PubMed

J Inherit Metab Dis. 2025 Jan;48(1):e12832. doi: 10.1002/jimd.12832.ABSTRACTX-linked adrenoleukodystrophy (ALD) is a peroxisomal disorder resulting from pathogenic variants in the ABCD1 gene that primarily affects the nervous system and is characterized by progressive axonal degeneration in the spinal cord and peripheral nerves and leukodystrophy. Dysfunction of peroxisomal very long-chain fatty acid (VLCFA) degradation has been implicated in ALD pathology, but the impact on astrocytes, which critically support neuronal function, remains poorly understood. Fibroblasts from four ALD patients were reprogrammed to generate human-induced pluripotent stem cells (hiPSC). hiPSC-derived astrocytes were generated to study the impact of ALD on astrocytic fatty acid homeostasis. Our study reveals significant changes in the lipidome of ALD hiPSC-derived astrocytes, characterized by an enrichment of VLCFAs across multiple lipid classes, including triacylglycerols, cholesteryl esters, and phosphatidylcholines. Importantly, ALD hiPSC-derived astrocytes not only exhibit intrinsic lipid dysregulation but also affect the dendritic tree complexity of neurons in co-culture systems. These findings highlight the cell-autonomous effects of pathogenic variants in the ABCD1 protein on astrocytes and their microenvironment, shed light on potential mechanisms underlying ALD neuropathology, and underscore the critical role of astrocytes in neuronal health.PMID:39704488 | DOI:10.1002/jimd.12832

Environ Sci Technol. 2024 Dec 20. doi: 10.1021/acs.est.4c07881. Online ahead of print.ABSTRACTFoliar application of beneficial nanoparticles exhibits potential in mitigating combined stresses from heavy metals and polycyclic aromatic hydrocarbons (PAHs) in crops, necessitating a comprehensive understanding of plant-rhizosphere-microbial processes to promote sustainable nanotechnology in agriculture. Herein, we investigated the mitigating mechanisms of foliar application of zinc oxide nanoparticles (nZnO) on lettuce growth under phenanthrene (Phe) and cadmium (Cd) costress. Compared to Phe + Cd treatment, low (L-nZnO) and high (H-nZnO) concentration of nZnO increased fresh biomass (27.2% and 8.42%) and root length (20.4% and 39.6%) and decreased MDA (35.0% and 40.0%) and H2O2 (29.0% and 15.6%) levels. L-nZnO and H-nZnO decreased Cd in roots (26.8% and 41.8%) and enhanced Zn in roots (19.9% and 107%), stems (221% and 2510%), and leaves (233% and 1500%), suggesting the long-distance migration of Zn from leaves to roots and subsequently regulating the metabolic pathways and microbial communities. Metabolomics revealed that nZnO modulated leaf glycerophospholipid metabolism and amino acid pathways and promoted rhizosphere soil carbon and phosphorus metabolism. Additionally, nZnO enriched the plant-growth-promoting, extreme, and stress-resistant bacteria in roots and leaves and heavy-metal-resistant and PAH-degrading bacteria in rhizosphere soil. These findings underscore the promising nanostrategy of nZnO to benefit plant growth in soil cocontaminated with heavy metals and PAHs.PMID:39704184 | DOI:10.1021/acs.est.4c07881

Haematologica. 2024 Dec 19. doi: 10.3324/haematol.2024.285154. Online ahead of print.ABSTRACTPlasma metabolomics analysis was performed on 44 patients with relapsed/refractory B-cell non-Hodgkin lymphoma (r/r/B-NHL) infused with approved CD19.CAR-T cell products at the time of pre-lymphodepletion (PLD) and at day +1, +7, and +30 after CAR-T cell infusion. At the PLD time point, a metabolic profile characterized by high lipoproteins and lactate and low glucose contributed to poor outcome prediction in association with high lactate dehydrogenase levels. At day+1, higher plasma levels of lipid metabolism products and lower glucose and glycoproteins levels were observed in tisa-cel compared to axi-cel-treated patients. At day+30, discriminant analysis found two clusters in a subgroup of patients, one with CR lasting one year after therapy, and another who relapsed within one year (relapsed>D30). This latter showed a higher content of N-GlycA, a known biomarker of systemic inflammation that is also correlated with C-reactive protein in our case setting of relapsing patients. Our data show complex metabolomic changes that track the evolution of the disease and drug activity in the first 30 days of CAR-T cell therapy. Conceivably, a pro-inflammatory drift may be linked to a forthcoming disease relapse in CAR-T patients.PMID:39704157 | DOI:10.3324/haematol.2024.285154

J Sci Food Agric. 2024 Dec 20. doi: 10.1002/jsfa.14084. Online ahead of print.ABSTRACTBACKGROUND: Alterations in the degrees of milling (DOM) could significantly influence the odor of rice. A gas chromatography-mass spectrometry (GC-MS)-based untargeted metabolomics method has been effectively employed to identify the differential volatiles among rice from various origins or varieties, although it has not been utilized to identify the differential volatiles among cooked rice with different DOM.RESULTS: Fifty volatile compounds were detected in cooked brown rice (CBR), cooked medium-milled rice (CMMR) and cooked well-milled rice (CWMR) of the four fragrant Simiao rice by GC-MS. A comprehensive GC-MS-based untargeted metabolomics analysis revealed 25 differential volatiles among CBR, CMMR and CWMR. Among them, seven differential volatiles, namely hexanal, octanal, decanal, (E,E)-2,4-decadienal, vanillin, acetoin and pentanol, as well as one differential volatile (dibutyl phthalate), were determined as volatile markers for CBR and CMMR, respectively. Moreover, acetoin was identified to distinguish among CBR, CMMR, and CWMR of fragrant Simiao rice.CONCLUSION: GC-MS-based untargeted metabolomics could be effectively applied to screen differential volatiles in cooked rice with different DOM. The 25 differential volatiles identified could significantly contribute to the distinctive odor in cooked fragrant Simiao rice with different DOM. © 2024 Society of Chemical Industry.PMID:39704026 | DOI:10.1002/jsfa.14084

Front Oncol. 2024 Dec 5;14:1418283. doi: 10.3389/fonc.2024.1418283. eCollection 2024.ABSTRACTBACKGROUND: Previous epidemiological studies have yielded inconclusive results regarding the causality between blood metabolites and the risk of gastric cancer (GC). To address this shortcoming, we conducted a two-sample Mendelian randomization (MR) study, combined with metabolomics techniques, to elucidate the causality between 486 genetically predicted blood metabolites and GC.METHODS: MR analysis and metabolomics techniques such as ultra-high performance liquid chromatography/tandem mass spectrometry (UPLC-MS/MS) and gas chromatography/tandem mass spectrometry (GC-MS/MS) technologies were employed to assess the causality of 486 genetically predicted blood metabolites on the risk of GC. The genome-wide association study (GWAS) summary data for 486 blood metabolites from 7,824 individuals. The GWAS summary data for GC (ebi-a-GCST90018849) were obtained from the IEU Open GWAS project, including 1,029 GC cases and 474,841 controls. Primary causality estimates were obtained using inverse variance weighting (IVW), supplemented with the weighted median, MR-Egger, weighted mode, and simple mode. In addition, we conducted sensitivity analyses (including Cochran's Q, MR-Egger intercept, MR-PRESSO, and leave-one-out tests),Steiger's test, linked disequilibrium score regression, and multivariate MR (MVMR) to improve the assessment of causality between GC and blood metabolite. Finally, we recruited a total of 11 patients diagnosed with gastric cancer from the First Affiliated Hospital of Air Force Military Medical University between September and October 2024. The control group comprised 11 healthy individuals. Serum samples were collected from both groups for the evaluation of blood-related metabolite expression levels using advanced techniques such as ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and gas chromatography-mass spectrometry (GC-MS/MS).RESULTS: The MVMR analysis revealed a significant association between genetically predicted elevated levels of tryptophan (odds ratio [OR] = 0.523, 95% confidence interval [CI] = 0.313-0.872, p = 0.013), nonadecanoate (19:0) (odds ratio [OR] = 0.460, 95% confidence interval [CI] = 0.225-0.943, p = 0.034), and erythritol (odds ratio [OR] = 0.672, 95% confidence interval [CI] = 0.468-0.930, p = 0.016) with a decreased risk of gastric cancer. Based on metabolomic techniques such as UPLC-MS/MS and GC-MS/MS analyses, it has been demonstrated that the expression levels of tryptophan, nonadecanoate (19:0), and erythritol are reduced in patients with gastric cancer. This finding aligns with the results obtained from our MR analysis and provides further confirmation regarding the protective role of tryptophan, nonadecanoate (19:0), and erythritol against gastric cancer.CONCLUSIONS: These findings indicate that three blood metabolites are causally related to GC and provide new perspectives for combining genomics and metabolomics to study the mechanisms of metabolite-mediated GC development.PMID:39703854 | PMC:PMC11655336 | DOI:10.3389/fonc.2024.1418283

Front Oncol. 2024 Dec 5;14:1389529. doi: 10.3389/fonc.2024.1389529. eCollection 2024.ABSTRACTCentral nervous system (CNS) tumours are the most common cancer cause of death in under 40s in the UK, largely because they persist and recur and sometimes metastasise during treatment. Therefore, longitudinal monitoring of patients during and following treatment must be undertaken to understand the course of the disease and alter treatment plans reactively. This monitoring must be specific, sensitive, rapid, low cost, simple, and accepted by the patient. Cerebrospinal fluid (CSF) examination obtained following lumbar puncture, already a routine part of treatment in paediatric cases, could be better utilised with improved biomarkers. In this review, we discuss the potential for metabolites in the CSF to be used as biomarkers of CNS tumour remission, progression, response to drugs, recurrence and metastasis. We confer the clinical benefits and risks of this approach and conclude that there are many potential advantages over other tests and the required instrumentation is already present in UK hospitals. On the other hand, the approach needs more research investment to find more metabolite biomarkers, better understand their relation to the tumour, and validate those biomarkers in a standardised assay in order for the assay to become a clinical reality.PMID:39703845 | PMC:PMC11655469 | DOI:10.3389/fonc.2024.1389529

Int J Gen Med. 2024 Dec 14;17:6229-6241. doi: 10.2147/IJGM.S498965. eCollection 2024.ABSTRACTOBJECTIVE: To explore the types of pathogens causing lower respiratory tract infections (LTRIs) in children and construction of a predictive model for monitoring secondary asthma caused by LTRIs.METHODS: Seven hundred and seventy-five children with LTRIs treated from June 2017 to July 2024 were selected as research subjects. Bacterial isolation and culture were performed on all children, and drug sensitivity tests were conducted on the isolated pathogens; And according to whether the child developed secondary asthma during treatment, they were divided into asthma group (n = 116) and non-asthma group (n = 659); Using logistic regression model to analyze the risk factors affecting secondary asthma in children with LTRIs, and establishing machine learning (ie nomogram and decision tree) prediction models; Using ROC curve analysis machine learning algorithms to predict AUC values, sensitivity, and specificity of secondary asthma in children with LTRIs.RESULTS: 792 pathogenic bacteria were isolated from 775 children with LTRIs through bacterial culture, including 261 Gram positive bacteria (32.95%) and 531 Gram negative bacteria (67.05%). Logistic regression model analysis showed that Glycerophospholipids, Sphingolipids and radiomics characteristics were risk factors for secondary asthma in children with LTRIs (P < 0.05). The AUC, sensitivity, and specificity of nomogram prediction for secondary asthma in children with LTRIs were 0.817(95CI: 0.760-0.874), 82.3%, and 76.6%, respectively; The AUC of decision tree prediction for secondary asthma in children with LTRIs is 0.926(95% CI: 0.869-0.983), with a sensitivity of 96.7% and a specificity of 87.8%.CONCLUSION: LTRIs in children are mainly caused by Staphylococcus aureus, Streptococcus pneumoniae, Staphylococcus epidermidis, Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa; In addition, machine learning combined with multi-omics prediction models has shown good ability in predicting LTRIs combined with asthma, providing a non-invasive and effective method for clinical decision-making.PMID:39703797 | PMC:PMC11656193 | DOI:10.2147/IJGM.S498965

Physiol Plant. 2024 Nov-Dec;176(6):e70016. doi: 10.1111/ppl.70016.ABSTRACTPlant-derived nanovesicles (PDNVs) and extracellular vesicles (EVs) represent a promising area of research due to their unique properties and potential therapeutic applications. Pinellia ternata (P. ternata) is well-known for its pharmacological properties but the PDNVs and EVs derived from it have been largely understudied. Previous studies have shown that a Temporary Immersion Bioreactor System (TIBS) plays an important role in controlling plant growth in order to obtain reproducible EVs and PDNVs. PDNVs were isolated from P. ternata plants and EVs were collected in the TIBS medium via ultra-high-speed differential centrifugation. Particle size, Zeta potentials and particle concentrations were assessed for PDNVs and EVs. Furthermore, non-targeted metabolomics was used to assess metabolic compositional differences between EVs and PDNVs, enabling the evaluation of the TIBS's quality control efficacy. Metabolomic profiling revealed 1072 metabolites in PDNVs and EVs, including 426 differential metabolites (DMs) distinguishing PDNVs from EVs: 362 DMs were positively correlated with PDNVs and 64 DMs were positively correlated with EVs; they were enriched across 17 KEGG pathways. PCA, PLS-DA, and metabolite sample correlation analyses showed high consistency between the replicates (PDNVs >0.87, EVs >0.93). This study demonstrated that TIBS is a performant system allowing consistency in generating PDNVs and EVs from P. ternata. We also highlighted the metabolic differences between PDNVs and EVs, guiding researchers in finding the bet system to produce efficient nanodrugs containing P. ternata pharmacological compounds.PMID:39703077 | DOI:10.1111/ppl.70016

Physiol Plant. 2024 Nov-Dec;176(6):e70021. doi: 10.1111/ppl.70021.ABSTRACTUrban vegetation provides many ecosystem services like heat island mitigation. However, urban trees are subjected to the stresses that they are meant to alleviate, with drought being a main constraint. We investigated the drought response strategy of plane trees (Platanus × hispanica), focusing on stomatal regulation and metabolic remodelling. To address this question, a semi-controlled experiment was performed in an urban site with fourteen plane trees grown in containers. From May to June 2022, those trees were physiologically characterized in response to a controlled edaphic drought completed by a targeted metabolome analysis focused on amino acids, sugars, polyols and organic acids. Early P. × hispanica response to drought consisted in stomatal closure limiting carbon assimilation and osmotic adjustment, which was likely related to malate and trehalose accumulation. Both allowed the maintenance of stem water potential and Relative Water Content. As the drought became severe, when the extractable soil water content (eSWC) dropped below 30%, a non-stomatal limitation of photosynthesis was observed and was associated with photosynthetic apparatus damage (reduced chlorophyll content and decrease in Fv/Fm) and a further decline in carbon assimilation. When eSWC decreased below 25%, severe drought induced defoliation. Together, these results highlight the isohydric strategy of P. × hispanica, based notably on osmotic adjustment and explain its resistance to drought combined with other urban constraints. In the context of climatic change in cities, it would be interesting to analyse the impact of successive drought cycles in the long term, aiming for sustainable planning and management of urban trees.PMID:39703071 | DOI:10.1111/ppl.70021

Mol Plant. 2024 Dec 18:S1674-2052(24)00394-0. doi: 10.1016/j.molp.2024.12.010. Online ahead of print.ABSTRACTAngraecum sesquipedale, also known as Darwin's orchid, possesses an exceptionally long nectar spur. Charles Darwin predicted the orchid to be pollinated by a hawkmoth with a correspondingly long proboscis, later identified as Xanthopan praedicta. In this plant-pollinator interaction, the A. sesquipedale flower emits a complex blend of scent compounds dominated by diurnally regulated oximes (R1R2C=N-OH) to attract crepuscular and nocturnal pollinators. The molecular mechanism of oxime biosynthesis remains unclear in orchids. Here, we present the chromosome-level genome of A. sesquipedale. The haploid genome size is 2.10 Gb and represents 19 pseudochromosomes. Cytochrome P450 encoding genes of the CYP79 family known to be involved in oxime biosynthesis in seed plants are not present in the A. sesquipedale genome nor in the genomes of other members of the orchid family. Metabolomic analysis of the A. sesquipedale flower revealed a substantial release of oximes at dusk during the blooming stage. By integrating metabolomic and transcriptomic correlation approaches, flavin-containing monooxygenases (FMOs) encoded by six tandem-repeat genes in the A. sesquipedale genome are identified as catalyzing the formation of oximes present. Further in vitro and in vivo assays confirm the function of FMOs in the oxime biosynthesis. We designate these FMOs as Orchid Oxime Synthases 1-6. The evolutionary aspects related to the CYP79 gene losses and neofunctionalization of FMO-catalyzed biosynthesis of oximes in Darwin's orchid provide new insights into the convergent evolution of biosynthetic pathways.PMID:39702965 | DOI:10.1016/j.molp.2024.12.010

Magn Reson Chem. 2024 Dec 19. doi: 10.1002/mrc.5504. Online ahead of print.ABSTRACTCatharanthus roseus, also known as Madagascar periwinkle, is a perennial plant renowned for its extensive pharmacological properties. It produces vital chemotherapeutic compounds, including vinblastine and vincristine, and exhibits anti-inflammatory, antidiabetic, and antioxidant activities. In this study, we utilized a range of two-dimensional (2D) nuclear magnetic resonance (NMR) techniques, such as 1H-1H correlation spectroscopy (COSY), 1H-1H J-resolved NMR, and 1H-13C heteronuclear single quantum coherence (HSQC) sensitivity-enhanced NMR spectroscopy, to identify key metabolites in C. roseus leaf extracts. Given the presence of numerous metabolites with closely spaced multiplet resonances, the 1H NMR spectra often exhibit significant signal overlap, making metabolite identification difficult or even impossible. However, the use of 2D NMR techniques effectively overcame this challenge, allowing for the precise identification of important alkaloids, such as vindoline, vinblastine, serpentine, and ajmalicine, along with essential metabolites like organic acids, amino acids, and carbohydrates. The extract contained a variety of bioactive compounds, including organic acids crucial for the tricarboxylic acid (TCA) cycle, branched-chain amino acids vital for metabolic functions, and alkaloids with substantial therapeutic potential. This comprehensive study underscores the continued significance of C. roseus in both traditional and modern medicine, emphasizing its intricate metabolic network and its potential in the development of novel therapeutics.PMID:39702899 | DOI:10.1002/mrc.5504

Metabolomics. 2024 Dec 19;21(1):11. doi: 10.1007/s11306-024-02209-9.ABSTRACTINTRODUCTION: Metabolic disorders are a global health concern, necessitating the development of drugs with fewer side effects and more efficacy. Traditional Indian medicine uses Tinospora cordifolia and Tinospora sinensis, but their metabolite fingerprints and impact on geographical location remains unknown.OBJECTIVE: The present study aimed to identify metabolite fingerprints from T. cordifolia and T. sinensis species from different geographic locations and also to identify potential quality markers for treating metabolic disorders.METHODS: Non-targeted metabolite fingerprinting of T. cordifolia and T. sinensis was performed using HPLC-QTOF-MS/MS analysis. Network pharmacology, molecular docking and molecular dynamics simulation analysis were performed to identify potential quality markers, hub targets, and key pathways associated with metabolic disorders.RESULTS: In this study, six potential marker compounds and twenty-five differential compounds were identified between T. cordifolia and T. sinensis. Based on geography, five and one metabolite marker compounds were identified in T. cordifolia and T. sinensis respectively. Network pharmacology, molecular docking, and molecular dynamics simulation analysis revealed trans piceid, crustecdysone in T. cordifolia, and gallic acid in T. sinensis as potential quality markers against metabolic disorder related hub targets.CONCLUSION: Integration of non-targeted metabolomics and network pharmacology approach deciphers the pharmacological mechanism of action in terms of identifying potential quality markers from Tinospora species that can be used against metabolic disorders. However, further research is required to validate these findings in in vitro and in vivo studies for better assertion.PMID:39702870 | DOI:10.1007/s11306-024-02209-9

Eye (Lond). 2024 Dec 19. doi: 10.1038/s41433-024-03517-z. Online ahead of print.ABSTRACTThe human microbiome has progressively been recognised for its role in various disease processes. In ophthalmology, complex interactions between the gut and distinct ocular microbiota within each structure and microenvironment of the eye has advanced our knowledge on the multi-directional relationships of these ecosystems. Increasingly, studies have shown that modulation of the microbiome can be achieved through faecal microbiota transplantation and synbiotics producing favourable outcomes for ophthalmic diseases. As ophthalmologists, we are obliged to educate our patients on measures to cultivate a healthy gut microbiome through a range of holistic measures. Further integrative studies combining microbial metagenomics, metatranscriptomics and metabolomics are necessary to fully characterise the human microbiome and enable targeted therapeutic interventions.PMID:39702789 | DOI:10.1038/s41433-024-03517-z

Appl Microbiol Biotechnol. 2024 Dec 19;108(1):538. doi: 10.1007/s00253-024-13384-z.ABSTRACTMetabolomics is a cutting-edge omics technology that identifies metabolites in organisms and their environments and tracks their fluctuations. This field has been extensively utilized to elucidate previously unknown metabolic pathways and to identify the underlying causes of metabolic changes, given its direct association with phenotypic alterations. However, metabolomics inherently has limitations that can lead to false positives and false negatives. First, most metabolites function as intermediates in multiple biochemical reactions, making it challenging to pinpoint which specific reaction is responsible for the observed changes in metabolite levels. Consequently, metabolic processes that are anticipated to vary with metabolite concentrations may not exhibit significant changes, generating false positives. Second, the range of metabolites identified is contingent upon the analytical conditions employed. Until now, no analytical instrument or protocol has been developed that can capture all metabolites simultaneously. Therefore, some metabolites are changed but are not detected, generating false negatives. In this review, we offer a novel and systematic assessment of the limitations of omics technologies and propose-specific strategies to minimize false positives and false negatives through multi-omics approaches. Additionally, we provide examples of multi-omics applications in microbial metabolic engineering and host-microbiome interactions, helping other researchers gain a better understanding of these strategies. KEY POINTS: • Metabolomics identifies metabolic shifts but has inherent false positive/negatives. • Multi-omics approaches help overcome metabolomics' inherent limitations.PMID:39702677 | DOI:10.1007/s00253-024-13384-z

Metabolomics. 2024 Dec 19;21(1):10. doi: 10.1007/s11306-024-02208-w.ABSTRACTINTRODUCTION: Metabolomics is the comprehensive study of small molecules in biological systems. It has recently garnered attention for its wide variety of applications such as diseases, drug treatments, agriculture, and more. As the interest in metabolomics grow, meeting the demands of cutting-edge research requires software tools that not only advance analytical capabilities, but also prioritize user-friendly features.OBJECTIVES: In response to this need, we present two new computer programs, A-SIMA: Advanced-Software for Interactive Metabolite Analysis and A-MAP: A Multivariate Analysis Program. These tools aim to introduce new capabilities for metabolite identification and data analysis, and thereby advancing the computational methodology in NMR-based metabolomics.METHODS: A-SIMA is designed with an easy-to-use graphical user interface which allows users to perform metabolite identification on 1D and 2D NMR data effortlessly with complete control over the identification process. Similarly, A-MAP facilitates multivariate statistical analysis of metabolite data through a straightforward process. It offers analysis options such as Principal Component Analysis and Orthogonal Partial Least Squares-Discriminant Analysis using regions of interests as inputs.RESULTS: Both A-SIMA and A-MAP are pre-built in the POKY suite, available at https://poky.clas.ucdenver.edu , with tutorial videos on YouTube for guidance on not only the programs, but also installation. The POKY suite is a software program for NMR biomolecular analysis. With the addition of these programs in POKY, researchers and professionals can experience a fully integrated process for every step of their metabolite analysis. Data can also be easily exported from these programs to be applied elsewhere.CONCLUSION: The introduction of A-SIMA and A-MAP can be promising tools that can lead significant advancements in metabolomics research. These tools offer enhanced capabilities for metabolite analysis and statistical modelling in a user-friendly manner. Their integration into the POKY suite ensures accessibility, usability, and efficiency.PMID:39702618 | DOI:10.1007/s11306-024-02208-w

Commun Biol. 2024 Dec 19;7(1):1666. doi: 10.1038/s42003-024-07306-y.ABSTRACTThe left and right ventricles of the human heart are functionally and developmentally distinct such that genetic or acquired insults can cause dysfunction in one or both ventricles resulting in heart failure. To better understand ventricle-specific molecular changes influencing heart failure development, we first performed unbiased quantitative mass spectrometry on pre-mortem non-diseased human myocardium to compare the metabolome and proteome between the normal left and right ventricles. Constituents of gluconeogenesis, glycolysis, lipogenesis, lipolysis, fatty acid catabolism, the citrate cycle and oxidative phosphorylation were down-regulated in the left ventricle, while glycogenesis, pyruvate and ketone metabolism were up-regulated. Inter-ventricular significance of these metabolic pathways was then found to be diminished within end-stage dilated cardiomyopathy and ischaemic cardiomyopathy, while heart failure-associated pathways were increased in the left ventricle relative to the right within ischaemic cardiomyopathy, such as fluid sheer-stress, increased glutamine-glutamate ratio, and down-regulation of contractile proteins, indicating a left ventricular pathological bias.PMID:39702518 | DOI:10.1038/s42003-024-07306-y

J Ovarian Res. 2024 Dec 19;17(1):250. doi: 10.1186/s13048-024-01573-3.ABSTRACTOBJECTIVE: To investigate the changes in bile acid (BA) metabolites within the follicular fluid (FF) of patients with diminished ovarian reserve (DOR) and to identify novel diagnostic markers that could facilitate early detection and intervention in DOR patients.DESIGN: A total of 182 patients undergoing assisted reproductive technology (ART) were enrolled and categorized into the normal ovarian reserve (NOR) group (n = 91) or the DOR group (n = 91) to measure BA levels in FF. To identify the changes in granulosa cells (GCs), we collected GCs from an additional 7 groups of patients for transcriptome sequencing.SETTING: Reproductive medicine center within a hospital and university research laboratory.POPULATION: A total of 182 patients undergoing assisted reproductive technology were enrolled and categorized into the NOR group (n = 91) or the DOR group (n = 91).METHODS: In this study, BA metabolites in FF of DOR and NOR patients were analyzed in detail by targeted metabolomics, and the correlation between BA levels in FF and clinical indicators was discussed. Then, we constructed a diagnostic model for DOR using the random forest algorithm based on five different BAs. Additionally, we performed a functional enrichment analysis on differentially expressed genes (DEGs) in GCs from both DOR and NOR patients.MAIN OUTCOME MEASURES: BA levels in FF and their correlation with clinical indicators; the areas under the curve (AUCs) of the random forest diagnostic model for DOR; and the DEGs and corresponding functional enrichment results of GC RNA analysis.RESULT (S): The levels of lithocholic acid, chenodeoxycholic acid, ursodeoxycholic acid, deoxycholic acid and cholic acid in FF of DOR group were lower than those of NOR group. And significant reductions in total, primary, secondary, and unconjugated BA levels were observed in the DOR group. The above five BAs levels were closely related to indicators of ovarian reserve. The AUC of the diagnostic model based on the above five BAs was 0.964. Based on transcriptome sequencing data from two groups of GCs, a total of 482 up-regulated and 654 down-regulated DEGs were identified. Gene ontology analysis revealed that the metabolic and biosynthetic processes of fatty acids, steroids, and cholesterol were enriched in these DEGs, whereas Kyoto Encyclopedia of Genes and Genomes analysis indicated enrichment of fatty acid and ovarian steroidogenesis.CONCLUSION(S): The levels of multiple BA metabolites in FF are significantly lower than those in patients with DOR and are closely related to the evaluation of ovarian reserve function.PMID:39702491 | DOI:10.1186/s13048-024-01573-3

Mil Med Res. 2024 Dec 20;11(1):83. doi: 10.1186/s40779-024-00584-x.ABSTRACTBACKGROUND: With the increasing risk of nuclear exposure, more attention has been paid to the prevention and treatment of acute radiation syndrome (ARS). Although amino acids are key nutrients involved in hematopoietic regulation, the impacts of amino acids on bone marrow hematopoiesis following irradiation and the associated mechanisms have not been fully elucidated. Hence, it is of paramount importance to study the changes in amino acid metabolism after irradiation and their effects on hematopoiesis as well as the related mechanisms.METHODS: The content of serum amino acids was analyzed using metabolomic sequencing. The survival rate and body weight of the irradiated mice were detected after altering the methionine content in the diet. Extracellular matrix (ECM) protein analysis was performed via proteomics analysis. Inflammatory factors were examined by enzyme-linked immunosorbent assay (ELISA). Flow cytometry, Western blotting, and immunofluorescence were employed to determine the mechanism by which S100 calcium-binding protein A4 (S100A4) regulates macrophage polarization.RESULTS: The survival time of irradiated mice was significantly associated with alterations in multiple amino acids, particularly methionine. A high methionine diet promoted irradiation tolerance, especially in the recovery of bone marrow hematopoiesis, yet with dose limitations. Folate metabolism could partially alleviate the dose bottleneck by reducing the accumulation of homocysteine. Mechanistically, high methionine levels maintained the abundance of ECM components, including collagens and glycoproteins, in the bone marrow post-irradiation, among which the level of S100A4 was significantly changed. S100A4 regulated macrophage polarization via the STAT3 pathway, inhibited bone marrow inflammation and facilitated the proliferation and differentiation of hematopoietic stem/progenitor cells.CONCLUSIONS: We have demonstrated that an appropriate elevation in dietary methionine enhances irradiation tolerance in mice and explains the mechanism by which methionine regulates bone marrow hematopoiesis after irradiation.PMID:39702305 | DOI:10.1186/s40779-024-00584-x

Chin Med. 2024 Dec 19;19(1):174. doi: 10.1186/s13020-024-01045-2.ABSTRACTBACKGROUND: As a typical medicinal food homology species, Chinese herbal medicine Astragali radix (AR) has been widely used to regulate the human immune system worldwide. However, the human immunomodulation of AR and its corresponding mechanisms remain unclear.METHODS: First, following a fortnight successive AR administration, the changes in immune cytokines and immune cells from 20 healthy human subjects were used as immune indicators to characterize the immunomodulatory effects of AR. Subsequently, ultra-high-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) based lipidomics and metabolomics analysis was performed on human serum, urine, and feces samples to investigate the changes in metabolic profiles. Then, 16S rRNA gene sequencing of feces samples was adopted for the changes of human gut microbiota. Finally, correlation analysis was conducted on the gut microbiome, metabolome/lipidome data, and immune indicators.RESULTS: AR displayed good safety in clinical use and posed a minor impact on gut microbiota major genera, global metabolic profiles, and immune cells. Meanwhile, AR could significantly up-regulate anti-inflammatory cytokines, down-regulate serum creatinine and pro-inflammatory cytokines, promote the anabolism of arginine, glycerolipid, sphingolipid, and purine, and the catabolism of phenylalanine and glycerophospholipid. Moreover, these AR-induced changes were closely correlated with significantly decreased Granulicatella, slightly higher Bifidobacterium, Ruminococcus, and Subdoligranulum, and slightly lower Blautia.CONCLUSION: The study clearly demonstrated that AR could modulate the human immune, by modifying the metabolism of amino acids, lipids, and purines in a microbiota-related way. Trial registration ChiCTR, ChiCTR2100054765. Registered 26 December 2021-Prospectively registered, https://www.chictr.org.cn/historyversionpub.html?regno=ChiCTR2100054765.PMID:39702294 | DOI:10.1186/s13020-024-01045-2

Mol Cancer. 2024 Dec 20;23(1):275. doi: 10.1186/s12943-024-02189-3.ABSTRACTBACKGROUND: Immunogenic cell death (ICD) inducers are often identified in phenotypic screening campaigns by the release or surface exposure of various danger-associated molecular patterns (DAMPs) from malignant cells. This study aimed to streamline the identification of ICD inducers by leveraging cellular morphological correlates of ICD, specifically the condensation of nucleoli (CON).METHODS: We applied artificial intelligence (AI)-based imaging analyses to Cell Paint-stained cells exposed to drug libraries, identifying CON as a marker for ICD. CON was characterized using SYTO 14 fluorescent staining and holotomographic microscopy, and visualized by AI-deconvoluted transmitted light microscopy. A neural network-based quantitative structure-activity relationship (QSAR) model was trained to link molecular descriptors of compounds to the CON phenotype, and the classifier was validated using an independent dataset from the NCI-curated mechanistic collection of anticancer agents.RESULTS: CON strongly correlated with the inhibition of DNA-to-RNA transcription. Cytotoxic drugs that inhibit RNA synthesis without causing DNA damage were as effective as conventional cytotoxicants in inducing ICD, as demonstrated by DAMPs release/exposure and vaccination efficacy in mice. The QSAR classifier successfully predicted drugs with a high likelihood of inducing CON.CONCLUSIONS: We developed AI-based algorithms for predicting CON-inducing drugs based on molecular descriptors and their validation using automated micrographs analysis, offering a new approach for screening ICD inducers with minimized adverse effects in cancer therapy.PMID:39702289 | DOI:10.1186/s12943-024-02189-3