Akiyoshi Hirayama

Quantitative Metabolome Profiling of Colon and Stomach Cancer Microenvironment by Capillary Electrophoresis Time-of-Flight Mass Spectrometry

Most cancer cells predominantly produce energy by glycolysis rather than oxidative phosphorylation via the tricarboxylic acid (TCA) cycle, even in the presence of an adequate oxygen supply (Warburg effect). However, little has been reported regarding the direct measurements of global metabolites in clinical tumor tissues. Here, we applied capillary electrophoresis time-of-flight mass spectrometry, which enables comprehensive and quantitative analysis of charged metabolites, to simultaneously measure their levels in tumor and grossly normal tissues obtained from 16 colon and 12 stomach cancer patients. Quantification of 94 metabolites in colon and 95 metabolites in stomach involved in glycolysis, the pentose phosphate pathway, the TCA and urea cycles, and amino acid and nucleotide metabolisms resulted in the identification of several cancer-specific metabolic traits. Extremely low glucose and high lactate and glycolytic intermediate concentrations were found in both colon and stomach tumor tissues, which indicated enhanced glycolysis and thus confirmed the Warburg effect. Significant accumulation of all amino acids except glutamine in the tumors implied autophagic degradation of proteins and active glutamine breakdown for energy production, i.e., glutaminolysis. In addition, significant organ-specific differences were found in the levels of TCA cycle intermediates, which reflected the dependency of each tissue on aerobic respiration according to oxygen availability. The results uncovered unexpectedly poor nutritional conditions in the actual tumor microenvironment and showed that capillary electrophoresis coupled to mass spectrometry-based metabolomics, which is capable of quantifying the levels of energy metabolites in tissues, could be a powerful tool for the development of novel anticancer agents that target cancer-specific metabolism.

Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles

Saliva is a readily accessible and informative biofluid, making it ideal for the early detection of a wide range of diseases including cardiovascular, renal, and autoimmune diseases, viral and bacterial infections and, importantly, cancers. Saliva-based diagnostics, particularly those based on metabolomics technology, are emerging and offer a promising clinical strategy, characterizing the association between salivary analytes and a particular disease. Here, we conducted a comprehensive metabolite analysis of saliva samples obtained from 215 individuals (69 oral, 18 pancreatic and 30 breast cancer patients, 11 periodontal disease patients and 87 healthy controls) using capillary electrophoresis time-of-flight mass spectrometry (CE-TOF-MS). We identified 57 principal metabolites that can be used to accurately predict the probability of being affected by each individual disease. Although small but significant correlations were found between the known patient characteristics and the quantified metabolites, the profiles manifested relatively higher concentrations of most of the metabolites detected in all three cancers in comparison with those in people with periodontal disease and control subjects. This suggests that cancer-specific signatures are embedded in saliva metabolites. Multiple logistic regression models yielded high area under the receiver-operating characteristic curves (AUCs) to discriminate healthy controls from each disease. The AUCs were 0.865 for oral cancer, 0.973 for breast cancer, 0.993 for pancreatic cancer, and 0.969 for periodontal diseases. The accuracy of the models was also high, with cross-validation AUCs of 0.810, 0.881, 0.994, and 0.954, respectively. Quantitative information for these 57 metabolites and their combinations enable us to predict disease susceptibility. These metabolites are promising biomarkers for medical screening.

Mitochondrial dysfunction associated with increased oxidative stress and α-synuclein accumulation in PARK2 iPSC-derived neurons and postmortem brain tissue

BackgroundParkinson’s disease (PD) is a neurodegenerative disease characterized by selective degeneration of dopaminergic neurons in the substantia nigra (SN). The familial form of PD, PARK2, is caused by mutations in the parkin gene. parkin-knockout mouse models show some abnormalities, but they do not fully recapitulate the pathophysiology of human PARK2.ResultsHere, we generated induced pluripotent stem cells (iPSCs) from two PARK2 patients. PARK2 iPSC-derived neurons showed increased oxidative stress and enhanced activity of the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway. iPSC-derived neurons, but not fibroblasts or iPSCs, exhibited abnormal mitochondrial morphology and impaired mitochondrial homeostasis. Although PARK2 patients rarely exhibit Lewy body (LB) formation with an accumulation of α-synuclein, α-synuclein accumulation was observed in the postmortem brain of one of the donor patients. This accumulation was also seen in the iPSC-derived neurons in the same patient.ConclusionsThus, pathogenic changes in the brain of a PARK2 patient were recapitulated using iPSC technology. These novel findings reveal mechanistic insights into the onset of PARK2 and identify novel targets for drug screening and potential modified therapies for PD.

Serum metabolomics reveals γ-glutamyl dipeptides as biomarkers for discrimination among different forms of liver disease

BACKGROUND & AIMS We applied a metabolome profiling approach to serum samples obtained from patients with different liver diseases, to discover noninvasive and reliable biomarkers for rapid-screening diagnosis of liver diseases. METHODS Using capillary electrophoresis and liquid chromatography mass spectrometry, we analyzed low molecular weight metabolites in a total of 248 serum samples obtained from patients with nine types of liver disease and healthy controls. RESULTS We found that γ-glutamyl dipeptides, which were biosynthesized through a reaction with γ-glutamylcysteine synthetase, were indicative of the production of reduced glutathione, and that measurement of their levels could distinguish among different liver diseases. Multiple logistic regression models facilitated the discrimination between specific and other liver diseases and yielded high areas under receiver-operating characteristic curves. The area under the curve values in training and independent validation data were 0.952 and 0.967 in healthy controls, 0.817 and 0.849 in drug-induced liver injury, 0.754 and 0.763 in asymptomatic hepatitis B virus infection, 0.820 and 0.762 in chronic hepatitis B, 0.972 and 0.895 in hepatitis C with persistently normal alanine transaminase, 0.917 and 0.707 in chronic hepatitis C, 0.803 and 0.993 in cirrhosis type C, and 0.762 and 0.803 in hepatocellular carcinoma, respectively. Several γ-glutamyl dipeptides also manifested potential for differentiating between nonalcoholic steatohepatitis and simple steatosis. CONCLUSIONS γ-Glutamyl dipeptides are novel biomarkers for liver diseases, and varying levels of individual or groups of these peptides have the power to discriminate among different forms of hepatic disease.

Gene Knockout and Metabolome Analysis of Carnitine/Organic Cation Transporter OCTN1

ABSTRACTPurposeSolute carrier OCTN1 (SLC22A4) is an orphan transporter, the physiologically important substrate of which is still unidentified. The aim of the present study was to examine physiological roles of OCTN1.MethodsWe first constructed octn1 gene knockout (octn1−/−) mice. Metabolome analysis was then performed to identify substrates in vivo. The possible association of the substrate identified with diseased conditions was further examined.ResultsThe metabolome analysis of blood and several organs indicated complete deficiency of a naturally occurring potent antioxidant ergothioneine in octn1−/− mice among 112 metabolites examined. Pharmacokinetic analyses after oral administration revealed the highest distribution to small intestines and extensive renal reabsorption of [3H]ergothioneine, both of which were much reduced in octn1−/− mice. The octn1−/− mice exhibited greater susceptibility to intestinal inflammation under the ischemia and reperfusion model. The blood ergothioneine concentration was also much reduced in Japanese patients with Crohn’s disease, compared with healthy volunteers and patients with another inflammatory bowel disease, ulcerative colitis.ConclusionsThese results indicate that OCTN1 plays a pivotal role for maintenance of systemic and intestinal exposure of ergothioneine, which could be important for protective effects against intestinal tissue injuries, providing a possible diagnostic tool to distinguish the inflammatory bowel diseases.

Metabolic profiling reveals new serum biomarkers for differentiating diabetic nephropathy

Capillary electrophoresis coupled with time-of-flight mass spectrometry was used to explore new serum biomarkers with high sensitivity and specificity for diabetic nephropathy (DN) diagnosis, through comprehensive analysis of serum metabolites with 78 diabetic patients. Multivariate analyses were used for identification of marker candidates and development of discriminative models. Of the 289 profiled metabolites, orthogonal partial least-squares discriminant analysis identified 19 metabolites that could distinguish between DN with macroalbuminuria and diabetic patients without albuminuria. These identified metabolites included creatinine, aspartic acid, γ-butyrobetaine, citrulline, symmetric dimethylarginine (SDMA), kynurenine, azelaic acid, and galactaric acid. Significant correlations between all these metabolites and urinary albumin-to-creatinine ratios (p < 0.009, Spearman’s rank test) were observed. When five metabolites (including γ-butyrobetaine, SDMA, azelaic acid and two unknowns) were selected from 19 metabolites and applied for multiple logistic regression model, AUC value for diagnosing DN was 0.927 using the whole dataset, and 0.880 in a cross-validation test. In addition, when four known metabolites (aspartic acid, SDMA, azelaic acid and galactaric acid) were applied, the resulting AUC was still high at 0.844 with the whole dataset and 0.792 with cross-validation. Combination of serum metabolomics with multivariate analyses enabled accurate discrimination of DN patients. The results suggest that capillary electrophoresis-mass spectrometry based metabolome analysis could be used for DN diagnosis.

Alteration of the Intestinal Environment by Lubiprostone Is Associated with Amelioration of Adenine-Induced CKD

The accumulation of uremic toxins is involved in the progression of CKD. Various uremic toxins are derived from gut microbiota, and an imbalance of gut microbiota or dysbiosis is related to renal failure. However, the pathophysiologic mechanisms underlying the relationship between the gut microbiota and renal failure are still obscure. Using an adenine-induced renal failure mouse model, we evaluated the effects of the ClC-2 chloride channel activator lubiprostone (commonly used for the treatment of constipation) on CKD. Oral administration of lubiprostone (500 µg/kg per day) changed the fecal and intestinal properties in mice with renal failure. Additionally, lubiprostone treatment reduced the elevated BUN and protected against tubulointerstitial damage, renal fibrosis, and inflammation. Gut microbiome analysis of 16S rRNA genes in the renal failure mice showed that lubiprostone treatment altered their microbial composition, especially the recovery of the levels of the Lactobacillaceae family and Prevotella genus, which were significantly reduced in the renal failure mice. Furthermore, capillary electrophoresis-mass spectrometry-based metabolome analysis showed that lubiprostone treatment decreased the plasma level of uremic toxins, such as indoxyl sulfate and hippurate, which are derived from gut microbiota, and a more recently discovered uremic toxin, trans-aconitate. These results suggest that lubiprostone ameliorates the progression of CKD and the accumulation of uremic toxins by improving the gut microbiota and intestinal environment.

Prediction of metabolite identity from accurate mass, migration time prediction and isotopic pattern information in CE-TOFMS data.

CE‐TOFMS is a powerful method for profiling charged metabolites. However, the limited availability of metabolite standards hinders the process of identifying compounds from detected features in CE‐TOFMS data sets. To overcome this problem, we developed a method to identify unknown peaks based on the predicted migration time (tm) and accurate m/z values. We developed a predictive model using 375 standard cationic metabolites and support vector regression. The model yielded good correlations between the predicted and measured tm (R=0.952 and 0.905 using complete and cross‐validation data sets, respectively). Using the trained model, we subsequently predicted the tm for 2938 metabolites available from the public databases and assigned tentative identities to noise‐filtered features in human urine samples. While 38.9% of the peaks were assigned metabolite names by matching with the standard library alone, the proportion increased to 52.2%. The proposed methodology increases the value of metabolomic data sets obtained from CE‐TOFMS profiling.

Capillary electrophoresis‐mass spectrometry‐based metabolome analysis of serum and saliva from neurodegenerative dementia patients

Despite increasing global prevalence, the precise pathogenesis and terms for objective diagnosis of neurodegenerative dementias remain controversial, and comprehensive understanding of the disease remains lacking. Here, we conducted metabolomic analysis of serum and saliva obtained from patients with neurodegenerative dementias (n = 10), including Alzheimers disease, frontotemporal lobe dementia, and Lewy body disease, as well as from age‐matched healthy controls (n = 9). Using CE‐TOF‐MS, six metabolites in serum (β‐alanine, creatinine, hydroxyproline, glutamine, iso‐citrate, and cytidine) and two in saliva (arginine and tyrosine) were significantly different between dementias and controls. Using multivariate analysis, serum was confirmed as a more efficient biological fluid for diagnosis compared to saliva; additionally, 45 metabolites in total were identified as candidate markers that could discriminate at least one pair of diagnostic groups from the healthy control group. These metabolites possibly provide an objective method for diagnosing dementia‐type by multiphase screening. Moreover, diagnostic‐type‐dependent differences were observed in several tricarboxylic acid cycle compounds detected in serum, indicating that some pathways in glucose metabolism may be altered in dementia patients. This pilot study revealed novel alterations in metabolomic profiles between various neurodegenerative dementias, which would contribute to etiological investigations.

Global metabolic network reorganization by adaptive mutations allows fast growth of Escherichia coli on glycerol

Comparative whole-genome sequencing enables the identification of specific mutations during adaptation of bacteria to new environments and allelic replacement can establish their causality. However, the mechanisms of action are hard to decipher and little has been achieved for epistatic mutations, especially at the metabolic level. Here we show that a strain of Escherichia coli carrying mutations in the rpoC and glpK genes, derived from adaptation in glycerol, uses two distinct metabolic strategies to gain growth advantage. A 27-bp deletion in the rpoC gene first increases metabolic efficiency. Then, a point mutation in the glpK gene promotes growth by improving glycerol utilization but results in increased carbon wasting as overflow metabolism. In a strain carrying both mutations, these contrasting carbon/energy saving and wasting mechanisms work together to give an 89% increase in growth rate. This study provides insight into metabolic reprogramming during adaptive laboratory evolution for fast cellular growth.