Chromatography Mass Spectrometry-based Untargeted Metabolomics Research Articles

Exploring stingless bee honey from selected regions of Peninsular Malaysia through gas chromatography-mass spectrometry-based untargeted metabolomics.

Volatile organic compounds in honey are known for their considerable impact on the organoleptic properties of honey, such as aroma, flavor, taste, and texture. The type and composition of volatile organic compounds are influenced by entomological, geographical, and botanical origins; thus, these compounds have the potential to be chemical markers. Sixty-two volatile compounds were identified using gas chromatography-mass spectrometry from 30 Heterotrigona itama(H. itama) honey samples from 3 different geographical origins. Hydrocarbons and benzene derivatives were the dominant classes of volatile organic compounds in the samples. Both clustering and discriminant analyses demonstrated a clear separation between samples from distant origins (Kedah and Perak), and the volcano plot supported it. The reliability and predictability of the partial least squares-discriminant analysis model from the discriminant analysis were validated using cross-validation (R2 : 0.93; Q2 : 0.83; accuracy: 0.97) and the permutation test (p<0.001), and the output depicted that the model is legitimate. In combination with the variable importance of projection (VIP>1.0) and the Kruskal-Wallis test (p<0.01), 19 volatile organic compounds (encompassed aldehydes, benzene derivatives, esters, hydrocarbons, and terpenoids) were sorted and named potent chemical markers in classifying honey samples from three geographical origins. In brief, this study illustrated that volatile organic compounds of stingless honey originated from the same bee species, but different geographical origins could be applied as chemical markers.

The altered metabolic pathways of diffuse large B-cell lymphoma not otherwise specified

Altered metabolic fingerprints of Diffuse large B-cell lymphoma, not otherwise specified (DLBCL NOS) may offer novel opportunities to identify new biomarkers and improve the understanding of its pathogenesis. This study aimed to investigate the modified metabolic pathways in extranodal, germinal center B-cell (GCB) and non-GCB DLBCL NOS from the head and neck. Formalin-fixed paraffin-embedded (FFPE) tissues from eleven DLBCL NOS classified according to Hans’ algorithm using immunohistochemistry, and five normal lymphoid tissues (LT) were analyzed by high-performance liquid chromatography-mass spectrometry-based untargeted metabolomics. Partial Least Squares Discriminant Analysis showed that GCB and non-GCB DLBCL NOS have a distinct metabolomics profile, being the former more similar to normal lymphoid tissues. Metabolite pathway enrichment analysis indicated the following altered pathways: arachidonic acid, tyrosine, xenobiotics, vitamin E metabolism, and vitamin A. Our findings support that GCB and non-GCB DLBCL NOS has a distinct metabolomic profile, in which GCB possibly shares more metabolic similarities with LT than non-GCB DLBCL NOS.

The metabolomic profiling of total fat and fat distribution in a multi-cohort study of women and men

Currently studies aiming for the comprehensive metabolomics profiling of measured total fat (%) as well as fat distribution in both sexes are lacking. In this work, bioimpedance analysis was applied to measure total fat (%) and fat distribution (trunk to leg ratio). Liquid chromatography-mass spectrometry-based untargeted metabolomics was employed to profile the metabolic signatures of total fat (%) and fat distribution in 3447 participants from three Swedish cohorts (EpiHealth, POEM and PIVUS) using a discovery-replication cross-sectional study design. Total fat (%) and fat distribution were associated with 387 and 120 metabolites in the replication cohort, respectively. Enriched metabolic pathways for both total fat (%) and fat distribution included protein synthesis, branched-chain amino acids biosynthesis and metabolism, glycerophospholipid metabolism and sphingolipid metabolism. Four metabolites were mainly related to fat distribution: glutarylcarnitine (C5-DC), 6-bromotryptophan, 1-stearoyl-2-oleoyl-GPI (18:0/18:1) and pseudouridine. Five metabolites showed different associations with fat distribution in men and women: quinolinate, (12Z)-9,10-dihydroxyoctadec-12-enoate (9,10-DiHOME), two sphingomyelins and metabolonic lactone sulfate. To conclude, total fat (%) and fat distribution were associated with a large number of metabolites, but only a few were exclusively associated with fat distribution and of those metabolites some were associated with sex*fat distribution. Whether these metabolites mediate the undesirable effects of obesity on health outcomes remains to be further investigated.

Metabolite profiling of susceptible and resistant wheat (Triticum aestivum) cultivars responding to Puccinia striiformis f. sp. tritici infection

BackgroundPuccinia striiformis f. sp. tritici (Pst) is an economically devasting disease that is prominent in cereal crops such as wheat (Triticum aestivum). The fungal pathogen can cause approximately 30–70% losses in crop productivity and yields. Pst has become difficult to manage due to its ease of transmission through wind dispersal over long distances, and intercontinental dispersal has been previously reported. The ease of transmission has resulted in further destruction because of new and more virulent strains infecting crops previously resistant to a different strain.ResultsIn this study, a liquid chromatography-mass spectrometry-based untargeted metabolomics approach, in combination with multivariate data analytical tools, was used to elucidate the mechanistic nature of the defence systems of a Pst-resistant and a susceptible wheat cultivar infected with P. striiformis. We also investigated the time-dependant metabolic reconfiguration of infected plants over a four-week period. The untargeted metabolomic analysis revealed a time-course metabolic reprogramming involving phenylpropanoids (majority flavonoids), amino acids, lipids, benzoic acids, TCA cycle intermediates and benzoxazinoids responding to Pst infection. Interestingly, the results do not show a linear course for the decrease and increase (up-/down-regulation) of said classes of metabolites, but rather the up- or down-regulation of specific metabolites in response to the pathogen infection. The resistant Koonap cultivar had an abundance of phenolic compounds such as rutin, isoorintin-7-O-glucoside and luteolin-6-C-hexoside-O-hexoside. These compounds showed a decrease over time in control Koonap plants compared to an increase in Pst-infected plants. These metabolites were down-regulated in the susceptible Gariep cultivar, which could serve as biomarkers for plant responses to biotic stress and resistance against Pst.ConclusionsOverall, an LC-MS-based metabolomics approach allowed for the metabolic profiling and analysis of the impact of plant-pathogen interactions on the overall plant metabolome and provided a real-time snapshot of the differential significant metabolic perturbations occurring in wheat plants responding to the Pst pathogen. The Pst-resistant Koonap cultivar showed a rapid accumulation of defence metabolites in response to pathogen infection compared to the susceptible Gariep cultivar. These findings provide insight into the mechanistic biochemical nature of plant-microbe interactions and the prospects of metabolic engineering for improved plant tolerance and resistance to biotic stresses.

LC/MS-Based Untargeted Metabolomics Analysis in Women with Morbid Obesity and Associated Type 2 Diabetes Mellitus.

Obesity is a chronic and complex disease, with an increasing incidence worldwide that is associated with metabolic disorders such as type 2 diabetes mellitus (T2DM). Thus, it is important to determine the differences between metabolically healthy obese individuals and those with metabolic disorders. The aim of this study was to perform an untargeted metabolomics assay in women with morbid obesity (MO) compared to a normal weight group, and to differentiate the metabolome of these women with MO who present with T2DM. We carried out a liquid chromatography-mass spectrometry-based untargeted metabolomics assay using serum samples of 209 Caucasian women: 73 with normal weight and 136 with MO, of which 71 had T2DM. First, we found increased levels of choline and acylglycerols and lower levels of bile acids, steroids, ceramides, glycosphingolipids, lysophosphatidylcholines, and lysophosphatidylethanolamines in MO women than in the control group. Then, in MO women with T2DM, we found increased levels of glutamate, propionyl-carnitine, bile acids, ceramides, lysophosphatidylcholine 14:0, phosphatidylinositols and phosphoethanolamines, and lower levels of Phe-Ile/Leu. Thus, we found metabolites with opposite trends of concentration in the two metabolomic analyses. These metabolites could be considered possible new factors of study in the pathogenesis of MO and associated T2DM in women.

Gas chromatography-mass spectrometry-based untargeted metabolomics analysis reveals circulating biomarkers related to wooden breast myopathy in broilers: a preliminary study

Approaches for the diagnosis of wooden breast (WB) myopathy in live birds are urgently required before applying intervention strategies to reduce occurrence and severity for the poultry industry. The objective of this study was to characterize the serum metabolic profiles in male broilers affected by WB and to identify biomarkers related to this myopathy. Broilers were categorized into normal (CON) and WB groups based on gross scoring and histological evaluation. Gas chromatography-mass spectrometry-based metabolomics, multivariate analysis, and orthogonal partial least squares discriminant analysis revealed a clear separation between CON and WB. A total of 73 significantly different (P < 0.05) metabolites with 17 upregulated and 56 downregulated were identified, which were mainly involved in pathways of alanine, aspartate, and glutamate metabolism, carbohydrate metabolism, and taurine and hypotaurine metabolism. By using the nested cross-validation function of random forest analysis, 9 significantly altered (P < 0.05) metabolites (cerotinic acid, arabitol, phosphoenolpyruvate, terephthalic acid, cis-gondoic acid, N-acetyl-d-glucosamine, 4-hydroxymandelic acid, caffeine, and xanthurenic acid) were identified as biomarkers with an excellent discriminant performance for WB myopathy. Collectively, this study provides new insights for a deeper understanding of the pathogenesis and provides metabolites as biomarkers for diagnostic utilization of WB myopathy.

A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics

Ion mobility (IM) adds a new dimension to liquid chromatography-mass spectrometry-based untargeted metabolomics which significantly enhances coverage, sensitivity, and resolving power for analyzing the metabolome, particularly metabolite isomers. However, the high dimensionality of IM-resolved metabolomics data presents a great challenge to data processing, restricting its widespread applications. Here, we develop a mass spectrum-oriented bottom-up assembly algorithm for IM-resolved metabolomics that utilizes mass spectra to assemble four-dimensional peaks in a reverse order of multidimensional separation. We further develop the end-to-end computational framework Met4DX for peak detection, quantification and identification of metabolites in IM-resolved metabolomics. Benchmarking and validation of Met4DX demonstrates superior performance compared to existing tools with regard to coverage, sensitivity, peak fidelity and quantification precision. Importantly, Met4DX successfully detects and differentiates co-eluted metabolite isomers with small differences in the chromatographic and IM dimensions. Together, Met4DX advances metabolite discovery in biological organisms by deciphering the complex 4D metabolomics data.

ORAL LEUKOPLAKIA WITH AND WITHOUT MALIGNANT TRANSFORMATION ARE METABOLICALLY DISTINCT

<h3>Objectives</h3> To compare the metabolic profile of oral leukoplakia with and without malignant transformation. <h3>Study Design</h3> We evaluated samples of untransformed oral leukoplakia (<i>n</i> = 15) and those that underwent malignant transformation (<i>n</i> = 5) by high-performance liquid chromatography-mass spectrometry-based untargeted metabolomics. Univariate and multivariate statistical methods were applied to detect potentially discriminating compounds between groups. The mummichog and gene set enrichment analysis algorithms were associated to predict the enriched biological pathways. <h3>Results</h3> Seventy-two compounds were statistically significant (<i>P</i> < .05) for discriminating our study groups. Multivariate statistical analysis demonstrated the distinct metabolic profile of the samples, revealing a trend for the separation of transformed and untransformed oral leukoplakias. The main enriched pathways were related to lipid metabolism, synthesis of proinflammatory molecules, and retinoic and vitamin E metabolism. <h3>Conclusions</h3> The current results suggest untargeted metabolomics' potential to discriminate malignantly transformed leukoplakias from untransformed ones. Statistical analysis showed compounds that may characterize each evaluated group. Also, the enriched metabolic pathways were consistent with carcinogenesis-related processes. Further evaluation of the significantly altered compounds may be promising for developing a biomarker panel for the early diagnosis of leukoplakia with an increased risk of malignant progression.

AGE-RELATED METABOLIC PATHWAYS CHANGES IN DENTAL FOLLICLES: A PILOT STUDY

<h3>Objectives</h3> To assess age-related changes in metabolic pathways of dental follicles associated with unerupted/impacted mandibular third molars from young and adult individuals. <h3>Study Design</h3> Formalin-fixed paraffin-embedded dental follicle tissue samples from individuals younger than age 16 (<i>n</i> = 13) and older than age 26 (<i>n</i> = 7) were selected. Samples were analyzed by high-performance liquid chromatography-mass spectrometry-based untargeted metabolomics. Multivariate and univariate analyses were conducted. The prediction of altered metabolic pathways was performed by mummichog and gene set enrichment analysis approaches. <h3>Results</h3> Dental follicles from young and adult individuals showed differences in pathways related to C21-steroid hormone biosynthesis, bile acid biosynthesis, galactose metabolism, androgen and estrogen biosynthesis, starch and sucrose metabolism, and lipoate metabolism. <h3>Conclusions</h3> Metabolic pathway differences related to aging, some of which have been previously linked to aging phenotype acquisition, were observed between our experimental groups. Our findings support that, similar to other human tissues, dental follicles show alterations at a metabolic level with aging, which can pave the way for further studies on oral pathology, oral biology, and physiology.

Computational Metabolomics Tools Reveal Metabolic Reconfigurations Underlying the Effects of Biostimulant Seaweed Extracts on Maize Plants under Drought Stress Conditions.

Drought is one of the major abiotic stresses causing severe damage and losses in economically important crops worldwide. Drought decreases the plant water status, leading to a disruptive metabolic reprogramming that negatively affects plant growth and yield. Seaweed extract-based biostimulants show potential as a sustainable strategy for improved crop health and stress resilience. However, cellular, biochemical, and molecular mechanisms governing the agronomically observed benefits of the seaweed extracts on plants are still poorly understood. In this study, a liquid chromatography–mass spectrometry-based untargeted metabolomics approach combined with computational metabolomics strategies was applied to unravel the molecular ‘stamps’ that define the effects of seaweed extracts on greenhouse-grown maize (Zea mays) under drought conditions. We applied mass spectral networking, substructure discovery, chemometrics, and metabolic pathway analyses to mine and interpret the generated mass spectral data. The results showed that the application of seaweed extracts induced alterations in the different pathways of primary and secondary metabolism, such as phenylpropanoid, flavonoid biosynthesis, fatty acid metabolism, and amino acids pathways. These metabolic changes involved increasing levels of phenylalanine, tryptophan, coumaroylquinic acid, and linolenic acid metabolites. These metabolic alterations are known to define some of the various biochemical and physiological events that lead to enhanced drought resistance traits. The latter include root growth, alleviation of oxidative stress, improved water, and nutrient uptake. Moreover, this study demonstrates the use of molecular networking in annotating maize metabolome. Furthermore, the results reveal that seaweed extract-based biostimulants induced a remodeling of maize metabolism, subsequently readjusting the plant towards stress alleviation, for example, by increasing the plant height and diameter through foliar application. Such insights add to ongoing efforts in elucidating the modes of action of biostimulants, such as seaweed extracts. Altogether, our study contributes to the fundamental scientific knowledge that is necessary for the development of a biostimulants industry aiming for a sustainable food security.

Gas chromatography-mass spectrometry-based untargeted metabolomics reveals metabolic perturbations in medullary thyroid carcinoma

Medullary thyroid cancer (MTC) is a rare tumor that arises from parafollicular cells within the thyroid gland. The molecular mechanism underlying MTC has not yet been fully understood. Here, we aimed to perform plasma metabolomics profiling of MTC patients to explore the perturbation of metabolic pathways contributing to MTC tumorigenesis. Plasma samples from 20 MTC patients and 20 healthy subjects were obtained to carry out an untargeted metabolomics by gas chromatography–mass spectrometry. Multivariate and univariate analyses were employed as diagnostic tools via MetaboAnalyst and SIMCA software. A total of 76 features were structurally annotated; among them, 13 metabolites were selected to be differentially expressed in MTC patients compared to controls (P < 0.05). These metabolites were mainly associated with the biosynthesis of unsaturated fatty acids and amino acid metabolisms, mostly leucine, glutamine, and glutamate, tightly responsible for tumor cells' energy production. Moreover, according to the receiver operating characteristic curve analysis, metabolites with the area under the curve (AUC) value up to 0.90, including linoleic acid (AUC = 0.935), linolenic acid (AUC = 0.92), and leucine (AUC = 0.948) could discriminate MTC from healthy individuals. This preliminary work contributes to existing knowledge of MTC metabolism by providing evidence of a distinctive metabolic profile in MTC patients relying on the metabolomics approach.

Machine Learning-Based Retention Time Prediction of Trimethylsilyl Derivatives of Metabolites.

In gas chromatography–mass spectrometry-based untargeted metabolomics, metabolites are identified by comparing mass spectra and chromatographic retention time with reference databases or standard materials. In that sense, machine learning has been used to predict the retention time of metabolites lacking reference data. However, the retention time prediction of trimethylsilyl derivatives of metabolites, typically analyzed in untargeted metabolomics using gas chromatography, has been poorly explored. Here, we provide a rationalized framework for machine learning-based retention time prediction of trimethylsilyl derivatives of metabolites in gas chromatography. We compared different machine learning paradigms, in addition to exploring the influence of the computational molecular structure representation to train the prediction models: fingerprint class and fingerprint calculation software. Our study challenged predicted retention time when using chemical ionization and electron impact ionization sources in simulated and real cases, demonstrating a good correct identity ranking capability by machine learning, despite observing a limited false identity filtering power in cases where a spectrum or a monoisotopic mass match to multiple candidates. Specifically, machine learning prediction yielded median absolute and relative retention index (relative retention time) errors of 37.1 retention index units and 2%, respectively. In addition, fingerprint class and fingerprint calculation software, as well as the molecular structural similarity between the training and test or real case sets, showed to be critical modulators of the prediction performance. Finally, we leveraged the structural similarity between the training and test or real case set to determine the probability that the prediction error is below a specific threshold. Overall, our study demonstrates that predicted retention time can provide insights into the true structure of unknown metabolites by ranking from the most to the least plausible molecular identity, and sets the guidelines to assess the confidence in metabolite identification using predicted retention time data.

Metabolic Signature of Ethanol-Induced Hepatotoxicity in HepaRG Cells by Liquid Chromatography-Mass Spectrometry-Based Untargeted Metabolomics.

Alcoholic liver disease is highly prevalent but poorly identified and characterized, leading to knowledge gaps, which impairs early diagnosis. Excessive alcohol consumption is known to alter lipid metabolism, followed by progressive intracellular lipid accumulation, resulting in alcoholic fatty liver disease. In this study, HepaRG cells were exposed to ethanol at IC10 and 1/10 IC10 for 24 and 48 h. Metabolic alterations were investigated intra- and extracellularly with liquid chromatography-high-resolution mass spectrometry. Ion mobility was added as an extra separation dimension for untargeted lipidomics to improve annotation confidence. Distinctive patterns between exposed and control cells were consistently observed, with intracellular upregulation of di- and triglycerides, downregulation of phosphatidylcholines and phosphatidylethanolamines, sphingomyelins, and S-adenosylmethionine, among others. Several intracellular metabolic patterns could be related to changes in the extracellular environment, such as increased intracellular hydrolysis of sphingomyelins, leading to increased phosphorylcholine secretion. Carnitines showed alterations depending on the size of their carbon chain, which highlights the interplay between β-oxidation in mitochondria and peroxisomes. Potential new biomarkers of ethanol-induced hepatotoxicity have been observed, such as ceramides with a sphingadienine backbone, octanoylcarnitine, creatine, acetylcholine, and ethoxylated phosphorylcholine. The combination of the metabolic fingerprint and footprint enabled a comprehensive investigation of the pathophysiology behind ethanol-induced hepatotoxicity.

Development of a widely targeted volatilomics method for profiling volatilomes in plants

Volatile organic compounds play essential roles in plant environment interactions as well as determining the fragrance of plants. Although gas chromatography-mass spectrometry-based untargeted metabolomics is commonly used to assess plant volatiles, it suffers from high spectral convolution, low detection sensitivity, a limited number of annotated metabolites, and relatively poor reproducibility. Here, we report a widely targeted volatilomics (WTV) method that involves using a “targeted spectra extraction” algorithm to address spectral convolution, constructing a high-coverage MS2 spectral tag library to expand volatile annotation, adapting a multiple reaction monitoring mode to improve sensitivity, and using regression models to adjust for signal drift. The newly developed method was used to profile the volatilome of rice grains. Compared with the untargeted method, the newly developed WTV method shows higher sensitivity (for example, the signal-to-noise ratio of guaicol increased from 4.1 to 18.8), high annotation coverage (the number of annotated volatiles increased from 43 to 132), and better reproducibility (the number of volatiles in quality control samples with relative standard deviation value below 30.0% increased from 14 to 92 after normalization). Using the WTV method, we studied the metabolic responses of tomato to environmental stimuli and profiled the volatilomes of different rice accessions. The results identified benzothiazole as a potential airborne signal priming tomato plants for enhanced defense and 2-nonanone and 2-heptanone as novel aromatic compounds contributing to rice fragrance. These case studies suggest that the widely targeted volatilomics method is more efficient than those currently used and may considerably promote plant volatilomics studies.

Unveiling metabolic changes in marsupialized odontogenic keratocyst: A pilot study.

We aimed to assess which metabolic pathways would be implicated in the phenotypic changes of the epithelial lining of odontogenic keratocyst after marsupialization, comparing pre- and post-marsupialized lesions with adjacent oral mucosa. Eighteen formalin-fixed and paraffin-embedded tissues from six subjects were divided into three paired groups: odontogenic keratocyst pre- (n=6) and post-marsupialization (n=6), and adjacent oral mucosa (n=6). The metabolic pathways found in these groups were obtained by high-performance liquid chromatography-mass spectrometry-based untargeted metabolomics performed. Through putative metabolite annotation followed by pathway enrichment and predictive analysis with automated algorithms (Mummichog and Gene Set Enrichment Analysis), we found differences in many cellular processes that may be involved in inflammation, oxidative stress response, keratinocyte-basal membrane attachment, differentiation, and proliferation functions, all relevant to odontogenic keratocyst pathobiology and the phenotype acquired after marsupialization. Our study was able to identify several metabolic pathways potentially involved in the metaplastic changes induced by marsupialization of odontogenic keratocysts. An improved comprehension of this process could pave the way for the development of targeted therapies.

Liquid chromatography-mass spectrometry-based metabolomics study of the efficacy of Chinese medicine asthma-relieving decoction on respiratory syncytial virus infection

呼吸道合胞病毒(RSV)容易引发下呼吸道感染,尤其是小儿毛细支气管炎。中药定喘汤用于RSV感染的治疗在中国有着广泛的临床实践基础。由于中药体系的复杂性,其治疗机制和主要药效成分还不够明确。采用代谢组学方法分析中药药效,可以为传统中药治疗提供现代科学论证。该研究对大鼠各干预组分别采用中药定喘汤全方,宣、降、清分解剂以及利巴韦林灌胃治疗,正常组大鼠和RSV模型组大鼠均采用生理盐水灌胃。分别取大鼠在第0、3、7天的眼底血浆进行基于液相色谱-串联质谱法的非靶向代谢组学分析,以研究RSV感染引起的代谢组改变、定喘汤对RSV感染相关代谢物的调控及定喘汤的主要药效来源。对检测的代谢物离子进行多变量分析,建立主成分分析模型,结果显示在RSV感染及治疗进程中,中药定喘汤组与利巴韦林阳性对照组对RSV感染大鼠产生的总体代谢调控效果类似。在3种定喘汤分解剂中,组成成分为麻黄和白果的宣法分解剂对代谢组的调控作用与定喘汤更为接近,可能为定喘汤的主要药效来源。基于配对t检验发现大鼠在感染RSV后有83种代谢物发生了显著性改变,表明RSV感染可造成大鼠多条代谢通路紊乱。第3天时中药定喘汤对包括胆汁酸、氨基酸、有机酸、脂质等在内的代谢物有治疗效果。3种分解剂中,宣法分解剂对代谢物的调节作用与定喘汤类似,而降法分解剂和清法分解剂对大鼠因感染RSV引起的酰基肉碱的异常升高有明显下调作用。各组都有维持肠道菌群和免疫系统稳定的药效。该研究采用代谢组学方法评价定喘汤的药效,发现了定喘汤治疗后改变的代谢物的种类及变化规律,为定喘汤的治疗机理和活性成分的研究提供了理论支持。

Metabolic markers for diagnosis and risk-prediction of multiple myeloma

AimsTo discriminate metabolic biomarkers for diagnosis and risk prediction of multiple myeloma (MM) on a basis of metabolic characteristics in systemic circulation and local pathogenic niche. Main methodsA gas chromatography mass spectrometry-based untargeted metabolomics analysis was performed within the bone marrow (BM) supernatants and peripheral plasma from healthy donors and patients with MM. Key findingsDistinct metabolic features between MM patients and healthy volunteers were profiled in both BM and plasma. Metabolic profiles of subgroups in which MM patients undergo high/medium/low risk displayed risk-dependent metabolic shift especially in BM. In MM patients, up-regulated glutamate level and down-regulated glutamine level in BM indicated enhanced glutamate metabolism which provided NH4+ for ammonia utilization. This resulted in increased level of urea and creatinine produced from urea cycle, arginine and proline metabolism in both BM and plasma collected from MM patients. The disorders of tricarboxylic acid cycle and carnitine synthesis were unique in BM of MM patients. Receiver operating characteristic curve analysis indicated that aspartate was a candidate plasma biomarker for diagnosis with the highest sensitivity and specificity in both BM and plasma. Threonine was identified as a preferential plasma biomarker for risk prediction due to significant relation with various risk indexes of MM in both BM and plasma. SignificanceThe perturbed glutamate metabolism and carnitine synthesis in BM of MM patients provided a new sight on pathogenesis of MM. The plasma level of aspartate and threonine may become a preferential metabolic marker for diagnosis and risk prediction of MM, respectively.

PSVI-2 Effects of dietary supplementation of a Saccharomyces cerevisiae-based direct-fed microbial product on plasma carbonyl-metabolome and fecal bacterial community of beef steers

Abstract We examined the effects of dietary supplementation of a Saccharomyces cerevisiae-based direct-fed microbial on plasma carbonyl-metabolome and fecal bacterial community of beef steers during a 42-d receiving period. Forty newly-weaned beef steers were used in this study. The steers were stratified by BW and randomly assigned to receive a basal diet with no additive (CON; n = 20) or a basal diet supplemented with 19 g of CommenceTM (PROB; n = 20). CommenceTM (PMI, Arden Hills, MN) contains a blend of 6.2 × 1011 cfu/g of S. cerevisiae, 3.5 × 1010 cfu/g of a mixture of Enterococcus lactis, Bacillus subtilis, Enterococcus faecium, and L. casei. On d 0 and 40, rectal fecal samples were collected for bacterial community analysis via sequencing of the V3-V4 region of 16S rRNA gene. On d 42, blood was collected for analysis of carbonyl-containing metabolites in plasma using a chemical isotope labeling/liquid chromatography mass spectrometry-based untargeted metabolomics. The data were analyzed using the GLIMMIX procedure of SAS with treatment as a fixed effect) and (random effect). A total number of 812 unique plasma metabolites were detected. Up to 305 metabolites [fold change (FC) ≥ 1.5, FDR ≤ 0.01] including glucose, hippuric acid, glycoaldehyde, and 5-hydroxykynurenamine were increased by PROB supplementation, whereas 199 metabolites (FC ≤ 0.63, FDR ≤ 0.01) including acetoacetate were reduced. Correlation analysis showed that plasma concentrations of 5-oxopentanoate, 2-aceto-2-hydroxybutanoate, and 3-methyl-2-oxopentanoic acid were positively correlated (P ≤ 0.10) with average daily gain. Supplemental PROB increased (P ≤ 0.05) the relative abundance of Prevotellaceae, Megasphaera, Dorea, Acetitomaculum, and Blautia. In contrast, the relative abundance of Elusimicrobium, Moheibacter, and Stenotrophomonas were reduced (P ≤ 0.05). This study demonstrated that PROB altered the plasma carbonyl-metabolome and fecal bacterial community of the beef steers.

Online Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry-based untargeted volatile metabolomics for studying emerging complex biopesticides: A proof of concept

This work introduces a novel online Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry-based untargeted metabolomics approach, suggested as an alternative tool to study the environmental fate of volatile xenometabolites in emerging complex biopesticides, e.g. the Myrica gale methanolic extract herbicide containing several unknown metabolites. A “living” microcosm sample was designed for non-destructive analysis by a 35-min HS-SPME automated extraction and a 36-min GC-MS run. A 38-day kinetics study was then applied on two groups of soil samples: control and spiked. Statistical tools were used for the comparative kinetics study. The Principal Component Analysis revealed and explained the evolution and the dissipation of the herbicide volatile xenometabolome over time. The time-series Heatmap and Multivariate Empirical Bayes Analysis of Variance allowed the prioritization of 101 relevant compounds including 22 degradation by-products. Out of them, 96 xenometabolites were putatively identified. They included 63 compounds that are identified as herbicide components for the first time. The Orthogonal Projections to Latent Structures Discriminant Analysis and its Cross-Validation test were used to assess the total dissipation of the herbicide volatile residues and method detection limit. The reproducibility of the method was also assessed. The highest inter-samples (n = 3) Peak Area RSD was 7.75 %. The highest inter-samples (n = 3) and inter-days (n = 8) Retention Time SD were 0.43 sec and 3.44 sec, respectively. The work presents a green, non-laborious and high-throughput approach. It required a small number of environmental samples (6 microcosms) that were analyzed 8 times and were not destroyed during the study.

Population-based case-control study revealed metabolomic biomarkers of suboptimal health status in Chinese population-potential utility for innovative approach by predictive, preventive, and personalized medicine.

Suboptimal health status (SHS) is a subclinical stage of chronic diseases, and the identification of SHS provides an opportunity for the predictive, preventive, and personalized medicine (PPPM) of chronic diseases. Previous studies have reported the associations between metabolic signatures and early signs of chronic diseases. This study aimed to detect the metabolic biomarkers for the identification of SHS in a case-control study. SHS questionnaire-25 (SHSQ-25) was used in a population-based health survey to measure the SHS levels of participants. The liquid chromatography-mass spectrometry-based untargeted metabolomics analysis was conducted on plasma samples collected from 50 SHS participants and 50 age- and sex-matched healthy controls. After adjusting for the confounders, 24 significantly differential metabolites, such as sphingomyelin, sphingosine, sphinganine, progesterone, pregnanolone, and bilirubin, were identified as the candidate biomarkers for SHS. Pathway analysis revealed that sphingolipid metabolism, taurine metabolism, and steroid hormone biosynthesis are the disturbed metabolic pathways related to SHS. A combination of four metabolic biomarkers (sphingosine, pregnanolone, taurolithocholate sulfate, cervonyl carnitine) can distinguish SHS individuals from the controls with a sensitivity of 94.0%, a specificity of 90.0%, and an area under the receiver operating characteristic curve of 0.977. Plasma metabolites are valuable biomarkers for SHS identification, and meanwhile, SHSQ-25 can be used as an alternative health screening tool in the population-based health survey. SHS-related metabolic disturbances could be detected at the early onset of SHS, and SHS-related metabolites could create a window opportunity for PPPM of chronic diseases.