PubMed

Anal Bioanal Chem. 2025 Jan 20. doi: 10.1007/s00216-025-05735-0. Online ahead of print.ABSTRACTSpecies identification of botanical products is a crucial aspect of research and regulatory compliance; however, botanical classification can be difficult, especially for morphologically similar species with overlapping genetic and metabolomic markers, like those in the genus Ocimum. Untargeted LC-MS metabolomics coupled with multivariate predictive modeling provides a potential avenue for improving herbal identity investigations, but the current dearth of reference materials for many botanicals limits the applicability of these approaches. This study investigated the potential of using greenhouse-grown authentic Ocimum to build predictive models for classifying commercially available Ocimum products. We found that three species, O. tenuiflorum, O. gratissimum, and O. basilicum, were chemically distinct based on their untargeted UPLC-MS/MS profiles when grown in controlled settings; combined with an orthogonal high-performance thin-layer chromatography (HPTLC) approach, O. tenuiflorum materials revealed two distinct chemotypes which could confound analysis. Three predictive models (partial least squares, LASSO regression, and random forest) were employed to extrapolate these findings to commercially available products; however, the controlled materials were significantly different from external samples, and all three chemometric models were unreliable in classifying external materials. LASSO was the most successful when classifying new greenhouse samples. Overall, this study highlights how growing and processing conditions can influence the complexity of botanical metabolome profiles; further studies are needed to characterize the factors driving herbal products' phytochemistry in conjunction with chemometric predictive modeling.PMID:39831958 | DOI:10.1007/s00216-025-05735-0

Metabolomics. 2025 Jan 20;21(1):15. doi: 10.1007/s11306-024-02211-1.ABSTRACTINTRODUCTION: ZenoTOF is new class of high-resolution mass spectrometer that combines resolution and sensitivity. This mass spectrometer is well designed to perform metabolomics.METHODS: In this context, we compared the performance of ZenoTOF 7600 system (Sciex) with QTOF6520 (Agilent Technologies) through the leaf metabolome analysis of two Coffea species, namely C. anthonyi and C. arabica.RESULTS: Both species were used to compare both TOF systems. Our results showed that the ZenoTOF 7600 system provided more features (3146 vs 2326 metabolites) and more nodes (1410 vs 379 metabolites) by molecular network in only one injection.CONCLUSION: These performances were attributed to the scan speed and sensitivity of the ZenotTOF and demonstrates its added value in the context of metabolomics.PMID:39831913 | DOI:10.1007/s11306-024-02211-1

Plant Biotechnol J. 2025 Jan 20. doi: 10.1111/pbi.14586. Online ahead of print.ABSTRACTSoybean cyst nematode (SCN, Heterodera glycines) is a major pathogen harmful to soybean all over the world, causing huge yield loss every year. Soybean resistance to SCN is a complex quantitative trait controlled by a small number of major genes (rhg1 and Rhg4) and multiple micro-effect genes. Therefore, the continuous identification of new resistant lines and genes is needed for the sustainable development of global soybean production. Here, a novel disease-resistance quantitative trait locus Rscn-16 was identified and fine mapped to an 8.4-kb interval on chromosome 16 using an F2 population. According to transcriptome and metabolome analysis, a UDP-glucosyltransferase encoding gene, GmUGT88A1, was identified as the most likely gene of Rscn-16. Soybean lines overexpressing GmUGT88A1 exhibited increased resistance to SCN, higher isoflavone glycosides and larger seed size while the phenotype of RNA-interference and knockout soybean lines showed sensitivity to SCN and decreased in seed size compared to wild-type plants. GmMYB29 gene could bind to the promoter of GmUGT88A1 and coordinate with GmUGT88A1 to regulate soybean resistance to SCN and isoflavone accumulation. Under SCN infection, GmUGT88A1 participated in the reorientation of isoflavone biosynthetic metabolic flow and the accumulation of isoflavone glycosides, thus protecting soybean from SCN stress. GmUGT88A1 was found to control soybean seed size by affecting transcription abundance of GmSWEET10b and GmFAD3C, which are known to control soybean seed weight. Our findings provide insights into the regulation of SCN resistance, isoflavone content and seed size through metabolic flux redirection, and offer a potential means for soybean improvement.PMID:39831827 | DOI:10.1111/pbi.14586

Eur J Neurosci. 2025 Jan;61(2):e16665. doi: 10.1111/ejn.16665.ABSTRACTParkinson's disease (PD) is a neurodegenerative disease involving multiple factors. We explored the connection between intestinal microbiome levels and PD by examining inflammatory cytokines, peripheral immune cell counts and plasma metabolomics as potential factors. By obtaining the Genome-Wide Association Study (GWAS) data needed for this study from GWAS Catalog, including summary data for 473 intestinal microbiota traits (N = 5959), 91 inflammatory cytokine traits (N = 14,824), 118 peripheral immune cell count traits (N = 3757), 1400 plasma metabolite traits (N = 8299) and PD traits (N = 482,730). We used two-step Mendelian randomization (MR) mediated analysis to investigate possible pathways from intestinal microbiota to PD mediated by inflammatory cytokines, peripheral immune cells and plasma metabolites. MR has revealed the causal effects of 19 intestinal microbiota, 1 inflammatory cytokine and 12 plasma metabolites on PD, whereas there is no significant causal relationship between immune cell count characteristics and the occurrence of PD. Mediation analysis showed that the associations between the genus Demequina and PD were mediated by tryptophan with mediated proportions of 17.51% (p = 0.0393). Our study demonstrates that genus Demequina may promote the occurrence of PD by reducing the levels of tryptophan.PMID:39831637 | DOI:10.1111/ejn.16665

Nat Prod Rep. 2025 Jan 20. doi: 10.1039/d4np00026a. Online ahead of print.ABSTRACTCovering: 1994 to 2024Historically, microbial natural product discovery has been predominantly guided by biological activity from crude microbial extracts with metabolite characterization proceeding one molecule at a time. Despite decades of bioactivity-guided isolations, genomic evidence now suggests that we have only accessed a small fraction of the total natural product potential from microorganisms and that the products of the vast majority of biosynthetic pathways remain to be identified. Here we describe recent advancements that have enabled high-throughput mass spectrometry and comparative metabolomics, which in turn facilitate high-throughput natural product discovery. These advancement promise to fully unlock the reservoir of microbial natural products.PMID:39831433 | DOI:10.1039/d4np00026a

Physiol Plant. 2025 Jan-Feb;177(1):e70049. doi: 10.1111/ppl.70049.ABSTRACTPinellia ternata is an herb species in the Pinellia genus with significant economic value due to its medicinal properties. Understanding the accumulation and spatial distribution characteristics of metabolites during the development of the medicinal part, the rhizome of P. ternata (PR), provides a basis for targeted metabolic regulation and quality evaluation. In this study, we used matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI) and MS/MS to analyse metabolites at 5 representative stages (S1 to S5) of rhizome development in cross and longitudinal sections of the rhizome. A total of 168 metabolites were detected, with 13 being metabolites previously reported in PR. Additionally, Venn analysis revealed 12 bioactive differential metabolites during the growth process. Their spatial distribution and composition were analyzed, showing that alkaloids and amino acids were significantly distributed throughout the entire region and had higher relative contents compared to other metabolites. Flavonoids were more distributed in the outer regions of PR, potentially playing a greater role in combating biotic or abiotic stresses. Specifically, in cross-sections, arginine, nicotinamide, and 2-pentylpyridine showed a clear trend of accumulation from the outer to the inner from S1 to S5, while trigonelline, adenosine, cytidine, 3,4-dihydroxycinnamyl alcohol, raffinose, choline alfoscerate, liquiritin, and apii exhibited the opposite trend. For longitudinal sections, trigonelline, 2-pentylpyridine, choline alfoscerate and baicalein showed a trend of accumulation from the area of bud end to the far region during S1 to S5, while arginine showed opposite distribution trends. These findings deepen our understanding of the metabolic processes involved in the development of PR and have potential implications for variety improvement and quality control.PMID:39831343 | DOI:10.1111/ppl.70049

Food Chem X. 2024 Jul 6;23:101634. doi: 10.1016/j.fochx.2024.101634. eCollection 2024 Oct 30.ABSTRACTGeographical regions profoundly influence the flavor characteristics of Congou black tea (CBT). In this study, 35 CBT samples from 7 geographical regions were comprehensively characterized by integrated multiple intelligent sensory technologies and untargeted metabolomics analysis. A satisfactory discrimination was achieved through the fusion of multiple intelligent sensory technologies (R2Y = 0.918, Q2 = 0.859). A total of 104 non-volatile and 169 volatile metabolites were identified by UHPLC-HRMS and GC-MS, respectively. Of these, 45 critical differential non-volatile metabolites and 76 pivotal differential volatile metabolites were pinpointed based on variable importance in projection >1 and p < 0.05. Moreover, 52 key odorants with OAV ≥ 1 were identified, with hexanal, phenylacetaldehyde, linalool, β-cyclocitral, methyl salicylate, geraniol, α-ethylidene phenylacetaldehyde, and trans-β-ionone being recognized as the common odorants across 7 geographical regions. The results provide theoretical support for a comprehensive understanding of the effect of geographical regions on the flavor of black tea.PMID:39831178 | PMC:PMC11740800 | DOI:10.1016/j.fochx.2024.101634

Front Microbiol. 2025 Jan 3;15:1509117. doi: 10.3389/fmicb.2024.1509117. eCollection 2024.ABSTRACTThe gut microbiota actually shares the host's physical space and affects the host's physiological functions and health indicators through a complex network of interactions with the host. However, its role as a determinant of host health and disease is often underestimated. With the emergence of new technologies including next-generation sequencing (NGS) and advanced techniques such as microbial community sequencing, people have begun to explore the interaction mechanisms between microorganisms and hosts at various omics levels such as genomics, transcriptomics, metabolomics, and proteomics. With the enrichment of multi-omics integrated analysis methods based on the microbiome, an increasing number of complex statistical analysis methods have also been proposed. In this review, we summarized the multi-omics research analysis methods currently used to study the interaction between the microbiome and the host. We analyzed the advantages and limitations of various methods and briefly introduced their application progress.PMID:39831120 | PMC:PMC11739165 | DOI:10.3389/fmicb.2024.1509117

World J Psychiatry. 2025 Jan 19;15(1):101186. doi: 10.5498/wjp.v15.i1.101186. eCollection 2025 Jan 19.ABSTRACTBACKGROUND: The use of network pharmacology and blood metabolomics to study the pathogenesis of violent aggression in patients with schizophrenia and the related drug mechanisms of action provides new directions for reducing the risk of violent aggression and optimizing treatment plans.AIM: To explore the metabolic regulatory mechanism of olanzapine in treating patients with schizophrenia with a moderate to high risk of violent aggression.METHODS: Metabolomic technology was used to screen differentially abundant metabolites in patients with schizophrenia with a moderate to high risk of violent aggression before and after olanzapine treatment, and the related metabolic pathways were identified. Network pharmacology was used to establish protein-protein interaction networks of the core targets of olanzapine. Gene Ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were subsequently performed.RESULTS: Compared with the healthy group, the patients with schizophrenia group presented significant changes in the levels of 24 metabolites related to the disruption of 9 metabolic pathways, among which the key pathways were the alanine, aspartate and glutamate metabolism and arginine biosynthesis pathways. After treatment with olanzapine, the levels of 10 differentially abundant metabolites were significantly reversed in patients with schizophrenia. Olanzapine effectively regulated six metabolic pathways, among which the key pathways were alanine, aspartate and glutamate metabolism and arginine biosynthesis pathways. Ten core targets of olanzapine were involved in several key pathways.CONCLUSION: The metabolic pathways of alanine, aspartate, and glutamate metabolism and arginine biosynthesis are the key pathways involved in olanzapine treatment for aggressive schizophrenia.PMID:39831024 | PMC:PMC11684224 | DOI:10.5498/wjp.v15.i1.101186

Front Pharmacol. 2025 Jan 3;15:1514293. doi: 10.3389/fphar.2024.1514293. eCollection 2024.ABSTRACTBACKGROUND: Abdominal aortic aneurysm (AAA) is one of the most dangerous types of vascular diseases worldwide. Metabolic disturbance affects disease risk and provide underlying therapeutic targets. Previous studies have reported an association between metabolic disorders and AAA. However, evidence of a causal relationship between blood metabolites and AAA is still lacking at present.METHODS: Using Mendelian randomization (MR), we assessed the causal association between 1,400 serum metabolites and AAA. The inverse variance weighted method (IVW), weighted median, MR-Egger regression, simple mode, as well as weighted mode methods were used for evaluating the causality between blood metabolites and AAA. Pleiotropy and heterogeneity tests were further conducted.RESULTS: Through strict screening, 17 known metabolites, 7 unknown metabolites and 5 metabolite ratios related to AAA were identified. Among all the metabolites, 24 were found to have negative associations, while 5 exhibited positive associations. The top five metabolites associated with an increased risk of AAA were Oleoyl-linoleoyl-glycerol (18:1/18:2) [2], Glycosyl-N-(2-hydroxynervonoyl)-sphingosine (d18:1/24:1(2OH)), Glycochenodeoxycholate 3-sulfate, X-21441 and X-24328. In contrast, the top five metabolites that were linked to a reduced risk of AAA included Uridine to pseudouridine ratio, Octadecanedioate, Phosphate to oleoyl-linoleoyl-glycerol (18:1 to 18:2) [2] ratio, 1-(1-enyl-palmitoyl)-GPE (p-16:0), and 1-stearoyl-GPG (18:0).CONCLUSION: Among the 1,400 blood metabolites, we identified 17 known metabolites, 7 unknown metabolites, and 5 metabolite ratios associated with AAA. This MR study may provide a novel significant insight for the screening and prevention of AAA.PMID:39830355 | PMC:PMC11739154 | DOI:10.3389/fphar.2024.1514293

Front Pharmacol. 2025 Jan 3;15:1499142. doi: 10.3389/fphar.2024.1499142. eCollection 2024.ABSTRACTBACKGROUND: Indole-3-carbinol (I3C) is a compound derived from Cruciferous vegetables. We aim to ascertain whether I3C mediates the relations between mouse gut microbiota, intestinal barrier function, and metabolism to treat obesity in mice.METHODS: The experimental analyses focused on the changes in lipid distribution, inflammatory cytokines, glucose tolerance, gut microbiota composition, and serum metabolomics of 60 C57BL/6N mice.RESULTS: The experimental results demonstrated that I3C reduced body weight, hepatic steatosis, and systemic inflammation and improved insulin resistance in mice on a high-fat diet (HFD). Furthermore, I3C remarkably enhanced the enrichment of probiotics Akkermansia and Ligilactobacillus as well as SCFA-producing bacteria (Eubacterium, Lactococcus, and Coprococcus), while reducing the abundance of Eisenbergiella and Rikenellaceae_RC9_gut_group. Also, I3C notably up-regulated the levels of Claudin4, Occludin, and ZO-1 proteins and modulated the metabolism of argininosuccinic acid and galactose.CONCLUSION: The aforementioned findings suggest that I3C exerts a significant anti-obesity effect in mice by regulating abnormal gut microbiome, enhancing intestinal barrier function, and improving metabolic disorders.PMID:39830328 | PMC:PMC11739362 | DOI:10.3389/fphar.2024.1499142

Hortic Res. 2024 Sep 28;12(1):uhae276. doi: 10.1093/hr/uhae276. eCollection 2025 Jan.ABSTRACTFresh-cut fruit browning severely affects the appearance of fruit. Light treatment can effectively inhibit fresh-cut apple fruit browning, but the regulatory mechanism remains unknown. Here, we discovered that violet LED (Light-Emitting-Diode) light treatment significantly reduced fresh-cut apple fruit browning. Metabolomic analysis revealed that violet LED light treatment enhanced the phenolic accumulation of fresh-cut apple fruit. Transcriptomic analysis showed that the expression of phenolic degradation genes POLYPHENOL OXIDASE (MdPPO) and PEROXIDASE (MdPOD) was reduced, and the expression of phenolic synthesis gene PHENYLALANINE AMMONIA LYASE (MdPAL) was activated by violet LED light treatment. Moreover, two ELONGATED HYPOCOTYL 5 (MdHY5 and MdHYH) transcription factors involved in light signaling were identified. The expression of MdHY5 and MdHYH was activated by violet LED light treatment. Violet LED light treatment no longer inhibited fresh-cut apple fruit browning in MdHY5- or MdHYH- silenced fruit. Further experiments revealed that MdHY5 and MdHYH suppressed MdPPO and MdPOD expression and promoted MdPAL expression by binding to their promoters. In addition, MdHY5 and MdHYH bound to each other's promoters and enhanced their expression. Overall, our findings revealed that violet LED light-activated MdHY5 and MdHYH formed a positive transcriptional loop to regulate the transcription of MdPPO, MdPOD, and MdPAL, which in turn inhibited the degradation of phenolics and promoted the synthesis of phenolics, thus inhibiting fresh-cut apple fruit browning. These results provide a theoretical basis for improving the appearance and quality of fresh-cut apple fruit.PMID:39830309 | PMC:PMC11739620 | DOI:10.1093/hr/uhae276

medRxiv [Preprint]. 2025 Jan 7:2025.01.06.25320083. doi: 10.1101/2025.01.06.25320083.ABSTRACTBACKGROUND: Aneurysmal subarachnoid hemorrhage (aSAH) causes systemic changes that contribute to delayed cerebral ischemia (DCI) and morbidity. Circulating metabolites reflecting underlying pathophysiological mechanisms warrant investigation as biomarker candidates.METHODS: Blood samples, prospectively collected within 24 hours (T1) of admission and 7-days (T2) post ictus, from patients with acute aSAH from two tertiary care centers were retrospectively analyzed. Samples from healthy subjects and patients with non-neurologic critical illness served as controls. A validated external analysis platform was used to perform untargeted metabolomics. Bioinformatics analyses were conducted to identify metabolomic profiles defining each group and delineate metabolic pathways altered in each group. Machine learning (ML) models were developed incorporating key metabolites to improve DCI prediction.RESULTS: Among 70 aSAH, 30 healthy control, and 17 sick control subjects, a total of 1,117 metabolites were detected. Groups were matched among key clinical variables. DCI occurred in 36% of aSAH subjects, and poor functional outcome was observed in 70% at discharge. Metabolomic profiles readily discriminated the groups. aSAH subjects demonstrated a robust mobilization of lipid metabolites, with increased levels of free fatty acids (FFAs), mono- and diacylglycerols (MAG, DAG) compared with both control groups. aSAH subjects also had decreased circulating amino acid derived metabolites, consistent with increased catabolism. DCI was associated with increased sphingolipids (sphingosine and sphinganine) and decreased acylcarnitines and S- adenosylhomocysteine at T1. Decreased lysophospholipids and acylcarnitines were associated with poor outcomes. Incorporating metabolites into ML models improved prediction of DCI compared with clinical variables alone.CONCLUSIONS: Profound metabolic shifts occur after aSAH with characteristic increases in lipid and decreases in amino acid metabolites. Key lipid metabolites associated with outcomes (sphingolipids, lysophospholipids, and acylcarnitines) provide insight into systemic changes driving secondary complications. These metabolites may also prove to be useful biomarkers to improve prognostication and personalize aSAH care.PMID:39830284 | PMC:PMC11741492 | DOI:10.1101/2025.01.06.25320083

medRxiv [Preprint]. 2025 Jan 12:2025.01.08.25320192. doi: 10.1101/2025.01.08.25320192.ABSTRACTMetabolic diseases such as type 2 diabetes and obesity pose a significant global health burden. Plant-based diets, including vegan diets, are linked to favorable metabolic outcomes, yet the underlying mechanisms remain unclear. In a randomized trial involving 21 pairs of identical twins, we investigated the effects of vegan and omnivorous diets on the host metabolome, immune system, and gut microbiome. Vegan diets induced significant shifts in serum and stool metabolomes, cytokine profiles, and gut microbial composition. Notably, vegan diet subjects exhibited elevated serum glycine levels despite lower dietary glycine intake, linked to reduced abundance of the gut pathobiont Bilophila wadsworthia . Functional studies demonstrated that B. wadsworthia metabolizes glycine via the glycine reductase pathway and modulates host glycine availability. Removing B. wadsworthia from gnotobiotic mice elevated glycine levels and improved metabolic markers. These findings reveal a previously underappreciated mechanism by which the gut microbiota regulates host metabolic status through diet.PMID:39830242 | PMC:PMC11741504 | DOI:10.1101/2025.01.08.25320192

Front Vet Sci. 2025 Jan 3;11:1525897. doi: 10.3389/fvets.2024.1525897. eCollection 2024.ABSTRACTAIM: This study aimed to investigate the antibacterial efficacy of probiotic-derived cell-free supernatants (CFS) encapsulated within nanostructured lipid carriers (NLCs) against multidrug-resistant Pseudomonas aeruginosa and Staphylococcus pseudintermedius. Additionally, it aimed to identify specific bioactive compounds that contribute to the reported antibacterial properties by characterizing the metabolite substances present in the CFS using a metabolomic analysis technique.METHODS: Eight strains of lactic acid bacteria including Lactiplantibacillus plantarum (L22F and L25F), Pediococcus acidilactici (P72N, BF9, BF 14, BYF 20 and BYF 26) and Ligilactobacillus salivarius (BF 12) were selected as probiotic candidates. The inhibitory activity of their cell free supernatant (CFS) was tested against clinical strains of P. aeruginosa and S. pseudintermedius isolated from skin wounds of dogs and cats. An untargeted metabolomic approach based on liquid chromatography-mass spectrometry (LC-MS) identified potential antibacterial metabolites in the CFS. Cell-Free Supernatants-Nanostructured Lipid Carriers (CFS-NLCs) were developed, and their antibacterial activity and minimum bactericidal concentration (MBC) were analysed.RESULTS: Despite the strong multidrug-resistant nature of the pathogens, CFS displayed a moderate antibacterial activity against most tested strains. The acidic nature of the CFS, combined with bioactive antibacterial metabolites like Kanzonol V and 1-Hexanol, likely contributed to its inhibitory effects against pathogenic bacteria; notably, Kanzonol V was abundant in the CFS of L22F, BF12 and BYF26 (L22F_CFS, BF12_CFS and BYF26_CFS), while 1-Hexanol was particularly enriched in CFS of P72N (P72N_CFS), with both compounds effectively targeting bacterial cell membranes to disrupt cell integrity, leading to bacterial cell death. Other beneficial compounds such as Pyroglutamylleucine, Trigoneoside VIII and 18-Nor-4(19),8,11,13-abietatetraene which are likely to have anti-inflammatory, antimicrobial and antioxidant activities, were also detected in the CFS. The CFS-NLCs maintained their antibacterial activity and 30-60% dilutions of product completely inhibited the growth of pathogen strains even after three-months storage at room temperature.CONCLUSION: These findings suggest that CFS-NLCs could be a promising biotic therapy for treating hospital infections such as canine dermatitis and otitis caused by multidrug-resistant P. aeruginosa and S. pseudintermedius.PMID:39830167 | PMC:PMC11739306 | DOI:10.3389/fvets.2024.1525897

Acad Biol. 2024;2(4). doi: 10.20935/acadbiol7428. Epub 2024 Nov 27.ABSTRACTThe increasing complexity of biological systems demands advanced analytical approaches to decode the underlying mechanisms of health and disease. Integrative multi-omics approaches use multi-layered datasets such as genomic, transcriptomic, proteomic, and metabolomic data to understand biological processes much more comprehensively compared to the single-omics analysis and to provide a comprehensive view of cellular and molecular processes. However, these integrative approaches have their own computational and analytical challenges due to the large volume and nature of multi-omics data. Machine learning has emerged as a powerful tool to help and resolve these challenges. It offers sophisticated algorithms that can identify and discover hidden patterns and provide insights into complex biological networks. By integrating machine learning in multi-omics, we can enhance our understanding of drug discovery, disease, pathway, and network analysis. Machine learning and ensemble methods allow researchers to model nonlinear relationships and manage high-dimensional data, improving the precision of predictions. This approach paves the way for personalized medicine by identifying unique molecular signatures for individual patients, which can provide valuable insights into treatment planning and support more effective treatment. As machine learning continues to evolve, its role in multi-omics analysis will be pivotal in advancing our ability to interpret biological complexity and translate findings into clinical applications.PMID:39830067 | PMC:PMC11741185 | DOI:10.20935/acadbiol7428

Food Chem X. 2024 Dec 24;25:102111. doi: 10.1016/j.fochx.2024.102111. eCollection 2025 Jan.ABSTRACTHyperlipidemia is a common endocrine metabolic disease in humans. Long-term medications often have adverse effects, making the search for safer and more effective treatments crucial. This study aimed to explore the impacts and mechanisms of Lactiplantibacillus plantarum HY127 fermentation on enhancing bile acid-binding capacity (BABC). We fermented corn by-product juice (CBJ) by HY127 and investigated the BABC of HY127 bacterial cells and their metabolites. Our results indicated that HY127 cells (95.25 %) played a major role in enhancing BABC, with metabolites (31.50 %-66.41 %) also contributing. Compared to unfermented CBJ, the contents of phenolics, flavonoids, polysaccharides, and organic acids were significantly higher. Non-targeted metabolomics revealed upregulated amino acids, alkaloids, terpenoids, and other bioactive substances associated with BABC in the supernatant. This study confirmed that HY127 fermentation enhances the BABC of CBJ (increased by 32.02 %-78.76 %), providing a research foundation and technical reference for the development of LAB-fermented corn by-product beverages with hypolipidemic activities.PMID:39830001 | PMC:PMC11742556 | DOI:10.1016/j.fochx.2024.102111

bioRxiv [Preprint]. 2025 Jan 8:2025.01.07.631819. doi: 10.1101/2025.01.07.631819.ABSTRACTThe rapid growth that occurs during Drosophila larval development requires a dramatic rewiring of central carbon metabolism to support biosynthesis. Larvae achieve this metabolic state, in part, by coordinately up-regulating the expression of genes involved in carbohydrate metabolism. The resulting metabolic program exhibits hallmark characteristics of aerobic glycolysis and establishes a physiological state that supports growth. To date, the only factor known to activate the larval glycolytic program is the Drosophila Estrogen-Related Receptor (dERR). However, dERR is dynamically regulated during the onset of this metabolic switch, indicating that other factors must be involved. Here we discover that Sima, the Drosophila ortholog of Hif1α, is also essential for establishing the larval glycolytic program. Using a multi-omics approach, we demonstrate that sima mutants fail to properly activate aerobic glycolysis and die during larval development with metabolic defects that phenocopy dERR mutants. Moreover, we demonstrate that dERR and Sima/Hif1α protein accumulation is mutually dependent, as loss of either transcription factor results in decreased abundance of the other protein. Considering that the mammalian homologs of ERR and Hif1α also cooperatively regulate aerobic glycolysis in cancer cells, our findings establish the fly as a powerful genetic model for studying the interaction between these two key metabolic regulators.STRUCTURED ABSTRACT: Objectives: The rapid growth that occurs during Drosophila larval development requires a dramatic rewiring of central carbon metabolism to support biosynthesis. Larvae achieve this metabolic state, in part, by coordinately up-regulating the expression of genes involved in carbohydrate metabolism. The resulting metabolic program exhibits hallmark characteristics of aerobic glycolysis and establishes a physiological state that supports growth. To date, the only factor known to activate the larval glycolytic program is the Drosophila Estrogen-Related Receptor (dERR). However, dERR is dynamically regulated during the onset of this metabolic switch, indicating that other factors must be involved. Here we examine the possibility the Drosophila ortholog of Hypoxia inducible factor 1α (Hif1α) is also required to activate the larval glycolytic program. Methods: CRISPR/Cas9 was used to generate new loss-of-function alleles in the Drosophila gene similar ( sima ), which encodes the sole fly ortholog of Hif1α. The resulting mutant strains were analyzed using a combination of metabolomics and RNAseq for defects in carbohydrate metabolism. Results: Our studies reveal that sima mutants fail to activate aerobic glycolysis and die during larval development with metabolic phenotypes that mimic those displayed by dERR mutants. Moreover, we demonstrate that dERR and Sima/Hif1α protein accumulation is mutually dependent, as loss of either transcription factor results in decreased abundance the other protein. Conclusions: These findings demonstrate that Sima/HIF1α is required during embryogenesis to coordinately up-regulate carbohydrate metabolism in preparation for larval growth. Notably, our study also reveals that the Sima-dependent gene expression profile shares considerable overlap with that observed in dERR mutant, suggesting that Sima/HIF1α and dERR cooperatively regulate embryonic and larval glycolytic gene expression.HIGHLIGHTS: The Drosophila melanogaster gene similar ( sima ), which encodes the sole fly ortholog of Hif1α, is required to up-regulate glycolysis in preparation for larval growth. sima mutant larvae exhibit severe defects in carbohydrate metabolism and die during the second larval instar. sima mutant larvae exhibit the same metabolic phenotypes as Drosophila Estrogen Related Receptor ( dERR ) mutants, suggesting that these two transcription factors coordinately regulate the larval glycolytic program. Sima/Hif1α and dERR accumulation is mutually dependent, as loss of either transcription factor results in decreased abundance of the other.PMID:39829828 | PMC:PMC11741260 | DOI:10.1101/2025.01.07.631819

J Cereb Blood Flow Metab. 2025 Jan 20:271678X251314366. doi: 10.1177/0271678X251314366. Online ahead of print.ABSTRACTIncreases in mean lesional iron content by quantitative susceptibility mapping (QSM) by ≥6% and/or vascular permeability by dynamic contrast enhanced quantitative perfusion (DCEQP) by ≥40% on MRI have been associated with new symptomatic hemorrhage (SH) in cerebral cavernous malformations (CCMs). It is not known if plasma biomarkers can reflect these changes within the lesion proper. This cohort study enrolled 46 CCM patients with SH in the prior year. Plasma samples, QSM and DCEQP were simultaneously acquired at the beginning and end of 60 one-year epochs of prospective follow-up. Plasma levels of 16 proteins and 12 metabolites linked to CCM hemorrhage were assessed by enzyme-linked immunosorbent assay and liquid-chromatography mass spectrometry, respectively. A weighted model combining the percent changes in plasma levels in roundabout guidance receptor-4, cluster of differentiation 14, thrombomodulin and acetyl-L-carnitine reflected a mean increase in QSM ≥ 6% (97.2% and 100% specificity/sensitivity, p = 3.1 × 10-13). A weighted combination of percent changes in plasma levels of endoglin, pipecolic acid, arachidonic acid and hypoxanthine correlated with an increase in mean DCEQP ≥40% (99.6% specificity and 100% sensitivity, p = 4.1 × 10-17). This is a first report linking with great accuracy changes of circulating molecules to imaging changes reflecting new SH during prospective follow-up of CCMs.PMID:39829356 | DOI:10.1177/0271678X251314366

Aging Cell. 2025 Jan 20:e14484. doi: 10.1111/acel.14484. Online ahead of print.ABSTRACTMetabolomics and epigenomics have been used to develop 'ageing clocks' that assess biological age and identify 'accelerated ageing'. While metabolites are subject to short-term variation, DNA methylation (DNAm) may capture longer-term metabolic changes. We aimed to develop a hybrid DNAm-metabolic clock using DNAm as metabolite surrogates ('DNAm-metabolites') for age prediction. Within the UK Airwave cohort (n = 820), we developed DNAm metabolites by regressing 594 metabolites on DNAm and selected 177 DNAm metabolites and 193 metabolites to construct 'DNAm-metabolic' and 'metabolic' clocks. We evaluated clocks in their age prediction and association with noncommunicable disease risk factors. We additionally validated the DNAm-metabolic clock for the prediction of age and health outcomes in The Irish Longitudinal Study of Ageing (TILDA, n = 488) and the Health and Retirement Study (HRS, n = 4018). Around 70% of DNAm metabolites showed significant metabolite correlations (Pearson's r: > 0.30, p < 10-4) in the Airwave test set and overall stronger age associations than metabolites. The DNAm-metabolic clock was enriched for metabolic traits and was associated (p < 0.05) with male sex, heavy drinking, anxiety, depression and trauma. In TILDA and HRS, the DNAm-metabolic clock predicted age (r = 0.73 and 0.69), disability and gait speed (p < 0.05). In HRS, it additionally predicted time to death, diabetes, cardiovascular disease, frailty and grip strength. DNAm metabolite surrogates may facilitate metabolic studies using only DNAm data. Clocks built from DNAm metabolites provided a novel approach to assess metabolic ageing, potentially enabling early detection of metabolic-related diseases for personalised medicine.PMID:39829316 | DOI:10.1111/acel.14484