Metabolomic analysis reveals Ligilactobacillus salivarius CCFM 1266 fermentation improves dairy product quality.

Previous studies have demonstrated that Ligilactobacillus salivarius CCFM 1266 exhibits anti-inflammatory properties and the capability to synthesize niacin. This study aimed to investigate the fermentative abilities of L. salivarius CCFM 1266 in fermented milk. Metabonomic analysis revealed that fermentation by L. salivarius CCFM 1266 altered volatile flavor compounds and metabolite profiles, including heptanal, nonanal, and increased niacin production. Genomic investigations confirmed that L. salivarius CCFM 1266 possess essential genes for the metabolism of fructose and mannose, affirming its proficiency in utilizing fructooligosaccharides and mannan oligosaccharides. The addition of fructooligosaccharides and mannan oligosaccharides during the fermentation process significantly facilitated the proliferation of L. salivarius CCFM 1266 in fermented milk, with growth exceeding 107 colony-forming units (CFU)/mL. This intervention not only augmented the microbial density but also modified the metabolite composition of fermented milk, resulting in an elevated presence of advantageous flavor compounds such as nonanal, 2,3-pentanedione, and 3-methyl-2-butanone. However, its influence on improving the texture of fermented milk was observed to be minimal. Co-fermentation of L. salivarius CCFM 1266 with commercial fermentation starters indicated that L. salivarius CCFM 1266 was compatible, similarly altering metabolite composition and increasing niacin content in fermented milk. In summary, the findings suggest that L. salivarius CCFM 1266 holds substantial promise as an adjunctive fermentation starter, capable of enhancing the nutritional diversity of fermented milk products.

[Differential analysis of aqueous humor metabolomics between presenile cataract and senile cataract].

Objective: To explore the differences in metabolites and metabolic pathways in the aqueous humor between patients with presenile cataracts and senile cataracts. Methods: This metabolomic study was conducted at Tianjin Medical University Eye Hospital from August 2020 to September 2022. Eight patients with presenile cataracts (8 eyes) and 8 patients with senile cataracts (9 eyes) were included. Data were collected, including age, gender, preoperative uncorrected visual acuity, intraocular pressure, lens dysfunction index, and axial length. Aqueous humor and anterior capsule tissue samples were obtained during cataract surgery. Metabolites in the aqueous humor were detected using Liquid Chromatography-Mass Spectrometry in a non-targeted approach. The principal component analysis, differential analysis, clustering analysis, and correlation analysis were performed to identify differentially expressed metabolites. These metabolites were ranked based on the fold change (FC). The receiver operating characteristic (ROC) curve analysis and metabolic enrichment analysis were used to identify differential pathways and potential biomarkers for presenile cataracts. Immunohistochemistry was conducted on anterior capsule tissues, and pyruvate levels were measured by colorimetry to validate metabolomic results. Results: Patients with presenile cataracts included 7 males and 1 female, with a mean age of (37.50±4.90) years. Patients with senile cataracts were 7 males and 1 female, with a mean age of (73.44±5.22) years. Except for age, there were no significant differences in baseline data (P>0.05). A total of 347 differential metabolites were identified, 10 of which were potential biomarkers for presenile cataract according to the ROC curve analysis (all P<0.05), including propoxycaine (log2FC=7.26), 2-methyl-2, 3, 4, 5-tetrahydro-1, 5-benzodiazepine-4-ketone (log2FC=6.35), l-pyroglutamic acid (log2FC=-1.72), leanly-proline (log2FC=-0.77), and choline (log2FC=-0.56) in the positive ion mode, and N-phenylacetyl glutamine (log2FC=-1.84), pyruvate (log2FC=1.07), ascorbic acid (log2FC=0.92), pseudouracil nucleoside (log2FC=-0.68), and palmitic acid (log2FC=-0.51) in the negative ion mode. The metabolic enrichment analysis identified 72 differential pathways (32 cationic and 40 anionic), with significant differences in glutathione metabolism, cysteine and methionine metabolism, glycolysis or gluconeogenesis, pyruvate metabolism, and the citric acid cycle (P<0.05). The experimental validation showed reduced lactate dehydrogenase and increased pyruvate levels in patients with presenile cataracts (P<0.05). Conclusions: Pyruvate and nine other metabolites may serve as potential biomarkers for presenile cataracts. Pathways involving glutathione metabolism, cysteine and methionine metabolism, glycolysis or gluconeogenesis, pyruvate metabolism, and the citric acid cycle are notably dysregulated in patients with presenile cataracts.

Metabolomics-based study on the significance of differential metabolite binding IgG isoforms in Hemolytic disease of newborn.

BACKGROUND: Hemolytic disease of the newborn (HDN) is a common condition that can have a severe impact on the health of newborns due to the hemolytic reactions it triggers. Although numerous studies have focused on understanding the pathogenesis of HDN, there are still many unanswered questions. METHODS: In this retrospective study, serum samples were collected from 15 healthy newborns and 8 infants diagnosed with hemolytic disease. The relationship between different metabolites and various IgG subtypes in Healthy, HDN and BLI groups was studied by biochemical technique and enzyme-linked immunosorbent assay (ELISA). Metabolomics analysis was conducted to identify the differential metabolites associated with HDN. Subsequently, Pearson's correlation analysis was used to determine the relation of these differential metabolites with IgG isoforms. The relationship between the metabolites and IgG subtypes was observed after treatment. RESULTS: The study results revealed that infants with hemolytic disease exhibited abnormal elevations in TBA, IgG1, IgG2a, IgG2b, IgG3, and IgG4 levels when compared to healthy newborns. Additionally, differences in metabolite contents were also observed. N, N-DIMETHYLARGININE showed negative correlations with TBA, IgG1, IgG2a, IgG2b, IgG3, and IgG4, while 2-HYDROXYBUTYRATE, AMINOISOBUTANOATE, Inosine, and ALLYL ISOTHIOCYANATE exhibited positive correlations with TBA, IgG1, IgG2a, IgG2b, IgG3, and IgG4. Through metabolomics-based research, we have discovered associations between differential metabolites and different IgG isoforms during the onset of HDN. CONCLUSION: These findings suggest that changes in metabolite and IgG isoform levels are linked to HDN. Understanding the involvement of IgG isoforms and metabolites can provide valuable guidance for the diagnosis and treatment of HDN.

Multi-omics reveals new links between Fructosamine-3-Kinase (FN3K) and core metabolic pathways.

Fructosamine-3-kinases (FN3Ks) are a conserved family of repair enzymes that phosphorylate reactive sugars attached to lysine residues in peptides and proteins. Although FN3Ks are present across the Tree of Life and share detectable sequence similarity to eukaryotic protein kinases, the biological processes regulated by these kinases are largely unknown. To address this knowledge gap, we leveraged the FN3K CRISPR Knock-Out (KO) HepG2 cell line alongside an integrative multi-omics study combining transcriptomics, metabolomics, and interactomics to place these enzymes in a pathway context. The integrative analyses revealed the enrichment of pathways related to oxidative stress response, lipid biosynthesis (cholesterol and fatty acids), and carbon and co-factor metabolism. Moreover, enrichment of nicotinamide adenine dinucleotide (NAD) binding proteins and localization of human FN3K (HsFN3K) to mitochondria suggests potential links between FN3K and NAD-mediated energy metabolism and redox balance. We report specific binding of HsFN3K to NAD compounds in a metal and concentration-dependent manner and provide insight into their binding mode using modeling and experimental site-directed mutagenesis. Our studies provide a framework for targeting these understudied kinases in diabetic complications and metabolic disorders where redox balance and NAD-dependent metabolic processes are altered.

An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases.

Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min-max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65-0.86) and 0.84 (0.70-0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.

Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine.

To tackle the difficulty of extracting features from one-dimensional spectral signals using traditional spectral analysis, a metabolomics analysis method is proposed to locate two-dimensional correlated spectral feature bands and combine it with deep learning classification for wine origin traceability. Metabolomics analysis was performed on 180 wine samples from 6 different wine regions using UPLC-Q-TOF-MS. Indole, Sulfacetamide, and caffeine were selected as the main differential components. By analyzing the molecular structure of these components and referring to the main functional groups on the infrared spectrum, characteristic band regions with wavelengths in the range of 1000-1400 nm and 1500-1800 nm were selected. Draw two-dimensional correlation spectra (2D-COS) separately, generate synchronous correlation spectra and asynchronous correlation spectra, establish convolutional neural network (CNN) classification models, and achieve the purpose of wine origin traceability. The experimental results demonstrate that combining two segments of two-dimensional characteristic spectra determined by metabolomics screening with convolutional neural networks yields optimal classification results. This validates the effectiveness of using metabolomics screening to determine spectral feature regions in tracing wine origin. This approach effectively removes irrelevant variables while retaining crucial chemical information, enhancing spectral resolution. This integrated approach strengthens the classification model's understanding of samples, significantly increasing accuracy.

Integrated transcriptomics and metabolomics study of embryonic breast muscle of Jiaji ducks.

Because number of matured muscle fibers in poultry does not increase after birth, the meat yield is mainly determined during embryogenesis. We previously indicated breast muscle grew rapidly from 18th day after hatching (E18) to E27, and almost stopped from E27 to E34 of Jiaji ducks, while the mechanism is unclear. This study utilized RNA-seq to explore the related genes of muscle development and their relationship with small molecule metabolites at E18, E27 and E34 of Jiaji ducks. Several thousand differentially expressed genes (DEGs) were detected among E18, E27 and E34. DEGs expression profiles included 8 trend maps, among which trend 1 was opposite to and trend 6 was consistent with breast muscle development trend of Jiaji ducks. Through joint analysis between trend 1 of DEGs and trend 1 of differential metabolites (DEMs), protein digestion and absorption pathway stood out. The decrease of COL8A2 gene expression will lead to the decrease of arginine content, which will inhibit the development of breast muscle in embryonic Jiaji duck. Similarly, joint analysis between trend 6 of DEGs and trend 6 of DEMs indicated the increase of GAMT gene expression will cause the increase of proline content, and then promote the development of breast muscle of Jiaji duck in embryonic period. These results will be helpful for further understanding the mechanism of muscle yields of Jiaji ducks.

Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data.

Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed.

The Regulatory Mechanism of Smilax China L. Saponins against Nonalcoholic Fatty Liver Is Revealed by Metabolomics and Transcriptomics.

This study was to investigate the effects of Smilax China L. saponins (SCS) on non-alcoholic fatty liver disease (NAFLD). Rats were fed a high-fat diet (HFD) for 8 weeks to induce NAFLD, followed by SCS treatment for 8 weeks. The effect of SCS on liver injury was observed by H&E staining and the regulative mechanism of SCS on lipid formation was exposed by detecting Oil red O, insulin resistance (IR), and fatty acids synthesis (FAS). Furthermore, transcriptomics and metabolomics were performed to analyze the potential targets. The experimental results indicated that SCS exerted a positive curative effect in alleviating HFD-induced overweight, hepatic injury, steatosis, and lipid formation and accumulation in rats, and the preliminary mechanism studies showed that SCS could alleviate IR, inhibit FAS expression, and reduce Acetyl-CoA levels. Besides, the integrative analysis of transcriptomics and metabolomics exposed the targets of SCS to regulate lipid production likely being the sphingolipid metabolism and glycerophospholipid metabolism pathways. This study demonstrates that SCS significantly ameliorates lipid metabolic disturbance in rats with NAFLD by relieving insulin resistance, inhibiting the FAS enzymes, and regulating the sphingolipid and glycerophospholipid metabolism pathways.

Integration of clinical phenoms and metabolomics facilitates precision medicine for lung cancer.

Lung cancer is a common malignancy that is frequently associated with systemic metabolic disorders. Early detection is pivotal to survival improvement. Although blood biomarkers have been used in its early diagnosis, missed diagnosis and misdiagnosis still exist due to the heterogeneity of lung cancer. Integration of multiple biomarkers or trans-omics results can improve the accuracy and reliability for lung cancer diagnosis. As metabolic reprogramming is a hallmark of lung cancer, metabolites, specifically lipids might be useful for lung cancer detection, yet systematic characterizations of metabolites in lung cancer are still incipient. The present study profiled the polar metabolome and lipidome in the plasma of lung cancer patients to construct an inclusive metabolomic atlas of lung cancer. A comprehensive analysis of lung cancer was also conducted combining metabolomics with clinical phenotypes. Furthermore, the differences in plasma lipid metabolites were compared and analyzed among different lung cancer subtypes. Alcohols, amides, and peptide metabolites were significantly increased in lung cancer, while carboxylic acids, hydrocarbons, and fatty acids were remarkably decreased. Lipid profiling revealed a significant increase in plasma levels of CER, PE, SM, and TAG in individuals with lung cancer as compared to those in healthy controls. Correlation analysis confirmed the association between a panel of metabolites and TAGs. Clinical trans-omics studies elucidated the complex correlations between lipidomic data and clinical phenotypes. The present study emphasized the clinical importance of lipidomics in lung cancer, which involves the correlation between metabolites and the expressions of other omics, ultimately influencing clinical phenotypes. This novel trans-omics network approach would facilitate the development of precision therapy for lung cancer.

Intercropping with maize and sorghum-induced saikosaponin accumulation in Bupleurum chinense DC. by liquid chromatography-mass spectrometry-based metabolomics.

Bupleuri Radix is an important medicinal plant, which has been used in China and other Asian countries for thousands of years. Cultivated Bupleurum chinense DC. (B. chinense) is the main commodity of Bupleuri Radix. The benefits of intercropping with various crops for B. chinense have been recognized; however, the influence of intercropping on the chemical composition of B. chinense is still unclear yet. In this study, intercropping with sorghum and maize exhibited little effect on the root length, root diameter, and single root mass of B. chinense. Only the intercropping with sorghum increased the root length of B. chinense slightly compared to the monocropping. In addition, 200 compounds were identified by UHPLC-Q-TOF-MS, and metabolomic combined with the Venn diagram and heatmap analysis showed apparent separation between the intercropped and monocropped B. chinense samples. Intercropping with sorghum and maize could both increase the saikosaponins, fatty acyls, and organic acids in B. chinense while decreasing the phospholipids. The influence of intercropping on the saikosaponin biosynthesis was probably related with the light intensity and hormone levels in B. chinense. Moreover, we found intercropping increased the anti-inflammatory activity of B. chinense. This study provides a scientific reference for the beneficial effect of intercropping mode of B. chinense.

Metabolomic pathways in food allergy.

Food allergy (FA) is a widespread issue, affecting as many as 10% of the population. Over the past two to three decades, the prevalence of FA has been on the rise, particularly in industrialized and westernized countries. FA is a complex, multifactorial disease mediated by type 2 immune responses and involving environmental and genetic factors. However, the precise mechanisms remain inadequately understood. Metabolomics has the potential to identify disease endotypes, which could beneficially promote personalized prevention and treatment. A metabolome approach would facilitate the identification of surrogate metabolite markers reflecting the disease activity and prognosis. Here, we present a literature overview of recent metabolomic studies conducted on children with FA.

Comparison of Three Widely Employed Extraction Methods for Metabolomic Analysis of Trypanosoma brucei.

Trypanosoma brucei (Tb) is the causative agent of human African trypanosomiasis (HAT), also known as sleeping sickness, which can be fatal if left untreated. An understanding of the parasite's cellular metabolism is vital for the discovery of new antitrypanosomal drugs and for disease eradication. Metabolomics can be used to analyze numerous metabolic pathways described as essential to Tb. brucei but has some limitations linked to the metabolites' physicochemical properties and the extraction process. To develop an optimized method for extracting and analyzing Tb. brucei metabolites, we tested the three most commonly used extraction methods, analyzed the extracts by hydrophilic interaction liquid chromatography high-resolution mass spectrometry (HILIC LC-HRMS), and further evaluated the results using quantitative criteria including the number, intensity, reproducibility, and variability of features, as well as qualitative criteria such as the specific coverage of relevant metabolites. Here, we present the resulting protocols for untargeted metabolomic analysis of Tb. brucei using (HILIC LC-HRMS). © 2024 Wiley Periodicals LLC. Basic Protocol 1: Culture of Trypanosoma brucei brucei parasites Basic Protocol 2: Preparation of samples for metabolomic analysis of Trypanosoma brucei brucei Basic Protocol 3: LC-HRMS-based metabolomic data analysis of Trypanosoma brucei brucei.

The impact of continuous cultivation of Ganoderma lucidum on soil nutrients, enzyme activity, and fruiting body metabolites.

To explore the impacts of continuous Ganoderma lucidum cultivation on soil physicochemical factors, soil enzyme activity, and the metabolome of Ganoderma lucidum fruiting bodies, this study conducted two consecutive years of cultivation on the same plot of land. Soil physicochemical factors and enzyme activity were assessed, alongside non-targeted metabolomic analysis of the Ganoderma lucidum fruiting bodies under continuous cultivation. The findings unveiled that in the surface soil layer (0-15 cm), there was a declining trend in organic matter, ammonium nitrogen, available phosphorus, available potassium, pH, polyphenol oxidase, peroxidase, alkaline phosphatase, and sucrase, whereas nitrate nitrogen, electrical conductivity (EC), and salt content exhibited an upward trend. Conversely, in the deeper soil layer (15-30 cm), organic matter, ammonium nitrogen, available potassium, alkaline phosphatase, and sucrase demonstrated a decreasing trend, while nitrate nitrogen, available phosphorus, pH, EC, salt content, polyphenol oxidase, and soil peroxidase showed an increasing trend. Metabolomic analysis of Ganoderma lucidum fruiting bodies distinguished 64 significantly different metabolites between the GCK and GT groups, with 39 components having markedly higher relative contents in GCK and 25 components having significantly lower relative contents in GCK compared to GT. Moreover, among these metabolites, there were more types with higher contents in the fruiting bodies harvested in the first year (GCK) compared to those harvested in the second year (GT), with pronounced differences. KEGG pathway analysis revealed that GCK exhibited more complex metabolic pathways compared to GT. The metabolites of Ganoderma lucidum fruiting bodies were predominantly influenced by soil physicochemical factors and soil enzyme activity. In the surface soil layer (0-15 cm), the metabolome was significantly affected by soil pH, soil organic matter, available phosphorus, and soil alkaline phosphatase, while in the deeper soil layer (15-30 cm), differences in the Ganoderma lucidum metabolome were more influenced by soil alkaline phosphatase, soil catalase, pH, nitrate nitrogen, and soil sucrase.

Dynamic molecular signatures of acute myocardial infarction based on transcriptomics and metabolomics.

Acute myocardial infarction (AMI) commonly precedes ventricular remodeling, heart failure. Few dynamic molecular signatures have gained widespread acceptance in mainstream clinical testing despite the discovery of many potential candidates. These unmet needs with respect to biomarker and drug discovery of AMI necessitate a prioritization. We enrolled patients with AMI aged between 30 and 70. RNA-seq analysis was performed on the peripheral blood mononuclear cells collected from the patients at three time points: 1 day, 7 days, and 3 months after AMI. PLC/LC-MS analysis was conducted on the peripheral blood plasma collected from these patients at the same three time points. Differential genes and metabolites between groups were screened by bio-informatics methods to understand the dynamic changes of AMI in different periods. We obtained 15 transcriptional and 95 metabolite expression profiles at three time points after AMI through high-throughput sequencing. AMI-1d: enrichment analysis revealed the biological features of 1 day after AMI primarily included acute inflammatory response, elevated glycerophospholipid metabolism, and decreased protein synthesis capacity. Phosphatidylcholine (PC) and phosphatidylethanolamine (PE) might stand promising biomarkers to differentiate post-AMI stage. Anti-inflammatory therapy during the acute phase is an important direction for preventing related pathology. AMI-7d: the biological features of this stage primarily involved the initiation of cardiac fibrosis response and activation of platelet adhesion pathways. Accompanied by upregulated TGF-beta signaling pathway and ECM receptor interaction, GP5 help assess platelet activation, a potential therapeutic target to improve haemostasis. AMI-3m: the biological features of 3 months after AMI primarily showed a vascular regeneration response with VEGF signaling pathway, NOS3 and SHC2 widely activated, which holds promise for providing new therapeutic approaches for AMI. Our analysis highlights transcriptional and metabolomics signatures at different time points after MI, which deepens our understanding of the dynamic biological responses and associated molecular mechanisms that occur during cardiac repair.

Study on plasma metabolomics profiling of depression in Chinese community-dwelling older adults based on untargeted LC/GC‒MS.

Depression is a serious psychiatric illness that causes great inconvenience to the lives of elderly individuals. However, the diagnosis of depression is somewhat subjective. Nontargeted gas chromatography (GC)/liquid chromatography (LC)-mass spectrometry (MS) was used to study the plasma metabolic profile and identify objective markers for depression and metabolic pathway variation. We recruited 379 Chinese community-dwelling individuals aged ≥ 65. Plasma samples were collected and detected by GC/LC‒MS. Orthogonal partial least squares discriminant analysis and a heatmap were utilized to distinguish the metabolites. Receiver operating characteristic curves were constructed to evaluate the diagnostic value of these differential metabolites. Additionally, metabolic pathway enrichment was performed to reveal metabolic pathway variation. According to our standard, 49 people were included in the depression cohort (DC), and 49 people age- and sex-matched individuals were included in the non-depression cohort (NDC). 64 metabolites identified via GC‒MS and 73 metabolites identified via LC‒MS had significant contributions to the differentiation between the DC and NDC, with VIP values > 1 and p values < 0.05. Three substances were detected by both methods: hypoxanthine, phytosphingosine, and xanthine. Furthermore, 1-(sn-glycero-3-phospho)-1D-myo-inositol had the largest area under the curve (AUC) value (AUC = 0.842). The purine metabolic pathway is the most important change in metabolic pathways. These findings show that there were differences in plasma metabolites between the depression cohort and the non-depression cohort. These identified differential metabolites may be markers of depression and can be used to study the changes in depression metabolic pathways.

Metabolomic analysis of rumen-protected branched-chain amino acids in primiparous dairy cows.

Introduction: Peripartal cows are susceptible to a negative energy balance due to inadequate nutrient intake and high energy requirements for lactation. Improving the energy metabolism of perinatal dairy cows is crucial in increasing production in dairy cows. Methods: In this study, we investigated the impact of rumen-protected branched-chain amino acid (RPBCAA) on the production performance, energy and lipid metabolism, oxidative stress, and immune function of primiparous dairy cows using metabolomics through a single-factor experiment. Twenty healthy primiparous Holstein cows were selected based on body condition scores and expected calving date, and were randomly divided into RPBCAA (n = 10) and control (n = 10) groups. The control group received a basal diet from calving until 21 d in milk, and the RPBCAA group received the basal diet and 44.6 g/d RPLeu, 25.14 g/d RPIle, and 25.43 g/d RPVal. Results: In comparison to the control group, the supplementation of RPBCAA had no significant effect on milk yield and milk composition of the dairy cows. Supplementation with RPBCAA significantly increased the concentrations of insulin, insulin growth factor 1, glucagon, and growth hormones, which are indicators of energy metabolism in postpartum cows. The very low density lipoprotein, fatty acid synthase, acetyl coenzyme A carboxylase, and hormone-sensitive lipase contents of the RPBCAA group were significantly greater than that of the control group; these metrics are related to lipid metabolism. In addition, RPBCAA supplementation significantly increased serum glutathione peroxidase and immunoglobulin G concentrations and decreased malondialdehyde concentrations. Liquid chromatography-mass spectrometry (LC-MS) analysis revealed 414 serum and 430 milk metabolic features. Supplementation with RPBCAA primarily increased concentrations of amino acid and lipid metabolism pathways and upregulated the abundance of serotonin, glutamine, and phosphatidylcholines. Discussion: In summary, adding RPBCAA to the daily ration can influence endocrine function and improve energy metabolism, regulate amino acid and lipid metabolism, mitigate oxidative stress and maintain immune function on primiparous cows in early lactation.

GC/MS-based untargeted metabolomics reveals the differential metabolites for discriminating vintage of Chenxiang-type baijiu.

The "outstanding and unique aged aroma" of Chinese Chenxiang-type baijiu (CXB)-Daoguang 25 (DG25) mainly originates from a "extraordinary storage technology" of Mujiuhai (a wooden container), so it is mysterious and interesting. In this study, an untargeted GC/MS-based metabolomics was used to reveals the volatile differential metabolites for discriminating six different vintages of DG25 combing with chemometrics. A total of 100 volatile metabolites (including unknowns) were extracted and identified, including esters (41%), alcohols (10%) and acids (7%) so on. Finally, 33 differential metabolites were identified as aging-markers. Among them, 25 aging-markers showed a downtrend, including 17 esters such as ethyl acetate, ethyl hexanoate and ethyl palmitate so on. Moreover, it was interesting and to further study that furans showed a significant downtrend. Statistically speaking, ethyl benzoate played an important role in discriminating vintage of 1Y and 3Y, and the other 24 differential metabolites with downtrend discriminating the unstored (0Y-aged) DG25. Eight differential metabolites, such as ethyl octanoate, benzaldehyde, 3-methylbutanol and 1,1-diethoxyaccetal so on increased during aging of DG25, and they played a statistical role in discriminating the 5Y-, 10Y- and 20Y-aged DG25. This study provides a theoretical basis way for the formation mechanism of aging aroma for CXB.

Metabolic network analysis of pre-ASD newborns and 5-year-old children with autism spectrum disorder.

Classical metabolomic and new metabolic network methods were used to study the developmental features of autism spectrum disorder (ASD) in newborns (n = 205) and 5-year-old children (n = 53). Eighty percent of the metabolic impact in ASD was caused by 14 shared biochemical pathways that led to decreased anti-inflammatory and antioxidant defenses, and to increased physiologic stress molecules like lactate, glycerol, cholesterol, and ceramides. CIRCOS plots and a new metabolic network parameter, V ° net, revealed differences in both the kind and degree of network connectivity. Of 50 biochemical pathways and 450 polar and lipid metabolites examined, the developmental regulation of the purine network was most changed. Purine network hub analysis revealed a 17-fold reversal in typically developing children. This purine network reversal did not occur in ASD. These results revealed previously unknown metabolic phenotypes, identified new developmental states of the metabolic correlation network, and underscored the role of mitochondrial functional changes, purine metabolism, and purinergic signaling in autism spectrum disorder.

Metabolomic screening of radioiodine refractory thyroid cancer patients and the underlying chemical mechanism of iodine resistance.

Radioiodine refractory (RAIR) patients do not benefit from iodine-131 therapy. Thus, timely identification of RAIR patients is critical for avoiding ineffective radioactive iodine therapy. In addition, determining the causes of iodine resistance will facilitate the development of novel treatment strategies. This study was comprised of 20 RAIR and 14 non-radioiodine refractory (non-RAIR) thyroid cancer patients. Liquid chromatography-mass spectrometry was used to identify differences in the serum metabolites of RAIR and non-RAIR patients. In addition, chemical assays were performed to determine the effects of the differential metabolites on iodine uptake. Metabolic pathway enrichment analysis of the differential metabolites revealed significant differences in the phenylalanine and tyrosine metabolic pathways. Notably, quinate and shikimic acid, metabolites of the tyrosine pathway, were significantly increased in the RAIR group. In contrast, the phenylalanine pathway metabolites, hippuric acid and 2-phenylacetamide, were markedly decreased in the RAIR group. Thyroid peroxidase plays an important role in catalyzing the iodination of tyrosine residues, while the ionic state of iodine promotes the iodination reaction. Quinate, shikimic acid, hippuric acid, and 2-phenylacetamide were found to be involved in the iodination of tyrosine, which is a key step in thyroid hormone synthesis. Specifically, quinate and shikimic acid were found to inhibit iodination, while hippuric acid and 2-phenylacetamide promoted iodination. Abnormalities in phenylalanine and tyrosine metabolic pathways are closely associated with iodine resistance. Tyrosine is required for thyroid hormone synthesis and could be a potential cause of iodine resistance.