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1
Yu, Ji-Woo, Min-Ho Song, Ji-Ho Lee, et al. "Urinary Metabolomic Differentiation of Infants Fed on Human Breastmilk and Formulated Milk." Metabolites 14, no. 2 (2024): 128. http://dx.doi.org/10.3390/metabo14020128.
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Human breastmilk is an invaluable nutritional and pharmacological resource with a highly diverse metabolite profile, which can directly affect the metabolism of infants. Application of metabolomics can discriminate the complex relationship between such nutrients and infant health. As the most common biological fluid in metabolomic study, infant urinary metabolomics may provide the physiological impacts of different nutritional resources, namely human breastmilk and formulated milk. In this study, we aimed to identify possible differences in the urine metabolome of 30 infants (1–14 days after birth) fed with breast milk (n = 15) or formulated milk (n = 15). From metabolomic analysis with gas chromatography-mass spectrometry, 163 metabolites from single mass spectrometry (GC-MS), and 383 metabolites from tandem mass spectrometry (GC-MS/MS) were confirmed in urinary samples. Various multivariate statistical analysis were performed to discriminate the differences originating from physiological/nutritional variables, including human breastmilk/formulate milk feeding, sex, and duration of feeding. Both unsupervised and supervised discriminant analyses indicated that feeding resources (human breastmilk/formulated milk) gave marginal but significant differences in urinary metabolomes, while other factors (sex, duration of feeding) did not show notable discrimination between groups. According to the biomarker analyses, several organic acid and amino acids showed statistically significant differences between different feeding resources, such as 2-hydroxyhippurate.
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Nakhod, Valeriya I., Tatiana V. Butkova, Kristina A. Malsagova, et al. "Sample Preparation for Metabolomic Analysis in Exercise Physiology." Biomolecules 14, no. 12 (2024): 1561. https://doi.org/10.3390/biom14121561.
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Metabolomics investigates final and intermediate metabolic products in cells. Assessment of the human metabolome relies principally on the analysis of blood, urine, saliva, sweat, and feces. Tissue biopsy is employed less frequently. Understanding the metabolite composition of biosamples from athletes can significantly improve our knowledge of molecular processes associated with the efficiency of training and recovery. Such knowledge may also lead to new management opportunities. Successful execution of metabolomic studies requires simultaneous qualitative and quantitative analyses of numerous small biomolecules in samples under test. Unlike genomics and proteomics, which do not allow for direct assessment of enzymatic activity, metabolomics focuses on biochemical phenotypes, providing unique information about health and physiological features. Crucial factors in ensuring the efficacy of metabolomic analysis are the meticulous selection and pre-treatment of samples.
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Szczerbinski, Lukasz, Gladys Wojciechowska, Adam Olichwier, et al. "Untargeted Metabolomics Analysis of the Serum Metabolic Signature of Childhood Obesity." Nutrients 14, no. 1 (2022): 214. http://dx.doi.org/10.3390/nu14010214.
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Obesity rates among children are growing rapidly worldwide, placing massive pressure on healthcare systems. Untargeted metabolomics can expand our understanding of the pathogenesis of obesity and elucidate mechanisms related to its symptoms. However, the metabolic signatures of obesity in children have not been thoroughly investigated. Herein, we explored metabolites associated with obesity development in childhood. Untargeted metabolomic profiling was performed on fasting serum samples from 27 obese Caucasian children and adolescents and 15 sex- and age-matched normal-weight children. Three metabolomic assays were combined and yielded 726 unique identified metabolites: gas chromatography–mass spectrometry (GC–MS), hydrophilic interaction liquid chromatography coupled to mass spectrometry (HILIC LC–MS/MS), and lipidomics. Univariate and multivariate analyses showed clear discrimination between the untargeted metabolomes of obese and normal-weight children, with 162 significantly differentially expressed metabolites between groups. Children with obesity had higher concentrations of branch-chained amino acids and various lipid metabolites, including phosphatidylcholines, cholesteryl esters, triglycerides. Thus, an early manifestation of obesity pathogenesis and its metabolic consequences in the serum metabolome are correlated with altered lipid metabolism. Obesity metabolite patterns in the adult population were very similar to the metabolic signature of childhood obesity. Identified metabolites could be potential biomarkers and used to study obesity pathomechanisms.
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Sebastiani, Paola, and Nalini Raghavachari. "METABOLOMICS OF LONGEVITY AND LIFESPAN." Innovation in Aging 7, Supplement_1 (2023): 631. http://dx.doi.org/10.1093/geroni/igad104.2056.
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Abstract Serum metabolomics has been an important source of biomarkers of aging and longevity for years. This symposium will bring together investigators from large studies of human longevity to provide an overview of recent discoveries on serum metabolomics of aging and extreme human longevity, their connections to genetic variations, and highlight the challenges of correlating metabolomic profiles of aging in human studies and across multiple species. Dr. Sebastiani will describe results from analyses of serum metabolomics of participants enrolled in the Long Life Family Study, highlight similarities and differences between metabolomic profiles of old age and extreme old age, and some connections with genetics of extreme human longevity. Dr. Rappaport will connect specific variations of the APOE alleles to metabolomic profiles and describe a possible role of bioenergetics pathways in mediating the effect of APOE to longevity and resistance to Alzheimer’s disease. Dr. Monti will expand the characterization of metabolomics of aging and extreme human longevity in a large metabolomic study of very old centenarians by using traditional statistical analyses and novel machine learning techniques. His analysis identifies rich signatures of aging and longevity that include well known metabolites and point to bile acids and several classes of steroids as important marker of longevity. Analytical innovations will be taken further by Dr. Schork who will introduce a novel approach based on distance of profiles to analyze multiple metabolites simultaneously and show the value of this approach to analyze metabolomic profiles of maximum lifespan across multiple species. This is a Geroscience Interest Group Sponsored Symposium.
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Qi, Jinwei, Kang Li, Yunxia Shi, et al. "Cross-Species Comparison of Metabolomics to Decipher the Metabolic Diversity in Ten Fruits." Metabolites 11, no. 3 (2021): 164. http://dx.doi.org/10.3390/metabo11030164.
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Fruits provide humans with multiple kinds of nutrients and protect humans against worldwide nutritional deficiency. Therefore, it is essential to understand the nutrient composition of various fruits in depth. In this study, we performed LC-MS-based non-targeted metabolomic analyses with ten kinds of fruit, including passion fruit, mango, starfruit, mangosteen, guava, mandarin orange, grape, apple, blueberry, and strawberry. In total, we detected over 2500 compounds and identified more than 300 nutrients. Although the ten fruits shared 909 common-detected compounds, each species accumulated a variety of species-specific metabolites. Additionally, metabolic profiling analyses revealed a constant variation in each metabolite’s content across the ten fruits. Moreover, we constructed a neighbor-joining tree using metabolomic data, which resembles the single-copy protein-based phylogenetic tree. This indicates that metabolome data could reflect the genetic relationship between different species. In conclusion, our work enriches knowledge on the metabolomics of fruits, and provides metabolic evidence for the genetic relationships among these fruits.
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Kim, Hyun Woo. "Metabolomic Approaches to Investigate the Effect of Metformin: An Overview." International Journal of Molecular Sciences 22, no. 19 (2021): 10275. http://dx.doi.org/10.3390/ijms221910275.
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Metformin is the first-line antidiabetic drug that is widely used in the treatment of type 2 diabetes mellitus (T2DM). Even though the various therapeutic potential of metformin treatment has been reported, as well as the improvement of insulin sensitivity and glucose homeostasis, the mechanisms underlying those benefits are still not fully understood. In order to explain the beneficial effects on metformin treatment, various metabolomics analyses have been applied to investigate the metabolic alterations in response to metformin treatment, and significant systemic metabolome changes were observed in biofluid, tissues, and cells. In this review, we compare the latest metabolomic research including clinical trials, animal models, and in vitro studies comprehensively to understand the overall changes of metabolome on metformin treatment.
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Li, Jie, Huan Liu, Panpan Yang, Feng Zhu, Fei Shen, and Geyu Liang. "Identifying Aberrant 1CM-Related Pathways by Multi-Omics Analysis and Validating Tumor Inhibitory Effect of One-Carbon Donor Betaine in Gastric Cancer." International Journal of Molecular Sciences 26, no. 8 (2025): 3841. https://doi.org/10.3390/ijms26083841.
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Metabolic reprogramming, a well-established hallmark of gastric carcinogenesis, has been implicated in driving tumor progression. Nevertheless, the precise mechanisms through which these metabolic alterations orchestrate gastric cancer (GC) pathogenesis remain incompletely elucidated. We conducted metabolomic analyses of plasma samples obtained from 334 patients with GC and healthy individuals to identify differential metabolites and metabolic pathways. Transcriptome sequencing was conducted on six pairs of tissues, and a joint analysis of the transcriptome and metabolome was performed. Single-cell sequencing data were acquired and co-analyzed with metabolomics to investigate metabolic abnormalities at the single-cell level. Finally, four representative metabolites selected using Random Forest analysis were subjected to cellular experiments to elucidate the mechanisms through which these metabolites exert their effects. Metabolomic analyses revealed that serine and glycine metabolism, glycolysis, and glutamate metabolism were significantly altered in GC, suggesting that one-carbon metabolism (1CM)-related pathways are aberrantly activated. A combined analysis of the transcriptome, single-cell transcriptome, and metabolomics indicated that pathways related to oxidative phosphorylation, nucleotide metabolism, and amino acid metabolism in epithelial cells were altered in GC. Cellular experiments demonstrated that the one-carbon donor metabolite betaine could inhibit the activity, invasion, and migration of GC cells while activating the phosphorylation of AMPKα. In conclusion, the 1CM-related pathway and the metabolite betaine play significant roles in GC, and the mechanisms through which the one-carbon donor betaine influences GC warrant further investigation.
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Patterson, Jeffrey, Xiaojian Shi, William Bresette, et al. "A Metabolomic Analysis of the Sex-Dependent Hispanic Paradox." Metabolites 11, no. 8 (2021): 552. http://dx.doi.org/10.3390/metabo11080552.
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In Mexican Americans, metabolic conditions, such as obesity and type 2 diabetes (T2DM), are not necessarily associated with an increase in mortality; this is the so-called Hispanic paradox. In this cross-sectional analysis, we used a metabolomic analysis to look at the mechanisms behind the Hispanic paradox. To do this, we examined dietary intake and body mass index (BMI; kg/m2) in men and women and their effects on serum metabolomic fingerprints in 70 Mexican Americans (26 men, 44 women). Although having different BMI values, the participants had many similar anthropometric and biochemical parameters, such as systolic and diastolic blood pressure, total cholesterol, and LDL cholesterol, which supported the paradox in these subjects. Plasma metabolomic phenotypes were measured using liquid chromatography tandem mass spectrometry (LC-MS/MS). A two-way ANOVA assessing sex, BMI, and the metabolome revealed 23 significant metabolites, such as 2-pyrrolidinone (p = 0.007), TMAO (p = 0.014), 2-aminoadipic acid (p = 0.019), and kynurenine (p = 0.032). Pathway and enrichment analyses discovered several significant metabolic pathways between men and women, including lysine degradation, tyrosine metabolism, and branch-chained amino acid (BCAA) degradation and biosynthesis. A log-transformed OPLS-DA model was employed and demonstrated a difference due to BMI in the metabolomes of both sexes. When stratified for caloric intake (<2200 kcal/d vs. >2200 kcal/d), a separate OPLS-DA model showed clear separation in men, while females remained relatively unchanged. After accounting for caloric intake and BMI status, the female metabolome showed substantial resistance to alteration. Therefore, we provide a better understanding of the Mexican-American metabolome, which may help demonstrate how this population—particularly women—possesses a longer life expectancy despite several comorbidities, and reveal the underlying mechanisms of the Hispanic paradox.
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Kelly, Patricia E., H. Jene Ng, Gillian Farrell, et al. "An Optimised Monophasic Faecal Extraction Method for LC-MS Analysis and Its Application in Gastrointestinal Disease." Metabolites 12, no. 11 (2022): 1110. http://dx.doi.org/10.3390/metabo12111110.
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Liquid chromatography coupled with mass spectrometry (LC-MS) metabolomic approaches are widely used to investigate underlying pathogenesis of gastrointestinal disease and mechanism of action of treatments. However, there is an unmet requirement to assess faecal metabolite extraction methods for large-scale metabolomics studies. Current methods often rely on biphasic extractions using harmful halogenated solvents, making automation and large-scale studies challenging. The present study reports an optimised monophasic faecal extraction protocol that is suitable for untargeted and targeted LC-MS analyses. The impact of several experimental parameters, including sample weight, extraction solvent, cellular disruption method, and sample-to-solvent ratio, were investigated. It is suggested that a 50 mg freeze-dried faecal sample should be used in a methanol extraction (1:20) using bead beating as the means of cell disruption. This is revealed by a significant increase in number of metabolites detected, improved signal intensity, and wide metabolic coverage given by each of the above extraction parameters. Finally, we addressed the applicability of the method on faecal samples from patients with Crohn’s disease (CD) and coeliac disease (CoD), two distinct chronic gastrointestinal diseases involving metabolic perturbations. Untargeted and targeted metabolomic analysis demonstrated the ability of the developed method to detect and stratify metabolites extracted from patient groups and healthy controls (HC), highlighting characteristic changes in the faecal metabolome according to disease. The method developed is, therefore, suitable for the analysis of patients with gastrointestinal disease and can be used to detect and distinguish differences in the metabolomes of CD, CoD, and HC.
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Kreuzer, Kathrin, Alexandra Reiter, Anna Maria Birkl-Töglhofer, et al. "The PROVIT Study—Effects of Multispecies Probiotic Add-on Treatment on Metabolomics in Major Depressive Disorder—A Randomized, Placebo-Controlled Trial." Metabolites 12, no. 8 (2022): 770. http://dx.doi.org/10.3390/metabo12080770.
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The gut–brain axis plays a role in major depressive disorder (MDD). Gut-bacterial metabolites are suspected to reduce low-grade inflammation and influence brain function. Nevertheless, randomized, placebo-controlled probiotic intervention studies investigating metabolomic changes in patients with MDD are scarce. The PROVIT study (registered at clinicaltrials.com NCT03300440) aims to close this scientific gap. PROVIT was conducted as a randomized, single-center, double-blind, placebo-controlled multispecies probiotic intervention study in individuals with MDD (n = 57). In addition to clinical assessments, metabolomics analyses (1H Nuclear Magnetic Resonance Spectroscopy) of stool and serum, and microbiome analyses (16S rRNA sequencing) were performed. After 4 weeks of probiotic add-on therapy, no significant changes in serum samples were observed, whereas the probiotic groups’ (n = 28) stool metabolome shifted towards significantly higher concentrations of butyrate, alanine, valine, isoleucine, sarcosine, methylamine, and lysine. Gallic acid was significantly decreased in the probiotic group. In contrast, and as expected, no significant changes resulted in the stool metabolome of the placebo group. Strong correlations between bacterial species and significantly altered stool metabolites were obtained. In summary, the treatment with multispecies probiotics affects the stool metabolomic profile in patients with MDD, which sets the foundation for further elucidation of the mechanistic impact of probiotics on depression.
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Billet, Kévin, Sébastien Salvador-Blanes, Thomas Dugé De Bernonville, et al. "Terroir Influence on Polyphenol Metabolism from Grape Canes: A Spatial Metabolomic Study at Parcel Scale." Molecules 28, no. 11 (2023): 4555. http://dx.doi.org/10.3390/molecules28114555.
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The composition of bioactive polyphenols from grape canes, an important viticultural byproduct, was shown to be varietal-dependent; however, the influence of soil-related terroir factors remains unexplored. Using spatial metabolomics and correlation-based networks, we investigated how continuous changes in soil features and topography may impact the polyphenol composition in grape canes. Soil properties, topography, and grape cane extracts were analyzed at georeferenced points over 3 consecutive years, followed by UPLC-DAD-MS-based metabolomic analysis targeting 42 metabolites. Principal component analyses on intra-vintage metabolomic data presented a good reproducibility in relation to geographic coordinates. A correlation-driven approach was used to explore the combined influence of soil and topographic variables on metabolomic responses. As a result, a metabolic cluster including flavonoids was correlated with elevation and curvature. Spatial metabolomics driven by correlation-based networks represents a powerful approach to spatialize field-omics data and may serve as new field-phenotyping tool in precision agriculture.
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Rosolanka, Robert, Peter Liptak, Eva Baranovicova, et al. "Changes in the Urine Metabolomic Profile in Patients Recovering from Severe COVID-19." Metabolites 13, no. 3 (2023): 364. http://dx.doi.org/10.3390/metabo13030364.
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Metabolomics is a relatively new research area that focuses mostly on the profiling of selected molecules and metabolites within the organism. A SARS-CoV-2 infection itself can lead to major disturbances in the metabolite profile of the infected individuals. The aim of this study was to analyze metabolomic changes in the urine of patients during the acute phase of COVID-19 and approximately one month after infection in the recovery period. We discuss the observed changes in relation to the alterations resulting from changes in the blood plasma metabolome, as described in our previous study. The metabolome analysis was performed using NMR spectroscopy from the urine of patients and controls. The urine samples were collected at three timepoints, namely upon hospital admission, during hospitalization, and after discharge from the hospital. The acute COVID-19 phase induced massive alterations in the metabolic composition of urine was linked with various changes taking place in the organism. Discriminatory analyses showed the feasibility of successful discrimination of COVID-19 patients from healthy controls based on urinary metabolite levels, with the highest significance assigned to citrate, Hippurate, and pyruvate. Our results show that the metabolomic changes persist one month after the acute phase and that the organism is not fully recovered.
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Delporte, Cédric, Nausicaa Noret, Cécile Vanhaverbeke, et al. "Does the Phytochemical Diversity of Wild Plants Like the Erythrophleum genus Correlate with Geographical Origin?" Molecules 26, no. 6 (2021): 1668. http://dx.doi.org/10.3390/molecules26061668.
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Secondary metabolites are essential for plant survival and reproduction. Wild undomesticated and tropical plants are expected to harbor highly diverse metabolomes. We investigated the metabolomic diversity of two morphologically similar trees of tropical Africa, Erythrophleum suaveolens and E. ivorense, known for particular secondary metabolites named the cassaine-type diterpenoids. To assess how the metabolome varies between and within species, we sampled leaves from individuals of different geographic origins but grown from seeds in a common garden in Cameroon. Metabolites were analyzed using reversed phase LC-HRMS(/MS). Data were interpreted by untargeted metabolomics and molecular networks based on MS/MS data. Multivariate analyses enabled us to cluster samples based on species but also on geographic origins. We identified the structures of 28 cassaine-type diterpenoids among which 19 were new, 10 were largely specific to E. ivorense and five to E. suaveolens. Our results showed that the metabolome allows an unequivocal distinction of morphologically-close species, suggesting the potential of metabolite fingerprinting for these species. Plant geographic origin had a significant influence on relative concentrations of metabolites with variations up to eight (suaveolens) and 30 times (ivorense) between origins of the same species. This shows that the metabolome is strongly influenced by the geographical origin of plants (i.e., genetic factors).
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Cheng, Leo L., Adam S. Feldman, Lindsey A. Vandergrift, et al. "Abstract 2222: Detecting clinically significant prostate cancers: Tissue metabolomics refines multiparametric MRI-ultrasound fusion prostate biopsy." Cancer Research 82, no. 12_Supplement (2022): 2222. http://dx.doi.org/10.1158/1538-7445.am2022-2222.
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Abstract The advent of prostate specific antigen (PSA) testing led to increased early prostate cancer (PCa) detection and has decreased PCa-related death. However, PSA is not cancer-specific, and the challenge persists of differentiating those PCa patients with indolent tumors from those requiring definitive therapy. Metabolomic profiles have the potential to capture molecular dynamics of disease and to reflect disease status before cellular manifestations become observable by histopathology. With clinical, multiparametric magnetic resonance imaging (mpMRI)-positive, fusion biopsy-targeted tissue cores and mpMRI-negative controls in a training-testing cohort design, we studied the potential of magnetic resonance spectroscopy (MRS) to yield cancer metabolomic profiles that could help discriminate likely indolent from clinically significant disease. Using MRS-based PCa metabolomic analyses, performed prior to histology, our approach is able to: determine metabolomic relevations identified in fusion biopsy targets, estimate the scale of PCa metabolomic fields, and detect clinically significant disease in tissues deemed benign or low-risk PCa by pathology and imaging. Our intact tissue MRS metabolomics evaluations indicated significant differences in individual prostate tissue metabolites based on Target-Contralateral (Contral) paired comparisons for both Training and Testing cohorts. We identified metabolomic differences among Target prostate biopsy cores obtained from mpMRI lesions of different PI-RADS scores, and between Target and non-target Contral cores. As a retrospective study, we also analyzed data collected at the time of the initial prostate biopsy alongside patient status across follow up. By introducing metabolomics, as compared with using PSAd or PI-RADS alone, the sensitivity predictions increased by 80.0% and 25.0%, respectively; NPV increased by 18.1% and 8.0%; and accuracy for PSAd increased by 13.0%. PI-RADS accuracy stayed the same Our results show that tissue metabolomic profiles could augment current MR-based imaging findings and histopathological evaluations of fusion biopsies for certain patient populations by more accurately characterizing them into clinically significant or insignificant subgroups. In our analyses, tissue metabolomics alone, or its combination with other clinical parameters, improved sensitivity and negative predictive values, as well as overall accuracy, for our testing cohort. This method, which relies on performing tissue MRS of needle biopsy cores prior to histopathologic analysis, causes no interruption to patient care. Findings from our study demonstrate the utility and translational potential of cancer metabolomics in personalized treatment for PCa and encourages the development of in vivo PCa metabolomic imaging to enhance the diagnostic utility of mpMRI. Citation Format: Leo L. Cheng, Adam S. Feldman, Lindsey A. Vandergrift, Isabella H. Muti, Florian Rumpf, Andrew Gusev, Yannick Berker, Marcella R. Cardoso, Taylor L. Fuss, Emily D. Negroponte, Shulin Wu, Felix Ehret, Christopher A. Dietz, Sarah S. Dinges, Thitinan Chulroek, Edouard Nicaise, Piet Habbel, Martin Ayree, Johannes Nowak, Douglas M. Dahl, Chin-Lee Wu, Mukesh Harisinghani. Detecting clinically significant prostate cancers: Tissue metabolomics refines multiparametric MRI-ultrasound fusion prostate biopsy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2222.
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Loras, Alba, M. Carmen Martínez-Bisbal, Guillermo Quintás, Salvador Gil, Ramón Martínez-Máñez, and José Luis Ruiz-Cerdá. "Urinary Metabolic Signatures Detect Recurrences in Non-Muscle Invasive Bladder Cancer." Cancers 11, no. 7 (2019): 914. http://dx.doi.org/10.3390/cancers11070914.
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Patients with non-muscle invasive bladder cancer (NMIBC) undergo lifelong monitoring based on repeated cystoscopy and urinary cytology due to the high recurrence rate of this tumor. Nevertheless, these techniques have some drawbacks, namely, low accuracy in detection of low-grade tumors, omission of pre-neoplastic lesions and carcinomas in situ (CIS), invasiveness, and high costs. This work aims to identify a urinary metabolomic signature of recurrence by proton Nuclear Magnetic Resonance (1H NMR) spectroscopy for the follow-up of NMIBC patients. To do this, changes in the urinary metabolome before and after transurethral resection (TUR) of tumors are analyzed and a Partial Least Square Discriminant Analysis (PLS-DA) model is developed. The usefulness of this discriminant model for the detection of tumor recurrences is assessed using a cohort of patients undergoing monitoring. The trajectories of the metabolomic profile in the follow-up period provide a negative predictive value of 92.7% in the sample classification. Pathway analyses show taurine, alanine, aspartate, glutamate, and phenylalanine perturbed metabolism associated with NMIBC. These results highlight the potential of 1H NMR metabolomics to detect bladder cancer (BC) recurrences through a non-invasive approach.
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Byerley, Lauri O., Karyn M. Gallivan, Courtney J. Christopher, et al. "Gut Microbiome and Metabolome Variations in Self-Identified Muscle Builders Who Report Using Protein Supplements." Nutrients 14, no. 3 (2022): 533. http://dx.doi.org/10.3390/nu14030533.
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Muscle builders frequently consume protein supplements, but little is known about their effect on the gut microbiota. This study compared the gut microbiome and metabolome of self-identified muscle builders who did or did not report consuming a protein supplement. Twenty-two participants (14 males and 8 females) consumed a protein supplement (PS), and seventeen participants (12 males and 5 females) did not (No PS). Participants provided a fecal sample and completed a 24-h food recall (ASA24). The PS group consumed significantly more protein (118 ± 12 g No PS vs. 169 ± 18 g PS, p = 0.02). Fecal metabolome and microbiome were analyzed by using untargeted metabolomics and 16S rRNA gene sequencing, respectively. Metabolomic analysis identified distinct metabolic profiles driven by allantoin (VIP score = 2.85, PS 2.3-fold higher), a catabolic product of uric acid. High-protein diets contain large quantities of purines, which gut microbes degrade to uric acid and then allantoin. The bacteria order Lactobacillales was higher in the PS group (22.6 ± 49 No PS vs. 136.5 ± 38.1, PS (p = 0.007)), and this bacteria family facilitates purine absorption and uric acid decomposition. Bacterial genes associated with nucleotide metabolism pathways (p < 0.001) were more highly expressed in the No PS group. Both fecal metagenomic and metabolomic analyses revealed that the PS group’s higher protein intake impacted nitrogen metabolism, specifically altering nucleotide degradation.
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Mok, Jeong-Hun, Minjoong Joo, Van-An Duong, et al. "Proteomic and Metabolomic Analyses of Maggots in Porcine Corpses for Post-Mortem Interval Estimation." Applied Sciences 11, no. 17 (2021): 7885. http://dx.doi.org/10.3390/app11177885.
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Post-mortem interval (PMI) estimation is a critical task in forensic science. In this study, we used maggots collected from pig carcasses and applied an integrated proteomics and metabolomics approach to determine potential candidate substances for the estimation of PMI. After methanol precipitation, the supernatant containing metabolites and the protein pellet were separated and subjected to metabolomic and proteomic analyses using liquid chromatography-tandem mass spectrometry (LC-MS/MS). MS/MS data were analyzed for identification and quantification using Proteome Discoverer and Compound Discoverer software. A total of 573 metabolites and more than 800 porcine proteins were identified in maggots. This is the first dataset of proteins and metabolites in maggots collected from porcine carcasses. In this study, guanosine monophosphate, xanthine, inosine, adenosine, and guanine were detected with a similar tendency to increase during early days of maggot development and then decreased gradually. We broadly profiled various biomolecules through analysis in the spot of incident. Especially, we confirmed that proteome and metabolome profiling could be performed directly and indirectly.
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Fitzpatrick, Garrett, Maryam Rahman, Timothy Garrett, and Jesse Kresak. "MNGI-11. HIGH-GRADE AND LOW-GRADE MENINGIOMAS HARBOR DIFFERING METABOLOMIC PROFILES." Neuro-Oncology 21, Supplement_6 (2019): vi141—vi142. http://dx.doi.org/10.1093/neuonc/noz175.593.
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Abstract BACKGROUND Meningiomas are the most common primary brain tumor in adults. While the majority of meningiomas are low-grade and effectively treated by resection alone, there is a subset of tumors that have a high incidence of recurrence, metastatic potential, and morbidity. Radiation has been employed with variable success for high-grade meningiomas. No chemotherapeutic approaches have proven effective against these tumors to date. There is a need for a better understanding of this tumor type in order to provide our patients with better treatment options. OBJECTIVE The purpose of this study is to investigate the metabolomic profile of meningiomas with a focus on comparing low- and high-grade tumors and identifying biologically significant metabolites which could correlate with overall and disease-free survival. METHODS Ten tumor samples of each meningioma grade (WHO grades I-III) were collected from the Florida Center for Brain Tumor Research. Global metabolomic profiling by liquid chromatography mass spectrometry was performed on the frozen tumor samples. Statistical analyses were performed using the Southeast Center for Integrated Metabolomics Galaxy interface. Select metabolites which significantly differed between low-grade (WHO Grade I) and high-grade (WHO grade II-III) were identified using the Human Metabolome Database. RESULTS Differing metabolomic profiles between low-grade and high-grade meningiomas were confirmed by multivariate analysis and demonstrated by unsupervised hierarchical clustering. Notably, lysophospholipid and sphingolipid metabolism was increased in the high-grade tumors, while FAPy-adenine, an oxidized nucleoside which may serve as a tumor marker, was decreased. Guanine was found to be consistently decreased in patients with negative outcomes. CONCLUSIONS High-grade and low-grade meningiomas harbor different metabolomic profiles. The significance of these specific differences requires further investigation.
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Tasci, Erdal, Shreya Chappidi, Ying Zhuge, et al. "GLIO-Select: Machine Learning-Based Feature Selection and Weighting of Tissue and Serum Proteomic and Metabolomic Data Uncovers Sex Differences in Glioblastoma." International Journal of Molecular Sciences 26, no. 9 (2025): 4339. https://doi.org/10.3390/ijms26094339.
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Glioblastoma (GBM) is a fatal brain cancer known for its rapid and aggressive growth, with some studies indicating that females may have better survival outcomes compared to males. While sex differences in GBM have been observed, the underlying biological mechanisms remain poorly understood. Feature selection can lead to the identification of discriminative key biomarkers by reducing dimensionality from high-dimensional medical datasets to improve machine learning model performance, explainability, and interpretability. Feature selection can uncover unique sex-specific biomarkers, determinants, and molecular profiles in patients with GBM. We analyzed high-dimensional proteomic and metabolomic profiles from serum biospecimens obtained from 109 patients with pathology-proven glioblastoma (GBM) on NIH IRB-approved protocols with full clinical annotation (local dataset). Serum proteomic analysis was performed using Somalogic aptamer-based technology (measuring 7289 proteins) and serum metabolome analysis using the University of Florida’s SECIM (Southeast Center for Integrated Metabolomics) platform (measuring 6015 metabolites). Machine learning-based feature selection was employed to identify proteins and metabolites associated with male and female labels in high-dimensional datasets. Results were compared to publicly available proteomic and metabolomic datasets (CPTAC and TCGA) using the same methodology and TCGA data previously structured for glioma grading. Employing a machine learning-based and hybrid feature selection approach, utilizing both LASSO and mRMR, in conjunction with a rank-based weighting method (i.e., GLIO-Select), we linked proteomic and metabolomic data to clinical data for the purposes of feature reduction to identify molecular biomarkers associated with biological sex in patients with GBM and used a separate TCGA set to explore possible linkages between biological sex and mutations associated with tumor grading. Serum proteomic and metabolomic data identified several hundred features that were associated with the male/female class label in the GBM datasets. Using the local serum-based dataset of 109 patients, 17 features (100% ACC) and 16 features (92% ACC) were identified for the proteomic and metabolomic datasets, respectively. Using the CPTAC tissue-based dataset (8828 proteomic and 59 metabolomic features), 5 features (99% ACC) and 13 features (80% ACC) were identified for the proteomic and metabolomic datasets, respectively. The proteomic data serum or tissue (CPTAC) achieved the highest accuracy rates (100% and 99%, respectively), followed by serum metabolome and tissue metabolome. The local serum data yielded several clinically known features (PSA, PZP, HCG, and FSH) which were distinct from CPTAC tissue data (RPS4Y1 and DDX3Y), both providing methodological validation, with PZP and defensins (DEFA3 and DEFB4A) representing shared proteomic features between serum and tissue. Metabolomic features shared between serum and tissue were homocysteine and pantothenic acid. Several signals emerged that are known to be associated with glioma or GBM but not previously known to be associated with biological sex, requiring further research, as well as several novel signals that were previously not linked to either biological sex or glioma. EGFR, FAT4, and BCOR were the three features associated with 64% ACC using the TCGA glioma grading set. GLIO-Select shows remarkable results in reducing feature dimensionality when different types of datasets (e.g., serum and tissue-based) were used for our analyses. The proposed approach successfully reduced relevant features to less than twenty biomarkers for each GBM dataset. Serum biospecimens appear to be highly effective for identifying biologically relevant sex differences in GBM. These findings suggest that serum-based noninvasive biospecimen-based analyses may provide more accurate and clinically detailed insights into sex as a biological variable (SABV) as compared to other biospecimens, with several signals linking sex differences and glioma pathology via immune response, amino acid metabolism, and cancer hallmark signals requiring further research. Our results underscore the importance of biospecimen choice and feature selection in enhancing the interpretation of omics data for understanding sex-based differences in GBM. This discovery holds significant potential for enhancing personalized treatment plans and patient outcomes.
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Merwin, Nishanth J., Walaa K. Mousa, Chris A. Dejong, et al. "DeepRiPP integrates multiomics data to automate discovery of novel ribosomally synthesized natural products." Proceedings of the National Academy of Sciences 117, no. 1 (2019): 371–80. http://dx.doi.org/10.1073/pnas.1901493116.
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Microbial natural products represent a rich resource of evolved chemistry that forms the basis for the majority of pharmacotherapeutics. Ribosomally synthesized and posttranslationally modified peptides (RiPPs) are a particularly interesting class of natural products noted for their unique mode of biosynthesis and biological activities. Analyses of sequenced microbial genomes have revealed an enormous number of biosynthetic loci encoding RiPPs but whose products remain cryptic. In parallel, analyses of bacterial metabolomes typically assign chemical structures to only a minority of detected metabolites. Aligning these 2 disparate sources of data could provide a comprehensive strategy for natural product discovery. Here we present DeepRiPP, an integrated genomic and metabolomic platform that employs machine learning to automate the selective discovery and isolation of novel RiPPs. DeepRiPP includes 3 modules. The first, NLPPrecursor, identifies RiPPs independent of genomic context and neighboring biosynthetic genes. The second module, BARLEY, prioritizes loci that encode novel compounds, while the third, CLAMS, automates the isolation of their corresponding products from complex bacterial extracts. DeepRiPP pinpoints target metabolites using large-scale comparative metabolomics analysis across a database of 10,498 extracts generated from 463 strains. We apply the DeepRiPP platform to expand the landscape of novel RiPPs encoded within sequenced genomes and to discover 3 novel RiPPs, whose structures are exactly as predicted by our platform. By building on advances in machine learning technologies, DeepRiPP integrates genomic and metabolomic data to guide the isolation of novel RiPPs in an automated manner.
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Xi, Dandan, Xiaofeng Li, Changwei Zhang, et al. "The Combined Analysis of Transcriptome and Metabolome Provides Insights into Purple Leaves in Eruca vesicaria subsp. sativa." Agronomy 12, no. 9 (2022): 2046. http://dx.doi.org/10.3390/agronomy12092046.
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Background: Arugula is an essential oil crop of cruciferous species worldwide and serves as a salad vegetable. Purple plant leaves provide nutrients benefiting human beings and are mainly attributed to high anthocyanins. In this study, we collected a purple arugula cultivar with purple leaves and a green arugula with green leaves. The genetic bases and mechanisms underlying purple leaf formation in arugula remain unclear. Therefore, we conducted integrative metabolomics and transcriptomics of two arugula cultivars with different leaf colors. Methods: To study the underlying mechanisms, transcriptomic and metabolomic analyses were carried out. Results: Metabolomic analysis revealed that 84 of 747 metabolites were significantly differentially expressed, comprising 30 depleted and 49 enriched metabolites. Further analysis showed that cyanidin is the main components responsible for the purple color. A total of 144,790 unigenes were obtained by transcriptomic analysis, with 13,204 unigenes differentially expressed, comprising 8120 downregulated and 5084 upregulated unigenes. Seven structural genes, PAL, C4H, 4CL, CHS, CCoAOMT, LDOX, and UFGT, were identified as candidate genes associated with anthocyanin accumulation through combined analysis of transcriptome and metabolome. Conclusions: Collectively, the differences in the expression levels of PAL, C4H, 4CL, CHS, CCoAOMT, LDOX, and UFGT might be responsible for purple leaf coloration, providing important data for the discovery of candidate genes and molecular bases controlling the purple leaves in arugula.
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Dabbousy, Ranin, Mohamad Rima, Rabih Roufayel, et al. "Plant Metabolomics: The Future of Anticancer Drug Discovery." Pharmaceuticals 17, no. 10 (2024): 1307. http://dx.doi.org/10.3390/ph17101307.
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Drug development from medicinal plants constitutes an important strategy for finding natural anticancer therapies. While several plant secondary metabolites with potential antitumor activities have been identified, well-defined mechanisms of action remained uncovered. In fact, studies of medicinal plants have often focused on the genome, transcriptome, and proteome, dismissing the relevance of the metabolome for discovering effective plant-based drugs. Metabolomics has gained huge interest in cancer research as it facilitates the identification of potential anticancer metabolites and uncovers the metabolomic alterations that occur in cancer cells in response to treatment. This holds great promise for investigating the mode of action of target metabolites. Although metabolomics has made significant contributions to drug discovery, research in this area is still ongoing. In this review, we emphasize the significance of plant metabolomics in anticancer research, which continues to be a potential technique for the development of anticancer drugs in spite of all the challenges encountered. As well, we provide insights into the essential elements required for performing effective metabolomics analyses.
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Patti, Gary J., Ralf Tautenhahn, Bryan R. Fonslow, et al. "Meta-analysis of global metabolomics and proteomics data to link alterations with phenotype." Spectroscopy 26, no. 3 (2011): 151–54. http://dx.doi.org/10.1155/2011/923017.
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Global metabolomics has emerged as a powerful tool to interrogate cellular biochemistry at the systems level by tracking alterations in the levels of small molecules. One approach to define cellular dynamics with respect to this dysregulation of small molecules has been to consider metabolic flux as a function of time. While flux measurements have proven effective for model organisms, acquiring multiple time points at appropriate temporal intervals for many sample types (e.g., clinical specimens) is challenging. As an alternative, meta-analysis provides another strategy for delineating metabolic cause and effect perturbations. That is, the combination of untargeted metabolomic data from multiple pairwise comparisons enables the association of specific changes in small molecules with unique phenotypic alterations. We recently developed metabolomic software called metaXCMS to automate these types of higher order comparisons. Here we discuss the potential of metaXCMS for analyzing proteomic datasets and highlight the biological value of combining meta-results from both metabolomic and proteomic analyses. The combined meta-analysis has the potential to facilitate efforts in functional genomics and the identification of metabolic disruptions related to disease pathogenesis.
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Anlar, Gulsen Guliz, Najeha Anwardeen, Sarah Al Ashmar, Shona Pedersen, Mohamed A. Elrayess, and Asad Zeidan. "Metabolomics Profiling of Stages of Coronary Artery Disease Progression." Metabolites 14, no. 6 (2024): 292. http://dx.doi.org/10.3390/metabo14060292.
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Coronary artery disease (CAD) and atherosclerosis pose significant global health challenges, with intricate molecular changes influencing disease progression. Hypercholesterolemia (HC), hypertension (HT), and diabetes are key contributors to CAD development. Metabolomics, with its comprehensive analysis of metabolites, offers a unique perspective on cardiovascular diseases. This study leveraged metabolomics profiling to investigate the progression of CAD, focusing on the interplay of hypercholesterolemia, hypertension, and diabetes. We performed a metabolomic analysis on 221 participants from four different groups: (I) healthy individuals, (II) individuals with hypercholesterolemia (HC), (III) individuals with both HC and hypertension (HT) or diabetes, and (IV) patients with self-reported coronary artery disease (CAD). Utilizing data from the Qatar Biobank, we combined clinical information, metabolomic profiling, and statistical analyses to identify key metabolites associated with CAD risk. Our data identified distinct metabolite profiles across the study groups, indicating changes in carbohydrate and lipid metabolism linked to CAD risk. Specifically, levels of mannitol/sorbitol, mannose, glucose, and ribitol increased, while pregnenediol sulfate, oleoylcarnitine, and quinolinate decreased with higher CAD risk. These findings suggest a significant role of sugar, steroid, and fatty acid metabolism in CAD progression and point to the need for further research on the correlation between quinolinate levels and CAD risk, potentially guiding targeted treatments for atherosclerosis. This study provides novel insights into the metabolomic changes associated with CAD progression, emphasizing the potential of metabolites as predictive biomarkers.
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He, Dongling, Shaocong Hu, Zhi Huang, et al. "Metabolomics analyses of serum metabolites perturbations associated with Naja atra bite." PLOS Neglected Tropical Diseases 17, no. 8 (2023): e0011507. http://dx.doi.org/10.1371/journal.pntd.0011507.
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Naja atra bite is one of the most common severe snakebites in emergency departments. Unfortunately, the pathophysiological changes caused by Naja atra bite are unclear due to the lack of good animal models. In this study, an animal model of Naja atra bite in Guangxi Bama miniature pigs was established by intramuscular injection at 2 mg/kg of Naja atra venom, and serum metabolites were systematically analyzed using untargeted metabolomic and targeted metabolomic approaches. Untargeted metabolomic analysis revealed that 5045 chromatographic peaks were obtained in ESI+ and 3871 chromatographic peaks were obtained in ESI-. Screening in ESI+ modes and ESI- modes identified 22 and 36 differential metabolites compared to controls. The presence of 8 core metabolites of glutamine, arginine, proline, leucine, phenylalanine, inosine, thymidine and hippuric acid in the process of Naja atra bite was verified by targeted metabolomics significant difference (P<0.05). At the same time, during the verification process of the serum clinical samples with Naja atra bite, we found that the contents of three metabolites of proline, phenylalanine and inosine in the serum of the patients were significantly different from those of the normal human serum (P<0.05). By conducting functional analysis of core and metabolic pathway analysis, we revealed a potential correlation between changes in key metabolites after the Naja atra bite and the resulting pathophysiological alterations, and our research aims to establish a theoretical foundation for the prompt diagnosis and treatment of Naja atra bite.
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Salzer, Liesa, and Michael Witting. "Quo Vadis Caenorhabditis elegans Metabolomics—A Review of Current Methods and Applications to Explore Metabolism in the Nematode." Metabolites 11, no. 5 (2021): 284. http://dx.doi.org/10.3390/metabo11050284.
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Metabolomics and lipidomics recently gained interest in the model organism Caenorhabditis elegans (C. elegans). The fast development, easy cultivation and existing forward and reverse genetic tools make the small nematode an ideal organism for metabolic investigations in development, aging, different disease models, infection, or toxicology research. The conducted type of analysis is strongly depending on the biological question and requires different analytical approaches. Metabolomic analyses in C. elegans have been performed using nuclear magnetic resonance (NMR) spectroscopy, direct infusion mass spectrometry (DI-MS), gas-chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS) or combinations of them. In this review we provide general information on the employed techniques and their advantages and disadvantages in regard to C. elegans metabolomics. Additionally, we reviewed different fields of application, e.g., longevity, starvation, aging, development or metabolism of secondary metabolites such as ascarosides or maradolipids. We also summarised applied bioinformatic tools that recently have been used for the evaluation of metabolomics or lipidomics data from C. elegans. Lastly, we curated metabolites and lipids from the reviewed literature, enabling a prototypic collection which serves as basis for a future C. elegans specific metabolome database.
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De Moraes Salgado, Carla, Laís Rosa Viana, and Maria Cristina Cintra Gomes-Marcondes. "Placental, Foetal, and Maternal Serum Metabolomic Profiles in Pregnancy-Associated Cancer: Walker-256 Tumour Model in a Time-Course Analysis." International Journal of Molecular Sciences 24, no. 17 (2023): 13026. http://dx.doi.org/10.3390/ijms241713026.
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Cancer during pregnancy presents a delicate coexistence, imposing ethical and professional challenges on both the patient and medical team. In this study, we aimed to explore in a pre-clinical model the impact of tumour evolution in serum, placental and foetal metabolomics profiles during pregnancy in a time-course manner. Pregnant Wistar rats were distributed into two experimental groups: Control (C) and Walker-256 tumour-bearing (W). The rats were euthanised on three different gestational periods: at 12 days post-conception (dpc), at 16 dpc, and at 19 dpc. Serum, placenta and foetal metabolomic profiles were performed by 1H-NMR spectra following the analyses using Chenomx NMR Analysis Software V8.3. The tumour evolution was exponential, affecting the placental metabolomic profile during all the pregnancy stages. The placental tissue in tumour-bearing dams developed at a lower speed, decreasing the foetus’s weight. Associated with the serum metabolomic changes related to tumour growth, the placental metabolomic alterations impacted many metabolic pathways related to energy provision, protein synthesis and signalling, which directly harmed the foetus’s development. The development of the foetus is clearly affected by the damage induced by the tumour evolution, which alters the metabolic profile of both the serum and the placenta, impairing early embryonic development.
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El-Heliebi, Amin, Tadeja Urbanic-Purkart, Kariem Mahdy-Ali, et al. "EXTH-04. PATIENT-DERIVED CELLS FOR EX VIVO DRUG SCREENING STUDIES OF GLIOMAS." Neuro-Oncology 24, Supplement_7 (2022): vii209. http://dx.doi.org/10.1093/neuonc/noac209.803.
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Abstract BACKGROUND In precision oncology ex vivo drug screening systems have the potential to improve clinical outcomes. Traditionally, cancer drugs are tested on cancer cell line models, but these cannot represent an individual patient and are biologically too distinct. Drug screening systems usually rely on viability assays and correlations to genomic alterations. Beside genomic alterations, the cellular metabolism is significantly altered which may lead to drug resistance. Here we aim to establish a drug screening platform using tumor cells derived directly from the individual patient glial tumor tissue, create patient derived tumor cells (PDCs) and combine the outcomes from standardized viability- and genetic-assays with a new developed metabolomics platform. Materials and METHODS Fresh native tissue from patients harbouring low- and high-grade glioma are collected (n=46). Tumor tissue used for NMR-based metabolomic analyses and targeted sequencing analyses as well as PDC isolation. To preserve the original tumor similarity, tissue is short term cultured for two weeks, and PDCs are seeded and treated with a panel of clinical- and preclinical drugs followed by viability assessment, sequencing and metabolomic profiling. RESULTS Culturing of PDCs is successful in ≥ 85% of patient cases, provided that at least 2 g of tumor tissue is available. The automatized high throughput ex vivo drug response identifies drug candidates, which might become relevant for therapeutic approaches in future. It is possible to distinguish between IDH1-wild-type and IDH1-mutant tumors based on the metabolomic profile, which is confirmed by immunohistochemical staining and molecular analysis of IDH1 R132H-mutation. Strong metabolomic variations have been identified, including GABA, lactate, and myo-inositol levels between tumor and healthy tissue. CONCLUSION Entangling drug screening and genetic assays with metabolomic profiling of glial tumors enriches the information about cellular drug response and paves the way for future clinical studies and better understanding of underlying drug resistance mechanisms in gliomas.
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Bailleux, Caroline, David Chardin, Jocelyn Gal, et al. "Metabolomic Signatures of Scarff–Bloom–Richardson (SBR) Grade in Non-Metastatic Breast Cancer." Cancers 15, no. 7 (2023): 1941. http://dx.doi.org/10.3390/cancers15071941.
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Purpose: Identification of metabolomic biomarkers of high SBR grade in non-metastatic breast cancer. Methods: This retrospective bicentric metabolomic analysis included a training set (n = 51) and a validation set (n = 49) of breast cancer tumors, all classified as high-grade (grade III) or low-grade (grade I–II). Metabolomes of tissue samples were studied by liquid chromatography coupled with mass spectrometry. Results: A molecular signature of the top 12 metabolites was identified from a database of 602 frequently predicted metabolites. Partial least squares discriminant analyses showed that accuracies were 0.81 and 0.82, the R2 scores were 0.57 and 0.55, and the Q2 scores were 0.44431 and 0.40147 for the training set and validation set, respectively; areas under the curve for the Receiver Operating Characteristic Curve were 0.882 and 0.886. The most relevant metabolite was diacetylspermine. Metabolite set enrichment analyses and metabolic pathway analyses highlighted the tryptophan metabolism pathway, but the concentration of individual metabolites varied between tumor samples. Conclusions: This study indicates that high-grade invasive tumors are related to diacetylspermine and tryptophan metabolism, both involved in the inhibition of the immune response. Targeting these pathways could restore anti-tumor immunity and have a synergistic effect with immunotherapy. Recent studies could not demonstrate the effectiveness of this strategy, but the use of theragnostic metabolomic signatures should allow better selection of patients.
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Liu, Yin Allison, Orwa Aboud, Lina A. Dahabiyeh, Orin Bloch, and Oliver Fiehn. "Metabolomic characterization of human glioblastomas and patient plasma: a pilot study." F1000Research 13 (September 19, 2024): 98. http://dx.doi.org/10.12688/f1000research.143642.5.
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Background Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling. Methods A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence. Results Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype. Conclusions In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.
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Liu, Yin Allison, Orwa Aboud, Lina A. Dahabiyeh, Orin Bloch, and Oliver Fiehn. "Metabolomic characterization of human glioblastomas and patient plasma: a pilot study." F1000Research 13 (June 25, 2024): 98. http://dx.doi.org/10.12688/f1000research.143642.2.
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Background Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling. Methods A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence. Results Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype. Conclusions In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.
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Liu, Yin Allison, Orwa Aboud, Lina A. Dahabiyeh, Orin Bloch, and Oliver Fiehn. "Metabolomic characterization of human glioblastomas and patient plasma: a pilot study." F1000Research 13 (July 23, 2024): 98. http://dx.doi.org/10.12688/f1000research.143642.3.
Full textAbstract:
Background Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling. Methods A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence. Results Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype. Conclusions In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.
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Liu, Yin Allison, Orwa Aboud, Lina A. Dahabiyeh, Orin Bloch, and Oliver Fiehn. "Metabolomic characterization of human glioblastomas and patient plasma: a pilot study." F1000Research 13 (August 30, 2024): 98. http://dx.doi.org/10.12688/f1000research.143642.4.
Full textAbstract:
Background Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling. Methods A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence. Results Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype. Conclusions In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.
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Liu, Yin Allison, Orwa Aboud, Lina A. Dahabiyeh, Orin Bloch, and Oliver Fiehn. "Metabolomic characterization of human glioblastomas and patient plasma: a pilot study." F1000Research 13 (February 16, 2024): 98. http://dx.doi.org/10.12688/f1000research.143642.1.
Full textAbstract:
Background Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling. Methods A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence. Results Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype. Conclusions In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.
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Iwamoto, Hitoshi, Masaaki Okihara, Isao Akashi, et al. "Metabolomic Profiling of Plasma, Urine, and Saliva of Kidney Transplantation Recipients." International Journal of Molecular Sciences 23, no. 22 (2022): 13938. http://dx.doi.org/10.3390/ijms232213938.
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Kidney biopsy is commonly used to diagnose kidney transplant dysfunction after transplantation. Therefore, the development of minimally invasive and quantitative methods to evaluate kidney function in transplant recipients is necessary. Here, we used capillary electrophoresis-mass spectrometry to analyze the biofluids collected from transplant recipients with impaired (Group I, n = 31) and stable (Group S, n = 19) kidney function and from donors (Group D, n = 9). Metabolomics analyses identified and quantified 97 metabolites in plasma, 133 metabolites in urine, and 108 metabolites in saliva. Multivariate analyses revealed apparent differences in the metabolomic profiles of the three groups. In plasma samples, arginine biosynthesis and purine metabolism between the I and S Groups differed. In addition, considerable differences in metabolomic profiles were observed between samples collected from participants with T cell-mediated rejection (TCR), antibody-mediated rejection, and other kidney disorders (KD). The metabolomic profiles in the three types of biofluids showed different patterns between TCR and KD, wherein 3-indoxyl sulfate showed a significant increase in TCR consistently in both plasma and urine samples. These results suggest that each biofluid has different metabolite features to evaluate kidney function after transplantation and that 3-indoxyl sulfate could predict acute rejection.
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Watanabe, Masahiro, Masamitsu Maekawa, Keitaro Miyoshi, et al. "Global and Targeted Metabolomics for Revealing Metabolomic Alteration in Niemann-Pick Disease Type C Model Cells." Metabolites 14, no. 10 (2024): 515. http://dx.doi.org/10.3390/metabo14100515.
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Background: Niemann-Pick disease type C (NPC) is an inherited disorder characterized by a functional deficiency of cholesterol transport proteins. However, the molecular mechanisms and pathophysiology of the disease remain unknown. Methods: In this study, we identified several metabolite characteristics of NPC that may fluctuate in a cellular model of the disease, using both global and targeted metabolomic analyses by liquid chromatography/tandem mass spectrometry (LC-MS/MS). Three cell lines, HepG2 cells (wild-type[WT]) and two NPC model HepG2 cell lines in which NPC1 was genetically ablated (knockout [KO]1 and KO2), were used for metabolomic analysis. Data were subjected to enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Results: The enrichment analysis of global metabolomics revealed that 8 pathways in KO1 and 16 pathways in KO2 cells were notably altered. In targeted metabolomics for 15 metabolites, 4 metabolites in KO1 and 10 metabolites in KO2 exhibited statistically significant quantitative changes in KO1 or KO2 relative to WT. Most of the altered metabolites were related to creatinine synthesis and cysteine metabolism pathways. Conclusions: In the future, our objective will be to elucidate the relationship between these metabolic alterations and pathophysiology.
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Botticelli, Andrea, Pamela Vernocchi, Federico Marini, et al. "Gut metabolomics profiling of non‑small cell lung cancer (NSCLC) patients under immunotherapy treatment." Journal of Translational Medicine 18 (February 3, 2020): 49. https://doi.org/10.1186/s12967-020-02231-0.
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<strong>Background:</strong> Despite the efficacy of immune checkpoint inhibitors (ICIs) only the 20–30% of treated patients present long term benefits. The metabolic changes occurring in the gut microbiota metabolome are herein proposed as a factor potentially influencing the response to immunotherapy. <strong>Methods:</strong> The metabolomic profiling of gut microbiota was characterized in 11 patients affected by non-small cell lung cancer (NSCLC) treated with nivolumab in second-line treatment with anti-PD-1 nivolumab. The metabolomics analyses were performed by GC–MS/SPME and <sup>1</sup>H-NMR in order to detect volatile and non-volatile metabolites. Metabolomic data were processed by statistical profiling and chemometric analyses. <strong>Results:</strong> Four out of 11 patients (36%) presented early progression, while the remaining 7 out of 11 (64%) presented disease progression after 12 months. 2-Pentanone (ketone) and tridecane (alkane) were significantly associated with early progression, and on the contrary short chain fatty acids (SCFAs) (i.e., propionate, butyrate), lysine and nicotinic acid were significantly associated with long-term beneficial effects. <strong>Conclusions:</strong> Our preliminary data suggest a significant role of gut microbiota metabolic pathways in affecting response to immunotherapy. The metabolic approach could be a promising strategy to contribute to the personalized management of cancer patients by the identification of microbiota-linked “indicators” of early progressor and long responder patients.
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Solanki, Hiren, Manon Pierdet, Olivier P. Thomas, and Mayalen Zubia. "Insights into the Metabolome of the Cyanobacterium Leibleinia gracilis from the Lagoon of Tahiti and First Inspection of Its Variability." Metabolites 10, no. 5 (2020): 215. http://dx.doi.org/10.3390/metabo10050215.
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Cyanobacteria are known to produce a large diversity of specialized metabolites that can cause severe (eco)toxicological effects. In the lagoon of Tahiti, the benthic cyanobacterium Leibleinia gracilis is commonly found overgrowing the proliferative macroalga Turbinaria ornata or dead branching corals. The specialized metabolome of the cyanobacterium L. gracilis was therefore investigated together with its variability on both substrates and changes in environmental parameters. For the study of the metabolome variability, replicates of L. gracilis were collected in the same location of the lagoon of Tahiti before and after a raining event, both on dead corals and on T. ornata. The variability in the metabolome was inferred from a comparative non-targeted metabolomic using high resolution mass spectrometry (MS) data and a molecular network analysis built through MS/MS analyses. Oxidized fatty acid derivatives including the unusual 11-oxopalmitelaidic acid were found as major constituents of the specialized metabolome of this species. Significant variations in the metabolome of the cyanobacteria were observed, being more important with a change in environmental factors. Erucamide was found to be the main chemical marker highly present when the cyanobacterium grows on the macroalga. This study highlights the importance of combined approaches in metabolomics and molecular networks to inspect the variability in the metabolome of cyanobacteria with applications for ecological questions.
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Cao, M., L. Johnson, R. Johnson, A. Koulman, G. A. Lane, and S. Rasmussen. "joint analyses of transcriptomic and metabolomic data to probe ryegrass-endophyte symbiosis." NZGA: Research and Practice Series 13 (January 1, 2007): 195–98. http://dx.doi.org/10.33584/rps.13.2006.3051.
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Fungal endophytes (Neotyphodium lolii) in perennial ryegrass (Lolium perenne) produce a range of bioactive alkaloids which are implicated in both toxicity to grazing animals and resistance to insects. The understanding of regulatory and biochemical mechanisms of the symbiosis will provide clues for the genetic manipulation of beneficial alkaloid production. This paper presents approaches to analyse data from high-throughput microarray experiments and targeted metabolomic analyses. Combined with bioinformatics analyses, potential genes were found associated with the accumulation of alkaloids and other metabolites. The advantages and limitations of our approach to address the molecular mechanisms of the symbiosis will be discussed. Keywords: Lolium perenne, Neotyphodium lolii, metabolomics, microarray
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Suissa, Laurent, Jean-Marie Guigonis, Fanny Graslin, et al. "Combined Omic Analyzes of Cerebral Thrombi: A New Molecular Approach to Identify Cardioembolic Stroke Origin." Stroke 52, no. 9 (2021): 2892–901. http://dx.doi.org/10.1161/strokeaha.120.032129.
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Background and Purpose: The diagnosis of cardioembolic stroke can be challenging for patient management in secondary stroke prevention, particularly in the case of covert paroxysmal atrial fibrillation. The molecular composition of a cerebral thrombus is related to its origin. Therefore, proteomic and metabolomic analyses of the retrieved thrombotic material should allow the identification of biomarkers or signatures to improve the etiological diagnosis of stroke. Methods: In this pilot study, the proteome and metabolome of cerebral thrombi from atherothrombotic and cardioembolic stroke patients were studied according to ASCOD phenotyping (A: atherosclerosis; S: small-vessel disease; C: cardiac pathology; O: other causes; D: dissection), with the highest causality grade, from the ThrombiOMIC cohort (consecutive patients with stroke recanalized by mechanical thrombectomy in an acute phase). Proteomic and metabolomic results were used separately or combined, and the obtained omic signatures were compared with classical cardioembolic stroke predictors using pairwise comparisons of the area under receiver operating characteristics. Results: Among 59 patients of the ThrombiOMIC cohort, 34 patients with stroke showed a cardioembolic phenotype and 7 had an atherothrombotic phenotype. Two thousand four hundred fifty-six proteins and 5019 molecular features of the cerebral thrombi were identified using untargeted proteomic and metabolomic approaches, respectively. Area under receiver operating characteristics to predict the cardioembolic origin of stroke were calculated using the proteomic results (0.945 [95% CI, 0.871–1]), the metabolomic results (0.836 [95% CI, 0.714–0.958]), and combined signatures (0.996 [95% CI, 0.984–1]). The diagnostic performance of the combined signatures was significantly higher than that of classical predictors such as the plasmatic BNP (B-type natriuretic peptide) level (area under receiver operating characteristics, 0.803 [95% CI, 0.629–0.976]). Conclusions: The combined proteomic and metabolomic analyses of retrieved cerebral thrombi is a very promising molecular approach to predict the cardioembolic cause of stroke and to improve secondary stroke prevention strategies.
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Stockard, Bradley, Timothy Garrett, Soheil Meshinchi, and Jatinder K. Lamba. "Metabolomic Profiling Defines Distinct Metabolic Signature Associated with FLT3/ITD AML." Blood 128, no. 22 (2016): 1692. http://dx.doi.org/10.1182/blood.v128.22.1692.1692.
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Abstract AML is a hematological disorder resulting from proliferation and expansion of malignant myeloid cells. Clinical outcome for AML remains dismal despite intensive therapy in part due to the disease heterogeneity with various cytogenetic and molecular lesions. Fms-Like Tyrosine Kinase-3 (FLT3) is a receptor tyrosine kinase expressed hematopoietic stem/progenitor cells. Activating mutations of FLT3 gene due to internal tandem duplication of the juxtamembrane domain coding sequence (FLT3/ITD) causes autonomous cellular proliferations leading to disease progression. Metabolomic profiling has been successfully utilized to identify metabolic alterations in hematological disorders. However, no studies on metabolic alterations associated with pediatric AML have been reported at this time. In this study we propose to establish the metabolomic landscape in pediatric AML patients and identify differential expression of metabolites based on FLT3/ITD status. Cellular and plasma metabolomics profile was generated from 32 matching diagnostic material from 16 patients with and without FLT3/ITD (N=8 for each cohort and each sample was run in duplicate) treated on COG-AAML0531 study. Global metabolomics profiling was performed on a Thermo Q-Exactive Oribtrap mass spectrometer with Dionex UHPLC and autosampler. All samples were analyzed in duplicate in positive and negative heated electrospray ionization with a mass resolution of 35,000 at m/z 200 as separate injections. Separation was achieved on an ACE 18-pfp 100 x 2.1 mm, 2 µm column with mobile phase A as 0.1% formic acid in water and mobile phase B as acetonitrile. This is a polar embedded stationary phase that provides comprehensive coverage, but does have some limitation is the coverage of very polar species. The flow rate was 350 µL/min with a column temperature of 25¡C. 4 µL was injected for negative ions and 2 µL for positive ions. Statistical analysis was performed using MetaboAnalyst software using all metabolites (known and unknown) as well as only the annotated metabolites. Univariate analysis was performed by volcano plot and Multivariate analysis was performed using PCA, PLS-DA and OPLS-DA. Total of 2966 plasma metabolome (779 negative and 2187 positive) and 1742 (227 negative and 1515 positive) cellular metabolome features were identified. All subsequent data analyses were normalized to the sum of metabolites for each sample. Comparison of the cellular metabolome in patients with and without FLT3/ITD identified 12 known and 135 unknown metabolites that were significantly different between two cohorts (p<0.05). Similar comparison of the plasma metabolome identified19 known and 300 unknown metabolites in the patient with and without FLT3/ITD (top results are shown in Fig.1). Orthogonal partial least squares-discriminant analysis (OPLS-DA) showed clear separation between the 2 groups (Fig.2). Some of the top known plasma metabolites (p<0.01) differentiating patients by FLT3 status include guanine, pyrimidine-2-3dicarboxylate, acetylglycine, acetyl-L-alanine, aminobutonate gaba, L-carnitine, methyl-2 oxovaleric acid, asparagine, acetyl arginine, Hydroxydecanoic acid, cysteic acid and glycocholic acid. Within leukemic cells top metabolites differentiating between FLT3 status included actyly carnitine, adenosine monophosphate, hypoxanthine, diaminohepatanedioate, guanine and sphingosine. Metaboanalyst Pathway analysis module mapped the differentiating metabolites to aminoacyl-tRNA biosynthesis, Glycerophospholipid metabolism, Cysteine and methionine metabolism, Pantothenate and CoA biosynthesis, Purine metabolism. This pilot study defines distinct metabolomics signature associated with genomic subtype of AML (FLT3/ITD). As metabolomics provides an insight into the ultimate metabolic destination of normal and malignant hematopoiesis, it has a potential to provide a unique insight into the altered metabolic pathways in AML and identify pathways and networks that might be shared by varied genomic subtypes of AML. Such data can help merge rare genomic variants based on shared metabolic signatures and more appropriately guide directed therapies. Disclosures No relevant conflicts of interest to declare.
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Furukawa, Hiroshi, Shomi Oka, Kota Shimada, et al. "Serum Metabolomic Profiles of Rheumatoid Arthritis Patients With Acute-Onset Diffuse Interstitial Lung Disease." Biomarker Insights 14 (January 2019): 117727191987047. http://dx.doi.org/10.1177/1177271919870472.
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Objective: Acute-onset diffuse interstitial lung disease (AoDILD) includes acute exacerbation of interstitial lung disease (ILD), drug-induced ILD, and Pneumocystis pneumonia, and frequently occurs in patients with rheumatoid arthritis (RA). Since AoDILD causes a poor prognosis in RA, biomarkers for AoDILD were eagerly desired. Metabolomic analyses were extensively performed in cancer patients and successfully generated better diagnostic biomarkers. In the present study, serum metabolomic profiles of AoDILD in RA were investigated to generate better potential metabolomic biomarkers. Methods: Serum samples of 10 RA patients with AoDILD were collected on admission and in a stable state, more than 3 months before the admission. Serum metabolomic analyses were conducted on the samples from these RA patients with AoDILD. Results: Apparently distinct serum metabolomic profiles in AoDILD were not observed in univariate or hierarchical cluster analyses. Partial least squares-discriminant analysis (PLS-DA) was performed to select candidate metabolites based on variable importance in projection (VIP) scores. The PLS-DA model generated from the four metabolites with VIP scores more than 2.25 (mannosamine, alliin, kynurenine, and 2-hydroxybutyric acid) could successfully discriminate AoDILD from the stable condition (area under the curve: 0.962, 95% confidence interval: 0.778–1.000). Conclusion: It was demonstrated that metabolomic profiling was useful to generate better biomarkers in AoDILD.
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Zhai, Yaying, Fan Xia, Luting Shi, et al. "Early Pregnancy Markers in the Serum of Ewes Identified via Proteomic and Metabolomic Analyses." International Journal of Molecular Sciences 24, no. 18 (2023): 14054. http://dx.doi.org/10.3390/ijms241814054.
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The diagnosis of ewes’ pregnancy status at an early stage is an efficient way to enhance the reproductive output of sheep and allow producers to optimize production and management. The techniques of proteomics and metabolomics have been widely used to detect regulatory factors in various physiological processes of animals. The aim of this study is to explore the differential metabolites and proteins in the serum of pregnant and non-pregnant ewes by proteomics and metabolomics. The serum of ewes at 21, 28 and 33 days after artificial insemination (AI) were collected. The pregnancy stratus of the ewes was finally determined through ultrasound examination and then the ewes were grouped as Pregnant (n = 21) or N on-pregnant (n = 9). First, the serum samples from pregnant or non-pregnant ewes at 21 days after AI were selected for metabolomic analysis. It was found that the level of nine metabolites were upregulated and 20 metabolites were downregulated in the pregnant animals (p < 0.05). None of these differential metabolomes are suitable as markers of pregnancy due to their small foldchange. Next, the proteomes of serum from pregnant or non-pregnant ewes were evaluated. At 21 days after AI, the presence of 321 proteins were detected, and we found that the level of three proteins were upregulated and 11 proteins were downregulated in the serum of pregnant ewes (p < 0.05). The levels of serum amyloid A (SAA), afamin (AFM), serpin family A member 6 (SERPINA6) and immunoglobulin-like domain-containing protein between pregnant and non-pregnant ewes at 21-, 28- and 33-days post-AI were also analyzed via enzyme-linked immunosorbent assay (ELISA). The levels of SAA and AFM were significantly higher in pregnant ewes than in non-pregnant ewes, and could be used as markers for early pregnancy detection. Overall, our results show that SAA and AFM are potential biomarkers to determine the early pregnancy status of ewes.
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Bremer, Parker Ladd, and Oliver Fiehn. "SMetaS: A Sample Metadata Standardizer for Metabolomics." Metabolites 13, no. 8 (2023): 941. http://dx.doi.org/10.3390/metabo13080941.
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Metabolomics has advanced to an extent where it is desired to standardize and compare data across individual studies. While past work in standardization has focused on data acquisition, data processing, and data storage aspects, metabolomics databases are useless without ontology-based descriptions of biological samples and study designs. We introduce here a user-centric tool to automatically standardize sample metadata. Using such a tool in frontends for metabolomic databases will dramatically increase the FAIRness (Findability, Accessibility, Interoperability, and Reusability) of data, specifically for data reuse and for finding datasets that share comparable sets of metadata, e.g., study meta-analyses, cross-species analyses or large scale metabolomic atlases. SMetaS (Sample Metadata Standardizer) combines a classic database with an API and frontend and is provided in a containerized environment. The tool has two user-centric components. In the first component, the user designs a sample metadata matrix and fills the cells using natural language terminology. In the second component, the tool transforms the completed matrix by replacing freetext terms with terms from fixed vocabularies. This transformation process is designed to maximize simplicity and is guided by, among other strategies, synonym matching and typographical fixing in an n-grams/nearest neighbors model approach. The tool enables downstream analysis of submitted studies and samples via string equality for FAIR retrospective use.
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Philbin, Casey S., Matthew Paulsen, and Lora A. Richards. "Opposing Effects of Ceanothus velutinus Phytochemistry on Herbivore Communities at Multiple Scales." Metabolites 11, no. 6 (2021): 361. http://dx.doi.org/10.3390/metabo11060361.
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Identifying the interactions of functional, biotic, and abiotic factors that define plant–insect communities has long been a goal of community ecologists. Metabolomics approaches facilitate a broader understanding of how phytochemistry mediates the functional interactions among ecological factors. Ceanothus velutinus communities are a relatively unstudied system for investigating chemically mediated interactions. Ceanothus are nitrogen-fixing, fire-adapted plants that establish early post-fire, and produce antimicrobial cyclic peptides, linear peptides, and flavonoids. This study takes a metabolomic approach to understanding how the diversity and variation of C. velutinus phytochemistry influences associated herbivore and parasitoid communities at multiple spatiotemporal scales. Herbivores and foliar samples were collected over three collection times at two sites on the east slope of the Sierra Nevada Mountain range. Foliar tissue was subjected to LC-MS metabolomic analysis, and several novel statistical analyses were applied to summarize, quantify, and annotate variation in the C. velutinus metabolome. We found that phytochemistry played an important role in plant–insect community structure across an elevational gradient. Flavonoids were found to mediate biotic and abiotic influences on herbivores and associated parasitoids, while foliar oligopeptides played a significant positive role in herbivore abundance, even more than abundance of host plants and leaf abundance. The importance of nutritional and defense chemistry in mediating ecological interactions in C. velutinus plant–herbivore communities was established, justifying larger scale studies of this plant system that incorporate other mediators of phytochemistry such as genetic and metageomic contributions.
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Aliakbari, Amir, Alireza Ehsani, Rasoul Vaez Torshizi, et al. "Genetic variance of metabolomic features and their relationship with body weight and body weight gain in Holstein cattle1." Journal of Animal Science 97, no. 9 (2019): 3832–44. http://dx.doi.org/10.1093/jas/skz228.
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Abstract In recent years, metabolomics has been used to clarify the biology underlying biological samples. In the field of animal breeding, investigating the magnitude of genetic control on the metabolomic profiles of animals and their relationships with quantitative traits adds valuable information to animal improvement schemes. In this study, we analyzed metabolomic features (MFs) extracted from the metabolomic profiles of 843 male Holstein calves. The metabolomic profiles were obtained using nuclear magnetic resonance (NMR) spectroscopy. We investigated 2 alternative methods to control for peak shifts in the NMR spectra, binning and aligning, to determine which approach was the most efficient for assessing genetic variance. Series of univariate analyses were implemented to elucidate the heritability of each MF. Furthermore, records on BW and ADG from 154 to 294 d of age (ADG154–294), 294 to 336 d of age (ADG294–336), and 154 to 336 d of age (ADG154–336) were used in a series of bivariate analyses to establish the genetic and phenotypic correlations with MFs. Bivariate analyses were only performed for MFs that had a heritability significantly different from zero. The heritabilities obtained in the univariate analyses for the MFs in the binned data set were low (<0.2). In contrast, in the aligned data set, we obtained moderate heritability (0.2 to 0.5) for 3.5% of MFs and high heritability (more than 0.5) for 1% of MFs. The bivariate analyses showed that ~12%, ~3%, ~9%, and ~9% of MFs had significant additive genetic correlations with BW, ADG154–294, ADG294–336, and ADG154–336, respectively. In all of the bivariate analyses, the percentage of significant additive genetic correlations was higher than the percentage of significant phenotypic correlations of the corresponding trait. Our results provided insights into the influence of the underlying genetic mechanisms on MFs. Further investigations in this field are needed for better understanding of the genetic relationship among the MFs and quantitative traits.
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Jiang, Baoping, Liang Le, Wenting Wan, et al. "The Flower Tea Coreopsis tinctoria Increases Insulin Sensitivity and Regulates Hepatic Metabolism in Rats Fed a High-Fat Diet." Endocrinology 156, no. 6 (2015): 2006–18. http://dx.doi.org/10.1210/en.2015-1015.
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AbstractAn infusion of Coreopsis tinctoria (CT) flowering tops is traditionally used in Portugal to control hyperglycemia; however, the effects of CT protection against high-fat diet (HFD)-induced hepatic insulin resistance have not been systematically studied and the precise mechanism of action is not clear. The metabolomic profiles of insulin-resistant rats fed a HFD and a CT-supplemented diet (HFD supplemented with CT drinking) for 8 weeks were investigated. Serum samples for clinical biochemistry and liver samples for histopathology and liquid chromatography-mass spectrometry-based metabolomic research were collected. Western blot and quantitative real-time PCR analyses were further used to measure the expression of several relevant enzymes together with perturbed metabolic pathways. Using analysis software, the CT treatment was found to significantly ameliorate the disturbance in 10 metabolic pathways. Combined metabolomic, Western blot, and quantitative real-time PCR analyses revealed that CT treatment significantly improved the glucose homeostasis by, on the one hand, through inhibiting the expression of gluconeogenic pathway key proteins glucose-6-phosphatase and phosphoenolpyruvate carboxykinase and, on the other hand, via regulating the mRNA or protein levels of the Krebs cycle critical enzymes (citrate synthase, succinate dehydrogenase complex, subunit A, flavoprotein, and dihydrolipoamide S-succinyltransferase). These results provide metabolic evidence of the complex pathogenic mechanism involved in hepatic insulin resistance and that the supplementation with CT improves insulin resistance at a global scale. Liquid chromatography-mass spectrometry-based metabolomics approaches are helpful to further understand diabetes-related mechanisms.
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Tee, Khim Boon, Luqman Ibrahim, Najihah Mohd Hashim, Mohd Zuwairi Saiman, Zaril Harza Zakaria, and Hasniza Zaman Huri. "Pharmacokinetics and Metabolomic Profiling of Metformin and Andrographis paniculata: A Protocol for a Crossover Randomised Controlled Trial." Journal of Clinical Medicine 11, no. 14 (2022): 3931. http://dx.doi.org/10.3390/jcm11143931.
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This protocol aims to profile the pharmacokinetics of metformin and Andrographis paniculata (AP) and continue with untargeted pharmacometabolomics analysis on pre-dose and post-dose samples to characterise the metabolomics profiling associated with the human metabolic pathways. This is a single-centre, open-labelled, three periods, crossover, randomised-controlled, single-dose oral administration pharmacokinetics and metabolomics trial of metformin 1000 mg (n = 18), AP 1000 mg (n = 18), or AP 2000 mg (n = 18) in healthy volunteers under the fasting condition. Subjects will be screened according to a list of inclusion and exclusion criteria. Investigational products will be administered according to the scheduled timeline. Vital signs and adverse events will be monitor periodically, and standardized meals will be provided to the subjects. Fifteen blood samples will be collected over 24 h, and four urine samples will be collected within a 12 h period. Onsite safety monitoring throughout the study and seven-day phone call safety follow-up will be compiled after the last dose of administration. The plasma samples will be analysed for the pharmacokinetics parameters to estimate the drug maximum plasma concentration. Untargeted metabolomic analysis between pre-dose and maximum plasma concentration (Cmax) samples will be performed for metabolomic profiling to identify the dysregulation of human metabolic pathways that link to the pharmacodynamics effects. The metformin arm will focus on the individualised Cmax plasma concentration for metabolomics study and used as a model drug. After this, an investigation of the dose-dependent effects will be performed between pre-dose samples and median Cmax concentration samples in the AP 1000 mg and AP 2000 mg arms for metabolomics study. The study protocol utilises a crossover study design to incorporate a metabolomics-based study into pharmacokinetics trial in the drug development program. The combination analyses will complement the interpretation of pharmacological effects according to the bioavailability of the drug.
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Lazcano-Ramírez, Hugo Gerardo, Roberto Gamboa-Becerra, Irving J. García-López, et al. "Effects of the Developmental Regulator BOLITA on the Plant Metabolome." Genes 12, no. 7 (2021): 995. http://dx.doi.org/10.3390/genes12070995.
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Transcription factors are important regulators of gene expression. They can orchestrate the activation or repression of hundreds or thousands of genes and control diverse processes in a coordinated way. This work explores the effect of a master regulator of plant development, BOLITA (BOL), in plant metabolism, with a special focus on specialized metabolism. For this, we used an Arabidopsis thaliana line in which the transcription factor activity can be induced. Fingerprinting metabolomic analyses of whole plantlets were performed at different times after induction. After 96 h, all induced replicas clustered as a single group, in contrast with all controls which did not cluster. Metabolomic analyses of shoot and root tissues enabled the putative identification of differentially accumulated metabolites in each tissue. Finally, the analysis of global gene expression in induced vs. non-induced root samples, together with enrichment analyses, allowed the identification of enriched metabolic pathways among the differentially expressed genes and accumulated metabolites after the induction. We concluded that the induction of BOL activity can modify the Arabidopsis metabolome. Future work should investigate whether its action is direct or indirect, and the implications of the metabolic changes for development regulation and bioprospection.
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Antikainen, Anni A., Stefan Mutter, Valma Harjutsalo, Lena M. Thorn, Per-Henrik Groop, and Niina Sandholm. "Urinary metabolomics provide insights into coronary artery disease in individuals with type 1 diabetes." Cardiovascular Diabetology 23, no. 1 (2024). http://dx.doi.org/10.1186/s12933-024-02512-8.
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Abstract Background Type 1 diabetes increases the risk of coronary artery disease (CAD). High-throughput metabolomics may be utilized to identify metabolites associated with disease, thus, providing insight into disease pathophysiology, and serving as predictive markers in clinical practice. Urine is less tightly regulated than blood, and therefore, may enable earlier discovery of disease-associated markers. We studied urine metabolomics in relation to incident CAD in individuals with type 1 diabetes. Methods We prospectively studied CAD in 2501 adults with type 1 diabetes from the Finnish Diabetic Nephropathy Study. 209 participants experienced incident CAD within the 10-year follow-up. We analyzed the baseline urine samples with a high-throughput targeted urine metabolomics platform, which yielded 54 metabolites. With the data, we performed metabolome-wide survival analyses, correlation network analyses, and metabolomic state profiling for prediction of incident CAD. Results Urinary 3-hydroxyisobutyrate was associated with decreased 10-year incident CAD, which according to the network analysis, likely reflects younger age and improved kidney function. Urinary xanthosine was associated with 10-year incident CAD. In the network analysis, xanthosine correlated with baseline urinary allantoin, which is a marker of oxidative stress. In addition, urinary trans-aconitate and 4-deoxythreonate were associated with decreased 5-year incident CAD. Metabolomic state profiling supported the usage of CAD-associated urinary metabolites to improve prediction accuracy, especially during shorter follow-up. Furthermore, urinary trans-aconitate and 4-deoxythreonate were associated with decreased 5-year incident CAD. The network analysis further suggested glomerular filtration rate to influence the urinary metabolome differently between individuals with and without future CAD. Conclusions We have performed the first high-throughput urinary metabolomics analysis on CAD in individuals with type 1 diabetes and found xanthosine, 3-hydroxyisobutyrate, trans-aconitate, and 4-deoxythreonate to be associated with incident CAD. In addition, metabolomic state profiling improved prediction of incident CAD. Graphical abstract