The evolving landscape of untargeted metabolomics

Cysteine-S-sulfate promotes arteriosclerosis obliterans by inducing TH17 differentiation and promoting pyroptosis

Arteriosclerotic obliterans (ASO) is a peripheral vascular disease with a high rate of amputation, the mechanism of which remains unclear and markers for early diagnosis are seriously lacking. Therefore, we first used untargeted metabolomics analysis to reveal significant differences in metabolites between the ASO cohort and the healthy volunteer cohort. Combined with targeted analysis, it was confirmed that cysteine-S-sulfate could be identified as a potential biomarker for ASO, with good diagnostic performance. In addition, we analyzed the distribution of helper T cells (TH1, TH2, TH9, TH17 and TH22), and the results showed that TH17 was highly expressed in the ASO cohort, and IL-17 was also elevated. Through in vitro experiments, we found that cysteine-S-sulfate can promote IL-17 secretion and induce HUVCEs pyroptosis. To further clarify the pathogenicity of cysteine-S-sulfate, we used an acute lower limb ischemia model and gave a high methionine diet to simulate the high cysteine-S-sulfate state in vivo. What was shocking was that the high methionine diet group mice had lower blood flow than the control group, as well as higher levels of cysteine-S-sulfate, higher levels of TH17, and higher levels of pyroptosis. This also confirms that cysteine-S-sulfate promotes the differentiation of TH17, the secretion of cytokines, and the occurrence of pyroptosis, promoting the progression of ASO. This study is valuable in providing a diagnostic marker for ASO and elucidating its pathogenesis. This suggests that blocking IL-17 may be a new strategy to treat ASO, offering clinicians a therapeutic possibility.

Genetic and environmental drivers of intraspecific variation in foliar metabolites in a tropical tree community

Plant interactions with abiotic and biotic environments are mediated by diverse metabolites, which are crucial for stress response and defense. These metabolites can not only support diversity by shaping species niche differences but also display heritable and plastic intraspecific variation, which few studies have quantified in terms of their relative contributions. To address this shortcoming, we used untargeted metabolomics to annotate and quantify foliar metabolites and restriction-site associated DNA (RAD) sequencing to assess genetic distances among 300 individuals of 10 locally abundant species from a diverse tropical community in Southwest China. We quantified the relative contributions of relatedness and the abiotic and biotic environment to intraspecific metabolite variation, considering different biosynthetic pathways. Intraspecific variation contributed most to community-level metabolite diversity, followed by species-level variation. Biotic factors had the largest effect on total and secondary metabolites, while abiotic factors strongly influenced primary metabolites, particularly carbohydrates. The relative importance of these factors varied widely across different biosynthetic pathways and different species. Our findings highlight that intraspecific variation is an essential component of community-level metabolite diversity. Furthermore, species rely on distinct classes of metabolites to adapt to environmental pressures, with genetic, abiotic, and biotic factors playing pathway-specific roles in driving intraspecific variation.

Denoising Search doubles the number of metabolite and exposome annotations in human plasma using an Orbitrap Astral mass spectrometer

Chemical exposures may affect human metabolism and contribute to the etiology of neurodegenerative disorders such as Alzheimer's disease. Identifying these small metabolites involves matching experimental spectra to reference spectra in databases. However, environmental chemicals or physiologically active metabolites are usually present at low concentrations in human specimens. The presence of noise ions can substantially degrade spectral quality, leading to false negatives and reduced identification rates. In response to this challenge, the Spectral Denoising algorithm removes both chemical and electronic noise. Spectral Denoising outperformed alternative methods in benchmarking studies on 240 tested metabolites. It improved high confident compound identifications at an average 35-fold lower concentrations than previously achievable. Spectral Denoising proved highly robust against varying levels of both chemical and electronic noise even with a greater than 150-fold higher intensity of noise ions than true fragment ions. For human plasma samples from patients with Alzheimer's disease that were analyzed on the Orbitrap Astral mass spectrometer, Denoising Search detected 2.5-fold more annotated compounds compared to the Exploris 240 Orbitrap instrument, including drug metabolites, household and industrial chemicals, and pesticides.

Evaluation of ion source parameters and liquid chromatography methods for plasma untargeted metabolomics using orbitrap mass spectrometer

Although untargeted metabolomics holds promise for study of metabolites in human health and disease, robust method development and optimization are needed to reduce potential analytical biases and to ensure comprehensive, high-throughput results. In this study, the effect of mass spectrometer (MS) ion source parameters on the signal reproducibility and number of metabolite annotations during untargeted metabolomics is shown. Furthermore, different mobile phase gradients and columns (five reversed phase (RP)-C18 and two hydrophilic interaction liquid chromatography (HILIC) columns) were evaluated for untargeted metabolomics of blood plasma extracts. Positioning the electrospray needle at the farthest on the Z-direction and the closest tested position on the Y-direction with respect to the mass spectrometry inlet produced the best signal reproducibility and the greatest number of metabolite annotations. Moreover, optimal ion source conditions included a positive spray voltage between 2.5 and 3.5 kV, a negative spray voltage between 2.5 and 3.0 kV, vaporization and ion transfer tube (ITT) temperature between 250 and 350 °C, 30 to 50 arbitrary units of sheath gas, and at least 10 auxiliary gas units. Despite the differences in chromatographic characteristics, the different RP columns assessed showed comparable performance in terms of number of metabolites annotated. For HILIC columns, a zwitterionic column demonstrated better performance than an amide column. Finally, as compared with use of a RP column alone, use of both the optimal RP and HILIC approaches expanded metabolome coverage: the number of metabolites annotated increased by 60 %. This study highlights the significance of fine-tuning the MS ion source parameters and optimizing chromatographic conditions on metabolome coverage during untargeted metabolomics of plasma samples.

Non-targeted metabolomic study in plasma in rats with post-traumatic osteoarthritis model

PURPOSE

This study aimed to examine the differential expression profiles of plasma metabolites in rat models of post-traumatic osteoarthritis (PTOA) and elucidate the roles of metabolites and their pathways in the progression of PTOA using bioinformatics analysis.

METHOD

Plasma samples were collected from 24 SD female rats to model PTOA, and metabolomic assays were conducted. The samples were divided into three groups: the surgically induced mild PTOA group (Group A: 3 weeks postoperative using the modified Hulth model; age 2 months), the surgically induced severe PTOA group (Group B: 5 weeks postoperative using the modified Hulth model; age 2 months), and the normal control group (Group C: healthy rats aged 2 months). Metabolites were structurally identified by comparing the retention times, molecular masses, secondary fragmentation spectra, collision energies, and other metabolite data with a database (provided by Shanghai Applied Protein Technology Co., Ltd.). Target prediction and pathway analysis were subsequently performed using bioinformatics analysis.

RESULTS

The experiment revealed that in the mild PTOA group, levels of Alpha-ketoglutarate, Isocitric acid, Dichloroacetate, and other metabolites increased significantly compared with the normal group, whereas Linolenic acid, Lactose, and others decreased significantly. These findings suggest that these metabolites can serve as biomarkers for the diagnosis of early PTOA. In the severe PTOA group, Diosgenin, Indoleacrylic acid, Alpha-ketoglutarate, Isocitric acid, and others were elevated and may also be used as biomarkers for PTOA diagnosis. Adrenosterone, (+)-chlorpheniramine, and Phenanthridine levels were higher in the severe PTOA group compared to the mild PTOA group, while Menadione, Adenosine 5'-monophosphate, and Arg-Gly-Asp levels were lower.

CONCLUSIONS

Taurocholate, indoleacrylic acid, alpha-ketoglutarate, and isocitric acid may serve as biomarkers for PTOA joint injury in rats. Menadione, adenosine 5'-monophosphate, and Arg-Gly-Asp exhibited differential expression between severe and mild PTOA groups in rats, potentially reflecting the injury's severity. Further investigation into these molecules in human tissues is warranted to ascertain their utility as biomarkers for PTOA in humans.

Application of integrated omics in aseptic loosening of prostheses after hip replacement

Aseptic loosening (AL) of artificial hip joints is the most common complication following hip replacement surgery. A total of eight patients diagnosed with AL following total hip arthroplasty (THA) undergoing total hip replacement and eight control patients diagnosed with avascular necrosis of femoral head (ANFH) or femoral neck fracture undergoing THA were enrolled. The samples of the AL group were from synovial tissue surrounding the lining/head/neck of the prosthesis, and the samples of the control group were from the synovium in the joint cavity. The present study utilized second‑generation high‑throughput sequencing and mass spectrometry to detect differentially expressed genes, proteins and metabolites in the samples, as well as Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Key genes cytokine receptor‑like factor‑1 (CRLF1) and glutathione‑S transferase µ1 (GSTM1) expression levels were verified by reverse transcription‑quantitative PCR and western blotting. The integrated transcriptomics, proteomics and untargeted metabolomics analyses revealed characteristic metabolite changes (biosynthesis of guanine, L‑glycine and adenosine) and decreased CRLF1 and GSTM1 in AL, which were primarily associated with amino acid metabolism and lipid metabolism. In summary, the present study may uncover the underlying mechanisms of AL pathology and provide stable and accurate biomarkers for early warning and diagnosis.

Pre-diagnostic circulating metabolomics and prostate cancer risk: A systematic review and meta-analysis

Background

Metabolomic dysregulation contributes to prostate cancer (PCa) pathogenesis, and studies suggest that circulating metabolites have strong clinical potential to act as biomarkers. However, evidence of circulating metabolite associations has not been quantitively aggregated.

Methods

Systematic searches were performed in PubMed and Embase (October 17th, 2024) to identify pre-diagnostic untargeted serum metabolomic studies of PCa risk. After harmonizing metabolite names across studies, restricted maximum likelihood was used to conduct meta-analyses to quantify associations between metabolites and risk of overall PCa, low- to intermediate-risk PCa, high- to very high-risk PCa and lethal PCa, as defined by the NCCN. Statistical significance was defined as FDR-adjusted P<0.05. Enrichment analyses were conducted on significant metabolites to identify biologically relevant pathways. Correlation of effect estimates between PCa outcomes was assessed via Pearson correlation.

Results

We identified 12 untargeted pre-diagnostic circulating metabolomic studies in a systematic review and meta-analyzed associations between up to 408 metabolites with four PCa outcomes. Three, eleven and nineteen metabolites were significantly associated with risk of overall, high/very high-risk and lethal PCa, respectively. Metabolites associated with high/very high-risk PCa were significantly enriched for lipids. Limited evidence of correlation between metabolite effects across outcomes was identified, highlighting potentially unique metabolite drivers of high-risk and lethal PCa. Follow-up analyses found that 13 of the significant metabolites were drug and/or dietary modifiable.

Conclusions

These findings suggest the strong potential for metabolites to inform risk of lethal PCa, which could inform risk-stratified screening strategies and facilitate the identification of targets for PCa prevention.

Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning

Polycystic ovary syndrome (PCOS) is a primary endocrine disorder affecting premenopausal women involving metabolic dysregulation. We aimed to screen serum biomarkers in PCOS patients using untargeted lipidomics and ensemble machine learning. Serum from PCOS patients and non-PCOS subjects were collected for untargeted lipidomics analysis. Through analyzing the classification of differential lipid metabolites and the association between differential lipid metabolites and clinical indexes, ensemble machine learning, data preprocessing, statistical test pre-screening, ensemble learning method secondary screening, biomarkers verification and evaluation, and diagnostic panel model construction and verification were performed on the data of untargeted lipidomics. Results indicated that different lipid metabolites not only differ between groups but also have close effects on different corresponding clinical indexes. PI (18:0/20:3)-H and PE (18:1p/22:6)-H were identified as candidate biomarkers. Three machine learning models, logistic regression, random forest, and support vector machine, showed that screened biomarkers had better classification ability and effect. In addition, the correlation of candidate biomarkers was low, indicating that the overlap between the selected biomarkers was low, and the combination of panels was more optimized. When the AUC value of the test set of the constructed diagnostic panel model was 0.815, the model's accuracy in the test set was 0.74, specificity was 0.88, and sensitivity was 0.7. This study demonstrated the applicability and robustness of machine learning algorithms to analyze lipid metabolism data for efficient and reliable biomarker screening. PI (18:0/20:3)-H and PE (18:1p/22:6)-H showed great potential in diagnosing PCOS.

Current Role and Potential of Triple Quadrupole Mass Spectrometry in Biomedical Research and Clinical Applications

Mass-spectrometry-based assays nowadays play an essential role in biomedical research and clinical applications. There are different types of commercial mass spectrometers on the market today, and triple quadrupole (QqQ) is one of the time-honored systems. Here, we overview the main areas of QqQ applications in biomedicine and assess the current level, evolution, and trends in the use of QqQ in these areas. Relevant data were extracted from the Scopus database using the specified terms and Boolean operators defined for each field of the QqQ application. We also discuss the recent advances in QqQ and QqQ-based analytical platforms, which promote the clinical application of these systems, and explain the indicated substantial increase in triple quadrupole use in biomedicine. The number of biomedical studies utilizing QqQ increased 2-3 times this decade. Triple quadrupole is most intensively used in the field of endocrine research and testing. On the contrary, the relative rate of immunoassay utilization-a major competitor of chromatography-mass spectrometry-decreased in this area as well as its use within Therapeutic drug monitoring (TDM) and forensic toxicology. Nowadays, the applications of high-resolution accurate mass (HRAM) mass spectrometers in the investigated areas represent only a small fraction of the total amount of research using mass spectrometry; however, their application substantially increased during the last decade in the untargeted search for new biomarkers.

Research progress on factors affecting the human milk metabolome

Human milk is the gold standard for infant nutrition and contains macronutrients, micronutrients, and various bioactive substances. The human milk composition and metabolite profiles are complex and dynamic, complicating its specific analysis. Metabolomics, a recently emerging technology, has been used to identify human milk metabolites classes. Applying metabolomics to study the factors affecting human milk metabolites can provide significant insights into the relationship between infant nutrition, health, and development and better meet the nutritional needs of infants during growth. Here, we systematically review the current status of human milk metabolomic research, and related methods, offering an in-depth analysis of the influencing factors and results of human milk metabolomics from a metabolic perspective to provide novel ideas to further advance human milk metabolomics.

First Insights into the Fecal Metabolome of Healthy, Free-Roaming Giraffes (Giraffa camelopardalis): An Untargeted GCxGC/TOF-MS Metabolomics Study

BACKGROUND/OBJECTIVES

This study provides the first insights to the fecal metabolome of the giraffe (Giraffa camelopardalis). By using untargeted metabolomics via gas chromatography time-of-flight mass spectrometry (GCxGC/TOF-MS), this study primarily aims to provide results of the impact that external stimuli, such as supplemental feeding (SF) practices, seasonal variation and sex, might have on the fecal metabolome composition of healthy, free-roaming giraffes.

METHODS

Untargeted GCxGC/TOF-MS analysis was applied to the feces collected from thirteen giraffes (six males and seven females) from six different locations within the central Free State Province of South Africa over a period of two years. Statistical analysis of the generated data was used to identify the metabolites that were significantly different between the giraffes located in environments that provided SF and others where the giraffes only fed on the natural available vegetation. The same metabolomics analysis was used to investigate metabolite concentrations that were significantly different between the wet and dry seasons for a single giraffe male provided with SF over the two-year period, as well as for age and sex differences.

RESULTS

A total of 2042 features were detected from 26 giraffe fecal samples. Clear variations between fecal metabolome profiles were confirmed, with higher levels of amino acid-related and carbohydrate-related metabolites for giraffes receiving SF. In addition, a separation between the obtained profiles of samples collected from a single adult male giraffe during the wet and dry seasons was identified. Differences, such as higher levels of carbohydrate-related metabolites and organic compounds during the wet season were noted. Distinct variations in profiles were also identified for the metabolites from fecal samples collected from the six males and seven females, with higher concentrations in carbohydrate-related metabolites and alkanes for female giraffes comparatively.

CONCLUSIONS

This is the first study to investigate the composition of the fecal metabolome of free-roaming giraffes, as well as the effects that external factors, such as environmental exposures, feeding practices, seasonal variations, age and sex, have on it. This novel use of fecal metabolomics assists in developing non-invasive techniques to determine giraffe populations' health that do not require additional stressors such as capture, restraint and blood collection. Ultimately, such non-invasive advances are beneficial towards the conservation of wildlife species on a larger scale.

Integrating metabolomics and network pharmacology analysis to explore mechanism of Pueraria lobata against pulmonary fibrosis: Involvement of arginine metabolism pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Pueraria lobata (Willd.) Ohwi is a typical medicinal and edible plant with a long application history in China and Southeast Asia. As a widely used traditional medicine, P. lobata exhibits the properties of anti-inflammatory, antipyretic, antioxidant, relieving cough and asthma. Particularly, the increasing evidence indicates that the P. lobata has the therapeutic effect on fibrotic-related diseases in terms of metabolic regulation. However, the mechanisms of P. lobata on pulmonary fibrosis (PF) has not been thoroughly explored.

AIM OF THE STUDY

This study aimed to explore the effect of arginine metabolites of P. lobata against PF model by integrating metabolomics and network pharmacology analysis. It might provide a new idea for the target finding of P. lobata anti-pulmonary fibrosis.

MATERIALS AND METHODS

In this study, the Sprague Dawley (SD) rats were randomly divided into five experimental groups: saline-treated control group, bleomycin-induced fibrosis group, prednisolone acetate group, P. lobata 3.2 g/kg group and P. lobata 6.4 g/kg group. The therapeutic effect of P. lobata on bleomycin-induced PF in rats was evaluated by clinical symptoms such as lung function, body weight, hematoxylin eosin staining (HE), Masson staining and hydroxyproline assay. Next, the plasma metabolomics analysis was carried out by LC-MS to explore the pathological differences between the group of control, PF and P. lobata-treated rats. Then, the network pharmacology study coupled with experimental validation was conducted to analysis the results of metabolic research. We constructed the "component-target-disease" network of P. lobata in the treatment of PF. In addition, the molecular docking method was used to verify the interaction between potential active ingredients and core targets of P. lobata. Finally, we tested NOS2 and L-OT in arginine-related metabolic pathway in plasma of the rats by enzyme-linked immunosorbent assay (ELISA). Real-time PCR was performed to observe the level of TNF-α mRNA and MMP9 mRNA. And we tested the expression of TNF-α and MMP9 by Western blot analysis.

RESULTS

Our findings revealed that P. lobata improved lung function and ameliorated the pathological symptoms, such as pathological damage, collagen deposition, and body weight loss in PF rats. Otherwise, the plasma metabolomics were employed to screen the differential metabolites of amino acids, lipids, flavonoids, arachidonic acid metabolites, glycoside, etc. Finally, we found that the arginine metabolism signaling mainly involved in the regulating of P. lobata on the treatment of PF rats. Furtherly, the network pharmacology predicted that the arginine metabolism pathway was contained in the top 20 pathways. Next, we integrated metabolomics and network pharmacology that identified NOS2, MMP9 and TNF-α as the P. lobata regulated hub genes by molecular docking. Importantly, it indicated a strong affinity between the puerarin and the NOS2. P. lobata attenuated TNF-α, MMP-9 and NOS2 levels, suppressed TNF-α and MMP-9 protein expression, and decreased L-OT and NOS2 content in PF rats. These results indicated that the effects of P. lobata may ameliorated PF via the arginine metabolism pathway in rats. Therefore, P. lobata may be a potential therapeutic agent to ameliorated PF.

CONCLUSION

In this work, we used metabolomics and network pharmacology to explore the mechanisms of P. lobata in the treatment of PF. Finally, we confirmed that P. lobata alleviated BLM-induced PF in rats by regulating arginine metabolism pathway based on reducing the L-OT and NOS2-related signal molecular. The search for the biomarkers finding of arginine metabolism pathway revealed a new strategy for P. lobata in the treatment of PF.

Identification of Chagas disease biomarkers using untargeted metabolomics

Untargeted metabolomic analysis is a powerful tool used for the discovery of novel biomarkers. Chagas disease (CD), caused by Trypanosoma cruzi, is a neglected tropical disease that affects 6-7 million people with approximately 30% developing cardiac manifestations. The most significant clinical challenge lies in its long latency period after acute infection, and the lack of surrogate markers to predict disease progression or cure. In this cross-sectional study, we analyzed sera from 120 individuals divided into four groups: 31 indeterminate CD, 41 chronic chagasic cardiomyopathy (CCC), 18 Latin Americans with other cardiomyopathies and 30 healthy volunteers. Using a high-throughput panel of 986 metabolites, we identified three distinct profiles among individuals with cardiomyopathy, indeterminate CD and healthy volunteers. After a more stringent analysis, we identified some potential biomarkers. Among peptides, phenylacetylglutamine and fibrinopeptide B (1-13) exhibited an increasing trend from controls to ICD and CCC. Conversely, reduced levels of bilirubin and biliverdin alongside elevated urobilin correlated with disease progression. Finally, elevated levels of cystathionine, phenol glucuronide and vanillactate among amino acids distinguished CCC individuals from ICD and controls. Our novel exploratory study using metabolomics identified potential biomarker candidates, either alone or in combination that if confirmed, can be translated into clinical practice.

Metabolic disruptions and impaired reproductive fitness in wild-caught freshwater turtles (Emydura macquarii macquarii) exposed to elevated per- and polyfluoroalkyl substances (PFAS)

Per- and poly-fluoroalkyl substances (PFAS) pose a threat to organisms and ecosystems due to their persistent nature. Ecotoxicology endpoints used in regulatory guidelines may not reflect multiple, low-level but persistent stressors. This study examines the biological effects of PFAS on Eastern short-necked turtles in Queensland, Australia. In this study, blood samples were collected and analysed for PFAS, hormone levels, and functional omics endpoints. High levels of PFAS were found in turtles at the impacted site, with PFOS being the dominant constituent. The PFAS profiles of males and females differed, with males having higher PFAS concentrations. Hormone concentrations differed between impacted and reference sites in male turtles, with elevated testosterone and corticosterone indicative of stress. Further, energy utilisation, nucleotide synthesis, nitrogen metabolism, and amino acid synthesis were altered in both male and female turtles from PFAS-impacted sites. Both sexes show similar metabolic responses to environmental stressors from the PFAS-contaminated site, which may adversely affect their reproductive fitness. Purine metabolism, caffeine metabolism, and ferroptosis pathway changes in turtles can cause gout, cell death, and overall health problems. Further, the study showed that prolonged exposure to elevated PFAS levels in the wild could compromise turtle reproductive fitness by disrupting reproductive steroids and metabolic pathways.

The anti-depression effect and potential mechanism of the petroleum ether fraction of CDB: Integrated network pharmacology and metabolomics

The combination of Chaidangbo (CDB) is an antidepressant traditional Chinese medicine (TCM) prescription simplified by Xiaoyaosan (a classic antidepressant TCM prescription) through dismantling research, which has the effect of dispersing stagnated liver qi and nourishing blood in TCM theory. Although the antidepressant effect of CBD has been confirmed in animal studies, the material basis and possible molecular mechanism for antidepressant activity in CBD have not been clearly elucidated. Herein, we investigated the effects and potential mechanisms of CDB antidepressant fraction (petroleum ether fraction of CDB, PEFC) on chronic unpredictable mild stress (CUMS)-induced depression-like behavior in mice using network pharmacology and metabolomics. First, a UPLC-QE/MS was employed to identify the components of PEFC. To extract active ingredients, SwissADME screening was used to the real PEFC components that were found. Potential PEFC antidepressant targets were predicted based on a network pharmacology approach, and a pathway enrichment analysis was performed for the predicted targets. Afterward, a CUMS mouse depression model was established and LC-MS-based untargeted hippocampal metabolomics was performed to identify differential metabolites, and related metabolic pathways. Finally, the protein expressions in mouse hippocampi were determined by Western blot to validate the network pharmacology and metabolomics deduction. A total of 16 active compounds were screened in SwissADME that acted on 73 core targets of depression, including STAT3, MAPKs, and NR3C1; KEGG enrichment analysis showed that PEFC modulated signaling pathways such as PI3K-Akt signaling pathway, endocrine resistance, and MAPK to exert antidepressant effects. PEFC significantly reversed abnormalities of hippocampus metabolites in CUMS mice, mainly affecting the synthesis and metabolism of glycine, serine, and threonine, impacting catecholamine transfer and cholinergic synapses and regulating the activity of the mTOR signaling pathway. Furthermore, Western blot analysis confirmed that PEFC significantly influenced the main protein levels of the PI3K/Akt/mTOR signaling pathways in the hippocampus of mice subjected to CUMS. This study integrated metabolomics, network pharmacology and biological verification to explore the potential mechanism of PEFC in treating depression, which is related to the regulation of amino acid metabolism dysfunction and the activation of PI3K/Akt/mTOR signaling pathways in the hippocampus. The comprehensive strategy also provided a reasonable way for unveiling the pharmacodynamic mechanisms of multi-components, multi-targets, and multi-pathways in TCM with antidepressant effect.

Untargeted Metabolomics Based on UPLC-Q-Exactive-Orbitrap-MS/MS Revealed the Differences and Correlations between Different Parts of the Root of Paeonia lactiflora Pall

BACKGROUND

Paeonia lactiflora Pall. (PLP) is a plant with excellent ornamental and therapeutic value that can be utilized in traditional Chinese medicine as Paeoniae Radix Alba (PRA) and Paeoniae Radix Rubra (PRR). PRA must undergo the "peeling" process, which involves removing the cork and a portion of the phloem. PLP's biological function is strongly linked to its secondary metabolites, and the distribution of metabolites in different regions of the PLP rhizome causes changes in efficacy when PLP is processed into various therapeutic compounds.

METHODS

The metabolites of the cork (cor), phloem (phl), and xylem (xyl) were examined in the roots of PLP using a metabolomics approach based on UPLC-Q-Exactive-Orbitrap-MS/MS (UPLC-MS/MS), and the differential metabolites were evaluated using multivariate analysis.

RESULTS

Significant changes were observed among the cor, phl, and xyl samples. In both positive and negative ion modes, a total of 15,429 peaks were detected and 7366 metabolites were identified. A total of 525 cor-phl differential metabolites, 452 cor-xyl differential metabolites, and 328 phl-xyl differential metabolites were evaluated. Flavonoids, monoterpene glycosides, fatty acids, sugar derivatives, and carbohydrates were among the top 50 dissimilar chemicals. The key divergent metabolic pathways include linoleic acid metabolism, galactose metabolism, ABC transporters, arginine biosynthesis, and flavonoid biosynthesis.

CONCLUSION

The cor, phl, and xyl of PLP roots exhibit significantly different metabolite types and metabolic pathways; therefore, "peeling" may impact the pharmaceutical effect of PLP. This study represents the first metabolomics analysis of the PLP rhizome, laying the groundwork for the isolation and identification of PLP pharmacological activity, as well as the quality evaluation and efficacy exploration of PLP.

GC/MS and LC/MS serum metabolomic analysis of Chinese LN patients

China, being a densely populated nation, faces a substantial economic burden due to a high incidence of lupus nephritis (LN) cases. The concealed onset of LN has resulted in many individuals have missed the optimal timing for treatment. The aim of the research is to study the serum metabolomics of Chinese LN patients using gas chromatography (GC)/mass spectrometry (MS) and liquid chromatography (LC)/MS to identify potential diagnostic markers. Fifty LN patients and fifty normal controls, matched for Body Mass Index (BMI) and age, were selected. Serum analysis was conducted using GC/MS and LC/MS, followed by multivariate statistical analysis. Various multidimensional analyses, including principal component analysis, partial least squares discrimination analysis, and orthogonal partial least squares discrimination analysis, along with one-dimensional analyses such as t-tests, were performed. Metabolites with variable importance in projection value > 1 and a p-value < 0.05 were considered critical biomarkers for LN. Furthermore, identified biomarkers delineated relevant metabolic pathways, and a metabolic pathway map was obtained from the database. Forty-one metabolites were identified as potential LN biomarkers, primarily associated with immune regulation, energy metabolism, intestinal microbial metabolism, renal damage, and oxidative stress. The potential for diagnosing LN and other diseases through metabolomics is demonstrated. Future research should explore larger sample sizes, metabolomic comparisons across different diseases and health states, and integration of metabolomics with clinical diagnostics. Such studies will enhance the understanding of metabolomics in medical diagnosis and provide robust support for its practical application.

COLMARppm: A Web Server Tool for the Accurate and Rapid Prediction of 1H and 13C NMR Chemical Shifts of Organic Molecules and Metabolites

Despite rapid progress in metabolomics research, a major bottleneck is the large number of metabolites whose chemical structures are unknown or whose spectra have not been deposited in metabolomics databases. Nuclear magnetic resonance (NMR) spectroscopy has a long history of elucidating chemical structures from experimentally measured 1H and 13C chemical shifts. One approach to characterizing the chemical structures of an unknown metabolite is to predict the 1H and 13C chemical shifts of candidate compounds (e.g., metabolites from the Human Metabolome Database (HMDB)) and compare them with chemical shifts of the unknown. However, accurate prediction of NMR chemical shifts in aqueous solution is challenging due to limitations of experimental chemical shift libraries and the high computational cost of quantum chemical methods. To improve NMR prediction accuracy and applicability, an empirical prediction strategy is introduced here to provide an accurately predicted chemical shift for organic molecules and metabolites within seconds. Unique features of COLMARppm include (i) the training library exclusively consisting of high quality NMR spectra measured under standard conditions in aqueous solution, (ii) utilization of NMR motif information, and (iii) leveraging of the improved prediction accuracy for the automated assignment of experimental chemical shifts for candidate structures. COLMARppm is demonstrated in terms of accuracy and speed for a set of 20 compounds taken from the HMDB for chemical shift prediction and resonance assignment. COLMARppm is applicable to a wide range of small molecules and can be directly incorporated into metabolomics workflows.

Metabolomics study reveals increased deoxycholic acid contributes to deoxynivalenol-mediated intestinal barrier injury

AIMS

Deoxynivalenol (DON), namely vomitoxin, is one of the most prevalent fungal toxins in cereal crops worldwide. However, the underlying toxic mechanisms of DON remain largely unknown.

MAIN METHODS

DON exposure-caused changes in the murine plasma metabolome and gut microbiome were investigated by an LC-MS/MS-based nontargeted metabolomics approach and sequencing of 16S rRNA in fecal samples, respectively. Cellular models were then used to validate the findings from the metabolomics study.

KEY FINDINGS

DON exposure increased intestinal barrier permeability evidenced by its-mediated decrease in colonic Claudin 5 and E-cadherin, as well as increases in colonic Ifn-γ, Cxcl9, Cxcl10, and Cxcr3. Furthermore, DON exposure resulted in a significant increase in murine plasma levels of deoxycholic acid (DCA). Also, DON exposure led to gut microbiota dysbiosis, which was associated with DON exposure-caused increase in plasma DCA. In addition, we found not only DON but also DCA dose-dependently caused a significant increase in the levels of IFN-γ, CXCL9, CXCL10, and/or CXCR3, as well as a significant decrease in the expression levels of Claudin 5 and/or E-cadherin in the human colonic epithelial cells (NCM460).

SIGNIFICANCE

DON-mediated increase in DCA contributes to DON-caused intestinal injury. DCA may be a potential therapeutic target for DON enterotoxicity.

Metabolomics of natural samples: A tutorial review on the latest technologies

Metabolomics is the study of metabolites present in a living system. It is a rapidly growing field aimed at discovering novel compounds, studying biological processes, diagnosing diseases, and ensuring the quality of food products. Recently, the analysis of natural samples has become important to explore novel bioactive compounds and to study how environment and genetics affect living systems. Various metabolomics techniques, databases, and data analysis tools are available for natural sample metabolomics. However, choosing the right method can be a daunting exercise because natural samples are heterogeneous and require untargeted approaches. This tutorial review aims to compile the latest technologies to guide an early-career scientist on natural sample metabolomics. First, different extraction methods and their pros and cons are reviewed. Second, currently available metabolomics databases and data analysis tools are summarized. Next, recent research on metabolomics of milk, honey, and microbial samples is reviewed. Finally, after reviewing the latest trends in technologies, a checklist is presented to guide an early-career researcher on how to design a metabolomics project. In conclusion, this review is a comprehensive resource for a researcher planning to conduct their first metabolomics analysis. It is also useful for experienced researchers to update themselves on the latest trends in metabolomics.

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.

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.

Metabolomic Analysis in Neurocritical Care Patients

Metabolomics is the analytical study of metabolites in biological matrices using high-throughput profiling. Traditionally, the metabolome has been studied to identify various biomarkers for the diagnosis and pathophysiology of disease. Over the last decade, metabolomic research has grown to include the identification of prognostic markers, the development of novel treatment strategies, and the prediction of disease severity. In this review, we summarized the available evidence on the use of metabolome profiling in neurocritical care populations. Specifically, we focused on aneurysmal subarachnoid hemorrhage, traumatic brain injury, and intracranial hemorrhage to identify the gaps in the current literature and to provide direction for future studies. A primary literature search of the Medline and EMBASE databases was conducted. Upon removing duplicate studies, abstract screening and full-text screening were performed. We screened 648 studies and extracted data from 17 studies. Based on the current evidence, the utility of metabolomic profiling has been limited due to inconsistencies amongst studies and a lack of reproducible data. Studies identified various biomarkers for diagnosis, prognosis, and treatment modification. However, studies evaluated and identified different metabolites, resulting in an inability to compare the study results. Future research towards addressing the gaps in the current literature, including reproducing data on the use of specific metabolite panels, is needed.

Metabolomics of infectious diseases in the era of personalized medicine

Infectious diseases continue to be a major cause of morbidity and mortality worldwide. Diseases cause perturbation of the host's immune system provoking a response that involves genes, proteins and metabolites. While genes are regulated by epigenetic or other host factors, proteins can undergo post-translational modification to enable/modify function. As a result, it is difficult to correlate the disease phenotype based solely on genetic and proteomic information only. Metabolites, however, can provide direct information on the biochemical activity during diseased state. Therefore, metabolites may, potentially, represent a phenotypic signature of a diseased state. Measuring and assessing metabolites in large scale falls under the omics technology known as "metabolomics". Comprehensive and/or specific metabolic profiling in biological fluids can be used as biomarkers of disease diagnosis. In addition, metabolomics together with genomics can be used to differentiate patients with differential treatment response and development of host targeted therapy instead of pathogen targeted therapy where pathogens are more prone to mutation and lead to antimicrobial resistance. Thus, metabolomics can be used for patient stratification, personalized drug formulation and disease control and management. Currently, several therapeutics and in vitro diagnostics kits have been approved by US Food and Drug Administration (FDA) for personalized treatment and diagnosis of infectious diseases. However, the actual number of therapeutics or diagnostics kits required for tailored treatment is limited as metabolomics and personalized medicine require the involvement of personnel from multidisciplinary fields ranging from technological development, bioscience, bioinformatics, biostatistics, clinicians, and biotechnology companies. Given the significance of metabolomics, in this review, we discussed different aspects of metabolomics particularly potentials of metabolomics as diagnostic biomarkers and use of small molecules for host targeted treatment for infectious diseases, and their scopes and challenges in personalized medicine.

Mass spectrometry-based metabolomics for discovering active ingredients and exploring action mechanism of herbal medicine

Natural products derived from herbal medicine are a fruitful source of lead compounds because of their structural diversity and potent bioactivities. However, despite the success of active compounds derived from herbal medicine in drug discovery, some approaches cannot effectively elucidate the overall effect and action mechanism due to their multi-component complexity. Fortunately, mass spectrometry-based metabolomics has been recognized as an effective strategy for revealing the effect and discovering active components, detailed molecular mechanisms, and multiple targets of natural products. Rapid identification of lead compounds and isolation of active components from natural products would facilitate new drug development. In this context, mass spectrometry-based metabolomics has established an integrated pharmacology framework for the discovery of bioactivity-correlated constituents, target identification, and the action mechanism of herbal medicine and natural products. High-throughput functional metabolomics techniques could be used to identify natural product structure, biological activity, efficacy mechanisms, and their mode of action on biological processes, assisting bioactive lead discovery, quality control, and accelerating discovery of novel drugs. These techniques are increasingly being developed in the era of big data and use scientific language to clarify the detailed action mechanism of herbal medicine. In this paper, the analytical characteristics and application fields of several commonly used mass spectrometers are introduced, and the application of mass spectrometry in the metabolomics of traditional Chinese medicines in recent years and its active components as well as mechanism of action are also discussed.

Untangling the Complexities of Processing and Analysis for Untargeted LC-MS Data Using Open-Source Tools

Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free-to-use data processing and analysis tools exist for various untargeted MS approaches, including liquid chromatography (LC), but choosing the 'correct' pipeline isn't straight-forward. This tutorial, in conjunction with a user-friendly online guide presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. We provide practical advice concerning experimental design, organisation of data and downstream analysis, and offer details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/changing methodologies and increased clarity and detail as user participation becomes more common. Hence, the authors welcome contributions and improvements to the workflow via the online repository. We believe that this workflow will streamline and condense complex mass-spectrometry approaches into easier, more manageable, analyses thereby generating opportunities for researchers previously discouraged by inaccessible and overly complicated software.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Metabolomics of asthma, COPD, and asthma-COPD overlap: an overview

The two common progressive lung diseases, asthma and chronic obstructive pulmonary disease (COPD), are the leading causes of morbidity and mortality worldwide. Asthma-COPD overlap, referred to as ACO, is another complex pulmonary disease that manifests itself with features of both asthma and COPD. The disease has no clear diagnostic or therapeutic guidelines, thereby making both diagnosis and treatment challenging. Though a number of studies on ACO have been documented, gaps in knowledge regarding the pathophysiologic mechanism of this disorder exist. Addressing this issue is an urgent need for improved diagnostic and therapeutic management of the disease. Metabolomics, an increasingly popular technique, reveals the pathogenesis of complex diseases and holds promise in biomarker discovery. This comprehensive narrative review, comprising 99 original research articles in the last five years (2017-2022), summarizes the scientific advances in terms of metabolic alterations in patients with asthma, COPD, and ACO. The analytical tools, nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS), commonly used to study the expression of the metabolome, are discussed. Challenges frequently encountered during metabolite identification and quality assessment are highlighted. Bridging the gap between phenotype and metabotype is envisioned in the future.

Morphology and gene expression profiling provide complementary information for mapping cell state

Morphological and gene expression profiling can cost-effectively capture thousands of features in thousands of samples across perturbations by disease, mutation, or drug treatments, but it is unclear to what extent the two modalities capture overlapping versus complementary information. Here, using both the L1000 and Cell Painting assays to profile gene expression and cell morphology, respectively, we perturb human A549 lung cancer cells with 1,327 small molecules from the Drug Repurposing Hub across six doses, providing a data resource including dose-response data from both assays. The two assays capture both shared and complementary information for mapping cell state. Cell Painting profiles from compound perturbations are more reproducible and show more diversity but measure fewer distinct groups of features. Applying unsupervised and supervised methods to predict compound mechanisms of action (MOAs) and gene targets, we find that the two assays not only provide a partially shared but also a complementary view of drug mechanisms. Given the numerous applications of profiling in biology, our analyses provide guidance for planning experiments that profile cells for detecting distinct cell types, disease phenotypes, and response to chemical or genetic perturbations.

Metabolic signatures in human follicular fluid identify lysophosphatidylcholine as a predictor of follicular development

In order to investigate the metabolic characteristics of human follicular fluid (FF) and to reveal potential metabolic predictors of follicular development (FD) with clinical implications, we analyzed a total of 452 samples based on a two-stage study design. In the first stage, FF samples from both large follicles (LFs) and matched-small follicles (SFs) of 26 participants were analyzed with wide-spectrum targeted metabolomics. The metabolic signatures were described by multi-omics integration technology including metabolomic data and transcriptomic data. In the second stage, the potential biomarkers of FD were verified using enzyme-linked immunoassay with FF and blood serum from an independent 200 participants. We describe the FF metabolic signatures from ovarian follicles of different developmental stages. Lysophosphatidylcholine (LPC) can be used as a biomarker of FD and ovarian sensitivity, advancing the knowledge of metabolic regulation during FD and offering potential detection and therapeutic targets for follicle and oocyte health improvements in humans.

A two-stage metabolomic analysis for human follicular fluid characteristics and predictors of follicular development yields metabolic signatures and proposes lysophosphatidylcholine (LPC) as a biomarker for follicular development.

Metabolomics as a valid analytical technique in environmental exposure research: application and progress

BACKGROUND

In recent years, studies have shown that exposure to environmental pollutants (e.g., radiation, heavy metal substances, air pollutants, organic pollutants) is a leading cause of human non-communicable diseases. The key to disease prevention is to clarify the harmful mechanisms and toxic effects of environmental pollutants on the body. Metabolomics is a high-sensitivity, high-throughput omics technology that can obtain detailed metabolite information of an organism. It is a crucial tool for gaining a comprehensive understanding of the pathway network regulation mechanism of the organism. Its application is widespread in many research fields such as environmental exposure assessment, medicine, systems biology, and biomarker discovery.

AIM OF REVIEW

Recent findings show that metabolomics can be used to obtain molecular snapshots of organisms after environmental exposure, to help understand the interaction between environmental exposure and organisms, and to identify potential biomarkers and biological mechanisms.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review focuses on the application of metabolomics to understand the biological effects of radiation, heavy metals, air pollution, and persistent organic pollutants exposure, and examines some potential biomarkers and toxicity mechanisms.

Oxylipin profile in saliva from patients with cystic fibrosis reveals a balance between pro-resolving and pro-inflammatory molecules

Oxylipins are signaling molecules originated by fatty acids that modulate vascular and bronchial tone, bronchial secretion, cytokine production and immune cell activity. The unbalanced production of pro-inflammatory and pro-resolving (i.e., anti-inflammatory) oxylipins has a relevant role in the pathogenesis of pulmonary inflammation like in cystic fibrosis (CF). We analyzed by LC-MRM/MS 65 oxylipins and 4 fatty acids in resting saliva from 69 patients with CF and 50 healthy subjects (controls). The salivary levels of 48/65 oxylipins were significantly different between CF patients and controls. Among these, EpETE, DHET, 6ketoPGE1 and HDHA were significantly higher in saliva from CF patients than in controls. All these molecules display anti-inflammatory effects, i.e., releasing of bronchial and vascular tone, modulation of cytokine release. While 20-hydroxyPGF2A, PGB2, EpDPE, 9 K-12-ELA, bicyclo-PGE2, oleic acid, LTC4, linoleic acid, 15oxoEDE, 20 hydroxyPGE2 and DHK-PGD2/PGE2 (mostly associated to pro-inflammatory effects) resulted significantly lower in CF patients than in controls. Our data suggest that the salivary oxylipins profile in CF patients is addressed toward a global anti-inflammatory effect. Although these findings need be confirmed on larger populations in prospective studies, they will contribute to better understand the pathogenesis of CF chronic inflammation and to drive targeted therapies based on the modulation of oxylipins synthesis and degradation.

Metabolomic Study on Iohexol-Induced Nephrotoxicity in Rats Based on NMR and LC-MS Analyses

Iohexol, the raw material of nonionic X-ray computed tomography (X-CT) contrast medium, is usually injected into the vein before CT angiography diagnosis. It is used for angiography, urography, and lymphography. With the advantages of low contrast density and good tolerance, it is currently one of the most popular contrast media. However, the renal toxicity of iohexol seriously affects its safety use. Therefore, it is of great importance to identify new potential diagnostic biomarkers and therapeutic targets in the process of contrast medium-induced acute kidney injury (CI-AKI) in order to safely use iohexol in clinical practice. In this study, in order to understand the metabolic mechanism of CI-AKI, ultra-high-performance liquid chromatography/quadrupole-Orbitrap-mass spectrometry and ¹H NMR-based metabolomic techniques were utilized to study the metabolic spectra of kidney, plasma, and urine from CI-AKI rats, and a total of 30 metabolites that were closely related to kidney injury were screened out, which were mainly related to 9 metabolic pathways. The results further indicated that iohexol might intensify kidney dysfunction in vivo by disrupting the metabolic pathways in the body, especially through blocking energy metabolism, amino acid metabolism, and promoting inflammatory reactions.

Distinct lipid profiles of radiation-induced carotid plaques from atherosclerotic carotid plaques revealed by UPLC-QTOF-MS and DESI-MSI

BACKGROUND AND PURPOSE

Radiotherapy is a standard treatment for head and neck tumors that significantly increases patients' long-term survival rates. However, late cerebrovascular complications, especially carotid artery stenosis (CAS), have gained increasing attention. Investigation of biomarkers of radiation-induced CAS may help to elucidate the mechanism by which radiation induces damage to blood vessels and identify possible preventive measures against such damage.

MATERIALS AND METHODS

In this study, we used lipidomics strategy to characterize the lipids present in 8 radiation-induced carotid plaques (RICPs) and 12 atherosclerotic carotid plaques (ASCPs). We also used desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) to map the spatial distribution of the screened lipids from 2 RICPs samples and 2 ASCPs samples.

RESULTS

The results showed that 31 metabolites in RICPs were significantly higher than that in ASCPs, 24 of which were triglycerides (TGs). We used four machine learning models to select potential indicators from the 31 metabolites. Six TGs [TG(17:2/17:2/18:0), TG(17:1/17:2/18:0), TG(17:0/17:2/18:0), TG(17:2/17:2/20:0), TG(17:1/17:2/20:0), TG(15:0/22:0/22:2)] were found to be the potential markers for distinguishing RICPs and ASCPs (AUC = 0.83). The DESI-MSI results suggested that the 6 TGs were localized in the collagen fiber regions and confirmed the differences of these TGs between the two kinds of plaques.

CONCLUSIONS

The 6 TGs primarily localized in the collagen fiber regions of plaques are likely to be potential indicators for the differentiation of RICPs from ASCPs which may have implications in the mechanisms and possible preventive measures against RICPs.

Morphological and metabolomics impact of sublethal doses of natural compounds and its nanoemulsions in Bacillus cereus

Microbiological safety in food industry are always a concern regarding sublethal tolerance in bacteria for common and natural sanitizers. Natural bacteriocins, such as nisin (NIS), may negatively interfere in the efficiency of major compounds of essential oils against foodborne pathogenic bacteria. However, nanoemulsioned forms increase the bactericidal potential of natural compounds acting synergistically. In this study, cinnamaldehyde (CIN), citral (CIT), and linalool (LIN) were evaluated independently, associated with NIS, and in nanoemulsions (NEs) against Bacillus cereus using untargeted-metabolomics. Results revealed morphological changes in the structure of B. cereus treated with NEs of CIN and CIT, both NIS-associated. In addition, sensibility tests and UHPLC-QTOF-MS analyses indicated that NIS might react together with CIT reducing the bactericidal efficiency, while the nanoemulsion of CIT effect was enhanced by NIS in nanoemulsioned forms. This study highlights the importance of prudent administration of natural compounds as antimicrobial agents to prevent sublethal tolerance in pathogenic bacteria.

Plasma Metabolomics for Discovery of Early Metabolic Markers of Prostate Cancer Based on Ultra-High-Performance Liquid Chromatography-High Resolution Mass Spectrometry

BACKGROUND

The prevention and early screening of PCa is highly dependent on the identification of new biomarkers. In this study, we investigated whether plasma metabolic profiles from healthy males provide novel early biomarkers associated with future risk of PCa.

METHODS

Using the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort, we identified plasma samples collected from 146 PCa cases up to 13 years prior to diagnosis and 272 matched controls. Plasma metabolic profiles were characterized using ultra-high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS).

RESULTS

Orthogonal partial least squares discriminant analysis (OPLS-DA) discriminated PCa cases from controls, with a median area under the receiver operating characteristic curve (AU-ROC) of 0.92 using a 1000-time repeated random sub-sampling validation. Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) identified the top 10 most important metabolites (p < 0.001) discriminating PCa cases from controls. Among them, phosphate, ethyl oleate, eicosadienoic acid were higher in individuals that developed PCa than in the controls during the follow-up. In contrast, 2-hydroxyadenine, sphinganine, L-glutamic acid, serotonin, 7-keto cholesterol, tiglyl carnitine, and sphingosine were lower.

CONCLUSION

Our results support the dysregulation of amino acids and sphingolipid metabolism during the development of PCa. After validation in an independent cohort, these signatures may promote the development of new prevention and screening strategies to identify males at future risk of PCa.