PubMed

J Obstet Gynaecol Res. 2025 Feb;51(2):e16229. doi: 10.1111/jog.16229.ABSTRACTBACKGROUND: The pathogenesis of preeclampsia (PE) remains unclear, but the interaction between intestinal flora and PE has been attention in recent studies. Several studies have shown that imbalanced intestinal flora plays an important role in the inducement of PE.PURPOSE: The potential correlation among intestinal flora, metabolites, and fetal growth restriction was explored by integrating data and analyzing neonatal growth. The study is hoped to provide references for subsequent studies.METHODS: A comparison between the intestinal flora of healthy pregnant women and pregnant with PE was conducted using 16S rRNA gene sequencing. Subsequently, the feces, serum and umbilical cord blood collected from healthy pregnant women and those with PE were analyzed using global untargeted metabolomics to identify differences in metabolites.RESULTS: The results showed that Bifidobacterium, Chryseobacterium, Eubacterium, Pravotella and Bacteroides are the core species associated with many metabolites. Normal-birth weight had a negative correlation with the abundance of raclopride, Phe-Gly-O and Lys-Phe-OH showed a positive correlation with Phosphonate and Lys-Gly. Simultaneously, these core metabolites showed a strong correlation with other growth indices (BPD, AC, FL). In summary, the imbalance of intestinal flora in pregnant women may alter the abundance of the core metabolites, thereby affecting the neonatal growth.CONCLUSIONS: A global untargeted metabolomics was performed on the samples including feces, serum, and umbilical cord blood. The integrated multi-omics analysis revealed the interaction among intestinal flora, metabolites and the clinical indices, demonstrating the potential effects of the imbalance of intestinal flora on neonatal growth in the pregnant women with PE.PMID:39930654 | DOI:10.1111/jog.16229

Plant J. 2025 Feb;121(3):e17252. doi: 10.1111/tpj.17252.ABSTRACTGlobal metabolic and transcriptional reprogramming is a common event in plant abiotic stress responses, however, the relevant molecular mechanisms remain largely unknown. Here, we characterized the physiological function and molecular mechanism for the rice UGT706F1. We found that UGT706F1 can be potently induced by high temperature. Its overexpression can markedly enhance the heat tolerance of rice through improving the capacity of scavenging reactive oxygen species, whereas its functional deletion results in heat sensitivity in rice. To investigate the regulatory mechanism of UGT706F1 in response to high temperature, we carried out extensive screening of the in vitro enzymatic activity of UGT706F1 and discovered that UGT706F1 exhibits broad-spectrum activity toward flavonoid compounds. Through targeted flavonoid metabolomics analysis, we further revealed that the overexpression of UGT706F1 elevated the content of diverse flavonoids and flavonoid glycosides in rice. Subsequently, via transcriptome analysis, we found that following heat treatment, the overexpression of UGT706F1 was capable of enhancing the transcriptional activity of those genes including the flavonoid synthases, heat shock factors, heat shock proteins, glutathione S-transferase, and various antioxidant enzymes. Furthermore, we identified an R2R3 MYB-type transcription factor MYB61 and demonstrated that MYB61 could directly bind the promoter of UGT706F1 and activate the transcription of UGT706F1. The overexpression of MYB61 also enhanced the heat tolerance and increased flavonoid glycosides. Overall, this study unveiled a novel pathway of the plant heat tolerance response mediated by MYB61-UGT706F1 module and identified a new UGT player for the metabolic and transcriptional regulation under high-temperature circumstance.PMID:39930614 | DOI:10.1111/tpj.17252

J Exp Clin Cancer Res. 2025 Feb 10;44(1):47. doi: 10.1186/s13046-025-03302-0.ABSTRACTThe focus of cancer immunotherapy has traditionally been on immune cells and tumor cells themselves, often overlooking the tumor secretome. This review provides a comprehensive overview of the intricate relationship between tumor cells and the immune response in cancer progression. It highlights the pivotal role of the tumor secretome - a diverse set of molecules secreted by tumor cells - in significantly influencing immune modulation, promoting immunosuppression, and facilitating tumor survival. In addition to elucidating these complex interactions, this review discusses current clinical trials targeting the tumor secretome and highlights their potential to advance personalized medicine strategies. These trials aim to overcome the challenges of the tumor microenvironment by designing therapies tailored to the secretome profiles of individual cancer patients. In addition, advances in proteomic techniques are highlighted as essential tools for unraveling the complexity of the tumor secretome, paving the way for improved cancer treatment outcomes.PMID:39930476 | DOI:10.1186/s13046-025-03302-0

Adv Exp Med Biol. 2025;1468:207-212. doi: 10.1007/978-3-031-76550-6_34.ABSTRACTDysregulation of lipid metabolism has been linked with risk for age-related retinal diseases including age-related macular degeneration (AMD). However, how dysregulated lipid metabolism contributes to AMD development is unknown. In this study, we evaluated the retinal and plasma lipidomes of a mouse model displaying retinal pigmented epithelium (RPE) pathologies that are observed in AMD including RPE dysmorphia and degeneration. We found that the RPE phenotypes in mice overexpressing transmembrane protein 135 (Tmem135 TG) are correlated with retinal and plasma lipidome changes. While distinct lipid profiles were observed in the retina and plasma of Tmem135 TG mice, a common finding in both retinal and plasma lipidomes was an increase of lipids containing C22:5. This data suggests that accumulation of C22:5-containing lipids may contribute to the development of the RPE pathologies in Tmem135 TG mice.PMID:39930197 | DOI:10.1007/978-3-031-76550-6_34

BMC Plant Biol. 2025 Feb 10;25(1):174. doi: 10.1186/s12870-025-06189-3.ABSTRACTSeagrasses maintain cellular water balance by regulating ion concentrations and accumulating organic osmolytes, enabling them to survive in the fluctuating salinity of intertidal environments. However, the molecular mechanisms underlying seagrass responses to salinity changes remain relatively understudied. To address this, we conducted a multi-omics analysis of Ruppia sinensis under low, moderate, and high salinity conditions to uncover the mechanisms behind its adaptation to salinity fluctuations. Our research revealed that the transition from low to high salinity significantly altered the physiological characteristics of R. sinensis. Simultaneously, the species enhanced its ability to cope with and adapt to salinity fluctuations by increasing antioxidant enzyme activity. Integration of multi-omics data further indicated that under high salinity conditions, R. sinensis synthesizes more flavonoids to bolster its adaptive capacity. Additionally, the phenylpropanoid metabolic pathway appears to play a crucial role in the response of R. sinensis to changes in water salinity.PMID:39930400 | DOI:10.1186/s12870-025-06189-3

BMC Plant Biol. 2025 Feb 11;25(1):178. doi: 10.1186/s12870-025-06154-0.ABSTRACTGlobally, rice bacterial blight disease causes significant yield losses. Metabolomics is a vital tool for understanding this disease by analyzing metabolite levels and pathways involved in resistance and susceptibility. It enables the development of disease-resistant rice varieties and sustainable disease management strategies. This study has focused on the metabolic response to bacterial blight disease in three rice varieties: the near isogenic rice line IRBB27, wild rice (Oryza minuta-CG154:IRGC No. 93259, accession No. EC861737), and the susceptible control IR24. However, detailed metabolomics studies in wild rice remain largely unexplored. So, metabolic analysis with untargeted liquid chromatography mass spectrometry analysis (LC-MS/MS) was performed at various time points, including pre infection and post infection at 12 h and 24 h with Xanthomonas oryzae pv. oryzae (Xoo). In this study, a total of 6067 metabolites were identified. Pre-infection stage of the susceptible, resistant, and wild rice had 675, 660, and 702 identified metabolites, respectively, but these numbers were altered at post-infection stages. Various defense-related metabolites, including amino acids, flavonoids, alkaloids, terpenoids, nucleotide derivatives, organic acids, inorganic compounds, fatty acid and lipid derivatives have been identified. PCA and PLS-DA plots revealed differences in the metabolome among susceptible, resistant, and wild genotypes, suggesting distinct metabolic profiles for each. In this study, we found 149 metabolites were upregulated and 162 downregulated in the wild type (CG154) compared to the susceptible cultivar (IR24). Similarly, 85 metabolites were upregulated and 92 downregulated in the resistant near isogenic line (IRBB27) compared to IR24, while 156 were upregulated and 149 downregulated in CG154 compared to IRBB27. Key metabolites, including flavonoids, terpenoids, and phenolic compounds, showed significantly higher levels (P ≤ 0.01) in resistant varieties. These identified defense metabolites could serve as potential biomarkers for bacterial blight resistance in rice. The findings from this study have important implications for the development of new rice cultivars with tolerance to bacterial blight disease.PMID:39930388 | DOI:10.1186/s12870-025-06154-0

BMC Plant Biol. 2025 Feb 10;25(1):173. doi: 10.1186/s12870-025-06163-z.ABSTRACTBACKGROUND: Chrysanthemum (Chrysanthemum × morifolium) is one of the four major cut flowers worldwide and is valued for ornamental, culinary, and medicinal purposes. Terpenoids are key components of the fragrance of chrysanthemum; they not only serve to repel insect herbivores and promote pollination but also impact the value of the plant. However, the terpene production of chrysanthemum and the regulatory mechanisms involved remain unclear.RESULTS: We used gas chromatography‒mass spectrometry (GC‒MS) to identify 177 compounds, including 106 terpenes, in ten chrysanthemum cultivars. Monoterpene derivatives and sesquiterpenes were the most common. Next, we identified 27 candidate hub genes for terpene production in chrysanthemum via combined transcriptome and metabolome analysis, as well as weighted gene coexpression network analysis. The three terpenes synthesis-related genes were significantly expressed in the disc florets of the different chrysanthemum cultivars. We concluded that the transcription factors TCP8, TCP5, ATHB8, ATHB7, HAT22, TGA1, TGA4, and WHY1 may regulate terpene synthesis.CONCLUSIONS: In this study, we profiled terpenes in chrysanthemum florets and constructed a key terpene-transcription factor network related to terpene synthesis. These findings lay the groundwork for future research into the mechanism of terpene synthesis in chrysanthemum as well as in other plants.PMID:39930381 | DOI:10.1186/s12870-025-06163-z

BMC Genomics. 2025 Feb 11;26(1):130. doi: 10.1186/s12864-025-11330-5.ABSTRACTSolute carriers (SLC) are integral membrane proteins responsible for transporting a wide variety of metabolites, signaling molecules and drugs across cellular membranes. Despite key roles in metabolism, signaling and pharmacology, around one third of SLC proteins are 'orphans' whose substrates are unknown. Experimental determination of SLC substrates is technically challenging, given the wide range of possible physiological candidates. Here, we develop a predictive algorithm to identify correlations between SLC expression levels and intracellular metabolite concentrations by leveraging existing cancer multi-omics datasets. Our predictions recovered known SLC-substrate pairs with high sensitivity and specificity compared to simulated random pairs. CRISPR-Cas9 dependency screen data and metabolic pathway adjacency data further improved the performance of our algorithm. In parallel, we combined drug sensitivity data with SLC expression profiles to predict new SLC-drug interactions. Together, we provide a novel bioinformatic pipeline to predict new substrate predictions for SLCs, offering new opportunities to de-orphanise SLCs with important implications for understanding their roles in health and disease.PMID:39930358 | DOI:10.1186/s12864-025-11330-5

Geroscience. 2025 Feb 10. doi: 10.1007/s11357-025-01553-5. Online ahead of print.ABSTRACTChronic, low-grade inflammation is a hallmark of aging and various age-related diseases, including metabolic dysfunction-associated steatotic liver disease (MASLD). The prevalence of metabolic dysfunction-associated steatohepatitis (MASH), an advanced form of MASLD, increases with age and contributes to morbidity and mortality among the elderly. This study investigates the role of necroptosis, a programmed cell death pathway that promotes inflammation, in liver inflammaging and age-associated MASLD by utilizing genetic ablation models of two key necroptosis proteins, Mlkl or Ripk3. The absence of Mlkl or Ripk3 significantly reduced liver inflammation, steatosis, and fibrosis in aged male mice, supporting the role of necroptosis in age-associated MASLD. Additionally, Mlkl or Ripk3 deletion impacted other non-necroptotic cellular processes that drive inflammation and MASLD, such as cellular senescence, apoptosis, and autophagy in aged liver. Levels of plasma TNFα and IL6, key proinflammatory cytokines associated with inflammaging, are reduced in Mlkl-/- or Ripk3-/- aged mice, supporting a systemic effect of necroptosis inhibition on inflammation. Proteomic analysis of liver tissues emphasizes the critical role of lipid and immune regulatory processes in maintaining liver homeostasis when Mlkl or Ripk3 is absent in aging liver. While Mlkl deletion did not affect the lifespan of mice, Ripk3 deletion shortened it. Additionally, Mlkl deficiency improved insulin sensitivity, whereas Ripk3 deficiency exacerbated glucose intolerance in aged mice. Thus, selective inhibition of Mlkl, not Ripk3, represents a potential therapeutic avenue for mitigating age-related liver disease and enhancing metabolic outcomes in the elderly.PMID:39930289 | DOI:10.1007/s11357-025-01553-5

Clin Rheumatol. 2025 Feb 10. doi: 10.1007/s10067-025-07355-6. Online ahead of print.ABSTRACTRheumatoid arthritis (RA) is a systemic chronic autoimmune disease characterized by joint damage and systemic involvement. Despite advancements in understanding RA, early diagnosis and effective treatment remain challenging due to the complex pathogenesis and limited specificity of current biomarkers. Metabolomics, offers a promising approach for identifying new biomarkers to assess treatment responsiveness in RA. A systematic review was conducted to identify key metabolites and metabolic pathways that may reveal responsiveness to different drug therapy strategies (methotrexate, TNF, and IL-6 inhibitors) in RA treatment. The systematic search was conducted in PubMed and Google Scholar in accordance with PRISMA recommendations. The risk of bias and the quality of the final selected studies were assessed in duplicate using the Risk Of Bias In Non-randomized Studies - of Interventions (ROBINS-I) tool and using the QUADOMICS tool. Eighteen studies were eligible for data extraction. Metabolomic studies revealed distinct profiles for responders and non-responders to different RA treatments. For methotrexate therapy, key metabolites included for example: homocysteine, glycerol-3-phosphate, and diphosphoglyceric acid. TNF inhibitor response was associated mainly with changes in carbohydrate derivatives and amino acids. IL-6 inhibitor studies identified metabolites such as N-acetylglucosamine, N-acetylgalactosamine, and N-acetylneuraminic acid as potential predictors of response. Across studies, metabolomic profiles demonstrated high sensitivity and specificity in distinguishing responders from non-responders. These studies collectively highlight alterations in TCA cycle metabolites, amino acids, nucleotide metabolism, and lipid profiles, among others. This review supports the identification of better treatment strategies choosing methotrexate, TNF, or IL-6 inhibitors as therapeutic interventions based on metabolomics profiling.PMID:39930277 | DOI:10.1007/s10067-025-07355-6

Adv Exp Med Biol. 2025;1468:267-271. doi: 10.1007/978-3-031-76550-6_44.ABSTRACTThe retina is one of the most metabolically active tissues in the human body and has its own complex metabolic environment as the different cell types in this tissue are interconnected to maintain a healthy retinal homeostasis. Any disturbances in the homeostatic balance may have a severe impact on retinal function affecting vision. About 341 genes are listed in the RetNet database as being causative for monogenic inherited retinal diseases. By intersecting this list with the Mammalian Metabolic Enzyme Database, we identified 28 metabolic genes that can result in diseases such as retinitis pigmentosa, Leber congenital amaurosis, or optic atrophy when mutated. Alongside inherited retinal diseases, metabolism also plays a prominent role in acquired retinal diseases. Metabolomics studies have been performed on patients with age-related macular degeneration, diabetic retinopathy, and glaucoma revealing dysregulated metabolic pathways, such as lipid, amino acid, and purine metabolism, in the onset of disease. Although there are distinct pathophysiological differences between inherited and acquired retinal disorders, diving deeper into the role of metabolism and how metabolic dysfunction may overlap with different pathologies, could give us indications on how to design approaches to normalize the homeostatic balance in the retina as treatment options to protect vision.PMID:39930207 | DOI:10.1007/978-3-031-76550-6_44

World J Microbiol Biotechnol. 2025 Feb 11;41(2):68. doi: 10.1007/s11274-025-04285-y.ABSTRACTOmics technologies are a set of disciplines that analyze large-scale molecular data to understand biological systems in a holistic way. These technologies aim to reveal the structure, functions and interactions of organisms by studying processes at many levels of biomolecules, from the genome to metabolism. Lactobacillomics is introduced as an interdisciplinary field that integrates multiple "omics" technologies-including genomics, transcriptomics, proteomics, metabolomics, and metagenomics- to provide a comprehensive insight into "lactic acid bacteria" species. Lactobacillomics aims to elucidate the genetic, metabolic, and functional characteristics of lactic acid bacteria (LAB) species, providing insights into the mechanisms underlying their probiotic effects and contributions to the host microbiome. By analyzing genomes and metabolic pathways, researchers can identify specific genes responsible for health-promoting functions and desirable fermentation characteristics, which can guide the development of targeted probiotic strains with optimized health benefits. The integration of these omics data allows facilitating the discovery of biomarkers for health and disease states, the development of new probiotics tailored to specific populations or health conditions, and the optimization of fermentation processes to enhance the safety, flavor, and nutritional profile of fermented foods. A comprehensive review and bibliometric analysis were conducted to provide an overview of this promising field between 2005 and 2025 by examining Web of Science Core Collection data. Research results reveal trending topics, future perspectives, and key areas of growth within lactic acid bacteria (LAB) studies, particularly as they intersect with omics technologies.PMID:39930163 | DOI:10.1007/s11274-025-04285-y

Sci Rep. 2025 Feb 10;15(1):4963. doi: 10.1038/s41598-025-87288-x.ABSTRACTProfound metabolomic alterations occur during COVID-19. Early identification of the subset of hospitalised COVID-19 patients at risk of developing severe disease is critical for optimal resource utilization and prompt treatment. This work explores the metabolomic profile of hospitalised adult COVID-19 patients with severe disease, and establishes a predictive signature for disease progression. Within 48 hours of admission, serum samples were collected from 148 hospitalised patients for nuclear magnetic resonance (NMR) spectroscopy. Lipoprotein profiling was performed using the 1H-NMR-based Liposcale test, while low molecular weight metabolites were analysed using one-dimensional Carr-Purcell-Meiboom-Gill pulse spectroscopy and an adaptation of the Dolphin method for lipophilic extracts. Severe COVID-19, per WHO's Clinical Progression Scale, was characterized by altered lipoprotein distribution, elevated signals of glyc-A and glyc-B, a shift towards a catabolic state with elevated levels of branched-chain amino acids, and accumulation of ketone bodies. Furthermore, COVID-19 patients initially presenting with moderate disease but progressing to severe stages exhibited a distinct metabolic signature. Our multivariate model demonstrated a cross-validated AUC of 0.82 and 72% predictive accuracy for severity progression. NMR spectroscopy-based metabolomic profiling enables the identification of moderate COVID-19 patients at risk of disease progression, aiding in resource allocation and early intervention.PMID:39929875 | DOI:10.1038/s41598-025-87288-x

Compr Rev Food Sci Food Saf. 2025 Mar;24(2):e70138. doi: 10.1111/1541-4337.70138.ABSTRACTThere is an urgent need to address food safety concerns associated with multiple Maillard reaction‒derived chemical contaminants, such as acrylamide, heterocyclic aromatic amines, advanced glycation end products, and 5-hydroxymethylfurfural, which are present in processed foods. Current studies have focused on single contaminant generated by the Maillard reaction; however, there is a dearth of information regarding the interactions of multiple contaminants and their joint control methods. This review article comprehensively summarizes the state-of-the-art progress in the simultaneous analysis, coformation, joint hazardous control, and risk assessment of multiple food processing contaminants generated by the Maillard reaction. The Maillard reaction is associated with caramelization, lipid oxidation, protein oxidation, and ascorbic acid browning reactions. Mass spectrometry‒based chromatography is currently the preferred method for the simultaneous quantification of multiple contaminants, with metabolomics and indirect detection methodologies providing new insights. Mitigation strategies for multiple contaminants include optimizing pretreatment, introducing exogenous additives, regulating processing parameters, and utilizing emerging technologies. Limited animal studies on the metabolism of various contaminants have yielded diverse results, guided by biomarkers for deep understanding. Integrated risk assessment should be conducted to quantify multihazard health impacts. In future research, a unique framework should be developed for assessing multiple contaminants, characterizing their metabolic profiles, and optimizing control measures for Maillard reaction‒derived contaminants.PMID:39929674 | DOI:10.1111/1541-4337.70138

Zhongguo Zhong Yao Za Zhi. 2025 Jan;50(1):278-284. doi: 10.19540/j.cnki.cjcmm.20241005.601.ABSTRACTWith the advancement in big data and artificial intelligence technologies, the extensive application of omics technologies in traditional Chinese medicine(TCM) research has generated large experimental datasets, enabling the exploration of cross-scale correlations among massive data and thereby resulting in the shift toward a data-intensive research paradigm. The emerging approach of multi-omics data fusion analysis, emphasizing technical and computational tools, presents a potential breakthrough in this field. The holistic perspective of TCM aligns with the concept of multi-omics data fusion, yet the data types encountered exhibit high dimensionality with small sample sizes, necessitating data processing techniques such as dimensionality reduction. The current challenge lies in selecting suitable analytical methods for these data to enhance the systematic understanding of physiological functions and disease diagnosis/treatment processes. This paper explores the theories and frameworks of multi-omics data fusion, analyzes methods for fusing high-dimensional, small-sample multi-omics data in TCM, and aims to provide insights for advancing TCM research.PMID:39929669 | DOI:10.19540/j.cnki.cjcmm.20241005.601

Zhongguo Zhong Yao Za Zhi. 2025 Jan;50(1):19-31. doi: 10.19540/j.cnki.cjcmm.20240902.703.ABSTRACTTo explore the anti-depression effect of Suanzaoren Decoction(SZRD), the regulatory effects on endogenous metabolites in the liver of rats with depression induced by chronic unpredictable mild stress(CUMS) were analyzed by using LC-MS metabolomics. The rats were randomly divided into normal control group, model group, low-dose SZRD group, high-dose SZRD group, and positive drug group. The CUMS depression model was replicated by applying a variety of stimuli, such as fasting and water deprivation, ice water swimming, hot water swimming, day and night reversal, tail clamping, and restraint for rats. Modeling and treatment were conducted for 56 days. The behavioral indexes of rats in each group, including body weight, open field test, sucrose preference test, and tail suspension test, were observed. Plasma samples and liver tissue samples were collected, and the contents of 5-hydroxytryptamine(5-HT), dopamine(DA), and norepinephrine(NE) in plasma were measured using enzyme-linked immunosorbent assay(ELISA). Meanwhile, the regulatory effects of SZRD on the liver metabolic profile of CUMS model rats were analyzed by the LC-MS metabolomics method. The results show that SZRD can significantly improve the depression-like behavior of CUMS model rats and increase the neurotransmitter levels of 5-HT, DA, and NE in plasma. A total of 24 different metabolites in the rats' liver are identified using the LC-MS metabolomics method, and SZRD can reverse 13 of these metabolites. Metabolic pathway analysis indicates that nine metabolic pathways are found to be significantly associated with depression, and in the low-dose SZRD group, four pathways can be regulated, including pentose phosphate pathway, purine metabolism, inositol phosphate metabolism, and sphingolipid metabolism. In the high-dose SZRD group, two metabolic pathways can be regulated, including sphingolipid metabolism and glycerol glycerophospholipid metabolism. Sphingolipid metabolism is a metabolic pathway that can be regulated by SZRD at different doses, so it is speculated that it may be the primary pathway through which SZRD can alleviate metabolic disturbances in the liver of CUMS model rats.PMID:39929643 | DOI:10.19540/j.cnki.cjcmm.20240902.703

Zhongguo Zhong Yao Za Zhi. 2025 Jan;50(1):10-20. doi: 10.19540/j.cnki.cjcmm.20240712.710.ABSTRACTThe Baihe Dihuang Decoction(BDD) is a representative traditional Chinese medicine formula that has been used to treat depression. This study employed metabolomics and network pharmacology to investigate the mechanism of BDD in the treatment of depression. Fifty male Sprague-Dawley(SD) rats were randomly assigned to the normal control group, model group, fluoxetine group, and high-and low-dose BDD groups. A rat model of depression was established through chronic unpredictable mild stress(CUMS), and the behavioral changes were detected by forced swimming test and open field test. Metabolomics technology was used to analyze the metabolic profiles of serum and hippocampal tissue to screen differential metabolites and related metabolic pathways. Additionally, network pharmacology and molecular docking techniques were used to investigate the key targets and core active ingredients of BDD in improving metabolic abnormalities of depression. A "component-target-metabolite-pathway" regulatory network was constructed. BDD could significantly improve depressive-like behavior in CUMS rats and regulate 12 differential metabolites in serum and 27 differential metabolites in the hippocampus, involving tryptophan metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, alanine, aspartate, and glutamate metabolism, tyrosine metabolism, and purine metabolism. Verbascoside, isorbascoside, and regaloside B were the key active ingredients for improving metabolic abnormalities in depression. Epidermal growth factor receptor(EGFR), protooncogene tyrosine-protein kinase(SRC), glycogen synthase kinase 3β(GSK3β), and androgen receptor(AR) were the key core targets for improving metabolic abnormalities of depression. This study offered a preliminary insight into the mechanism of BDD in alleviating metabolic abnormalities of depression through network regulation, providing valuable guidance for its clinical use and subsequent research.PMID:39929642 | DOI:10.19540/j.cnki.cjcmm.20240712.710

Zhongguo Zhong Yao Za Zhi. 2025 Jan;50(2):322-342. doi: 10.19540/j.cnki.cjcmm.20240902.704.ABSTRACTThis study explored the action mechanism of Jiawei Xionggui Decoction in the treatment of Alzheimer's disease(AD) by integrating mouse brain tissue metabolomics and network pharmacology. Six-month-old amyloid precursor protein/presenilin 1(APP/PS1) mice were selected and divided into the APP/PS1 group and Jiawei Xionggui Decoction intervention group, with age-matched C57BL/6 mice serving as controls. Cognitive abilities and pathological damage in the mice were observed. Gas chromatography-mass spectrometry/mass spectrometry(GC-MS/MS) technology was utilized to analyze the metabolic profiles of mice brain tissue. Differential metabolites were screened, and relevant metabolic pathways were enriched. Network pharmacology was adopted to screen the active components of Jiawei Xionggui Decoction, so as to construct a protein-protein interaction network of its core targets for AD treatment and conduct Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis of potential targets for Jiawei Xionggui Decoction in treating AD. Finally, a "metabolite-reaction-enzyme-gene" network was constructed for combined analysis of metabolomics and network pharmacology. The results showed that Jiawei Xionggui Decoction significantly reversed the trends of 18 differential metabolites involved in 15 metabolic pathways such as glyoxylate and dicarboxylate metabolism, glycine, serine, and threonine metabolism, pyruvate metabolism, alanine, aspartate, and glutamate metabolism, and tricarboxylic acid cycle(TCA) in mouse brain tissue. Furthermore, 383 core targets of Jiawei Xionggui Decoction were implicated in pathways like the phosphoinositide 3-kinase(PI3K)/protein kinase B(Akt) signaling pathway and calcium signaling pathway. Overall analysis indicated that energy metabolism, amino acid metabolism, and fatty acid metabolism were crucial metabolic pathways for Jiawei Xionggui Decoction in treating AD. The findings suggest that Jiawei Xionggui Decoction can protect neuronal cells in mouse brain tissue, thus improving cognitive impairment.PMID:39929614 | DOI:10.19540/j.cnki.cjcmm.20240902.704

Annu Rev Anal Chem (Palo Alto Calif). 2025 Feb 12. doi: 10.1146/annurev-anchem-071524-125307. Online ahead of print.ABSTRACTExposomics refers to the comprehensive analysis of environmental exposures over the lifespan and assessment of their biological effects on human health. This new frontier in environmental research promises new insights for assessment of the hazards of complex chemical exposures as compared to targeted biomonitoring of a limited panel of known toxicant(s). Metabolomics plays a pivotal role in expanding exposomic initiatives that require orthogonal separation methods coupled to high-resolution mass spectrometry while using minimally invasive specimens from prospective cohort studies that can capture early life exposures. However, several grand analytical challenges remain, including high-throughput metabolomic data workflows that are scalable to large populations, the identification of unknown contaminants and their contact sources, and elucidating the impact of multiple co-exposures at critical stages of development. In this review, we outline new advances in metabolomic technologies for exposomics research over the past five years that are urgently needed to guide regulatory policies via better exposure mitigation and strategies to improve metabolic resilience.PMID:39929542 | DOI:10.1146/annurev-anchem-071524-125307

Environ Res. 2025 Feb 8:121082. doi: 10.1016/j.envres.2025.121082. Online ahead of print.ABSTRACTThe gut microbiota can act as a buffer against changes in energy and food availability and adapt plastically to fluctuations in the host's diet. However, it is unknown how changes in the gut microbiome with the seasons impact microbial metabolism and the accessibility of nutrients to hosts. The study utilized 16S rRNA and UHPLC-MS/MS approaches to examine seasonal fecal metabolome variations in the captive Yangtze finless porpoises (YFPs) to determine if these variations are linked to nutrient intake or gut microbiome composition changes. The YFPs were mostly fed a frozen and live fish diet, with different food intakes yearly. We found that gut microbial diversity remained constant, but community structure varied seasonally. Firmicutes and Cyanobacteria were higher in winter, Actinobacteria in spring and fall, and proteobacteria in summer. The genus Paeniclostridium was significantly higher in the spring season, Romboutsia and Clostridium_sensu_stricto_13 were significantly higher in the summer, while Terrisporobacter and Macrococcus were significantly higher in the fall group. The study reported that seasonal dietary variation significantly impacted the fecal metabolome by affecting the metabolism, including energy, amino acid, carbohydrate, and nucleotide metabolism of the captive YFP. Moreover, significant correlations between metabolome and microbiome were found, and these correlations may indicate that the captive YFP has adapted to cope with dietary variations and enhance energy acquisition. These findings improve our knowledge of the link between microbiota, diet, metabolites, and the physiology of the host and suggest that gut microbial populations may adapt continuously to changes in diet.PMID:39929417 | DOI:10.1016/j.envres.2025.121082