Metabolites最新文献

Introduction: This study aimed to evaluate whether glycoprotein and lipoprotein lipidomics profiles could enhance a clinical predictive model for carotid subclinical atherosclerosis in patients with type 1 diabetes (T1D). Additionally, we assessed the influence of cardiac autonomic neuropathy (CAN) on these predictive models. Methods: We conducted a cross-sectional study including 256 patients with T1D. Serum glycoprotein and lipoprotein lipidomics profiles were determined using ¹H-NMR spectroscopy. Subclinical atherosclerosis was defined as carotid intima-media thickness (cIMT) ≥ 1.5 mm. CAN was identified using the Clarke score. Predictive models were built and their performance evaluated using receiver operating characteristic curves and cross-validation. Results: Subclinical atherosclerosis was detected in 32% of participants. Patients with both CAN and atherosclerosis were older, had a longer duration of diabetes, and were more likely to present with bilateral carotid disease. Clinical predictors such as age, duration of diabetes, and smoking status remained the strongest determinants of subclinical atherosclerosis [AUC = 0.88 (95%CI: 0.84-0.93)]. While glycoprotein and lipoprotein lipidomics profiles were associated with atherosclerosis, their inclusion in the clinical model did not significantly improve its diagnostic performance. Stratification by the presence of CAN revealed no impact on the model's ability to predict subclinical atherosclerosis, underscoring its robustness across different risk subgroups. Conclusions: In a cohort of patients with T1D, subclinical atherosclerosis was strongly associated with traditional clinical risk factors. Advanced glycoprotein and lipoprotein lipidomics profiling, although associated with atherosclerosis, did not enhance the diagnostic accuracy of predictive models beyond clinical variables. The predictive model remained effective even in the presence of CAN, highlighting its reliability as a screening tool for identifying patients at risk of subclinical atherosclerosis.

Background/Objectives: Sarcopenia, characterized by the progressive loss of muscle mass and strength, is linked to physical disability, metabolic dysfunction, and an increased risk of mortality. Exercise therapy is currently acknowledged as a viable approach for addressing sarcopenia. Nevertheless, the molecular mechanisms behind exercise training or physical activity remain poorly understood. The disruption of mitochondrial homeostasis is implicated in the pathogenesis of sarcopenia. Exercise training effectively delays the onset of sarcopenia by significantly maintaining mitochondrial homeostasis, including promoting mitophagy, improving mitochondrial biogenesis, balancing mitochondrial dynamics, and maintaining mitochondrial redox. Exerkines (e.g., adipokines, myokines, hepatokines, and osteokines), signaling molecules released in response to exercise training, may potentially contribute to skeletal muscle metabolism through ameliorating mitochondrial homeostasis, reducing inflammation, and regulating protein synthesis as a defense against sarcopenia. Methods: In this review, we provide a detailed summary of exercise-induced exerkines and confer their benefit, with particular focus on their impact on mitochondrial homeostasis in the context of sarcopenia. Results: Exercise induces substantial adaptations in skeletal muscle, including increased muscle mass, improved muscle regeneration and hypertrophy, elevated hormone release, and enhanced mitochondrial function. An expanding body of research highlights that exerkines have the potential to regulate processes such as mitophagy, mitochondrial biogenesis, dynamics, autophagy, and redox balance. These mechanisms contribute to the maintenance of mitochondrial homeostasis, thereby supporting skeletal muscle metabolism and mitochondrial health. Conclusions: Through a comprehensive investigation of the molecular mechanisms within mitochondria, the context reveals new insights into the potential of exerkines as key exercise-protective sensors for combating sarcopenia.

Background: Drought stress has become one of the biggest concerns in threating the growth and yield of carrots (Daucus carota L.). Recent studies have shed light on the physiological and molecular metabolisms in response to drought in the carrot plant; however, tissue-specific responses and regulations are still not fully understood. Methods: To answer this curiosity, this study investigated the interplay among carrot tissues, such as leaves (L); storage roots (SRs); and lateral roots (LRs) under drought conditions. This study revealed that the SRs played a crucial role in an early perception by upregulating key genes, including DcNCED3 (ABA biosynthesis) and DcYUCCA6 (auxin biosynthesis). The abundance of osmolytes (proline; GABA) and carbohydrates (sucrose; glucose; fructose; mannitol; and inositol) was also significantly increased in each tissue. In particular, LRs accumulated high levels of these metabolites and promoted growth under drought conditions. Conclusions: Our findings suggest that the SR acts as a central regulator in the drought response of carrots by synthesizing ABA and auxin, which modulate the accumulation of metabolites and growth of LRs. This study provides new insights into the mechanisms of tissue-specific carrot responses to drought tolerance, emphasizing that the SR plays a key role in improving drought resistance.

Background: Hepatocellular carcinoma (HCC) is a prevalent and lethal form of liver cancer with limited treatment options. Silymarin, a flavonoid complex derived from milk thistle, has shown promise in liver disease treatment due to its antioxidant, anti-inflammatory, and anticancer properties. This study aims to explore the therapeutic potential of silymarin in HCC through a comprehensive in silico approach.

Methods: This study employed a network pharmacology approach to identify key molecular targets of silymarin in HCC. The Genecards and Metascape databases were used for target identification and functional annotation. Molecular docking analysis was conducted on the primary silymarin components against VEGFA and SRC proteins, which are critical in HCC progression. MD simulations followed to assess the stability and interactions of the docked complexes.

Results: Network pharmacology analysis identified several key molecular targets and pathways implicated in HCC. The molecular docking results revealed strong binding affinities of silymarin components to VEGFA and SRC, with Silybin A and Isosilybin B showing the highest affinities. MD simulations confirmed the stability of these interactions, indicating potential inhibitory effects on HCC progression.

Conclusions: This study provides a comprehensive in silico evaluation of silymarin's therapeutic potential in HCC. The findings suggest that silymarin, particularly its components Silybin A and Isosilybin B, may effectively target VEGFA and SRC proteins, offering a promising avenue for HCC treatment. Further experimental validation is warranted to confirm these findings and facilitate the development of silymarin-based therapeutics for HCC.

Background/objectives: Dartmoor Estate Tea plantation in Devon, UK, is renowned for its unique microclimate and varied soil conditions, which contribute to the distinctive flavours and chemical profiles of tea. The chemical diversity of fresh leaf samples from various garden locations was explored within the plantation.

Methods: Fresh leaf, which differed by location, cultivar, time of day, and variety, was analysed using Flow Infusion Electrospray Ionisation Mass Spectrometry (FIE-MS).

Results: Random forest classification revealed no significant differences between Georgian N2 cultivar garden locations. However, a significant degree of variability was observed within four tri-clonal variants (Tocklai Variety) with TV9 exhibiting greater similarity to the Georgian N2 cultivar compared to TV8 and TV11, while TV11 was found to be most like TV1. The intraclass variability in leaf composition was similar between the varieties. We explored the metabolic changes over the day in one variant (Camellia assamica Masters), yielding a model with a significant R² value of 0.617 (p < 0.01, 3000 permutations). Starch and sucrose metabolism was found to be significant where the abundance of these chemical features increased throughout the day and then began to decrease at night.

Conclusions: This research highlights the complex interplay of cultivars, geographical location, and temporal factors on the chemical composition of tea. It provides insightful data on the metabolic pathways influencing tea cultivation and production and underscores the importance of these variables in determining the final chemical profile and organoleptic characteristics of tea products.

Background: Millet peppers have rich and diverse germplasm resources. It is of great significance to characterize their phenotypes and physicochemical indicators.

Methods: 30 millet germplasms were selected to measure the fruit length and width, flesh thickness, number of ventricles, fruit stalk length, and single fruit weight, and the texture characteristics of fruit such as hardness, cohesiveness, springiness, gumminess, and chewiness were determined by a texture analyzer. At the same time, high-performance liquid chromatography (HPLC) and gas chromatography (GC) were used to determine the fruit of capsaicin, dihydrocapsaicin, nordihydrocapsaicin, fatty acids, vitamin E (VE), total phenol, total sugar, and total dietary fiber.

Results: M11 showed outstanding parameters in phenotype and texture. The coefficient of variation (CV) for VE was as high as 94.943% and the highest diversity index (H') was total soluble solid, at 1.988%. M5 and M18 contained rich and diverse fatty acids. At the same time, the content of capsaicinoids in M18 also ranks among the top, second only to M27 (with a total capsaicin content of 5623.96 μg/g). PCA analysis using phenotypic data and physicochemical data showed that the classification results were different. Further hierarchical group analysis was carried out using all the index data. The results showed that 30 millet pepper germplasms were divided into three new categories: M5, M9, M18, and M24 formed one group (C1), M10, M14, M16, M19, M20, M22, M25, M26, M28, M29, and M30 formed another cluster (C2), and the remaining germplasms formed a third cluster (C3). Among them, the abundance of fatty acids in the C1 germplasm was higher than that in the other two groups.

Conclusions: Our study showed that different germplasms had significant differences in morphological traits and nutritional metabolic components and were rich in genetic diversity. This study provides a theoretical basis for the improvement of millet varieties and the development of functional food.

Introduction: NMR spectroscopy is a powerful technique for studying metabolism, either in metabolomics settings or through tracing with stable isotope-enriched metabolic precursors. MetaboLabPy (version 0.9.66) is a free and open-source software package used to process 1D- and 2D-NMR spectra. The software implements a complete workflow for NMR data pre-processing to prepare a series of 1D-NMR spectra for multi-variate statistical data analysis. This includes a choice of algorithms for automated phase correction, segmental alignment, spectral scaling, variance stabilisation, export to various software platforms, and analysis of metabolic tracing data. The software has an integrated help system with tutorials that demonstrate standard workflows and explain the capabilities of MetaboLabPy. Materials and Methods: The software is implemented in Python and uses numerous Python toolboxes, such as numpy, scipy, pandas, etc. The software is implemented in three different packages: metabolabpy, qtmetabolabpy, and metabolabpytools. The metabolabpy package contains classes to handle NMR data and all the numerical routines necessary to process and pre-process 1D NMR data and perform multiplet analysis on 2D-¹H, ¹³C HSQC NMR data. The qtmetabolabpy package contains routines related to the graphical user interface. Results: PySide6 is used to produce a modern and user-friendly graphical user interface. The metabolabpytools package contains routines which are not specific to just handling NMR data, for example, routines to derive isotopomer distributions from the combination of NMR multiplet and GC-MS data. A deep-learning approach for the latter is currently under development. MetaboLabPy is available via the Python Package Index or via GitHub.

Endometriosis is a common gynecological disease that affects approximately 10-15% of reproductive-aged women worldwide. This debilitating disease has a negative impact on the quality of life of those affected. Despite this condition being very common, the pathogenesis is not well understood. Metabolomics is the study of the array of low-weight metabolites in a given sample. This emerging field of omics-based science has proved to be effective at furthering the understanding of endometriosis. In this systematic review, we seek to provide an overview of the application of metabolomics in endometriosis. We highlight the use of metabolomics in locating biomarkers for identification, understanding treatment mechanisms and symptoms, and relating external factors to endometriosis. The literature search took place in the Web of Science, Pubmed, and Google Scholar based on the keywords "metabolomics" AND "endometriosis" or "metabolome" AND "endometriosis". We found 58 articles from 2012 to 2024 that met our search criteria. Significant alterations of lipids, amino acids, as well as other compounds were present in human and animal models. Discrepancies among studies of significantly altered metabolites make it difficult to make general conclusions on the metabolic signature of endometriosis. However, several individual metabolites were elevated in multiple studies of women with endometriosis; these include 3-hydroxybutyrate, lactate, phosphatidic acids, succinate, pyruvate, tetradecenoylcarnitine, hypoxanthine, and xanthine. Accordingly, L-isoleucine and citrate were reduced in multiple studies of women with endometriosis. Including larger cohorts, standardizing testing methods, and studying the individual phenotypes of endometriosis may lead to more separable results.

Background: Frailty is an increasingly recognised complication of diabetes in older people and should be taken into consideration in management plans, including the use of the new therapies of sodium glucose cotransporter-2 (SGLT-2) inhibitors and glucagon like peptide-1 receptor agonists (GLP-1RA). The frailty syndrome appears to span across a spectrum, from a sarcopenic obese phenotype at one end, characterised by obesity, insulin resistance, and prevalent cardiovascular risk factors, to an anorexic malnourished phenotype at the other end, characterised by significant weight loss, reduced insulin resistance, and less prevalent cardiovascular risk factors. Therefore, the use of the new therapies may not be suitable for every frail older individual with diabetes. Objectives: To review the characteristics and phenotype of frail older people with diabetes who should benefit from the use of SGLT-2 inhibitors or GLP-1RA. Methods: A narrative review of the studies investigating the benefits of SGLT-2 inhibitors and GLP-1RA in frail older people with diabetes. Results: The current evidence is indirect, and the literature suggests that the new therapies are effective in frail older people with diabetes and the benefit appears to be proportional with the severity of frailty. However, frail patients described in the literature who benefited from such therapy appeared to be either overweight or obese, and to have a higher prevalence of unfavourable metabolism and cardiovascular risk factors such as dyslipidaemia, gout, and hypertension compared to non-frail subjects. They also have a higher prevalence of established cardiovascular disease compared with non-frail individuals. In absolute terms, their higher cardiovascular baseline risk meant that they benefited the most from such therapy. The characteristics of this group of frail patients fulfil the criteria of the sarcopenic obese frailty phenotype, which is likely to benefit most from the new therapies due to the unfavourable metabolic profile of this phenotype. There is no current evidence to suggest the benefit of the new therapies in the anorexic malnourished phenotype, which is underrepresented or totally excluded from these studies, such as in patients living in care homes. This phenotype is likely to be intolerant to such therapy due to its associated risk of inducing further weight loss, dehydration, and hypotension. Conclusions: Clinicians should consider the early use of the new therapies in frail older people with diabetes who are either of normal weight, overweight, or obese with prevalent cardiovascular risk factors, and avoid their use in those frail subjects who ae underweight, anorexic, and malnourished.

Background/Objectives: The integration of microbiome and metabolome data could unveil profound insights into biological processes. However, widely used multi-omic data analyses often employ a stepwise mining approach, failing to harness the full potential of multi-omic datasets and leading to reduced detection accuracy. Synergistic analysis incorporating microbiome/metabolome data are essential for deeper understanding. Method: This study introduces a Coupled Matrix and Tensor Factorization (CMTF) framework for the joint analysis of microbiome and metabolome data, overcoming these limitations. Two CMTF frameworks were developed to factorize microbial taxa, functional pathways, and metabolites into latent factors, facilitating dimension reduction and biomarker identification. Validation was conducted using three diverse microbiome/metabolome datasets, including built environments and human gut samples from inflammatory bowel disease (IBD) and COVID-19 studies. Results: Our results revealed biologically meaningful biomarkers, such as Bacteroides vulgatus and acylcarnitines associated with IBD and pyroglutamic acid and p-cresol associated with COVID-19 outcomes, which provide new avenues for research. The CMTF framework consistently outperformed traditional methods in both dimension reduction and biomarker detection, offering a robust tool for uncovering biologically relevant insights. Conclusions: Despite its stringent data requirements, including the reliance on stratified microbial-based pathway abundances and taxa-level contributions, this approach provides a significant step forward in multi-omics integration and analysis, with potential applications across biomedical, environmental, and agricultural research.