Analysis Of Metabolomics Data Research Articles

7-Ketocholesterol Induces Lipid Metabolic Reprogramming and Enhances Cholesterol Ester Accumulation in Cardiac Cells.

7-Ketocholesterol (7KCh) is a major oxidized cholesterol product abundant in lipoprotein deposits and atherosclerotic plaques. Our previous study has shown that 7KCh accumulates in erythrocytes of heart failure patients, and further investigation centered on how 7KCh may affect metabolism in cardiomyocytes. We applied metabolomics to study the metabolic changes in cardiac cell line HL-1 after treatment with 7KCh. Mevalonic acid (MVA) pathway-derived metabolites, such as farnesyl-pyrophosphate and geranylgeranyl-pyrophosphate, phospholipids, and triacylglycerols levels significantly declined, while the levels of lysophospholipids, such as lysophosphatidylcholines (lysoPCs) and lysophosphatidylethanolamines (lysoPEs), considerably increased in 7KCh-treated cells. Furthermore, the cholesterol content showed no significant change, but the production of cholesteryl esters was enhanced in the treated cells. To explore the possible mechanisms, we applied mRNA-sequencing (mRNA-seq) to study genes differentially expressed in 7KCh-treated cells. The transcriptomic analysis revealed that genes involved in lipid metabolic processes, including MVA biosynthesis and cholesterol transport and esterification, were differentially expressed in treated cells. Integrated analysis of both metabolomic and transcriptomic data suggests that 7KCh induces cholesteryl ester accumulation and reprogramming of lipid metabolism through altered transcription of such genes as sterol O-acyltransferase- and phospholipase A2-encoding genes. The 7KCh-induced reprogramming of lipid metabolism in cardiac cells may be implicated in the pathogenesis of cardiovascular diseases.

Phytotoxic effect and molecular mechanism induced by graphene towards alfalfa (Medicago sativa L.) by integrating transcriptomic and metabolomics analysis

Although the widespread use of nanoparticles has been reported in various fields, the toxic mechanisms of molecular regulation involved in the alfalfa treated by nanomaterials is still in the preliminary research stage. In this study, Bara 310 SC (Bara, tolerant genotype) and Gold Empress (Gold, susceptible genotype) were used to investigate how the leaves of alfalfa interpret the physiological responses to graphene stress based on metabolome and transcriptome characterizations. Herein, graphene at different concentrations (0, 1% and 2%, w/w) were selected as the analytes. Physiological results showed antioxidant defence system and photosynthesis was significantly disturbed under high environmental concentration of graphene. With Ultra high performance liquid chromatography electrospray tandem mass spectrometry (UPLC-ESI-MS/MS), 406 metabolites were detected and 62/13 and 110/58 metabolites significantly changed in the leaves of Gold/Bara under the 1% and 2%-graphene treatments (w/w), respectively. The most important metabolites which were accumulated under graphene stress includes amino acids, flavonoids, organic acids and sugars. Transcriptomic analysis reveals 1125 of core graphene-responsive genes in alfalfa that was robustly differently expressed in both genotypes. And differential expression genes (DEGs) potentially related to photosynthetic enzymes, antioxidant enzymes, amino acids metabolism, and sucrose and starch metabolic which finding was supported by the metabolome study. Gold was more disturbed by graphene stress at both transcriptional and metabolic levels, since more stress-responsive genes/metabolites were identified in Gold. A comprehensive analysis of transcriptomic and metabolomic data highlights the important role of amino acid metabolism and nicotinate and nicotinamide metabolism pathways for graphene tolerance in alfalfa. Our study provide necessary information for better understanding the phytotoxicity molecular mechanism underlying nanomaterials tolerance of plant.

Deep Learning for Rapid Identification of Microbes Using Metabolomics Profiles.

Rapid detection of viable microbes remains a challenge in fields such as microbial food safety. We here present the application of deep learning algorithms to the rapid detection of pathogenic and non-pathogenic microbes using metabolomics data. Microbes were incubated for 4 h in a protein-free defined medium, followed by 1D 1H nuclear magnetic resonance (NMR) spectroscopy measurements. NMR spectra were analyzed by spectral binning in an untargeted metabolomics approach. We trained multilayer (“deep”) artificial neural networks (ANN) on the data and used the resulting models to predict spectra of unknown microbes. ANN predicted unknown microbes in this laboratory setting with an average accuracy of 99.2% when using a simple feature selection method. We also describe learning behavior of the employed ANN and the optimization strategies that worked well with these networks for our datasets. Performance was compared to other current data analysis methods, and ANN consistently scored higher than random forest models and support vector machines, highlighting the potential of deep learning in metabolomics data analysis.

Metabolic Profile in Plasma AND CSF of LEVODOPA-induced Dyskinesia in Parkinson's Disease: Focus on Neuroinflammation.

The existence of few biomarkers and the lack of a better understanding of the pathophysiology of levodopa-induced dyskinesia (LID) in Parkinson's disease (PD) require new approaches, as the metabolomic analysis, for discoveries. We aimed to identify a metabolic profile associated with LID in patients with PD in an original cohort and to confirm the results in an external cohort (BioFIND). In the original cohort, plasma and CSF were collected from 20 healthy controls, 23 patients with PD without LID, and 24 patients with PD with LID. LC-MS/MS and metabolomics data analysis were used to perform untargeted metabolomics. Untargeted metabolomics data from the BioFIND cohort were analyzed. We identified a metabolic profile associated with LID in PD, composed of multiple metabolic pathways. In particular, the dysregulation of the glycosphingolipid metabolic pathway was more related to LID and was strongly associated with the severity of dyskinetic movements. Furthermore, bile acid biosynthesis metabolites simultaneously found in plasma and CSF have distinguished patients with LID from other participants. Data from the BioFIND cohort confirmed dysregulation in plasma metabolites from the bile acid biosynthesis pathway. There is a distinct metabolic profile associated with LID in PD, both in plasma and CSF, which may be associated with the dysregulation of lipid metabolism and neuroinflammation.

Binary Simplification as an Effective Tool in Metabolomics Data Analysis.

Metabolomics aims to perform a comprehensive identification and quantification of the small molecules present in a biological system. Due to metabolite diversity in concentration, structure, and chemical characteristics, the use of high-resolution methodologies, such as mass spectrometry (MS) or nuclear magnetic resonance (NMR), is required. In metabolomics data analysis, suitable data pre-processing, and pre-treatment procedures are fundamental, with subsequent steps aiming at highlighting the significant biological variation between samples over background noise. Traditional data analysis focuses primarily on the comparison of the features’ intensity values. However, intensity data are highly variable between experimental batches, instruments, and pre-processing methods or parameters. The aim of this work was to develop a new pre-treatment method for MS-based metabolomics data, in the context of sample profiling and discrimination, considering only the occurrence of spectral features, encoding feature presence as 1 and absence as 0. This “Binary Simplification” encoding (BinSim) was used to transform several benchmark datasets before the application of clustering and classification methods. The performance of these methods after the BinSim pre-treatment was consistently as good as and often better than after different combinations of traditional, intensity-based, pre-treatments. Binary Simplification is, therefore, a viable pre-treatment procedure that effectively simplifies metabolomics data-analysis pipelines.

Integrative metabolomics-genomics approach reveals key metabolic pathways and regulators of Alzheimer's disease.

Metabolites, the biochemical products of the cellular process, can be used to measure alterations in biochemical pathways related to the pathogenesis of Alzheimer's disease (AD). However, the relationships between systemic abnormalities in metabolism and the pathogenesis of AD are poorly understood. In this study, we aim to identify AD‐specific metabolomic changes and their potential upstream genetic and transcriptional regulators through an integrative systems biology framework for analyzing genetic, transcriptomic, metabolomic, and proteomic data in AD. Metabolite co‐expression network analysis of the blood metabolomic data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) shows short‐chain acylcarnitines/amino acids and medium/long‐chain acylcarnitines are most associated with AD clinical outcomes, including episodic memory scores and disease severity. Integration of the gene expression data in both the blood from the ADNI and the brain from the Accelerating Medicines Partnership Alzheimer's Disease (AMP‐AD) program reveals ABCA1 and CPT1A are involved in the regulation of acylcarnitines and amino acids in AD. Gene co‐expression network analysis of the AMP‐AD brain RNA‐seq data suggests the CPT1A‐ and ABCA1‐centered subnetworks are associated with neuronal system and immune response, respectively. Increased ABCA1 gene expression and adiponectin protein, a regulator of ABCA1, correspond to decreased short‐chain acylcarnitines and amines in AD in the ADNI. In summary, our integrated analysis of large‐scale multiomics data in AD systematically identifies novel metabolites and their potential regulators in AD and the findings pave a way for not only developing sensitive and specific diagnostic biomarkers for AD but also identifying novel molecular mechanisms of AD pathogenesis.

Multivariate analysis of NMR-based metabolomic data.

Nuclear magnetic resonance (NMR) spectroscopy allows for simultaneous detection of a wide range of metabolites and lipids. As metabolites act together in complex metabolic networks, they are often highly correlated, and optimal biological insight is achieved when using methods that take the correlation into account. For this reason, latent-variable-based methods, such as principal component analysis and partial least-squares discriminant analysis, are widely used in metabolomic studies. However, with increasing availability of larger population cohorts, and a shift from analysis of spectral data to using quantified metabolite levels, both more traditional statistical approaches and alternative machine learning methods have become more widely used. This review aims at providing an overview of the current state-of-the-art multivariate methods for the analysis of NMR-based metabolomic data as well as alternative methods, highlighting their strengths and limitations.

Metabolic Profiling Reveals Cellular Reprogramming of Acute Myeloid Leukemia By Omipalisib through Serine Synthesis Pathway

Background: More than 50% of AML patients had hyperactivation of PI3K-AKT-mTOR signaling. Those patients are supposed to be associated with poor prognosis and chemotherapy resistance. The PI3K-AKT-mTOR signaling involves many cellular processes, including mRNA translation, cellular metabolism, and protein turnover. Omipalisib is a dual PI3K/mTOR inhibitor that exhibits anti-tumor activity in several cancers. However, the precise metabolic consequences in response to PI3K/mTOR dual inhibitor are still not fully studied in AML.Aims: To evaluate the efficacy and to elucidate the metabolic alteration of the anti-cancer effects of omipalisib on leukemic cells from both in vitro and in vivo aspects.Materials and Methods: HL60, THP1, and OCI-AML3 myeloid leukemia cell lines were used in this study. Omipalisib (GSK2126458) was used for in vitro and in vivo experiments. Cell proliferation was measured by Cell Titer 96 AQueous One Solution Cell Proliferation Assay. The protein expression and phosphorylation status were analyzed by immunoblotting. Flow cytometry was used for cell cycle and mitochondrial analysis. The metabolomics profiles were analyzed by Agilent 1290 UHPLC system coupled with 6540-QTOF. RNA-seq was performed using an Illumina NovaSeq 6000 platform. Differentially expressed genes (DEGs) between control and omipalisib groups were identified by EBseq. A threshold of fold change ≥2 (or ≤0.5) and p ≤ 0.05 was used to select the DEGs. The mRNA quantification was measured by QuantStudio 3 Real-Time PCR Systems. The oxygen consumption rate (OCR) was analyzed by the XFe 24 extracellular flux analyzer. The CAnN.Cg-Foxn1 nu/CrlNarl mice were used for evaluating in vivo efficacy of omipalisib in murine model.Results: We demonstrated the anti-proliferation effect of omipalisib on AML cell lines with different genetic background. The IC 50 of OCI-AML3, THP1, and HL60 were 16.97nM, 9.35 nM, and 18.69 nM, respectively. Omipalisib could significantly induce G 0/G 1 cell cycle arrest in all three cell lines. As expected, omipalisib could significantly down-regulate the phosphorylation of AKT, mTOR, S6K and 4E-BP1. Metabolomics profiling analysis revealed that 24 of the 137 tested metabolites were significantly different between the control group and the omipalisib-treated groups in OCI-AML3 cells. Further metabolic pathway enrichment analysis demonstrated that metabolites related to amino acid metabolisms were significantly reduced following omipalisib treatment. In addition, we identified 300 DEGs between control and omipalisib-treated OCI-AML3 cells; of these, 251 were upregulated and 49 were downregulated. Further gene set enrichment analysis (GSEA) of hallmark gene sets indicated omipalisib treatment was significantly negatively associated with E2F targets, Myc targets, G2M checkpoint, mTORC1 signaling pathway, and oxidative phosphorylation. Joint-Pathway analysis (MetaboAnalyst 5.0) revealed that ‘glycine, serine and threonine metabolism’ was the most downregulated pathway in the omipalisib-treated group with p-value of 1.0076E-5 and impact value of 0.86567. qRT-PCR confirmed that several important genes, PHGDH, PSAT1, PSPH, SHMT1/2 and MTHFD1/2 in the serine and glycine synthesis pathway were significantly decrease in the OCI-AML3 cells following treated with omipalisib. OCR analysis indicated that the capacity of the mitochondria to produce energy was reduced after omipalisib treatment. Mitochondrial analysis showed that mitochondria mass and membrane potential decreased after omipalisib treatment, indicating the biosynthesis and functions of mitochondrial may be affected by omipalisib. In vivo studies showed that oral administration of 0.2 or 1 mg/kg omipalisib in mice could significantly retard tumor growth without obvious changes in body weight.Summary: We found that nanomolar levels of omipalisib could significantly inhibit cell growth and induce G 0/G 1 cell cycle arrest in myeloid leukemia cells. Joint-Pathway analysis of RNA-seq and metabolomics data revealed that omipalisib mainly altered serine and glycine metabolism. Further experiments indicated that serine synthesis pathway could be suppressed by omipalisib at least in part through disrupting PI3K-AKT-mTOR signaling. In vivo xenograft model, omipalisib could retard tumor growth at as low as 0.2 mg/kg. This information may be potentially suitable for future clinical application. [Display omitted] DisclosuresChou: Kirin: Honoraria, Research Funding; Bristol Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Other: Advisory Board; Pfizer: Honoraria, Other: Advisory Board; IQVIA: Honoraria, Other: Advisory Board; Abbvie: Honoraria, Other: Advisory Board, Research Funding; Celgene: Honoraria, Other: Advisory Board, Research Funding. Tien: AbbVie: Honoraria; Celgene: Honoraria, Research Funding; Novartis: Honoraria.

Rice metabolic regulatory network spanning the entire life cycle

As one of the most important crops in the world, rice (Oryza sativa) is a model plant for metabolome research. Although many studies have focused on the analysis of specific tissues, the changes in metabolite abundance across the entire life cycle have not yet been determined. In this study, combining both targeted and nontargeted metabolite profiling methods, a total of 825 annotated metabolites were quantified in rice samples from different tissues covering the entire life cycle. The contents of metabolites in different tissues of rice were significantly different, with various metabolites accumulating in the plumule and radicle during seed germination. Combining these data with transcriptome data obtained from the same time period, we constructed the Rice Metabolic Regulation Network. The metabolites and co-expressed genes were further divided into 12 clusters according to their accumulation patterns, with members within each cluster displaying a uniform and clear pattern of abundance across development. Using this dataset, we established a comprehensive metabolic profile of the rice life cycle and used two independent strategies to identify novel transcription factors—namely the use of known regulatory genes as bait to screen for new networks underlying lignin metabolism and the unbiased identification of new glycerophospholipid metabolism regulators on the basis of tissue specificity. This study thus demonstrates how guilt-by-association analysis of metabolome and transcriptome data spanning the entire life cycle in cereal crops provides novel resources and tools to aid in understanding the mechanisms underlying important agronomic traits.

Maplet: an extensible R toolbox for modular and reproducible metabolomics pipelines.

This article presents maplet, an open-source R package for the creation of highly customizable, fully reproducible statistical pipelines for metabolomics data analysis. It builds on the SummarizedExperiment data structure to create a centralized pipeline framework for storing data, analysis steps, results and visualizations. maplet’s key design feature is its modularity, which offers several advantages, such as ensuring code quality through the maintenance of individual functions and promoting collaborative development by removing technical barriers to code contribution. With over 90 functions, the package includes a wide range of functionalities, covering many widely used statistical approaches and data visualization techniques.Availability and implementationThe maplet package is implemented in R and freely available at https://github.com/krumsieklab/maplet

Comparison of bacteria disintegration methods and their influence on data analysis in metabolomics

Metabolomic experiments usually contain many different steps, each of which can strongly influence the obtained results. In this work, metabolic analyses of six bacterial strains were performed in light of three different bacterial cell disintegration methods. Three strains were gram-negative (Pseudomonas aeruginosa, Escherichia coli, and Klebsiella pneumoniae), and three were gram-positive (Corynebacterium glutamicum, Bacillus cereus, and Enterococcus faecalis). For extraction, the methanol–water extraction method (1:1) was chosen. To compare the efficiency of different cell disintegration methods, sonication, sand mill, and tissue lyser were used. For bacterial extract metabolite analysis, 1H NMR together with univariate and multivariate analyses were applied. The obtained results showed that metabolite concentrations are strongly dependent on the cell lysing methodology used and are different for various bacterial strains. The results clearly show that one of the disruption methods gives the highest concentration for most identified compounds (e. g. sand mill for E. faecalis and tissue lyser for B. cereus). This study indicated that the comparison of samples prepared by different procedures can lead to false or imprecise results, leaving an imprint of the disintegration method. Furthermore, the presented results showed that NMR might be a useful bacterial strain identification and differentiation method. In addition to disintegration method comparison, the metabolic profiles of each elaborated strain were analyzed, and each exhibited its metabolic profile. Some metabolites were identified by the 1H NMR method in only one strain. The results of multivariate data analyses (PCA) show that regardless of the disintegration method used, the strain group can be identified. Presented results can be significant for all types of microbial studies containing the metabolomic targeted and non-targeted analysis.

Caffeate may play an important role in the somatic embryogenesis of oil palm (Elaeis guineensis Jacq.)

Somatic embryogenesis (SE) is a key step in plant regeneration and propagation, but the mechanisms underlying SE in oil palm still stay unclear. Integrated analysis of metabolome and transcriptome data showed that 13 metabolites are specifically accumulated in somatic embryo, of which caffeate was 16.6-fold (p = 0.0002) higher than in callus. Moreover, caffeate was significantly correlated with phytohormone signaling genes including ethylene receptor (ETR2-2), auxin efflux carrier component 3a (PIN3A), auxin response factor (ARF15-2), and several SE transcription factors (TFs) including WUSCHEL-related homeobox (WOX), VIVIPAROUS1(VP1), BABY BOOM2 (BBM2). Furthermore, staining and microscopic observation showed that phenolic acids highly accumulated in the center of newly formed callus clusters and lignin is predominant in somatic embryo than in callus, indicated that caffeate (the major phenolic acids in glycosylated form and the precursor of lignin biosynthesis) may play an important role in SE. Our results provide a better understanding of the SE mechanism in oil palm.

Integrative Analysis of Metabolomics and Transcriptomics Data Identifies Prognostic Biomarkers Associated With Oral Squamous Cell Carcinoma.

BackgroundOral squamous cell carcinoma (OSCC) is the most malignant neoplasm in oral cancer. There is growing evidence that its progression involves altered metabolism. The current method of evaluating prognosis is very limited, and metabolomics may provide a new approach for quantitative evaluation. The aim of the study is to evaluate the use of metabolomics as prognostic markers for patients with OSCC.MethodsAn analytical platform, Ultra-Performance Liquid Chromatography-Quadrupole/Orbitrap High Resolution Mass Spectrometry (UHPLC-Q-Orbitrap HRMS), was used to acquire the serum fingerprinting profiles from a total of 103 patients of OSCC before and after the operation. In total, 103 OSCC patients were assigned to either a training set (n = 73) or a test set (n = 30). The potential biomarkers and the changes of serum metabolites were profiled and correlated with the clinicopathological parameters and survival of the patients by statistical analysis. To further verify our results, we linked them to gene expression using data from the Kyoto Encyclopedia of Genes and Genomes (KEGG).ResultsIn total, 14 differential metabolites and five disturbed pathways were identified between the preoperative group and postoperative group. Succinic acid change-low, hypoxanthine change-high tumor grade, and tumor stage indicated a trend towards improved recurrence-free survival (RFS), whether in a training set or a test set. In addition, succinic acid change-low, hypoxanthine change-high, and tumor grade provided the highest predictive accuracy of the patients with OSCC. KEGG enrichment analysis showed that the imbalance in the amino acid and purine metabolic pathway may affect the prognosis of OSCC.ConclusionsThe changes of metabolites before and after operation may be related to the prognosis of OSCC patients. UHPLC-Q-Orbitrap HRMS serum metabolomics analysis could be used to further stratify the prognosis of patients with OSCC. These results can better understand the mechanisms related to early recurrence and help develop more effective therapeutic targets.

Metabolic endophenotype associated with right ventricular glucose uptake in pulmonary hypertension.

Alterations in metabolism and bioenergetics are hypothesized in the mechanisms leading to pulmonary vascular remodeling and heart failure in pulmonary hypertension (PH). To test this, we performed metabolomic analyses on 30 PH individuals and 12 controls. Furthermore, using 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography, we dichotomized PH patients into metabolic phenotypes of high and low right ventricle (RV) glucose uptake and followed them longitudinally. In support of metabolic alterations in PH and its progression, the high RV glucose group had higher RV systolic pressure (p < 0.001), worse RV function as measured by RV fractional area change and peak global longitudinal strain (both p < 0.05) and may be associated with poorer outcomes (33% death or transplantation in the high glucose RV uptake group compared to 7% in the low RV glucose uptake group at five years follow-up, log-ranked p = 0.07). Pathway enrichment analysis identified key metabolic pathways including fructose catabolism, arginine-nitric oxide metabolism, tricarboxylic acid cycle, and ketones metabolism. Integrative human protein-protein interactome network analysis of metabolomic and transcriptomic data identified key pathobiological pathways: arginine biosynthesis, tricarboxylic acid cycle, purine metabolism, hypoxia-inducible factor 1, and apelin signaling. These findings identify a PH metabolomic endophenotype, and for the first time link this to disease severity and outcomes.

RMisbeta: A robust missing value imputation approach in transcriptomics and metabolomics data

Transcriptomics and metabolomics data often contain missing values or outliers due to limitations of the data acquisition techniques. Most of the statistical methods require complete datasets for downstream analysis. A number of methods have been developed for missing value imputation using the classical mean and variance based on maximum likelihood estimators, which are not robust against outliers. Consequently, the performance of these methods deteriorates in the presence of outliers. Hence precise imputation of missing values and outliers handling are both concurrently important. Therefore, in this paper, we developed a robust iterative approach using robust estimators based on the minimum beta divergence method, which simultaneously impute missing values and outliers. We investigate the performance of the proposed method in a comparison with six frequently used missing value imputation methods such as Zero, KNN, robust SVD, EM, random forest (RF) and weighted least square approach (WLSA) through feature selection using both simulated and real datasets. Ten performance indices were used to explore the optimal method such as Frobenius norm (FOBN), accuracy (ACC), sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), detection rate (DR), misclassification error rate (MER), the area under the ROC curve (AUC) and computational runtime. Evaluation based on both simulated and real data suggests the superiority of the proposed method over the other traditional methods in terms of various rates of outliers and missing values. The suggested approach also keeps almost equal performance in absence of outliers with the other methods. The proposed method is accurate, simple, and consumes lower computational time compared to the other methods. Therefore, our recommendation is to apply the proposed procedure for large-scale transcriptomics and metabolomics data analysis. The computational tool has been implemented in an R package, which is publicly available from https://CRAN.R-project.org/package=rMisbeta.

Integrated Metabolomics and Transcriptomics Analyses Reveal Histidine Metabolism Plays an Important Role in Imiquimod-Induced Psoriasis-like Skin Inflammation.

Psoriasis is a chronic inflammatory skin disease characterized by massive keratinocyte proliferation and immune cell infiltration into the epidermis. However, the specific mechanisms underlying the development of psoriasis remain unclear. Untargeted metabolomics and transcriptomics have been used separately to profile biomarkers and risk genes in the serum of psoriasis patients. However, the integration of metabolomics and transcriptomics to identify dysregulated metabolites and genes in the psoriatic skin is lacking. In this study, we performed an untargeted metabolomics analysis of imiquimod (IMQ)-induced psoriasis-like mice and healthy controls, and found that levels of a total of 4,188 metabolites differed in IMQ-induced psoriasis-like mice compared with those in control mice. Metabolomic data analysis using MetaboAnalyst showed that the metabolic pathways of primary metabolites, such as folate biosynthesis and galactose metabolism, were significantly altered in the skin of mice after treatment with IMQ. Furthermore, IMQ treatment also significantly altered metabolic pathways of secondary metabolites, including histidine metabolism, in mouse skin tissues. The metabolomic results were verified by transcriptomics analysis. RNA-seq results showed that histamine decarboxylase (HDC) mRNA levels were significantly upregulated after IMQ treatment. Targeted inhibition of histamine biosynthesis process using HDC-specific inhibitor, pinocembrin (PINO), significantly alleviated epidermal thickness, downregulated the expression of interleukin (IL)-17A and IL-23, and inhibited the infiltration of immune cells during IMQ-induced psoriasis-like skin inflammation. In conclusion, our study offers a validated and comprehensive understanding of metabolism during the development of psoriasis and demonstrated that PINO could protect against IMQ-induced psoriasis-like skin inflammation.

Aromatic amino acids play a harmonizing role in prostate cancer: A metabolomics-based cross-sectional study.

BackgroundProstate cancer (PCa) is a common health problem worldwide. The rate of this disease is likely to grow by 2021. PCa is a heterogeneous disorder, and various biochemical factors contribute to the development of this disease. The metabolome is the complete set of metabolites in a cell or biological sample and represents the downstream end product of the omics. Hence, to model PCa by computational systems biology, a preliminary metabolomics-based study was used to compare the metabolome profile pattern between healthy and PCa men.ObjectiveThis study was carried out to highlight energy metabolism modification and assist the prognosis and treatment of disease with unique biomarkers.Materials and MethodsIn this cross-sectional research, 26 men diagnosed with stage-III PCa and 26 healthy men with normal PSA levels were enrolled. Urine was analyzed with proton nuclear magnetic resonance (H-NMR) spectroscopy, accompanied by the MetaboAnalyst web-based platform tool for metabolomics data analysis. Partial least squares regression discriminant analysis was applied to clarify the separation between the two groups. Outliers were documented and metabolites determined, followed by identifying biochemical pathways.ResultsOur findings reveal that modifications in aromatic amino acid metabolism and some of their metabolites have a high potential for use as urinary PCa biomarkers. Tryptophan metabolism (p 0.001), tyrosine metabolism (p 0.001), phenylalanine, tyrosine and tryptophan biosynthesis (p 0.001), phenylalanine metabolism (p = 0.01), ubiquinone and other terpenoid-quinone biosynthesis (p = 0.19), nitrogen metabolism (p = 0.21), and thiamine metabolism (p = 0.41) with Q (0.198) and R (0.583) were significantly altered.ConclusionThe discriminated metabolites and their pathways play an essential role in PCa causes and harmony.

Integrative Network Analysis Revealed Genetic Impact of Pyruvate Kinase L/R on Hepatocyte Proliferation and Graft Survival after Liver Transplantation.

Background Pyruvate kinase L/R (PKLR) has been suggested to affect the proliferation of hepatocytes via regulation of the cell cycle and lipid metabolism. However, its impact on the global metabolome and its clinical implications remain unclear. Aims We aimed to clarify the genetic impact of PKLR on the metabolomic profiles of hepatoma cells and its potential effects on grafts for liver transplantation (LT). Methods Nontargeted and targeted metabolomic assays were performed in human hepatoma cells transfected with lentiviral vectors causing PKLR overexpression and silencing, respectively. We then constructed a molecular network based on integrative analysis of transcriptomic and metabolomic data. We also assessed the biological functions of PKLR in the global metabolome in LT grafts in patients via a weighted correlation network model. Results Multiomic analysis revealed that PKLR perturbations significantly affected the pyruvate, citrate, and glycerophospholipid metabolism pathways, as crucial steps in de novo lipogenesis (DNL). We also confirmed the importance of phosphatidylcholines (PC) and its derivative lyso-PC supply on improved survival of LT grafts in patients. Coexpression analysis revealed beneficial effects of PKLR overexpression on posttransplant prognosis by alleviating arachidonic acid metabolism of the grafts, independent of operational risk factors. Conclusion This systems-level analysis indicated that PKLR affected hepatoma cell viability via impacts on the whole process of DNL, from glycolysis to final PC synthesis. PKLR also improved prognosis after LT, possibly via its impact on the increased genesis of beneficial glycerophospholipids.

Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline.

Inborn errors of metabolism (IEM) are inherited conditions caused by genetic defects in enzymes or cofactors. These defects result in a specific metabolic fingerprint in patient body fluids, showing accumulation of substrate or lack of an end-product of the defective enzymatic step. Untargeted metabolomics has evolved as a high throughput methodology offering a comprehensive readout of this metabolic fingerprint. This makes it a promising tool for diagnostic screening of IEM patients. However, the size and complexity of metabolomics data have posed a challenge in translating this avalanche of information into knowledge, particularly for clinical application. We have previously established next-generation metabolic screening (NGMS) as a metabolomics-based diagnostic tool for analyzing plasma of individual IEM-suspected patients. To fully exploit the clinical potential of NGMS, we present a computational pipeline to streamline the analysis of untargeted metabolomics data. This pipeline allows for time-efficient and reproducible data analysis, compatible with ISO:15189 accredited clinical diagnostics. The pipeline implements a combination of tools embedded in a workflow environment for large-scale clinical metabolomics data analysis. The accompanying graphical user interface aids end-users from a diagnostic laboratory for efficient data interpretation and reporting. We also demonstrate the application of this pipeline with a case study and discuss future prospects.

Contribution of upregulated aminoacyl-tRNA biosynthesis to metabolic dysregulation in gastric cancer.

Background and AimMetabolic reprogramming is characterized by dysregulated levels of metabolites and metabolic enzymes. Integrated metabolomic and transcriptomic data analysis can help to elucidate changes in the levels of metabolites and metabolic enzymes, screen the core metabolic pathways, and develop novel therapeutic strategies for cancer.MethodsHere, the metabolome of gastric cancer tissues was determined using liquid chromatography–mass spectrometry. The transcriptome data from The Cancer Genome Atlas dataset were integrated with the liquid chromatography–mass spectrometry data to identify the common dysregulated gastric cancer‐specific metabolic pathways. Additionally, the protein expression and clinical significance of key metabolic enzymes were examined using a gastric cancer tissue array.ResultsMetabolomic analysis of 16 gastric cancer tissues revealed that among the 15 dysregulated metabolomic pathways, the aminoacyl‐tRNA biosynthesis pathway in the gastric tissues was markedly upregulated relative to that in the adjacent noncancerous tissues, which was consistent with the results of transcriptome analysis. Bioinformatic analysis revealed that among the key regulators in the aminoacyl‐tRNA biosynthesis pathway, the expression levels of threonyl‐tRNA synthetase (TARS) and phenylalanyl‐tRNA synthetase (FARSB) were correlated with tumor grade and poor survival, respectively. Additionally, gastric tissue array data analysis indicated that TARS and FARSB were upregulated in gastric cancer tissues and were correlated with poor prognosis and tumor metastasis.ConclusionsThis study demonstrated that the aminoacyl‐tRNA biosynthesis pathway is upregulated in gastric cancer and both TARS and FARSB play key roles in the progression of gastric cancer. Additionally, a novel therapeutic strategy for gastric cancer was proposed that involves targeting the aminoacyl‐tRNA biosynthesis pathway.