PubMed

J Agric Food Chem. 2024 Jan 5. doi: 10.1021/acs.jafc.3c06301. Online ahead of print.ABSTRACTTocotrienols and tocopherols (vitamin E) are potent antioxidants that are synthesized in green plants. Unlike ubiquitous tocopherols, tocotrienols predominantly accumulate in the endosperm of monocot grains, catalyzed by homogentiate geranylgeranyl transferase (HGGT). Previously, we generated a tocotrienol-deficient hvhggt mutant with shrunken barley grains. However, the relationship between tocotrienols and grain development remains unclear. Here, we found that the hvhggt lines displayed hollow endosperms with defective transfer cells and reduced aleurone layers. The carbohydrate and starch contents of the hvhggt endosperm decreased by approximately 20 and 23%, respectively. Weighted gene coexpression network analyses identified a critical gene module containing HvHGGT, which was strongly associated with the hvhggt mutation and enriched with gene functions in starch and sucrose metabolism. Metabolome measurements revealed an elevated soluble sugar content in the hvhggt endosperm, which was significantly associated with the identified gene modules. The hvhggt endosperm had significantly higher NAD(H) and NADP(H) contents and lower levels of ADPGlc (regulated by redox balance) than the wild-type, consistent with the absence of tocotrienols. Interestingly, exogenous α-tocotrienol spraying on developing hvhggt spikes partially rescued starch accumulation and endosperm defects. Our study supports a potential novel function of tocotrienols in grain starch accumulation and endosperm development in monocot crops.PMID:38181192 | DOI:10.1021/acs.jafc.3c06301

Cancer Biother Radiopharm. 2024 Jan 5. doi: 10.1089/cbr.2023.0116. Online ahead of print.ABSTRACTWith the development of the social economy and the deepening understanding of cancer, cancer has become a significant cause of death, threatening human health. Although researchers have made rapid progress in cancer treatment strategies in recent years, the overall survival of cancer patients is still not optimistic. Therefore, it is essential to reveal the spatial pattern of gene expression, spatial heterogeneity of cell populations, microenvironment interactions, and other aspects of cancer. Spatiotemporal transcriptomics can help analyze the mechanism of cancer occurrence and development, greatly help precise cancer treatment, and improve clinical prognosis. Here, we review the integration strategies of single-cell RNA sequencing and spatial transcriptomics data, summarize the recent advances in spatiotemporal transcriptomics in cancer studies, and discuss the combined application of spatial multiomics, which provides new directions and strategies for the precise treatment and clinical prognosis of cancer.PMID:38181185 | DOI:10.1089/cbr.2023.0116

Int J Surg. 2024 Jan 4. doi: 10.1097/JS9.0000000000001033. Online ahead of print.ABSTRACTOBJECTIVES: Prostate cancer (PCa) is one of the most common malignancies in men worldwide and has caused increasing clinical morbidity and mortality, making timely diagnosis and accurate staging crucial. We introduced a novel approach based on mass spectrometry (MS) for precise diagnosis and stratification of PCa to facilitate clinical decision-making.METHODS: Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) MS analysis of trace blood samples was combined with machine learning algorithms to construct diagnostic and stratification models. A total of 367 subjects, comprising 181 with PCa and 186 with non-PCa were enrolled. Additional 60 subjects, comprising 30 with PCa and 30 with non-PCa were enrolled as an external cohort for validation. Subsequent metabolomic analysis was carried out using Autoflex MALDI-TOF, and the mass spectra were introduced into various algorithms to construct different models.RESULTS: Serum metabolic fingerprints were successfully obtained from 181 patients with PCa and 186 patients with non-PCa. The diagnostic model based on the eight signals demonstrated a remarkable area under curve (AUC) of 100% and was validated in the external cohort with the AUC of 87.3%. Fifteen signals were selected for enrichment analysis, revealing the potential metabolic pathways that facilitate tumorigenesis. Furthermore, the stage prediction model with an overall accuracy of 85.9% precisely classified subjects with localized disease and those with metastasis. The risk stratification model, with an overall accuracy of 89.6%, precisely classified the subjects as low-risk and high-risk.CONCLUSIONS: Our study facilitated the timely diagnosis and risk stratification of PCa and provided new insights into the underlying mechanisms of metabolic alterations in PCa.PMID:38181121 | DOI:10.1097/JS9.0000000000001033

Proc Natl Acad Sci U S A. 2024 Jan 9;121(2):e2321437121. doi: 10.1073/pnas.2321437121. Epub 2024 Jan 5.NO ABSTRACTPMID:38181059 | DOI:10.1073/pnas.2321437121

J Infect Dis. 2024 Jan 5:jiad611. doi: 10.1093/infdis/jiad611. Online ahead of print.ABSTRACTBACKGROUND: Avian influenza viruses pose significant risk to human health. Vaccines targeting the hemagglutinin of these viruses are poorly immunogenic without the use of adjuvants.METHODS: Twenty healthy men and women (18-49 years of age) were randomized to receive two doses of inactivated influenza A/H5N1 vaccine alone (IIV) or with AS03 adjuvant (IIV-AS03) one month apart. Urine and serum samples were collected on day 0 and on days 1, 3, and 7 following first vaccination and subjected to metabolomics analyses to identify metabolites, metabolic pathways, and metabolite clusters associated with immunization.RESULTS: Seventy-three differentially abundant (DA) serum and 88 urine metabolites were identified for any post-vaccination day comparison. Pathway analysis revealed enrichment of tryptophan, tyrosine and nicotinate metabolism in urine and serum among IIV-AS03 recipients. Increased urine abundance of 4-vinylphenol sulfate on Day 1 was associated with serologic response based on hemagglutination inhibition responses. In addition, 9 DA urine metabolites were identified in participants with malaise compared to those without.CONCLUSIONS: Our findings suggest that tryptophan, tyrosine, and nicotinate metabolism are upregulated among IIV-AS03 recipients compared with IIV alone. Metabolites within these pathways may serve as measures of immunogenicity and may provide mechanistic insights for adjuvanted vaccines.PMID:38181048 | DOI:10.1093/infdis/jiad611

Metabolomics. 2024 Jan 5;20(1):13. doi: 10.1007/s11306-023-02081-z.ABSTRACTINTRODUCTION: The burden of stroke in patients with hypertension is very high, and its prediction is critical.OBJECTIVES: We aimed to use plasma lipidomics profiling to identify lipid biomarkers for predicting incident stroke in patients with hypertension.METHODS: This was a nested case-control study. Baseline plasma samples were collected from 30 hypertensive patients with newly developed stroke, 30 matched patients with hypertension, 30 matched patients at high risk of stroke, and 30 matched healthy controls. Lipidomics analysis was performed by ultrahigh-performance liquid chromatography-tandem mass spectrometry, and differential lipid metabolites were screened using multivariate and univariate statistical methods. Machine learning methods (least absolute shrinkage and selection operator, random forest) were used to identify candidate biomarkers for predicting stroke in patients with hypertension.RESULTS: Co-expression network analysis revealed that the key molecular alterations of the lipid network in stroke implicate glycerophospholipid metabolism and choline metabolism. Six lipid metabolites were identified as candidate biomarkers by multivariate statistical and machine learning methods, namely phosphatidyl choline(40:3p)(rep), cholesteryl ester(20:5), monoglyceride(29:5), triglyceride(18:0p/18:1/18:1), triglyceride(18:1/18:2/21:0) and coenzyme(q9). The combination of these six lipid biomarkers exhibited good diagnostic and predictive ability, as it could indicate a risk of stroke at an early stage in patients with hypertension (area under the curve = 0.870; 95% confidence interval: 0.783-0.957).CONCLUSIONS: We determined lipidomic signatures associated with future stroke development and identified new lipid biomarkers for predicting stroke in patients with hypertension. The biomarkers have translational potential and thus may serve as blood-based biomarkers for predicting hypertensive stroke.PMID:38180633 | DOI:10.1007/s11306-023-02081-z

Metabolomics. 2024 Jan 5;20(1):12. doi: 10.1007/s11306-023-02078-8.ABSTRACTINTRODUCTION: Alzheimer's Disease (AD) is complex and novel approaches are urgently needed to aid in diagnosis. Blood is frequently used as a source for biomarkers; however, its complexity prevents proper detection. The analytical power of metabolomics, coupled with statistical tools, can assist in reducing this complexity.OBJECTIVES: Thus, we sought to validate a previously proposed panel of metabolic blood-based biomarkers for AD and expand our understanding of the pathological mechanisms involved in AD that are reflected in the blood.METHODS: In the validation cohort serum and plasma were collected from 25 AD patients and 25 healthy controls. Serum was analysed for metabolites using nuclear magnetic resonance (NMR) spectroscopy, while plasma was tested for markers of neuronal damage and AD hallmark proteins using single molecule array (SIMOA).RESULTS: The diagnostic performance of the metabolite biomarker panel was confirmed using sparse-partial least squares discriminant analysis (sPLS-DA) with an area under the curve (AUC) of 0.73 (95% confidence interval: 0.59-0.87). Pyruvic acid and valine were consistently reduced in the discovery and validation cohorts. Pathway analysis of significantly altered metabolites in the validation set revealed that they are involved in branched-chain amino acids (BCAAs) and energy metabolism (glycolysis and gluconeogenesis). Additionally, strong positive correlations were observed for valine and isoleucine between cerebrospinal fluid p-tau and t-tau.CONCLUSIONS: Our proposed panel of metabolites was successfully validated using a combined approach of NMR and sPLS-DA. It was discovered that cognitive-impairment-related metabolites belong to BCAAs and are involved in energy metabolism.PMID:38180611 | DOI:10.1007/s11306-023-02078-8

Endocr Connect. 2024 Jan 1:EC-23-0384. doi: 10.1530/EC-23-0384. Online ahead of print.ABSTRACTDiabetic cardiomyopathy (DCM) is a serious complication of type 2 diabetes mellitus (T2DM) that contributes to cardiovascular morbidity and mortality. However, the metabolic alterations and specific biomarkers associated with DCM in T2DM remain unclear. In this study, we conducted a comprehensive metabolomic analysis using liquid chromatography-mass spectrometry (LC-MS) to investigate the plasma metabolite profiles of T2DM patients with and without DCM. We identified significant differences in metabolite levels between the groups, highlighting the dysregulation of various metabolic pathways, including starch and sucrose metabolism, steroid hormone biosynthesis, tryptophan metabolism, purine metabolism, and pyrimidine metabolism. Although several metabolites showed altered abundance in DCM, they were also shared characteristics of DCM and T2DM rather than specific to DCM. Additionally, through biomarker analyses, we identified potential biomarkers for DCM, such as cytidine triphosphate, 11-ketoetiocholanolone, saccharopine, nervonic acid, and erucic acid. These biomarkers demonstrated distinct patterns and associations with metabolic pathways related to DCM. Our findings provide insights into the metabolic changes associated with DCM in T2DM patients and highlight potential biomarkers for further validation and clinical application. Further research is needed to elucidate the underlying mechanisms and validate the diagnostic and prognostic value of these biomarkers in larger cohorts.PMID:38180052 | DOI:10.1530/EC-23-0384

Biomark Med. 2024 Jan 5. doi: 10.2217/bmm-2023-0420. Online ahead of print.ABSTRACTAsthma, chronic obstructive pulmonary disease (COPD) and asthma-COPD overlap are the third leading cause of mortality around the world. They share some common features, which can lead to misdiagnosis. To properly manage these conditions, reliable markers for early and accurate diagnosis are needed. Over the past 20 years, many molecules have been investigated in the exhaled breath condensate to better understand inflammation pathways and mechanisms related to these disorders. Recently, more advanced techniques, such as sensitive metabolomic and proteomic profiling, have been used to obtain a more comprehensive understanding. This article reviews the use of targeted and untargeted metabolomic methodology to study asthma, COPD and asthma-COPD overlap.PMID:38179966 | DOI:10.2217/bmm-2023-0420

Anal Chem. 2024 Jan 5. doi: 10.1021/acs.analchem.3c03660. Online ahead of print.ABSTRACTThe implementation of quality control strategies is crucial to ensure the reproducibility, accuracy, and meaningfulness of metabolomics data. However, this pivotal step is often overlooked within the metabolomics workflow and frequently relies on the use of nonstandardized and poorly reported protocols. To address current limitations in this respect, we have developed QComics, a robust, easily implementable and reportable method for monitoring and controlling data quality. The protocol operates in various sequential steps aimed to (i) correct for background noise and carryover, (ii) detect signal drifts and "out-of-control" observations, (iii) deal with missing data, (iv) remove outliers, (v) monitor quality markers to identify samples affected by improper collection, preprocessing, or storage, and (vi) assess overall data quality in terms of precision and accuracy. Notably, this tool considers important issues often neglected along quality control, such as the need of separately handling missing values and truly absent data to avoid losing relevant biological information, as well as the large impact that preanalytical factors may elicit on metabolomics results. Altogether, the guidelines compiled in QComics might contribute to establishing gold standard recommendations and best practices for quality control within the metabolomics community.PMID:38179935 | DOI:10.1021/acs.analchem.3c03660

Circ Heart Fail. 2024 Jan 5:e010813. doi: 10.1161/CIRCHEARTFAILURE.123.010813. Online ahead of print.ABSTRACTBACKGROUND: Metabolomics has become a valuable tool for identifying potential new biomarkers and metabolic profiles. It has the potential to improve the diagnosis and prognosis of different phenotypes of heart failure. To generate a distinctive metabolic profile, we assessed and compared the metabolic phenotypes of patients with acute decompensated heart failure (ADHF), patients with chronic heart failure (CHF), and healthy controls.METHODS: Plasma metabolites were analyzed by liquid-chromatography mass spectrometry/mass spectrometry and the MxP Quant 500 kit in 15 patients with ADHF, 50 patients with CHF (25 with dilated cardiomyopathy, 25 with ischemic cardiomyopathy), and 13 controls.RESULTS: Of all metabolites identified to be significantly altered, 3-indolepropionic acid and 1-methyl histidine showed the highest concentration differences in ADHF and CHF compared with control. Area under the curve-receiver operating characteristic analysis showed an area under the curve ≥0.8 for 3-indolepropionic acid and 1-methyl histidine, displaying good discrimination capabilities between control and patient cohorts. Additionally, symmetrical dimethylarginine (mean, 1.97±0.61 [SD]; P=0.01) was identified as a suitable biomarker candidate for ADHF and kynurenine (mean, 1.69±0.39 [SD]; P=0.009) for CHF when compared with control, both demonstrating an area under the curve ≥0.85.CONCLUSIONS: Our study provides novel insights into the metabolic differences between ADHF and CHF and healthy controls. We here identify new metabolites for potential diagnostic and prognostic purposes.PMID:38179791 | DOI:10.1161/CIRCHEARTFAILURE.123.010813

Front Biosci (Landmark Ed). 2023 Dec 26;28(12):344. doi: 10.31083/j.fbl2812344.ABSTRACTBACKGROUND: Activating transcription factor 4 (ATF4) is a fundamental basic-leucine zipper transcription factor that plays a pivotal role in numerous stress responses, including endoplasmic reticulum (ER) stress and the integrated stress response. ATF4 regulates adaptive gene expression, thereby triggering stress resistance in cells.METHODS: To characterize the metabolic status of atf4-⁣/- Drosophila larvae, we conducted both metabolomic and microarray analyses.RESULTS: Metabolomic analysis demonstrated an increase in lactate levels in atf4-⁣/- mutants when compared to wild-type flies. However, there was a significant reduction in adenosine triphosphate (ATP) synthesis in the atf4-⁣/- flies, suggesting an abnormal energy metabolism in the mutant larvae. Microarray analysis unveiled that Drosophila ATF4 controls gene expression related to diverse biological processes, including lipase activity, oxidoreductase activity, acyltransferase, immune response, cell death, and transcription factor, particularly under nutrient-restricted conditions. In situ hybridization analysis further demonstrated specific augmentation of CG6283, classified as a gastric lipase, within the gastric caeca of nutrient-restricted flies. Moreover, overexpression of lipases, CG6283 and CG6295, made the flies resistant to starvation.CONCLUSIONS: These findings underscore the role of Drosophila ATF4 in responding to metabolic fluctuations and modulating gene expression associated with metabolism and stress adaptation. Dysregulation of ATF4 may detrimentally impact the development and physiology of Drosophila.PMID:38179767 | DOI:10.31083/j.fbl2812344

Front Biosci (Landmark Ed). 2023 Dec 26;28(12):345. doi: 10.31083/j.fbl2812345.ABSTRACTBACKGROUND: Chinese cabbage (Brassica rapa L. ssp. pekinensis) is one of the most popular vegetables in China because of its taste and health benefits. The area of production has obvious effects on the quality of Chinese cabbage. However, metabolite profiling and variations in different production areas are still unclear.METHODS: Here, widely targeted metabolite analyses based on the ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) approach were performed to study the metabolite profiling of Chinese cabbage planted in the Jiaozhou and Jinan areas.RESULTS: A total of 531 metabolites were detected, of which 529 were present in the Chinese cabbage from both areas, 108 were found to be chemicals related to Chinese traditional medicine, and 79 were found to correspond to at least one disease. Chinese cabbage is rich in nutritious substances such as lipids, phenolic acids, amino acids and derivatives, nucleotides and derivatives, organic acids, flavonoids, glucosinolates, saccharides, alcohols, and vitamins. Comparative analysis showed that the metabolic profiles differed between areas, and 89 differentially altered metabolites (DAMs) were characterized. Of these, 78 DAMs showed higher levels in Jinan Chinese cabbage, whereas 11 had higher levels in Jiaozhou Chinese cabbage. Two metabolites, S-(Methyl)glutathione and nicotinic acid adenine dinucleotide, were unique in Jiaozhou Chinese cabbage. Based on Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, the DAMs were enriched into 23 pathways, of which tryptophan metabolism and thiamine metabolism were the significant enrichment pathways.CONCLUSIONS: This study provides new insights into the metabolite profiles and production areas affecting the metabolite variations of Chinese cabbage, which will be useful for functional Chinese cabbage cultivation.PMID:38179748 | DOI:10.31083/j.fbl2812345

Technol Cancer Res Treat. 2024 Jan-Dec;23:15330338231223080. doi: 10.1177/15330338231223080.ABSTRACTBACKGROUND: Acute myeloid leukemia (AML) with Fms-like tyrosine kinase 3 gene internal tandem duplication (FLT3-ITD) mutations has a poor prognosis. The combination of arsenic trioxide (ATO) and all-trans retinoic acid (ATRA) has a synergistic killing effect on leukemia cells with FLT3-ITD mutation. However, the mechanism, especially the changes of gene expression and metabolic activity remain unclear. Here we explore the transcriptome and metabolomics changes of FLT3-ITD AML cells treated with ATO/ATRA.METHODS: RNA-seq was used to identify differential expressed genes (DEGs), and ultra-high performance liquid chromatography-quadrupole electrostatic field orbital trap mass spectrometry (UHPLC-QE-MS) nontargeted metabolomics method was used to screen out the differential metabolites in FLT3-ITD mutant cell lines treated with ATRA and ATO. KEGG pathway database was utilized for pathway exploration and Seahorse XF24 was used to detect extracellular acidification rate (ECAR). Metabolic polymerase chain reaction (PCR) array and real-time quantitative PCR (RT-qPCR) were used to detect mRNA levels of key metabolic genes of glycolysis and fatty acid after drug treatment.RESULTS: A total of 3873 DEGs were identified and enriched in 281 Gene Ontology (GO) terms, among which 210 were related to biological processes, 43 were related to cellular components, and 28 were related to molecular functions. Besides, 1794 and 927 differential metabolites were screened in positive and negative ion mode separately, and 59 different metabolic pathways were involved, including alanine-aspartate-glutamate metabolic pathway, arginine, and proline metabolic pathway, glycerophospholipid metabolic pathways, etc. According to KEGG Pathway analysis of transcriptome combined with metabolome, glycolysis/gluconeogenesis pathway and fatty acid metabolism pathway were significantly founded enriched. ATRA + ATO may inhibit the glycolysis of FLT3-ITD AML cells by inhibiting FLT3 and its downstream AKT/HK2-VDAC1 signaling pathway.CONCLUSIONS: The gene transcription profile and metabolites of FLT3-ITD mutant cells changes significantly after treatment, which might be related to the anti-FLT3-ITD AML effect. The screened DEGs, differential metabolites pathway are helpful in studying the mechanism of anti-leukemia effects and drug targets.PMID:38179723 | DOI:10.1177/15330338231223080

Front Plant Sci. 2023 Dec 21;14:1274700. doi: 10.3389/fpls.2023.1274700. eCollection 2023.ABSTRACTFlavonoids are secondary metabolites that have economic value and are essential for health. Poplar is a model perennial woody tree that is often used to study the regulatory mechanisms of flavonoid synthesis. We used a poplar bud mutant, the red leaf poplar variety 2025 (Populus × euramericana 'Zhonghuahongye'), and green leaves as study materials and selected three stages of leaf color changes for evaluation. Phenotypic and biochemical analyses showed that the total flavonoid, polyphenol, and anthocyanin contents of red leaves were higher than those of green leaves in the first stage, and the young and tender leaves of the red leaf variety had higher antioxidant activity. The analyses of widely targeted metabolites identified a total of 273 flavonoid metabolites (114 flavones, 41 flavonols, 34 flavonoids, 25 flavanones, 21 anthocyanins, 18 polyphenols, 15 isoflavones, and 5 proanthocyanidins). The greatest difference among the metabolites was found in the first stage. Most flavonoids accumulated in red leaves, and eight anthocyanin compounds contributed to red leaf coloration. A comprehensive metabolomic analysis based on RNA-seq showed that most genes in the flavonoid and anthocyanin biosynthetic pathways were differentially expressed in the two types of leaves. The flavonoid synthesis genes CHS (chalcone synthase gene), FLS (flavonol synthase gene), ANS (anthocyanidin synthase gene), and proanthocyanidin synthesis gene LAR (leucoanthocyanidin reductase gene) might play key roles in the differences in flavonoid metabolism. A correlation analysis of core metabolites and genes revealed several candidate regulators of flavonoid and anthocyanin biosynthesis, including five MYB (MYB domain), three bHLH (basic helix-loop-helix), and HY5 (elongated hypocotyl 5) transcription factors. This study provides a reference for the identification and utilization of flavonoid bioactive components in red-leaf poplar and improves the understanding of the differences in metabolism and gene expression between red and green leaves at different developmental stages.PMID:38179486 | PMC:PMC10764563 | DOI:10.3389/fpls.2023.1274700

Front Microbiol. 2023 Dec 21;14:1251243. doi: 10.3389/fmicb.2023.1251243. eCollection 2023.ABSTRACTBACKGROUND: Myopia is one of the most common eye diseases leading to blurred distance vision. Inflammatory diseases could trigger or exacerbate myopic changes. Although gut microbiota bacteria are associated with various inflammatory diseases, little is known about its role in myopia.MATERIALS AND METHODS: The mice were randomly divided into control and model groups, with the model group being attached-30D lens onto the eyes for 3 weeks. Then, mouse cecal contents and plasma were collected to analyze their intestinal microbiota and plasma metabolome.RESULTS: We identified that the microbial composition differed considerably between the myopic and non-myopic mice, with the relative abundance of Firmicutes phylum decreased obviously while that of Actinobacteria phylum was increased in myopia. Furthermore, Actinobacteria and Bifidobacterium were positively correlated with axial lengths (ALs) of eyeballs while negatively correlated with refractive diopters. Untargeted metabolomic analysis identified 141 differentially expressed metabolites, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed considerable enrichment mainly in amino acid metabolism pathways. Notably, pathways involved glutamate metabolism including "Glutamine and D-glutamate metabolism" and "Alanine, aspartate and glutamate metabolism" was changed dramatically, which presented as the concentrations of L-Glutamate and L-Glutamine decreased obviously in myopia. Interestingly, microbiome dysbiosis and metabolites alternations in myopia have a disrupting gut barrier feature. We further demonstrated that the gut barrier function was impaired in myopic mice manifesting in decreased expression of Occludin, ZO-1 and increased permeation of FITC-dextran.DISCUSSION: Myopic mice had obviously altered gut microbiome and metabolites profiles compared to non-myopic mice. The dysbiosis and plasma metabolomics shift in myopia had an interrupting gut barrier feature. Our study provides new insights into the possible role of the gut microbiota in myopia and reinforces the potential feasibility of microbiome-based therapies in myopia.PMID:38179454 | PMC:PMC10764480 | DOI:10.3389/fmicb.2023.1251243

Front Endocrinol (Lausanne). 2023 Dec 21;14:1280248. doi: 10.3389/fendo.2023.1280248. eCollection 2023.ABSTRACTBACKGROUND: The metabolic characteristics of premature ovarian insufficiency (POI), a reproductive endocrine disease characterized by abnormal sex hormone metabolism and follicle depletion, remain unclear. Metabolomics is a powerful tool for exploring disease phenotypes and biomarkers. This study aims to identify metabolic markers and construct diagnostic models, and elucidate the underlying pathological mechanisms for POI.METHODS: Non-targeted metabolomics was utilized to characterize the plasma metabolic profile of 40 patients. The metabolic markers were identified through bioinformatics and machine learning, and constructed an optimal diagnostic model by classified multi-model analysis. Enzyme-linked immunosorbent assay (ELISA) was used to verify antioxidant indexes, mitochondrial enzyme complexes, and ATP levels. Finally, integrated transcriptomics and metabolomics were used to reveal the dysregulated pathways and molecular regulatory mechanisms of POI.RESULTS: The study identified eight metabolic markers significantly correlated with ovarian reserve function. The XGBoost diagnostic model was developed based on six machine learning models, demonstrating its robust diagnostic performance and clinical applicability through the evaluation of receiver operating characteristic (ROC) curve, decision curve analysis (DCA), calibration curve, and precise recall (PR) curve. Multi-omics analysis showed that mitochondrial respiratory chain electron carrier (CoQ10) and enzyme complex subunits were down-regulated in POI. ELISA validation revealed an elevation in oxidative stress markers and a reduction in the activities of antioxidant enzymes, CoQ10, and mitochondrial enzyme complexes in POI.CONCLUSION: Our findings highlight that mitochondrial dysfunction and energy metabolism disorders are closely related to the pathogenesis of POI. The identification of metabolic markers and predictive models holds significant implications for the diagnosis, treatment, and monitoring of POI.PMID:38179298 | PMC:PMC10764474 | DOI:10.3389/fendo.2023.1280248

J Appl Stat. 2022 Sep 2;51(1):114-138. doi: 10.1080/02664763.2022.2116746. eCollection 2024.ABSTRACTWe propose a novel approach to the estimation of multiple Graphical Models to analyse temporal patterns of association among a set of metabolites over different groups of patients. Our motivating application is the Southall And Brent REvisited (SABRE) study, a tri-ethnic cohort study conducted in the UK. We are interested in identifying potential ethnic differences in metabolite levels and associations as well as their evolution over time, with the aim of gaining a better understanding of different risk of cardio-metabolic disorders across ethnicities. Within a Bayesian framework, we employ a nodewise regression approach to infer the structure of the graphs, borrowing information across time as well as across ethnicities. The response variables of interest are metabolite levels measured at two time points and for two ethnic groups, Europeans and South-Asians. We use nodewise regression to estimate the high-dimensional precision matrices of the metabolites, imposing sparsity on the regression coefficients through the dynamic horseshoe prior, thus favouring sparser graphs. We provide the code to fit the proposed model using the software Stan, which performs posterior inference using Hamiltonian Monte Carlo sampling, as well as a detailed description of a block Gibbs sampling scheme.PMID:38179161 | PMC:PMC10763914 | DOI:10.1080/02664763.2022.2116746

Toxicol Res (Camb). 2023 Dec 2;13(1):tfad108. doi: 10.1093/toxres/tfad108. eCollection 2024 Feb.ABSTRACTLunar dust particles are an environmental threat to lunar astronauts, and inhalation of lunar dust can cause lung damage. The current study explored the mechanism of lunar dust simulant (CLDS-i) inducing inflammatory pulmonary injury. Wistar rats were exposed to CLDS-i for 4 h/d and 7d/week for 4 weeks. Pathological results showed that a large number of inflammatory cells gathered and infiltrated in the lung tissues of the simulated lunar dust group, and the alveolar structures were destroyed. Transcriptome analysis confirmed that CLDS-i was mainly involved in the regulation of activation and differentiation of immune inflammatory cells, activated signaling pathways related to inflammatory diseases, and promoted the occurrence and development of inflammatory injury in the lung. Combined with metabolomics analysis, the results of joint analysis of omics were found that the genes Kmo, Kynu, Nos3, Arg1 and Adh7 were involved in the regulation of amino acid metabolism in rat lung tissues, and these genes might be the key targets for the treatment of amino acid metabolic diseases. In addition, the imbalance of amino acid metabolism might be related to the activation of nuclear factor kappaB (NF-κB) signaling pathway. The results of quantitative real-time polymerase chain reaction and Western blot further confirmed that CLDS-i may promote the occurrence and development of lung inflammation and lead to abnormal amino acid metabolism by activating the B cell activation factor (BAFF)/ B cell activation factor receptor (BAFFR)-mediated NF-κB signaling pathway.PMID:38179001 | PMC:PMC10762671 | DOI:10.1093/toxres/tfad108

PNAS Nexus. 2023 Dec 18;3(1):pgad440. doi: 10.1093/pnasnexus/pgad440. eCollection 2024 Jan.ABSTRACTWe examined the effects of 1 month of a eucaloric, high-fat (48% of calories) diet (HFD) on gonadotropin secretion in normal-weight women to interrogate the role of free fatty acids and insulin in mediating the relative hypogonadotropic hypogonadism of obesity. Eighteen eumenorrheic women (body mass index [BMI] 18-25 kg/m2) were studied in the early follicular phase of the menstrual cycle before and after exposure to an HFD with frequent blood sampling for luteinizing hormone (LH) and follicle-stimulating hormone (FSH), followed by an assessment of pituitary sensitivity to gonadotropin-releasing hormone (GnRH). Mass spectrometry-based plasma metabolomic analysis was also performed. Paired testing and time-series analysis were performed as appropriate. Mean endogenous LH (unstimulated) was significantly decreased after the HFD (4.3 ± 1.0 vs. 3.8 ± 1.0, P < 0.01); mean unstimulated FSH was not changed. Both LH (10.1 ± 1.0 vs. 7.2 ± 1.0, P < 0.01) and FSH (9.5 ± 1.0 vs. 8.8 ± 1.0, P < 0.01) responses to 75 ng/kg of GnRH were reduced after the HFD. Mean LH pulse amplitude and LH interpulse interval were unaffected by the dietary exposure. Eucaloric HFD exposure did not cause weight change. Plasma metabolomics confirmed adherence with elevation of fasting free fatty acids (especially long-chain mono-, poly-, and highly unsaturated fatty acids) by the last day of the HFD. One-month exposure to an HFD successfully induced key reproductive and metabolic features of reprometabolic syndrome in normal-weight women. These data suggest that dietary factors may underlie the gonadotrope compromise seen in obesity-related subfertility and therapeutic dietary interventions, independent of weight loss, may be possible.PMID:38178979 | PMC:PMC10766410 | DOI:10.1093/pnasnexus/pgad440