PubMed

Metabolites. 2025 Jan 14;15(1):48. doi: 10.3390/metabo15010048.ABSTRACTIntroduction: NMR spectroscopy is a powerful technique for studying metabolism, either in metabolomics settings or through tracing with stable isotope-enriched metabolic precursors. MetaboLabPy (version 0.9.66) is a free and open-source software package used to process 1D- and 2D-NMR spectra. The software implements a complete workflow for NMR data pre-processing to prepare a series of 1D-NMR spectra for multi-variate statistical data analysis. This includes a choice of algorithms for automated phase correction, segmental alignment, spectral scaling, variance stabilisation, export to various software platforms, and analysis of metabolic tracing data. The software has an integrated help system with tutorials that demonstrate standard workflows and explain the capabilities of MetaboLabPy. Materials and Methods: The software is implemented in Python and uses numerous Python toolboxes, such as numpy, scipy, pandas, etc. The software is implemented in three different packages: metabolabpy, qtmetabolabpy, and metabolabpytools. The metabolabpy package contains classes to handle NMR data and all the numerical routines necessary to process and pre-process 1D NMR data and perform multiplet analysis on 2D-1H, 13C HSQC NMR data. The qtmetabolabpy package contains routines related to the graphical user interface. Results: PySide6 is used to produce a modern and user-friendly graphical user interface. The metabolabpytools package contains routines which are not specific to just handling NMR data, for example, routines to derive isotopomer distributions from the combination of NMR multiplet and GC-MS data. A deep-learning approach for the latter is currently under development. MetaboLabPy is available via the Python Package Index or via GitHub.PMID:39852390 | DOI:10.3390/metabo15010048

Metabolites. 2025 Jan 11;15(1):44. doi: 10.3390/metabo15010044.ABSTRACTBackground/Objectives: Respiratory viruses, including Influenza, RSV, and COVID-19, cause various respiratory infections. Distinguishing these viruses relies on diagnostic methods such as PCR testing. Challenges stem from overlapping symptoms and the emergence of new strains. Advanced diagnostics are crucial for accurate detection and effective management. This study leveraged nasopharyngeal metabolome data to predict respiratory virus scenarios including control vs. RSV, control vs. Influenza A, control vs. COVID-19, control vs. all respiratory viruses, and COVID-19 vs. Influenza A/RSV. Method: We proposed a stacking-based ensemble technique, integrating the top three best-performing ML models from the initial results to enhance prediction accuracy by leveraging the strengths of multiple base learners. Key techniques such as feature ranking, standard scaling, and SMOTE were used to address class imbalances, thus enhancing model robustness. SHAP analysis identified crucial metabolites influencing positive predictions, thereby providing valuable insights into diagnostic markers. Results: Our approach not only outperformed existing methods but also revealed top dominant features for predicting COVID-19, including Lysophosphatidylcholine acyl C18:2, Kynurenine, Phenylalanine, Valine, Tyrosine, and Aspartic Acid (Asp). Conclusions: This study demonstrates the effectiveness of leveraging nasopharyngeal metabolome data and stacking-based ensemble techniques for predicting respiratory virus scenarios. The proposed approach enhances prediction accuracy, provides insights into key diagnostic markers, and offers a robust framework for managing respiratory infections.PMID:39852387 | DOI:10.3390/metabo15010044

Metabolites. 2025 Jan 10;15(1):40. doi: 10.3390/metabo15010040.ABSTRACTBACKGROUND: The frequent occurrence of extreme temperature events causes significant economic losses to the livestock industry. Therefore, delving into the differences in the physiological and molecular mechanisms of heat stress across different sheep breeds is crucial for developing effective management and breeding strategies.METHODS: This study explores the differences in heat tolerance mechanisms between Hu sheep and Xinggao sheep by comparing their growth performance under normal and heat stress conditions, as well as examining the differences in physiological, biochemical, and antioxidant indicators related to heat tolerance, serum metabolomics, and gut microbiomics in a heat stress environment.RESULTS: The results indicate that with changes in the temperature-humidity index (THI), Hu sheep exhibit superior stability in respiratory rate (RR) and rectal temperature (RT) fluctuations compared to Xinggao sheep. In terms of biochemical indicators and antioxidant capacity, the levels of creatinine (Cr) and superoxide dismutase (SOD) in Hu sheep serum are significantly higher than those in Xinggao sheep. In comparison, alkaline phosphatase (ALP) and malondialdehyde (MDA) levels are significantly lower. Metabolomic results showed that, compared to Hu sheep, Xinggao sheep exhibited higher cortisol (COR) and dopamine (DA) levels under heat stress conditions, a stronger lipid mobilization capacity, and elevated levels of tricarboxylic acid (TCA) cycle-related metabolites. Furthermore, gut microbiome analysis results indicate that Hu sheep demonstrate stronger cellulose degradation capabilities, as evidenced by significantly higher abundances of microorganisms such as Ruminococcus, Fibrobacter, and Bacteroidales_RF16_group, compared to Xinggao sheep.CONCLUSIONS: In summary, Hu sheep exhibit stronger heat tolerance compared to Xinggao sheep. These findings provide an important theoretical basis for the breeding and selection of heat-tolerant meat sheep varieties and offer strong support for the region's livestock industry in addressing the challenges posed by global warming.PMID:39852383 | DOI:10.3390/metabo15010040

Metabolites. 2025 Jan 10;15(1):38. doi: 10.3390/metabo15010038.ABSTRACTBackground/Objectives: Gardeniae Fructus (GF) has been widely used as both food and medicinal purposes for thousands of years, but their antioxidant properties and potential metabolite biomarkers remain unclear. Methods: The purposes of this study were to examine antioxidant activities of 21 GF varieties from different geographical origins in China and identify potential biomarkers of antioxidant properties using an untargeted LC-MS-based metabolomics approach. Results: The results demonstrate that metabolomics had the ability to trace the geographical origins of GF. We found that antioxidant activities varied with different varieties of GF, which was dependent on their chemical compositions. The key chemical categories were obtained as the primary contributors of the antioxidant activity, including prenol lipids, flavonoids, coumarins and derivatives, as well as steroids and steroid derivatives. In addition, adouetine Y, coagulin R 3-glucoside and epicatechin 3-glucoside were identified as potential biomarkers for the antioxidant activity of GF. Conclusions: Therefore, our study sheds light on the metabolic characteristics and biomarkers of the antioxidant properties of GF, contributing to the selection and cultivation of a high antioxidant variety.PMID:39852381 | DOI:10.3390/metabo15010038

Metabolites. 2025 Jan 10;15(1):37. doi: 10.3390/metabo15010037.ABSTRACTCancer is one of the leading causes of death globally, and is ranked second in the United States. Early detection is crucial for more effective treatment and a higher chance of survival rates, reducing burdens on individuals and societies. Genitourinary cancers, in particular, face significant challenges in early detection. Finding new and cost-effective diagnostic methods is of clinical need. Metabolomic-based approaches, notably volatile organic compound (VOC) analysis, have shown promise in detecting cancer. VOCs are small organic metabolites involved in biological processes and disease development. They can be detected in urine, breath, and blood samples, making them potential candidates for sensitive and non-invasive alternatives for early cancer detection. However, developing robust VOC detection methods remains a hurdle. This review outlines the current landscape of major genitourinary cancers (kidney, prostate, bladder, and testicular), including epidemiology, risk factors, and current diagnostic tools. Furthermore, it explores the applications of using VOCs as cancer biomarkers, various analytical techniques, and comparisons of extraction and detection methods across different biospecimens. The potential use of VOCs in detection, monitoring disease progression, and treatment responses in the field of genitourinary oncology is examined.PMID:39852380 | DOI:10.3390/metabo15010037

Metabolites. 2025 Jan 9;15(1):34. doi: 10.3390/metabo15010034.ABSTRACTBACKGROUND: There is a high rate of depressive symptoms such as irritability, anhedonia, fatigue, and hypersomnia in patients with type 2 diabetes mellitus (T2DM). However, the causes and underlying mechanisms of the comorbidity of depression and diabetes remain unknown.METHODS: For the first time, we identified Decidual protein induced by progesterone 1 (Depp1), also known as DEPP autophagy regulator 1, as a hub gene in both depression and T2DM models. Depp1 levels were increased in the mPFC but not in other brain regions, such as the hippocampus or nucleus accumbens, according to Western blot and PCR assays.RESULTS: Glucose dysregulation and synaptic loss occur in both depression and T2DM. The typical hyperglycemia in T2DM was observed in two models of depression, namely, chronic social defeat stress (CSDS) and chronic restraint stress (CRS). Hyperglycemia, which occurred in T2DM, was observed, and metabolomics data clearly showed the perturbation of glucose levels and glucose metabolism in the medial prefrontal cortex (mPFC). Decreased protein levels of BDNF and PSD95 suggested significant synaptic loss in depressed and diabetic mice.CONCLUSION: These findings suggest that the comorbidity of depression and diabetes is involved in the dysfunction of Depp1 in the mPFC.PMID:39852377 | DOI:10.3390/metabo15010034

Metabolites. 2025 Jan 8;15(1):28. doi: 10.3390/metabo15010028.ABSTRACTBACKGROUND: Due to scientific advancements in high-throughput data production technologies, omics studies, such as genomics and metabolomics, often give rise to numerous measurements per sample/subject containing several noisy variables that potentially cloud the true signals relevant to the desired study outcome(s). Therefore, correcting for multiple testing is critical while performing any statistical test of significance to minimize the chances of false or missed discoveries. Such correction practice is commonplace in genome-wide association studies (GWAS) but is also becoming increasingly relevant to metabolome-wide association studies (MWAS). However, many existing procedures may be too conservative or too lenient, only assume a linear association between the features, or have not been evaluated on metabolomics data.METHODS: One such multiple testing correction strategy is to estimate the number of statistically independent tests, called the effective number of tests, based on the eigen-analysis of the correlation matrix between the features. This effective number is then used for a subsequent single-step adjustment to obtain the pointwise significance level. We propose a modification to the p-value adjustment based on a more general measure of association between two predictors, the distance correlation, with a specific focus on MWAS.RESULTS: We assessed common GWAS p-value adjustment procedures and one tailored for MWAS, which rely on eigen-analysis of the Pearson's correlation matrix. Our study, including varying sample size-to-feature ratios, response types, and metabolite groupings, highlights the superior performance of the distance correlation.CONCLUSION: We propose the distance-correlation-based p-value adjustment (DisCo P-ad) as a novel modification that can enhance existing eigen-analysis-based multiple testing correction procedures by increasing power or reducing false positives. While our focus is on metabolomics, DisCo P-ad can also readily be applied to other high-dimensional omics studies.PMID:39852371 | DOI:10.3390/metabo15010028

Metabolites. 2025 Jan 8;15(1):27. doi: 10.3390/metabo15010027.ABSTRACTBACKGROUND: Pregnancy induces significant physiological and metabolic changes in the mother to support fetal growth and prepare for childbirth. These adaptations impact various systems, including immune tolerance, metabolism, and endocrine function. While metabolomics has been utilized to study pregnancy-related metabolic changes, comprehensive comparisons between pregnant and non-pregnant states, particularly using ultra-high-performance liquid chromatography coupled with mass spectrometry (UHPLC-MS), remain limited.METHODS: This study aimed to explore the dynamic, longitudinal metabolic shifts during pregnancy by profiling plasma samples from 65 pregnant women across three time points (6-14 weeks, 14-22 weeks, and >24 weeks) and 42 postpartum women. Lipidomics was prioritized, and a solvent mixture was employed to enhance lipid extraction, using UHPLC-QTOF-ESI+-MS.RESULTS: A total of 290 metabolites were identified and analyzed. Our results revealed significant metabolic differences between pregnant and postpartum women, with lipid molecules such as estrogen derivatives, fatty acids, and ceramides showing strong potential as biomarkers. Further biomarker analysis highlighted distinct metabolic signatures between early and late pregnancy stages, particularly in lipid metabolism (with AUC values > 0.8).CONCLUSIONS: These findings contribute to a deeper understanding of pregnancy-related metabolic changes and may offer insights into maternal and neonatal health outcomes.PMID:39852370 | DOI:10.3390/metabo15010027

Metabolites. 2025 Jan 7;15(1):26. doi: 10.3390/metabo15010026.ABSTRACTBACKGROUND: Olive leaves are a rich source of polyphenols, predominantly secoiridoids, flavonoids, and simple phenols, which exhibit various biological properties. Extracts prepared from olive leaves are associated with hypoglycemic, hypotensive, diuretic, and antiseptic properties. Upon ingestion, a substantial fraction of these polyphenols reaches the colon where they undergo extensive metabolism by the gut microbiota. Host characteristics, like age, can influence the composition of the gut microbiome, potentially affecting the biotransformation of these compounds. Therefore, it can be hypothesised that differences in the gut microbiome between young and elderly individuals may impact the biotransformation rate and the type and amount of metabolites formed.METHODS: An in vitro biotransformation model was used to mimic the conditions in the stomach, small intestine and colon of two age groups of healthy participants (20-30 years old, ≥65 years old), using oleuropein as a single compound and an olive leaf extract as test compounds. The bacterial composition and metabolite content were investigated.RESULTS: The study revealed that, while the same metabolites were formed in both age groups, in the young age group, less metabolite formation was observed, likely due to a reduced viable cell count. Most biotransformation reactions took place within the first 24 h of colon incubation, and mainly, deglycosylation, hydrolysis, flavonoid ring cleavage, and demethylation reactions were observed. A bacterial composition analysis showed a steep drop in α-diversity after 24 h of colon incubation, likely due to favourable experimental conditions for certain bacterial species.CONCLUSIONS: Both age groups produced the same metabolites, suggesting that the potential for polyphenols to exert their health-promoting benefits persists in healthy older individuals.PMID:39852369 | DOI:10.3390/metabo15010026

Metabolites. 2025 Jan 7;15(1):24. doi: 10.3390/metabo15010024.ABSTRACTBACKGROUND: Bos taurus indicus cattle is known to be temperamental and to produce beef with greater variability in terms of quality compared to beef of Bos taurus taurus. Cattle adaptability and resilience are of great importance to sustain beef production worldwide.OBJECTIVE: The study aimed to understand early post-mortem metabolites among muscles with different fiber types profile of calm and excitable Nellore, as well as its relationship with fragmentation of beef aged up to 28 d.METHODS: Animals were evaluated based on chute score and exit velocity to calculate a temperament index, which was used to classify them as calm or excitable. At slaughter, the pH and temperature declines of Triceps brachii (TB) and Longissimus lumborum (LL) were measured, muscles were sampled, and aged up to 28 d. Metabolites were determined, and sarcomere length and myofibrillar fragmentation index (MFI) were quantified. Metabolomics data were analyzed using a multivariate approach, while other traits were investigated through ANOVA.RESULTS: The pH decline was affected by all three fixed effects investigated (temperament × muscle × time post-mortem: p = 0.016), while temperature decline was affected by muscle × time (p < 0.001). Metabolites differed among muscles and cattle temperament, with excitable cattle showing greater taurine abundance in LL, as well as greater creatine in TB 1 h post-mortem, based on the volcano plot. Sarcomere length and MFI results revealed faster and limited tenderization in excitable cattle beef.CONCLUSIONS: Altogether, results emphasized the upregulation of mitochondrial enzymes and reduced tenderization as determinants of inferior beef quality after prolonged aging in excitable cattle.PMID:39852367 | DOI:10.3390/metabo15010024

Metabolites. 2025 Jan 5;15(1):19. doi: 10.3390/metabo15010019.ABSTRACTSeed storability is a crucial agronomic trait and indispensable for the safe storage of rice seeds and grains. Nevertheless, the metabolite mechanisms governing Indica rice seed storability under natural conditions are still poorly understood.METHODS: Therefore, the seed storage tolerance of global rice core germplasms stored for two years under natural aging conditions were identified, and two extreme groups with different seed storabilities from the Indica rice group were analyzed using the UPLC-MS/MS metabolomic strategy.RESULTS: Our results proved that the different rice core accessions showed significant variability in storage tolerance, and the metabolite analysis of the two Indica rice pools exhibited different levels of storability. A total of 103 differentially accumulated metabolites (DAMs) between the two pools were obtained, of which 38 were up-regulated and 65 were down-regulated, respectively. Further analysis disclosed that the aging-resistant rice accessions had higher accumulation levels of flavonoids, terpenoids, phenolic acids, organic acids, lignans, and coumarins while exhibiting lower levels of lipids and alkaloids compared to the storage-sensitive rice accessions. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that several biosynthesis pathways were involved in the observed metabolite differences, including alpha-linolenic acid metabolism, butanoate metabolism, and propanoate metabolism. Notably, inhibition of the linolenic acid metabolic pathway could enhance seed storability. Additionally, increased accumulations of organic acids, such as succinic acid, D-malic acid, and methylmalonic acid, in the butanoate and propanoate metabolisms were identified as a beneficial factor for seed storage.CONCLUSIONS: These new findings will deepen our understanding of the underlying mechanisms governing rice storability.PMID:39852362 | DOI:10.3390/metabo15010019

Metabolites. 2025 Jan 4;15(1):17. doi: 10.3390/metabo15010017.ABSTRACTOBJECTIVE: This study aimed to explore the molecular response mechanisms of differential blood metabolites before and after 8 weeks of threshold and polarized training models using metabolomics technology combined with changes in athletic performance.METHODS: Twenty-four male rowers aged 14-16 were randomly divided into a THR group and a POL group (12 participants each). The THR group followed a threshold training model (72%, 24%, and 4% of training time in low-, moderate-, and high-intensity zones, respectively), while the POL group followed a polarized training model (78%, 8%, and 14% training-intensity distribution). Both groups underwent an 8-week training program. Aerobic endurance changes were assessed using a 2 km maximal rowing performance test, and untargeted metabolome analysis was conducted to examine blood metabolomic changes before and after the different training interventions. Aerobic endurance changes were assessed through a 2 km maximal rowing test. Non-targeted metabolomics analysis was employed to evaluate changes in blood metabolome profiles before and after the different training interventions.RESULTS: After 8 weeks of training, both the THR and POL groups exhibited significant improvements in 2 km maximal rowing performance (p < 0.05), with no significant differences between the groups. The THR and POL groups had 46 shared differential metabolites before and after the intervention, primarily enriched in sphingolipid metabolism, glutathione metabolism, and glycine, serine, and threonine metabolism pathways. Nine unique differential metabolites were identified in the THR group, mainly enriched in pyruvate metabolism, glycine, serine, and threonine metabolism, glutathione metabolism, and sphingolipid metabolism. A total of 14 unique differential metabolites were identified in the POL group, predominantly enriched in sphingolipid metabolism, glycine, serine, and threonine metabolism, aminoacyl-tRNA biosynthesis, and glutathione metabolism.CONCLUSIONS: The 8-week THR and POL training models demonstrated similar effects on enhancing aerobic performance in adolescent male rowers, indicating that both training modalities share similar blood metabolic mechanisms for improving aerobic endurance. Furthermore, both the THR group and the POL group exhibited numerous shared metabolites and some differential metabolites, suggesting that the two endurance training models share common pathways but also have distinct aspects in enhancing aerobic endurance.PMID:39852360 | DOI:10.3390/metabo15010017

Metabolites. 2025 Jan 3;15(1):14. doi: 10.3390/metabo15010014.ABSTRACTBackground/Objectives: Metabolic profiling of human milk (HM) is indispensable for elucidating mother-milk-infant relationships. Methods: We evaluated the Biocrates MxP® Quant 500 assay for HM-targeted metabolomics (106 small molecules, 524 lipids) and analyzed in a feasibility test HM from apparently healthy Brazilian mothers (A: 2-8, B: 28-50, C: 88-119 days postpartum, ntotal = 25). Results: Of the 630 possible signatures detectable with this assay, 506 were above the limits of detection in an HM-pool (10 µL) used for assay evaluation, 12 of them above the upper limit of quantitation. Analyzing five different HM-pool volumes (2-20 µL) revealed acceptable linearity for 458 metabolites. Intraday accuracy of 80-120% was attained by 469 metabolites after spiking and for 342 after a 1:2 dilution. Analyzing HM from Brazilian mothers revealed significantly lower concentrations in colostrum vs. mature milk for many flow-injection analyses (FIA) and only a few LC-MS metabolites, including triglycerides, sphingomyelins, and phosphatidylcholines. Higher concentrations at the later lactation stages were found predominantly for amino acids and related compounds. Conclusions: The MxP Quant® 500 assay is a useful tool for HM metabolic profiling, minimizing analytical bias between matrices, and enhancing our ability to study milk as a biological system.PMID:39852357 | DOI:10.3390/metabo15010014

Metabolites. 2025 Jan 3;15(1):12. doi: 10.3390/metabo15010012.ABSTRACTBackground/Objectives: This study explores the allelopathic effects of volatile organic compounds (VOCs) emitted by the invasive species Ailanthus altissima (Mill.) Swingle on the seed germination of Bidens pilosa. A. altissima is known for releasing allelopathic VOCs that suppress the growth of neighbouring plants, contributing to its invasive potential. Methods: To examine these effects, we exposed B. pilosa seeds to varying concentrations of A. altissima VOCs, assessing germination rates and metabolic changes through untargeted metabolomics. Results: Our findings revealed that VOCs from A. altissima significantly inhibited the germination speed and overall germination rates of B. pilosa in a dose-dependent manner. Metabolomic profiling showed disruptions in energy and amino acid metabolism pathways, specifically involving delayed breakdown of starch and key metabolites, indicating inhibition of critical metabolic processes during early germination stages. This metabolic delay likely impairs B. pilosa's establishment and competitiveness, enhancing A. altissima's ecological dominance. Conclusions: The results underscore the potential of VOC-based allelopathy as a mechanism of plant invasion, offering insights into the role of VOCs in interspecies plant competition and ecosystem dynamics.PMID:39852355 | DOI:10.3390/metabo15010012

Metabolites. 2025 Jan 2;15(1):10. doi: 10.3390/metabo15010010.ABSTRACTBACKGROUND: Huntington's disease (HD) is a multifaceted neurological disorder characterized by the progressive deterioration of motor, cognitive, and psychiatric functions. Despite a limited understanding of its pathogenesis, research has implicated abnormal trinucleotide cytosine-adenine-guanine CAG repeat expansion in the huntingtin gene (HTT) as a critical factor. The development of innovative strategies is imperative for the early detection of predictive biomarkers, enabling timely intervention and mitigating irreversible cellular damage. Lipidomics, a comprehensive analytical approach, has emerged as an indispensable tool for systematically characterizing lipid profiles and elucidating their role in disease pathology.METHOD: A MedLine search was performed to identify studies that use lipidomics for the characterization of HD. Search terms included "Huntington disease"; "lipidomics"; "biomarker discovery"; "NMR"; and "Mass spectrometry".RESULTS: This review highlights the significance of lipidomics in HD diagnosis and treatment, exploring changes in brain lipids and their functions. Recent breakthroughs in analytical techniques, particularly mass spectrometry and NMR spectroscopy, have revolutionized brain lipidomics research, enabling researchers to gain deeper insights into the complex lipidome of the brain.CONCLUSIONS: A comprehensive understanding of the broad spectrum of lipidomics alterations in HD is vital for precise diagnostic evaluation and effective disease management. The integration of lipidomics with artificial intelligence and interdisciplinary collaboration holds promise for addressing the clinical variability of HD.PMID:39852353 | DOI:10.3390/metabo15010010

Metabolites. 2024 Dec 31;15(1):7. doi: 10.3390/metabo15010007.ABSTRACTBackground: Sarcoidosis is a granulomatous disease affecting multiple organ systems and poses a diagnostic challenge due to its diverse clinical manifestations and absence of specific diagnostic tests. Currently, blood biomarkers such as ACE, sIL-2R, CD163, CCL18, serum amyloid A, and CRP are employed to aid in the diagnosis and monitoring of sarcoidosis. Metabolomics holds promise for identifying highly sensitive and specific biomarkers. This study aimed to leverage metabolomics for the early diagnosis of sarcoidosis and to identify metabolic phenotypes associated with disease progression. Methods: Serum samples from patients with sarcoidosis (n = 40, including stage 1 to stage 4), were analyzed for metabolite levels by semi-untargeted liquid chromatography-mass spectrometry (LC-MS). Metabolomics data from patients with sarcoidosis were compared with those from patients with COVID-19 and healthy controls to identify distinguishing metabolic biosignatures. Univariate and multivariate analyses were applied to obtain diagnostic and prognostic metabolic phenotypes. Results: Significant changes in metabolic profiles distinguished stage 1 sarcoidosis from healthy controls, with potential biomarkers including azelaic acid, itaconate, and glutarate. Distinct metabolic phenotypes were observed across the stages of sarcoidosis, with stage 2 exhibiting greater heterogeneity compared with stages 1, 3, and 4. Conclusions: we explored immunometabolic phenotypes by comparing patients with sarcoidosis with patients with COVID-19 and healthy controls, revealing potential metabolic pathways associated with acute and chronic inflammation across the stages of sarcoidosis.PMID:39852350 | DOI:10.3390/metabo15010007

Metabolites. 2024 Dec 30;15(1):5. doi: 10.3390/metabo15010005.ABSTRACTBackground: Owing to the progressive rise in saline waters globally, resulting in detrimental impacts on freshwater aquaculture, the underlying molecular distinctions governing the response to alkaline stress between diploid and triploid crucian carp remain unknown. Methods: This investigation explores the effects of 20 and 60 mmol NaHCO3 stress over 30 days on the gills of diploid and triploid crucian carp, employing histological, biochemical, and multi-omic analyses. Results: Findings reveal structural damage to gill lamellas in the examined tissue. Diploid crucian carp exhibit heightened activities of superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px), and alkaline phosphatase (AKP), alongside lower malondialdehyde (MDA) and urea nitrogen (BUN) levels compared to triploid counterparts. Metabolomic investigations suggest alterations in purine metabolism, lipid metabolism, sphingolipid metabolism, and aminoglycan and nucleotide sugar metabolism following NaHCO3 exposure. Transcriptomic data indicate differential expression of genes associated with nitrogen metabolism, complement and coagulation cascades, IL-17 signaling pathways, and Toll-like receptor signaling pathways. Conclusions: Overall, NaHCO3-induced stress leads to significant gill tissue damage, accompanied by reactive oxygen species (ROS) production causing oxidative stress and disruptions in lipid metabolism in crucian carp. Furthermore, an inflammatory response in gill cells triggers an immune response. Diploid crucian carp exhibit superior antioxidant and immune capacities compared to triploid counterparts, while also displaying reduced inflammatory responses in vivo. Notably, diploid carp efficiently excrete excess BUN through purine metabolism, mitigating protein metabolism and amino acid imbalances caused by BUN accumulation. This enables them to allocate less energy for coping with external environmental stress, redirecting surplus energy toward growth and development. The above results indicate that diploid organisms can better adapt to saline-alkaline environments. Overall, this study provides novel perspectives into species selection of crucian carp of different ploidy in saline-alkaline waters.PMID:39852348 | DOI:10.3390/metabo15010005

Metabolites. 2024 Dec 30;15(1):4. doi: 10.3390/metabo15010004.ABSTRACTIntroduction/Objectives: Since the biological activities and toxicities of 'foreign' and/or excess levels of metal ions are predominantly determined by their precise molecular nature, here we have employed high-resolution 1H NMR analysis to explore the 'speciation' of paramagnetic Ni(II) ions in human saliva, a potentially rich source of biomolecular Ni(II)-complexants/chelators. These studies are of relevance to the in vivo corrosion of nickel-containing metal alloy dental prostheses (NiC-MADPs) in addition to the dietary or adverse toxicological intake of Ni(II) ions by humans. Methods: Unstimulated whole-mouth human saliva samples were obtained from n = 12 pre-fasted (≥8 h) healthy participants, and clear whole-mouth salivary supernatants (WMSSs) were obtained from these via centrifugation. Microlitre aliquots of stock aqueous Ni(II) solutions were sequentially titrated into WMSS samples via micropipette. Any possible added concentration-dependent Ni(II)-mediated pH changes therein were experimentally controlled. 1H NMR spectra were acquired on a JEOL JNM-ECZ600R/S1 spectrometer. Results: Univariate and multivariate (MV) metabolomics and MV clustering analyses were conducted in a sequential stepwise manner in order to follow the differential effects of increasing concentrations of added Ni(II). The results acquired showed that important Ni(II)-responsive biomolecules could be clustered into distinguishable patterns on the basis of added concentration-dependent responses of their resonance intensities and line widths. At low added concentrations (71 µmol/L), low-WMSS-level N-donor amino acids (especially histidine) and amines with relatively high stability constants for this paramagnetic metal ion were the most responsive (severe resonance broadenings were observed). However, at higher Ni(II) concentrations (140-670 µmol/L), weaker carboxylate O-donor ligands such as lactate, formate, succinate, and acetate were featured as major Ni(II) ligands, a consequence of their much higher WMSS concentrations, which were sufficient for them to compete for these higher Ni(II) availabilities. From these experiments, the metabolites most affected were found to be histidine ≈ methylamines > taurine ≈ lactate ≈ succinate > formate > acetate ≈ ethanol ≈ glycine ≈ N-acetylneuraminate, although they predominantly comprised carboxylato oxygen donor ligands/chelators at the higher added Ni(II) levels. Removal of the interfering effects arising from the differential biomolecular compositions of the WMSS samples collected from different participants and those from the effects exerted by a first-order interaction effect substantially enhanced the statistical significance of the differences observed between the added Ni(II) levels. The addition of EDTA to Ni(II)-treated WMSS samples successfully reversed these resonance modifications, an observation confirming the transfer of Ni(II) from the above endogenous complexants to this exogenous chelator to form the highly stable diamagnetic octahedral [Ni(II)-EDTA] complex (Kstab = 1.0 × 1019 M-1). Conclusions: The results acquired demonstrated the value of linking advanced experimental design and multivariate metabolomics/statistical analysis techniques to 1H NMR analysis for such speciation studies. These provided valuable molecular information regarding the identities of Ni(II) complexes in human saliva, which is relevant to trace metal ion speciation and toxicology, the in vivo corrosion of NiC-MADPs, and the molecular fate of ingested Ni(II) ions in this biofluid. The carcinogenic potential of these low-molecular-mass Ni(II) complexes is discussed.PMID:39852347 | DOI:10.3390/metabo15010004

Metabolites. 2024 Dec 24;15(1):2. doi: 10.3390/metabo15010002.ABSTRACTBackground: Metabolomics measurements are noisy, often characterized by a small sample size and missing entries. While data-driven methods have shown promise in terms of analyzing metabolomics data, e.g., revealing biomarkers of various phenotypes, metabolomics data analysis can significantly benefit from incorporating prior information about metabolic mechanisms. This paper introduces a novel data analysis approach to incorporate mechanistic models in metabolomics data analysis. Methods: We arranged time-resolved metabolomics measurements of plasma samples collected during a meal challenge test from the COPSAC2000 cohort as a third-order tensor: subjects by metabolites by time samples. Simulated challenge test data generated using a human whole-body metabolic model were also arranged as a third-order tensor: virtual subjects by metabolites by time samples. Real and simulated data sets were coupled in the metabolites mode and jointly analyzed using coupled tensor factorizations to reveal the underlying patterns. Results: Our experiments demonstrated that the joint analysis of simulated and real data had better performance in terms of pattern discovery, achieving higher correlations with a BMI (body mass index)-related phenotype compared to the analysis of only real data in males, while in females, the performance was comparable. We also demonstrated the advantages of such a joint analysis approach in the presence of incomplete measurements and its limitations in the presence of wrong prior information. Conclusions: The joint analysis of real measurements and simulated data (generated using a mechanistic model) through coupled tensor factorizations guides real data analysis with prior information encapsulated in mechanistic models and reveals interpretable patterns.PMID:39852345 | DOI:10.3390/metabo15010002

Front Immunol. 2025 Jan 9;15:1444892. doi: 10.3389/fimmu.2024.1444892. eCollection 2024.ABSTRACTOBJECTIVE: We aimed to evaluate microbiome and microbiota-derived C18 dietary polyunsaturated fatty acids (PUFAs), such as conjugated linoleic acid (CLA), and to investigate their differences that correlate with arthritis severity in collagen-induced arthritis (CIA) mice.METHODS: On day 84 after induction, during the chronic phase of arthritis, cecal samples were analyzed using 16S rRNA sequencing, and plasma and cecal digesta were evaluated using liquid chromatography-tandem mass spectrometry. Differences in microbial composition between 10 control (Ctrl) and 29 CIA mice or between the mild and severe subgroups based on arthritis scores were identified. The cecal metabolite profile and its correlation with the microbiome were evaluated with respect to arthritis severity.RESULTS: The hydroxy and oxo metabolite levels were higher in CIA mice than in Ctrl mice, some of which, including 10-hydroxy-cis-6-18:1, were positively correlated with arthritis scores. The 9-trans,11-trans CLA levels in CIA mice had a negative linear correlation with arthritis scores. Microbial diversity was lower in severe CIA mice than in mild CIA or Ctrl mice. The abundance of Lactobacillus relatively increased in the severe subgroup of CIA mice compared with that in the mild subgroup and was positively correlated with arthritis severity.CONCLUSION: Alterations in gut microbiota and microbiota-derived C18 PUFA metabolites are associated in CIA mice and correlated with arthritis scores, indicating that plasma or fecal C18 PUFA metabolites can be potential biomarkers for arthritis severity and dysbiosis.PMID:39850876 | PMC:PMC11754244 | DOI:10.3389/fimmu.2024.1444892