PubMed

Molecules. 2023 Mar 8;28(6):2494. doi: 10.3390/molecules28062494.ABSTRACTJigucao capsules (JGCC) have the effects of soothing the liver and gallbladder and clearing heat and detoxification. It is a good medicine for treating acute and chronic hepatitis cholecystitis with damp heat of the liver and gallbladder. However, the existing quality standard of JGCC does not have content determination items, which is not conducive to quality control. In this study, serum pharmacochemistry technology and UNIFI data processing software were used to identify the blood prototype components and metabolites under the condition of the obvious drug effects of JGCC, and the referenced literature reports and the results from in vitro analysis of JGCC in the early stage revealed a total of 43 prototype blood components and 33 metabolites in JGCC. Quality markers (Q-markers) were discovered, such as abrine, trigonelline, hypaphorine and isoschaftoside. In addition, ultra-high-performance liquid chromatography-triple quadrupole mass spectrometry (UPLC-QQQ-MS) was used to determine the active ingredients in JGCC. The components of quantitative analysis have good correlation in the linear range with R2 ≥ 0.9993. The recovery rate is 93.15%~108.92% and the relative standard deviation (RSD) is less than 9.48%. The established UPLC-MS/MS quantitative analysis method has high sensitivity and accuracy, and can be used for the quality evaluation of JGCC.PMID:36985466 | DOI:10.3390/molecules28062494

Molecules. 2023 Mar 8;28(6):2462. doi: 10.3390/molecules28062462.ABSTRACTGreen tea is a popular beverage around the world and possesses a unique flavor. The flavor qualities of green tea are closely related to its grade and this relationship has not yet been studied. Three baked green teas with similar flavor were studied, namely, Huangshan Maofeng, Taiping Houkui, and Shucheng Xiaolanhua. A total of 34 odor compounds were identified by solid phase microextraction (SPME) combined with two-dimensional comprehensive gas chromatography-olfactometry-mass spectrometry analysis (GC×GC-O-MS). The results of the clustering analysis showed that the content of D-limonene and linalool in the high-grade (Grade A) tea was much higher than the content in other grades, so they were identified as odor markers of Grade A baked green tea. The taste components of different grades of green tea infusion were analyzed by high-performance liquid chromatography-mass spectrometry (HPLC-MS) and HPLC. A combination of clustering analysis, principal component analysis (PCA), and orthogonal partial least squares discrimination analysis (OPLS-DA) indicated that galloylglucose, digalloylglucose, trigalloyglucose, strictinin, and gallic acid could be used as taste markers of Grade A baked green tea. Therefore, the results in this paper reveal the substances responsible for the odor and taste markers of high-grade baked green tea.PMID:36985433 | DOI:10.3390/molecules28062462

Microorganisms. 2023 Mar 16;11(3):769. doi: 10.3390/microorganisms11030769.ABSTRACTMajor health issues, such as the rise in oxidative stress, incidences of Alzheimer's disease, and infections caused by antibiotic-resistant microbes, have prompted researchers to look for new therapeutics. Microbial extracts are still a good source of novel compounds for biotechnological use. The objective of the current work was to investigate marine fungal bioactive compounds with potential antibacterial, antioxidant, and acetylcholinesterase inhibitory effects. Penicillium chrysogenum strain MZ945518 was isolated from the Mediterranean Sea in Egypt. The fungus was halotolerant with a salt tolerance index of 1.3. The mycelial extract showed antifungal properties against Fusarium solani with an inhibitory percentage of 77.5 ± 0.3, followed by Rhizoctonia solani and Fusarium oxysporum with percentages of 52 ± 0.0 and 40 ± 0.5, respectively. The extract also showed antibacterial activity against both Gram-negative and Gram-positive bacterial strains using the agar diffusion technique. The fungal extract was significantly more effective with Proteus mirabilis ATCC 29906 and Micrococcus luteus ATCC 9341; inhibition zones recorded 20 and 12 mm, respectively, compared with the antibiotic gentamycin, which recorded 12 and 10 mm, respectively. The antioxidant activity of the fungus extract revealed that it successfully scavenged DPPH free radicals and recorded an IC50 of 542.5 µg/mL. Additionally, it was capable of reducing Fe3+ to Fe2+ and exhibiting chelating ability in the metal ion-chelating test. The fungal extract was identified as a crucial inhibitor of acetylcholinesterase with an inhibition percentage of 63% and an IC50 value of 60.87 µg/mL. Using gas chromatography-mass spectrometry (GC/MS), 20 metabolites were detected. The most prevalent ones were (Z)-18-octadec-9-enolide and 1,2-Benzenedicarboxylic acid, with ratios of 36.28 and 26.73%, respectively. An in silico study using molecular docking demonstrated interactions between the major metabolites and the target proteins, including: DNA Gyrase, glutathione S-transferase, and Acetylcholinesterase, confirming the extract's antimicrobial and antioxidant activity. Penicillium chrysogenum MZ945518, a halotolerant strain, has promising bioactive compounds with antibacterial, antioxidant, and acetylcholinesterase inhibitory activities.PMID:36985342 | DOI:10.3390/microorganisms11030769

Microorganisms. 2023 Mar 16;11(3):766. doi: 10.3390/microorganisms11030766.ABSTRACTIntegration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome-metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZOE 2.0; investigating early childhood dental caries, ECC) and find that the accuracy of the BZINB model-based correlation method is superior compared to Spearman's rank and Pearson correlations in terms of approximating the underlying relationships between microbial taxa and metabolites. The new method, BZINB-iMMPath, facilitates the construction of metabolite-species and species-species correlation networks using BZINB and identifies modules of (i.e., correlated) species by combining BZINB and similarity-based clustering. Perturbations in correlation networks and modules can be efficiently tested between groups (i.e., healthy and diseased study participants). Upon application of the new method in the ZOE 2.0 study microbiome-metabolome data, we identify that several biologically-relevant correlations of ECC-associated microbial taxa with carbohydrate metabolites differ between healthy and dental caries-affected participants. In sum, we find that the BZINB model is a useful alternative to Spearman or Pearson correlations for estimating the underlying correlation of zero-inflated bivariate count data and thus is suitable for integrative analyses of multi-omics data such as those encountered in microbiome and metabolome studies.PMID:36985339 | DOI:10.3390/microorganisms11030766

Microorganisms. 2023 Mar 8;11(3):697. doi: 10.3390/microorganisms11030697.ABSTRACTDespite considerable advances in medicine and technology, humanity still faces many deadly diseases such as cancer and malaria. In order to find appropriate treatments, the discovery of new bioactive substances is essential. Therefore, research is now turning to less frequently explored habitats with exceptional biodiversity such as the marine environment. Many studies have demonstrated the therapeutic potential of bioactive compounds from marine macro- and microorganisms. In this study, nine microbial strains isolated from an Indian Ocean sponge, Scopalina hapalia, were screened for their chemical potential. The isolates belong to different phyla, some of which are already known for their production of secondary metabolites, such as the actinobacteria. This article aims at describing the selection method used to identify the most promising microorganisms in the field of active metabolites production. The method is based on the combination of their biological and chemical screening, coupled with the use of bioinformatic tools. The dereplication of microbial extracts and the creation of a molecular network revealed the presence of known bioactive molecules such as staurosporin, erythromycin and chaetoglobosins. Molecular network exploration indicated the possible presence of novel compounds in clusters of interest. The biological activities targeted in the study were cytotoxicity against the HCT-116 and MDA-MB-231 cell lines and antiplasmodial activity against Plasmodium falciparum 3D7. Chaetomium globosum SH-123 and Salinispora arenicola SH-78 strains actually showed remarkable cytotoxic and antiplasmodial activities, while Micromonospora fluostatini SH-82 demonstrated promising antiplasmodial effects. The ranking of the microorganisms as a result of the different screening steps allowed the selection of a promising strain, Micromonospora fluostatini SH-82, as a premium candidate for the discovery of new drugs.PMID:36985270 | DOI:10.3390/microorganisms11030697

Microorganisms. 2023 Mar 7;11(3):678. doi: 10.3390/microorganisms11030678.ABSTRACTMikania micrantha is one of the worst invasive species globally and can cause significant negative impacts on agricultural and forestry economics, particularly in Asia and the Pacific region. The rust Puccinia spegazzinii has been used successfully as a biological control agent in several countries to help manage M. micrantha. However, the response mechanisms of M. micrantha to P. spegazzinii infection have never been studied. To investigate the response of M. micrantha to infection by P. spegazzinii, an integrated analysis of metabolomics and transcriptomics was performed. The levels of 74 metabolites, including organic acids, amino acids, and secondary metabolites in M. micrantha infected with P. spegazzinii, were significantly different compared to those in plants that were not infected. After P. spegazzinii infection, the expression of the TCA cycle gene was significantly induced to participate in energy biosynthesis and produce more ATP. The content of most amino acids, such as L-isoleucine, L-tryptophan and L-citrulline, increased. In addition, phytoalexins, such as maackiain, nobiletin, vasicin, arachidonic acid, and JA-Ile, accumulated in M. micrantha. A total of 4978 differentially expressed genes were identified in M. micrantha infected by P. spegazzinii. Many key genes of M. micrantha in the PTI (pattern-triggered immunity) and ETI (effector-triggered immunity) pathways showed significantly higher expression under P. spegazzinii infection. Through these reactions, M. micrantha is able to resist the infection of P. spegazzinii and maintain its growth. These results are helpful for us to understand the changes in metabolites and gene expression in M. micrantha after being infected by P. spegazzinii. Our results can provide a theoretical basis for weakening the defense response of M. micrantha to P. spegazzinii, and for P. spegazzinii as a long-term biological control agent of M. micrantha.PMID:36985252 | DOI:10.3390/microorganisms11030678

Microorganisms. 2023 Feb 24;11(3):572. doi: 10.3390/microorganisms11030572.ABSTRACTEarly detection of late-onset sepsis (LOS) in preterm infants is crucial since timely treatment initiation is a key prognostic factor. We hypothesized that fecal volatile organic compounds (VOCs), reflecting microbiota composition and function, could serve as a non-invasive biomarker for preclinical pathogen-specific LOS detection. Fecal samples and clinical data of all preterm infants (≤30 weeks' gestation) admitted at nine neonatal intensive care units in the Netherlands and Belgium were collected daily. Samples from one to three days before LOS onset were analyzed by gas chromatography-ion mobility spectrometry (GC-IMS), a technique based on pattern recognition, and gas chromatography-time of flight-mass spectrometry (GC-TOF-MS), to identify unique metabolites. Fecal VOC profiles and metabolites from infants with LOS were compared with matched controls. Samples from 121 LOS infants and 121 matched controls were analyzed using GC-IMS, and from 34 LOS infants and 34 matched controls using GC-TOF-MS. Differences in fecal VOCs were most profound one and two days preceding Escherichia coli LOS (Area Under Curve; p-value: 0.73; p = 0.02, 0.83; p < 0.002, respectively) and two and three days before gram-negative LOS (0.81; p < 0.001, 0.85; p < 0.001, respectively). GC-TOF-MS identified pathogen-specific discriminative metabolites for LOS. This study underlines the potential for VOCs as a non-invasive preclinical diagnostic LOS biomarker.PMID:36985146 | DOI:10.3390/microorganisms11030572

Metabolites. 2023 Mar 22;13(3):460. doi: 10.3390/metabo13030460.ABSTRACTDirect infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) is a promising tool for high-throughput metabolomics analysis. However, metabolite assignment is limited by the inadequate mass accuracy and chemical space of the metabolome database. Here, a serum metabolome characterization method was proposed to make full use of the potential of DI-nESI-HRMS. Different from the widely used database search approach, unambiguous formula assignments were achieved by a reaction network combined with mass accuracy and isotopic patterns filter. To provide enough initial known nodes, an initial network was directly constructed by known metabolite formulas. Then experimental formula candidates were screened by the predefined reaction with the network. The effects of sources and scales of networks on assignment performance were investigated. Further, a scoring rule for filtering unambiguous formula candidates was proposed. The developed approach was validated by a pooled serum sample spiked with reference standards. The coverage and accuracy rates for the spiked standards were 98.9% and 93.6%, respectively. A total of 1958 monoisotopic features were assigned with unique formula candidates for the pooled serum, which is twice more than the database search. Finally, a case study of serum metabolomics in diabetes was carried out using the developed method.PMID:36984900 | DOI:10.3390/metabo13030460

Metabolites. 2023 Mar 20;13(3):454. doi: 10.3390/metabo13030454.ABSTRACTThe study of plant metabolism is one of the most complex tasks, mainly due to the huge amount and structural diversity of metabolites, as well as the fact that they react to changes in the environment and ultimately influence each other. Metabolic profiling is most often carried out using tools that include mass spectrometry (MS), which is one of the most powerful analytical methods. All this means that even when analyzing a single sample, we can obtain thousands of data. Data science has the potential to revolutionize our understanding of plant metabolism. This review demonstrates that machine learning, network analysis, and statistical modeling are some techniques being used to analyze large quantities of complex data that provide insights into plant development, growth, and how they interact with their environment. These findings could be key to improving crop yields, developing new forms of plant biotechnology, and understanding the relationship between plants and microbes. It is also necessary to consider the constraints that come with data science such as quality and availability of data, model complexity, and the need for deep knowledge of the subject in order to achieve reliable outcomes.PMID:36984894 | DOI:10.3390/metabo13030454

Metabolites. 2023 Mar 20;13(3):453. doi: 10.3390/metabo13030453.ABSTRACTAcute stress response triggers various physiological responses such as energy mobilization to meet metabolic demands. However, the underlying molecular changes in the brain remain largely obscure. Here, we used a brief water avoidance stress (WAS) to elicit an acute stress response in mice. By employing RNA-sequencing and metabolomics profiling, we investigated the acute stress-induced molecular changes in the mouse whole brain. The aberrant expression of 60 genes was detected in the brain tissues of WAS-exposed mice. Functional analyses showed that the aberrantly expressed genes were enriched in various processes such as superoxide metabolism. In our global metabolomic profiling, a total of 43 brain metabolites were significantly altered by acute WAS. Metabolic pathways upregulated from WAS-exposed brain tissues relative to control samples included lipolysis, eicosanoid biosynthesis, and endocannabinoid synthesis. Acute WAS also elevated the levels of branched-chain amino acids, 5-aminovalerates, 4-hydroxy-nonenal-glutathione as well as mannose, suggesting complex metabolic changes in the brain. The observed molecular events in the present study provide a valuable resource that can help us better understand how acute psychological stress impacts neural functions.PMID:36984893 | DOI:10.3390/metabo13030453

Metabolites. 2023 Mar 20;13(3):452. doi: 10.3390/metabo13030452.ABSTRACTAromatase inhibitor-induced arthralgia (AIA) presents a major problem for patients with breast cancer but is poorly understood. This prospective study explored the inflammatory metabolomic changes in the development of AIA. This single-arm, prospective clinical trial enrolled 28 postmenopausal women with early-stage (0-3) ER+ breast cancer starting adjuvant anastrozole. Patients completed the Breast Cancer Prevention Trial (BCPT) Symptom Checklist and the Western Ontario and McMaster Universities Arthritis Index (WOMAC) at 0, 3, and 6 months. The plasma levels of four polyunsaturated fatty acids (PUFAs) and 48 oxylipins were quantified at each timepoint. The subscores for WOMAC-pain and stiffness as well as BCPT-total, hot flash, and musculoskeletal pain significantly increased from baseline to 6 months (all p < 0.05). PUFA and oxylipin levels were stable over time. The baseline levels of 8-HETE were positively associated with worsening BCPT-total, BCPT-hot flash, BCPT-musculoskeletal pain, WOMAC-pain, and WOMAC- stiffness at 6 months (all p < 0.05). Both 9-HOTrE and 13(S)-HOTrE were related to worsening hot flash, and 5-HETE was related to worsening stiffness (all p < 0.05). This is the first study to prospectively characterize oxylipin and PUFA levels in patients with breast cancer starting adjuvant anastrozole. The oxylipin 8-HETE should be investigated further as a potential biomarker for AIA.PMID:36984892 | DOI:10.3390/metabo13030452

Metabolites. 2023 Mar 20;13(3):451. doi: 10.3390/metabo13030451.ABSTRACT2-hydroxybutyric acid (2HB) serves as an important regulatory factor in a variety of diseases. The circulating level of 2HB in serum is significantly higher in multiple diseases, such as cancer and type 2 diabetes (T2D). However, there is currently no systematic study on 2HB-producing bacteria that demonstrates whether gut bacteria contribute to the circulating 2HB pool. To address this question, we used BLASTP to reveal the taxonomic profiling of 2HB-producing bacteria in the human microbiome, which are mainly distributed in the phylum Proteobacteria and Firmicutes. In vitro experiments showed that most gut bacteria (21/32) have at least one path to produce 2HB, which includes Aspartic acid, methionine, threonine, and 2-aminobutyric acid. Particularly, Fusobacterium nucleatum has the strongest ability to synthesize 2HB, which is sufficient to alter colon 2HB concentration in mice. Nevertheless, neither antibiotic (ABX) nor Fusobacterium nucleatum gavage significantly affected mouse serum 2HB levels during the time course of this study. Taken together, our study presents the profiles of 2HB-producing bacteria and demonstrates that gut microbiota was a major contributor to 2HB concentration in the intestinal lumen but a relatively minor contributor to serum 2HB concentration.PMID:36984891 | DOI:10.3390/metabo13030451

Metabolites. 2023 Mar 18;13(3):447. doi: 10.3390/metabo13030447.ABSTRACTThe inborn errors of metabolism (IEMs or Inherited Metabolic Disorders) are a heterogeneous group of diseases caused by a deficit of some specific metabolic pathways. IEMs may present with multiple overlapping symptoms, sometimes difficult delayed diagnosis and postponed therapies. Additionally, many IEMs are not covered in newborn screening and the diagnostic profiling in the metabolic laboratory is indispensable to reach a correct diagnosis. In recent years, Metabolomics helped to obtain a better understanding of pathogenesis and pathophysiology of IEMs, by validating diagnostic biomarkers, discovering new specific metabolic patterns and new IEMs itself. The expansion of Metabolomics in clinical biochemistry and laboratory medicine has brought these approaches in clinical practice as part of newborn screenings, as an exam for differential diagnosis between IEMs, and evaluation of metabolites in follow up as markers of severity or therapies efficacy. Lastly, several research groups are trying to profile metabolomics data in platforms to have a holistic vision of the metabolic, proteomic and genomic pathways of every single patient. In 2018 this team has made a review of literature to understand the value of Metabolomics in IEMs. Our review offers an update on use and perspectives of metabolomics in IEMs, with an overview of the studies available from 2018 to 2022.PMID:36984887 | DOI:10.3390/metabo13030447

Metabolites. 2023 Mar 18;13(3):445. doi: 10.3390/metabo13030445.ABSTRACTThe present study aims to identify the salivary metabolic profile of healthy infants and young children, and to correlate this with age, salivary gland maturation, and dentition. Forty-eight children were selected after clinical evaluation in which all intraoral structures were examined. Total unstimulated saliva was collected, and salivary metabolites were analyzed by 1H Nuclear Magnetic Resonance (NMR) at 25 °C. Partial least squares discriminant analysis (PLS-DA), orthogonal PLS-DA (O-PLS-DA), and univariate analysis were used, adopting a 95% confidence interval. The study showed a distinct salivary metabolomic profile related to age and developmental phase. The saliva of children in the pre-eruption teeth period showed a different metabolite profile than that of children after the eruption. However, more evident changes were observed in the saliva profile of children older than 30 months. Alanine, choline, ethanol, lactate, and sugar region were found in higher levels in the saliva of patients before 30 months old. Acetate, N-acetyl sugar, butyrate, caproate, creatinine, leucine, phenylalanine, propionate, valine, succinate, and valerate were found to be more abundant in the saliva of children after 30 months old. The saliva profile is a result of changes in age and dental eruption, and these findings can be useful for monitoring the physiological changes that occur in infancy.PMID:36984885 | DOI:10.3390/metabo13030445

Metabolites. 2023 Mar 17;13(3):444. doi: 10.3390/metabo13030444.ABSTRACTNoninvasive risk stratification is a challenging issue in the management of patients with nonalcoholic fatty liver disease (NAFLD). This study aimed to identify multiomics-based predictors of NAFLD progression, as assessed by changes in serial FibroScan-aspartate aminotransferase (FAST) scores during lifestyle modification. A total of 266 patients with available metabolomics and genotyping data were included. The follow-up sub-cohort included patients with paired laboratory and transient elastography results (n = 160). The baseline median FAST score was 0.37. The PNPLA3 rs738409 genotype was significantly associated with a FAST score > 0.35. Circulating metabolomics significantly associated with a FAST score > 0.35 included SM C24:0 (odds ratio [OR] = 0.642; 95% confidence interval [CI], 0.463-0.891), PC ae C40:6 (OR = 0.477; 95% CI, 0.340-0.669), lysoPC a C18:2 (OR = 0.570; 95% CI, 0.417-0.779), and tyrosine (OR = 2.743; 95% CI, 1.875-4.014). A combination of these metabolites and PNPLA3 genotype yielded a c-index = 0.948 for predicting a FAST score > 0.35. In the follow-up sub-cohort (median follow-up = 23.7 months), 47/76 patients (61.8%) with a baseline FAST score > 0.35 had a follow-up FAST score ≤ 0.35. An improved FAST score at follow-up was significantly associated with age, serum alanine aminotransferase, and tyrosine. In conclusion, baseline risk stratification in NAFLD patients may be assisted using a multiomics-based model. Particularly, patients with increased tyrosine may benefit from an earlier switch to pharmacologic approaches.PMID:36984884 | DOI:10.3390/metabo13030444

Metabolites. 2023 Mar 17;13(3):441. doi: 10.3390/metabo13030441.ABSTRACTBreast cancer (BC) is the most common type of cancer among women in almost all countries worldwide and is one of the oncological pathologies for which is indicated fertility preservation, a type of procedure used to help keep a person's ability to have children. Follicular fluid (FF) is a major component of oocyte microenvironment, which is involved in oocyte growth, follicular maturation, and in communication between germ and somatic cells; furthermore, it accumulates all metabolites during oocytes growth. To obtain information about changes on fertility due to cancer, we aimed at investigating potential biomarkers to discriminate between FF samples obtained from 16 BC patients and 10 healthy women undergoing in vitro fertilization treatments. An NMR-based metabolomics approach was performed to investigate the FF metabolic profiles; ELISA and western blotting assays were used to investigate protein markers of oxidative and inflammatory stress, which are processes closely related to cancer. Our results seem to suggest that FFs of BC women display some significant metabolic alterations in comparison to healthy controls, and these variations are also related with tumor staging.PMID:36984881 | DOI:10.3390/metabo13030441

Metabolites. 2023 Mar 16;13(3):436. doi: 10.3390/metabo13030436.ABSTRACTMicroalgae are attracting attention as a next-generation alternative source of protein and essential fatty acids that do not consume large amounts of water or land. Chaetoceros gracilis (C. gracilis)-a marine microalga-is rich in proteins, fucoxanthin, and eicosapentaenoic acid (EPA). Growing evidence indicates that dietary fucoxanthin and EPA have beneficial effects in humans. However, none of these studies have shown that dietary C. gracilis has beneficial effects in mammals. In this study, we investigated the effects of dietary C. gracilis on lipid abnormalities in Sprague-Dawley rats fed a high-sucrose cholesterol-containing diet. Dried C. gracilis was added to the control diet at a final dose of 2 or 5% (w/w). After four weeks, the soleus muscle weights were found to be dose-responsive to C. gracilis and showed a tendency to increase. The hepatic triglyceride and total cholesterol levels were significantly reduced by C. gracilis feeding compared to those in the control group. The activities of FAS and G6PDH, which are related to fatty acid de novo synthesis, were found to be dose-responsive to C. gracilis and tended to decrease. The hepatic glycerol content was also significantly decreased by C. gracilis feeding, and the serum HDL cholesterol levels were significantly increased, whereas the serum levels of cholesterol absorption markers (i.e., campesterol and β-sitosterol) and the hepatic mRNA levels of Scarb1 were significantly decreased. Water-soluble metabolite analysis showed that the muscular contents of several amino acids, including leucine, were significantly increased by C. gracilis feeding. The tendency toward an increase in the weight of the soleus muscle as a result of C. gracilis feeding may be due to the enhancement of muscle protein synthesis centered on leucine. Collectively, these results show that the oral administration of C. gracilis alleviates hepatic lipid accumulation in rats fed a high-sucrose and cholesterol-containing diet, indicating the potential use of C. gracilis as a food resource.PMID:36984876 | DOI:10.3390/metabo13030436

Metabolites. 2023 Mar 16;13(3):434. doi: 10.3390/metabo13030434.ABSTRACTMorphine administration causes system-level metabolic changes. Here, we show that morphine-tolerant mice exhibited distinct plasma metabolic signatures upon acute and chronic administration. We utilized a mouse model of morphine tolerance by exposing mice to increasing doses of the drug over 4 days. We collected plasma samples from mice undergoing acute or chronic morphine or saline injections and analyzed them using targeted GC-MS-based metabolomics to profile approximately 80 metabolites involved in the central carbon, amino acid, nucleotide, and lipid metabolism. Our findings reveal distinct alterations in plasma metabolite concentrations in response to acute or chronic morphine intake, and these changes were linked to the development of tolerance to morphine's analgesic effects. We identified several metabolites that had been differentially affected by acute versus chronic morphine use, suggesting that metabolic changes may be mitigated by prolonged exposure to the drug. Morphine-tolerant mice showed a restoration of amino acid and glycolytic metabolites. Additionally, we conducted reconstructed metabolic network analysis on the first 30 VIP-ranked metabolites from the PLSDA of the saline, acute, and morphine-tolerant mice groups, which uncovered four interaction networks involving the amino acid metabolism, the TCA cycle, the glutamine-phenylalanine-tyrosine pathway, and glycolysis. These pathways were responsible for the metabolic differences observed following distinct morphine administration regimens. Overall, this study provides a valuable resource for future investigations into the role of metabolites in morphine-induced analgesia and associated effects following acute or chronic use in mice.PMID:36984873 | DOI:10.3390/metabo13030434

Metabolites. 2023 Mar 16;13(3):433. doi: 10.3390/metabo13030433.ABSTRACTCentral metabolism has a profound impact on the clinical phenotypes and penetrance of neurological diseases such as Alzheimer's (AD) and Parkinson's (PD) diseases, Amyotrophic Lateral Sclerosis (ALS) and Autism Spectrum Disorder (ASD). In contrast to the multifactorial origin of these neurological diseases, neurodevelopmental impairment and neurodegeneration in Familial Dysautonomia (FD) results from a single point mutation in the ELP1 gene. FD patients represent a well-defined population who can help us better understand the cellular networks underlying neurodegeneration, and how disease traits are affected by metabolic dysfunction, which in turn may contribute to dysregulation of the gut-brain axis of FD. Here, 1H NMR spectroscopy was employed to characterize the serum and fecal metabolomes of FD patients, and to assess similarities and differences in the polar metabolite profiles between FD patients and healthy relative controls. Findings from this work revealed noteworthy metabolic alterations reflected in energy (ATP) production, mitochondrial function, amino acid and nucleotide catabolism, neurosignaling molecules, and gut-microbial metabolism. These results provide further evidence for a close interconnection between metabolism, neurodegeneration, and gut microbiome dysbiosis in FD, and create an opportunity to explore whether metabolic interventions targeting the gut-brain-metabolism axis of FD could be used to redress or slow down the progressive neurodegeneration observed in FD patients.PMID:36984872 | DOI:10.3390/metabo13030433

Metabolites. 2023 Mar 15;13(3):428. doi: 10.3390/metabo13030428.ABSTRACTCurrent treatment for Multiple Sclerosis (MS) consists of a multidisciplinary approach including disease-modifying therapies. The response to treatment is heterogeneous, representing a crucial challenge in the classification of patients. Metabolomics is an innovative tool that can identifies biomarkers/predictors of treatment response. We aimed to evaluate plasma metabolic changes in a group of naïve Relapsing-Remitting MS patients starting Fingolimod treatment, to find specific metabolomic features that predict the therapeutic response as well as the possible side effects. The study included 42 Relapsing-Remitting MS blood samples, of which 30 were classified as responders after two years of FINGO treatment, whereas 12 patients were Not-Responders. For fifteen patients, samples were collected at four time points: before starting the therapy; at six months after the initiation; at twelve months after; and at twenty-four months after initiation. The serum was analysed through Nuclear Magnetic Resonance and multivariate and univariate statistical analysis. Considering the single comparison between each time point, it was possible to identify a set of metabolites changing their concentrations based on the drug intake. FINGO influences aminoacidic and energy metabolisms and reduces oxidative stress and the activity of the immune system, both typical features of MS.PMID:36984868 | DOI:10.3390/metabo13030428