To date, the changes in maternal metabolic response associated with prenatal aflatoxin exposure remain largely unknown. This study investigated the effects of prenatal aflatoxin exposure on the maternal serum metabolome in rural Ethiopia. A total of 309 pregnant women were enrolled prospectively, and their serum aflatoxin concentrations were measured using targeted liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Serum metabolic fingerprints were obtained using laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS), followed by combination of univariate and multivariate statistical modelling to evaluate changes in circulating metabolic features between aflatoxin-exposed and unexposed mothers and to select discriminatory metabolic features. The analysis revealed that 81.8% of women were exposed to aflatoxins, with a median concentration of 12.9 pg/mg albumin. The orthogonal partial least square discriminant analysis (OPLS-DA) regression model demonstrated significant disparities in the serum metabolome when comparing Ethiopian pregnant women with low vs high aflatoxin exposure. Thirty-two differentially expressed metabolic features were identified, affecting aminoacyl-tRNA biosynthesis pathway. Several discriminatory metabolites have been identified, including glutamine, tryptophan, tyrosine, carnosine, and 1-methylnicotinamide. In conclusion, our findings indicate that aflatoxin exposure during pregnancy have shown disparities in the maternal serum metabolome, primarily affecting protein synthesis. Further research is needed to identify specific metabolite biomarkers and elucidate the underlying mechanisms.

UNASSIGNED: To analyze the effect of oral metformin on changes in gut microbiota characteristics and metabolite composition in normal weight type 2 diabetic patients.
UNASSIGNED: T2DM patients in the cross-sectional study were given metformin for 12 weeks. Patients with unmedicated T2DM were used as a control group to observe the metrics of T2DM patients treated with metformin regimen. 16S rDNA high-throughput gene sequencing of fecal gut microbiota of the study subjects was performed by llumina NovaSeq6000 platform. Targeted macro-metabolomics was performed on 14 cases of each of the gut microbiota metabolites of the study subjects using UPLC-MS/MS technology. Correlations between the characteristics of the gut microbiota and its metabolites, basic human parameters, glycolipid metabolism indicators, and inflammatory factors were analyzed using spearman analysis.
UNASSIGNED: Glycolipid metabolism indexes and inflammatory factors were higher in normal-weight T2DM patients than in the healthy population (P<0.05), but body weight, BMI, waist circumference, and inflammatory factor concentrations were lower in normal-weight T2DM patients than in obese T2DM patients (P<0.05). Treatment with metformin in T2DM patients improved glycolipid metabolism, but the recovery of glycolipid metabolism was more pronounced in obese T2DM patients. None of the differences in α-diversity indexes were statistically significant (P>0.05), and the differences in β-diversity were statistically significant (P <0.05). Community diversity and species richness recovered after metformin intervention compared to before, and were closer to the healthy population. We found that Anaerostipes/Xylose/Ribulose/Xylulose may play an important role in the treatment of normal-weight T2DM with metformin by improving glycemic lipids and reducing inflammation. And Metformin may play a role in obese T2DM through Romboutsia, medium-chain fatty acids (octanoic acid, decanoic acid, and dodecanoic acid).
UNASSIGNED: Gut microbial dysbiosis and metabolic disorders were closely related to glucose-lipid metabolism and systemic inflammatory response in normal-weight T2DM patients. Metformin treatment improved glucose metabolism levels, systemic inflammation levels in T2DM patients, closer to the state of healthy population. This effect may be mediated by influencing the gut microbiota and microbial host co-metabolites, mainly associated with Anaerostipes and xylose/Ribulose/Xylulose. Metformin may exert its effects through different pathways in normal-weight versus obese T2DM patients.

This study aimed to investigate the changes in physicochemical properties, bioactivities and metabolites of fermented goji juice (FGJ) by Lacticaseibacillus rhamnosus at different fermentation stages. The results showed that Lacticaseibacillus rhamnosus fermentation significantly decreased the content of soluble protein, total phenolic, total flavonoid and total sugar. Meanwhile, the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging ability and the inhibition rate of xanthine oxidase (XOD) activity were remarkably enhanced by Lacticaseibacillus rhamnosus fermentation. Flavor profiles analysis indicated that FGJ produced novel volatile compounds such as 4-methylpentanol and 2-butanol, which provide its distinct aroma. The non-targeted metabolomics analysis showed that the differential metabolites in the FGJ28 vs. FGJ0 group were mainly included 1,7-bis (3,4-dihydroxyphenyl) heptan-3-yl acetate, isoplumbagin, triacetylresveratrol, sulochrin, indole-3-acetaldehyde, etc., which might have an effect on the promotion of the bioactivity of goji juice. These findings will contribute to understanding the biotransformation effect of Lacticaseibacillus rhamnosus fermentation on goji juice.

UNASSIGNED: Lymphangioleiomyomatosis (LAM) is a rare and progressive disorder that usually arises in the lung and almost exclusively affects women of childbearing age. In recent years, a number of molecules have been shown to be differentially expressed between patients with LAM and healthy control individuals, and some of these molecules, in addition to vascular endothelial growth factor D (VEGF-D), have the potential to be novel biomarkers.
UNASSIGNED: This review summarizes the recent advances in omics research, including genomics, transcriptomics, proteomics, and metabolomics, in LAM biomarker discovery. It also retrieves the literature on LAM biomarkers studied by omics techniques in the last ten years using PubMed and other retrieval tools.
UNASSIGNED: Further research on expanded sample sizes can be conducted to construct specific models to study the role of these molecules in the pathogenesis of LAM and clarify the underlying mechanisms involved. In the future, in terms of technology, the combination of various omics methods is expected to result in novel biomarker discovery.

Slow Transit Constipation (STC) is characterized by impaired colonic motility, but its relationship with gut microbiota remains unclear. This study investigated the correlation between specific gut microbial populations and STC, focusing on the Lactobacillus acidophilus to Lactobacillus johnsonii (A/J) ratio. We used four rat groups: Control (CON), Loperamide-induced STC (LOP), antibiotic-treated (ABX), and antibiotic plus Loperamide (ABX + LOP). Fecal samples were analyzed using 16S rRNA gene sequencing, and serum metabolites were examined through LC-MS. The LOP group showed an increased A/J ratio, while ABX and ABX + LOP groups had decreased ratios. Notably, the ABX + LOP group did not develop STC symptoms. Metabolomic analysis revealed alterations in key metabolites across groups, including changes in levels of guanidinoacetate, glycine, L-glutamine, nicotine, and nicotinate D-ribonucleotide in the LOP group, and variations in L-glutamine, L-phenylalanine, L-tyrosine, histamine, D-ornithine, and lecithin in the ABX and ABX + LOP groups. Our findings suggest a correlation between the A/J ratio and STC development, offering insights into STC pathophysiology and potential microbiome-targeted therapies.

As insufficient sleep has become a widespread concern in modern society, potential sleep-improving effect of mulberry (Morus alba L.) leaf ethanol extract (MLE) and the related mechanism were investigated in the present study. According to the results, MLE could significantly shorten sleep latency by 33 %, extend sleep duration by 56 % and increase sleep ratio of mice through increasing 5-HT and GABA release in serum, hypothalamus and hippocampus. Metabonomic analysis showed that phenylalanine metabolism, arginine and proline metabolism might be the potential pathways of MLE to improve sleep. Network pharmacological and LC-MS analysis suggested that the key sleep-improving active ingredients in MLE might be luteolin, kaempferol, naringenin, morin, stigmasterol and β-sitosterol. Further molecular docking and qRT-PCR results demonstrated that the key targets for MLE to improve sleep might be MAOA, GABRA1 and GABRA2. In conclusion, MLE showed outstanding sleep-improving effect and great potential for the application as novel sleep-improving functional food.

Low-temperature cooking causes flavor weakness while improving the texture and digestive properties of meat. To enhance the flavor of low-temperature cooked Tibetan pork, samples were cooked at low-temperature with or without ultrasound-assisted (UBTP, BTP) for different times (30 min, 90 min) and then analyzed using GC-MS and LC-MS. The results showed that ultrasound-assisted cooking caused a significant increase in lipid oxidation by 9.10% in the early stage of the treatment. Additionally, at the later stage of ultrasound-assisted processing, proteins were oxidized and degraded, which resulted in a remarkable rise in the protein carbonyl content by 6.84%. With prolonged effects of ultrasound and low-temperature cooking, the formation of phenylacetaldehyde in UBTP-90 sample originated from the degradation of phenylalanine through multivariate statistics and correlation analysis. Meanwhile, trans, cis-2,6-nonadienal and 1-octen-3-one originated from the degradation of linolenic acid and arachidonic acid. This study clarified the mechanism of ultrasound-assisted treatment improving the flavor of low-temperature-cooked Tibetan pork based on the perspective of lipids and proteins oxidation, providing theoretical supports for flavor enhancement in Tibetan pork-related products.

The utilization of metabolomics approaches to explore the metabolic mechanisms underlying plant fitness and adaptation to dynamic environments is growing, highlighting the need for an efficient and user-friendly toolkit tailored for analyzing the extensive datasets generated by metabolomics studies. Current protocols for metabolome data analysis often struggle with handling large-scale datasets or require programming skills. To address this, we present MetMiner (https://github.com/ShawnWx2019/MetMiner), a user-friendly, full-functionality pipeline specifically designed for plant metabolomics data analysis. Built on R shiny, MetMiner can be deployed on servers to utilize additional computational resources for processing large-scale datasets. MetMiner ensures transparency, traceability, and reproducibility throughout the analytical process. Its intuitive interface provides robust data interaction and graphical capabilities, enabling users without prior programming skills to engage deeply in data analysis. Additionally, we constructed and integrated a plant-specific mass spectrometry database into the MetMiner pipeline to optimize metabolite annotation. We have also developed MDAtoolkits, which include a complete set of tools for statistical analysis, metabolite classification, and enrichment analysis, to facilitate the mining of biological meaning from the datasets. Moreover, we propose an iterative weighted gene co-expression network analysis strategy for efficient biomarker metabolite screening in large-scale metabolomics data mining. In two case studies, we validated MetMiner\'s efficiency in data mining and robustness in metabolite annotation. Together, the MetMiner pipeline represents a promising solution for plant metabolomics analysis, providing a valuable tool for the scientific community to use with ease.

Bladder cancer (BLCA) mortality is higher in African American (AA) patients compared with European American (EA) patients, but the molecular mechanism underlying race-specific differences are unknown. To address this gap, we conducted comprehensive RNA-Seq, proteomics, and metabolomics analysis of BLCA tumors from AA and EA. Our findings reveal a distinct metabolic phenotype in AA BLCA characterized by elevated mitochondrial oxidative phosphorylation (OXPHOS), particularly through the activation of complex I. The results provide insight into the complex I activation-driven higher OXPHOS activity resulting in glutamine-mediated metabolic rewiring and increased disease progression, which was also confirmed by [U]13C-glutamine tracing. Mechanistic studies further demonstrate that knockdown of NDUFB8, one of the components of complex I in AA BLCA cells, resulted in reduced basal respiration, ATP production, GLS1 expression, and proliferation. Moreover, preclinical studies demonstrate the therapeutic potential of targeting complex I, as evidenced by decreased tumor growth in NDUFB8-depleted AA BLCA tumors. Additionally, genetic and pharmacological inhibition of GLS1 attenuated mitochondrial respiration rates and tumor growth potential in AA BLCA. Taken together, these findings provide insight into BLCA disparity for targeting GLS1-Complex I for future therapy.

Age related cataracts (ARC) represent the main reason for blindness globally. The lens epithelial cells (LECs) participate not only in the metabolism of many substances in the lens but also in maintaining lens transparency. This study used lipidomics to investigate the metabolic differences in LECs of ARC patients with different severity, aiming at identifying potential metabolic biomarkers of ARC. Patients diagnosed with ARC and underwent cataract surgery at Shanghai Tongren Hospital were selected to participate in this study, which were classified as mild ARC group and severe ARC group. During their cataract surgery, anterior lens capsules(LCs) containing LECs were obtained. The lipidomics of LECs were analyzed using the liquid chromatography‑mass spectrometry (LC-MS). Potential pathways of lipids were searched for using databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and MetaboAnalyst platform. In LEC lipids, 26 lipids have been identified as potential biomarkers between mild ARC and severe ARC, with AUC values of 0.67-0.94. The pathway analysis results revealed that the Glycerophospholipid (GPL) metabolism was significantly influenced, indicating that these metabolic markers contribute significantly to regulating this pathway. The LEC metabolic spectrum demonstrates a proficient ability to differentiate between patients with varying levels of cataracts. Herein, we have successfully identified potential metabolic biomarkers and pathways that have proven to be valuable in enhancing our understanding of ARC pathogenesis. The finding has translational value for developing new cataract treatment methods in the future.