Establishment of Protocols for Global Metabolomics by LC-MS for Biomarker Discovery

Identifying critical age and gender-based metabolomic shifts in a Japanese population of the Tohoku Medical Megabank cohort

Understanding the physiological changes associated with aging and the associated disease risks is essential to establish biomarkers as indicators of biological aging. This study used the NMR-measured plasma metabolome to calculate age-specific metabolite indices. In doing so, the scope of the study was deliberately simplified to capture general trends and insights into age-related changes in metabolic patterns. In addition, changes in metabolite concentrations with age were examined in detail, with the period from 55-59 to 60-64 years being a period of significant metabolic change, particularly in men, and from 45-49 to 50-54 years in females. These results illustrate the different variations in metabolite concentrations by sex and provide new insights into the relationship between age and metabolic diseases.

A principal component analysis of metabolome and cognitive decline among Japanese older adults: cross-sectional analysis using Tohoku Medical Megabank Cohort Study

BackgroundDementia is the leading cause of disability and imposes a significant burden on society. Previous studies have suggested an association between metabolites and cognitive decline. Although the metabolite composition differs between Western and Asian populations, studies targeting Asian populations remain scarce.MethodsThis cross-sectional study used data from a cohort survey of community-dwelling older adults aged ≥ 60 years living in Miyagi, Japan, conducted by Tohoku Medical Megabank Organization between 2013 and 2016. Forty-three metabolite variables quantified using nuclear magnetic resonance spectroscopy were used as explanatory variables. Dependent variable was the presence of cognitive decline (≤ 23 points), assessed by the Mini-Mental State Examination. Principal component (PC) analysis was performed to reduce the dimensionality of metabolite variables, followed by logistic regression analysis to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for cognitive decline.ResultsA total of 2,940 participants were included (men: 49.0%, mean age: 67.6 years). Among them, 1.9% showed cognitive decline. The first 12 PC components (PC1-PC12) accounted for 71.7% of the total variance. Multivariate analysis showed that PC1, which mainly represented essential amino acids, was associated with lower odds of cognitive decline (OR = 0.89; 95% CI, 0.80-0.98). PC2, which mainly included ketone bodies, was associated with cognitive decline (OR = 1.29; 95% CI, 1.11-1.51). PC3, which included amino acids, was associated with lower odds of cognitive decline (OR = 0.81; 95% CI, 0.66-0.99).ConclusionAmino acids are protectively associated with cognitive decline, whereas ketone metabolites are associated with higher odds of cognitive decline.

Changes in Faecal Microbiota Profile and Plasma Biomarkers following the Administration of an Antioxidant Oleuropein-Rich Leaf Extract in a Rat Model Mimicking Colorectal Cancer

Oleuropein (OLE), a phenolic compound particularly abundant in the olive leaves, has been reported to have beneficial activities against colorectal cancer (CRC). In vitro studies suggested that these latter could be due to a modulation of the intestinal microbiota. Aiming to evaluate if OLE could affect the intestinal microbiota and the plasma metabolome, an antioxidant oleuropein-rich leaf extract (ORLE) was administered for one week to PIRC rats (F344/NTac-Apcam1137), a genetic model mimicking CRC. ORLE treatment significantly modulated the gut microbiota composition. Plasma metabolomic profiles revealed a significant predictive ability for amino acids, medium-chain fatty acids, and aldehydes. Pathway analysis revealed a significant decrease in phosphatidylcholine accumulation (LogFC = -1.67) in PIRC rats. These results suggest a significant effect of ORLE administration on faecal microbiota profiles and plasma metabolomes, thereby offering new omics-based insights into its protective role in CRC progression.

The drug-specific properties of hypoxia-inducible factor-prolyl hydroxylase inhibitors in mice reveal a significant contribution of the kidney compared to the liver to erythropoietin induction

AIMS

Kidney disease often leads to anemia due to a defect in the renal production of the erythroid growth factor erythropoietin (EPO), which is produced under the positive regulation of hypoxia-inducible transcription factors (HIFs). Chemical compounds that inhibit HIF-prolyl hydroxylases (HIF-PHs), which suppress HIFs, have been developed to reactivate renal EPO production in renal anemia patients. Currently, multiple HIF-PH inhibitors, in addition to conventional recombinant EPO reagents, are used for renal anemia treatment. This study aimed to elucidate the therapeutic mechanisms and drug-specific properties of HIF-PH inhibitors.

METHODS AND KEY FINDINGS

Gene expression analyses and mass spectrometry revealed that HIF-PH inhibitors (daprodustat, enarodustat, molidustat, and vadadustat) alter Epo gene expression levels in the kidney and liver in a drug-specific manner, with different pharmacokinetics in the plasma and urine after oral administration to mice. The drug specificity revealed the dominant contribution of EPO induction in the kidneys rather than in the liver to plasma EPO levels after HIF-PH inhibitor administration. We also found that several HIF-PH inhibitors directly induce duodenal gene expression related to iron intake, while these drugs indirectly suppress hepatic hepcidin expression to mobilize stored iron for hemoglobin synthesis through induction of the EPO-erythroferrone axis.

SIGNIFICANCE

Renal EPO induction is the major target of HIF-PH inhibitors for their therapeutic effects on erythropoiesis. Additionally, the drug-specific properties of HIF-PH inhibitors in EPO induction and iron metabolism have been shown in mice, providing useful information for selecting the proper HIF-PH inhibitor for each renal anemia patient.

Application of the Hydrophilic Interaction Liquid Chromatography (HILIC-MS) Novel Protocol to Study the Metabolic Heterogeneity of Glioblastoma Cells

Glioblastoma is a highly malignant brain tumor consisting of a heterogeneous cellular population. The transformed metabolism of glioblastoma cells supports their growth and division on the background of their milieu. One might hypothesize that the transformed metabolism of a primary glioblastoma could be well adapted to limitations in the variety and number of substrates imported into the brain parenchyma and present it their microenvironment. Additionally, the phenotypic heterogeneity of cancer cells could promote the variations among their metabolic capabilities regarding the utilization of available substrates and release of metabolic intermediates. With the aim to identify the putative metabolic footprint of different types of glioblastoma cells, we exploited the possibility for separation of polar and ionic molecules present in culture media or cell lysates by hydrophilic interaction liquid chromatography (HILIC). The mass spectrometry (MS) was then used to identify and quantify the eluted compounds. The introduced method allows the detection and quantification of more than 150 polar and ionic metabolites in a single run, which may be present either in culture media or cell lysates and provide data for polaromic studies within metabolomics. The method was applied to analyze the culture media and cell lysates derived from two types of glioblastoma cells, T98G and U118. The analysis revealed that even both types of glioblastoma cells share several common metabolic aspects, and they also exhibit differences in their metabolic capability. This finding agrees with the hypothesis about metabolic heterogeneity of glioblastoma cells. Furthermore, the combination of both analytical methods, HILIC-MS, provides a valuable tool for metabolomic studies based on the simultaneous identification and quantification of a wide range of polar and ionic metabolites-polaromics.

UHPLC-qTOF-MS-Based Nontargeted Metabolomics to Characterize the Effects of Capsaicin on Plasma and Skin Metabolic Profiles of C57BL/6 Mice-An In vivo Experimental Study

Background

Capsaicin is the main compound found in chili pepper and has complex pharmacologic effects. This study aimed to elucidate the mechanism of the effect of capsaicin on physiological processes by analyzing changes in metabolites and metabolic pathways.

Methods

Female C57BL/6 mice were divided into two groups(n = 10/group) and fed with capsaicin-soybean oil solution(group T) or soybean oil(group C) for 6 weeks. Ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC-qTOF-MS) based metabolomics was undertaken to assess plasma and skin metabolic profile changes and identify differential metabolites through multivariate analysis.

Results

According to the OPLS-DA score plots, the plasma and skin metabolic profiles in the group T and group C were significantly separated. In plasma, 38 significant differential metabolites were identified. KEGG pathway enrichment analysis revealed that the most significant plasma metabolic pathways included pyruvate metabolism and ABC transporters. In skin, seven significant differential metabolites were found. Four metabolic pathways with p values < 0.05 were detected, including sphingolipid metabolism, sphingolipid signaling pathway, apoptosis, and necroptosis.

Conclusion

These findings will provide metabolomic insights to assess the physiological functions of capsaicin and contribute to a better understanding of the potential effects of a capsaicin-rich diet on health.

User-friendly relative quantification procedure for gas chromatography/mass spectrometry-based plasma metabolome analysis

RATIONALE

Recently, metabolome analysis has been applied to a variety of research fields, but differences between batches or facilities can cause discrepancies in the results of such analyses. To resolve these issues using comprehensive metabolome analysis, in which it is difficult to perform quantitative analyses of all detected metabolites, internal standard compounds are used to obtain relative metabolite levels. This study investigated gas chromatography/mass spectrometry-based plasma metabolome analysis methods that are superior to relative quantification using internal standard compounds.

METHODS

In experiment I, four analyses were performed under different analytical conditions at one facility, and then the data from the four analyses were compared. In experiment II, the same samples were analyzed at three facilities, and then the data from the three facilities were compared.

RESULTS

Regarding the relative values obtained through comparisons with the internal standard compound, differences in the analytical results were observed among the four analytical conditions in experiment I and among the three facilities in experiment II, and the differences observed among the three facilities (experiment II) were larger. When correction was performed using plasma as a quality control, which is the procedure suggested in this study, these differences were markedly ameliorated.

CONCLUSION

The suggested procedure involves the analysis of a plasma standard as a quality control for each batch and the calculation of relative target plasma to quality-control plasma values for each metabolite. This is an easy and low-cost method and could be readily employed by researchers during comprehensive plasma metabolome analysis.

Translational response to mitochondrial stresses is orchestrated by tRNA modifications

Mitochondrial stress and dysfunction play important roles in many pathologies. However, how cells respond to mitochondrial stress is not fully understood. Here, we examined the translational response to electron transport chain (ETC) inhibition and arsenite induced mitochondrial stresses. Our analysis revealed that during mitochondrial stress, tRNA modifications (namely f5C, hm5C, queuosine and its derivatives, and mcm5U) dynamically change to fine tune codon decoding, usage, and optimality. These changes in codon optimality drive the translation of many pathways and gene sets, such as the ATF4 pathway and selenoproteins, involved in the cellular response to mitochondrial stress. We further examined several of these modifications using targeted approaches. ALKBH1 knockout (KO) abrogated f5C and hm5C levels and led to mitochondrial dysfunction, reduced proliferation, and impacted mRNA translation rates. Our analysis revealed that tRNA queuosine (tRNA-Q) is a master regulator of the mitochondrial stress response. KO of QTRT1 or QTRT2, the enzymes responsible for tRNA-Q synthesis, led to mitochondrial dysfunction, translational dysregulation, and metabolic alterations in mitochondria-related pathways, without altering cellular proliferation. In addition, our analysis revealed that tRNA-Q loss led to a domino effect on various tRNA modifications. Some of these changes could be explained by metabolic profiling. Our analysis also revealed that utilizing serum deprivation or alteration with Queuine supplementation to study tRNA-Q or stress response can introduce various confounding factors by altering many other tRNA modifications. In summary, our data show that tRNA modifications are master regulators of the mitochondrial stress response by driving changes in codon decoding.

Identification of kynurenine and quinolinic acid as promising serum biomarkers for drug-induced interstitial lung diseases

BACKGROUND

Drug-induced interstitial lung disease (DILD) is a lung injury caused by various types of drugs and is a serious problem in both clinical practice and drug development. Clinical management of the condition would be improved if there were DILD-specific biomarkers available; this study aimed to meet that need.

METHODS

Biomarker candidates were identified by non-targeted metabolomics focusing on hydrophilic molecules, and further validated by targeted approaches using the serum of acute DILD patients, DILD recovery patients, DILD-tolerant patients, patients with other related lung diseases, and healthy controls.

RESULTS

Serum levels of kynurenine and quinolinic acid (and kynurenine/tryptophan ratio) were elevated significantly and specifically in acute DILD patients. The diagnostic potentials of these biomarkers were superior to those of conventional lung injury biomarkers, Krebs von den Lungen-6 and surfactant protein-D, in discriminating between acute DILD patients and patients with other lung diseases, including idiopathic interstitial pneumonia and lung diseases associated with connective tissue diseases. In addition to identifying and evaluating the biomarkers, our data showed that kynurenine/tryptophan ratios (an indicator of kynurenine pathway activation) were positively correlated with serum C-reactive protein concentrations in patients with DILD, suggesting the potential association between the generation of these biomarkers and inflammation. Our in vitro experiments demonstrated that macrophage differentiation and inflammatory stimulations typified by interferon gamma could activate the kynurenine pathway, resulting in enhanced kynurenine levels in the extracellular space in macrophage-like cell lines or lung endothelial cells. Extracellular quinolinic acid levels were elevated only in macrophage-like cells but not endothelial cells owing to the lower expression levels of metabolic enzymes converting kynurenine to quinolinic acid. These findings provide clues about the molecular mechanisms behind their specific elevation in the serum of acute DILD patients.

CONCLUSIONS

The serum concentrations of kynurenine and quinolinic acid as well as kynurenine/tryptophan ratios are promising and specific biomarkers for detecting and monitoring DILD and its recovery, which could facilitate accurate decisions for appropriate clinical management of patients with DILD.

jMorp: Japanese Multi-Omics Reference Panel update report 2023

Modern medicine is increasingly focused on personalized medicine, and multi-omics data is crucial in understanding biological phenomena and disease mechanisms. Each ethnic group has its unique genetic background with specific genomic variations influencing disease risk and drug response. Therefore, multi-omics data from specific ethnic populations are essential for the effective implementation of personalized medicine. Various prospective cohort studies, such as the UK Biobank, All of Us and Lifelines, have been conducted worldwide. The Tohoku Medical Megabank project was initiated after the Great East Japan Earthquake in 2011. It collects biological specimens and conducts genome and omics analyses to build a basis for personalized medicine. Summary statistical data from these analyses are available in the jMorp web database (https://jmorp.megabank.tohoku.ac.jp), which provides a multidimensional approach to the diversity of the Japanese population. jMorp was launched in 2015 as a public database for plasma metabolome and proteome analyses and has been continuously updated. The current update will significantly expand the scale of the data (metabolome, genome, transcriptome, and metagenome). In addition, the user interface and backend server implementations were rewritten to improve the connectivity between the items stored in jMorp. This paper provides an overview of the new version of the jMorp.

Analysis of Metabolic Changes in Endogenous Metabolites and Diagnostic Biomarkers for Various Diseases Using Liquid Chromatography and Mass Spectrometry

Analysis of endogenous metabolites in various diseases is useful for searching diagnostic biomarkers and elucidating the molecular mechanisms of pathophysiology. The author and collaborators have developed some LC/tandem mass spectrometry (LC/MS/MS) methods for metabolites and applied them to disease-related samples. First, we identified urinary conjugated cholesterol metabolites and serum N-palmitoyl-O-phosphocholine serine as useful biomarkers for Niemann-Pick disease type C (NPC). For the purpose of intraoperative diagnosis of glioma patients, we developed the LC/MS/MS analysis methods for 2-hydroxyglutaric acid or cystine and found that they could be good differential biomarkers. For renal cell carcinoma, we searched for various biomarkers for early diagnosis, malignancy evaluation and recurrence prediction by global metabolome analysis and targeted LC/MS/MS analysis. In pathological analysis, we developed a simultaneous LC/MS/MS analysis method for 13 steroid hormones and applied it to NPC cells, we found 6 types of reductions in NPC model cells. For non-alcoholic steatohepatitis (NASH), model mice were prepared with special diet and plasma bile acids were measured, and as a result, hydrophilic bile acids were significantly increased. In addition, we developed an LC/MS/MS method for 17 sterols and analyzed liver cholesterol metabolites and found a decrease in phytosterols and cholesterol synthetic markers and an increase in non-enzymatic oxidative sterols in the pre-onset stage of NASH. We will continue to challenge themselves to add value to clinical practice based on cutting-edge analytical chemistry methodology.

Fatty acid binding protein type 7 deficiency preserves auditory function in noise-exposed mice

Fatty acid-binding protein 7 (FABP7) is vital for uptake and trafficking of fatty acids in the nervous system. To investigate the involvement of FABP7 in noise-induced hearing loss (NIHL) pathogenesis, we used Fabp7 knockout (KO) mice generated via CRISPR/Cas9 in the C57BL/6 background. Initial auditory brainstem response (ABR) measurements were conducted at 9 weeks, followed by noise exposure at 10 weeks. Subsequent ABRs were performed 24 h later, with final measurements at 12 weeks. Inner ears were harvested 24 h after noise exposure for RNA sequencing and metabolic analyses. We found no significant differences in initial ABR measurements, but Fabp7 KO mice showed significantly lower thresholds in the final ABR measurements. Hair cell survival was also enhanced in Fabp7 KO mice. RNA sequencing revealed that genes associated with the electron transport chain were upregulated or less impaired in Fabp7 KO mice. Metabolomic analysis revealed various alterations, including decreased glutamate and aspartate in Fabp7 KO mice. In conclusion, FABP7 deficiency mitigates cochlear damage following noise exposure. This protective effect was supported by the changes in gene expression of the electron transport chain, and in several metabolites, including excitotoxic neurotransmitters. Our study highlights the potential therapeutic significance of targeting FABP7 in NIHL.

Reduced glutathione level in the aqueous humor of patients with primary open-angle glaucoma and normal-tension glaucoma

Glaucoma is a leading cause of blindness worldwide in older people. Profiling the aqueous humor, including the metabolites it contains, is useful to understand physiological and pathological conditions in the eye. In the current study, we used mass spectrometry (MS) to characterize the aqueous humor metabolomic profile and biological features of patients with glaucoma. Aqueous humor samples were collected during trabeculectomy surgery or cataract surgery and analyzed with global metabolomics. We included 40 patients with glaucoma (32 with POAG, 8 with NTG) and 37 control subjects in a discovery study. VIP analysis revealed five metabolites that were elevated and three metabolites that were reduced in the glaucoma patients. The identified metabolomic profile had an area under the receiver operating characteristic curve of 0.953. Among eight selected metabolites, the glutathione level was significantly decreased in association with visual field defects. Moreover, in a validation study to confirm the reproducibility of our findings, the glutathione level was reduced in NTG and POAG patients compared with a cataract control group. Our findings demonstrate that aqueous humor profiling can help to diagnose glaucoma and that various aqueous humor metabolites are correlated with clinical parameters in glaucoma patients. In addition, glutathione is clearly reduced in the aqueous humor of glaucoma patients with both IOP-dependent and IOP-independent disease subtypes. These findings indicate that antioxidant agents in the aqueous humor reflect glaucomatous optic nerve damage and that excessive oxidative stress may be involved in the pathogenesis of glaucoma.

Small High-Density Lipoprotein and Omega-3 Fatty Acid Intake Differentiates Japanese and Japanese-Americans: The INTERLIPID Study

AIM

To identify the most differentiated serum lipids, especially concerning particle size and fractions, between Japanese living in Japan and Japanese-Americans in Hawaii, in the absence of possible genetic confounders, and cross-sectionally examine the associated modifiable lifestyle factors.

METHODS

Overall, 1,241 (aged 40-59 years) Japanese living in Japan and Japanese-Americans in Hawaii were included. We quantified 130 serum lipid profiles (VLDL 1-5, IDL, LDL 1-6, high-density lipoprotein [HDL] 1-4, and their subfractions) using Bruker's 1H-nuclear magnetic resonance spectrometer for the primary outcome. Modifiable lifestyle factors included body mass index (BMI), physical activity, alcohol and smoking habits, and 70 nutrient parameters. We evaluated the different lipids between the groups using partial least squares-discriminant analysis and association between extracted lipids and lifestyle factors using multivariable linear regression analysis.

RESULTS

Concentrations of HDL4, HDL with the smallest particle size, were lower in Japanese than in Japanese-Americans of both sexes. Higher fish-derived omega-3 fatty acid intake and lower alcohol intake were associated with lower HDL4 concentrations. A 1% higher kcal intake of total omega-3 fatty acids was associated with a 9.8-mg/dL lower HDL4. Fish-derived docosapentaenoic acid, eicosapentaenoic acid, and docosahexaenoic acid intake were inversely associated with HDL4 concentration. There was no relationship between country, sex, age, or BMI.

CONCLUSIONS

Japanese and Japanese-Americans can be differentiated based on HDL4 concentration. High fish intake among the Japanese may contribute to their lower HDL4 concentration. Thus, HDL particle size may be an important clinical marker for coronary artery diseases or a fish consumption biomarker.

Data Processing and Analysis in Liquid Chromatography-Mass Spectrometry-Based Targeted Metabolomics

Mass spectrometry (MS)-based metabolomics provides high-dimensional datasets; that is, the data include various metabolite features. Data analysis begins by converting the raw data obtained from the MS to produce a data matrix (metabolite × concentrations). This is followed by several steps, such as peak integration, alignment of multiple data, metabolite identification, and calculation of metabolite concentrations. Each step yields the analytical results and the accompanying information used for the quality assessment of the anterior steps. Thus, the measurement quality can be analyzed through data processing. Here, we introduce a typical data processing procedure and describe a method to utilize the intermediate data as quality control. Subsequently, commonly used data analysis methods for metabolomics data, such as statistical analyses, are also introduced.

dbTMM: an integrated database of large-scale cohort, genome and clinical data for the Tohoku Medical Megabank Project

To reveal gene-environment interactions underlying common diseases and estimate the risk for common diseases, the Tohoku Medical Megabank (TMM) project has conducted prospective cohort studies and genomic and multiomics analyses. To establish an integrated biobank, we developed an integrated database called “dbTMM” that incorporates both the individual cohort/clinical data and the genome/multiomics data of 157,191 participants in the Tohoku Medical Megabank project. To our knowledge, dbTMM is the first database to store individual whole-genome data on a variant-by-variant basis as well as cohort/clinical data for over one hundred thousand participants in a prospective cohort study. dbTMM enables us to stratify our cohort by both genome-wide genetic factors and environmental factors, and it provides a research and development platform that enables prospective analysis of large-scale data from genome cohorts.

A database integrating 1.3 trillion genome cohort data entries from 157,191 individuals in Japan will facilitate research into the gene-environment interactions underlying common diseases. The Tohoku Medical Megabank integrated database called dbTMM was developed by Soichi Ogishima, Masayuki Yamamoto and colleagues at Tohoku University in Japan. It incorporates the genome, metabolome, proteome, clinical, sociodemographic, lifestyle and environmental data from 84,073 adults, and 73,529 pregnant women and their families, including children. Blood and urine samples were collected from participants and analysed, then obtained genome/multiomics data were stored in dbTMM. Users can stratify the entire population into smaller populations based on multiple data variables, including whole genome variants, to search for statistically significant differences that might warrant further research. The dbTMM is expected to help clarify the genes and gene-environment interactions underlying common diseases and improve disease risk prediction.

Comparison of Kit-Based Metabolomics with Other Methodologies in a Large Cohort, towards Establishing Reference Values

Metabolic profiling is an omics approach that can be used to observe phenotypic changes, making it particularly attractive for biomarker discovery. Although several candidate metabolites biomarkers for disease expression have been identified in recent clinical studies, the reference values of healthy subjects have not been established. In particular, the accuracy of concentrations measured by mass spectrometry (MS) is unclear. Therefore, comprehensive metabolic profiling in large-scale cohorts by MS to create a database with reference ranges is essential for evaluating the quality of the discovered biomarkers. In this study, we tested 8700 plasma samples by commercial kit-based metabolomics and separated them into two groups of 6159 and 2541 analyses based on the different ultra-high-performance tandem mass spectrometry (UHPLC-MS/MS) systems. We evaluated the quality of the quantified values of the detected metabolites from the reference materials in the group of 2541 compared with the quantified values from other platforms, such as nuclear magnetic resonance (NMR), supercritical fluid chromatography tandem mass spectrometry (SFC-MS/MS) and UHPLC-Fourier transform mass spectrometry (FTMS). The values of the amino acids were highly correlated with the NMR results, and lipid species such as phosphatidylcholines and ceramides showed good correlation, while the values of triglycerides and cholesterol esters correlated less to the lipidomics analyses performed using SFC-MS/MS and UHPLC-FTMS. The evaluation of the quantified values by MS-based techniques is essential for metabolic profiling in a large-scale cohort.

Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis

The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed models to evaluate pregnancy status and predict gestational age using omics data from blood biospecimens; however, less invasive methods are desired. Here we propose a model to predict gestational age, using urinary metabolite information. In our prospective cohort study, we collected 2741 urine samples from 187 healthy pregnant women, 23 patients with hypertensive disorders of pregnancy, and 14 patients with spontaneous preterm birth. Using gas chromatography-tandem mass spectrometry, we identified 184 urinary metabolites that showed dynamic systematic changes in healthy pregnant women according to gestational age. A model to predict gestational age during normal pregnancy progression was constructed; the correlation coefficient between actual and predicted weeks of gestation was 0.86. The predicted gestational ages of cases with hypertensive disorders of pregnancy exhibited significant progression, compared with actual gestational ages. This is the first study to predict gestational age in normal and complicated pregnancies by using urinary metabolite information. Minimally invasive urinary metabolomics might facilitate changes in the prediction of gestational age in various clinical settings.

Metabolomic profile of milk fermented with Streptococcus thermophilus cocultured with Bifidobacterium animalis ssp. lactis, Lactiplantibacillus plantarum, or both during storage

In this study, the microbial interactions among cocultures of Streptococcus thermophilus (St) with potential probiotics of Bifidobacterium animalis ssp. lactis (Ba) and Lactiplantibacillus plantarum (Lp) in fermented milk were investigated during a storage period of 21 d at 4°C, in terms of acidifying activity (pH and titratable acidity), viable counts, and metabolites. A nontargeted metabolomics approach based on ultra-high-performance liquid chromatography coupled with mass spectrometry was employed for mapping the global metabolite profiles of fermented milk. Probiotic strains cocultured with St accelerated milk acidification, and improved the microbial viability compared with the single culture of St. The St-Ba/Lp treatment manifested a higher bacteria viability and acidification ability in comparison with the St-Ba or the St-Lp treatment. Relative quantitation of 179 significant metabolites was identified, including nucleosides, AA, short peptides, organic acids, lipid derivatives, carbohydrates, carbonyl compounds, and compounds related to energy metabolism. The principal component analysis indicated that St treatment and coculture treatments displayed a complete distinction in metabolite profiles, and Lp had a larger effect than Ba on metabolic profiles of fermented milk produced by cofermentation with St during storage. The heat map in combination with hierarchical cluster analysis showed that the abundance of metabolites significantly varied with the starter cultures over the storage, and high abundance of metabolites was observed in either St or coculture samples. The St-Ba/Lp treatment showed relatively high abundance for the vast majority of metabolites. These findings suggest that the profile of the metabolites characterizing fermented milk samples may depend on the starter cultures, and incorporation of probiotics may considerably influence the metabolomic activities of fermented milks.

Quality Assessment of Untargeted Analytical Data in a Large-Scale Metabolomic Study

Large-scale metabolomic studies have become common, and the reliability of the peak data produced by the various instruments is an important issue. However, less attention has been paid to the large number of uncharacterized peaks in untargeted metabolomics data. In this study, we tested various criteria to assess the reliability of 276 and 202 uncharacterized peaks that were detected in a gathered set of 30 plasma and urine quality control samples, respectively, using capillary electrophoresis-time-of-flight mass spectrometry (CE-TOFMS). The linear relationship between the amounts of pooled samples and the corresponding peak areas was one of the criteria used to select reliable peaks. We used samples from approximately 3000 participants in the Tsuruoka Metabolome Cohort Study to investigate patterns of the areas of these uncharacterized peaks among the samples and clustered the peaks by combining the patterns and differences in the migration times. Our assessment pipeline removed substantial numbers of unreliable or redundant peaks and detected 35 and 74 reliable uncharacterized peaks in plasma and urine, respectively, some of which may correspond to metabolites involved in important physiological processes such as disease progression. We propose that our assessment pipeline can be used to help establish large-scale untargeted clinical metabolomic studies.

High-Precision Automated Workflow for Urinary Untargeted Metabolomic Epidemiology

Urine is a noninvasive biofluid that is rich in polar metabolites and well suited for metabolomic epidemiology. However, because of individual variability in health and hydration status, the physiological concentration of urine can differ >15-fold, which can pose major challenges in untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics. Although numerous urine normalization methods have been implemented (e.g., creatinine, specific gravity-SG), most are manual and, therefore, not practical for population-based studies. To address this issue, we developed a method to measure SG in 96-well-plates using a refractive index detector (RID), which exhibited accuracy within 85-115% and <3.4% precision. Bland-Altman statistics showed a mean deviation of -0.0001 SG units (limits of agreement: -0.0014 to 0.0011) relative to a hand-held refractometer. Using this RID-based SG normalization, we developed an automated LC-MS workflow for untargeted urinary metabolomics in a 96-well-plate format. The workflow uses positive and negative ionization HILIC chromatography and acquires mass spectra in data-independent acquisition (DIA) mode at three collision energies. Five technical internal standards (tISs) were used to monitor data quality in each method, all of which demonstrated raw coefficients of variation (CVs) < 10% in the quality controls (QCs) and < 20% in the samples for a small cohort (n = 87 urine samples, n = 22 QCs). Application in a large cohort (n = 842 urine samples, n = 248 QCs) demonstrated CVQC < 5% and CVsamples < 16% for 4/5 tISs after signal drift correction by cubic spline regression. The workflow identified >540 urinary metabolites including endogenous and exogenous compounds. This platform is suitable for performing urinary untargeted metabolomic epidemiology and will be useful for applications in population-based molecular phenotyping.

Colorectal Cancer-Derived CAT1-Positive Extracellular Vesicles Alter Nitric Oxide Metabolism in Endothelial Cells and Promote Angiogenesis

Accumulating scientific evidences strongly support the importance of cancer-derived extracellular vesicles (EV) in organization of tumor microenvironment and metastatic niches, which are also considered as ideal tools for cancer liquid biopsy. To uncover the full scope of proteomic information packaged within EVs secreted directly from human colorectal cancer, we cultured surgically resected viable tissues and obtained tissue-exudative EVs (Te-EV). Our quantitative profiling of 6,307 Te-EV proteins and 8,565 tissue proteins from primary colorectal cancer and adjacent normal mucosa (n = 17) allowed identification of a specific cargo in colorectal cancer-derived Te-EVs, high-affinity cationic amino acid transporter 1 (CAT1, P = 5.0 × 10^-3, fold change = 6.2), in addition to discovery of a new class of EV markers, VPS family proteins. The EV sandwich ELISA confirmed escalation of the EV-CAT1 level in plasma from patients with colorectal cancer compared with healthy donors (n = 119, P = 3.8 × 10^-7). Further metabolomic analysis revealed that CAT1-overexpressed EVs drastically enhanced vascular endothelial cell growth and tubule formation via upregulation of arginine transport and downstream NO metabolic pathway. These findings demonstrate the potency of CAT1 as an EV-based biomarker for colorectal cancer and its functional significance on tumor angiogenesis. IMPLICATIONS: This study provides a proteome-wide compositional dataset for viable colorectal cancer tissue-derived EVs and especially emphasizes importance of EV-CAT1 as a key regulator of angiogenesis.

Representing the Metabolome with High Fidelity: Range and Response as Quality Control Factors in LC-MS-Based Global Profiling

Liquid chromatography-mass spectrometry (LC-MS) is a powerful and widely used technique for measuring the abundance of chemical species in living systems. Its sensitivity, analytical specificity, and direct applicability to biofluids and tissue extracts impart great promise for the discovery and mechanistic characterization of biomarker panels for disease detection, health monitoring, patient stratification, and treatment personalization. Global metabolic profiling applications yield complex data sets consisting of multiple feature measurements for each chemical species observed. While this multiplicity can be useful in deriving enhanced analytical specificity and chemical identities from LC-MS data, data set inflation and quantitative imprecision among related features is problematic for statistical analyses and interpretation. This Perspective provides a critical evaluation of global profiling data fidelity with respect to measurement linearity and the quantitative response variation observed among components of the spectra. These elements of data quality are widely overlooked in untargeted metabolomics yet essential for the generation of data that accurately reflect the metabolome. Advanced feature filtering informed by linear range estimation and analyte response factor assessment is advocated as an attainable means of controlling LC-MS data quality in global profiling studies and exemplified herein at both the feature and data set level.

jMorp updates in 2020: large enhancement of multi-omics data resources on the general Japanese population

In the Tohoku Medical Megabank project, genome and omics analyses of participants in two cohort studies were performed. A part of the data is available at the Japanese Multi Omics Reference Panel (jMorp; https://jmorp.megabank.tohoku.ac.jp) as a web-based database, as reported in our previous manuscript published in Nucleic Acid Research in 2018. At that time, jMorp mainly consisted of metabolome data; however, now genome, methylome, and transcriptome data have been integrated in addition to the enhancement of the number of samples for the metabolome data. For genomic data, jMorp provides a Japanese reference sequence obtained using de novo assembly of sequences from three Japanese individuals and allele frequencies obtained using whole-genome sequencing of 8,380 Japanese individuals. In addition, the omics data include methylome and transcriptome data from ∼300 samples and distribution of concentrations of more than 755 metabolites obtained using high-throughput nuclear magnetic resonance and high-sensitivity mass spectrometry. In summary, jMorp now provides four different kinds of omics data (genome, methylome, transcriptome, and metabolome), with a user-friendly web interface. This will be a useful scientific data resource on the general population for the discovery of disease biomarkers and personalized disease prevention and early diagnosis.

HPLC-HRMS Global Metabolomics Approach for the Diagnosis of "Olive Quick Decline Syndrome" Markers in Olive Trees Leaves

Olive quick decline syndrome (OQDS) is a multifactorial disease affecting olive plants. The onset of this economically devastating disease has been associated with a Gram-negative plant pathogen called Xylella fastidiosa (Xf). Liquid chromatography separation coupled to high-resolution mass spectrometry detection is one the most widely applied technologies in metabolomics, as it provides a blend of rapid, sensitive, and selective qualitative and quantitative analyses with the ability to identify metabolites. The purpose of this work is the development of a global metabolomics mass spectrometry assay able to identify OQDS molecular markers that could discriminate between healthy (HP) and infected (OP) olive tree leaves. Results obtained via multivariate analysis through an HPLC-ESI HRMS platform (LTQ-Orbitrap from Thermo Scientific) show a clear separation between HP and OP samples. Among the differentially expressed metabolites, 18 different organic compounds highly expressed in the OP group were annotated; results obtained by this metabolomic approach could be used as a fast and reliable method for the biochemical characterization of OQDS and to develop targeted MS approaches for OQDS detection by foliage analysis.

A Protocol for Untargeted Metabolomic Analysis: From Sample Preparation to Data Processing

Untargeted metabolomics has rapidly become a profiling method of choice in many areas of research, including mitochondrial biology. Most commonly, untargeted metabolomics is performed with liquid chromatography/mass spectrometry because it enables measurement of a relatively wide range of physiochemically diverse molecules. Specifically, to assess energy pathways that are associated with mitochondrial metabolism, hydrophilic interaction liquid chromatography (HILIC) is often applied before analysis with a high-resolution accurate mass instrument. The workflow produces large, complex data files that are impractical to analyze manually. Here, we present a protocol to perform untargeted metabolomics on biofluids such as plasma, urine, and cerebral spinal fluid with a HILIC separation and an Orbitrap mass spectrometer. Our protocol describes each step of the analysis in detail, from preparation of solvents for chromatography to selecting parameters during data processing.

Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics

Metabolomics has been widely used for investigating the biological functions of disease expression and has the potential to discover biomarkers in circulating biofluids or tissue extracts that reflect in phenotypic changes. Metabolic profiling has advantages because of the use of unbiased techniques, including multivariate analysis, and has been applied in pharmacological studies to predict therapeutic and adverse reactions of drugs, which is called pharmacometabolomics (PMx). Nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based metabolomics has contributed to the discovery of recent disease biomarkers; however, the optimal strategy for the study purpose must be selected from many established protocols, methodologies and analytical platforms. Additionally, information on molecular localization in tissue is essential for further functional analyses related to therapeutic and adverse effects of drugs in the process of drug development. MS imaging (MSI) is a promising technology that can visualize molecules on tissue surfaces without labeling and thus provide localized information. This review summarizes recent uses of MS-based global and wide-targeted metabolomics technologies and the advantages of the MSI approach for PMx and highlights the PMx technique for the biomarker discovery of adverse drug effects.

Metabolomic Analysis to Elucidate Mechanisms of Sunitinib Resistance in Renal Cell Carcinoma

Metabolomics analysis possibly identifies new therapeutic targets in treatment resistance by measuring changes in metabolites accompanying cancer progression. We previously conducted a global metabolomics (G-Met) study of renal cell carcinoma (RCC) and identified metabolites that may be involved in sunitinib resistance in RCC. Here, we aimed to elucidate possible mechanisms of sunitinib resistance in RCC through intracellular metabolites. We established sunitinib-resistant and control RCC cell lines from tumor tissues of RCC cell (786-O)-injected mice. We also quantified characteristic metabolites identified in our G-Met study to compare intracellular metabolism between the two cell lines using liquid chromatography-mass spectrometry. The established sunitinib-resistant RCC cell line demonstrated significantly desuppressed protein kinase B (Akt) and mesenchymal-to-epithelial transition (MET) phosphorylation compared with the control RCC cell line under sunitinib exposure. Among identified metabolites, glutamine, glutamic acid, and α-KG (involved in glutamine uptake into the tricarboxylic acid (TCA) cycle for energy metabolism); fructose 6-phosphate, D-sedoheptulose 7-phosphate, and glucose 1-phosphate (involved in increased glycolysis and its intermediate metabolites); and glutathione and myoinositol (antioxidant effects) were significantly increased in the sunitinib-resistant RCC cell line. Particularly, glutamine transporter (SLC1A5) expression was significantly increased in sunitinib-resistant RCC cells compared with control cells. In this study, we demonstrated energy metabolism with glutamine uptake and glycolysis upregulation, as well as antioxidant activity, was also associated with sunitinib resistance in RCC cells.

A Customizable Analysis Flow in Integrative Multi-Omics

The number of researchers using multi-omics is growing. Though still expensive, every year it is cheaper to perform multi-omic studies, often exponentially so. In addition to its increasing accessibility, multi-omics reveals a view of systems biology to an unprecedented depth. Thus, multi-omics can be used to answer a broad range of biological questions in finer resolution than previous methods. We used six omic measurements—four nucleic acid (i.e., genomic, epigenomic, transcriptomics, and metagenomic) and two mass spectrometry (proteomics and metabolomics) based—to highlight an analysis workflow on this type of data, which is often vast. This workflow is not exhaustive of all the omic measurements or analysis methods, but it will provide an experienced or even a novice multi-omic researcher with the tools necessary to analyze their data. This review begins with analyzing a single ome and study design, and then synthesizes best practices in data integration techniques that include machine learning. Furthermore, we delineate methods to validate findings from multi-omic integration. Ultimately, multi-omic integration offers a window into the complexity of molecular interactions and a comprehensive view of systems biology.

Identification of critical genetic variants associated with metabolic phenotypes of the Japanese population

We performed a metabolome genome-wide association study for the Japanese population in the prospective cohort study of Tohoku Medical Megabank. By combining whole-genome sequencing and nontarget metabolome analyses, we identified a large number of novel associations between genetic variants and plasma metabolites. Of the identified metabolite-associated genes, approximately half have already been shown to be involved in various diseases. We identified metabolite-associated genes involved in the metabolism of xenobiotics, some of which are from intestinal microorganisms, indicating that the identified genetic variants also markedly influence the interaction between the host and symbiotic bacteria. We also identified five associations that appeared to be female-specific. A number of rare variants that influence metabolite levels were also found, and combinations of common and rare variants influenced the metabolite levels more profoundly. These results support our contention that metabolic phenotyping provides important insights into how genetic and environmental factors provoke human diseases.

Untargeted Metabolomic Profiling Using UHPLC-QTOF/MS Reveals Metabolic Alterations Associated with Autism

Autism spectrum disorder (ASD) is a clinical spectrum of neurodevelopment disorder characterized by deficits in social communication and social interaction along with repetitive/stereotyped behaviors. The current diagnosis for autism relies entirely on clinical evaluation and has many limitations. In this study, we aim to elucidate the potential mechanism behind autism and establish a series of potential biomarkers for diagnosis. Here, we established an ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry- (UHPLC-QTOF/MS-) based metabonomic approach to discriminate the metabolic modifications between the cohort of autism patients and the healthy subjects. UHPLC-QTOF/MS analysis revealed that 24 of the identified potential biomarkers were primarily involved in amino acid or lipid metabolism and the tryptophan kynurenine pathway. The combination of nicotinamide, anthranilic acid, D-neopterin, and 7,8-dihydroneopterin allows for discrimination between ASD patients and controls, which were validated in an independent autism case-control cohort. The results indicated that UHPLC-QTOF/MS-based metabolomics is capable of rapidly profiling autism metabolites and is a promising technique for the discovery of potential biomarkers related to autism.

Nrf2 contributes to the weight gain of mice during space travel

Space flight produces an extreme environment with unique stressors, but little is known about how our body responds to these stresses. While there are many intractable limitations for in-flight space research, some can be overcome by utilizing gene knockout-disease model mice. Here, we report how deletion of Nrf2, a master regulator of stress defense pathways, affects the health of mice transported for a stay in the International Space Station (ISS). After 31 days in the ISS, all flight mice returned safely to Earth. Transcriptome and metabolome analyses revealed that the stresses of space travel evoked ageing-like changes of plasma metabolites and activated the Nrf2 signaling pathway. Especially, Nrf2 was found to be important for maintaining homeostasis of white adipose tissues. This study opens approaches for future space research utilizing murine gene knockout-disease models, and provides insights into mitigating space-induced stresses that limit the further exploration of space by humans.

Using Nrf2 knockout mice, Suzuki, Uruno, Yumoto et al. show that space travel activates Nrf2 signaling, which contributes to the weight gain of mice by regulating fat metabolism of white adipose tissues. This study provides insights into potential interventions to mitigate stresses that accompany space travels.

Metabolomics Research Conducted by Nurse Scientists: A Systematic Scoping Review

Metabolomics, one of the newest omics, allows for investigation of holistic responses of living systems to myriad biological, behavioral, and environmental factors. Researcher use metabolomics to examine the underlying mechanisms of clinically observed phenotypes. However, these methods are complex, potentially impeding their uptake by scientists. In this scoping review, we summarize literature illustrating nurse scientists' use of metabolomics. Using electronic search methods, we identified metabolomics investigations conducted by nurse scientists and published in English-language journals between 1990 and November 2019. Of the studies included in the review (N = 30), 9 (30%) listed first and/or senior authors that were nurses. Studies were conducted predominantly in the United States and focused on a wide array of clinical conditions across the life span. The upward trend we note in the use of these methods by nurse scientists over the past 2 decades mirrors a similar trend across scientists of all backgrounds. A broad range of study designs were represented in the literature we reviewed, with the majority involving untargeted metabolomics (n = 16, 53.3%) used to generate hypotheses (n = 13, 76.7%) of potential metabolites and/or metabolic pathways as mechanisms of clinical conditions. Metabolomics methods match well with the unique perspective of nurse researchers, who seek to integrate the experiences of individuals to develop a scientific basis for clinical practice that emphasizes personalized approaches. Although small in number, metabolomics investigations by nurse scientists can serve as the foundation for robust programs of research to answer essential questions for nursing.

Unravelling the Potential of Salivary Volatile Metabolites in Oral Diseases. A Review

Fostered by the advances in the instrumental and analytical fields, in recent years the analysis of volatile organic compounds (VOCs) has emerged as a new frontier in medical diagnostics. VOCs analysis is a non-invasive, rapid and inexpensive strategy with promising potential in clinical diagnostic procedures. Since cellular metabolism is altered by diseases, the resulting metabolic effects on VOCs may serve as biomarkers for any given pathophysiologic condition. Human VOCs are released from biomatrices such as saliva, urine, skin emanations and exhaled breath and are derived from many metabolic pathways. In this review, the potential of VOCs present in saliva will be explored as a monitoring tool for several oral diseases, including gingivitis and periodontal disease, dental caries, and oral cancer. Moreover, the analytical state-of-the-art for salivary volatomics, e.g., the most common extraction techniques along with the current challenges and future perspectives will be addressed unequivocally.

Accurate quantification of urinary metabolites for predictive models manifest clinicopathology of renal cell carcinoma

Using surgically resected tissue, we identified characteristic metabolites related to the diagnosis and malignant status of clear cell renal cell carcinoma (ccRCC). Specifically, we quantified these metabolites in urine samples to evaluate their potential as clinically useful noninvasive biomarkers of ccRCC. Between January 2016 and August 2018, we collected urine samples from 87 patients who had pathologically diagnosed ccRCC and from 60 controls who were patients with benign urological conditions. Metabolite concentrations in urine samples were investigated using liquid chromatography‐mass spectrometry with an internal standard and adjustment based on urinary creatinine levels. We analyzed the association between metabolite concentration and predictability of diagnosis and of malignant status by multiple logistic regression and receiver operating characteristic (ROC) curves to establish ccRCC predictive models. Of the 47 metabolites identified in our previous study, we quantified 33 metabolites in the urine samples. Multiple logistic regression analysis revealed 5 metabolites (l‐glutamic acid, lactate, d‐sedoheptulose 7‐phosphate, 2‐hydroxyglutarate, and myoinositol) for a diagnostic predictive model and 4 metabolites (l‐kynurenine, l‐glutamine, fructose 6‐phosphate, and butyrylcarnitine) for a predictive model for clinical stage III/IV. The sensitivity and specificity of the diagnostic predictive model were 93.1% and 95.0%, respectively, yielding an area under the ROC curve (AUC) of 0.966. The sensitivity and specificity of the predictive model for clinical stage were 88.5% and 75.4%, respectively, with an AUC of 0.837. In conclusion, quantitative analysis of urinary metabolites yielded predictive models for diagnosis and malignant status of ccRCC. Urinary metabolites have the potential to be clinically useful noninvasive biomarkers of ccRCC to improve patient outcomes.

Identification of novel biomarkers of hepatocellular carcinoma by high-definition mass spectrometry: Ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry and desorption electrospray ionization mass spectrometry imaging

RATIONALE

Hepatocellular carcinoma (HCC) is a highly malignant disease for which the development of prospective or prognostic biomarkers is urgently required. Although metabolomics is widely used for biomarker discovery, there are some bottlenecks regarding the comprehensiveness of detected features, reproducibility of methods, and identification of metabolites. In addition, information on localization of metabolites in tumor tissue is needed for functional analysis. Here, we developed a wide-polarity global metabolomics (G-Met) method, identified HCC biomarkers in human liver samples by high-definition mass spectrometry (HDMS), and demonstrated localization in cryosections using desorption electrospray ionization MS imaging (DESI-MSI) analysis.

METHODS

Metabolic profiling of tumor (n = 38) and nontumor (n = 72) regions in human livers of HCC was performed by an ultrahigh-performance liquid chromatography quadrupole time-of-flight MS (UHPLC/QTOFMS) instrument equipped with a mixed-mode column. The HCC biomarker candidates were extracted by multivariate analyses and identified by matching values of the collision cross section and their fragment ions on the mass spectra obtained by HDMS. Cryosections of HCC livers, which included both tumor and nontumor regions, were analyzed by DESI-MSI.

RESULTS

From the multivariate analysis, m/z 904.83 and m/z 874.79 were significantly high and low, respectively, in tumor samples and were identified as triglyceride (TG) 16:0/18:1(9Z)/20:1(11Z) and TG 16:0/18:1(9Z)/18:2(9Z,12Z) using the synthetic compounds. The TGs were clearly localized in the tumor or nontumor areas of the cryosection.

CONCLUSIONS

Novel biomarkers for HCC were identified by a comprehensive and reproducible G-Met method with HDMS using a mixed-mode column. The combination analysis of UHPLC/QTOFMS and DESI-MSI revealed that the different molecular species of TGs were associated with tumor distribution and were useful for characterizing the progression of tumor cells and discovering prospective biomarkers.

Lipidome-based rapid diagnosis with machine learning for detection of TGF-β signalling activated area in head and neck cancer

Background

Several pro-oncogenic signals, including transforming growth factor beta (TGF-β) signalling from tumour microenvironment, generate intratumoural phenotypic heterogeneity and result in tumour progression and treatment failure. However, the precise diagnosis for tumour areas containing subclones with cytokine-induced malignant properties remains clinically challenging.

Methods

We established a rapid diagnostic system based on the combination of probe electrospray ionisation-mass spectrometry (PESI-MS) and machine learning without the aid of immunohistological and biochemical procedures to identify tumour areas with heterogeneous TGF-β signalling status in head and neck squamous cell carcinoma (HNSCC). A total of 240 and 90 mass spectra were obtained from TGF-β-unstimulated and -stimulated HNSCC cells, respectively, by PESI-MS and were used for the construction of a diagnostic system based on lipidome.

Results

This discriminant algorithm achieved 98.79% accuracy in discrimination of TGF-β1-stimulated cells from untreated cells. In clinical human HNSCC tissues, this approach achieved determination of tumour areas with activated TGF-β signalling as efficiently as a conventional histopathological assessment using phosphorylated-SMAD2 staining. Furthermore, several altered peaks on mass spectra were identified as phosphatidylcholine species in TGF-β-stimulated HNSCC cells.

Conclusions

This diagnostic system combined with PESI-MS and machine learning encourages us to clinically diagnose intratumoural phenotypic heterogeneity induced by TGF-β.

A pathogen-derived metabolite induces microglial activation via odorant receptors

Microglia (MG), the principal neuroimmune sentinels in the brain, continuously sense changes in their environment and respond to invading pathogens, toxins, and cellular debris, thereby affecting neuroinflammation. Microbial pathogens produce small metabolites that influence neuroinflammation, but the molecular mechanisms that determine whether pathogen-derived small metabolites affect microglial activation of neuroinflammation remain to be elucidated. We hypothesized that odorant receptors (ORs), the largest subfamily of G protein-coupled receptors, are involved in microglial activation by pathogen-derived small metabolites. We found that MG express high levels of two mouse ORs, Olfr110 and Olfr111, which recognize a pathogenic metabolite, 2-pentylfuran, secreted by Streptococcus pneumoniae. These interactions activate MG to engage in chemotaxis, cytokine production, phagocytosis, and reactive oxygen species generation. These effects were mediated through the G_αs -cyclic adenosine monophosphate-protein kinase A-extracellular signal-regulated kinase and G_βγ -phospholipase C-Ca²⁺ pathways. Taken together, our results reveal a novel interplay between the pathogen-derived metabolite and ORs, which has major implications for our understanding of microglial activation by pathogen recognition. DATABASE: Model data are available in the PMDB database under the accession number PM0082389.

Ferroptosis is controlled by the coordinated transcriptional regulation of glutathione and labile iron metabolism by the transcription factor BACH1

Ferroptosis is an iron-dependent programmed cell death event, whose regulation and physiological significance remain to be elucidated. Analyzing transcriptional responses of mouse embryonic fibroblasts exposed to the ferroptosis inducer erastin, here we found that a set of genes related to oxidative stress protection is induced upon ferroptosis. We considered that up-regulation of these genes attenuates ferroptosis induction and found that the transcription factor BTB domain and CNC homolog 1 (BACH1), a regulator in heme and iron metabolism, promotes ferroptosis by repressing the transcription of a subset of the erastin-induced protective genes. We noted that these genes are involved in the synthesis of GSH or metabolism of intracellular labile iron and include glutamate-cysteine ligase modifier subunit (Gclm), solute carrier family 7 member 11 (Slc7a11), ferritin heavy chain 1 (Fth1), ferritin light chain 1 (Ftl1), and solute carrier family 40 member 1 (Slc40a1). Ferroptosis has also been previously shown to induce cardiomyopathy, and here we observed that Bach1-/- mice are more resistant to myocardial infarction than WT mice and that the severity of ischemic injury is decreased by the iron-chelator deferasirox, which suppressed ferroptosis. Our findings suggest that BACH1 represses genes that combat labile iron-induced oxidative stress, and ferroptosis is stimulated at the transcriptional level by BACH1 upon disruption of the balance between the transcriptional induction of protective genes and accumulation of iron-mediated damage. We propose that BACH1 controls the threshold of ferroptosis induction and may represent a therapeutic target for alleviating ferroptosis-related diseases, including myocardial infarction.

LC/MS analysis of three-dimensional model cells exposed to cigarette smoke or aerosol from a novel tobacco vapor product

A novel tobacco vapor product (NTV) contains tobacco leaves and generates nicotine-containing aerosols using heating elements. Subchronic biological effects have been evaluated previously using three-dimensional bronchial epithelial model cells by repeated exposure to cigarette smoke (CS) and the NTV aerosols; however, the intracellular exposure characteristics have not been studied in detail. In this study, cells were initially exposed to an aqueous extract (AqE) of cigarette smoke (CS) at two concentration levels, and the cell lysate underwent untargeted analysis by LC-high resolution mass spectrometry to determine the exogenous compounds present in the cells. Among the thousands of peaks detected, four peaks showed a CS-dependency, which were reproducibly detected. Two of the peaks were nicotine and nicotine N-oxide, and the other two putative compounds were myosmine and norharman. The cells were then exposed to an AqE of CS in various combinations of exposure and post-exposure culture durations. Post-exposure culturing of cells with fresh medium markedly decreased the peak areas of the four compounds. The in-vitro switching study of CS to NTV aerosols was investigated by intermittently exposing cells to an AqE of CS four times, followed by exposure to either an AqE of CS, NTV aerosol or medium another four times. Switching to NTV reduced myosmine and norharman levels, which are known CS constituents. The results indicate that extracellular compounds inside cells reflect the exposure state outside cells. Thus, monitoring functional changes to cells in these exposure experiments is feasible.

Comprehensive and semi-quantitative analysis of carboxyl-containing metabolites related to gut microbiota on chronic kidney disease using 2-picolylamine isotopic labeling LC-MS/MS

Carboxyl-containing metabolites, such as bile acids and fatty acids, have many important functions and microbiota is involved in the production of them. In the previous study, we found that the chronic kidney disease (CKD) model mice raised under germ-free conditions provided more severe renal damage than the mice with commensal microbiota. However, the precise influence by the microbiome and carboxyl-containing metabolites to the renal functions is unknown. In this study, we aimed to develop a novel chemical isotope labeling-LC-MS/MS method using the 2-picolylamine and its isotopologue and applied the analysis of effects of microbiome and CKD pathophysiology. The developed semi-quantitative method provided the high accuracy not inferior to the absolute quantification. By comparing of four groups of mice, we found that both microbiota and renal function can alter the composition and level of these metabolites in both plasma and intestine. In particular, the intestinal level of indole-3-acetic acid, short-chain fatty acids and n-3 type of polyunsaturated fatty acid, which play important roles in the endothelial barrier function, were significantly lower in germ-free conditions mice with renal failure. Accordingly, it is suggested these metabolites might have a renoprotective effect on CKD by suppressing epithelial barrier disruption.

Biobank Establishment and Sample Management in the Tohoku Medical Megabank Project

The Tohoku Medical Megabank biobank (TMM biobank) is the first major population-based biobank established in Japan. The TMM biobank was established based on two population cohorts and is a reconstruction program from the Great East Japan Earthquake and Tsunami of 2011. The biobank stores more than 3.4 million tubes of biospecimens and associated health and analytic data obtained from approximately 150,000 TMM cohort participants between May 2013 and December 2018, and the TMM biobank currently shares high-quality specimens and data. Various biospecimens, including peripheral and cord blood mononuclear cells, buffy coat, plasma, serum, urine, breast milk and saliva have been collected in the TMM biobank. To minimize human error and maintain the quality of data and specimens, we have been utilizing laboratory information management system into various biobank procedures from registration to storage with various automation systems, such as liquid dispensing, DNA extraction and their storage. The biobank procedures for the quality management system (ISO 9001:2015) and information security management system (ISO 27001:2013) are certified by the International Organization for Standardization. The quality of our biobank samples fulfills the pre-analytical requirements for researchers conducting next-generation whole genome sequencing, DNA array analyses, proteomics, metabolomics, etc. We established analytical centers to conduct standard genomic and multiomic analyses in-house and share the generated data. Additionally, we generate thousands of Epstein-Barr virus (EBV)-transformed lymphoblastoid cell lines and proliferating T cells for functional studies. The TMM biobank serves as an indispensable infrastructure for academic, clinical and industrial research to actualize next-generation medicine in Japan.

jMorp: Japanese Multi Omics Reference Panel

We developed jMorp, a new database containing metabolome and proteome data for plasma obtained from >5000 healthy Japanese volunteers from the Tohoku Medical Megabank Cohort Study, which is available at https://jmorp.megabank.tohoku.ac.jp. Metabolome data were measured by proton nuclear magnetic resonance (NMR) and liquid chromatography–mass spectrometry (LC–MS), while proteome data were obtained by nanoLC–MS. We released the concentration distributions of 37 metabolites identified by NMR, distributions of peak intensities of 257 characterized metabolites by LC–MS, and observed frequencies of 256 abundant proteins. Additionally, correlation networks for the metabolites can be observed using an interactive network viewer. Compared with some existing databases, jMorp has some unique features: (i) Metabolome data were obtained using a single protocol in a single institute, ensuring that measurement biases were significantly minimized; (ii) The database contains large-scale data for healthy volunteers with various health records and genome data and (iii) Correlations between metabolites can be easily observed using the graphical viewer. Metabolites data are becoming important intermediate markers for evaluating the health states of humans, and thus jMorp is an outstanding resource for a wide range of researchers, particularly those in the fields of medical science, applied molecular biology, and biochemistry.

Validated comprehensive metabolomics and lipidomics analysis of colon tissue and cell lines

Current untargeted approaches for metabolic fingerprinting of colon tissue and cell lines lack validation of reproducibility and/or focus on a selection of metabolites as opposed to the entire metabolome. Yet, both are critical to ensure reliable results and pursue a fully holistic analysis. Therefore, we have optimized and validated a platform for analyzing the polar metabolome and lipidome of colon-derived cell and tissue samples based on a consecutive extraction of polar and apolar components. Peak areas of selected targeted analytes and the number of untargeted components were assessed. Analysis was performed using ultra-high performance liquid-chromatography (UHPLC) coupled to hybrid quadrupole-Orbitrap high-resolution mass spectrometry (HRMS). This resulted in an optimized extraction protocol using 50% methanol/ultrapure water to obtain the polar fraction followed by a dichloromethane-based lipid extraction. Using this comprehensive approach, we have detected more than 15,000 components with CV < 30% in internal quality control (IQC) samples and were able to discriminate the non-transformed (NT) and transformed (T) state in human colon tissue and cell lines based on validated OPLS-DA models (R²Y > 0.719 and Q² > 0.674). To conclude, our validated polar metabolomics and lipidomics fingerprinting approach could be of great value to reveal gastrointestinal disease-associated biomarkers and mechanisms.

Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy

PURPOSE

A prospective cohort study for pregnant women, the Maternity Log study, was designed to construct a time-course high-resolution reference catalogue of bioinformatic data in pregnancy and explore the associations between genomic and environmental factors and the onset of pregnancy complications, such as hypertensive disorders of pregnancy, gestational diabetes mellitus and preterm labour, using continuous lifestyle monitoring combined with multiomics data on the genome, transcriptome, proteome, metabolome and microbiome.

PARTICIPANTS

Pregnant women were recruited at the timing of first routine antenatal visits at Tohoku University Hospital, Sendai, Japan, between September 2015 and November 2016. Of the eligible women who were invited, 65.4% agreed to participate, and a total of 302 women were enrolled. The inclusion criteria were age ≥20 years and the ability to access the internet using a smartphone in the Japanese language.

FINDINGS TO DATE

Study participants uploaded daily general health information including quality of sleep, condition of bowel movements and the presence of nausea, pain and uterine contractions. Participants also collected physiological data, such as body weight, blood pressure, heart rate and body temperature, using multiple home healthcare devices. The mean upload rate for each lifelog item was ranging from 67.4% (fetal movement) to 85.3% (physical activity), and the total number of data points was over 6 million. Biospecimens, including maternal plasma, serum, urine, saliva, dental plaque and cord blood, were collected for multiomics analysis.

FUTURE PLANS

Lifelog and multiomics data will be used to construct a time-course high-resolution reference catalogue of pregnancy. The reference catalogue will allow us to discover relationships among multidimensional phenotypes and novel risk markers in pregnancy for the future personalised early prediction of pregnancy complications.

Value of global metabolomics in association with diagnosis and clinicopathological factors of renal cell carcinoma

Renal cell carcinoma (RCC) is a malignant tumor that currently lacks clinically useful biomarkers indicative of early diagnosis or disease status. RCC has commonly been diagnosed based on imaging results. Metabolomics offers a potential technology for discovering biomarkers and therapeutic targets by comprehensive screening of metabolites from patients with various cancers. We aimed to identify metabolites associated with early diagnosis and clinicopathological factors in RCC using global metabolomics (G-Met). Tumor and nontumor tissues were sampled from 20 cases of surgically resected clear cell RCC. G-Met was performed by liquid chromatography mass spectrometry and important metabolites specific to RCC were analyzed by multivariate statistical analysis for cancer diagnostic ability based on area under the curve (AUC) and clinicopathological factors (tumor volume, pathological T stage, Fuhrman grade, presence of coagulation necrosis and distant metastasis). We identified 58 metabolites showing significantly increased levels in tumor tissues, 34 of which showed potential early diagnostic ability (AUC >0.8), but 24 did not discriminate between tumor and nontumor tissues (AUC ≤0.8). We recognized 6 pathways from 9 metabolites with AUC >0.8 and 7 pathways from 10 metabolites with AUC ≤0.8 about malignant status. Clinicopathological factors involving malignant status correlated significantly with metabolites showing AUC ≤0.8 (p = 0.0279). The tricarboxylic acid cycle (TCA) cycle, TCA cycle intermediates, nucleotide sugar pathway and inositol pathway were characteristic pathways for the malignant status of RCC. In conclusion, our study found that metabolites and their pathways allowed discrimination between early diagnosis and malignant status in RCC according to our G-Met protocol.

Impact of post-collection freezing delay on the reliability of serum metabolomics in samples reflecting the California mid-term pregnancy biobank

BACKGROUND

Population-based biorepositories are important resources, but sample handling can affect data quality.

OBJECTIVE

Identify metabolites of value for clinical investigations despite extended postcollection freezing delays, using protocols representing a California mid-term pregnancy biobank.

METHODS

Blood collected from non-pregnant healthy female volunteers (n = 20) underwent three handling protocols after 30 min clotting at room temperature: (1) ideal-samples frozen (- 80 °C) within 2 h of collection; (2) delayed freezing-samples held at room temperature for 3 days, then 4 °C for 9 days, the median times for biobank samples, and then frozen; (3) delayed freezing with freeze-thaw-the delayed freezing protocol with a freeze-thaw cycle simulating retrieved sample sub-aliquoting. Mass spectrometry-based untargeted metabolomic analyses of primary metabolism and complex lipids and targeted profiling of oxylipins, endocannabinoids, ceramides/sphingoid-bases, and bile acids were performed. Metabolite concentrations and intraclass correlation coefficients (ICC) were compared, with the ideal protocol as the reference.

RESULTS

Sixty-two percent of 428 identified compounds had good to excellent ICCs, a metric of concordance between measurements of samples handled with the different protocols. Sphingomyelins, phosphatidylcholines, cholesteryl esters, triacylglycerols, bile acids and fatty acid diols were the least affected by non-ideal handling, while sugars, organic acids, amino acids, monoacylglycerols, lysophospholipids, N-acylethanolamides, polyunsaturated fatty acids, and numerous oxylipins were altered by delayed freezing. Freeze-thaw effects were assay-specific with lipids being most stable.

CONCLUSIONS

Despite extended post-collection freezing delays characteristic of some biobanks of opportunistically collected clinical samples, numerous metabolomic compounds had both stable levels and good concordance.

Metabolomic changes in the mouse retina after optic nerve injury

In glaucoma, although axonal injury drives retinal ganglion cell (RGC) death, little is known about the underlying pathomechanisms. To provide new mechanistic insights and identify new biomarkers, we combined latest non-targeting metabolomics analyses to profile altered metabolites in the mouse whole retina 2, 4, and 7 days after optic nerve crush (NC). Ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry and liquid chromatography Fourier transform mass spectrometry covering wide spectrum of metabolites in combination highlighted 30 metabolites that changed its concentration after NC. The analysis displayed similar changes for purine nucleotide and glutathione as reported previously in another animal model of axonal injury and detected multiple metabolites that increased after the injury. After studying the specificity of the identified metabolites to RGCs in histological sections using imaging mass spectrometry, two metabolites, i.e., L-acetylcarnitine and phosphatidylcholine were increased not only preceding the peak of RGC death in the whole retina but also at the RGC layer (2.3-fold and 1.2-fold, respectively). These phospholipids propose novel mechanisms of RGC death and may serve as early biomarkers of axonal injury. The combinatory metabolomics analyses promise to illuminate pathomechanisms, reveal biomarkers, and allow the discovery of new therapeutic targets of glaucoma.

Berberine Could Ameliorate Cardiac Dysfunction via Interfering Myocardial Lipidomic Profiles in the Rat Model of Diabetic Cardiomyopathy

Background: Diabetic cardiomyopathy (DCM) is considered to be a distinct clinical entity independent of concomitant macro- and microvascular disorders, which is initiated partly by disturbances in energy substrates. This study was to observe the dynamic modulations of berberine in DCM rats and explore the changes of lipidomic profiles of myocardial tissue. Methods: Sprague-Dawley (SD) rats were fed high-sucrose and high-fat diet (HSHFD) for totally 22 weeks and intraperitoneally (i.p.) injected with 30 mg/kg of streptozotocin (STZ) at the fifth week to induce DCM. Seventy-two hours after STZ injection, the rats were orally given with berberine at 10, 30 mg/kg and metformin at 200 mg/kg, respectively. Dynamic changes of cardiac function, heart mass ratios and blood lipids were observed at f 4, 10, 16, and 22, respectively. Furthermore, lipid metabolites in myocardial tissue at week 16 were profiled by the ultra-high-performance liquid chromatography coupled to a quadruple time of flight mass spectrometer (UPLC/Q-TOF/MS) approach. Results: Berberine could protect against cardiac diastolic and systolic dysfunctions, as well as cardiac hypertrophy, and the most effective duration is with 16-week of administration. Meanwhile, 17 potential biomarkers of phosphatidylcholines (PCs), phosphatidylethanolamines (PEs) and sphingolipids (SMs) of DCM induced by HSFD/STZ were identified. The perturbations of lipidomic profiles could be partly reversed with berberine intervention, i.e., PC (16:0/20:4), PC (18:2/0:0), PC (18:0/18:2), PC (18:0/22:5), PC (20:4/0:0), PC (20:4/18:0), PC (20:4/18:1), PC (20:4/20:2), PE (18:2/0:0), and SM (d18:0/16:0). Conclusions: These results indicated a close relationship between PCs, PEs and SMs and cardiac damage mechanisms during development of DCM. The therapeutic effects of berberine on DCM are partly caused by interferences with PCs, PEs, and SMs metabolisms.

Omics research project on prospective cohort studies from the Tohoku Medical Megabank Project

Population-based prospective cohort studies are indispensable for modern medical research as they provide important knowledge on the influences of many kinds of genetic and environmental factors on the cause of disease. Although traditional cohort studies are mainly conducted using questionnaires and physical examinations, modern cohort studies incorporate omics and genomic approaches to obtain comprehensive physical information, including genetic information. Here, we report the design and midterm results of multi-omics analysis on population-based prospective cohort studies from the Tohoku Medical Megabank (TMM) Project. We have incorporated genomic and metabolomic studies in the TMM cohort study as both metabolome and genome analyses are suitable for high-throughput analysis of large-scale cohort samples. Moreover, an association study between the metabolome and genome show that metabolites are an important intermediate phenotype connecting genetic and lifestyle factors to physical and pathologic phenotypes. We apply our metabolome and genome analyses to large-scale cohort samples in the following studies.