Butyrylcarnitine

Quantitation of Butyrylcarnitine, Isobutyrylcarnitine, and Glutarylcarnitine in Urine Using Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry (UPLC-MS/MS)

Acylcarnitines are formed when an acyl group is transferred from coenzyme A to a molecule of L-carnitine. In organic acidemias, and in fatty acid oxidation disorders, specific acylcarnitine species accumulate in a pattern that is characteristic for each disease. For this reason, acylcarnitine analysis is widely used for screening and diagnosis of inherited disorders of metabolism. The most common method for acylcarnitine analysis uses flow injection tandem mass spectrometry. Flow injection analysis allows for high throughput, however, does not provide separation of isomeric and isobaric compounds. Among the acylcarnitine species which can be affected by the presence of isomeric/isobaric compounds, C4-carnitine and C5DC-carnitine are probably the ones encountered most often. The method presented here is performed on urine and utilizes butanolic HCL to derivatize acylcarnitines, ultra-performance liquid chromatography to resolve C4- and C5-DC isomers and isobars, and quantitation of these species using multiple-reaction monitoring (MRM).

Plasma metabolomic profile in nonalcoholic fatty liver disease

The plasma profile of subjects with nonalcoholic fatty liver disease (NAFLD), steatosis, and steatohepatitis (NASH) was examined using an untargeted global metabolomic analysis to identify specific disease-related patterns and to identify potential noninvasive biomarkers. Plasma samples were obtained after an overnight fast from histologically confirmed nondiabetic subjects with hepatic steatosis (n = 11) or NASH (n = 24) and were compared with healthy, age- and sex-matched controls (n = 25). Subjects with NAFLD were obese, were insulin resistant, and had higher plasma concentrations of homocysteine and total cysteine and lower plasma concentrations of total glutathione. Metabolomic analysis showed markedly higher levels of glycocholate, taurocholate, and glycochenodeoxycholate in subjects with NAFLD. Plasma concentrations of long-chain fatty acids were lower and concentrations of free carnitine, butyrylcarnitine, and methylbutyrylcarnitine were higher in NASH. Several glutamyl dipeptides were higher whereas cysteine-glutathione levels were lower in NASH and steatosis. Other changes included higher branched-chain amino acids, phosphocholine, carbohydrates (glucose, mannose), lactate, pyruvate, and several unknown metabolites. Random forest analysis and recursive partitioning of the metabolomic data could separate healthy subjects from NAFLD with an error rate of approximately 8% and separate NASH from healthy controls with an error rate of 4%. Hepatic steatosis and steatohepatitis could not be separated using the metabolomic profile. Plasma metabolomic analysis revealed marked changes in bile salts and in biochemicals related to glutathione in subjects with NAFLD. Statistical analysis identified a panel of biomarkers that could effectively separate healthy controls from NAFLD and healthy controls from NASH. These biomarkers can potentially be used to follow response to therapeutic interventions.

Metabolomics in Glaucoma: A Systematic Review

Purpose: Glaucoma remains a poorly understood disease, and identifying biomarkers for early diagnosis is critical to reducing the risk of glaucoma-related visual impairment and blindness. The aim of this review is to provide current metabolic profiles for glaucoma through a summary and analysis of reported metabolites associated with glaucoma.
Methods: We searched PubMed and Web of Science for metabolomics studies of humans on glaucoma published before November 11, 2020. Studies were included if they assessed the biomarkers of any types of glaucoma and performed mass spectrometry-based or nuclear magnetic resonance-based metabolomics approach. Pathway enrichment analysis and topology analysis were performed to generate a global view of metabolic signatures related to glaucoma using the MetaboAnalyst 3.0.
Results: In total, 18 articles were included in this review, among which 13 studies were focused on open-angle glaucoma (OAG). Seventeen metabolites related to OAG were repeatedly identified, including seven amino acids (arginine, glycine, alanine, lysine, methionine, phenylalanine, tyrosine), two phosphatidylcholine (PC aa C34:2, PC aa C36:4), three complements (acetylcarnitine, propionylcarnitine, butyrylcarnitine), carnitine, glutamine, hypoxanthine, spermine, and spermidine. The pathway analysis implied a major role of amino metabolism in OAG pathophysiology and revealed the metabolic characteristics between different biological samples.
Conclusions: In this review, we summarize existing metabolomic studies related to glaucoma biomarker identification and point out a series of metabolic disorders in OAG patients, providing information on the molecular mechanism changes in glaucoma. Additional studies are needed to validate existing findings, and future research will need to explore the potential overlap between different biological fluids.

Association Between Human Blood Metabolome and the Risk of Psychiatric Disorders

Background and hypothesis: To identify promising drug targets for psychiatric disorders, we applied Mendelian randomization (MR) design to systematically screen blood metabolome for potential mediators of psychiatric disorders and further predict target-mediated side effects.
Study design: We selected 92 unique blood metabolites from 3 metabolome genome-wide association studies (GWASs) with totally 147 827 participants. Summary statistics for bipolar disorder (BIP), attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), major depressive disorder (MDD), schizophrenia (SCZ), panic disorder (PD), autistic spectrum disorder (ASD), and anorexia nervosa (AN) originated from the Psychiatric Genomics Consortium, involving 1 143 340 participants. Mendelian randomization (MR) analyses were conducted to estimate associations of blood metabolites with psychiatric disorders. Phenome-wide MR analysis was further performed to predict side effects mediated by metabolite-targeted interventions.
Results: Eight metabolites were identified associated with psychiatric disorders, including five established mediators: N-acetylornithine (BIP: OR, 0.72 [95% CI, 0.66-0.79]; SCZ: OR, 0.74 [0.64-0.84]), glycine (BIP: OR, 0.62 [0.50-0.77]), docosahexaenoic acid (MDD: OR, 0.96 [0.94-0.97]), 3-Hydroxybutyrate (MDD: OR, 1.14 [1.08-1.21]), butyrylcarnitine (SCZ: OR, 1.22 [1.12-1.32]); and three novel mediators: 1-arachidonoylglycerophosphocholine (1-arachidonoyl-GPC)(BIP: OR, 0.31 [0.23-0.41]), glycoproteins (BIP: OR, 0.94 [0.92-0.97]), sphingomyelins (AN: OR, 1.12 [1.06-1.19]). Phenome-wide MR analysis showed that all identified metabolites except for N-acetylornithine and 3-Hydroxybutyrate had additional effects on nonpsychiatric diseases, while glycine, 3-Hydroxybutyrate, N-acetylornithine, and butyrylcarnitine had no adverse side effects.
Conclusions: This MR study identified five established and three novel mediators for psychiatric disorders. N-acetylornithine, glycine, 3-Hydroxybutyrate, and butyrylcarnitine might be promising targets against psychiatric disorders with no predicted adverse side effects.

Combination of host immune metabolic biomarkers for the PD-1 blockade cancer immunotherapy

BACKGROUNDCurrent clinical biomarkers for the programmed cell death 1 (PD-1) blockade therapy are insufficient because they rely only on the tumor properties, such as programmed cell death ligand 1 expression frequency and tumor mutation burden. Identifying reliable, responsive biomarkers based on the host immunity is necessary to improve the predictive values.METHODSWe investigated levels of plasma metabolites and T cell properties, including energy metabolism markers, in the blood of patients with non-small cell lung cancer before and after treatment with nivolumab (n = 55). Predictive values of combination markers statistically selected were evaluated by cross-validation and linear discriminant analysis on discovery and validation cohorts, respectively. Correlation between plasma metabolites and T cell markers was investigated.RESULTSThe 4 metabolites derived from the microbiome (hippuric acid), fatty acid oxidation (butyrylcarnitine), and redox (cystine and glutathione disulfide) provided high response probability (AUC = 0.91). Similarly, a combination of 4 T cell markers, those related to mitochondrial activation (PPARγ coactivator 1 expression and ROS), and the frequencies of CD8+PD-1hi and CD4+ T cells demonstrated even higher prediction value (AUC = 0.96). Among the pool of selected markers, the 4 T cell markers were exclusively selected as the highest predictive combination, probably because of their linkage to the abovementioned metabolite markers. In a prospective validation set (n = 24), these 4 cellular markers showed a high accuracy rate for clinical responses of patients (AUC = 0.92).CONCLUSIONCombination of biomarkers reflecting host immune activity is quite valuable for responder prediction.FUNDINGAMED under grant numbers 18cm0106302h0003, 18gm0710012h0105, and 18lk1403006h0002; the Tang Prize Foundation; and JSPS KAKENHI grant numbers JP16H06149, 17K19593, and 19K17673.