarkers for OC.