PubMed

Insects. 2023 Oct 3;14(10):800. doi: 10.3390/insects14100800.ABSTRACTPlenty of freshwater species, especially macroinvertebrates that are essential to the provision of numerous ecosystem functions, encounter higher mortality due to acute hypoxia. However, within the family Chironomidae, a wide range of tolerance to hypoxia/anoxia is displayed. Propsilocerus akamusi depends on this great tolerance to become a dominant species in eutrophic lakes. To further understand how P. akamusi responds to acute hypoxic stress, we used multi-omics analysis in combination with histomorphological characteristics and physiological indicators. Thus, we set up two groups-a control group (DO 8.4 mg/L) and a hypoxic group (DO 0.39 mg/L)-to evaluate enzyme activity and the transcriptome, metabolome, and histomorphological characteristics. With blue-black chromatin, cell tightness, cell membrane invagination, and the production of apoptotic vesicles, tissue cells displayed typical apoptotic features in the hypoxic group. Although lactate dehydrogenase (LDH), alcohol dehydrogenase (ADH), catalase (CAT), and Na+/K+ -ATPase (NKA) activities were dramatically enhanced under hypoxic stress, glycogen content, and superoxide dismutase (SOD) activities were significantly reduced compared to the control group. The combined analysis of the transcriptome and metabolome, which further demonstrated, in addition to carbohydrates, including glycogen, the involvement of energy metabolism pathways, including fatty acid, protein, trehalose, and glyoxylate cycles, provided additional support for the aforementioned findings. Lactate is the end product of glycogen degradation, and HIF-1 plays an important role in promoting glycogenolysis in acute hypoxic conditions. However, we discovered that the ethanol tested under hypoxic stress likely originates from the symbiodinium of P. akamusi. These results imply that some parameters related to energy metabolism, antioxidant enzyme activities, and histomorphological features may be used as biomarkers of eutrophic lakes in Chironomus riparius larvae. The study also provides a scientific reference for assessing toxicity and favoring policies to reduce their impact on the environment.PMID:37887812 | DOI:10.3390/insects14100800

Metabolites. 2023 Oct 23;13(10):1104. doi: 10.3390/metabo13101104.ABSTRACTSouth Africa is rich in diverse medicinal plants, and it is reported to have over 35% of the global Helichrysum species, many of which are utilized in traditional medicine. Various phytochemical studies have offered valuable insights into the chemistry of Helichrysum plants, hinting at bioactive components that define the medicinal properties of the plant. However, there are still knowledge gaps regarding the size and diversity of the Helichrysum chemical space. As such, continuous efforts are needed to comprehensively characterize the phytochemistry of Helichrysum, which will subsequently contribute to the discovery and exploration of Helichrysum-derived natural products for drug discovery. Thus, reported herein is a computational metabolomics work to comprehensively characterize the metabolic landscape of the medicinal herb Helichrysum splendidum, which is less studied. Metabolites were methanol-extracted and analyzed on a liquid chromatography-tandem mass spectrometry (LC-MS/MS) system. Spectral data were mined using molecular networking (MN) strategies. The results revealed that the metabolic map of H. splendidum is chemically diverse, with chemical superclasses that include organic polymers, benzenoids, lipid and lipid-like molecules, alkaloids, and derivatives, phenylpropanoids and polyketides. These results point to a vastly rich chemistry with potential bioactivities, and the latter was demonstrated through computationally assessing the binding of selected metabolites with CDK-2 and CCNB1 anti-cancer targets. Molecular docking results showed that flavonoids (luteolin, dihydroquercetin, and isorhamnetin) and terpenoids (tiliroside and silybin) interact strongly with the CDK-2 and CCNB1 targets. Thus, this work suggests that these flavonoid and terpenoid compounds from H. splendidum are potentially anti-cancer agents through their ability to interact with these proteins involved in cancer pathways and progression. As such, these actionable insights are a necessary step for further exploration and translational studies for H. splendidum-derived compounds for drug discovery.PMID:37887429 | DOI:10.3390/metabo13101104

Metabolites. 2023 Oct 21;13(10):1101. doi: 10.3390/metabo13101101.ABSTRACTMetabolomics provides a unique snapshot into the world of small molecules and the complex biological processes that govern the human, animal, plant, and environmental ecosystems encapsulated by the One Health modeling framework. However, this "molecular snapshot" is only as informative as the number of metabolites confidently identified within it. The spectral similarity (SS) score is traditionally used to identify compound(s) in mass spectrometry approaches to metabolomics, where spectra are matched to reference libraries of candidate spectra. Unfortunately, there is little consensus on which of the dozens of available SS metrics should be used. This lack of standard SS score creates analytic uncertainty and potentially leads to issues in reproducibility, especially as these data are integrated across other domains. In this work, we use metabolomic spectral similarity as a case study to showcase the challenges in consistency within just one piece of the One Health framework that must be addressed to enable data science approaches for One Health problems. Here, using a large cohort of datasets comprising both standard and complex datasets with expert-verified truth annotations, we evaluated the effectiveness of 66 similarity metrics to delineate between correct matches (true positives) and incorrect matches (true negatives). We additionally characterize the families of these metrics to make informed recommendations for their use. Our results indicate that specific families of metrics (the Inner Product, Correlative, and Intersection families of scores) tend to perform better than others, with no single similarity metric performing optimally for all queried spectra. This work and its findings provide an empirically-based resource for researchers to use in their selection of similarity metrics for GC-MS identification, increasing scientific reproducibility through taking steps towards standardizing identification workflows.PMID:37887426 | DOI:10.3390/metabo13101101

Metabolites. 2023 Oct 20;13(10):1099. doi: 10.3390/metabo13101099.ABSTRACTNepeta nuda L. is a medicinal plant enriched with secondary metabolites serving to attract pollinators and deter herbivores. Phenolics and iridoids of N. nuda have been extensively investigated because of their beneficial impacts on human health. This study explores the chemical profiles of in vitro shoots and wild-grown N. nuda plants (flowers and leaves) through metabolomic analysis utilizing gas chromatography and mass spectrometry (GC-MS). Initially, we examined the differences in the volatiles' composition in in vitro-cultivated shoots comparing them with flowers and leaves from plants growing in natural environment. The characteristic iridoid 4a-α,7-β,7a-α-nepetalactone was highly represented in shoots of in vitro plants and in flowers of plants from nature populations, whereas most of the monoterpenes were abundant in leaves of wild-grown plants. The known in vitro biological activities encompassing antioxidant, antiviral, antibacterial potentials alongside the newly assessed anti-inflammatory effects exhibited consistent associations with the total content of phenolics, reducing sugars, and the identified metabolic profiles in polar (organic acids, amino acids, alcohols, sugars, phenolics) and non-polar (fatty acids, alkanes, sterols) fractions. Phytohormonal levels were also quantified to infer the regulatory pathways governing phytochemical production. The overall dataset highlighted compounds with the potential to contribute to N. nuda bioactivity.PMID:37887424 | DOI:10.3390/metabo13101099

Metabolites. 2023 Oct 19;13(10):1095. doi: 10.3390/metabo13101095.ABSTRACTRecently, a clinical blood metabogram was developed as a fast, low-cost and reproducible test that allows the implementation of metabolomics in clinical practice. The components of the metabogram are functionally related groups of blood metabolites associated with humoral regulation, the metabolism of lipids, carbohydrates and amines, lipid intake into the organism, and liver function, thereby providing clinically relevant information. It is known that the gut microbiota affects the blood metabolome, and the components of the blood metabolome may affect the composition of the gut microbiota. Therefore, before using the metabogram in the clinic, the link between the metabogram components and the level of gut microorganisms should be established. For this purpose, the metabogram and microbiota data were obtained in this work for the same individuals. Metabograms of blood plasma were obtained by direct mass spectrometry of blood plasma, and the gut microbiome was determined by a culture-based method and real-time polymerase chain reaction (PCR). This study involved healthy volunteers and individuals with varying degrees of deviation in body weight (n = 44). A correlation analysis determined which metabogram components are linked to which gut microorganisms and the strength of this link. Moreover, diagnostic parameters (sensitivity, specificity and accuracy) confirmed the capacity of metabogram components to be used for diagnosing gut microbiota alterations. Therefore, the obtained results allow the use of the metabogram in a clinical setting, taking into account its relationship with gut microbiota.PMID:37887420 | DOI:10.3390/metabo13101095

Metabolites. 2023 Oct 19;13(10):1094. doi: 10.3390/metabo13101094.ABSTRACTCold is an important environmental stress affecting the growth, productivity, and geographic distribution of tree species. Oaks are important for environmental conservation and wood supplies. Oak metabolites respond to low temperatures (LTs). In this study, the physiological and metabolic responses of two oak species to cold stress were investigated and compared. The field observations and physiological responses showed that Quercus wutaishanica was more cold-tolerant than Q. acutissima. After frost, the one-year-old twigs of Q. wutaishanica had higher survival rates, accumulated more soluble sugar and protein, and exhibited higher superoxide dismutase (SOD) activity than those of Q. acutissima. Untargeted metabolomics identified 102 and 78 differentially accumulated metabolites in Q. acutissima and Q. wutaishanica, respectively, when the leaves were subjected to LTs (4 °C for 24 h). The carbohydrate and flavonoid metabolites contributed to the cold tolerance of both oak species. Succinate, an intermediate in the citric acid cycle, was significantly inhibited by LTs, a potential energy conservation strategy. Unlike Q. acutissima, Q. wutaishanica underwent metabolic reprogramming that significantly increased the contents of phosphatidylcholine, gallic acid, oxidized glutathione, shikimate, and phenylpyruvate under LTs. Our data provide a reference for characterizing the mechanisms involved in the response of oak species to cold temperatures and enhancing the cold tolerance of forest trees.PMID:37887419 | DOI:10.3390/metabo13101094

Metabolites. 2023 Oct 18;13(10):1092. doi: 10.3390/metabo13101092.ABSTRACTOral hormonal contraceptive users carry the risk of venous thrombosis and increased mortality. This study aimed to comprehensively profile the serum metabolome of participants using a combination of drospirenone (DRSP) and ethinyl estradiol (EE) containing oral contraceptives (COCs). The MxP Quant 500 kit for liquid chromatography mass tandem spectrometry (LC-MS/MS) was used to analyse the 22 controls and 44 COC users (22 on a low EE dose (DRSP/20EE) and 22 on a higher EE dose (DRSP/30EE)). The kit's results were compared to our internally developed untargeted and targeted metabolomics methods previously applied to this cohort. Of the 630 metabolites included in the method, 277 provided desirable results (consistently detected above their detection limits), and of these, 5 had p-values < 0.05, including betaine, glutamine, cortisol, glycine, and choline. Notably, these variations were observed between the control and COC groups, rather than among the two COC groups. Partial least squares-discriminant analysis revealed 49 compounds with VIP values ≥ 1, including amino acids and their derivatives, ceramides, phosphatidylcholines, and triglycerides, among others. Ten differential compounds were consistent with our previous studies, reinforcing the notion of COCs inducing a prothrombotic state and increased oxidative stress. Although only a limited number of compounds were deemed usable, these were quantified with high reliability and facilitated the identification of meaningful biological differences among the sample groups. In addition to substantiating known drug-induced variations, new hypotheses were also generated.PMID:37887417 | DOI:10.3390/metabo13101092

Metabolites. 2023 Oct 17;13(10):1088. doi: 10.3390/metabo13101088.ABSTRACTThe Animal Metabolite Database (AMDB, https://amdb.online) is a freely accessible database with built-in statistical analysis tools, allowing one to browse and compare quantitative metabolomics data and raw NMR and MS data, as well as sample metadata, with a focus on the metabolite concentrations rather than on the raw data itself. AMDB also functions as a platform for the metabolomics community, providing convenient deposition and exchange of quantitative metabolomic data. To date, the majority of the data in AMDB relate to the metabolite content of the eye lens and blood of vertebrates, primarily wild species from Siberia, Russia and laboratory rodents. However, data on other tissues (muscle, heart, liver, brain, and more) are also present, and the list of species and tissues is constantly growing. Typically, every sample in AMDB contains concentrations of 60-90 of the most abundant metabolites, provided in nanomoles per gram of wet tissue weight (nmol/g). We believe that AMDB will become a widely used tool in the community, as typical metabolite baseline concentrations in tissues of animal models will aid in a wide variety of fundamental and applied scientific fields, including, but not limited to, animal modeling of human diseases, assessment of medical formulations, and evolutionary and environmental studies.PMID:37887413 | DOI:10.3390/metabo13101088

Metabolites. 2023 Oct 17;13(10):1086. doi: 10.3390/metabo13101086.ABSTRACTGrowing evidence indicates that metabolites and energy metabolism play an active rather than consequential role in regulating cellular fate. Cardiac development requires dramatic metabolic remodeling from relying primarily on glycolysis in pluripotent stem cells (PSCs) to oxidizing a wide array of energy substrates to match the high bioenergetic demands of continuous contraction in the developed heart. However, a detailed analysis of how remodeling of energy metabolism contributes to human cardiac development is lacking. Using dynamic multiple reaction monitoring metabolomics of central carbon metabolism, we evaluated temporal changes in energy metabolism during human PSC 3D cardiac lineage specification. Significant metabolic remodeling occurs during the complete differentiation, yet temporal analysis revealed that most changes occur during transitions from pluripotency to mesoderm (day 1) and mesoderm to early cardiac (day 5), with limited maturation of cardiac metabolism beyond day 5. Real-time metabolic analysis demonstrated that while hPSC cardiomyocytes (hPSC-CM) showed elevated rates of oxidative metabolism compared to PSCs, they still retained high glycolytic rates, confirming an immature metabolic phenotype. These observations support the opportunity to metabolically optimize the differentiation process to support lineage specification and maturation of hPSC-CMs.PMID:37887411 | DOI:10.3390/metabo13101086

Metabolites. 2023 Oct 13;13(10):1079. doi: 10.3390/metabo13101079.ABSTRACTIn this investigation, we outline the applications of a data mining technique known as Subgroup Discovery (SD) to the analysis of a sample size-limited metabolomics-based dataset. The SD technique utilized a supervised learning strategy, which lies midway between classificational and descriptive criteria, in which given the descriptive property of a dataset (i.e., the response target variable of interest), the primary objective was to discover subgroups with behaviours that are distinguishable from those of the complete set (albeit with a differential statistical distribution). These approaches have, for the first time, been successfully employed for the analysis of aromatic metabolite patterns within an NMR-based urinary dataset collected from a small cohort of patients with the lysosomal storage disorder Niemann-Pick class 1 (NPC1) disease (n = 12) and utilized to distinguish these from a larger number of heterozygous (parental) control participants. These subgroup discovery strategies discovered two different NPC1 disease-specific metabolically sequential rules which permitted the reliable identification of NPC1 patients; the first of these involved 'normal' (intermediate) urinary concentrations of xanthurenate, 4-aminobenzoate, hippurate and quinaldate, and disease-downregulated levels of nicotinate and trigonelline, whereas the second comprised 'normal' 4-aminobenzoate, indoxyl sulphate, hippurate, 3-methylhistidine and quinaldate concentrations, and again downregulated nicotinate and trigonelline levels. Correspondingly, a series of five subgroup rules were generated for the heterozygous carrier control group, and 'biomarkers' featured in these included low histidine, 1-methylnicotinamide and 4-aminobenzoate concentrations, together with 'normal' levels of hippurate, hypoxanthine, quinolinate and hypoxanthine. These significant disease group-specific rules were consistent with imbalances in the combined tryptophan-nicotinamide, tryptophan, kynurenine and tyrosine metabolic pathways, along with dysregulations in those featuring histidine, 3-methylhistidine and 4-hydroxybenzoate. In principle, the novel subgroup discovery approach employed here should also be readily applicable to solving metabolomics-type problems of this nature which feature rare disease classification groupings with only limited patient participant and sample sizes available.PMID:37887404 | DOI:10.3390/metabo13101079

Metabolites. 2023 Oct 13;13(10):1075. doi: 10.3390/metabo13101075.ABSTRACTThe untargeted metabolomics analysis of biological samples with nuclear magnetic resonance (NMR) provides highly complex data containing various signals from different molecules. To use these data for classification, e.g., in the context of food authentication, machine learning methods are used. These methods are usually applied as a black box, which means that no information about the complex relationships between the variables and the outcome is obtained. In this study, we show that the random forest-based approach surrogate minimal depth (SMD) can be applied for a comprehensive analysis of class-specific differences by selecting relevant variables and analyzing their mutual impact on the classification model of different truffle species. SMD allows the assignment of variables from the same metabolites as well as the detection of interactions between different metabolites that can be attributed to known biological relationships.PMID:37887402 | DOI:10.3390/metabo13101075

Metabolites. 2023 Oct 12;13(10):1073. doi: 10.3390/metabo13101073.ABSTRACTHepatocellular carcinoma (HCC) is one of the most refractory cancers with a high rate of recurrence. Iron is an essential trace element, and iron chelation has garnered attention as a novel therapeutic strategy for cancer. Since intracellular metabolism is significantly altered by inhibiting various proteins by iron chelation, we investigated combination anticancer therapy targeting metabolic changes that are forcibly modified by iron chelator administration. The deferoxamine (DFO)-resistant cell lines were established by gradually increasing the DFO concentration. Metabolomic analysis was conducted to evaluate the metabolic alterations induced by DFO administration, aiming to elucidate the resistance mechanism in DFO-resistant strains and identify potential novel therapeutic targets. Metabolom analysis of the DFO-resistant Huh7 cells revealed enhanced glycolysis and salvage cycle, alternations in glutamine metabolism, and accumulation of dipeptides. Huh7 cultured in the absence of glutamine showed enhanced sensitivity to DFO, and glutaminase inhibitor (CB839) showed a synergistic effect with DFO. Furthermore, the effect of DFO was enhanced by an autophagy inhibitor (chloroquine) in vitro. DFO-induced metabolic changes are specific targets for the development of efficient anticancer combinatorial therapies using DFO. These findings will be useful for the development of new cancer therapeutics in refractory liver cancer.PMID:37887398 | DOI:10.3390/metabo13101073

Metabolites. 2023 Oct 12;13(10):1071. doi: 10.3390/metabo13101071.ABSTRACTThe Omega-3 Index (O3I) reflects eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) content in erythrocytes. While the O3I is associated with numerous health outcomes, its widespread use is limited. We investigated whether urinary metabolites could be used to non-invasively monitor the O3I in an exploratory analysis of a previous placebo-controlled, parallel arm randomized clinical trial in males and females (n = 88) who consumed either ~3 g/d olive oil (OO; control), EPA, or DHA for 12 weeks. Fasted blood and first-void urine samples were collected at baseline and following supplementation, and they were analyzed via gas chromatography and multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS), respectively. We tentatively identified S-carboxypropylcysteamine (CPCA) as a novel urinary biomarker reflecting O3I status, which increased following both EPA and DHA (p < 0.001), but not OO supplementation, and was positively correlated to the O3I (R = 0.30, p < 0.001). Additionally, an unknown dianion increased following DHA supplementation, but not EPA or OO. In ROC curve analyses, CPCA outperformed all other urinary metabolites in distinguishing both between OO and EPA or DHA supplementation groups (AUC > 80.0%), whereas the unknown dianion performed best in discriminating OO from DHA alone (AUC = 93.6%). Candidate urinary biomarkers of the O3I were identified that lay the foundation for a non-invasive assessment of omega-3 status.PMID:37887396 | DOI:10.3390/metabo13101071

Metabolites. 2023 Oct 10;13(10):1069. doi: 10.3390/metabo13101069.ABSTRACTIdiopathic pulmonary fibrosis (IPF) is a chronic and progressive condition characterized by the impairment of alveolar epithelial cells. Despite continued research efforts, the effective therapeutic medication is still absent due to an incomplete understanding of the underlying etiology. It has been shown that rhythmic alterations are of significant importance in the pathophysiology of IPF. However, a comprehensive understanding of how metabolite level changes with circadian rhythms in individuals with IPF is lacking. Here, we constructed an extensive metabolite database by utilizing an unbiased reference system culturing with 13C or 15N labeled nutrients. Using LC-MS analysis via ESI and APCI ion sources, 1300 potential water-soluble metabolites were characterized and applied to evaluate the metabolic changes with rhythm in the lung from both wild-type mice and mice with IPF. The metabolites, such as glycerophospholipids and amino acids, in WT mice exhibited notable rhythmic oscillations. The concentrations of phospholipids reached the highest during the fast state, while those of amino acids reached their peak during fed state. Similar diurnal variations in the metabolite rhythm of amino acids and phospholipids were also observed in IPF mice. Although the rhythmic oscillation of metabolites in the urea cycle remained unchanged, there was a significant up-regulation in their levels in the lungs of IPF mice. 15N-ammonia in vivo isotope tracing further showed an increase in urea cycle activity in the lungs of mice with IPF, which may compensate for the reduced efficiency of the hepatic urea cycle. In sum, our metabolomics database and method provide evidence of the periodic changes in lung metabolites, thereby offering valuable insights to advance our understanding of metabolic reprogramming in the context of IPF.PMID:37887394 | DOI:10.3390/metabo13101069

Metabolites. 2023 Oct 9;13(10):1061. doi: 10.3390/metabo13101061.ABSTRACTThe gut microbiome is of tremendous relevance to human health and disease, so it is a hot topic of omics-driven biomedical research. However, a valid identification of gut microbiota-associated molecules in human blood or urine is difficult to achieve. We hypothesize that bowel evacuation is an easy-to-use approach to reveal such metabolites. A non-targeted and modifying group-assisted metabolomics approach (covering 40 types of modifications) was applied to investigate urine samples collected in two independent experiments at various time points before and after laxative use. Fasting over the same time period served as the control condition. As a result, depletion of the fecal microbiome significantly affected the levels of 331 metabolite ions in urine, including 100 modified metabolites. Dominating modifications were glucuronidations, carboxylations, sulfations, adenine conjugations, butyrylations, malonylations, and acetylations. A total of 32 compounds, including common, but also unexpected fecal microbiota-associated metabolites, were annotated. The applied strategy has potential to generate a microbiome-associated metabolite map (M3) of urine from healthy humans, and presumably also other body fluids. Comparative analyses of M3 vs. disease-related metabolite profiles, or therapy-dependent changes may open promising perspectives for human gut microbiome research and diagnostics beyond analyzing feces.PMID:37887386 | DOI:10.3390/metabo13101061

Metabolites. 2023 Oct 8;13(10):1059. doi: 10.3390/metabo13101059.ABSTRACTHepatocellular carcinoma (HCC) is the main liver malignancy and has a high mortality rate. The discovery of novel biomarkers for early diagnosis, prognosis, and stratification purposes has the potential to alleviate its disease burden. Mass spectrometry (MS) is one of the principal technologies used in metabolomics, with different experimental methods and machine types for different phases of the biomarker discovery process. Here, we review why MS applications are useful for liver cancer, explain the MS technique, and briefly summarise recent findings from metabolomic MS studies on HCC. We also discuss the current challenges and the direction for future research.PMID:37887384 | DOI:10.3390/metabo13101059

Metabolites. 2023 Oct 7;13(10):1058. doi: 10.3390/metabo13101058.ABSTRACTAnkylosing spondylitis (AS) is a type of chronic rheumatic immune disease, and the crucial point of AS treatment is identifying the correct stage of the disease. However, there is a lack of effective diagnostic methods for AS staging. The primary objective of this study was to perform an untargeted metabolomic approach in AS patients in an effort to reveal metabolic differences between patients in remission and acute stages. Serum samples from 40 controls and 57 AS patients were analyzed via gas chromatography-mass spectrometry (GC-MS). Twenty-four kinds of differential metabolites were identified between the healthy controls and AS patients, mainly involving valine/leucine/isoleucine biosynthesis and degradation, phenylalanine/tyrosine/tryptophan biosynthesis, glutathione metabolism, etc. Furthermore, the levels of fatty acids (linoleate, dodecanoate, hexadecanoate, and octadecanoate), amino acids (serine and pyroglutamate), 2-hydroxybutanoate, glucose, etc., were lower in patients in the acute stage than those in the remission stage, which may be associated with the aggravated inflammatory response and elevated oxidative stress in the acute stage. Multiple stage-specific metabolites were significantly correlated with inflammatory indicators (CRP and ESR). In addition, the combination of serum 2-hydroxybutanoate and hexadecanoate plays a significant role in the diagnosis of AS stages. These metabolomics-based findings provide new perspectives for AS staging, treatment, and pathogenesis studies.PMID:37887383 | DOI:10.3390/metabo13101058

Metabolites. 2023 Oct 7;13(10):1057. doi: 10.3390/metabo13101057.ABSTRACTAlcoholic liver disease (ALD) represents a significant global health concern, yet the available treatment options remain limited. Numerous studies have shown that gut microbiota is a critical target for the treatment of ALD. Additionally, there is increasing evidence that host metabolism also plays a crucial role in the development of ALD. Akkermansia muciniphila has been demonstrated to ameliorate experimental ALD through its modulatory effects on the intestinal vascular barrier, enhancement of mucus layer thickness, and promotion of intestinal tight junction proteins. Nevertheless, there is a dearth of studies investigating the impact of A. muciniphila on host metabolism and gut microbiota. Here, C57BL/6 mice were utilized to establish a modified NIAAA model in order to investigate the impact of the oral administration of A. muciniphila during the development of ALD. Furthermore, we employed targeted metabolomics to analyze the serum metabolomic profiles of the mice and 2bRAD-M sequencing to comprehensively examine the underlying mechanisms of the efficacy of A. muciniphila on ALD. Our results illustrated that the oral administration of A. muciniphila alleviated alcohol-induced liver injury in conjunction with encouraged serum levels of ornithine and diminished the elevation of oxalic acid levels induced by alcohol intake. In addition, A. muciniphila also inhibited the proliferation of harmful bacteria, such as Escherichia coli and Helicobacter hepaticus, induced by alcohol consumption while promoting the growth of butyrate-producing and commensal bacteria, including Paramuribaculum intestinale and Bacteroides ovatus. In conclusion, this study suggests that A. muciniphila restores ALD by regulating the gut microbiota, and this corrective effect is associated with alterations in the serum metabolism. Our research supplies a theoretical basis for developing A. muciniphila as an innovative generation of probiotic for preventing and managing ALD.PMID:37887381 | DOI:10.3390/metabo13101057

Metabolites. 2023 Oct 7;13(10):1055. doi: 10.3390/metabo13101055.ABSTRACTWe developed a machine-learning system for the selective diagnostics of adenocarcinoma (AD), squamous cell carcinoma (SQ), and small-cell carcinoma lung (SC) cancers based on their metabolomic profiles. The system is organized as two-stage binary classifiers. The best accuracy for classification is 92%. We used the biomarkers sets that contain mostly metabolites related to cancer development. Compared to traditional methods, which exclude hierarchical classification, our method splits a challenging multiclass task into smaller tasks. This allows a two-stage classifier, which is more accurate in the scenario of lung cancer classification. Compared to traditional methods, such a "divide and conquer strategy" gives much more accurate and explainable results. Such methods, including our algorithm, allow for the systematic tracking of each computational step.PMID:37887380 | DOI:10.3390/metabo13101055

Metabolites. 2023 Oct 5;13(10):1052. doi: 10.3390/metabo13101052.ABSTRACTCellular metabolomics provides insights into the metabolic processes occurring within cells and can help researchers understand how these processes are regulated and how they relate to cellular function, health, and disease. In this technical note, we investigated the effects of solvent evaporation equipment and storage condition on high-coverage cellular metabolomics. We previously introduced a robust CIL LC-MS-based cellular metabolomics workflow that encompasses various steps, including cell harvest, metabolic quenching, cell lysis, metabolite extraction, differential chemical isotope labeling, and LC-MS analysis. This workflow has consistently served as the cornerstone of our collaborative research and service projects. As a core facility catering to users with diverse research needs and financial resources, we have encountered scenarios requiring short-term sample storage. For example, the need often arises to transport samples at room temperature from user sites to our core facility. Herein, we present a study in which we compared different solvent evaporation methods (specifically, the nitrogen blowdown evaporator, SpeedVac concentrator, and lyophilizer) and diverse storage conditions (including dried samples stored in a freezer, samples stored in a freezer with methanol, dried samples stored at room temperature, and samples stored at room temperature with methanol). Our findings indicate that the choice of solvent evaporation equipment did not significantly impact the cellular metabolome. However, we observed a noteworthy change in the metabolome after 7 days of storage when cells were stored with methanol, regardless of whether they were kept at -80 °C or room temperature, in contrast to cells that were dried and frozen. Importantly, we detected no significant alterations in cells that were dried and stored at room temperature. In conclusion, to ensure the production of high-quality CIL LC-MS metabolomics results, we strongly recommend that, in situations where low-temperature storage is not feasible, cell samples should be thoroughly dried before storage or shipment at room temperature.PMID:37887377 | DOI:10.3390/metabo13101052