Evaluation of Small Molecules in Blank EDTA Plasma Tubes and Optimization of Metabolomic Workflow for Biomarker Studies Using Plasma Samples.

As the first step of metabolomic analysis in biomarker identification studies, various types of blood collection tubes are used in clinical practice. However, little attention is paid to potential contamination caused by the blank tube itself. Here, we evaluated small molecules in blank EDTA plasma tubes through LC-MS-based untargeted metabolomic analysis and identified small molecules with markedly varied levels among different production batches or specifications. Our data demonstrate possible contamination and data interference caused by blank EDTA plasma tubes when employing large clinical cohorts for biomarker identification. Therefore, we propose a workflow of filtering metabolites in blank tubes prior to statistical analysis to improve the fidelity of biomarker identification.

Proteomics and metabolomics profiling reveal panels of circulating diagnostic biomarkers and molecular subtypes in stable COPD.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease with high morbidity and mortality, especially in advanced patients. We aimed to develop multi-omics panels of biomarkers for the diagnosis and explore its molecular subtypes. METHODS: A total of 40 stable patients with advanced COPD and 40 controls were enrolled in the study. Proteomics and metabolomics techniques were applied to identify potential biomarkers. An additional 29 COPD and 31 controls were enrolled for validation of the obtained proteomic signatures. Information on demographic, clinical manifestation, and blood test were collected. The ROC analyses were carried out to evaluate the diagnostic performance, and experimentally validated the final biomarkers on mild-to-moderate COPD. Next, molecular subtyping was performed using proteomics data. RESULTS: Theophylline, palmitoylethanolamide, hypoxanthine, and cadherin 5 (CDH5) could effectively diagnose advanced COPD with high accuracy (auROC = 0.98, sensitivity of 0.94, and specificity of 0.95). The performance of the diagnostic panel was superior to that of other single/combined results and blood tests. Proteome based stratification of COPD revealed three subtypes (I-III) related to different clinical outcomes and molecular feature: simplex COPD, COPD co-existing with bronchiectasis, and COPD largely co-existing with metabolic syndrome, respectively. Two discriminant models were established using the auROC of 0.96 (Principal Component Analysis, PCA) and 0.95 (the combination of RRM1 + SUPV3L1 + KRT78) in differentiating COPD and COPD with co-morbidities. Theophylline and CDH5 were exclusively elevated in advanced COPD but not in its mild form. CONCLUSIONS: This integrative multi-omics analysis provides a more comprehensive understanding of the molecular landscape of advanced COPD, which may suggest molecular targets for specialized therapy.

Applications of liquid chromatography-mass spectrometry based metabolomics in predictive and personalized medicine.

Metabolomics is a fast-developing technique used in biomedical researches focusing on pathological mechanism illustration or novel biomarker development for diseases. The ability of simultaneously quantifying thousands of metabolites in samples makes metabolomics a promising technique in predictive or personalized medicine-oriented researches and applications. Liquid chromatography-mass spectrometry is the most widely employed analytical strategy for metabolomics. In this current mini-review, we provide a brief update on the recent developments and novel applications of LC-MS based metabolomics in the predictive and personalized medicine sector, such as early diagnosis, molecular phenotyping or prognostic evaluation. COVID-19 related metabolomic studies are also summarized. We also discuss the prospects of metabolomics in precision medicine-oriented researches, as well as critical issues that need to be addressed when employing metabolomic strategy in clinical applications.

Metabolic detection of malignant brain gliomas through plasma lipidomic analysis and support vector machine-based machine learning.

BACKGROUND: Most malignant brain gliomas (MBGs) are associated with dismal outcomes, mainly due to their late diagnosis. Current diagnostic methods for MBGs are based on imaging and histological examination, which limits their early detection. Here, we aimed to identify reliable plasma lipid biomarkers for non-invasive diagnosis for MBGs. METHODS: Untargeted lipidomic analysis was firstly performed using a discovery cohort (n=107). The data were processed by a support vector machine (SVM)-based discriminating model to retrieve a panel of candidate biomarkers. Then, a targeted quantification method was developed, and the SVM-based diagnostic model was constructed using a training cohort (n=750) and tested using a test cohort (n=225). Finally, the performance of the diagnostic model was further evaluated in an independent validation cohort (n=920) enrolled from multiple medical centers. FINDINGS: A panel of 11 plasma lipids was identified as candidate biomarkers with an accuracy of 0.999. The diagnostic model developed achieved a high performance in distinguishing MBGs patients from normal controls with an area under the receiver-operating characteristic curve (AUC) of 0.9877 and 0.9869 in the training and test cohorts, respectively. In the validation cohort, the 11 lipid panel still achieved an accuracy of 0.9641 and an AUC of 0.9866. INTERPRETATION: The present study demonstrates the applicability and robustness of utilizing a machine learning algorithm to analyze lipidomic data for efficient and reliable biomarker screening. The 11 lipid biomarkers show great potential for the non-invasive diagnosis of MBGs with high throughput. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgments section.

Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis.

Lung cancer is the leading cause of cancer mortality, and early detection is key to improving survival. However, there are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. Here, we performed single-cell RNA sequencing of different early-stage lung cancers and found that lipid metabolism was broadly dysregulated in different cell types, with glycerophospholipid metabolism as the most altered lipid metabolism-related pathway. Untargeted lipidomics was carried out in an exploratory cohort of 311 participants. Through support vector machine algorithm-based and mass spectrum-based feature selection, we identified nine lipids (lysophosphatidylcholines 16:0, 18:0, and 20:4; phosphatidylcholines 16:0-18:1, 16:0-18:2, 18:0-18:1, 18:0-18:2, and 16:0-22:6; and triglycerides 16:0-18:1-18:1) as the features most important for early-stage cancer detection. Using these nine features, we developed a liquid chromatography-mass spectrometry (MS)-based targeted assay using multiple reaction monitoring. This target assay achieved 100.00% specificity on an independent validation cohort. In a hospital-based lung cancer screening cohort of 1036 participants examined by low-dose computed tomography and a prospective clinical cohort containing 109 participants, the assay reached more than 90.00% sensitivity and 92.00% specificity. Accordingly, matrix-assisted laser desorption/ionization MS imaging confirmed that the selected lipids were differentially expressed in early-stage lung cancer tissues in situ. This method, designated as Lung Cancer Artificial Intelligence Detector, may be useful for early detection of lung cancer or large-scale screening of high-risk populations for cancer prevention.

Identification of diagnostic markers and lipid dysregulation in oesophageal squamous cell carcinoma through lipidomic analysis and machine learning.

BACKGROUND: Oesophageal cancer (EC) ranks high in both morbidity and mortality. A non-invasive and high-sensitivity diagnostic approach is necessary to improve the prognosis of EC patients. METHODS: A total of 525 serum samples were subjected to lipidomic analysis. We combined serum lipidomics and machine-learning algorithms to select important metabolite features for the detection of oesophageal squamous cell carcinoma (ESCC), the major subtype of EC in developing countries. A diagnostic model using a panel of selected features was developed and evaluated. Integrative analyses of tissue transcriptome and serum lipidome were conducted to reveal the underlying mechanism of lipid dysregulation. RESULTS: Our optimised diagnostic model with a panel of 12 lipid biomarkers together with age and gender reaches a sensitivity of 90.7%, 91.3% and 90.7% and an area under receiver-operating characteristic curve of 0.958, 0.966 and 0.818 in detecting ESCC for the training cohort, validation cohort and independent validation cohort, respectively. Integrative analysis revealed matched variation trend of genes encoding key enzymes in lipid metabolism. CONCLUSIONS: We have identified a panel of 12 lipid biomarkers for diagnostic modelling and potential mechanisms of lipid dysregulation in the serum of ESCC patients. This is a reliable, rapid and non-invasive tumour-diagnostic approach for clinical application.

Comprehensive plasma metabolomic and lipidomic analyses reveal potential biomarkers for heart failure.

Heart failure is a syndrome with symptoms or signs caused by cardiac dysfunction. In clinic, four stages (A, B, C, and D) were used to describe heart failure progression. This study was aimed to explore plasma metabolomic and lipidomic profiles in different HF stages to identify potential biomarkers. Metabolomics and lipidomics were performed using plasma of heart failure patients at stages A (n = 49), B (n = 61), and C+D (n = 26). Analysis of Variance (ANOVA) was used for screening dysregulated molecules. Bioinformatics was used to retrieve perturbed metabolic pathways. Univariate and multivariate receiver operating characteristic curve (ROC) analyses were used for potential biomarker screening. Stage A showed significant difference to other stages, and 142 dysregulated lipids and 134 dysregulated metabolites were found belonging to several metabolic pathways. Several marker panels were proposed for the diagnosis of heart failure stage A versus stage B-D. Several molecules, including lysophosphatidylcholine 18:2, cholesteryl ester 18:1, alanine, choline, and Fructose, were found correlated with B-type natriuretic peptide or left ventricular ejection fractions. In summary, using untargeted metabolomic and lipidomic profiling, several dysregulated small molecules were successfully identified between HF stages A and B-D. These molecules would provide valuable information for further pathological researches and biomarker development.

Proteomic Analyses Identify Differentially Expressed Proteins and Pathways Between Low-Risk and High-Risk Subtypes of Early-Stage Lung Adenocarcinoma and Their Prognostic Impacts.

The histopathological subtype of lung adenocarcinoma (LUAD) is closely associated with prognosis. Micropapillary or solid predominant LUAD tends to relapse after surgery at an early stage, whereas lepidic pattern shows a favorable outcome. However, the molecular mechanism underlying this phenomenon remains unknown. Here, we recruited 31 lepidic predominant LUADs (LR: low-risk subtype group) and 28 micropapillary or solid predominant LUADs (HR: high-risk subtype group). Tissues of these cases were obtained and label-free quantitative proteomic and bioinformatic analyses were performed. Additionally, prognostic impact of targeted proteins was validated using The Cancer Genome Atlas databases (n = 492) and tissue microarrays composed of early-stage LUADs (n = 228). A total of 192 differentially expressed proteins were identified between tumor tissues of LR and HR and three clusters were identified via hierarchical clustering excluding eight proteins. Cluster 1 (65 proteins) showed a sequential decrease in expression from normal tissues to tumor tissues of LR and then to HR and was predominantly enriched in pathways such as tyrosine metabolism and ECM-receptor interaction, and increased matched mRNA expression of 18 proteins from this cluster predicted favorable prognosis. Cluster 2 (70 proteins) demonstrated a sequential increase in expression from normal tissues to tumor tissues of LR and then to HR and was mainly enriched in pathways such as extracellular organization, DNA replication and cell cycle, and high matched mRNA expression of 25 proteins indicated poor prognosis. Cluster 3 (49 proteins) showed high expression only in LR, with high matched mRNA expression of 20 proteins in this cluster indicating favorable prognosis. Furthermore, high expression of ERO1A and FEN1 at protein level predicted poor prognosis in early-stage LUAD, supporting the mRNA results. In conclusion, we discovered key differentially expressed proteins and pathways between low-risk and high-risk subtypes of early-stage LUAD. Some of these proteins could serve as potential biomarkers in prognostic evaluation.

Proteomic analysis reveals distinctive protein expression patterns of thrombus in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a type of malignant tumor of the urinary system. The renal vein or vena cava thrombus can be found in a subset of ccRCC patients in whom it leads to worse prognosis. However, the protein expression profile and molecular features of ccRCC thrombus remain largely unclear. Here, a comparative proteomic analysis was performed using the 2D-LC-MS strategy for the thrombus-tumor-normal tissue triples of 15 ccRCC patients. Statistical analysis, GO enrichment analysis, protein-protein interaction network construction, and mRNA-based survival analysis were used to interpret the proteomic data. Three dysregulated proteins, GGT5 (gamma-glutamyl transferase 5), KRT7 (keratin 7) and CFHR1 (complement factor H related 1), were analyzed using western blot (WB) and immunohistochemistry (IHC) to validate the reliability of the proteomic analysis. The result of this analysis revealed 251 dysregulated proteins, which could be divided into 11 clusters depending on the changing trends, among the thrombus, tumor, and normal tissues. Several pathways and regulation networks were found to be associated with the thrombus, and some dysregulated proteins showed potential values for prognosis prediction. WB and IHC results were in accordance with the proteomic results, further validating the reliability of this study. In conclusion, our findings provide an overview of the thrombus at the molecular level as well as valuable information for further pathological studies or research on biomarkers and therapeutic targets.

Integrated proteomics and phosphoproteomics reveal perturbed regulative pathways in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis largely owing to its inefficient diagnosis, rapid progress, and tenacious drug resistance. Here, we aimed to analyze the expressive patterns of proteins and phosphorylation in PDAC tissue samples and compare them to normal pancreatic tissue to investigate the underlying mechanisms and to reveal potential protein targets for diagnosis and drug development. Liquid chromatography coupled to mass spectrometry (LC-MS) based proteomics and phosphoproteomics analyses were performed using 20 pairs of patient-derived PDAC tissue and normal pancreatic tissue samples. Protein identification and quantification were conducted using MaxQuant software. Bioinformatics analysis was used to retrieve PDAC-relevant pathways and gene ontology (GO) terms. 4985 proteins and 3643 phosphoproteins were identified with high confidence; of these, 322 proteins and 235 phosphoproteins were dysregulated in PDAC. Several pathways, including several extracellular matrix-related pathways, were found to be strongly associated with PDAC. Further, the expression levels of filamin A (FLNA), integrin alpha-V (ITGAV), thymidine phosphorylase (TYMP), medium-chain specific acyl-CoA dehydrogenase, mitochondrial (ACADM), short-chain specific acyl-CoA dehydrogenase, mitochondrial (ACADS), and acetyl-CoA acetyltransferase, mitochondrial (ACAT1) were examined through western blot and immunohistochemistry analysis, and the results confirmed the validity of the proteomics analysis results. These findings provide comprehensive insight into the dysregulated regulative networks in PDAC tissue samples at the protein and phosphorylation levels, and they provide clues for further pathological studies and drug-target development.

Plasma Metabolomics and Lipidomics Reveal Perturbed Metabolites in Different Disease Stages of Chronic Obstructive Pulmonary Disease.

Background: Chronic obstructive pulmonary disease (COPD) is a common disease characterized by persistent respiratory symptoms and airflow restriction. It is usually manifested as airway and/or alveolar abnormalities caused by significant exposure to harmful particulates or gases. Objective: We aim to explore plasma metabolomic changes in the acute exacerbation stage of COPD (AECOPD) and stable stage of COPD (Stable COPD) to identify potential biomarkers for diagnosis or prognosis in clinical practice. Methods: Untargeted metabolomics and lipidomics analyses were performed to investigate dysregulated molecules in blood plasma of AECOPD patients (n=48) and Stable COPD (n=48), and a cohort of healthy people were included as a control group (n=48). Statistical analysis and bioinformatics analysis were performed to reveal dysregulated metabolites and perturbed metabolic pathways. SVM-based multivariate ROC analysis was used for candidate biomarker screening. Results: A total of 142 metabolites and 688 lipids were dysregulated in COPD patients. Pathway enrichment analysis showed that several metabolic pathways were perturbed after COPD onset. Several biomarker panels were proposed for diagnosis of COPD vs healthy control and AECOPD vs Stable COPD with AUC greater than 0.9. Conclusion: Numerous plasma metabolites and several metabolic pathways were detected relevant to COPD disease onset or progression. These metabolites may be considered as candidate biomarkers for diagnosis or prognosis of COPD. The perturbed pathways involved in COPD provide clues for further pathological mechanism studies of COPD.

Proteomic Analysis of Preoperative CSF Reveals Risk Biomarkers of Postoperative Delirium.

Objective: To analyze the proteome of preoperative cerebrospinal fluid (CSF) in older orthopedic patients with or without postoperative delirium (POD) using untargeted proteomics. Methods: A prospective cohort study was conducted. Eighty hip fracture patients aged ≥65 years were recruited. After successful spinal anesthesia, CSF was collected. The patients were divided into POD and No-POD groups based on the Confusion Assessment Method, and patients with POD were graded using the Memorial Delirium Assessment Scale (MDAS). Thirty No-POD patients were matched to 10 POD patients by age (±2 years) and Mini-Mental State Examination score (±2 scores). Label-free proteomic analysis was performed using a liquid chromatography coupled to mass spectrometry (LC-MS) workflow. Validation was performed using mass-spectrometry-based parallel reaction monitoring (PRM) for the 30 No-POD and 10 POD patients, as well as for an additional 5 POD patients. Bioinformatics were used to investigate possible relevant pathological mechanisms. Results: The incidence of POD in older orthopedic patients was 18.8% in our cohort of 80 patients. Proteomics results revealed 63 dysregulated CSF proteins, and PRM analysis validated these results. The preoperative CSF levels of both V-set and transmembrane domain-containing protein 2B (VSTM2B) and coagulation factor V (FA5) were positively correlated with MDAS scores on postoperative day 1 (r > 0.8, p < 0.05). Bioinformatic analysis revealed that several nervous-system-related pathways are relevant to POD development. Conclusion: We identified and validated several novel CSF proteins that are dysregulated in POD, and revealed several pathways that are relevant to POD development. Our results not only provide risk biomarkers for POD, but also give clues for further investigations into the pathological mechanisms of delirium. Clinical trial registration: This study was registered in the Chinese Clinical Trial Registry (ChiCTR1900021533).

PTENα regulates endocytosis and modulates olfactory function.

Phosphatase and tensin homolog deleted on chromosome 10 (PTEN) α is the first identified isoform of the well-known tumor suppressor PTEN. PTENα has an evolutionarily conserved 173-aa N terminus compared with canonical PTEN. Recently, PTENα has been shown to play roles in multiple biologic processes including learning and memory, cardiac homeostasis, and antiviral immunity. Here, we report that PTENα maintains mitral cells in olfactory bulb (OB), regulates endocytosis in OB neurons, and controls olfactory behaviors in mice. We show that PTENα directly dephosphorylates the endocytic protein amphiphysin and promotes its binding to adaptor-related protein complex 2 subunit ß1 (Ap2b1). In addition, we identified mutations in the N terminus of PTENα in patients with Parkinson disease and Lewy-body dementia, which are neurodegenerative disorders with early olfactory loss. Overexpression of PTENα mutant H169N in mice OB reduces odor sensitivity. Our data demonstrate a role of PTENα in olfactory function and provide insight into the mechanism of olfactory dysfunction in neurologic disorders.-Yuan, Y., Zhao, X., Wang, P., Mei, F., Zhou, J., Jin, Y., McNutt, M. A., Yin, Y. PTENα regulates endocytosis and modulates olfactory function.

Comprehensive metabolomic and proteomic analyses reveal candidate biomarkers and related metabolic networks in atrial fibrillation.

INTRODUCTION: Atrial fibrillation (AF) is an abnormal heart rhythm characterized by an irregular beating of the atria and is associated with an increased risk of heart failure, dementia, and stroke. Currently, the perturbation of plasma content due to AF disease onset is not well known. OBJECTIVES: To investigate dysregulated molecules in blood plasma of untreated AF patients, with the goal of identifying biomarkers for disease screening and pathological studies. METHODS: LC-MS based untargeted metabolomics, lipidomics and proteomics analyses were performed to find candidate biomarkers. A targeted quantification assay and an ELISA were performed to validate the results of the omics analyses. RESULTS: We found that 24 metabolites, 16 lipids and 16 proteins were significantly dysregulated in AF patients. Pathway enrichment analysis showed that the purine metabolic pathway and fatty acid metabolism were perturbed by AF onset. FA 20:2 and FA 22:4 show great linear correlational relationship with the left atrial area and could be considered for AF disease stage monitoring or prognosis evaluation. CONCLUSION: we used a comprehensive multiple-omics strategy to systematically investigate the dysregulated molecules in the plasma of AF patients, thereby revealing potential biomarkers for diagnosis and providing information for pathological studies.

Metabolomics identifies serum and exosomes metabolite markers of pancreatic cancer.

INTRODUCTION: Pancreatic cancer (PC) is one of the most aggressive malignancies, and it's difficult to diagnosis PC at an early stage, which leads to the poor prognosis of PC. OBJECTIVES: To identifiy the possible prognosis or dignosis metabolite biomarkers in the serum exosome of PC patients. METHODS: We employed LC-DDA-MS based untargeted lipidomic analysis to search for potential candidate biomarkers in the serum exosome of PC patients. Then LC-MRM-MS based targeted lipid quantification was used to validate the trends of the candidate biomarkers in larger sample cohorts. RESULTS: About 270 lipids belonging to 20 lipid species were found significantly dysregulated between the serum exosome of PC patients and healthy controls. 61 of them were validated in larger samples size. We further analysis the correlation between these dysregulated lipids and other PC related factors, and results show that LysoPC 22:0, PC (P-14:0/22:2) and PE (16:0/18:1) are all associated with tumor stage, CA19-9, CA242 and tumor diameter. What's more, PE (16:0/18:1) is also found to be significantly correlated with the patient's overall survival. CONCLUSION: These data reveal dysregulated lipids in serum exosome of PC patients, which have potential to be biomarkers for diagnosis, or unveil pathological relationship between exosome and PC progress.

Discovery of 2,4-diarylaminopyrimidine derivatives bearing dithiocarbamate moiety as novel FAK inhibitors with antitumor and anti-angiogenesis activities.

A series of 2,4-diarylaminopyrimidine derivatives containing dithiocarbamate moiety were designed by molecular hybridization strategy and synthesized for screening as inhibitors of focal adhesion kinase (FAK). Most of these compounds exhibit significant antiproliferative activities on human cancer cell lines expressing high levels of FAK at nanomolar concentrations. The compound 14z was identified as the most potent FAK inhibitor among these candidates. 14z has excellent anti-proliferative effect with IC50 values from 0.001 µM to 0.06 µM on HCT116, PC-3, U87-MG and MCF-7 cell lines and relatively less cytotoxicity to a nonmalignant cell line MCF-10A compared with MCF-7 cells (SI value > 10). 14z also exhibits significant FAK inhibitory activity (IC50 = 0.07 nM). In addition, compound 14z causes cell cycle arrest at G2/M and prompted apoptosis in both HCT116 and MCF-7 cells in a dose-dependent manner. Further studies show that compound 14z inhibits migration of MCF-7 and has anti-angiogenesis effect on HUVEC cells.

Use of Liquid Chromatography-Mass Spectrometry-Based Metabolomics to Identify Biomarkers of Tuberculosis.

Liquid chromatography-mass spectrometry (LC-MS) based metabolomics has proven to be a powerful analytical tool for biomarker screening. Here we describe two workflows which employ untargeted metabolomics to study serum biomarkers in tuberculosis patients. Expression profiles for samples of hydrophilic metabolites and hydrophobic metabolites (lipids) may be obtained by this method.

Lipidomic profiling of plasma samples from patients with mitochondrial disease.

Mitochondrial disease (MD) is a rare mitochondrial respiratory chain disorder with a high mortality and extremely challenging to treat. Although genomic, transcriptomic, and proteomic analyses have been performed to investigate the pathogenesis of MD, the role of metabolomics in MD, particularly of lipidomics remains unclear. This study was undertaken to identify potential lipid biomarkers of MD. An untargeted lipidomic approach was used to compare the plasma lipid metabolites in 20 MD patients and 20 controls through Liquid Chromatography coupled to Mass Spectrometry. Volcano plot analysis was performed to identify the different metabolites. Receiver operating characteristic (ROC) curves were constructed and the area under the ROC curves (AUC) was calculated to determine the potentially sensitive and specific biomarkers. A total of 41 lipids were significantly different in MD patients and controls. ROC curve analysis showed the top 5 AUC values of lipids (phosphatidylinositols 38:6, lysoPC 20:0, 19:0, 18:0, 17:0) are more than 0.99. Multivariate ROC curve based exploratory analysis showed the AUC of combination of top 5 lipids is 1, indicating they may be potentially sensitive and specific biomarkers for MD. We propose combination of these lipid species may be more valuable in predicting the development and progression of MD, and this will have important implications for the diagnosis and treatment of MD.

Lipidomic analysis of serum samples from migraine patients.

BACKGROUND: Migraine is a prevalent, disabling type of primary headache disorder associated with a high socioeconomic burden. The clinical management of migraine is challenging. This study was to identify potential serum lipidomic biomarkers of migraine. METHODS: The serum lipidomic profile of migraine sufferers was compared with healthy individuals using Liquid Chromatography coupled to Mass Spectrometry (LC-MS). Volcano plot analysis by Student's t-test was performed to identify the differential metabolites. Receiver operating characteristic (ROC) curves were constructed and the area under ROC curves (AUC) was calculated to evaluate whether the metabolites could be efficiently exploited for constructing a sensitive biomarker of migraine. RESULTS: A total of 29 serum metabolites from 4 classes of lipids including acylcarnitines, non-alpha-hydroxy-sphingosine ceramides (Cer_NSs), lysophosphatidylcholines (lysoPCs) and lysophosphatidylethanolamines (lysoPEs) were significantly different in migraine patients and controls. Of note, Cer_NSs were significantly elevated and lysoPEs were significantly decreased in migraine patients. LysoPE 18:1, lysoPE 18:2 and lysoPE 22:5 were found to be decreased in both positive and negative ion mode. Moreover, except for lysoPC 20:0, other lysoPCs were decreased in migraine patients. ROC curve analysis indicated that lysoPC 16:0 and lysoPC 20:0 are potential sensitive and specific biomarkers for migraine. CONCLUSION: LysoPC 16:0 and lysoPC 20:0 may be potential biomarkers for migraine. We suggest therapeutic management of these metabolites may be helpful in the prevention and treatment of migraine.

Low levels of serum serotonin and amino acids identified in migraine patients.

Migraine is a highly disabling primary headache associated with a high socioeconomic burden and a generally high prevalence. The clinical management of migraine remains a challenge. This study was undertaken to identify potential serum biomarkers of migraine. Using Liquid Chromatography coupled to Mass Spectrometry (LC-MS), the metabolomic profile of migraine was compared with healthy individuals. Principal component analysis (PCA) and Orthogonal partial least squares-discriminant analysis (orthoPLS-DA) showed the metabolomic profile of migraine is distinguishable from controls. Volcano plot analysis identified 10 serum metabolites significantly decreased during migraine. One of these was serotonin, and the other 9 were amino acids. Pathway analysis and enrichment analysis showed tryptophan metabolism (serotonin metabolism), arginine and proline metabolism, and aminoacyl-tRNA biosynthesis are the three most prominently altered pathways in migraine. ROC curve analysis indicated Glycyl-l-proline, N-Methyl-dl-Alanine and l-Methionine are potential sensitive and specific biomarkers for migraine. Our results show Glycyl-l-proline, N-Methyl-dl-Alanine and l-Methionine may be as specific or more specific for migraine than serotonin which is the traditional biomarker of migraine. We propose that therapeutic manipulation of these metabolites or metabolic pathways may be helpful in the prevention and treatment of migraine.