Metabolic Profiling Approach Research Articles

Multiple-matrix metabolomics analysis for the distinct detection of colorectal cancer and adenoma.

Although colorectal cancer (CRC) is the leading cause of cancer-related morbidity and mortality, current diagnostic tests for early-stage CRC and colorectal adenoma (CRA) are suboptimal. Therefore, there is an urgent need to explore less invasive screening procedures for CRC and CRA diagnosis. Untargeted gas chromatography-mass spectrometry (GC-MS) metabolic profiling approach was applied to identify candidate metabolites. We performed metabolomics profiling on plasma samples from 412 subjects including 200 CRC patients, 160 CRA patients and 52 normal controls (NC). Among these patients, 45 CRC patients, 152 CRA patients and 50 normal controls had their fecal samples tested simultaneously. Differential metabolites were screened in the adenoma-carcinoma sequence. Three diagnostic models were further developed to identify cancer group, cancer stage, and cancer microsatellite status using those significant metabolites. The three-metabolite-only classifiers used to distinguish the cancer group always keeps the area under the receiver operating characteristic curve (AUC) greater than 0.7. The AUC performance of the classifiers applied to discriminate CRC stage is generally greater than 0.8, and the classifiers used to distinguish microsatellite status of CRC is greater than 0.9. This finding highlights potential early-driver metabolites in CRA and early-stage CRC. We also find potential metabolic markers for discriminating the microsatellite state of CRC. Our study and diagnostic model have potential applications for non-invasive CRC and CRA detection.

Abstract 441: Bioluminescent dehydrogenase assays: A novel approach for metabolic profiling and inhibitor screening in cancer biology

Abstract Dehydrogenases play a pivotal role in cellular metabolism, acting as gatekeepers in key metabolic pathways. Their activity reflects the metabolic state of the cell, which is particularly relevant in the context of cancer and the immune response, where metabolic reprogramming is a hallmark of tumor progression and differentiation stages. Here, we present the development and application of novel bioluminescent dehydrogenase assays for direct measurement of metabolic activity in cell lysates. Our assays are highly sensitive, requiring as few as 100-500 cells, and have been successfully miniaturized to a 384-well plate format conducive to high-throughput screening. This technological advance allows for the precise monitoring of dehydrogenase activity across the pentose phosphate pathway, glycolysis, and the mitochondrial tricarboxylic acid (TCA) cycle. We demonstrate how shifts in dehydrogenase activity can serve as indicators of the metabolic status of cells, and their modulation during differentiation. Moreover, our bioluminescent assays provide a rapid screening platform for pathway-specific metabolic inhibitors, with potential applications in identifying new therapeutic targets within cancer metabolism. Our data reveal a dynamic landscape of metabolic activity during T cell maturation, underscoring the potential of dehydrogenase activity profiling in cancer immunobiology and the development of targeted cancer therapies. Citation Format: Kim Haupt, Natasha Karassina, Anthony Lauer, Kayla Sylvester, Jolanta Vidugiriene. Bioluminescent dehydrogenase assays: A novel approach for metabolic profiling and inhibitor screening in cancer biology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 441.

A GC-MS-based untargeted metabolomics approach for comprehensive metabolic profiling of mycophenolate mofetil-induced toxicity in mice.

Background: Mycophenolate mofetil (MMF), the morpholinoethyl ester of mycophenolic acid, is widely used for maintenance immunosuppression in transplantation. The gastrointestinal toxicity of MMF has been widely uncovered. However, the comprehensive metabolic analysis of MMF-induced toxicity is lacking. This study is aimed to ascertain the metabolic changes after MMF administration in mice. Methods: A total of 700mg MMF was dissolved in 7mL dimethyl sulfoxide (DMSO), and then 0.5mL of mixture was diluted with 4.5mL of saline (100mg/kg). Mice in the treatment group (n = 9) were given MMF (0.1mL/10g) each day via intraperitoneal injection lasting for 2weeks, while those in the control group (n = 9) received the same amount of blank solvent (DMSO: saline = 1:9). Gas chromatography-mass spectrometry was utilized to identify the metabolic profiling in serum samples and multiple organ tissues of mice. The potential metabolites were identified using orthogonal partial least squares discrimination analysis. Meanwhile, we used the MetaboAnalyst 5.0 (http://www.metaboanalyst.ca) and Kyoto Encyclopedia of Genes and Genomes database (http://www.kegg.jp) to depict the metabolic pathways. The percentages of lymphocytes in spleens were assessed by multiparameter flow cytometry analysis. Results: Compared to the control group, we observed that MMF treatment induced differential expression of metabolites in the intestine, hippocampus, lung, liver, kidney, heart, serum, and cortex tissues. Subsequently, we demonstrated that multiple amino acids metabolism and fatty acids biosynthesis were disrupted following MMF treatment. Additionally, MMF challenge dramatically increased CD4+ T cell percentages but had no significant influences on other types of lymphocytes. Conclusion: MMF can affect the metabolism in various organs and serum in mice. These data may provide preliminary judgement for MMF-induced toxicity and understand the metabolic mechanism of MMF more comprehensively.

Blood-Derived Metabolic Signatures as Biomarkers of Injury Severity in Traumatic Brain Injury: A Pilot Study.

Metabolomic biomarkers hold promise in aiding the diagnosis and prognostication of traumatic brain injury. In Canada, over 165,000 individuals annually suffer from a traumatic brain injury (TBI), making it one of the most prevalent neurological conditions. In this pilot investigation, we examined blood-derived biomarkers as proxy measures that can provide an objective approach to TBI diagnosis and monitoring. Using a 1H nuclear magnetic resonance (NMR)-based quantitative metabolic profiling approach, this study determined whether (1) blood-derived metabolites change during recovery in male participants with mild to severe TBI; (2) biological pathway analysis reflects mechanisms that mediate neural damage/repair throughout TBI recovery; and (3) changes in metabolites correlate to initial injury severity. Eight male participants with mild to severe TBI (with intracranial lesions) provided morning blood samples within 1-4 days and again 6 months post-TBI. Following NMR analysis, the samples were subjected to multivariate statistical and machine learning-based analyses. Statistical modelling displayed metabolic changes during recovery through group separation, and eight significant metabolic pathways were affected by TBI. Metabolic changes were correlated to injury severity. L-alanine (R= -0.63, p < 0.01) displayed a negative relationship with the Glasgow Coma Scale. This study provides pilot data to support the feasibility of using blood-derived metabolites to better understand changes in biochemistry following TBI.

Implementation of pure shift 1 H NMR in metabolic phenotyping for structural information recovery of biofluid metabolites with complex spin systems.

NMR spectroscopy is a mainstay of metabolic profiling approaches to investigation of physiological and pathological processes. The one-dimensional proton pulse sequences typically used in phenotyping large numbers of samples generate spectra that are rich in information but where metabolite identification is often compromised by peak overlap. Recently developed pure shift (PS) NMR spectroscopy, where all J-coupling multiplicities are removed from the spectra, has the potential to simplify the complex proton NMR spectra that arise from biosamples and hence to aid metabolite identification. Here we have evaluated two complementary approaches to spectral simplification: the HOBS (band-selective with real-time acquisition) and the PSYCHE (broadband with pseudo-2D interferogram acquisition) pulse sequences. We compare their relative sensitivities and robustness for deconvolving both urine and serum matrices. Both methods improve resolution of resonances ranging from doublets, triplets and quartets to more complex signals such as doublets of doublets and multiplets in highly overcrowded spectral regions. HOBS is the more sensitive method and takes less time to acquire in comparison with PSYCHE, but can introduce unavoidable artefacts from metabolites with strong couplings, whereas PSYCHE is more adaptable to these types of spin system, although at the expense of sensitivity. Both methods are robust and easy to implement. We also demonstrate that strong coupling artefacts contain latent connectivity information that can be used to enhance metabolite identification. Metabolite identification is a bottleneck in metabolic profiling studies. In the case of NMR, PS experiments can be included in metabolite identification workflows, providing additional capability for biomarker discovery.

Exploration of chemical interactions between Viscum combreticola Engl. and its hosts through a metabolic profiling approach and molecular networking

ABSTRACT Viscum combreticola Engl., an evergreen semi-parasitic mistletoe plant, is utilized in herbal medicines. Herein, a UHPLC-q-TOF-MS profiling and molecular networking approach were used to investigate the chemical interactions between V. combreticola and two of its host plants, Combretum erythrophyllum and Pseudolachnostylis maprouneifolia Pax. Moreover, in vitro grown V. combreticola seedlings were used to explore the host independence of the phytochemistry of these plants. The phytochemistry of V. combreticola was found to be independent and distinct from that of the host plants. Moreover, both mature V. combreticola and in vitro seedlings displayed diverse polyphenolic compounds but exhibited distinct metabolic profiles. Notably, the esterification of phenolic acids (hydroxycinnamic and benzoic acids) to quinic acid was a unique chemistry that was observed in both mature and in vitro grown V. combreticola. Beyond providing metabolic fingerprints of the studied samples, the UHPLC-q-TOF-MS and molecular networking effectively visualized plant-plant chemical relationships at a metabolite level.

Application of the UHPLC-q-TOF-MS and molecular networking in revealing chemo-taxonomical markers of Viscum combreticola Engl. and Viscum album L.

A metabolic profiling approach employing the UHPLC-q-TOF-MS and classical molecular networking was used to investigate the polyphenol profile differences between Viscum combreticola Engl. (African mistletoe) and Viscum album L. (European mistletoe). The findings of this study revealed that V. combreticola was predominantly characterized by a diverse hydroxycinnamic acid derivatives profile, mainly comprising of structural isomers of monoacylated, homodiacylated, heterodiacylated and heterotriacylated chlorogenic acids. Remarkably, a succinoyl and two new hydroxybenzoyl containing chlorogenic acids were identified for the first time in Viscum species in this study. In contrast, V. album contained flavonoids (flavonols, flavanones and flavones) showing various glycosylation patterns, where the majority of these compounds were acylated to a 3-hydroxy-3-methylglutaroyl moiety. Moreover, V. combreticola also displayed a different flavonoid profile that exhibited different glycosylation patterns from that observed in V. album. As such, the distinct polyphenol profiles of these two Viscum species can serve as a chemo-taxonomical markers of these plants. Additionally, this study demonstrated that molecular networking is a powerful tool in visualizing differences between the chemical composition of different samples. The diverse polyphenol profile of these two Viscum species might be attributed to their unique metabolism which could be influenced by their genetic make-up, thereby, allowing them to survive various biotic and abiotic stresses.

Comprehensive serum lipidomic analyses reveal potential biomarkers for malignant breast cancer: A case-control study.

Breast cancer is the most worldwide commonly found malignancy among women. The evidence for lipidomic studies of breast cancer in the Chinese population is relatively limited. Our current study aimed to identify peripheral lipids capable of distinguishing adults with and without malignant breast cancer in a Chinese population and to explore the potential lipid metabolism pathways implicated in breast cancer. Lipidomics was performed with an Ultimate 3000 UHPLC system coupled with a Q-Exactive HF MS platform by using the serum of 71 female patients with malignant breast cancer and 92 age-matched (± 2 years) healthy women. The data were uploaded to and processed by the specialized online software Metaboanalyst 5.0. Both univariate and multivariate analyses were carried out for potential biomarker screening. Areas under the receiver-operating characteristic (ROC) curves (AUCs) of identified differential lipids were obtained for evaluating their classification capacity. A total of 47 significantly different lipids were identified by applying the following criteria: false discovery rate-adjusted P < 0.05, variable importance in projection ⩾ 1.0, and fold change ⩾ 2.0 or ⩽ 0.5. Among them, 13 lipids were identified as diagnostic biomarkers with the area under curve (AUC) greater than 0.7. Multivariate ROC curves indicated that AUCs greater than 0.8 could be achieved with 2-47 lipids. Using an untargeted LC-MS-based metabolic profiling approach, our study provides preliminary evidence that extensive dysregulations of OxPCs, PCs, SMs and TAGs were involved in the pathological processes of breast cancer. We provided clues for furtherly investigating the role of lipid alterations in the pathoetiology of breast cancer.

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine.

Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

Comparison of Balanites aegyptiaca parts: metabolome providing insights into plant health benefits and valorization purposes as analyzed using multiplex GC-MS, LC-MS, NMR-based metabolomics, and molecular networking.

Balanites aegyptiaca (L.) Delile (Zygophyllaceae), also known as the desert date, is an edible fruit-producing tree popular for its nutritional and several health benefits. In this study, multi-targeted comparative metabolic profiling and fingerprinting approaches were conducted for the assessment of the nutrient primary and secondary metabolite heterogeneity in different parts, such as leaves, stems, seeds, unripe, and ripe fruits of B. aegyptiaca using nuclear magnetic resonance (NMR), ultra-performance liquid chromatography (UPLC-MS), and gas chromatography mass-spectrometry (GC-MS) based metabolomics coupled to multivariate analyses and in relation to its cytotoxic activities. NMR-based metabolomic study identified and quantified 15 major primary and secondary metabolites belonging to alkaloids, saponins, flavonoids, sugars, and amino and fatty acids. Principal component analysis (PCA) of the NMR dataset revealed α-glucose, sucrose, and isorhamnetin as markers for fruit and stem and unsaturated fatty acids for predominated seeds. Orthogonal projections to latent structure discriminant analysis (OPLS-DA) revealed trigonelline as a major distinctive metabolite in the immature fruit and isorhamnetin as a major distinct marker in the mature fruit. UPLC-MS/MS analysis using feature-based molecular networks revealed diverse chemical classes viz. steroidal saponins, N-containing metabolites, phenolics, fatty acids, and lipids as the constitutive metabolome in Balanites. Gas chromatography-mass spectroscopy (GC-MS) profiling of primary metabolites led to the detection of 135 peaks belonging to sugars, fatty acids/esters, amino acids, nitrogenous, and organic acids. Monosaccharides were detected at much higher levels in ripe fruit and disaccharides in predominate unripe fruits, whereas B. aegyptiaca vegetative parts (leaves and stem) were rich in amino acids and fatty acids. The antidiabetic compounds, viz, nicotinic acid, and trigonelline, were detected in all parts especially unripe fruit in addition to the sugar alcohol d-pinitol for the first time providing novel evidence for B. aegyptiaca use in diabetes. In vitro cytotoxic activity revealed the potential efficacy of immature fruit and seeds as cytotoxic agents against human prostate cancer (PC3) and human colorectal cancer (HCT-116) cell lines. Collectively, such detailed profiling of parts provides novel evidence for B. aegyptiaca medicinal uses.

Metabolic profiling of colorectal cancer organoids: A comparison between high-resolution magic angle spinning magnetic resonance spectroscopy and solution nuclear magnetic resonance spectroscopy of polar extracts.

Patient-derived cancer cells cultured in vitro are a cornerstone of cancer metabolism research. More recently, the introduction of organoids has provided the research community with a more versatile model system. Physiological structure and organization of the cell source tissue are maintained in organoids, representing a closer link to in vivo tumor models. High-resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS) is a commonly applied analytical approach for metabolic profiling of intact tissue, but its use has not been reported for organoids. The aim of the current work was to compare the performance of HR MAS MRS and extraction-based nuclear magnetic resonance (NMR) in metabolic profiling of wild-type and tumor progression organoids (TPOs) from human colon cancer, and further to investigate how the sequentially increased genetic alterations of the TPOs affect the metabolic profile. Sixteen metabolites were reliably identified and quantified both in spectra based on NMR of extracts and HR MAS MRS of intact organoids. The metabolite concentrations from the two approaches were highly correlated (r = 0.94), and both approaches were able to capture the systematic changes in metabolic features introduced by the genetic alterations characteristic of colorectal cancer progression (e.g., increased levels of lactate and decreased levels of myo-inositol and phosphocholine with an increasing number of mutations). The current work highlights that HR MAS MRS is a well-suited method for metabolic profiling of intact organoids, with the additional benefit that the nondestructive nature of HR MAS enables subsequent recovery of the organoids for further analyses based on nucleic acids or proteins.

A GC–MS-based untargeted metabolomics approach for comprehensive metabolic profiling of vancomycin-induced toxicity in mice

BackgroundVancomycin is a glycopeptide antibiotic that is commonly used for severe drug-resistant infections treatment. Application of vancomycin frequently leads to severe ototoxicity, hepatotoxicity, and nephrotoxicity; however, the comprehensive metabolic analysis of vancomycin-induced toxicity is lacking. PurposeThis study attempted to investigate the metabolic changes after vancomycin administration in mice. MethodsExperimental mice (n = 9) received continuous intraperitoneal injection of vancomycin (400 mg/kg) every day for 7 days, and mice in control group (n = 9) were treated with the same amount of normal saline. Pathological changes of the kidney were examined using haematoxylin and eosin (HE) staining. A gas chromatography-mass spectrometry (GC-MS) approach was used to identify discriminant metabolites in serum and various organs including the heart, liver, kidney, spleen, cerebral cortex, hippocampus, inner ear, lung, and intestine. The potential metabolites were identified using orthogonal partial least squares discrimination analysis (OPLS-DA). Subsequently, the MetaboAnalyst 5.0 (http://www.metaboanalyst.ca) and Kyoto Encyclopedia of Genes and Genomes database (KEGG, http://www.kegg.jp) were employed to depict the metabolic pathways. ResultsCompared with the control group, the vancomycin induced 13, 17, 27, 22, 16, 10, 17, 11, 10, and 7 differential metabolites in the serum, liver, kidney, heart, cerebral cortex, lung, spleen, intestine, hippocampus, and inner ear, respectively. Further pathway analyses identified that amino acids metabolism, fatty acids biosynthesis, energy metabolism, and lipid metabolism were disrupted after VCM exposure. ConclusionVancomycin affects the metabolism in various organs in mice, which provides new insights for identification of vancomycin-induced toxicity, and facilitate to better understanding of the metabolic pathogenesis of vancomycin.

Spectroscopic Analysis of Lagos spinach- seed (Celosia argentea Linn.)

Background: Celosia argentea Linn. (C. argentea) commonly known as Cock’s Comb. It’s a leading leafy vegetable used for treatment of antiinflammatory, antioxidant and possessing immunostimulating activities. This species contains a surplus of phytochemicals including terpenoids, glycosides, flavonoids and tannins. The full complement of bioactive chemicals has yet to be identified, which is a critical step in explaining its medical use. The Celosia genus contains around 30 species, the majority of which have had little phytochemical or pharmacological research done on them. The aim of this study is to get a better chemosemantics of Celosia species, large-scale multi-targeted metabolic profiling and fingerprinting approaches was used. Methods: FTIR, GC-MS and LC-MS spectra of extracted components were analyzed with multivariate data from C. argentea. The crude extract was examined at different wavelengths from 4000 cm-1 and 400 cm-1 by using Shimadzu 8400 Spectrophotometry system and the distinctive peaks were detected. For GC-MS analysis, sample was extracted with methanol (as per API procedure), filtered in ashless filter paper and extract was concentrated. The NIST Ver. 2.0 - Year 2005 library was used for chemical detection. Results: The FTIR profile showed different peaks ranging from 4000 cm-1 and 400 cm-1 and spectrum with confirmed presence of alcohols and phenols, amines, alkanes, alkynes aldehydes and ketones, carboxylic acids functional groups in methanol extract of C. argentea. Analysis of the mass spectrum GC-MS analysis provides different peaks determining presence of Pentadeconic acid, n-Hexadecoic acid, 9-12-Octadecadienoic acid, Cis-vaccenic acid, Octadecanoic acid. The results of LC-MS analysis provide specific data of bioactive components which may suggest the Butralin, Fulvine, Dihydrocapsaicin, Lisinopril, 9Z-Octadecen-12-yonic acid, N-Hexadecanoylpyrrodine etc. The phytoconstituents may use to treat diseases and further insight in future researches on Celosia argentea. Conclusion: The molecules reported through above exploration of spectral techniques may justify traditional therapeutic manifestation of C. argentea and useful in activity guided isolation.

Genomics-Based Reconstruction and Predictive Profiling of Amino Acid Biosynthesis in the Human Gut Microbiome

The human gut microbiota (HGM) have an impact on host health and disease. Amino acids are building blocks of proteins and peptides, also serving as precursors of many essential metabolites including nucleotides, cofactors, etc. Many HGM community members are unable to synthesize some amino acids (auxotrophs), while other members possess complete biosynthetic pathways for these nutrients (prototrophs). Metabolite exchange between auxotrophs and prototrophs affects microbial community structure. Previous studies of amino acid biosynthetic phenotypes were limited to model species or narrow taxonomic groups of bacteria. We analyzed over 2800 genomes representing 823 cultured HGM species with the aim to reconstruct biosynthetic pathways for proteinogenic amino acids. The genome context analysis of incomplete pathway variants allowed us to identify new potential enzyme variants in amino acid biosynthetic pathways. We further classified the studied organisms with respect to their pathway variants and inferred their prototrophic vs. auxotrophic phenotypes. A cross-species comparison was applied to assess the extent of conservation of the assigned phenotypes at distinct taxonomic levels. The obtained reference collection of binary metabolic phenotypes was used for predictive metabolic profiling of HGM samples from several large metagenomic datasets. The established approach for metabolic phenotype profiling will be useful for prediction of overall metabolic properties, interactions, and responses of HGM microbiomes as a function of dietary variations, dysbiosis and other perturbations.

Novelty detection for metabolic dynamics established on breast cancer tissue using 2D NMR TOCSY spectra

Most metabolic profiling approaches focus only on identifying pre-known metabolites on NMR TOCSY spectrum using configured parameters. However, there is a lack of tasks dealing with automating the detection of new metabolites that might appear during the dynamic evolution of biological cells. Novelty detection is a category of machine learning that is used to identify data that emerge during the test phase and were not considered during the training phase. We propose a novelty detection system for detecting novel metabolites in the 2D NMR TOCSY spectrum of a breast cancer-tissue sample. We build one- and multi-class recognition systems using different classifiers such as, Kernel Null Foley-Sammon Transform, Kernel Density Estimation, and Support Vector Data Description. The training models were constructed based on different sizes of training data and are used in the novelty detection procedure. Multiple evaluation measures were applied to test the performance of the novelty detection methods. Depending on the training data size, all classifiers were able to achieve 0% false positive rates and total misclassification error in addition to 100% true positive rates. The median total time for the novelty detection process varies between 1.5 and 20 seconds, depending on the classifier and the amount of training data. The results of our novel metabolic profiling method demonstrate its suitability, robustness and speed in automated metabolic research.

Urinary metabolomic signatures as indicators of injury severity following traumatic brain injury: A pilot study.

BackgroundAnalysis of fluid metabolites has the potential to provide insight into the neuropathophysiology of injury in patients with traumatic brain injury (TBI).ObjectiveUsing a 1H nuclear magnetic resonance (NMR)-based quantitative metabolic profiling approach, this study determined (1) if urinary metabolites change during recovery in patients with mild to severe TBI; (2) whether changes in urinary metabolites correlate to injury severity; (3) whether biological pathway analysis reflects mechanisms that mediate neural damage/repair throughout TBI recovery.MethodsUrine samples were collected within 7 days and at 6-months post-injury in male participants (n = 8) with mild-severe TBI. Samples were analyzed with NMR-based quantitative spectroscopy for metabolomic profiles and analyzed with multivariate statistical and machine learning-based analyses.ResultsLower levels of homovanillate (R = −0.74, p ≤ 0.001), L-methionine (R = −0.78, p < 0.001), and thymine (R = −0.85, p < 0.001) negatively correlated to injury severity. Pathway analysis revealed purine metabolism to be a primary pathway (p < 0.01) impacted by TBI.ConclusionThis study provides pilot data to support the use of urinary metabolites in clinical practice to better interpret biochemical changes underlying TBI severity and recovery. The discovery of urinary metabolites as biomarkers may assist in objective and rapid identification of TBI severity and prognosis. Thus, 1H NMR metabolomics has the potential to facilitate the adaptation of treatment programs that are personalized to the patient’s needs.

Metabolomic Variability of Different Soybean Genotypes: β-Carotene-Enhanced (Glycine max), Wild (Glycine soja), and Hybrid (Glycine max × Glycine soja) Soybeans.

We obtained a new hybrid soybean (Hybrid) by hybridizing β-carotene-enhanced soybean (BCE; Glycine max L.) containing the phytoene synthase-2A-carotene desaturase gene and wild-type soybean (Wild; Glycine soja). To investigate metabolic changes between variants, we performed metabolic profiling of leaves (three growth stages) and seeds. Multivariate analyses revealed significant metabolic differences between genotypes in seeds and leaves, with seeds showing accumulation of phytosterols, tocopherols, and carotenoids (BCE only), indicating co-induction of the methylerythritol 4-phosphate and mevalonic acid pathways. Additionally, Hybrid produced intermediate levels of carotenoids and high levels of amino acids. Principal component analysis revealed metabolic discrimination between growth stages of soybean leaves and identified differences in leaf groups according to different genotypes at 8, 12, and 16 weeks, with Wild showing higher levels of environmental stress-related compounds relative to BCE and Hybrid leaves. The metabolic profiling approach could be a useful tool to identify metabolic links in various soybean cultivars.

Organ-on-a-chip: Determine feasibility of a human liver microphysiological model to assess long-term steroid metabolites in sports drug testing.

A fundamental challenge in preventive doping research is the study of metabolic pathways of substances banned in sport. However, the pharmacological predictions obtained by conventional in vitro or in vivo animal studies are occasionally of limited transferability to humans according to an inability of in vitro models to mimic higher order system physiology or due to various species-specific differences using animal models. A more recently established technology for simulating human physiology is the "organ-on-a-chip" principle. In a multichannel microfluidic cell culture chip, 3-dimensional tissue spheroids, which can constitute artificial and interconnected microscale organs, imitate principles of the human physiology. The objective of this study was to determine if the technology is suitable to adequately predict metabolic profiles of prohibited substances in sport. As model compounds, the frequently misused anabolic steroids, stanozolol and dehydrochloromethyltestosterone (DHCMT) were subjected to human liver spheroids in microfluidic cell culture chips. The metabolite patterns produced and circulating in the chip media were then assessed by LC-HRMS/(MS) at different time points of up to 14 days of incubation at 37°C. The overall profile of observed glucurono-conjugated stanozolol metabolites excellently matched the commonly found urinary pattern of metabolites, including 3'OH-stanozolol-glucuronide and stanozolol-N-glucuronides. Similarly, but to a lower extent, the DHCMT metabolic profile was in agreement with phase-I and phase-II biotransformation products regularly seen in postadministration urine specimens. In conclusion, this pilot study indicates that the "organ-on-a-chip" technology provides a high degree of conformity with traditional human oral administration studies, providing a promising approach for metabolic profiling in sports drug testing.

Untargeted Plasma Metabolomics and Gut Microbiome Profiling Provide Novel Insights into the Regulation of Platelet Reactivity in Healthy Individuals.

Considerable variation exists in platelet reactivity to stimulation among healthy individuals. Various metabolites and metabolic pathways influence platelet reactivity, but a comprehensive overview of these associations is missing. The gut microbiome has a strong influence on the plasma metabolome. Here, we investigated the association of platelet reactivity with results of untargeted plasma metabolomics and gut microbiome profiling. We used data from a cohort of 534 healthy adult Dutch volunteers (the 500 Functional Genomics study). Platelet activation and reactivity were measured by the expression of the alpha-granule protein P-selectin and the binding of fibrinogen to the activated integrin αIIbβ3, both in unstimulated blood and after ex vivo stimulation with platelet agonists. Plasma metabolome was measured using an untargeted metabolic profiling approach by quadrupole time-of-flight mass spectrometry. Gut microbiome data were measured by shotgun metagenomic sequencing from stool samples. Untargeted metabolomics yielded 1,979 metabolites, of which 422 were identified to play a role in a human metabolic pathway. Overall, 92/422 (21.8%) metabolites were significantly associated with at least one readout of platelet reactivity. The majority of associations involved lipids, especially members of eicosanoids, including prostaglandins and leukotrienes. Dietary-derived polyphenols were also found to inhibit platelet reactivity. Validation of metabolic pathways with functional microbial profiles revealed two overlapping metabolic pathways ("alanine, aspartate, and glutamate metabolism" and "arginine biosynthesis") that were associated with platelet reactivity. This comprehensive overview is an resource for understanding the regulation of platelet reactivity by the plasma metabolome and the possible contribution of the gut microbiota.

Metabolic Profiling Revealed Prediction Biomarkers for Infantile Hemangioma in Umbilical Cord Blood Sera: A Prospective Study.

Infantile hemangioma (IH), the most common benign tumor in infancy, mostly arises and has rapid growth before 3 months of age. Because irreversible skin changes occur in the early proliferative stage, early medical treatment is essential to reduce the permanent sequelae caused by IH. Yet there are still no early screening biomarkers for IH before its visible emergence. This study aimed to explore prediction biomarkers using noninvasive umbilical cord blood (UCB). A prospective study of the metabolic profiling approach was performed on UCB sera from 28 infants with IH and 132 matched healthy controls from a UCB population comprising over 1500 infants (PeptideAtlas: PASS01675) using liquid chromatography-mass spectrometry. The metabolic profiling results exhibited the characteristic metabolic aberrance of IH. Machine learning suggested a panel of biomarkers to predict the occurrence of IH, with the area under curve (AUC) values in the receiver operating characteristic analysis all >0.943. Phenylacetic acid had potential to predict infants with large IH (diameter >2 cm) from those with small IH (diameter <2 cm), with an AUC of 0.756. The novel biomarkers in noninvasive UCB sera for predicting IH before its emergence might lead to a revolutionary clinical utility.