Multivariate Analysis in Metabolomics

Analysis of Key Differential Metabolites in Intervertebral Disc Degeneration Based on Untargeted Metabolomics

Background

Intervertebral disc degeneration disease (IVDD) is a prevalent orthopedic condition that causes chronic lower back pain, imposing a substantial economic burden on patients and society. Despite its high incidence, the pathophysiological mechanisms of IVDD remain incompletely understood.

Objective

This study aimed to identify metabolomic alterations in IVDD patients and explore the key metabolic pathways and metabolites involved in its pathogenesis.

Methods

Serum samples from 20 IVDD patients and 20 healthy controls were analyzed using ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). The identified metabolites were mapped to metabolic pathways using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

Results

Significant alterations were observed in metabolites such as 2-methyl-1,3-cyclohexadiene, stearoyl sphingomyelin, methylcysteine, L-methionine, and cis, cis-muconic acid. These metabolites were involved in pathways including glycine, serine, and threonine metabolism, cyanoamino acid metabolism, and the citrate cycle (TCA cycle).

Conclusion

The identified metabolic alterations provide insights into the pathogenesis of IVDD and suggest potential therapeutic targets for future investigation.

Advances in bioinformatics and multi-omics integration: transforming viral infectious disease research in veterinary medicine

The world is changing due to factors like bioterrorism, massive environmental changes, globalization of trade and commerce, growing urbanization, changing climate, and pollution. Numerous diseases have emerged because of these factors, especially in companion and food-producing animals. Numerous pathogens have established themselves in naïve populations, harming reproduction, productivity, and health. Bioinformatics is considered a valuable tool in infectious disease research, as it provides a comprehensive overview of the identification of pathogens, their genetic makeup, and their evolutionary relationship. Therefore, there is an urgent need for a novel bioinformatics approach to help decipher and model viral epidemiology and informatics on domestic animals and livestock. With significant advancements in bioinformatics and NGS, researchers can now identify contigs, which are contiguous sequences of DNA that are assembled from overlapping fragments, assemble a complete genome, perform phylogenetic analysis to diagnose, investigate the risk of viral diseases in animals, handle and share large biological datasets across various species. Additionally, multi-omics data integration further deepens our understanding of homology, divergence, mutations, and evolutionary relationships, providing a comprehensive perspective on the molecular mechanisms driving animal pathogens infections. This review aims to reveal the importance of utilizing the multidisciplinary areas of bioinformatics, genomics, proteomics, transcriptomics, metabolomics, and metagenomics and their roles in studying viral infectious diseases in veterinary medicine that will eventually improve the health of animals.

Unveiling Impurity Profiling of Synthetic Pathways of Organophosphorus Chlorpyrifos Through LC-HRMS Metabolomics-Based Approaches

Sourcing in chemical forensic science refers to the attribution of a sample to a specific source using a characteristic signature. It relies on the identification of chemical attribution signatures (CAS), including chemical markers such as residual synthetic precursors, impurities, reaction by-products and degradation products, or even metabolites. Undertaking CAS for chemical threat agents (CTA) can be used to provide an evidentiary link between the use of a given chemical and its precursor(s) to support forensic investigations. Organophosphorus compounds, a class of nerve agents, can be produced by different, more or less complex synthesis routes that can lead to specific CAS. Chlorpyrifos (CPF), an organophosphorus pesticide, was selected as model compound. To assess the specificity of impurity markers originated from a chemical synthesis, untargeted fingerprints of crude CPF from different synthesis pathways were analyzed as a first use-case using metabolomics-based trace discovery strategies. Seven different CPF synthesis routes were considered, and their crude mixtures were analyzed with a minimal sample preparation. Analyses were performed on a trapped ion mobility spectrometry (TIMS) coupled to liquid chromatography (LC) and high-resolution mass spectrometry (HRMS). Chemometrics analyses were conducted with multivariate methods to extract discriminating features (i.e., relevant impurities), annotate, and identify them. Then, unknown samples were analyzed in blind conditions without any information of the synthesis pathway employed. The aim is to validate the methodology seeking some discriminating impurities identified in the first section to attribute and classify them according to the synthesis route.

Invasive lobular carcinoma integrated multi-omics analysis reveals silencing of Arginosuccinate synthase and upregulation of nucleotide biosynthesis in tamoxifen resistance

Invasive Lobular Carcinoma (ILC), a distinct subtype of breast cancer is hallmarked by E-Cadherin loss, slow proliferation, and strong hormone receptor positivity. ILC faces significant challenges in clinical management due to advanced stage at diagnosis, late recurrence, and development of resistance to endocrine therapy - a cornerstone of ILC treatment. To elucidate the mechanisms underlying endocrine resistance in ILC, ILC cell lines (MDA-MB-134-VI, SUM44PE) were generated to be resistant to tamoxifen, a selective estrogen receptor modulator. The tamoxifen-resistant (TAMR) cells exhibit a 2-fold increase tamoxifen IC50 relative to parental cells. Metabolomics and RNA-sequencing revealed deregulation of alanine, aspartate, and glutamate metabolism, purine metabolism, and arginine and proline metabolism in TAMR cells. Among the fifteen commonly dysregulated genes in these pathways, low ASS1 expression was identified in the TAMR cells and was significantly correlated with poor outcome in ILC patients, specifically in the context of endocrine therapy. Our study reveals methylation mediated silencing of ASS1 in TAMR cells as a likely mechanism of downregulation. Demethylation restored ASS1 expression and correspondingly reduced tamoxifen IC50 toward parental levels. Nucleic acid biosynthesis is augmented in TAMR cells, evidenced by increase in nucleotide intermediates. Both TAMR cell lines demonstrated increased expression of several nucleic acid biosynthesis enzymes, including PAICS, PRPS1, ADSS2, CAD, and DHODH. Furthermore, CAD, the key multifunctional protein of de novo pyrimidine biosynthesis pathway is differentially activated in TAMR cells. Treating TAMR cell with Decitabine, a demethylating agent, or Farudodstat, a pyrimidine biosynthesis inhibitor, markedly augmented efficacy of tamoxifen. Collectively, our study unveils ASS1 downregulation as a novel mechanism underlying acquired tamoxifen resistance in ILC and establishes a metabolic link between ASS1 and nucleic acid biosynthesis. Restoring ASS1 expression or inhibiting pyrimidine biosynthesis restored tamoxifen sensitivity. ASS1 could be a potential biomarker and therapeutic target in tamoxifen resistant ILC patients, warranting further investigation.

Discrimination of Healthy and Botrytis cinerea-Infected Grapes Using Untargeted Metabolomic Analysis with Direct Electrospray Ionization Mass Spectrometry

Botrytis cinerea infections of grapes significantly reduce yield and quality and increase phenolic compound oxidation, resulting in color loss, off-flavors, and odors in wine. In this study, metabolites were extracted from grape homogenates comprising healthy or infected grapes from different vintages, cultivars, regions, and maturity stages. Samples were randomly analyzed by direct injection into an ion trap mass spectrometer, with data collected from 50 to 2000 m/z for 1 min. Molecular feature abundances from 0.1 to 0.4 min were normalized prior to Principal Components Analysis assessment of workflow. Samples were randomly assigned to a calibration and independent test sample set, with feature reduction, a two-class model Partial Least Squares-Discriminant Analysis, cross-validation, and permutation testing performed with the calibration data set. Prediction of sample class in the independent test samples demonstrated an overall predictive error of less than 5%. Feature importance was assessed using a combined variable importance in projection and selectivity ratio plot. Annotation of important molecular features using a high-resolution LC-QTOF mass spectrometry MS/MS of selected samples enabled key metabolites palmitic, oleic, linoleic and linolenic acids, succinate, and epicatechin to be identified and associated with infection. The proposed workflow establishes sensitive high-throughput rapid MS-based methods for phytosanitary testing of grape and fruit samples.

Active biomonitoring of stream ecosystems: untargeted metabolomic and proteomic responses and free radical scavenging activities in mussels

Many contaminants from scattered sources constantly endanger streams that flow through heavily inhabited areas, commercial districts, and industrial hubs. The responses of transplanted mussels in streams in active biomonitoring programs will reflect the dynamics of environmental stream conditions. This study evaluated the untargeted metabolomic and proteomic responses and free radical scavenging activities of transplanted mussels Sinanodonta woodiana in the Winongo Stream at three stations (S1, S2, S3) representing different pollution levels: low (S1), high (S2), and moderate (S3). The investigation examined untargeted metabolomic and proteomic responses in the gills and 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) activities in the gills, mantle, and digestive glands. Metabolomic analysis revealed a clear separation between mussel responses from the three stations after 28 days of exposure, with specific metabolites responding to different pollution levels. Proteomic analysis identified β-Actin protein in all stations. The β-Actin protein sequence of unexposed mussels had coverage of 17%, and increased to 23% at S1 on day 28 and 34% at S2 and S3 on day 28. All tissues showed increased DPPH and ABTS activities from day 3 to day 28, mainly in stations S2 and S3. These findings underscore the impact of pollution levels on the metabolomic and proteomic responses of S. woodiana and the importance of these discoveries as early indicators (biomarkers) of long-term aquatic environmental problems. In the face of current environmental challenges, this research raises concerns about the health of water bodies. It underscores the importance of developing robust, standardized, and dependable analytical techniques for monitoring the health of aquatic environments.

Investigation of alpha-glucosidase inhibition activity of Artabotrys sumatranus leaf extract using metabolomics, machine learning and molecular docking analysis

One way to treat diabetes mellitus type II is by using α-glucosidase inhibitor, that will slow down the postprandial glucose intake. Metabolomics analysis of Artabotrys sumatranus leaf extract was used in this research to predict the active compounds as α-glucosidase inhibitors from this extract. Both multivariate statistical analysis and machine learning approaches were used to improve the confidence of the predictions. After performance comparisons with other machine learning methods, random forest was chosen to make predictive model for the activity of the extract samples. Feature importance analysis (using random feature permutation and Shapley score calculation) was used to identify the predicted active compound as the important features that influenced the activity prediction of the extract samples. The combined analysis of multivariate statistical analysis and machine learning predicted 9 active compounds, where 6 of them were identified as mangiferin, neomangiferin, norisocorydine, apigenin-7-O-galactopyranoside, lirioferine, and 15,16-dihydrotanshinone I. The activities of norisocorydine, apigenin-7-O-galactopyranoside, and lirioferine as α-glucosidase inhibitors have not yet reported before. Molecular docking simulation, both to 3A4A (α-glucosidase enzyme from Saccharomyces cerevisiae, usually used in bioassay test) and 3TOP (a part of α-glucosidase enzyme in human gut) showed strong to very strong binding of the identified predicted active compounds to both receptors, with exception of neomangiferin which only showed strong binding to 3TOP receptor. Isolation based on bioassay guided fractionation further verified the metabolomics prediction by succeeding to isolate mangiferin from the extract, which showed strong α-glucosidase activity when subjected to bioassay test. The correlation analysis also showed a possibility of 3 groups in the predicted active compounds, which might be related to the biosynthesis pathway (need further research for verification). Another result from correlation analysis was that in general the α-glucosidase inhibition activity in the extract had strong correlation to antioxidant activity, which was also reflected in the predicted active compounds. Only one predicted compound had very low positive correlation to antioxidant activity.

Unveiling Colombia's medicinal Cannabis sativa treasure trove: Phenotypic and Chemotypic diversity in legal cultivation

INTRODUCTION

Cannabis sativa is a highly versatile plant with a long history of cultivation and domestication. It produces multiple compounds that exert distinct and valuable therapeutic effects by modulating diverse biological systems, including the endocannabinoid system (ECS). Access to standardized, metabolically diverse, and reproducible C. sativa chemotypes and chemovars is essential for physicians to optimize individualized patient treatment and for industries to conduct drug-discovery campaigns.

OBJECTIVE

This study aimed to characterize and assess the phytochemical diversity of C. sativa chemotypes in diverse ecological regions of Colombia, South America.

METHODOLOGY

Ten cannabinoids and 23 terpenes were measured using liquid and gas chromatography, in addition to other phenotypic traits, in 156 C. sativa plants that were grown in diverse ecological regions in Colombia, a hotspot for global biodiversity.

RESULTS

Our results reveal significant phytochemical diversity in Colombian-grown C. sativa plants, with four distinct chemotypes based on cannabinoid profile. The significant amount of usually uncommon terpenes suggests that Colombia's environments may have unique capabilities that allow the plant to express these compounds. Colombia's diverse climates offer enormous cultivation potential, making it a key player in both domestic and international medicinal and recreational C. sativa trade.

CONCLUSION

These findings underscore Colombia's capacity to pioneer global C. sativa production diversification, particularly in South America with new emerging markets.

Temperature-based investigation of rhamnolipids congeners production by the non-pathogenic Burkholderia thailandensis E264 using LC-QToF-MS metabolomics

INTRODUCTION

Burkholderia thailandensis E264 is a non-pathogenic soil bacterium that produces rhamnolipids (RLs), which are utilised in various fields. Although studies have illustrated changes in RLs congeners in response to environmental factors, studies on the influence of temperature on the RLs congeners produced by B. thailandensis E264 are scarce.

OBJECTIVE

It was hypothesised that RL congeners will be distributed differently at different temperature, which caused the produced RL to have different properties. This brought about the idea of a tailored production of RL for specific application through temperature control. Thus, this study aimed to investigate the distribution of RLs congeners by B. thailandensis E264 in response to different temperatures.

METHODOLOGY

B. thailandensis E264 was grown at three different temperatures (25 °C, 30 °C, and 37 °C) for nine days and subjected to metabolomic analysis using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QToF-MS).

RESULTS

The findings indicated that temperature significantly affected the metabolomic distribution of B. thailandensis E264, with mono-rhamno-mono-lipid and mono-rhamno-di-lipid being the predominant metabolites at 37 °C and 30 °C, with relative abundances of 64.1% and 65.3%, respectively. In comparison, di-rhamno-di-lipid was detected at 25 °C with an overall relative abundance of 77.7%.

CONCLUSION

This investigation showed that changing the cultivation temperature of the non-pathogenic B. thailandensis E264 produces diverse rhamnolipid congeners, which could enable the targeted synthesis of specific RLs for various applications and increase the market value of biosurfactants.

The potential correlations between cell-free extracts from Rhodobacter sphaeroides grown under low-oxygen conditions and volatile organic compounds in Chinese-style sausage

Limited research has explored the use of Rhodobacter sphaeroides cell-free extracts (RCFE) in meat processing. To examine the potential application of RCFE in improving the flavor quality of Chinese-style sausage, in this study, we investigated the effects and mechanisms of RCFE grown under low-oxygen conditions on the flavor development of Chinese-style sausage, using GC-MS and 4D label-free proteomics. The GC-MS analysis detected 60 volatile organic compounds, with significant increases in acids, esters, and alcohols following the addition of RCFE (p < 0.01). Fifteen differential flavor compounds were identified as potential biomarkers to distinguish sausages. From a total of 2689 proteins, 364 differentially expressed proteins were identified (p < 0.05, |Log2FC| > 1, and VIP > 1,) in RCFE grown under low- and high-oxygen conditions. KEGG pathway analysis suggested that the RCFE grown under low-oxygen conditions may enhance alcohol and acid levels by upregulating the expression of related enzymes, which subsequently increases ester levels in the sausage.

HPTLC Combined with sHetCA and Multivariate Statistics for the Detection of Bioactive Compounds in Complex Mixtures

High-Performance Thin Layer Chromatography (HPTLC) is widely utilized in natural products research due to its simplicity, low cost, and short total analysis time, including data treatment. While bioautography can be used for rapid detection of bioactive compounds in extracts, the number of available bioautographic methods is limited mainly due to the high cost and difficulty in developing protocols that lead to accurate and reproducible results. For this reason, an alternative method for the detection of bioactive compounds in plant extracts prior to their isolation using HPTLC, combined with multivariate chemometrics, was previously explored by our lab. To evaluate this method and compare it to other chemometrics-based methods, an artificial mixture (ArtExtr) of 59 standard compounds was used as a case study. The ArtExtr was fractionated by FCPC and the inhibitory activity of all fractions against DPPH was evaluated, while their chemical profiles were recorded using HPTLC. Multivariate statistics and the heterocovariance approach (HetCA) were employed and compared, with the success rate in detecting the ArtExtr bioactive substances being 85.7% via sparse heterocovariance (sHetCA). HPTLC combined with sHetCA can serve as a valuable tool for the detection of bioactive compounds in complex mixtures when bioautography is not feasible.

A comprehensive guide to volatolomics data analysis

Volatolomics (or volatilomics), the study of volatile organic compounds, has emerged as a significant branch of metabolomics due to its potential for non-invasive diagnostics and disease monitoring. However, the analysis of high-resolution data from mass spectrometry and gas sensor array-based instruments remains challenging. The careful consideration of experimental design, data collection, and processing strategies is essential to enhance the quality of results obtained from subsequent analyses. This comprehensive guide provides an in-depth exploration of volatolomics data analysis, highlighting the essential steps, such as data cleaning, pretreatment, and the application of statistical and machine learning techniques, including dimensionality reduction, clustering, classification, and variable selection. The choice of these methodologies, along with data handling practices, such as missing data imputation, outlier detection, model validation, and data integration, is crucial for identifying meaningful metabolites and drawing accurate diagnostic conclusions. By offering researchers the tools and knowledge to navigate the complexities of volatolomics data analysis, this guide emphasizes the importance of understanding the strengths and limitations of each method. Such informed decision-making enhances the reliability of findings, ultimately advancing the field and improving the understanding of metabolic processes in health and disease.

From Tea to Functional Foods: Exploring Caryopteris mongolica Bunge for Anti-Rheumatoid Arthritis and Unraveling Its Potential Mechanisms

BACKGROUND

Caryopteris mongolica Bunge (CM) shows promising potential for managing rheumatoid arthritis (RA) and digestive disorders, attributed to its rich content of bioactive compounds such as polyphenols and flavonoids. Despite its common use in herbal tea, the specific mechanisms underlying CM's anti-inflammatory and joint-protective effects remain unclear, limiting its development as a functional food. This study investigated the effects of aqueous CM extract on RA in collagen-induced arthritis (CIA) rats and explored the underlying mechanisms.

METHODS

Forty-eight female Sprague-Dawley rats were randomly assigned to six groups (n = 8): normal control, CIA model, methotrexate (MTX), and CM high-, middle-, and low-dose groups. Anti-inflammatory and joint-protective effects were evaluated using biochemical and histological analyses. To elucidate the mechanisms, we applied metabolomics, network pharmacology, and transcriptomics approaches.

RESULTS

The results demonstrated that CM extract effectively suppressed synovial inflammation in CIA rats, reducing joint degradation. CM's anti-inflammatory effects were mediated through the TNF signaling pathway, modulating glycerophospholipid and amino acid metabolism, including reduced levels of tryptophan, LysoPC, and asparagine. Molecular docking identified scutellarin and apigenin as key bioactive compounds. Additionally, immunofluorescence analysis revealed CM's therapeutic effects via TNF signaling inhibition and suppression of M1 macrophage polarization.

CONCLUSIONS

These findings highlight the therapeutic potential of CM for RA and support its development as a functional food or pharmaceutical product.

Investigation of antibacterial mode of action of ω-aminoalkoxylxanthones by NMR-based metabolomics and molecular docking

INTRODUCTION

The knowledge of the mode of action of an antimicrobial is essential for drug development and helps to fight against bacterial resistance. Thus, it is crucial to use analytical techniques to study the mechanism of action of substances that have potential to act as antibacterial agents OBJECTIVE: To use NMR-based metabolomics combined with chemometrics and molecular docking to identify the metabolic responses of Staphylococcus aureus following exposure to commercial antibiotics and some synthesized ω-aminoalkoxylxanthones.

METHODS

Intracellular metabolites of S. aureus were extracted after treatment with four commercial antibiotics and three synthesized ω-aminoalkoxylxanthones. NMR spectra were obtained and 1H NMR data was analyzed using both unsupervised and supervised algorithms (PCA and PLS-DA, respectively). Docking simulations on DNA topoisomerase IV protein were also performed for the ω-aminoalkoxylxanthones.

RESULTS

Through chemometric analysis, we distinguished between the control group and antibiotics with extracellular (ampicillin) and intracellular targets (kanamycin, tetracycline, and ciprofloxacin). We identified 21 metabolites, including important metabolites that differentiate the groups, such as betaine, acetamide, glutamate, lysine, alanine, isoleucine/leucine, acetate, threonine, proline, and ethanol. Regarding the xanthone-type derivatives (S6, S7 and S8), we observed a greater similarity between S7 and ciprofloxacin, which targets bacterial DNA replication. The molecular docking analysis showed high affinity of the ω-aminoalkoxylxanthones with the topoisomerase IV enzyme, as well as ciprofloxacin.

CONCLUSION

NMR-based metabolomics has shown to be an effective technique to assess the metabolic profile of S. aureus after treatment with certain antimicrobial compounds, helping the investigation of their mechanism of action.

Lopes T, de Oliveira JM, Raimundo JRS, Furtado DZS, Fonseca FLA, Oliveira RV, Cardoso DR, Carrilho E, Assunção NA. Multiplatform Metabolomics: Enhancing the Severity Risk Prognosis of SARS-CoV-2 Infection

Concerns about the SARS-CoV-2 outbreak (COVID-19) continue to persist even years later, with the emergence of new variants and the risk of disease severity. Common clinical symptoms, like cough, fever, and respiratory symptoms, characterize the noncritical patients, classifying them from mild to moderate. In a more severe and complex scenario, the virus infection can affect vital organs, resulting, for instance, in pneumonia and impaired kidney and heart function. However, it is well-known that subclinical symptoms at a metabolic level can be observed previously but require a proper diagnosis because viral replication on the host leaves a track with a different profile depending on the severity of the illness. Metabolomic profiles of mild, moderate, and severe COVID-19 patients were obtained by multiple platforms (LC-HRMS and MALDI-MS), increasing the chance to elucidate a prognosis for severity risk. A strong link was discovered between phenylalanine metabolism and increased COVID-19 severity symptoms, a pathway linked to cardiac and neurological consequences. Glycerophospholipids and sphingolipid metabolisms were also dysregulated linearly with the increasing symptom severity, which can be related to virus proliferation, immune system avoidance, and apoptosis escaping. Our data, endorsed by other literature, strengthens the notion that these pathways might play a vital role in a patient's prognosis.

Altered colonic microflora and its metabolic profile in mice with acute viral myocarditis induced by coxsackievirus B3

Mounting evidence suggests that the gut-heart axis is critical in the pathogenesis of cardiovascular diseases. The gut serves as the primary pathway through which Coxsackievirus B3 (CVB3) infects its host, leading to acute viral myocarditis (AVMC). However, little is known about the role of gut microflora and its metabolites in the development of AVMC. The AVMC model was established by intraperitoneal injection of CVB3 in mice. Then, 16S ribosomal RNA (16S rRNA) gene sequencing and ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) untargeted metabolomics profiling were performed to analyze the microflora composition and metabolic profile of colonic contents. Compared to the Control mice, the AVMC mice displayed a significant reduction in gut microflora richness and diversity, as revealed by an increased abundance of Proteobacteria and a decreased abundance of Cyanobacteria and Desulfobacterota. LEfSe analysis indicated that the main genera differing between the two groups were Escherichia-Shigella, Lactobacillus, Clostridium_sensu_stricto_1, Prevotellaceae_UCG-001, and Odoribacter. Based on the criterion of OPLS-DA VIP ≥ 1.0 and p-value < 0.05, a total of 198 differential metabolites (DMs) were identified in the gut, including 79 upregulated and 119 downregulated metabolites, of which lipids and lipid-like molecules accounted for the largest proportion. Notably, both altered gut bacterial taxa and metabolites were significantly enriched in the Lipid metabolism pathway, with Traumatic acid (TA), Alpha-Linolenic acid (ALA), Eicosapentaenoic acid (EPA), and Docosahexaenoic acid (DHA) being the key DMs in the pathway. Additionally, significant positive correlations (|r| > 0.80 and p < 0.05) were found between TA levels and Anaerotruncus and Bilophila abundance, between EPA levels and Clostridium_sensu_stricto_1 abundance, and between DHA levels and Escherichia-Shigella abundance, respectively. CVB3 infection leads to notable alterations in gut microflora composition and its metabolic profile, which may participate in AVMC development. Our findings provide important clues for future in-depth studies on AVMC etiology.

Comparative analysis of White and African American groups reveals unique lipid and inflammatory features of diabetes

Importance

African Americans have a higher prevalence of Type 2 Diabetes (T2D) compared to White groups. T2D is a health disparity clinically characterized by dysregulation of lipids and chronic inflammation. However, how the relationships among biological and sociological predictors of T2D drive this disparity remains to be addressed.

Objective

To determine characteristic plasma lipids and systemic inflammatory biomarkers contributing to diabetes presentation between White and African American groups.

Design

We performed a cross-sectional retrospective cohort study using pre-existing demographic and clinical data from two diverse studies: Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) and AllofUs. From HANDLS (N=40), we used information from wave 1 (2004). From AllofUs (N=17,339), we used data from the Registered Tier Dataset v7, available in the AllofUs researcher workbench.

Setting

HANDLS is a population-based cohort study involving 3720 participants in the Baltimore area supported by the Intramural Research Program of the National Institute on Aging. HANDLS is a longitudinal study designed to understand the sources of persistent health disparities in overall longevity and chronic disease in White and African American individuals. The AllofUs study is an NIH funded multicenter study consisting of patient-level data from 331,382 individuals from 35 hospitals in the United States aimed at sampling one million or more people living in the United States to provide a collection of broadly accessible data.

Participants

The HANDLS subcohort participants (N=40) were divided into four groups equally distributed by race, sex, and diabetes status. Groups were also matched by age, body mass index, and poverty status. The analysis pipeline consisted of evaluating the significance of the variables race and disease status using the 2-way ANOVA test and post-ANOVA comparisons using Fisher LSD test, reporting unadjusted p-values. Additionally, unsupervised (PCA) and supervised (OPLS-DA) clustering analysis was performed to determine putative biological drivers of variability and main immunological and metabolic features characterizing diabetes in White and African American groups from HANDLS. Major clinical findings were validated in a large cohort of White and African American groups with T2D in the AllofUS research study (N=17,339). AllofUs groups were of similar range in age and BMI as HANDLS. Furthermore, a linear regression model was built adjusting for age and BMI to determine differences in clinical findings between White and African American groups with T2D.

Main Outcomes and Measures

Primary outcomes using a HANDLS subcohort (N=40) were clinical parameters related to diabetes, plasma lipids determined by lipidomics and measured by mass spectrometry, and cytokine profiling using a customized panel of 52 cytokines and growth factors measured by Luminex. Outcomes evaluated in the AllofUs study (N=17,339) were clinical: cholesterol to HDL ratio, triglycerides, fasting glucose, insulin, and hemoglobin A1C.

Results

In the HANDLS subcohort, White individuals with diabetes had elevated cholesterol to HDL ratio (mean difference -1.869, p =0.0053 ) , high-sensitivity C-reactive protein (mean difference -9.135, p =0.0040), and clusters of systemic triglycerides measured by lipidomics, compared to White individuals without diabetes. These clinical markers of dyslipidemia (cholesterol to HDL ratio and triglycerides) and inflammation (hs-CRP) were not significantly elevated in diabetes in African Americans from the HANDLS subcohort. These results persisted even when controlling for statin use. Diabetes in White individuals in the HANDLS cohort was characterized by a marked elevation in plasma lipids, while an inflammatory status characterized by Th17-cytokines was predominant in the African American group from the HANDLS subcohort. We validated the key findings of elevated triglycerides and cholesterol to HDL ratio in White individuals with T2D in a sample (N=17,339) of the AllofUs study.

Conclusions and Relevance

Our results show that diabetes can manifest with healthy lipid profiles, particularly in these cohorts of African Americans. This study suggests that Th17-inflammation associated with diabetes is characteristic of African Americans, while a more classic inflammation is distinctive of White individuals from HANDLS cohort. Further, clinical markers of dyslipidemia seem to characterize diabetes presentation only in White groups, and not in African Americans.

Potential mechanism of Laportea bulbifera on treating inflammation and tumor via metabolomics, network pharmacology and molecular docking

This study aimed to utilize metabolomics, network pharmacology, and molecular docking techniques to identify the major active components of Laportea bulbifera and investigate their anti-inflammatory and potential anti-tumor mechanisms. The metabolic constituents of L. bulbifera were examined utilizing UPLC-ESI-MS/MS. PPI networks and compound-target-pathway networks were established using resources such as TCMSP, Swiss Target Prediction, DAVID, STRING database, and Cytoscape software. Molecular docking analysis of the most important compounds and targets was conducted using Autodock4, followed by validation of the molecular docking results' stability using GROMACS. The UPLC-ESI-MS/MS analysis identified a total of 798 compounds. A network pharmacology-based analysis was conducted, revealing that eight compounds and four molecular targets-namely, TNF, IL6, PIK3CA, and HDAC1-were enriched in the network. Pathway analysis of the identified targets demonstrated enrichment in 217 KEGG pathways. Molecular docking analysis and molecular dynamics simulations demonstrated strong therapeutic potential of N-feruloyltyramine, N-feruloylagmatine, and Ellagic acid against various inflammatory and tumor diseases. This study, for the first time, employed an integrated strategy of metabolomics, network pharmacology, molecular docking, and molecular dynamics, elucidating the mechanisms underlying the anti-inflammatory and potential anti-tumor effects of L. bulbifera, laying the foundation for subsequent drug development endeavors.

Multimodal analysis identifies microbiome changes linked to stem cell transplantation-associated diseases

BACKGROUND

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is one of the most efficient therapeutic options available to cure many hematological malignancies. However, severe complications derived from this procedure, including graft-versus-host disease (GVHD) and infections, can limit its success and negatively impact survival. Previous studies have shown that alterations in the microbiome are associated with the development of allo-HSCT-derived complications. However, most studies relied on single techniques that can only analyze a unique aspect of the microbiome, which hinders our ability to understand how microbiome alterations drive allo-HSCT-associated diseases.

RESULTS

Here, we have applied multiple "omic" techniques (16S rRNA and shotgun sequencing, targeted and un-targeted metabolomics) in combination with machine learning approaches to define the most significant microbiome changes following allo-HSCT at multiple modalities (bacterial taxa, encoded functions, and derived metabolites). In addition, multivariate approaches were applied to study interactions among the various microbiome modalities (the interactome). Our results show that the microbiome of transplanted patients exhibits substantial changes in all studied modalities. These include depletion of beneficial microbes, mainly from the Clostridiales order, loss of their bacterial encoded functions required for the synthesis of key metabolites, and a reduction in metabolic end products such as short chain fatty acids (SCFAs). These changes were followed by an expansion of bacteria that frequently cause infections after allo-HSCT, including several Staphylococcus species, which benefit from the reduction of bacteriostatic SCFAs. Additionally, we found specific alterations in all microbiome modalities that distinguished those patients who subsequently developed GVHD, including depletion of anti-inflammatory commensals, protective reactive oxygen detoxifying enzymes, and immunoregulatory metabolites such as acetate or malonate. Moreover, extensive shifts in the homeostatic relationship between bacteria and their metabolic products (e.g., Faecalibacterium and butyrate) were detected mainly in patients who later developed GVHD.

CONCLUSIONS

We have identified specific microbiome changes at different modalities (microbial taxa, their encoded genes, and synthetized metabolites) and at the interface between them (the interactome) that precede the development of complications associated with allo-HSCT. These identified microbial features provide novel targets for the design of microbiome-based strategies to prevent diseases associated with stem cell transplantation. Video Abstract.

Postprandial Metabolomic Profiling: Insights into Macronutrient-Specific Metabolic Responses in Healthy Individuals

BACKGROUND/OBJECTIVES

Understanding the metabolic responses to different macronutrients is crucial for assessing their impacts on health. This study aims to investigate the postprandial metabolomic profiles of healthy individuals following the consumption of glucose, protein, and lipids.

METHODS

Twenty-three healthy, normal-weight adults participated in the study, randomly assigned to consume 300 kcal from glucose, protein, or lipids after an overnight fast. Blood samples were collected at baseline and at 1, 2, and 3 h post-ingestion. An untargeted metabolomic approach using mass spectrometry was employed to analyze plasma metabolites.

RESULTS

In total, 21, 59, and 156 dysregulated metabolites were identified after glucose, protein, and lipid intake, respectively. Notably, 3'-O-methylguanosine levels decreased significantly after glucose consumption while remaining stable during lipid intake before increasing at 2 h. Common metabolites shared between glucose and lipid groups included 3'-O-methylguanosine, 3-oxotetradecanoic acid, poly-g-D-glutamate, and triglyceride (TG) (15:0/18:4/18:1).

CONCLUSIONS

The findings highlight distinct metabolic responses to macronutrient intake, emphasizing the role of specific metabolites in regulating postprandial metabolism. These insights contribute to understanding how dietary components influence metabolic health and insulin sensitivity.

Unveiling Pathophysiological Insights: Serum Metabolic Dysregulation in Acute Respiratory Distress Syndrome Patients with Acute Kidney Injury

Acute respiratory distress syndrome (ARDS) is associated with high mortality rates, which are further exacerbated when accompanied by acute kidney injury (AKI). Presently, there is a lack of comprehensive studies thoroughly elucidating the metabolic dysregulation in ARDS patients with AKI leading to poor outcomes. We hypothesized that metabolomics can be a potent tool to highlight the differences in the metabolic profile unraveling unidentified pathophysiological mechanisms of ARDS patients with and without AKI. 1H nuclear magnetic resonance spectroscopy was used to identify key metabolites in the serum samples of 75 patients. Distinct clusters of both groups were obtained as the study's primary outcome using multivariate analysis. Notable alternations in the levels of nine metabolites were identified. Pathway analysis revealed the dysregulation of five significant cycles, which resulted in various complications, such as hyperammonemia, higher energy requirements, and mitochondrial dysfunction causing oxidative stress. Identified metabolites also showed a significant correlation with clinical scores, indicating severity. This study shows the alterations in the metabolite concentration highlighting the difference in the pathophysiology of both patient groups and its association with outcome, pointing in the direction of a personalized medicine approach and holding significant promise for application in critical care settings to improve clinical outcomes.

Advancing drug safety and mitigating health concerns: High-resolution mass spectrometry in the levothyroxine case study

Levothyroxine is a drug with a narrow therapeutic index. Changing the drug formulation composition or switching between pharmaceutical brands can alter the bioavailability, which can result in major health problems. However, the increased adverse drug reactions have not been fully explained scientifically yet and a thorough investigation of the formulations is needed. In this study, we used a non-targeted analytical approach to examine the various levothyroxine formulations in detail and to reveal possible chemical changes. Ultra-high-performance liquid chromatography coupled with a data-independent acquisition high-resolution mass spectrometry (UHPLC-DIA-HRMS) was employed. UHPLC-DIA-HRMS allowed not only the detection of levothyroxine degradation products, but also the presence of non-expected components in the formulations. Among these, we identified compounds resulting from reactions between mannitol and other excipients, such as citric acid, stearate, and palmitate, or from reactions between an excipient and an active pharmaceutical ingredient, such as levothyroxine-lactose adduct. In addition to these compounds, undeclared phospholipids were also found in three formulations. This non-targeted approach is not common in pharmaceutical quality control analysis. Revealing the presence of unexpected compounds in drug formulations proved that the current control mechanisms do not have to cover the full complexity of pharmaceutical formulations necessarily.

Using matrix assisted laser desorption ionisation mass spectrometry combined with machine learning for vaccine authenticity screening

The global population is increasingly reliant on vaccines to maintain population health with billions of doses used annually in immunisation programmes. Substandard and falsified vaccines are becoming more prevalent, caused by both the degradation of authentic vaccines but also deliberately falsified vaccine products. These threaten public health, and the increase in vaccine falsification is now a major concern. There is currently no coordinated global infrastructure or screening methods to monitor vaccine supply chains. In this study, we developed and validated a matrix-assisted laser desorption/ionisation-mass spectrometry (MALDI-MS) workflow that used open-source machine learning and statistical analysis to distinguish authentic and falsified vaccines. We validated the method on two different MALDI-MS instruments used worldwide for clinical applications. Our results show that multivariate data modelling and diagnostic mass spectra can be used to distinguish authentic and falsified vaccines providing proof-of-concept that MALDI-MS can be used as a screening tool to monitor vaccine supply chains.

Edaphoclimatic variation and harvest seasonality as determining factors of multidimensional quality in avocado cv. hass grown in the tropics

The increase in cultivated areas in tropical zones such as Colombia for avocado cv. Hass and the lack of knowledge on edaphoclimatic relationships with factors associated with quality led to the present research. The aim of this research was to establish the relationship of soil, climatic, spatial factors (plot location), and harvest seasonality (principal and transitory) with the multidimensional quality of avocado cv. Hass planted under tropical conditions. This research was carried out on eight farms located in three producing subregions. Soil, environmental and harvest data were recorded for three years (2015-2017) in each plot. Avocado fruit samples were used to determine the parameters of macronutrient, fatty acids, minerals, and vitamin E. Descriptive, inferential statistics, multivariate analysis, effect size, second-order exponential model, and causal relationships were used to determine variables associated with soil, climate, harvest seasonality, and spatial location, and to determine quality parameters. The results established a relationship between nutritional quality and the origin region. Similarly, it was possible to identify parameters associated with differential quality with a robust statistical methodology to propose origin as a differentiating factor for quality. This study provided useful information for the value chain that selected the best areas for avocado crops according to market expectations and nutritional quality criteria.

Dynamic relationships among pathways producing hydrocarbons and fatty acids of maize silk cuticular waxes

The hydrophobic cuticle is the first line of defense between aerial portions of plants and the external environment. On maize (Zea mays L.) silks, the cuticular cutin matrix is infused with cuticular waxes, consisting of a homologous series of very long-chain fatty acids (VLCFAs), aldehydes, and hydrocarbons. Together with VLC fatty-acyl-CoAs (VLCFA-CoAs), these metabolites serve as precursors, intermediates, and end-products of the cuticular wax biosynthetic pathway. To deconvolute the potentially confounding impacts of the change in silk microenvironment and silk development on this pathway, we profiled cuticular waxes on the silks of the inbreds B73 and Mo17, and their reciprocal hybrids. Multivariate interrogation of these metabolite abundance data demonstrates that VLCFA-CoAs and total free VLCFAs are positively correlated with the cuticular wax metabolome, and this metabolome is primarily affected by changes in the silk microenvironment and plant genotype. Moreover, the genotype effect on the pathway explains the increased accumulation of cuticular hydrocarbons with a concomitant reduction in cuticular VLCFA accumulation on B73 silks, suggesting that the conversion of VLCFA-CoAs to hydrocarbons is more effective in B73 than Mo17. Statistical modeling of the ratios between cuticular hydrocarbons and cuticular VLCFAs reveals a significant role of precursor chain length in determining this ratio. This study establishes the complexity of the product-precursor relationships within the silk cuticular wax-producing network by dissecting both the impact of genotype and the allocation of VLCFA-CoA precursors to different biological processes and demonstrates that longer chain VLCFA-CoAs are preferentially utilized for hydrocarbon biosynthesis.

Effects of Hypoxia and Reoxygenation on Metabolic Profiles of Cardiomyocytes

In vitro cellular models provide valuable insights into the adaptive biochemical mechanisms triggered by cells to cope with the stress situation induced by hypoxia and reoxygenation cycles. The first biological data generated in studies based on this micrometric life-scale has the potential to provide us a global overview about the main biochemical phenomena presented in some reported preconditioning therapies in life-scale of higher dimensions. Thus, in this study, a cell incubator was designed and manufactured to produce a cellular model of heart hypoxia followed by reoxygenation (HfR) through consecutive repetitions of hypoxia-normoxia gas exchange. Samples of cellular extracts and culture media were obtained from non-proliferative cardiomyocytes (CMs) cultivated under challenging HfR (stressed CMs) and regular cultivation (unstressed CMs) in rounds of four days for each case. Metabolomic based on proton magnetic resonance spectroscopy (1H-MRS) was used as an analytical approach to identify and quantify the metabolomes of these samples, the endo- and exo-metabolome. Despite the stressed CMs presented over 90% higher cellular death rate compared to the unstressed CMs, the metabolic profiles indicates that the surviving cells up-regulate their amino acid metabolism either by active protein degradation or by the consumption of culture media components to increase coenzyme A-dependent metabolic pathways. This cell auto-regulation mechanism could be well characterized in the first two days when the difference smears off under once the metabolomes become similar. The metabolic adaptations of stressed CMs identified the relevance of the cyclic oxidation/reduction reactions of nicotinamide adenine dinucleotide phosphate molecules, NADP+/NADPH, and the increased tricarboxylic acid cycle activity in an environment overloaded with such a powerful antioxidant agent to survive an extreme HfR challenge. Thus, the combination of cellular models based on CMs, investigative methods, such as metabolomic and 1H-MRS, and the instrumental development of hypoxia incubator shown in this work were able to provide the first biochemical evidences behind therapies of gaseous exchanges paving the way to future assays.

Identification of antioxidant and flavour marker compounds in Kalosi-Enrekang Arabica brewed coffee processed using different postharvest treatment methods

This research aims to predict the presence of marker compounds that differentiate tubruk brew from coffee beans with different postharvest processing. This research also aims to predict compounds correlating with antioxidant activity and sensory flavour attributes. This research used Kalosi-Enrekang Arabica coffee beans, which were processed with three different postharvest processing (honey, full-washed and natural), roasted at medium level, and brewed using the tubruk method. Each brew was analyzed for chemical profiles using LC-MS and GC-MS, antioxidant analysis using the DPPH IC50 and FRAP methods, and sensory analysis for flavour using the QDA and SCAA methods for cupping scores. OPLS-DA analysis revealed the presence of marker compounds from each brew, and the dried fruit flavour attribute was to be an inter-process marker. After that, OPLS analysis showed marker compounds that correlate to antioxidant activity and flavour attributes. Rhaponticin is thought to be one of the marker compounds in natural coffee brews and is one of the compounds that correlates to the antioxidant activity of the DPPH method (IC50); prunin is thought to be one of the marker compounds for full-washed coffee brews and is one of the compounds that correlates to the activity antioxidants of FRAP method. Triacetin, which is thought to be a marker compound in natural brewed coffee, correlates with fruity flavour. 3-acetylpyridine, as a marker in honey-brewed coffee, correlates with nutty flavour. Even though there are differences in dominant flavours, the cupping score shows the brew is categorized as a specialty. This research shows that different post-harvest processing processes influence the compound profile, antioxidant activity and flavour attributes of Tubruk brewed coffee.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13197-024-05948-8.

Differences of Typical Wuyi Rock Tea in Taste and Nonvolatiles Profile Revealed by Multisensory Analysis and LC-MS-Based Metabolomics

Wuyi Rock tea, specifically Shuixian and Rougui, exhibits distinct sensory characteristics. In this study, we investigated the sensory and metabolite differences between Shuixian and Rougui. Quantitative description analysis revealed that Rougui exhibited higher intensity in bitter, thick, harsh, and numb tastes, while Shuixian had stronger salty and umami tastes. Nontargeted metabolomics identified 151 compounds with 66 compounds identified as key differential metabolites responsible for metabolic discrimination. Most of the catechins and flavonoids were enriched in Rougui tea, while epigallocatechin-3,3'-di-O-gallate, epigallocatechin-3,5-di-O-gallate, gallocatechin-3,5-di-O-gallate, isovitexin, and theaflavanoside I were enriched in Shuixian tea. Catechins, kaempferol, quercetin, and myricetin derivatives were positively correlated with bitter taste and numb sensation. Sour taste was positively correlated to organic acids. Amino acids potentially contributed to salty and umami tastes. These results provide further insights into the taste characteristics and the relationship between taste attributes and specific metabolites in Wuyi Rock tea.

UV-A radiation increases biomass yield by enhancing energy flow and carbon assimilation in the edible cyanobacterium Nostoc sphaeroides

Ultraviolet (UV) A radiation (315-400 nm) is the predominant component of solar UV radiation that reaches the Earth's surface. However, the underlying mechanisms of the positive effects of UV-A on photosynthetic organisms have not yet been elucidated. In this study, we investigated the effects of UV-A radiation on the growth, photosynthetic ability, and metabolome of the edible cyanobacterium Nostoc sphaeroides. Exposures to 5-15 W m-2 (15-46 µmol photons m-2 s-1) UV-A and 4.35 W m-2 (20 μmol photons m-2 s-1) visible light for 16 days significantly increased the growth rate and biomass production of N. sphaeroides cells by 18%-30% and 15%-56%, respectively, compared to the non-UV-A-acclimated cells. Additionally, the UV-A-acclimated cells exhibited a 1.8-fold increase in the cellular nicotinamide adenine dinucleotide phosphate (NADP) pool with an increase in photosynthetic capacity (58%), photosynthetic efficiency (24%), QA re-oxidation, photosystem I abundance, and cyclic electron flow (87%), which further led to an increase in light-induced NADPH generation (31%) and ATP content (83%). Moreover, the UV-A-acclimated cells showed a 2.3-fold increase in ribulose-1,5-bisphosphate carboxylase/oxygenase activity, indicating an increase in their carbon-fixing capacity. Gas chromatography-mass spectrometry-based metabolomics further revealed that UV-A radiation upregulated the energy-storing carbon metabolism, as evidenced by the enhanced accumulation of sugars, fatty acids, and citrate in the UV-A-acclimated cells. Therefore, our results demonstrate that UV-A radiation enhances energy flow and carbon assimilation in the cyanobacterium N. sphaeroides.IMPORTANCEUltraviolet (UV) radiation exerts harmful effects on photo-autotrophs; however, several studies demonstrated the positive effects of UV radiation, especially UV-A radiation (315-400 nm), on primary productivity. Therefore, understanding the underlying mechanisms associated with the promotive effects of UV-A radiation on primary productivity can facilitate the application of UV-A for CO2 sequestration and lead to the advancement of photobiological sciences. In this study, we used the cyanobacterium Nostoc sphaeroides, which has an over 1,700-year history of human use as food and medicine, to explore its photosynthetic acclimation response to UV-A radiation. As per our knowledge, this is the first study to demonstrate that UV-A radiation increases the biomass yield of N. sphaeroides by enhancing energy flow and carbon assimilation. Our findings provide novel insights into UV-A-mediated photosynthetic acclimation and provide a scientific basis for the application of UV-A radiation for optimizing light absorption capacity and enhancing CO2 sequestration in the frame of a future CO2 neutral, circular, and sustainable bioeconomy.

A Workflow for Meaningful Interpretation of Classification Results from Handheld Ambient Mass Spectrometry Analysis Probes

While untargeted analysis of biological tissues with ambient mass spectrometry analysis probes has been widely reported in the literature, there are currently no guidelines to standardize the workflows for the experimental design, creation, and validation of molecular models that are utilized in these methods to perform class predictions. By drawing parallels with hurdles that are faced in the field of food fraud detection with untargeted mass spectrometry, we provide a stepwise workflow for the creation, refinement, evaluation, and assessment of the robustness of molecular models, aimed at meaningful interpretation of mass spectrometry-based tissue classification results. We propose strategies to obtain a sufficient number of samples for the creation of molecular models and discuss the potential overfitting of data, emphasizing both the need for model validation using an independent cohort of test samples, as well as the use of a fully characterized feature-based approach that verifies the biological relevance of the features that are used to avoid false discoveries. We additionally highlight the need to treat molecular models as "dynamic" and "living" entities and to further refine them as new knowledge concerning disease pathways and classifier feature noise becomes apparent in large(r) population studies. Where appropriate, we have provided a discussion of the challenges that we faced in our development of a 10 s cancer classification method using picosecond infrared laser mass spectrometry (PIRL-MS) to facilitate clinical decision-making at the bedside.

Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging

BACKGROUND

The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse.

AIM OF REVIEW

The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.

Amino acid profile alteration in age-related atrial fibrillation

BACKGROUND

Amino acids (AAs) are one of the primary metabolic substrates for cardiac work. The correlation between AAs and both atrial fibrillation (AF) and aging has been documented. However, the relationship between AAs and age-related AF remains unclear.

METHODS

Initially, the plasma AA levels of persistent AF patients and control subjects were assessed, and the correlations between AA levels, age, and other clinical indicators were explored. Subsequently, the age-related AF mouse model was constructed and the untargeted myocardial metabolomics was conducted to detect the level of AAs and related metabolites. Additionally, the gut microbiota composition associated with age-related AF was detected by a 16S rDNA amplicon sequencing analysis on mouse fecal samples.

RESULTS

Higher circulation levels of lysine (Student's t-test, P = 0.001), tyrosine (P = 0.002), glutamic acid (P = 0.008), methionine (P = 0.008), and isoleucine (P = 0.014), while a lower level of glycine (P = 0.003) were observed in persistent AF patients. The feature AAs identified by machine learning algorithms were glutamic acid and methionine. The association between AAs and age differs between AF and control subjects. Distinct patterns of AA metabolic profiles were observed in the myocardial metabolites of aged AF mice. Aged AF mice had lower levels of Betaine, L-histidine, L-alanine, L-arginine, L-Pyroglutamic acid, and L-Citrulline compared with adult AF mice. Aged AF mice also presented a different gut microbiota pattern, and its functional prediction analysis showed AA metabolism alteration.

CONCLUSION

This study provided a comprehensive network of AA disturbances in age-related AF from multiple dimensions, including plasma, myocardium, and gut microbiota. Disturbances of AAs may serve as AF biomarkers, and restoring their homeostasis may have potential benefits for the management of age-related AF.

FNICM: A New Methodology To Identify Core Metabolites Based on Significantly Perturbed Metabolic Subnetworks

Metabolomics has emerged as a powerful tool in biomedical research to understand the pathophysiological processes and metabolic biomarkers of diseases. Nevertheless, it is a significant challenge in metabolomics to identify the reliable core metabolites that are closely associated with the occurrence or progression of diseases. Here, we proposed a new research framework by integrating detection-based metabolomics with computational network biology for function-guided and network-based identification of core metabolites, namely, FNICM. The proposed FNICM methodology is successfully utilized to uncover ulcerative colitis (UC)-related core metabolites based on the significantly perturbed metabolic subnetwork. First, seed metabolites were screened out using prior biological knowledge and targeted metabolomics. Second, by leveraging network topology, the perturbations of the detected seed metabolites were propagated to other undetected ones. Ultimately, 35 core metabolites were identified by controllability analysis and were further hierarchized into six levels based on confidence level and their potential significance. The specificity and generalizability of the discovered core metabolites, used as UC's diagnostic markers, were further validated using published data sets of UC patients. More importantly, we demonstrated the broad applicability and practicality of the FNICM framework in different contexts by applying it to multiple clinical data sets, including inflammatory bowel disease, colorectal cancer, and acute coronary syndrome. In addition, FNICM was also demonstrated as a practicality methodology to identify core metabolites correlated with the therapeutic effects of Clematis saponins. Overall, the FNICM methodology is a new framework for identifying reliable core metabolites for disease diagnosis and drug treatment from a systemic and a holistic perspective.

Comparative transcriptome and metabolome profiles of the leaf and fruits of a Xianjinfeng litchi budding mutant and its mother plant

Background: Litchi (Litchi chinensis) is an important sub-tropical fruit in the horticulture market in China. Breeding for improved fruit characteristics is needed for satisfying consumer demands. Budding is a sustainable method for its propagation. During our ongoing breeding program, we observed a litchi mutant with flat leaves and sharp fruit peel cracking in comparison to the curled leaves and blunt fruit peel cracking fruits of the mother plant. Methods: To understand the possible molecular pathways involved, we performed a combined metabolome and transcriptome analysis. Results: We identified 1,060 metabolites in litchi leaves and fruits, of which 106 and 101 were differentially accumulated between the leaves and fruits, respectively. The mutant leaves were richer in carbohydrates, nucleotides, and phenolic acids, while the mother plant was rich in most of the amino acids and derivatives, flavonoids, lipids and organic acids and derivatives, and vitamins. Contrastingly, mutant fruits had higher levels of amino acids and derivatives, carbohydrates and derivatives, and organic acids and derivatives. However, the mother plant's fruits contained higher levels of flavonoids, scopoletin, amines, some amino acids and derivatives, benzamidine, carbohydrates and derivatives, and some organic acids and derivatives. The number of differentially expressed genes was consistent with the metabolome profiles. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway-enriched gene expressions showed consistent profiles as of metabolome analysis. Conclusion: These results provide the groundwork for breeding litchi for fruit and leaf traits that are useful for its taste and yield.

Phenylpropane biosynthesis and alkaloid metabolism pathways involved in resistance of Amorphophallus spp. against soft rot disease

Soft rot of konjac (Amorphophallus spp.) is a devastating disease caused by the bacterium Pectobacterium carotovorum subsp. carotovorum (Pcc) with serious adverse effects on plantation development, corm quality and crop yield due to the current lack of effective control measures. The main objective of the present study was to elucidate the mechanisms underlying plant resistance to soft rot disease. A combination of transcriptomic and metabolomic analyses demonstrated significant enrichment of differentially expressed genes (DEG) and differentially accumulated metabolites (DAM) associated with plant hormones, phenylpropanoid biosynthesis and, in particular, alkaloid metabolism, in Amorphophallus muelleri following Pcc infection compared with A. konjac, these data implicate alkaloid metabolism as the dominant mechanism underlying disease resistance of A. muelleri. Quantitative real-time polymerase chain reaction analysis further revealed involvement of PAL, CYP73A16, CCOAOMT1, RBOHD and CDPK20 genes in the response of konjac to Pcc. Analysis of the bacteriostatic activities of total alkaloid from A. muelleri validated the assumption that alkaloid metabolism positively regulates disease resistance of konjac. Our collective results provide a foundation for further research on the resistance mechanisms of konjac against soft rot disease.

Plasma metabolomics for diagnostic biomarkers on ectopic pregnancy

Metabolomics is a relatively novel omics tool to provide potential biomarkers for early diagnosis of the diseases and to insight the pathophysiology not having discussed ever before. In the present study, an ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) was employed to the plasma samples of Group T1: Patients with ectopic pregnancy diagnosed using ultrasound, and followed-up with beta-hCG level (n = 40), Group T2: Patients with ectopic pregnancy diagnosed using ultrasound, underwent surgical treatment and confirmed using histopathology (n = 40), Group P: Healthy pregnant women (n = 40) in the first prenatal visit of pregnancy, Group C: Healthy volunteers (n = 40) scheduling a routine gynecological examination. Metabolite extraction was performed using 3 kDa pores - Amicon® Ultra 0.5 mL Centrifugal Filters. A gradient elution program (mobile phase composition was water and acetonitrile consisting of 0.1% formic acid) was applied using a C18 column (Agilent Zorbax 1.8 μM, 100 x 2.1 mm). Total analysis time was 25 min when the flow rate was 0.2 mL/min. The raw data was processed through XCMS - R program language edition where the optimum parameters detected using Isotopologue Parameter Optimization (IPO). The potential metabolites were identified using MetaboAnalyst 5.0 and finally 27 metabolites were evaluated to be proposed as potential biomarkers to be used for the diagnosis of ectopic pregnancy.

A Genome-Wide Association Study of Serum Metabolite Profiles in Septic Shock Patients

OBJECTIVES

We sought to assess whether genetic associations with metabolite concentrations in septic shock patients could be used to identify pathways of potential importance for understanding sepsis pathophysiology.

DESIGN

Retrospective multicenter cohort studies of septic shock patients.

SETTING

All participants who were admitted to 27 participating hospital sites in three countries (Australia, New Zealand, and the United Kingdom) were eligible for inclusion.

PATIENTS

Adult, critically ill, mechanically ventilated patients with septic shock (n = 230) who were a subset of the Adjunctive Corticosteroid Treatment in Critically Ill Patients with Septic Shock trial (ClinicalTrials.gov number: NCT01448109).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A genome-wide association study was conducted for a range of serum metabolite levels for participants. Genome-wide significant associations (p ≤ 5 × 10-8) were found for the two major ketone bodies (3-hydroxybutyrate [rs2456680] and acetoacetate [rs2213037] and creatinine (rs6851961). One of these single-nucleotide polymorphisms (SNPs) (rs2213037) was located in the alcohol dehydrogenase cluster of genes, which code for enzymes related to the metabolism of acetoacetate and, therefore, presents a plausible association for this metabolite. None of the three SNPs showed strong associations with risk of sepsis, 28- or 90-day mortality, or Acute Physiology and Chronic Health Evaluation score (a measure of sepsis severity).

CONCLUSIONS

We suggest that the genetic associations with metabolites may reflect a starvation response rather than processes involved in sepsis pathophysiology. However, our results require further investigation and replication in both healthy and diseased cohorts including those of different ancestry.

Metabolic Fingerprinting of Serum and Seminal Plasma of Testicular Cancer Patients Using Raman Spectroscopy: A Pilot Study

Background

Testicular cancer (TC) is a relatively rare type of cancer in men. Early diagnosis of TC remains challenging. Metabolomics holds promise in offering valuable insights in this regard. In this study, a metabolic fingerprinting approach was employed to identify potential biomarkers in both serum and seminal plasma of TC patients.

Methods

A total of 9 patients with testicular cancer and 10 controls were included in the study. The metabolic fingerprinting approach was utilized as a rapid diagnostic tool to analyze the metabolome in serum and seminal plasma of TC patients in comparison to fertile men. Raman spectroscopy was applied for the analysis of metabolites in these biological samples.

Results

Principal component analysis (PCA) and functional group analysis showed that the differentiation between serum samples from healthy men and TC patients was not possible. However, when analyzing seminal plasma, a significant difference was found between the two groups (p<0.05). Functional group analysis of serum only showed an increase in tryptophan concentration ratio in TC patients as compared to healthy men (p=0.03). In contrast, in seminal plasma of TC patients, this increase was observed in all analyzed compounds, including phenylalanine, tyrosine, lipids, proteins, phenols (p<0.001).

Conclusion

Our study highlights the potential of metabolic fingerprinting as a fast diagnostic tool for screening TC patients, with seminal plasma serving as a valuable biological sample. Furthermore, several potential biomarkers, particularly phenylalanine, were identified in seminal plasma. This research contributes to our understanding of TC pathogenesis and has the potential to pave the way for early detection and personalized treatment approaches.

Maternal and newborn metabolomic changes associated with urinary polycyclic aromatic hydrocarbon metabolite concentrations at delivery: an untargeted approach

INTRODUCTION

Prenatal exposure to polycyclic aromatic hydrocarbons (PAHs) has been associated with adverse human health outcomes. To explore the plausible associations between maternal PAH exposure and maternal/newborn metabolomic outcomes, we conducted a cross-sectional study among 75 pregnant people from Cincinnati, Ohio.

METHOD

We quantified 8 monohydroxylated PAH metabolites in maternal urine samples collected at delivery. We then used an untargeted high-resolution mass spectrometry approach to examine alterations in the maternal (n = 72) and newborn (n = 63) serum metabolome associated with PAH metabolites. Associations between individual maternal urinary PAH metabolites and maternal/newborn metabolome were assessed using linear regression adjusted for maternal and newborn factors while accounting for multiple testing with the Benjamini-Hochberg method. We then conducted functional analysis to identify potential biological pathways.

RESULTS

Our results from the metabolome-wide associations (MWAS) indicated that an average of 1% newborn metabolome features and 2% maternal metabolome features were associated with maternal urinary PAH metabolites. Individual PAH metabolite concentrations in maternal urine were associated with maternal/newborn metabolome related to metabolism of vitamins, amino acids, fatty acids, lipids, carbohydrates, nucleotides, energy, xenobiotics, glycan, and organic compounds.

CONCLUSION

In this cross-sectional study, we identified associations between urinary PAH concentrations during late pregnancy and metabolic features associated with several metabolic pathways among pregnant women and newborns. Further studies are needed to explore the mediating role of the metabolome in the relationship between PAHs and adverse pregnancy outcomes.

Feature-Based Molecular Networking Combined with Multivariate Analysis for the Characterization of Glutathione Adducts as a Smoking Gun of Bioactivation

Feature-based molecular networking (FBMN) is a powerful analytical tool for mass spectrometry (MS)-based untargeted metabolomics data analysis. FBMN plays an important role in drug metabolism studies, enabling the visualization of complex metabolomics data to achieve metabolite characterization. In this study, we propose a strategy for the characterization of glutathione (GSH) adducts formed via in vitro metabolic activation using FBMN assisted by multivariate analysis (MVA). Acetaminophen was used as a model substrate for method development, and the practical potential of the method was investigated by its application to 2-aminophenol (2-AP) and 2,4-dinitrochlorobenzene (DNCB). Two 2-AP GSH adducts and one DNCB GSH adduct were successfully characterized by forming networks with GSH even though the mass spectral information obtained for the parent compound was deficient. False positives were effectively filtered out by the variable influence on projection cutoff criteria obtained from orthogonal partial least-squares-discriminant analysis. The GSH adducts formed by enzymatic or nonenzymatic reactions were intuitively distinguished by the pie chart of FBMN results. In summary, our approach effectively characterizes GSH adducts, which serve as compelling evidence of bioactivation. It can be widely utilized to enhance risk assessment in the context of drug metabolism.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Characterisation of Sclerocarya birrea (marula) seed oil and investigation of the geographical origin by applying similarity calculations, differential NMR and hierarchical cluster analysis

INTRODUCTION

The marula fruit is an important indigenous African fruit since various commercial products are produced from the pulp and the seed oil. The increased demand requires methods for authentication, quality control and determination of geographical origin.

OBJECTIVE

The study aimed to establish a fast and reliable method for characterisation and authentication of marula seed oil. Furthermore, to identify marker compounds that can distinguish marula seed oils from other commercial oils and indicate regional differences.

MATERIALS AND METHODS

Metabolic profiling of 44 commercial marula seed oils was performed using proton nuclear magnetic resonance (1 H NMR). For rapid classification similarity calculations were compared with principal component analysis. Differential NMR was used to determine marker compounds.

RESULTS

Marula seed oil was found to be similar to macadamia and olive oils and was distinguished from these oils by the detection of minor components. Marula seed oil is differentiated from the other two oils by the absence of α-linolenic acid, relatively high levels of monoglycerides and diglycerides, and an approximately 1:1 ratio of 1,2- and 1,3-diglycerides. When comparing marula seed oils from various regions using hierarchical cluster analysis, clustering of the marula seed oils from Namibia and Zimbabwe was observed and was related to the quantities of linoleic acid and monoglycerides and diglycerides. Some samples displayed deviations in their composition which might indicate adulteration or contamination during the production process.

CONCLUSION

The study demonstrates the potential of NMR as a tool in the quality control of marula seed oil. This technique requires very little sample preparation, circumvents derivatisation of the oil components with fast run-times. In addition, samples with chemical profiles that differ from the general signature profile can easily be identified.

Multi-omics analysis of hospital-acquired diarrhoeal patients reveals biomarkers of enterococcal proliferation and Clostridioides difficile infection

Hospital-acquired diarrhoea (HAD) is common, and often associated with gut microbiota and metabolome dysbiosis following antibiotic administration. Clostridioides difficile is the most significant antibiotic-associated diarrhoeal (AAD) pathogen, but less is known about the microbiota and metabolome associated with AAD and C. difficile infection (CDI) with contrasting antibiotic treatment. We characterised faecal microbiota and metabolome for 169 HAD patients (33 with CDI and 133 non-CDI) to determine dysbiosis biomarkers and gain insights into metabolic strategies C. difficile might use for gut colonisation. The specimen microbial community was analysed using 16 S rRNA gene amplicon sequencing, coupled with untargeted metabolite profiling using gas chromatography-mass spectrometry (GC-MS), and short-chain fatty acid (SCFA) profiling using GC-MS. AAD and CDI patients were associated with a spectrum of dysbiosis reflecting non-antibiotic, short-term, and extended-antibiotic treatment. Notably, extended antibiotic treatment was associated with enterococcal proliferation (mostly vancomycin-resistant Enterococcus faecium) coupled with putative biomarkers of enterococcal tyrosine decarboxylation. We also uncovered unrecognised metabolome dynamics associated with concomitant enterococcal proliferation and CDI, including biomarkers of Stickland fermentation and amino acid competition that could distinguish CDI from non-CDI patients. Here we show, candidate metabolic biomarkers for diagnostic development with possible implications for CDI and vancomycin-resistant enterococci (VRE) treatment.

Metabolomics of Personalized Body Elements in Thai Traditional Medicine Response to Herbal Medicine for Body Elements Balancing in Healthy Volunteers

Background

In Thai traditional medicine (TTM), the dominant body element called "Dhat Chao Ruean" (DCR) is an integral part in the diagnostic process of Thai traditional medicine. TTM practitioners usually use Thai herbal Benjakul formula (BKF) for adjusting and balancing the body elements. However, the effects of BKF on metabolism and individual response to it have not been studied yet.

Methods

This study proposed to investigate the metabolic profiling in 24 volunteers categorized by their types of birth month DCR (bDCR) after the administration of BKF (450 mg, three tablets three times a day before meals) for seven days. Differences in metabolic profiling between bDCR groups were investigated by using liquid chromatography coupled with mass spectrometry for untargeted analysis, and in addition, the safety was assessed by testing the plasma biochemical level.

Results

This study identified 57 biomarkers in positive ESI and 12 in negative ESI. Piperine was found in varying amount among the participants but it was the highest in the earth group. In addition, this study found that elemicin, phenylpropionic acid, ricinoleic acid, and β-sitosterol are important substances in a single herb of BKF. Regarding biochemical tests, the results indicated that BKF can decrease the lipid profile and it has no toxic effects on liver and kidney functions.

Conclusion

The findings indicated that it is safe to use BKF which can help to improve health in chronic diseases by adjusting abnormality of the elements of the body. In addition, the information gathered from this study is valuable for further study in the field of Thai traditional medicine.

Metabolomic signatures distinguish extracellular vesicles from osteoclasts and odontoclasts

AIMS

Pathological dental root resorption and alveolar bone loss are often detected only after irreversible damage. Biomarkers in the gingival crevicular fluid or saliva could provide a means for early detection; however, such biomarkers have proven elusive. We hypothesize that a multiomic approach might yield reliable diagnostic signatures for root resorption and alveolar bone loss. Previously, we showed that extracellular vesicles (EVs) from osteoclasts and odontoclasts differ in their protein composition. In this study, we investigated the metabolome of EVs from osteoclasts, odontoclasts and clasts (non-resorbing clastic cells).

MATERIALS AND METHODS

Mouse haematopoietic precursors were cultured on dentine, bone or plastic, in the presence of recombinant RANKL and CSF-1 to trigger differentiation along the clastic line. On Day 7, the cells were fixed and the differentiation state and resorptive status of the clastic cells were confirmed. EVs were isolated from the conditioned media on Day 7 and characterized by nanoparticle tracking and electron microscopy to ensure quality. Global metabolomic profiling was performed using a Thermo Q-Exactive Orbitrap mass spectrometer with a Dionex UHPLC and autosampler.

RESULTS

We identified 978 metabolites in clastic EVs. Of those, 79 are potential biomarkers with Variable Interdependent Parameters scores of 2 or greater. Known metabolites cytidine, isocytosine, thymine, succinate and citrulline were found at statistically higher levels in EVs from odontoclasts compared with osteoclasts.

CONCLUSION

We conclude that numerous metabolites found in odontoclast EVs differ from those in osteoclast EVs, and thus represent potential biomarkers for root resorption and periodontal tissue destruction.

Aroma-based discrimination of Egyptian versus Indian guava fruits and in response to probiotics as analyzed via SPME/GC-MS and chemometric tools

Guava tree (Psidium guajava L., Myrtaceae) is an economic grown worldwide, particularly in tropical and subtropical regions. Guavas encompass numerous cultivars (cvs.) that were discriminated in previous studies based on leaf morphological features and profile of volatile organic compounds (VOCs). Nevertheless, fruit VOCs have also shown outstanding potential for discrimination of other plant taxa, which has not been utilized in guava. Hence, the current study investigates the various guava cvs. harvested from India and Egypt. A total of 5 samples were analyzed by solid phase microextraction coupled to gas chromatography/mass spectrometry. Results led to the detection of 42 VOCs belonging to aldehydes, alcohols, esters, ketones, aliphatic and aromatic hydrocarbons, in addition to monoterpene and sesquiterpene hydrocarbons. Butylated hydroxytoluene and β-caryophyllene were predominant reaching 77% and 41% in Egyptian and Indian guava, respectively. The impact of probiotic fermentation, i.e., Lactobacillus acidophilus and L. plantarum on aroma profile was not significantly different (p > 0.05). Multivariate data analyses were further applied for samples classification and markers determination, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). PCA score plot showed clear segregation of Egyptian from Indian specimens, whereas OPLS-DA revealed that β-caryophyllene was associated with white fruit versus 3-butenyl isothiocyanate and muurolol in red fruit type in the case of Indian guava. The richness of Egyptian guava in butylated hydroxytoluene in addition to the presence of vitamin C may potentiate its antioxidant activity, to be followed in subsequent studies regarding its health effects.

Identification of potential quality markers in Indonesia's Arabica specialty coffee using GC/MS-based metabolomics approach

INTRODUCTION

The cupping test is a widely used method for quality assessment of Arabica coffee. However, the cupping test is limited by the low number of certified panelists and the low throughput. Therefore, an analytical-based quality assessment may be a promising tool to complement the cupping test. A present, there is no report investigating quality marker candidates, focusing only on "specialty" grade Arabica coffee from Indonesia.

OBJECTIVE

This study identified the potential quality marker(s) in Arabica Specialty coffee at different stages (green beans, roasted beans, and brewed coffee.

METHODS

The metabolite profiles of ten different Arabica specialty-grade coffees were analyzed with different cup scores using gas chromatography-mass spectrometry (GC/MS). From the ten samples, green coffee beans, roasted coffee beans, and brewed coffee were selected. In addition, an orthogonal projection to latent structure (OPLS) regression analysis was conducted to obtain a potential quality marker based on the variable importance in projection (VIP). The potential quality marker(s) were validated by GC/MS metabolome profiling and OPLS analysis of different sets of samples consisting of 35 Arabica specialty-grade coffee samples.

RESULTS

In Arabica coffee samples, the OPLS model of the three stages showed galactinol to have a high VIP score. Galactinol showed a consistent positive correlation with cup scores at all stages of coffee production (green beans, roasted beans, and brewed coffee). The correlation suggests galactinol is a potential quality marker after further validation using different samples.

CONCLUSION

GC/MS combined with OPLS regression analysis suggested galactinol as a quality marker and provide an early screening method for Arabica coffee quality that complements the cupping test performed by certified panelists.

Light effects on Lasiodiplodia theobromae metabolome cultured in vitro

INTRODUCTION

The present work identified and compared intracellular metabolites and metabolic networks in mycelial cultures of Lasiodiplodia theobromae grown under 12 natural light and 24 hours' dark using a 1 H NMR-based metabolomics approach.

MATERIALS AND METHODS

Fungal cultures were grown in potato dextrose media, and metabolites were extracted by sonication with sodium phosphate-buffered saline (pH = 6.0, 10% D2O, 0.1 mM TSP) from mycelium samples collected every week over four weeks.

RESULTS

Multivariate analyses revealed that the light exposure group showed a positive correlation within beta-hydroxybutyrate, acetoacetate, acetone, betaine, choline, glycerol, and phosphocholine. On the other hand, phenyl acetate, leucine, isoleucine, valine, and tyrosine were positively correlated with dark conditions. Light favored the oxidative degradation of valine, leucine, and isoleucine, leading to the accumulation of choline, phosphocholine, betaine, and ketone bodies (ketogenesis). Ketogenesis, gluconeogenesis, and the biosynthesis of choline, phosphocholine, and betaine, were considered discriminatory routes for light conditions. The light-sensing pathways were interlinked with fungal development, as verified by the increased production of mycelia biomass without fruiting bodies and stress signaling, as demonstrated by the increased production of pigments.

Lipopolysaccharides from Ralstonia solanacearum induce a broad metabolomic response in Solanum lycopersicum

Ralstonia solanacearum, one of the most destructive crop pathogens worldwide, causes bacterial wilt disease in a wide range of host plants. The major component of the outer membrane of Gram-negative bacteria, lipopolysaccharides (LPS), has been shown to function as elicitors of plant defense leading to the activation of signaling and defense pathways in several plant species. LPS from a R. solanacearum strain virulent on tomato (LPSR. sol.), were purified, chemically characterized, and structurally elucidated. The lipid A moiety consisted of tetra- to hexa-acylated bis-phosphorylated disaccharide backbone, also decorated by aminoarabinose residues in minor species, while the O-polysaccharide chain consisted of either linear tetrasaccharide or branched pentasaccharide repeating units containing α-L-rhamnose, N-acetyl-β-D-glucosamine, and β-L-xylose. These properties might be associated with the evasion of host surveillance, aiding the establishment of the infection. Using untargeted metabolomics, the effect of LPSR. sol. elicitation on the metabolome of Solanum lycopersicum leaves was investigated across three incubation time intervals with the application of UHPLC-MS for metabolic profiling. The results revealed the production of oxylipins, e.g., trihydroxy octadecenoic acid and trihydroxy octadecadienoic acid, as well as several hydroxycinnamic acid amide derivatives, e.g., coumaroyl tyramine and feruloyl tyramine, as phytochemicals that exhibit a positive correlation to LPSR. sol. treatment. Although the chemical properties of these metabolite classes have been studied, the functional roles of these compounds have not been fully elucidated. Overall, the results suggest that the features of the LPSR. sol. chemotype aid in limiting or attenuating the full deployment of small molecular host defenses and contribute to the understanding of the perturbation and reprogramming of host metabolism during biotic immune responses.

Hyperfructosemia in sleep disordered breathing: metabolome analysis of Nagahama study

Sleep disordered breathing (SDB), mainly obstructive sleep apnea (OSA), constitutes a major health problem due to the large number of patients. Intermittent hypoxia caused by SDB induces alterations in metabolic function. Nevertheless, metabolites characteristic for SDB are largely unknown. In this study, we performed gas chromatography-mass spectrometry-based targeted metabolome analysis using data from The Nagahama Study (n = 6373). SDB-related metabolites were defined based on their variable importance score in orthogonal partial least squares discriminant analysis and fold changes in normalized peak-intensity levels between moderate-severe SDB patients and participants without SDB. We identified 20 metabolites as SDB-related, and interestingly, these metabolites were frequently included in pathways related to fructose. Multivariate analysis revealed that moderate-severe SDB was a significant factor for increased plasma fructose levels (β = 0.210, P = 0.006, generalized linear model) even after the adjustment of confounding factors. We further investigated changes in plasma fructose levels after continuous positive airway pressure (CPAP) treatment using samples from patients with OSA (n = 60) diagnosed by polysomnography at Kyoto University Hospital, and found that patients with marked hypoxemia exhibited prominent hyperfructosemia and their plasma fructose levels lowered after CPAP treatment. These data suggest that hyperfructosemia is the abnormality characteristic to SDB, which can be reduced by CPAP treatment.