Analysis Of Metabolomics Data Research Articles

PSIII-7 Effects of Saccharomyces cerevisiae fermentation product on total tract digestibility, ruminal fermentation, and plasma metabolome of beef steers fed a finishing diet

Abstract Saccharomyces cerevisiae fermentation product (SCFP) has been widely studied; however, its mode of action on high-concentrate diets has not been fully elucidated. This study evaluated the impact of SCFP on total tract digestibility, fecal excretions of N, P, Zn, and Cu, rumen fermentation, and plasma metabolome of finishing beef steers. Ruminally cannulated Holstein steers [n = 8; body weight (BW) = 580 ± 29.2 kg] were enrolled in a crossover design study with 25-d treatment periods and a 24-d washout period. Steers were fed ad libitum a finishing diet consisting of 20% corn silage and 80% concentrate (on DM basis). Steers were blocked based on initial BW and randomly assigned to two treatments in period 1 and switched to the other treatment in period 2. The treatments were CON (Control, basal diet only) and SCFP (basal diet top-dressed with 12 gּ steer-1ּ ּ d-1 SCFP, NaturSafe, Diamond V, Cedar Rapids, IA). Feed and fecal samples were collected during d 22 to 24 for determination of digestibility. Blood was collected before morning feeding on d 25. Plasma samples were analyzed for metabolome with liquid chromatography-mass spectrometry (LC-MS). Rumen fluid was collected via the rumen cannula at 0, 4, 8, and 12 h post-morning feeding on d 25 and analyzed for pH, volatile fatty acids (VFA), and NH3-N. Performance and ruminal fermentation characteristics data were analyzed using the GLIMMIX procedure of SAS 9.4. Metabolomic data analysis was conducted with Metaboanalyst 6.0. Supplementing steers with SCFP reduced DMI (10.6 vs. 11.4 kg/d, P = 0.05) without affecting (P > 0.05) average daily gain, feed efficiency, and digestibility of organic matter, crude protein, and neutral detergent fiber. Treatment did not affect (P > 0.10) fecal excretions of N, P, Cu, and Zn. Feeding SCFP increased ruminal pH (6.29 vs. 6.01, P = 0.01), numerically reduced concentrations of NH3-N (5.01 vs 6.25 mM, P = 0.16), and total VFA (96.8 vs. 102.5 mM, P = 0.15). SCFP also reduced the ruminal molar proportion of butyrate (11.2 vs. 12.8 molar %, P = 0.01) and tended to increase those of isobutyrate (1.03 vs. 0.90, molar %, P = 0.06) and isovalerate (2.62 vs. 2.29, molar %, P = 0.10). Supplementation of SCFP enriched (P ≤ 0.05) purine metabolism, amino sugar and nucleotide sugar metabolism, and lysine degradation pathways in the plasma. Additionally, tryptophan metabolism and pyrimidine metabolism pathways tended to be enriched (0.05 < P ≤ 0.10) by SCFP. Our findings indicate that feeding SCFP improved the ruminal pH of finishing cattle, which may be beneficial for preventing subacute ruminal acidosis in feedlot cattle. Additionally, SCFP enriched several important plasma nitrogen metabolism pathways, potentially associated with microbial nitrogen metabolism in the rumen, warranting further research.

MetMiner: A user-friendly pipeline for large-scale plant metabolomics data analysis.

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.

Metabolic Phenotypes and Vitamin D response in the Critically Ill: a metabolomic cohort study

Background & AimsAlthough vitamin D deficiency is common in critically ill patients, randomized controlled trials fail to demonstrate benefits of supplementation. We aimed to identify distinct vitamin D3 responsive metabolic phenotypes prior to trial intervention of high-dose vitamin D3 by applying machine learning clustering method to metabolomics data from the Correction of Vitamin D Deficiency in Critically Ill Patients (VITdAL-ICU) trial. MethodsIn the randomized, placebo-controlled VITdAL-ICU trial, critically ill adults received placebo or high-dose vitamin D3. To distinguish vitamin D3 responsive metabolic phenotypes prior to intervention, we implemented consensus clustering with partitioning around medoids algorithm to the plasma metabolome data before randomization. Individual metabolite differences were determined utilizing linear mixed-effects regression models stratified for metabolomic phenotypes with false discovery rate adjustment. The association between vitamin D3 supplementation and 180-day mortality was evaluated in each metabolic phenotype, applying multivariable logistic regression analysis. ResultsIn 453 critically ill adults, the study identified 4 distinct metabolic phenotypes (clusters A. N=134; B. N=123; C. N=92; D. N=104). We found differential metabolic pathway patterns in the four clusters. Specifically, branched chain amino acid catabolic metabolites, long-chain acylcarnitines and diacylglycerol species are significantly increased in a specific metabolic phenotype (cluster D) following high-dose vitamin D3. Further, in cluster D high-dose vitamin D3 supplementation had a significantly lower adjusted odds of 180-day mortality after controlling age, sex, Simplified Acute Physiology Score II, admission diagnosis, and baseline 25-hydroxyvitamin D (OR 0.28 (95%CI, 0.09–0.89); P=0.03). In metabotype A, B, and C, high-dose vitamin D3 supplementation was not significantly associated with lower 180-day mortality following multivariable adjustment. ConclusionIn this post-hoc cohort study of the VITdAL-ICU trial, the clustering analysis of plasma metabolome data identified biologically distinct metabolic phenotypes. Among clusters, we found the different associations between high-dose vitamin D3 supplementation and specific metabolite pathways as well as 180-day mortality. Our findings facilitate further research to validate metabolic phenotype-targeted strategies for critical illness treatments.

HerbMet: Enhancing metabolomics data analysis for accurate identification of Chinese herbal medicines using deep learning.

Chinese herbal medicines have been utilized for thousands of years to prevent and treat diseases. Accurate identification is crucial since their medicinal effects vary between species and varieties. Metabolomics is a promising approach to distinguish herbs. However, current metabolomics data analysis and modeling in Chinese herbal medicines are limited by small sample sizes, high dimensionality, and overfitting. This study aims to use metabolomics data to develop HerbMet, a high-performance artificial intelligence system for accurately identifying Chinese herbal medicines, particularly those from different species of the same genus. We propose HerbMet, an AI-based system for accurately identifying Chinese herbal medicines. HerbMet employs a 1D-ResNet architecture to extract discriminative features from input samples and uses a multilayer perceptron for classification. Additionally, we design the double dropout regularization module to alleviate overfitting and improve model's performance. Compared to 10 commonly used machine learning and deep learning methods, HerbMet achieves superior accuracy and robustness, with an accuracy of 0.9571 and an F1-score of 0.9542 for distinguishing seven similar Panax ginseng species. After feature selection by 25 different feature ranking techniques in combination with prior knowledge, we obtained 100% accuracy and an F1-score for discriminating P. ginseng species. Furthermore, HerbMet exhibits acceptable inference speed and computational costs compared to existing approaches on both CPU and GPU. HerbMet surpasses existing solutions for identifying Chinese herbal medicines species. It is simple to use in real-world scenarios, eliminating the need for feature ranking and selection in classical machine learning-based methods.

Revealing static and dynamic biomarkers from postprandial metabolomics data through coupled matrix and tensor factorizations.

Longitudinal metabolomics data from a meal challenge test contains both fasting and dynamic signals, that may be related to metabolic health and diseases. Recent work has explored the multiway structure of time-resolved metabolomics data by arranging it as a three-way array with modes: subjects, metabolites, and time. The analysis of such dynamic data (where the fasting data is subtracted from postprandial states) reveals dynamic markers of various phenotypes, and differences between fasting and dynamic states. However, there is still limited success in terms of extracting static and dynamic biomarkers for the same subject stratifications. Through joint analysis of fasting and dynamic metabolomics data, our goal is to capture static and dynamic biomarkers of a phenotype for the same subject stratifications providing a complete picture, that will be more effective for precision health. We jointly analyze fasting and dynamic metabolomics data collected during a meal challenge test from the COPSAC cohort using coupled matrix and tensor factorizations (CMTF), where the dynamic data (subjects by metabolites by time) is coupled with the fasting data (subjects by metabolites) in the subjects mode. The proposed data fusion approach extracts shared subject stratifications in terms of BMI (body mass index) from fasting and dynamic signals as well as the static and dynamic metabolic biomarker patterns corresponding to those stratifications. Specifically, we observe a subject stratification showing the positive association with all fasting VLDLs and higher BMI. For the same subject stratification, a subset of dynamic VLDLs (mainly the smaller sizes) correlates negatively with higher BMI. Higher correlations of the subject quantifications with the phenotype of interest are observed using such a data fusion approach compared to individual analyses of the fasting and postprandial state. The CMTF-based approach provides a complete picture of static and dynamic biomarkers for the same subject stratifications-when markers are present in both fasting and dynamic states.

Metabolomic Disparities in Intraocular Fluid Across Varied Stages of Cataract Progression: Implications for the Analysis of Cataract Development.

Introduction: The lens's metabolic demands are met through a continuous circulation of aqueous humor, encompassing a spectrum of components such as organic and inorganic ions, carbohydrates, glutathione, urea, amino acids, proteins, oxygen, carbon dioxide, and water. Metabolomics is a pivotal tool, offering an initial insight into the complexities of integrated metabolism. In this investigative study, we systematically scrutinize the composition of intraocular fluid in individuals afflicted with cataracts. Methods: The investigation involved a comprehensive analysis of aqueous humor samples from a cohort comprising 192 patients. These individuals were stratified by utilizing the SPONCS classification system, delineating distinct groups characterized by the hardness of cataracts. The analytical approach employed targeted quantitative metabolite analysis using HILIC-based liquid chromatography coupled with high-resolution mass spectrometric detection. The metabolomics data analysis was performed with MetaboAnalyst 5.0. Results: The results of the enrichment analysis have facilitated the inference that the discerned disparities among groups arise from disruptions in taurine and hypotaurine metabolism, variations in tryptophan metabolism, and modifications in mitochondrial beta-oxidation of short-chain saturated fatty acids and pyrimidine metabolism. Conclusion: A decline in taurine concentration precipitates diminished glutathione activity, prompting an elevated requirement for NAD+ and instigating tryptophan metabolism along the kynurenine pathway. Activation of this pathway is additionally prompted by interferon-gamma and UV radiation, leading to the induction of IDO. Concurrently, heightened mitochondrial beta-oxidation signifies a distinctive scenario in translocating fatty acids into the mitochondria, enhancing energy production.

MetaboLink: A web application for Streamlined Processing and Analysis of Large-Scale Untargeted Metabolomics Data.

The post-processing and analysis of large-scale untargeted metabolomics data face significant challenges due to the intricate nature of correction, filtration, imputation, and normalization steps. Manual execution across various applications often leads to inefficiencies, human-induced errors, and inconsistencies within the workflow. Addressing these issues, we introduce MetaboLink, a novel web application designed to process LC-MS metabolomics datasets combining established methodologies and offering flexibility and ease of implementation. It offers visualization options for data interpretation, an interface for statistical testing, and integration with PolySTest for further tests and with VSClust for clustering analysis. Fully functional tool is publicly available at https://computproteomics.bmb.sdu.dk/Metabolomics/. The source code is available at https://github.com/anitamnd/MetaboLink and a detailed description of the app can be found at https://github.com/anitamnd/MetaboLink/wiki. A tutorial video can be found at https://youtu.be/ZM6j10S6Z8Q. Supplementary data are available at Bioinformatics online.

Metabolomics in Radiotherapy-Induced Early Adverse Skin Reactions of Breast Cancer Patients.

Early adverse skin reactions (EASRs) are common side effects of radiotherapy (RT) that impact the quality of life of breast cancer patients. This study used global metabolomics profiles of breast cancer populations to identify metabolic pathways and biomarkers significantly associated with RT-induced EASRs to identify potential targets for precision interventions. We used a frequency-matched study design to identify pre-RT urine samples from 60 female breast cancer patients (30 with high and 30 with low EASRs) for metabolomic analysis by Metabolon Inc. using UPLC-MS/MS and GC-MS. Using MetaboAnalyst, we performed metabolomic data analysis and visualization on 84 candidate metabolites from 478 total compounds. We used the Oncology Nursing Society (ONS) Skin Toxicity Criteria (0-6) for EASRs assessment. Seven metabolic pathways were significantly associated with RT-induced EASRs, including alanine, aspartate, and glutamate metabolism (p = 0.0028), caffeine metabolism (p = 0.0360), pentose and glucuronate interconversions (p = 0.0028), glycine, serine, and threonine metabolism (p = 0.0360), beta-alanine metabolism (p = 0.0210), pantothenate and CoA biosynthesis (p = 0.0028), and glutathione metabolism (p = 0.0490). The alanine, aspartate, and glutamate metabolic pathway had the lowest false discovery rate (FDR)-adjusted p-value and the highest impact value of 0.60. Thirteen metabolite biomarkers were significantly associated with RT-induced EASRs. Our data show that the alanine, aspartate, and glutamate metabolism pathways had the highest impact value on RT-induced EASRs. Future larger studies are warranted to validate our findings and facilitate targeted interventions for preventing or mitigating RT-induced EASRs, offering a promising direction for further research and clinical applications.

Integrated Transcriptome and Metabolome Analysis Reveals Molecular Mechanisms Underlying Resistance to Phytophthora Root Rot.

Soybean production is significantly impacted by Phytophthora root rot (PRR), which is caused by Phytophthora sojae. The nucleotide-binding leucine-rich repeat (NLR) gene family plays a crucial role in plant disease resistance. However, current understanding of the function of soybean NLR genes in resistance to PRR is limited. To address this knowledge gap, transgenic soybean plants overexpressing the NLR gene (Glyma.18g283200) were generated to elucidate the molecular mechanism of resistance. Here, transcript changes and metabolic differences were investigated at three time points (12, 24, and 36 h) after P. sojae infection in hypocotyls of two soybean lines, Dongnong 50 (susceptible line, WT) and Glyma.18g283200 overexpression line (resistant line, OE). Based on the changes in differentially expressed genes (DEGs) in response to P. sojae infection in different lines and at different time points, it was speculated that HOPZ-ACTIVATED RESISTANCE 1 (ZAR1), valine, leucine, and isoleucine degradation, and phytohormone signaling may be involved in the defense response of soybean to P. sojae at the transcriptome level by GO term and KEGG pathway enrichment analysis. Differentially accumulated metabolites (DAMs) analysis revealed that a total of 223 and 210 differential metabolites were identified in the positive ion (POS) and negative ion (NEG) modes, respectively. An integrated pathway-level analysis of transcriptomics (obtained by RNA-seq) and metabolomics data revealed that isoflavone biosynthesis was associated with disease resistance. This work provides valuable insights that can be used in breeding programs aiming to enhance soybean resistance against PRR.

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline.

To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

MetaboReport: from metabolomics data analysis to comprehensive reporting.

Metabolomics, as an essential tool in systems biology, is now widely accessible to researchers of all levels. Yet challenges remain in data analysis and result interpretation. To address these challenges, we introduced MetaboReport, a versatile and interactive web app that simplifies metabolomics experiment design, data preprocessing, exploration, statistical analysis, visualization, and reporting. MetaboReport produces a comprehensive HTML report, including project details, an introduction, interactive plots and tables, statistical results and an in-depth explanations and interpretation of the results. MetaboReport is particularly tailored for research labs and metabolomics core facilities that provide metabolomics services, allowing them to efficiently manage and document different metabolomics projects, and effectively report the metabolomics results to users. MetaboReport is freely accessible on https://metaboreport.com, with source code available on GitHub (https://github.com/YonghuiDong/MetReport). Alternatively, users can install MetaboReport as a standalone desktop app (https://metaboreport.sourceforge.io).

Combined analysis of metabolomics and 16S rRNA sequencing for ankylosing spondylitis patients before and after secukinumab therapy.

Alterations in gut microbiota have been implicated in the pathogenesis of ankylosing spondylitis (AS), but the underlying mechanisms remain elusive. This study aims to investigate changes in gut microbiota and metabolites in individuals with AS before and after treatment with secukinumab, to identify the biological characteristics specific to AS patients and investigate the potential biomarkers, for optimizing therapeutic strategies more effectively. Fecal microbiome data were collected from 30 AS patients before and after secukinumab therapy and compared with data from 40 healthy controls (HC). Additionally, we analyzed the metabolic profile of both groups from plasma. Findings indicated that the treatment-induced changes in the composition of several crucial bacterial groups, including Megamonas, Prevotella_9, Faecalibacterium, Roseburia, Bacteroides, and Agathobacter. Post-treatment, these groups exhibited a distribution more akin to that of the healthy populations compared with their pretreatment status. We identified three gut microbial taxa, namely Prevotellaceae_bacterium_Marseille_P2831, Prevotella_buccae, and Elusimicrobiota, as potential biomarkers for diagnosing individuals at a higher risk of developing AS and assessing disease outcomes. Plasma metabolomics analysis revealed 479 distinct metabolites and highlighted three disrupted metabolic pathways. Integration of microbiome and metabolomics datasets demonstrated a significant degree of correlation, underscoring the impact of the microbiome on metabolic activity. Secukinumab can restore the balance of the gut microbiome and metabolites in AS patients, rendering them more similar to those found in the healthy population. The analysis of microbiome and metabolomics data have unveiled some candidate biomarkers capable of evaluating treatment efficacy.

Metabolomics reveals ascorbic acid inhibits ferroptosis in hepatocytes and boosts the effectiveness of anti-PD1 immunotherapy in hepatocellular carcinoma

BackgroundImmunotherapy combined with molecular targeted therapy is increasingly popular in patients with advanced hepatocellular carcinoma (HCC). However, immune-related adverse events(irAEs) brought on by immunotherapy increase the likelihood of side effects, thus it is important to look into ways to address this issue.MethodsDifferent metabolite patterns were established by analyzing metabolomics data in liver tissue samples from 10 patients(divided into severe and mild liver injury) before and after immuno-targeted therapy. After establishing a subcutaneous tumor model of HCC, the mice were divided into PBS group, ascorbic acid(AA) group, and anti-PD1 + tyrosine kinase inhibitor (TKI) group, anti-PD1 + TKI + AA group. Liver tissue were stained with hematoxylin-eosin staining(HE) and the content of aspartate transaminase (AST) and alanine transaminase(ALT) in blood were determined. The mechanism was confirmed by western blotting, mass cytometry, and other techniques.ResultsThrough metabolomics analysis, AA was significantly reduced in the sample of patients with severe liver injury caused by immuno-targeted therapy compared to patients with mild liver injury. The addition of AA in vivo experiments demonstrated a reduction in liver injury in mice. In the liver tissues of the anti-PD1 + TKI + AA group, the protein expressions of SLC7A11,GPX4 and the level of glutathione(GSH) were found to be higher compared to the anti-PD1 + TKI group. Mass cytometry analysis revealed a significant increase in the CD11b+CD44+ PD-L1+ cell population in the AA group when compared to the PBS group.ConclusionsAA could reduce liver injury by preventing hepatocyte SLC7A11/GPX4 ferroptosis and improve the immunotherapy effect of anti-PD1 by boosting CD11b+CD44+PD-L1+cell population in HCC.

Lipopolysaccharide-induced alterations in the liver metabolome of St. Croix and Suffolk sheep

The development of resistance in parasites due to overuse of anthelmintics has resulted in a marked decrease in the efficacy of these drug classes. Recent research efforts have focused on exploring alternatives such as selection for parasite-resistant breeds with the implication that immunocompetence may align with parasite resistance. Two breeds that are often investigated are the St. Croix (STC), a resistant hair breed, and Suffolk (SUF), a susceptible wool breed sheep. The liver plays a vital role in metabolism in the body and metabolizes lipopolysaccharide (LPS), which triggers whole body response through the production of appropriate metabolites, cytokines and immune cells. The objective of this study was to investigate the breed differences in liver metabolome of sheep, with divergent resistance to parasites, in response to LPS. Both STC and SUF sheep (n = 9/breed) were challenged with LPS intravenously. Rectal temperatures and sheep grimace score (SGS) were recorded hourly, for each animal, and averaged across the study for both breeds. The average rectal temperature throughout the study was similar for STC and SUF sheep (40.4°C and 40.2°C respectively), but the pattern of response was different. STC had an average SGS of 0.8 while SUF had an average of 3.3. Liver biopsies were collected from 3 sheep that were not challenged with LPS (HR0; n = 3/breed), two hours post-challenge (HR2; n = 3/breed), and six hours post-challenge (HR6; n = 3/breed). Liver tissue samples were subjected to quantitative untargeted metabolome analysis using chemical isotope labeling/liquid chromatography-mass spectrometry. Pathway analysis of the HR0 metabolome data revealed that 8 pathways (and their associated metabolites) including beta-alanine metabolism, arginine and proline metabolism and glutathione metabolism were altered (false discovery rate-adjusted P-value (FDR) ≤ 0.05) between STC and SUF sheep. At HR2, 10 altered pathways such as folate biosynthesis, taurine and hypotaurine metabolism, and glutathione metabolism. At HR6, only 2 pathways (glycerophospholipid metabolism and purine metabolism) were altered (FDR ≤ 0.05) between STC and SUF sheep. Results highlight the differences in hepatic metabolome and physiological response to LPS challenge that exist between SUF and STC. These findings suggest breed-specific differences in metabolic response to immune challenge, potentially influencing the divergent resistance of the two breeds to parasitic infections.

Comparative Metabolome and Transcriptome Analyses Reveal Differential Enrichment of Metabolites with Age in Panax notoginseng Roots.

Panax notoginseng is a perennial plant well known for its versatile medicinal properties, including hepatoprotective, antioxidant, anti-inflammatory, anti-tumor, estrogen-like, and antidepressant characteristics. It has been reported that plant age affects the quality of P. notoginseng. This study aimed to explore the differential metabolome and transcriptome of 2-year (PN2) and 3-year-old (PN3) P. notoginseng plant root samples. Principal component analysis of metabolome and transcriptome data revealed major differences between the two groups (PN2 vs. PN3). A total of 1813 metabolites and 28,587 genes were detected in this study, of which 255 metabolites and 3141 genes were found to be differential (p < 0.05) between PN2 vs. PN3, respectively. Among differential metabolites and genes, 155 metabolites and 1217 genes were up-regulated, while 100 metabolites and 1924 genes were down-regulated. The KEGG pathway analysis revealed differentially enriched metabolites belonging to class lipids ("13S-hydroperoxy-9Z, 11E-octadecadionic acid", "9S-hydroxy-10E, 12Z-octadecadionic acid", "9S-oxo-10E, 12Z-octadecadionic acid", and "9,10,13-trihydroxy-11-octadecadionic acid"), nucleotides and derivatives (guanine and cytidine), and phenolic acids (chlorogenic acid) were found to be enriched (p < 0.05) in PN3 compared to PN2. Further, these differentially enriched metabolites were found to be significantly (p < 0.05) regulated via linoleic acid metabolism, nucleotide metabolism, plant hormone signal transduction, and arachidonic acid metabolism pathways. Furthermore, the transcriptome analysis showed the up-regulation of key genes MAT, DMAS, SDH, gallate 1-beta-glucosyltransferase, and beta-D-glucosidase in various plants' secondary metabolic pathways and SAUR, GID1, PP2C, ETR, CTR1, EBF1/2, and ERF1/2 genes observed in phytohormone signal transduction pathway that is involved in plant growth and development, and protection against the various stressors. This study concluded that the roots of a 3-year-old P. notoginseng plant have better metabolome and transcriptome profiles compared to a 2-year-old plant with importantly enriched metabolites and genes in pathways related to metabolism, plant hormone signal transduction, and various biological processes. These findings provide insights into the plant's dynamic biochemical and molecular changes during its growth that have several implications regarding its therapeutic use.

Methionine as a regulator of bone remodeling with fasting.

Caloric restriction improves metabolic health but is often complicated by bone loss. We studied bone parameters in humans during a 10-day fast and identified candidate metabolic regulators of bone turnover. Pro-collagen 1 intact N-terminal pro-peptide (P1NP), a bone formation marker, decreased within 3 days of fasting. Whereas dual-energy x-ray absorptiometry measures of bone mineral density were unchanged after 10 days of fasting, high-resolution peripheral quantitative CT demonstrated remodeling of bone microarchitecture. Pathway analysis of longitudinal metabolomics data identified one-carbon metabolism as fasting dependent. In cultured osteoblasts, we tested the functional significance of one-carbon metabolites modulated by fasting, finding that methionine - which surged after 3 days of fasting - affected markers of osteoblast cell state in a concentration-dependent manner, in some instances exhibiting a U-shaped response with both low and high concentrations driving putative antibone responses. Administration of methionine to mice for 5 days recapitulated some fasting effects on bone, including a reduction in serum P1NP. In conclusion, a 10-day fast in humans led to remodeling of bone microarchitecture, potentially mediated by a surge in circulating methionine. These data support an emerging model that points to a window of optimal methionine exposure for bone health.

Essential Oil of Hymenocrater longiflorus Benth. as a Pro-Apoptotic Agent in Human Breast Cancer Cells

Background: Breast cancer is one of the most common cancers in women. Current treatments, such as chemotherapy and radiation therapy, often involve numerous side effects. Consequently, it is essential to discover new anticancer compounds that are safer and more effective. Objectives: The aim of this research is to investigate the anticancer effects of Hymenocrater longiflorus Benth. essential oil. Methods: The anticancer properties of H. longiflorus Benth. essential oil were investigated on MDA-MB-231 and MCF-7 breast cancer cells using MTT assay, double staining with acridine orange/ethidium bromide, and flow cytometry. The essential oil of H. longiflorus Benth. was analyzed using gas chromatography-mass spectrometry (GC-MS). Results: The essential oil significantly reduced the viability of breast cancer cells depending on the concentration (dose) and duration. Results from double fluorescent staining and flow cytometric analysis also indicated that apoptosis is the primary mechanism of cell death. Non-targeted GC-MS metabolomic data analysis of the essential oil revealed the presence of various anticancer compounds, including α-pinene, linalool, and caryophyllene, in the essential oil. Conclusions: In conclusion, the essential oil of H. longiflorus Benth. is a promising source of anticancer metabolites that can be further evaluated for drug therapy or as a co-treatment with conventional chemotherapeutic agents.

Machine Learning Approach to Metabolomic Data Predicts Type 2 Diabetes Mellitus Incidence.

Metabolomics, with its wealth of data, offers a valuable avenue for enhancing predictions and decision-making in diabetes. This observational study aimed to leverage machine learning (ML) algorithms to predict the 4-year risk of developing type 2 diabetes mellitus (T2DM) using targeted quantitative metabolomics data. A cohort of 279 cardiovascular risk patients who underwent coronary angiography and who were initially free of T2DM according to American Diabetes Association (ADA) criteria was analyzed at baseline, including anthropometric data and targeted metabolomics, using liquid chromatography (LC)-mass spectroscopy (MS) and flow injection analysis (FIA)-MS, respectively. All patients were followed for four years. During this time, 11.5% of the patients developed T2DM. After data preprocessing, 362 variables were used for ML, employing the Caret package in R. The dataset was divided into training and test sets (75:25 ratio) and we used an oversampling approach to address the classifier imbalance of T2DM incidence. After an additional recursive feature elimination step, identifying a set of 77 variables that were the most valuable for model generation, a Support Vector Machine (SVM) model with a linear kernel demonstrated the most promising predictive capabilities, exhibiting an F1 score of 50%, a specificity of 93%, and balanced and unbalanced accuracies of 72% and 88%, respectively. The top-ranked features were bile acids, ceramides, amino acids, and hexoses, whereas anthropometric features such as age, sex, waist circumference, or body mass index had no contribution. In conclusion, ML analysis of metabolomics data is a promising tool for identifying individuals at risk of developing T2DM and opens avenues for personalized and early intervention strategies.

Integrated analysis of transcriptomic and metabolomic data reveals how slurry ice treatment affects sugar metabolism in sweet corn (Zea mays L. var saccharata) during cold storage

AbstractSweet corn rapidly loses its sweetness, along with other nutrients, once it is harvested, which reduces its economic value. The present study was conducted to elucidate the regulatory mechanisms by which a rapid cooling slurry ice (SI) treatment followed by storage at 0 ± 0.5°C extends the postharvest quality of sweet corn relative to slower direct cooling at 0 ± 0.5°C. In this study, we found that SI treatment can maintain the appearance quality of sweet corn and inhibit grain shrinkage, respiration, and ethylene release. SI treatment delayed the aging of corn by regulating the expression levels of genes and the levels of metabolites. SI treatment also maintains its carbohydrate content, which is mainly related to starch and sucrose metabolism, glycolysis, citric acid cycle, pentose phosphate pathway, and cutin biosynthesis. The results provide a possible target for improving the postharvest quality of sweet corn.

Transcriptomic and metabolomic dissection of skeletal muscle of crossbred Chongming white goats with different meat production performance

BackgroundThe transcriptome and metabolome dissection of the skeletal muscle of high- and low- growing individuals from a crossbred population of the indigenous Chongming white goat and the Boer goat were performed to discover the potential functional differentially expressed genes (DEGs) and differential expression metabolites (DEMs).ResultsA total of 2812 DEGs were detected in 6 groups at three time stages (3,6,12 Month) in skeletal muscle using the RNA-seq method. A DEGs set containing seven muscle function related genes (TNNT1, TNNC1, TNNI1, MYBPC2, MYL2, MHY7, and CSRP3) was discovered, and their expression tended to increase as goat muscle development progressed. Seven DEGs (TNNT1, FABP3, TPM3, DES, PPP1R27, RCAN1, LMOD2) in the skeletal muscle of goats in the fast-growing and slow-growing groups was verified their expression difference by reverse transcription-quantitative polymerase chain reaction. Further, through the Liquid chromatography-mass spectrometry (LC-MS) approach, a total of 183 DEMs in various groups of the muscle samples and these DEMs such as Queuine and Keto-PGF1α, which demonstrated different abundance between the goat fast-growing group and slow-growing group. Through weighted correlation network analysis (WGCNA), the study correlated the DEGs with the DEMs and identified 4 DEGs modules associated with 18 metabolites.ConclusionThis study benefits to dissection candidate genes and regulatory networks related to goat meat production performance, and the joint analysis of transcriptomic and metabolomic data provided insights into the study of goat muscle development.