PubMed

J Oral Microbiol. 2024 Oct 30;16(1):2422164. doi: 10.1080/20002297.2024.2422164. eCollection 2024.ABSTRACTBACKGROUND: Oral lichen planus (OLP) is a chronic oral mucosal inflammatory disease with a risk of becoming malignant. Emerging evidence suggests that microbial imbalance plays an important role in the development of OLP. However, the association between the oral microbiota and the metabolic features in OLP is still unclear.METHODS: We conducted 16S rRNA sequencing and metabolomics profiling on 95 OLP patients and 105 healthy controls (HC).To study oral microbes and metabolic changes in OLP, we applied differential analysis, Spearman correlation analysis and four machine learning algoeithms.RESULTS: The alpha and beta diversity both differed between OLP and HC. After adjustment for gender and age, we found an increase in the relative abundance of Pseudomonas, Aggregatibacter, Campylobacter, and Lautropia in OLP, while 18 genera decreased in OLP. A total of 153 saliva metabolites distinguishing OLP from HC were identified. Notably, correlations were found between Oribacterium, specific lipid and amino acid metabolites, and OLP's clinical phenotype. Additionally, the combination of Pseudomonas, Rhodococcus and (±)10-HDoHE effectively distinguished OLP from HC.CONCLUSIONS: Based on multi-omics data, this study provides comprehensive evidence of a novel interplay between oral microbiome and metabolome in OLP pathogenesis using the oral microbiota and metabolites of OLP patients.PMID:39498115 | PMC:PMC11533246 | DOI:10.1080/20002297.2024.2422164

Heliyon. 2024 Sep 20;10(20):e38220. doi: 10.1016/j.heliyon.2024.e38220. eCollection 2024 Oct 30.ABSTRACTBACKGROUND: This study investigates the molecular mechanisms behind firmicutes-mediated macrophage (Mψ) polarization and glycolytic metabolic reprogramming through HIF-1α in response to intrinsic mucosal barrier injury induced by high-altitude hypoxia.METHODS: Establishing a hypoxia mouse model of high altitude, we utilized single-cell transcriptome sequencing to identify key cell types involved in regulating intestinal mucosal barrier damage caused by high-altitude hypoxia. Through proteomic analysis of colonic tissue Mψ and metabolomic analysis of Mψ metabolites, we determined crucial proteins and metabolic pathways influencing intestinal mucosal barrier damage induced by high-altitude hypoxia. Mechanistic validation was conducted using RAW264.7 Mψ in vitro by assessing cell viability with CCK-8 assay following treatment with different metabolites. The hypoxia mouse model was further validated in vivo by transplanting gut microbiota of Firmicutes. Histological examinations through H&E staining assessed colonic cell morphology and structure, while the FITC-dextran assay evaluated intestinal tissue permeability. Hypoxia probe signal intensity in mouse colonic tissue was assessed via metronidazole staining. Various experimental techniques, including flow cytometry, immunofluorescence, ELISA, Western blot, and RT-qPCR, were employed to study the impact of HIF-1α/glycolysis pathway and different gut microbiota metabolites on Mψ polarization.RESULTS: Bioinformatics analysis revealed that single-cell transcriptomics identified Mψ as a key cell type, with their polarization pattern playing a crucial role in the intestinal mucosal barrier damage induced by high-altitude hypoxia. Proteomics combined with metabolomics analysis indicated that HIF-1α and the glycolytic pathway are pivotal proteins and signaling pathways in the intestinal mucosal barrier damage caused by high-altitude hypoxia. In vitro cell experiments demonstrated that activation of the glycolytic pathway by HIF-1α led to a significant upregulation of mRNA levels of IL-1β, IL-6, and TNFα while downregulating mRNA levels of IL-10 and TGFβ, thereby promoting M1 Mψ activation and inhibiting M2 Mψ polarization. Further mechanistic validation experiments revealed that the metabolite butyric acid from Firmicutes bacteria significantly downregulated the protein expression of HIF-1α, GCK, PFK, PKM, and LDH, thus inhibiting the HIF-1α/glycolytic pathway that suppresses M1 Mψ and activates M2 Mψ, consequently alleviating the hypoxic symptoms in RAW264.7 cells. Subsequent animal experiments confirmed that Firmicutes bacteria inhibited the HIF-1α/glycolytic pathway to modulate Mψ polarization, thereby mitigating intestinal mucosal barrier damage in high-altitude hypoxic mice.CONCLUSION: The study reveals that firmicutes, through the inhibition of the HIF-1α/glycolysis pathway, mitigate Mψ polarization, thereby alleviating intrinsic mucosal barrier injury in high-altitude hypoxia.PMID:39498080 | PMC:PMC11534185 | DOI:10.1016/j.heliyon.2024.e38220

Heliyon. 2024 Oct 18;10(20):e39554. doi: 10.1016/j.heliyon.2024.e39554. eCollection 2024 Oct 30.ABSTRACTOBJECTIVE: Blood-stasis syndrome (BSS), an important syndrome in Type 2 diabetes mellitus(T2DM), is associated with the pathophysiological mechanisms underlying diabetic vascular complications. However, BSS has not been fully characterized as of yet. Due to the strong correlation between BSS and vasculopathy, we hypothesized that the metabolic characteristics of BSS in T2DM (T2DM BSS) are highly specific. By combining untargeted metabolomics and pseudotargeted lipidomics approaches, this study aimed to comprehensively elucidate the metabolic traits of T2DM BSS, thereby providing novel insights into the vascular complications of diabetes and establishing a foundation for precision medicine.METHODS: The survey was conducted in Haidian District of Beijing from October 2021 to November 2021, and data collection was completed in January 2022. Liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics and liquid chromatography-tandem mass spectrometry (LC-MS/MS) based pseudotargeted lipidomics were performed to detect metabolites and lipids. Multivariate, univariate, and pathway analyses were utilized to investigate metabolic changes. The unique metabolites of BSS were obtained by inter-group comparisons and screening. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic accuracy of metabolites.RESULTS: A total of 1189 participants completed the survey, of which 120 participants were recruited in this study and further divided into a discovery cohort (n = 90) and a validation cohort (n = 30). Among these, 21 participants were selected for psuedotargeted lipidomics analysis. 81 metabolites, mainly involving glycerophospholipids, were identified as unique metabolites of T2DM BSS, while fatty acyls (FAs) were identified as unique lipids. T2DM BSS was associated with significant dysregulation in glycerophospholipid metabolism and choline metabolism within cancer pathways as major metabolic disturbances. Furthermore, analyses of both the discovery and validation cohorts, indicated that LysoPC (20:5(5Z,8Z,11Z,14Z,17Z)/0:0) and LysoPC (15:0) had the greatest impact on distinguishing BSS.CONCLUSION: Altered levels of glycerophospholipids and FAs have been associated with T2DM BSS. These results provide valuable mechanistic insights linked with the development of BSS in T2DM subjects.PMID:39498030 | PMC:PMC11533630 | DOI:10.1016/j.heliyon.2024.e39554

Heliyon. 2024 Oct 9;10(20):e39104. doi: 10.1016/j.heliyon.2024.e39104. eCollection 2024 Oct 30.ABSTRACTBACKGROUND: Amyloid beta (Aβ) accumulation in the brain is one of the earliest findings in Alzheimer's disease (AD). The dog is a natural animal model for amyloid processing and early brain amyloid pathology. The goal of this study is to examine which differences in metabolomic profiles in cerebrospinal fluid (CSF) could be detected in dogs with a difference in CSF Aβ concentrations before amyloid accumulation occurs.METHOD: Metabolic profiling was performed on CSF from 4 to 8 year old dogs with different CSF Aβ concentrations.RESULTS: Metabolomic profiling of CSF showed differences in brain energy metabolism. More specifically, increases in N-acetylation of amino acids and amino sugars, creatine and pentose metabolism, and a decrease in tricarboxylic acid (TCA) cycle were seen in dogs with a high CSF Aβ concentration. In addition, signs of elevated oxidative stress, higher methionine, lipid and nucleotide metabolism and increased levels of cysteine, myo-inositol and trimethylamine N-oxide were noted in these animals.CONCLUSIONS: Differences in energy metabolism and stress mediated metabolic changes are seen in the brain of dogs with different CSF Aβ concentrations, before any amyloid deposition occurs. Similar metabolic changes, as in the high Aβ dogs, have been described in AD in humans and/or transgenic AD mice, some of them in very early phases.GENERAL SIGNIFICANCE: The differences observed in metabolomic profiles could help in identifying potential biomarkers for an increased risk of developing amyloid pathology in the brain and open the door to the evaluation of preventive treatments for amyloid pathology in humans.PMID:39498015 | PMC:PMC11532822 | DOI:10.1016/j.heliyon.2024.e39104

Heliyon. 2024 Oct 1;10(20):e38788. doi: 10.1016/j.heliyon.2024.e38788. eCollection 2024 Oct 30.ABSTRACTUntargeted metabolomics can be used for the comprehensive analysis of metabolite profiles in biological samples without preset targets, making them particularly suitable for exploring metabolic characteristics and potential mechanisms in complex diseases. Therefore, in this study, we employed gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) techniques to analyze the serum metabolic characteristics of patients with pregnancy-associated venous thromboembolism (PA-VTE). In this study, 11 pregnant women with VTE and 11 healthy pregnant women were included in the experimental and control groups, respectively. Using GC-MS, we identified 325 metabolites, with the highest proportion being organic oxygen compounds. Using LC-MS, we identified 3104 metabolites, with the highest proportion being acylcarnitine. The results revealed significant differences in the levels of lipids, organic compounds, and other metabolites between patients compared to healthy pregnant women. Pathways such as pyrimidine metabolism, linoleic acid metabolism, and mineral absorption differed between patients with PA-VTE and controls. Furthermore, we identified biomarkers associated with metabolic processes, such as fatty acids and amino acids (2-hydroxyhexanedioic acid, hexadecenal, palmitoylethanolamide, glycerol-1-phosphate, and N-acetyl-beta-D-glucosamine). These findings revealed the metabolic characteristics of PA-VTE and provided important clues for further research on its pathophysiological mechanisms. Our findings may contribute to the development of new diagnostic markers and support early diagnosis and treatment of PA-VTE.PMID:39497961 | PMC:PMC11532815 | DOI:10.1016/j.heliyon.2024.e38788

Front Cell Infect Microbiol. 2024 Oct 21;14:1455605. doi: 10.3389/fcimb.2024.1455605. eCollection 2024.ABSTRACTThe increasing prevalence of non-tuberculous mycobacterium (NTM) infections alongside tuberculosis (TB) underscores a pressing public health challenge. Yet, the mechanisms governing their infection within the lung remain poorly understood. Here, we integrate metagenomic sequencing, metabolomic sequencing, machine learning classifiers, SparCC, and MetOrigin methods to profile bronchoalveolar lavage fluid (BALF) samples from NTM/TB patients. Our aim is to unravel the intricate interplay between lung microbial communities and NTM/Mycobacterium tuberculosis infections. Our investigation reveals a discernible reduction in the compositional diversity of the lung microbiota and a diminished degree of mutual interaction concomitant with NTM/TB infections. Notably, NTM patients exhibit a distinct microbial community characterized by marked specialization and notable enrichment of Pseudomonas aeruginosa and Staphylococcus aureus, driving pronounced niche specialization for NTM infection. Simultaneously, these microbial shifts significantly disrupt tryptophan metabolism in NTM infection, leading to an elevation of kynurenine. Mycobacterium intracellulare, Mycobacterium paraintracellulare, Mycobacterium abscessus, and Pseudomonas aeruginosa have been implicated in the metabolic pathways associated with the conversion of indole to tryptophan via tryptophan synthase within NTM patients. Additionally, indoleamine-2,3-dioxygenase converts tryptophan into kynurenine, fostering an immunosuppressive milieu during NTM infection. This strategic modulation supports microbial persistence, enabling evasion from immune surveillance and perpetuating a protracted state of NTM infection. The elucidation of these nuanced microbial and metabolic dynamics provides a profound understanding of the intricate processes underlying NTM and TB infections, offering potential avenues for therapeutic intervention and management.PMID:39497924 | PMC:PMC11532197 | DOI:10.3389/fcimb.2024.1455605

Drug Des Devel Ther. 2024 Oct 31;18:4859-4875. doi: 10.2147/DDDT.S475838. eCollection 2024.ABSTRACTBACKGROUND: Rehmanniae Radix (RR) has received attention for its antithrombotic effect. However, few studies have independently explored the bioactive components responsible for its antithrombotic bioactivity and the potential mechanism. We aimed to reveal the antithrombotic mechanisms of RR by using metabolomics integrated with network pharmacology.METHODS: A thrombosis model was established by intraperitoneal injection of type I carrageenan in rats, and antithrombotic function was evaluated at different doses of RR. Metabolomics was used to identify the differential metabolites in the serum. Network pharmacology was then applied to identify the potential targets for the antithrombotic activity of the RR. An integrated network of metabolomics and network pharmacology was constructed using Cytoscape. Finally, key targets were verified using molecular docking.RESULTS: RR at 5.4 g/kg significantly alleviated the thrombosis. Thirteen potentially significant metabolites were involved in the therapeutic effects of RR against thrombosis, most of which were regulated for recovery after RR treatment. An integrated analysis of metabolomics and network pharmacology showed that the antithrombosis effect of RR was closely associated with the regulation of PLA2G2A, PTGS1, ALOX5, and CYP2C9. Molecular docking showed high affinity between the key targets and components of RR. We speculated that the components of RR, such as catalpol, ferulic acid methyl ester, and methyl 4-hydroxycinnamate, might act on key proteins, including PLA2G2A, PTGS1, and ALOX5, to exert antithrombosis effects.CONCLUSION: This study confirmed the antithrombotic effect of high-dose RR, revealed the antithrombotic mechanism and potential material basis, and laid the foundation for the antithrombotic clinical application of RR. Furthermore, it provides a successful case reference for screening natural herbal components and exploring their potential pharmacological mechanisms.PMID:39497835 | PMC:PMC11533886 | DOI:10.2147/DDDT.S475838

Food Chem (Oxf). 2024 Oct 16;9:100227. doi: 10.1016/j.fochms.2024.100227. eCollection 2024 Dec 30.ABSTRACTTo elucidate the formation of characteristic aroma over enzymatic-catalyzed processes (ECP), GC-MS-based volatile-metabolomic combined with desorption-electrospray-ionization coupled mass-spectrometry-imaging (DESI-MSI) were employed to analyze the changes of volatile organic compounds (VOCs) in Tieguanyin tea. A total of 579 VOCs were obtained, from which 24 components involved in five pathways were identified as biomarkers. Among these, four VOCs including 2-furancarboxylic acid, 4-methylbenzaldehyde, N-benzylformamide, cuminaldehyde, were detected in both DESI-MSI and GC-MS analysis, exhibiting dynamic changes along processing steps. RNA-sequencing analysis indicated the genes referring to stress response were activated during tea processing, facilitating the accumulation of flora-fruity aroma in tea leaf. Metabolic pathways analysis revealed that the increase in floral-fruity related components such as volatile terpenoids, phenylpropanoids/benzenoids, indole, alongside a decrease in green leaf volatiles including (E)-2-Hexenal, (Z)-3-Hexenol, played a crucial role in development of characteristic aroma, which could be a feasible index for evaluating processing techniques or quality of oolong tea.PMID:39497732 | PMC:PMC11533622 | DOI:10.1016/j.fochms.2024.100227

Front Oncol. 2024 Oct 21;14:1424293. doi: 10.3389/fonc.2024.1424293. eCollection 2024.ABSTRACTBladder cancer (BC) is the most common malignancy of the urinary tract. About 75% of all BC patients present with non-muscle-invasive BC (NMIBC), of which up to 70% will recur, and 15% will progress in stage and grade. As the recurrence and progression rates of NMIBC are strongly associated with some clinical and pathological factors, several risk stratification models have been developed to individually predict the short- and long-term risks of disease recurrence and progression. The NMIBC patients are stratified into four risk groups as low-, intermediate-, high-risk, and very high-risk by the European Association of Urology (EAU). Significant heterogeneity in terms of oncological outcomes and prognosis has been observed among NMIBC patients within the same EAU risk group, which has been partly attributed to the intrinsic heterogeneity of BC at the molecular level. Currently, we have a poor understanding of how to distinguish intermediate- and (very-)high-risk NMIBC with poor outcomes from those with a more benign disease course and lack predictive/prognostic tools that can specifically stratify them according to their pathologic and molecular properties. There is an unmet need for developing a more accurate scoring system that considers the treatment they receive after TURBT to enable their better stratification for further follow-up regimens and treatment selection, based also on a better response prediction to the treatment. Based on these facts, by employing a multi-layered -omics (namely, genomics, epigenetics, transcriptomics, proteomics, lipidomics, metabolomics) and immunohistopathology approach, we hypothesize to decipher molecular heterogeneity of intermediate- and (very-)high-risk NMIBC and to better stratify the patients with this disease. A combination of different -omics will provide a more detailed and multi-dimensional characterization of the tumor and represent the broad spectrum of NMIBC phenotypes, which will help to decipher the molecular heterogeneity of intermediate- and (very-)high-risk NMIBC. We think that this combinatorial multi-omics approach has the potential to improve the prediction of recurrence and progression with higher precision and to develop a molecular feature-based algorithm for stratifying the patients properly and guiding their therapeutic interventions in a personalized manner.PMID:39497708 | PMC:PMC11532112 | DOI:10.3389/fonc.2024.1424293

Obesity (Silver Spring). 2024 Nov;32(11):2024-2034. doi: 10.1002/oby.24137.ABSTRACTOBJECTIVE: Individuals living with obesity are differentially susceptible to cardiometabolic diseases. We hypothesized that an integrative multi-omics approach might improve identification of subgroups of individuals with obesity who have distinct cardiometabolic disease patterns.METHODS: We performed machine learning-based, integrative unsupervised clustering to identify proteomics- and metabolomics-defined subpopulations of individuals living with obesity (BMI ≥ 30 kg/m2), leveraging data from 243 individuals in the Multi-Ethnic Study of Atherosclerosis (MESA) cohort. Omics that contributed to the observed clusters were functionally characterized. We performed multivariate regression to assess whether the individuals in each cluster demonstrated differential patterns of cardiometabolic traits.RESULTS: We identified two distinct clusters (iCluster1 and 2). iCluster2 had significantly higher average BMI values, fasting blood glucose, and inflammation. iCluster1 was associated with higher levels of total cholesterol and high-density lipoprotein cholesterol. Pathways mediating cell growth, lipogenesis, and energy expenditures were positively associated with iCluster1. Inflammatory response and insulin resistance pathways were positively associated with iCluster2.CONCLUSIONS: Although the two identified clusters may represent progressive obesity-related pathologic processes measured at different stages, other mechanisms in combination could also underpin the identified clusters given no significant age difference between the comparative groups. For instance, clusters may reflect differences in dietary/behavioral patterns or differential rates of metabolic damage.PMID:39497627 | DOI:10.1002/oby.24137

J Anim Sci. 2024 Nov 5:skae339. doi: 10.1093/jas/skae339. Online ahead of print.ABSTRACTThe gastrointestinal microbiota plays a key role in the host physiology and health through a complex host-microbiota co-metabolism. Metabolites produced by microbial metabolism can travel through the bloodstream to reach distal organs and affect their function, ultimately influencing the development of relevant production traits such as meat quality. Meat quality is a complex trait made up of a number of characteristics and intramuscular fat content (IMF) is considered to be one of the most important parameters. In this study, 52 rabbits from two lines divergently selected for IMF (high-IMF (H) and low-IMF (L) lines) were used to perform an untargeted metabolomic analysis of their caecal content, with the aim to obtain information on genetically determined microbial metabolism related to IMF. A large, correlated response to selection was found in their caecal metabolome composition. Partial least squares discriminant analysis was used to identify the pathways differentiating the lines, which showed a classification accuracy of 99%. On the other hand, two linear partial least squares analyses were performed, one for each line, to extract evidence on the specific pathways associated with IMF deposition within each line, which showed predictive abilities (estimated using the Q2) of approximately 60%. The most relevant pathways differentiating the lines were those related to amino acids (aromatic, branched-chain and gamma-glutamyl), secondary bile acids, and purines. The higher content of secondary bile acids in the L-line was related to greater lipid absorption, while the differences found in purines suggested different fermentation activities, which could be related to greater nitrogen utilisation and energy efficiency in the L-line. The linear analyses showed that lipid metabolism had a greater relative importance for IMF deposition in the L-line, whereas a more complex microbial metabolism was associated in the H-line. The lysophospholipids and gamma-glutamyl amino acids were associated with IMF in both lines; the nucleotide and secondary bile acid metabolisms were mostly associated in the H-line; and the long-chain and branched-chain fatty acids were mostly associated in the L-line. A metabolic signature consisting of two secondary bile acids and two protein metabolites was found with 88% classification accuracy, pointing to the interaction between lipid absorption and protein metabolism as a relevant driver of the microbiome activity influencing IMF.PMID:39497598 | DOI:10.1093/jas/skae339

J Alzheimers Dis. 2024 Nov;102(1):237-253. doi: 10.3233/JAD-240280. Epub 2024 Oct 25.ABSTRACTBACKGROUND: Blood metabolites have emerged as promising candidates in the search for biomarkers for Alzheimer's disease (AD), as evidence shows that various metabolic derangements contribute to neurodegeneration in AD.OBJECTIVE: We aim to identify metabolic biomarkers for AD diagnosis.METHODS: We conducted an in-depth analysis of the serum metabolome of AD patients and age, sex-matched cognitively unimpaired older adults using ultra-high-performance liquid chromatography-high resolution mass spectrometry. The biomarkers associated with AD were identified using machine learning algorithms.RESULTS: Using the discovery dataset and support vector machine (SVM) algorithm, we identified a panel of 14 metabolites predicting AD with a 1.00 area under the curve (AUC) of receiver operating characteristic (ROC). The SVM model was tested against the verification dataset using an independent cohort and retained high predictive accuracy with a 0.97 AUC. Using the random forest (RF) algorithm, we identified a panel of 13 metabolites that predicted AD with a 0.96 AUC when tested against the verification dataset.CONCLUSIONS: These findings pave the way for an efficient, blood-based diagnostic test for AD, holding promise for clinical screenings and diagnostic procedures.PMID:39497321 | DOI:10.3233/JAD-240280

J Agric Food Chem. 2024 Nov 4. doi: 10.1021/acs.jafc.4c07416. Online ahead of print.ABSTRACTChrysoeriol, a natural flavonoid, has shown potential in inhibiting melanoma. However, the detailed molecular mechanisms of its action still need to be clarified. In this study, chrysoeriol showed significant suppressive effects on melanoma progression in a mouse model. The integrative gut microbiota and metabolomic analyses revealed that chrysoeriol modulates multiple pathways associated with apoptosis, necroptosis, pyroptosis, and ferroptosis. Morphological changes in chrysoeriol-treated melanoma cells showed PANoptosis- and ferroptosis-related characteristics. Additionally, chrysoeriol induced apoptosis, altered mitochondrial membrane potential, increased ROS production, promoted necroptosis, and also upregulated molecules linked to pyroptosis and ferroptosis. Molecular-level experiments confirmed that chrysoeriol promoted the upregulation of crucial proteins associated with the PANoptosis and ferroptosis pathways. Inhibition of PANoptosis and ferroptosis pathways by inhibitors or gene knockdown significantly attenuated the inhibitory effects of chrysoeriol on melanoma cell viability. This study provides robust evidence that chrysoeriol triggers both PANoptosis and ferroptosis in melanoma cells, underscoring its promise as a treatment option for melanoma.PMID:39497239 | DOI:10.1021/acs.jafc.4c07416

Lipids Health Dis. 2024 Nov 4;23(1):359. doi: 10.1186/s12944-024-02337-0.ABSTRACTBACKGROUND: Metabolic disruptions were observed in patients with optic neurodegenerative diseases (OND). However, evidence for the causal association between metabolites and OND is limited.METHODS: Two-sample Mendelian randomization (MR). Summary data for 128 blood metabolites was selected from three genome-wide association study (GWASs) involving 147,827 participants of European descent. GWASs Data for glaucoma (20906 cases and 391275 controls) and age-related macular degeneration (AMD, 9721 cases and 381339 controls) came from FinnGen consortium. A bi-directional MR was conducted to assess causality, and a Mediation MR was further applied to explore the indirect effect, a phenome-wide MR analysis was then performed to identify possible side-effects of the therapies.RESULTS: All the results underwent correction for multiple testing and rigorous sensitivity analyses. We identified N-acetyl glycine, serine, uridine were linked to an elevated risk of glaucoma. 1-arachidonic-glycerol-phosphate-ethanolamine, 4-acetamido butanoate, o-methylascorbate, saturated fatty acids, monounsaturated fatty acids, VLDL cholesterol, serum total cholesterol, X-11,529 were linked to reduced risk of glaucoma. There were 4 metabolites linked to a reduced risk of AMD, including tryptophan betaine, 4-androsten-3beta-17beta-diol disulfate, apolipoprotein B, VLDL cholesterol. We discovered IOP, AS, T2D as glaucoma risk factors, while BMI, AS, GCIPL as AMD factors. And 6 metabolites showed associations with risk factors in the same direction as their associations with glaucoma/AMD. Phenome-wide MR indicated that selected metabolites had protective/adverse effects on other diseases.CONCLUSIONS: By integrating genomics and metabolomics, this study supports new insights into the intricate mechanisms, and helps prevent and screen glaucoma and AMD.PMID:39497194 | DOI:10.1186/s12944-024-02337-0

Funct Integr Genomics. 2024 Nov 4;24(6):207. doi: 10.1007/s10142-024-01486-w.ABSTRACTPlants acts as living chemical factories that may create a large variety of secondary metabolites, most of which are used in pharmaceutical products. The production of these secondary metabolites is often much lower. Moreover, the primary constraint after discovering potential metabolites is the capacity to manufacture sufficiently for use in industrial and therapeutic contexts. The development of omics technology has brought revolutionary discoveries in various scientific fields, including transcriptomics, metabolomics, and genome sequencing. The metabolic pathways leading to the utilization of new secondary metabolites in the pharmaceutical industry can be identified with the use of these technologies. Genome editing (GEd) is a versatile technology primarily used for site-directed DNA insertions, deletions, replacements, base editing, and activation/repression at the targeted locus. Utilizing GEd techniques such as clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 (CRISPR-associated protein 9), metabolic pathways engineered to synthesize bioactive metabolites optimally. This article will briefly discuss omics and CRISPR/Cas9-based methods to improve secondary metabolite production in medicinal plants.PMID:39496976 | DOI:10.1007/s10142-024-01486-w

Pediatr Res. 2024 Nov 4. doi: 10.1038/s41390-024-03690-7. Online ahead of print.ABSTRACTBACKGROUND: Extrauterine growth restriction (EUGR) affects a substantial proportion of preterm infants and may influence both short-term complications and long-term sequelae. While many preterm infants with EUGR are secondary to small for gestational age (SGA) or very low birth weight (VLBW), a subset of EUGR infants do not exhibit these conditions. The purpose of this study is to investigate the metabolic profiles and biomarkers of EUGR infants in the absence of SGA and VLBW.METHODS: A total of 100 feces (n = 50) and plasma samples (n = 50) were collected from participants categorized as either EUGR (EUGR group) or non-EUGR (NonEUGR group) in the absence of SGA and VLBW. Metabolites were characterized via UPLC-MS/MS using the Discovery HD4® platform. Data normalization, partial least squares discriminant analysis (PLSDA), and KEGG enrichment analysis of metabolite profiles were performed using the MetaboAnalyst 6.0.RESULTS: The clinical characteristics of preterm infants differed significantly between the EUGR and NonEUGR groups at discharge, including length of stay, weight Z-score, weight, height Z-score, height, head circumference, and fat-free mass. The PLSDA model exhibited clustering within groups and separation between groups. A total of 58 and 71 differential metabolites were identified in feces and plasma samples, respectively. They were involved in pathways such as caffeine, galactose, glutathione, cysteine, and methionine metabolisms. In the feces sample, 1-palmitoyl-galactosylglycerol exhibited a significant negative correlation with the growth characteristics of preterm infants, while 1-palmitoyl-2-palmitoleoyl-GPC displayed the opposite pattern. In plasma samples, androsterone glucuronide displayed a significant positive correlation with the growth characteristics of preterm infants, whereas 2-methoxyhydroquinone sulfate generated an opposite pattern. Moreover, 2-oleoylglycerol and sphinganine-1-phosphate exhibited the highest area under the curve in feces and plasma samples, respectively, according to diagnostic ROC curves.CONCLUSION: Preterm infants with EUGR, in the absence of SGA and VLBW, exhibit specific clinical characteristics and metabolomic profiles. Sphinganine-1-phosphate and 2-oleoylglycerol may hold promise as diagnostic markers for EUGR in the absence of SGA and VLBW.IMPACT: The objective of this study is to identify the differential metabolites in preterm infants with extrauterine growth restriction (EUGR) in the absence of small for gestational age (SGA) or very low birth weight (VLBW). Preterm infants with EUGR without SGA and VLBW exhibit specific clinical characteristics and metabolomic profiles. Sphinganine-1-phosphate and 2-oleoylglycerol emerged as potential diagnostic biomarkers for EUGR. This study enhances our understanding of the metabolomic profile in preterm infants with EUGR without SGA or VLBW. Our findings will offer valuable evidence for improving nutritional management and shedding light on the associated pathophysiological mechanisms of EUGR.PMID:39496876 | DOI:10.1038/s41390-024-03690-7

Arch Gynecol Obstet. 2024 Nov 4. doi: 10.1007/s00404-024-07796-5. Online ahead of print.ABSTRACTBACKGROUND: Endometriosis affects the quality of life in women during their reproductive years, causing immense pain and can result in infertility. It is characterized by inflammation and the growth of the endometrium outside the uterine cavity. Metabolomics has the potential to resolve the major bottleneck of endometriosis which is delay in diagnosis due to the invasive diagnostic approach.In this review, the author has summarized the identified biomarkers of endometriosis from different bodily fluids. Metabolomics promises a non-invasive diagnostic approach for endometriosis that could aid in earlier diagnosis and prognosis.METHODS: Patients with endometriosis keywords were searched in correspondence with the assigned keywords, including metabolomics from PubMed, from its inception to Dec 2023. The relevant studies from this search were extracted and included in the study.RESULTS: This article provides information regarding metabolomics studies in endometrisis.CONCLUSIONS: We demonstrated that metabolomics is about to change the world of endometriosis by analyzing and detecting the diagnosis, prognosis, mortality and treatment response biomarkers.PMID:39496808 | DOI:10.1007/s00404-024-07796-5

Anal Bioanal Chem. 2024 Nov 4. doi: 10.1007/s00216-024-05622-0. Online ahead of print.ABSTRACTThe study of the dialogue between microorganisms at the molecular level is becoming essential to understand their relationship (antagonist, neutral, or beneficial interactions) and its impact on the organization of the microbial community. Mass spectrometry imaging (MSI) with matrix-assisted laser desorption/ionization (MALDI) is a technique that reveals the spatial distribution of molecules on a sample surface that may be involved in interactions between organisms. An experimental limitation to perform MALDI MSI is a flat sample surface, which in many cases could not be achieved for bacterial colonies such as filamentous bacteria (e.g., Streptomyces). In addition, sample heterogeneity affects sample dryness and MALDI matrix deposition prior to MSI. To avoid such problems, we introduce an additional step in the sample preparation. A polymeric membrane is interposed between the microorganisms and the agar-based culture medium, allowing the removal of bacterial colonies prior to MSI of the homogeneous culture medium. A proof of concept was evaluated on Streptomyces ambofaciens (a soil bacterium) cultures on solid media. As the mycelium was removed at the same time as the polymeric membrane, the metabolites released into the medium were spatially resolved by MALDI MSI. In addition, extraction of the recovered mycelium from the membrane confirmed the identification of the metabolites by ESI MS/MS analysis. This approach allows both the spatial distribution of metabolites produced by microorganisms in an agar medium to be studied under well-controlled sample preparation and their structure to be elucidated. This capability is illustrated using desferrioxamine E, a siderophore produced by S. ambofaciens.PMID:39496785 | DOI:10.1007/s00216-024-05622-0

Sci Rep. 2024 Nov 4;14(1):26705. doi: 10.1038/s41598-024-77494-4.ABSTRACTClinical proteomics analysis is of great significance for analyzing pathological mechanisms and discovering disease-related biomarkers. Using computational methods to accurately predict disease types can effectively improve patient disease diagnosis and prognosis. However, how to eliminate the errors introduced by peptide precursor identification and protein identification for pathological diagnosis remains a major unresolved issue. Here, we develop a powerful end-to-end deep learning model, termed "MS1Former", that is able to classify hepatocellular carcinoma tumors and adjacent non-tumor (normal) tissues directly using raw MS1 spectra without peptide precursor identification. Our model provides accurate discrimination of subtle m/z differences in MS1 between tumor and adjacent non-tumor tissue, as well as more general performance predictions for data-dependent acquisition, data-independent acquisition, and full-scan data. Our model achieves the best performance on multiple external validation datasets. Additionally, we perform a detailed exploration of the model's interpretability. Prospectively, we expect that the advanced end-to-end framework will be more applicable to the classification of other tumors.PMID:39496730 | DOI:10.1038/s41598-024-77494-4

Sci Rep. 2024 Nov 4;14(1):26635. doi: 10.1038/s41598-024-75627-3.ABSTRACTIdentifying biomarkers able to discriminate individuals on different health trajectories is crucial to understand the molecular basis of age-related morbidity. We investigated multi-omics signatures of general health and organ-specific morbidity, as well as their interconnectivity. We examined cross-sectional metabolome and proteome data from 3,142 adults of the Cooperative Health Research in South Tyrol (CHRIS) study, an Alpine population study designed to investigate how human biology, environment, and lifestyle factors contribute to people's health over time. We had 174 metabolites and 148 proteins quantified from fasting serum and plasma samples. We used the Cumulative Illness Rating Scale (CIRS) Comorbidity Index (CMI), which considers morbidity in 14 organ systems, to assess health status (any morbidity vs. healthy). Omics-signatures for health status were identified using random forest (RF) classifiers. Linear regression models were fitted to assess directionality of omics markers and health status associations, as well as to identify omics markers related to organ-specific morbidity. Next to age, we identified 21 metabolites and 10 proteins as relevant predictors of health status and results confirmed associations for serotonin and glutamate to be age-independent. Considering organ-specific morbidity, several metabolites and proteins were jointly related to endocrine, cardiovascular, and renal morbidity. To conclude, circulating serotonin was identified as a potential novel predictor for overall morbidity.PMID:39496618 | DOI:10.1038/s41598-024-75627-3