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.

Metabolic reprogramming in Nrf2-driven proliferation of normal rat hepatocytes

BACKGROUND AND AIMS

Cancer cells reprogram their metabolic pathways to support bioenergetic and biosynthetic needs and to maintain their redox balance. In several human tumors, the Keap1-Nrf2 system controls proliferation and metabolic reprogramming by regulating the pentose phosphate pathway (PPP). However, whether this metabolic reprogramming also occurs in normal proliferating cells is unclear.

APPROACH AND RESULTS

To define the metabolic phenotype in normal proliferating hepatocytes, we induced cell proliferation in the liver by 3 distinct stimuli: liver regeneration by partial hepatectomy and hepatic hyperplasia induced by 2 direct mitogens: lead nitrate (LN) or triiodothyronine. Following LN treatment, well-established features of cancer metabolic reprogramming, including enhanced glycolysis, oxidative PPP, nucleic acid synthesis, NAD + /NADH synthesis, and altered amino acid content, as well as downregulated oxidative phosphorylation, occurred in normal proliferating hepatocytes displaying Nrf2 activation. Genetic deletion of Nrf2 blunted LN-induced PPP activation and suppressed hepatocyte proliferation. Moreover, Nrf2 activation and following metabolic reprogramming did not occur when hepatocyte proliferation was induced by partial hepatectomy or triiodothyronine.

CONCLUSIONS

Many metabolic changes in cancer cells are shared by proliferating normal hepatocytes in response to a hostile environment. Nrf2 activation is essential for bridging metabolic changes with crucial components of cancer metabolic reprogramming, including the activation of oxidative PPP. Our study demonstrates that matured hepatocytes exposed to LN undergo cancer-like metabolic reprogramming and offers a rapid and useful in vivo model to study the molecular alterations underpinning the differences/similarities of metabolic changes in normal and neoplastic hepatocytes.

Dietary habits and plasma lipid concentrations in a general Japanese population

INTRODUCTION

Accumulating data on the associations between food consumption and lipid composition in the body is essential for understanding the effects of dietary habits on health.

OBJECTIVES

As part of omics research in the Tohoku Medical Megabank Community-Based Cohort Study, this study sought to reveal the dietary impact on plasma lipid concentration in a Japanese population.

METHODS

We conducted a correlation analysis of food consumption and plasma lipid concentrations measured using mass spectrometry, for 4032 participants in Miyagi Prefecture, Japan.

RESULTS

Our analysis revealed 83 marked correlations between six food categories and the concentrations of plasma lipids in nine subclasses. Previously reported associations, including those between seafood consumption and omega-3 fatty acids, were validated, while those between dairy product consumption and odd-carbon-number fatty acids (odd-FAs) were validated for the first time in an Asian population. Further analysis suggested that dairy product consumption is associated with odd-FAs via sphingomyelin (SM), which suggests that SM is a carrier of odd-FAs. These results are important for understanding odd-FA metabolism with regards to dairy product consumption.

CONCLUSION

This study provides insight into the dietary impact on plasma lipid concentration in a Japanese population.

Study Profile of the Tsuruoka Metabolomics Cohort Study (TMCS)

The Tsuruoka Metabolomics Cohort Study (TMCS) is an ongoing population-based cohort study being conducted in the rural area of Yamagata Prefecture, Japan. This study aimed to enhance the precision prevention of multi-factorial, complex diseases, including non-communicable and aging-associated diseases, by improving risk stratification and prediction measures. At baseline, 11,002 participants aged 35-74 years were recruited in Tsuruoka City, Yamagata Prefecture, Japan, between 2012 and 2015, with an ongoing follow-up survey. Participants underwent various measurements, examinations, tests, and questionnaires on their health, lifestyle, and social factors. This study used an integrative approach with deep molecular profiling to identify potential biomarkers linked to phenotypes that underpin disease pathophysiology and provide better mechanistic insights into social health determinants. The TMCS incorporates multi-omics data, including genetic and metabolomic analyses of 10,933 participants and comprehensive data collection ranging from physical, psychological, behavioral, and social to biological data. The metabolome is used as a phenotypic probe because it is sensitive to changes in physiological and external conditions. The TMCS focuses on collecting outcomes for cardiovascular disease, cancer incidence and mortality, disability, functional decline due to aging and disease sequelae, and the variation in health status within the body represented by omics analysis that lies between exposure and disease. It contains several sub-studies on aging, heated tobacco products, and women's health. This study is notable for its robust design, high participation rate (89%), and long-term repeated surveys. Moreover, it contributes to precision prevention in Japan and East Asia as a well-established multi-omics platform.

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.

Effect of Nicotinamide Mononucleotide Concentration in Human Milk on Neurodevelopmental Outcome: The Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study

(1) Background: Breast milk is the only source of nutrition for breastfed infants, but few studies have examined the relationship between breast milk micronutrients and infant neurodevelopmental outcome in exclusively breastfed infants. The aim of this study was to characterize the association between nicotinamide adenine dinucleotide (NAD)-related compounds in the breast milk of Japanese subjects and infant neurodevelopmental outcome. (2) Methods: A total of 150 mother-child pairs were randomly selected from the three-generation cohort of the Tohoku Medical Megabank in Japan. Infants were exclusively breastfed for up to 6 months. Breast milk was collected at 1 month postpartum, and the quantity of NAD-related substances in the breast milk was quantified. The mothers also completed developmental questionnaires at 6, 12, and 24 months. The relationship between the concentration of NAD-related substances in breast milk and developmental indicators was evaluated via ordinal logistic regression analysis. (3) Results: Nicotinamide mononucleotide (NMN) was quantified as the major NAD precursor in breast milk. The median amount of NMN in the breast milk was 9.2 μM. The NMN concentration in breast milk was the only NAD-related substance in breast milk that showed a significant positive correlation with neurodevelopmental outcome in infants at 24 months. (4) Conclusions: The results suggest that NMN in human milk may be an important nutrient for early childhood development.

Global Proteomics for Identifying the Alteration Pathway of Niemann-Pick Disease Type C Using Hepatic Cell Models

Niemann-Pick disease type C (NPC) is an autosomal recessive disorder with progressive neurodegeneration. Although the causative genes were previously identified, NPC has unclear pathophysiological aspects, and patients with NPC present various symptoms and onset ages. However, various novel biomarkers and metabolic alterations have been investigated; at present, few comprehensive proteomic alterations have been reported in relation to NPC. In this study, we aimed to elucidate proteomic alterations in NPC and perform a global proteomics analysis for NPC model cells. First, we developed two NPC cell models by knocking out NPC1 using CRISPR/Cas9 (KO1 and KO2). Second, we performed a label-free (LF) global proteomics analysis. Using the LF approach, more than 300 proteins, defined as differentially expressed proteins (DEPs), changed in the KO1 and/or KO2 cells, while the two models shared 35 DEPs. As a bioinformatics analysis, the construction of a protein-protein interaction (PPI) network and an enrichment analysis showed that common characteristic pathways such as ferroptosis and mitophagy were identified in the two model cells. There are few reports of the involvement of NPC in ferroptosis, and this study presents ferroptosis as an altered pathway in NPC. On the other hand, many other pathways and DEPs were previously suggested to be associated with NPC, supporting the link between the proteome analyzed here and NPC. Therapeutic research based on these results is expected in the future.

Identification of predictive biomarkers for endometrial cancer diagnosis and treatment response monitoring using plasma metabolome profiling

BACKGROUND

Endometrial cancer (EMC) is the most common female genital tract malignancy with an increasing prevalence in many countries including Japan, a fact that renders early detection and treatment necessary to protect health and fertility. Although early detection and treatment are necessary to further improve the prognosis of women with endometrial cancer, biomarkers that accurately reflect the pathophysiology of EMC patients are still unclear. Therefore, it is clinically critical to identify biomarkers to assess diagnosis and treatment efficacy to facilitate appropriate treatment and development of new therapies for EMC.

METHODS

In this study, wide-targeted plasma metabolome analysis was performed to identify biomarkers for EMC diagnosis and the prediction of treatment responses. The absolute quantification of 628 metabolites in plasma samples from 142 patients with EMC was performed using ultra-high-performance liquid chromatography with tandem mass spectrometry.

RESULTS

The concentrations of 111 metabolites increased significantly, while the concentrations of 148 metabolites decreased significantly in patients with EMC compared to healthy controls. Specifically, LysoPC and TGs, including unsaturated fatty acids, were reduced in patients with stage IA EMC compared to healthy controls, indicating that these metabolic profiles could be used as early diagnostic markers of EMC. In contrast, blood levels of amino acids such as histidine and tryptophan decreased as the risk of recurrence increased and the stages of EMC advanced. Furthermore, a marked increase in total TG and a decrease in specific TGs and free fatty acids including polyunsaturated fatty acids levels were observed in patients with EMC. These results suggest that the polyunsaturated fatty acids in patients with EMC are crucial for disease progression.

CONCLUSIONS

Our data identified specific metabolite profiles that reflect the pathogenesis of EMC and showed that these metabolites correlate with the risk of recurrence and disease stage. Analysis of changes in plasma metabolite profiles could be applied for the early diagnosis and monitoring of the course of treatment of EMC patients.

Identification of predictive biomarkers for diagnosis and radiation sensitivity of uterine cervical cancer using wide-targeted metabolomics

AIM

Uterine cervical cancer (UCC) is the fourth most common cancer in women, responsible for more than 300 000 deaths worldwide. Its early detection, by cervical cytology, and prevention, by vaccinating against human papilloma virus, greatly contribute to reducing cervical cancer mortality in women. However, penetration of the effective prevention of UCC in Japan remains low. Plasma metabolome analysis is widely used for biomarker discovery and the identification of cancer-specific metabolic pathways. Here, we aimed to identify predictive biomarkers for the diagnosis and radiation sensitivity of UCC using wide-targeted plasma metabolomics.

METHODS

We analyzed 628 metabolites in plasma samples obtained from 45 patients with UCC using ultra-high-performance liquid chromatography with tandem mass spectrometry.

RESULTS

The levels of 47 metabolites were significantly increased and those of 75 metabolites were significantly decreased in patients with UCC relative to healthy controls. Increased levels of arginine and ceramides, and decreased levels of tryptophan, ornithine, glycosylceramides, lysophosphatidylcholine, and phosphatidylcholine were characteristic of patients with UCC. Comparison of metabolite profiles in groups susceptible and non-susceptible to radiation therapy, a treatment for UCC, revealed marked variations in polyunsaturated fatty acid, nucleic acid, and arginine metabolism in the group not susceptible to treatment.

CONCLUSIONS

Our findings suggest that the metabolite profile of patients with UCC may be an important indicator for distinguishing these patients from healthy cohorts, and may also be useful for predicting sensitivity to radiotherapy.

Transcription factor c-Maf deletion improves streptozotocin-induced diabetic nephropathy by directly regulating Sglt2 and Glut2

The transcription factor c-Maf has been widely studied and has been reported to play a critical role in embryonic kidney development; however, the postnatal functions of c-Maf in adult kidneys remain unknown as c-Maf-null C57BL/6J mice exhibit embryonic lethality. In this study, we investigated the role of c-Maf in adult mouse kidneys by comparing the phenotypes of tamoxifen-inducible (TAM-inducible) c-Maf-knockout mice (c-Maffl/fl; CAG-Cre-ERTM mice named "c-MafΔTAM") with those of c-Maffl/fl control mice, 10 days after TAM injection [TAM(10d)]. In addition, we examined the effects of c-Maf deletion on diabetic conditions by injecting the mice with streptozotocin, 4 weeks before TAM injection. c-MafΔTAM mice displayed primary glycosuria caused by sodium-glucose cotransporter 2 (Sglt2) and glucose transporter 2 (Glut2) downregulation in the kidneys without diabetes, as well as morphological changes and life-threatening injuries in the kidneys on TAM(10d). Under diabetic conditions, c-Maf deletion promoted recovery from hyperglycemia and suppressed albuminuria and diabetic nephropathy by causing similar effects as did Sglt2 knockout and SGLT2 inhibitors. In addition to demonstrating the potentially unique gene regulation of c-Maf, these findings highlight the renoprotective effects of c-Maf deficiency under diabetic conditions and suggest that c-Maf could be a novel therapeutic target gene for treating diabetic nephropathy.

Decreased β-hydroxybutyrate and ketogenic amino acid levels in depressed human adults

β-hydroxybutyrate (BHB) is a major ketone body synthesized mainly in the liver mitochondria and is associated with stress and severity of depression in humans. It is known to alleviate depressive-like behaviors in mouse models of depression. In this study, plasma BHB, ketogenic and glucogenic amino acids selected from the Tohoku Medical Megabank Project Community-Based Cohort Study were analysed and measured using nuclear magnetic resonance spectroscopy. The Center for Epidemiologic Studies Depression Scale (CES-D) was utilized to select adult participants with depressive symptoms (CES-D ≥ 16; n = 5722) and control participants (CES-D < 16; n = 18,150). We observed significantly reduced plasma BHB, leucine, and tryptophan levels in participants with depressive symptoms. Using social defeat stress (SDS) mice models, we found that BHB levels in mice sera increased after acute SDS, but showed no change after chronic SDS, which differed from human plasma results. Furthermore, acute SDS increased mitochondrial BHB levels in the prefrontal cortex at 6 h. In contrast, chronic SDS significantly increased the amount of food intake but reduced hepatic mitochondrial BHB levels in mice. Moreover, gene transcriptions of voltage-dependent anion-selective channel 1 (Vdac1) and monocarboxylic acid transporter 1 (Mct1), major molecules relevant to mitochondrial biogenesis and BHB transporter, significantly decreased in the liver and PFC after chronic SDS exposure. These results provide evidence that hepatic and prefrontal mitochondrial biogenesis plays an important role in BHB synthesis under chronic stress and in humans with depressive symptoms.

Genome-wide association study of the risk of chronic kidney disease and kidney-related traits in the Japanese population: J-Kidney-Biobank

Chronic kidney disease (CKD) is a syndrome characterized by a gradual loss of kidney function with decreased estimated glomerular filtration rate (eGFR), which may be accompanied by an increase in the urine albumin-to-creatinine ratio (UACR). Although trans-ethnic genome-wide association studies (GWASs) have been conducted for kidney-related traits, there have been few analyses in the Japanese population, especially for the UACR trait. In this study, we conducted a GWAS to identify loci related to multiple kidney-related traits in Japanese individuals. First, to detect loci associated with CKD, eGFR, and UACR, we performed separate GWASs with the following two datasets: 475 cases of CKD diagnosed at seven university hospitals and 3471 healthy subjects (dataset 1) and 3664 cases of CKD-suspected individuals with eGFR <60 ml/min/1.73 m² or urinary protein ≥ 1+ and 5952 healthy subjects (dataset 2). Second, we performed a meta-analysis between these two datasets and detected the following associated loci: 10 loci for CKD, 9 loci for eGFR, and 22 loci for UACR. Among the loci detected, 22 have never been reported previously. Half of the significant loci for CKD were shared with those for eGFR, whereas most of the loci associated with UACR were different from those associated with CKD or eGFR. The GWAS of the Japanese population identified novel genetic components that were not previously detected. The results also suggest that the group primarily characterized by increased UACR possessed genetically different features from the group characterized by decreased eGFR.

Association Between Olfactory Test Data with Multiple Levels of Odor Intensity and Suspected Cognitive Impairment: A Cross-Sectional Study

BACKGROUND

Olfactory function decline has recently been reported to be associated with a risk of cognitive impairment. Few population-based studies have included younger adults when examining the association between olfactory test data with multiple odor intensities and suspected cognitive impairment.

OBJECTIVE

We investigated the association between high-resolution olfactory test data with fewer odors and suspected cognitive impairments. We also examined the differences between older and younger adults in this association.

METHODS

The Japanese version of the Montreal Cognitive Assessment (MoCA-J) was administered to 1,450 participants, with three odor-intensity-level olfactometry using six different odors. Logistic regressions to discriminate suspected cognitive impairment were conducted to examine the association, adjusted for age, sex, education duration, and smoking history. Data were collected from the Program by Tohoku University Tohoku Medical Megabank Organization, with an additional olfactory test conducted between 2019 and 2021.

RESULTS

We generally observed that the lower the limit of distinguishable odor intensity was, the higher the MoCA-J score was. The combination of spearmint and stuffy socks contributed most to the distinction between suspected and unsuspected cognitive impairment. Furthermore, the association was significant in women aged 60-74 years (adjusted odds ratio 0.881, 95% confidence interval [0.790, 0.983], p = 0.024).

CONCLUSIONS

The results indicate an association between the limit of distinguishable odor intensity and cognitive function. The olfactory test with multiple odor intensity levels using fewer odors may be applicable for the early detection of mild cognitive impairment, especially in older women aged 60-74 years.

Plasma metabolic disturbances during pregnancy and postpartum in women with depression

Examining plasma metabolic profiling during pregnancy and postpartum could help clinicians understand the risk factors for postpartum depression (PPD) development. This analysis targeted paired plasma metabolites in mid-late gestational and 1 month postpartum periods in women with (n = 209) or without (n = 222) PPD. Gas chromatogram-mass spectrometry was used to analyze plasma metabolites at these two time points. Among the 170 objected plasma metabolites, principal component analysis distinguished pregnancy and postpartum metabolites but failed to discriminate women with and without PPD. Compared to women without PPD, those with PPD exhibited 37 metabolites with disparate changes during pregnancy and the 1-month postpartum period and an enriched citrate cycle. Machine learning and multivariate statistical analysis identified two or three compounds that could be potential biomarkers for PPD prediction during pregnancy. Our findings suggest metabolic disturbances in women with depression and may help to elucidate metabolic processes associated with PPD development.

Esterification promotes the intracellular accumulation of roxadustat, an activator of hypoxia-inducible factors, to extend its effective duration

Kidney injury often causes anemia due to a lack of production of the erythroid growth factor erythropoietin (EPO) in the kidneys. Roxadustat is one of the first oral medicines inducing EPO production in patients with renal anemia by activating hypoxia-inducible factors (HIFs), which are activators of EPO gene expression. In this study, to develop prodrugs of roxadustat with improved permeability through cell membrane, we investigated the effects of 8 types of esterification on the pharmacokinetics and bioactivity of roxadustat using Hep3B hepatoma cells that HIF-dependently produce EPO. Mass spectrometry of cells incubated with the esterified roxadustat derivatives revealed that the designed compounds were deesterified after being taken up by cells and showed low cytotoxicity compared to the original compound. Esterification prolonged the effective duration of roxadustat with respect to EPO gene induction and HIF activation in cells transiently exposed to the compounds. In the kidneys and livers of mice, both of which are unique sites of EPO production, a majority of the methyl-esterified roxadustat was deesterified within 6 h after drug administration. The deesterified roxadustat derivative was continuously detectable in plasma and urine for at least 48 h after administration, while the administered compound became undetectable 24 h after administration. Additionally, we confirmed that methyl-esterified roxadustat activated erythropoiesis in mice by inducing Epo mRNA expression exclusively in renal interstitial cells, which have intrinsic EPO-producing potential. These data suggest that esterification could lead to the development of roxadustat prodrugs with improvements in cell membrane permeability, effective duration and cytotoxicity.

Nrf2 plays a critical role in the metabolic response during and after spaceflight

Space travel induces stresses that contribute to health problems, as well as inducing the expression of Nrf2 (NF-E2-related factor-2) target genes that mediate adaptive responses to oxidative and other stress responses. The volume of epididymal white adipose tissue (eWAT) in mice increases during spaceflight, a change that is attenuated by Nrf2 knockout. We conducted metabolome analyses of plasma from wild-type and Nrf2 knockout mice collected at pre-flight, in-flight and post-flight time points, as well as tissues collected post-flight to clarify the metabolic responses during and after spaceflight and the contribution of Nrf2 to these responses. Plasma glycerophospholipid and sphingolipid levels were elevated during spaceflight, whereas triacylglycerol levels were lower after spaceflight. In wild-type mouse eWAT, triacylglycerol levels were increased, but phosphatidylcholine levels were decreased, and these changes were attenuated in Nrf2 knockout mice. Transcriptome analyses revealed marked changes in the expression of lipid-related genes in the liver and eWAT after spaceflight and the effects of Nrf2 knockout on these changes. Based on these results, we concluded that space stress provokes significant responses in lipid metabolism during and after spaceflight; Nrf2 plays critical roles in these responses.

A cross-population atlas of genetic associations for 220 human phenotypes

Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan (n = 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen (n_total = 628,000) identified ~5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics.

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.

Identification and Validation of Combination Plasma Biomarker of Afamin, Fibronectin and Sex Hormone-Binding Globulin to Predict Pre-eclampsia

The purpose of the present study was to identify a plasma protein biomarker able to predict pre-eclampsia (PE). Comprehensive quantitative proteomics using mass spectrometry with sequential window acquisition of all theoretical fragment ion spectra (SWATH-MS) was applied to plasma samples of 7 PE and 14 healthy pregnant women (for PE subjects, plasma samples were taken before onset of PE), and 11 proteins were selected as candidates potentially able to differentiate the two groups. Plasmas collected at gestational weeks 14-24 from 36 PE and 120 healthy pregnant women (for PE subjects, plasma samples were taken before onset of PE) were used to conduct selected reaction monitoring quantification analysis, optimize protein combinations and conduct internal validation, which consisted of 30 iterations of 10-fold cross-validation using multivariate logistic regression and receiver operating characteristic (ROC) analysis. The combination of afamin, fibronectin, and sex-hormone-binding globulin was selected as the best candidate. The 3-protein combination predictive model (predictive equation and cut-off value) generated using the internal validation subjects was successfully validated in another group of validation subjects (36 PE and 54 healthy (for PE subjects, plasma samples were taken before onset of PE)) and showed good predictive performance, with the area under the curve (AUC) 0.835 and odds ratio 13.43. In conclusion, we newly identified a 3-protein combination biomarker and established a predictive equation and cut-off value that can predict the onset of PE based on analysis of plasma samples collected during gestational weeks 14-24.

Design and Progress of Oral Health Examinations in the Tohoku Medical Megabank Project

In order to assess the long-term impact of the Great East Japan Earthquake on the oral health of disaster victims and to evaluate gene-environmental interactions in the development of major oral diseases and oral-systemic associations, the oral part of two large-scale genome cohort studies by the Tohoku Medical Megabank Organization (ToMMo), including the Community-based cohort (CommCohort) study and the Birth and Three-Generation cohort (BirThree) study, have been conducted. The study population comprised 32,185 subjects, including 16,886 participants in the CommCohort study and 15,299 participants in the BirThree cohort study, recruited from 2013 to 2017. The oral studies consist of a questionnaire regarding oral hygiene behavior, clinical examinations by dentists, and oral plaque and saliva sampling for microbiome analyses, which were carried out at seven community support centers in Miyagi prefecture. The median age of all participants was 55.0 years, and 66.1% of participants were women. Almost all participants reported that they brushed their teeth more than once a day. The median number of present teeth was 27.0, and the decayed, missing and filled tooth number was 16.0, with a significant difference according to age and sex. The median periodontal pocket and clinical attachment level was 2.48 mm and 4.00 mm, respectively. Periodontal parameters increased significantly according to age, except for the accumulation of dental calculus. The oral part of these extensive cross-sectional studies provides a unique and important platform for future studies on oral health and diseases that elicit through interactions with systemic diseases, lifestyles, life events and genetic backgrounds, and contributes to researches clarifying the long-term effects of disasters on oral health.

Molecular basis for the disruption of Keap1-Nrf2 interaction via Hinge & Latch mechanism

The Keap1-Nrf2 system is central for mammalian cytoprotection against various stresses and a drug target for disease prevention and treatment. One model for the molecular mechanisms leading to Nrf2 activation is the Hinge-Latch model, where the DLGex-binding motif of Nrf2 dissociates from Keap1 as a latch, while the ETGE motif remains attached to Keap1 as a hinge. To overcome the technical difficulties in examining the binding status of the two motifs during protein-protein interaction (PPI) simultaneously, we utilized NMR spectroscopy titration experiments. Our results revealed that latch dissociation is triggered by low-molecular-weight Keap1-Nrf2 PPI inhibitors and occurs during p62-mediated Nrf2 activation, but not by electrophilic Nrf2 inducers_. This study demonstrates that Keap1 utilizes a unique Hinge-Latch mechanism for Nrf2 activation upon challenge by non-electrophilic PPI-inhibiting stimuli, and provides critical insight for the pharmacological development of next-generation Nrf2 activators targeting the Keap1-Nrf2 PPI.

Using NMR spectroscopy, Horie, Suzuki, Inoue et al. show that the dissociation of Keap1 from Nrf2, or the Hinge-Latch mechanism, is triggered by Keap1-Nrf2 inhibitors and occurs during p62- mediated Nrf2 activation, but not by electrophilic Nrf2 inducers. This study provides insights into the design of Nrf2 activators targeting the Keap1-Nrf2 interaction.

Japonica Array NEO with increased genome-wide coverage and abundant disease risk SNPs

Ethnic-specific SNP arrays are becoming more important to increase the power of genome-wide association studies in diverse population. In the Tohoku Medical Megabank Project, we have been developing a series of Japonica Arrays (JPA) for genotyping participants based on reference panels constructed from whole-genome sequence data of the Japanese population. Here, we designed a novel version of the SNP array for the Japanese population, called Japonica Array NEO (JPA NEO), comprising a total of 666,883 markers. Among them, 654,246 tag SNPs of autosomes and X chromosome were selected from an expanded reference panel of 3,552 Japanese, 3.5KJPNv2, using pairwise r² of linkage disequilibrium measures. Additionally, 28,298 markers were included for the evaluation of previously identified disease risk markers from the literature and databases, and those present in the Japanese population were extracted using the reference panel. Through genotyping 286 Japanese samples, we found that the imputation quality r² and INFO score in the minor allele frequency bin >2.5–5% were >0.9 and >0.8, respectively, and >12 million markers were imputed with an INFO score >0.8. From these results, JPA NEO is a promising tool for genotyping the Japanese population with genome-wide coverage, contributing to the development of genetic risk scores.

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.

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.

Metabolic Profiling of the Cerebrospinal Fluid in Pediatric Epilepsy

To characterize metabolic profiles within the central nervous system in epilepsy, we performed gas chromatography-tandem mass spectrometry (GC-MS/MS)-based metabolome analysis of the cerebrospinal fluid (CSF) in pediatric patients with and without epilepsy. The CSF samples obtained from 64 patients were analyzed by GC-MS/MS. Multivariate analyses were performed for two age groups, 0-5 years of age and 6-17 years of age, to elucidate the effects of epilepsy and antiepileptic drugs on the metabolites. In patients aged 0-5 years (22 patients with epilepsy, 13 without epilepsy), epilepsy patients had reduced 2-ketoglutaric acid and elevated pyridoxamine and tyrosine. In patients aged 6-17 years (12 with epilepsy, 17 without epilepsy), epilepsy patients had reduced 1,5-anhydroglucitol. Valproic acid was associated with elevated 2-aminobutyric acid, 2-ketoisocaproic acid, 4-hydroxyproline, acetylglycine, methionine, N-acetylserine, and serine. Reduced energy metabolism and alteration of vitamin B6 metabolism may play a role in epilepsy in young children. The roles of 1,5-anhydroglucitol in epilepsy in older children and in levetiracetam and zonisamide treatment remain to be explained. Valproic acid influenced the levels of amino acids and related metabolites involved in the metabolism of serine, methionine, and leucine.

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.

Correction: Biallelic GALM pathogenic variants cause a novel type of galactosemia

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection

To solve major limitations in algorithms for the metabolite-based prediction of psychiatric phenotypes, a novel prediction model for depressive symptoms based on nonlinear feature selection machine learning, the Hilbert-Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) algorithm, was developed and applied to a metabolomic dataset with the largest sample size to date. In total, 897 population-based subjects were recruited from the communities affected by the Great East Japan Earthquake; 306 metabolite features (37 metabolites identified by nuclear magnetic resonance measurements and 269 characterized metabolites based on the intensities from mass spectrometry) were utilized to build prediction models for depressive symptoms as evaluated by the Center for Epidemiologic Studies-Depression Scale (CES-D). The nested fivefold cross-validation was used for developing and evaluating the prediction models. The HSIC Lasso-based prediction model showed better predictive power than the other prediction models, including Lasso, support vector machine, partial least squares, random forest, and neural network. L-leucine, 3-hydroxyisobutyrate, and gamma-linolenyl carnitine frequently contributed to the prediction. We have demonstrated that the HSIC Lasso-based prediction model integrating nonlinear feature selection showed improved predictive power for depressive symptoms based on metabolome data as well as on risk metabolites based on nonlinear statistics in the Japanese population. Further studies should use HSIC Lasso-based prediction models with different ethnicities to investigate the generality of each risk metabolite for predicting depressive symptoms.

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.

Amino-acid selective isotope labeling enables simultaneous overlapping signal decomposition and information extraction from NMR spectra

Signal overlapping is a major bottleneck for protein NMR analysis. We propose a new method, stable-isotope-assisted parameter extraction (SiPex), to resolve overlapping signals by a combination of amino-acid selective isotope labeling (AASIL) and tensor decomposition. The basic idea of Sipex is that overlapping signals can be decomposed with the help of intensity patterns derived from quantitative fractional AASIL, which also provides amino-acid information. In SiPex, spectra for protein characterization, such as ¹⁵N relaxation measurements, are assembled with those for amino-acid information to form a four-order tensor, where the intensity patterns from AASIL contribute to high decomposition performance even if the signals share similar chemical shift values or characterization profiles, such as relaxation curves. The loading vectors of each decomposed component, corresponding to an amide group, represent both the amino-acid and relaxation information. This information link provides an alternative protein analysis method that does not require "assignments" in a general sense; i.e., chemical shift determinations, since the amino-acid information for some of the residues allows unambiguous assignment according to the dual selective labeling. SiPex can also decompose signals in time-domain raw data without Fourier transform, even in non-uniformly sampled data without spectral reconstruction. These features of SiPex should expand biological NMR applications by overcoming their overlapping and assignment problems.

Study Profile of the Tohoku Medical Megabank Community-Based Cohort Study

Background

We established a community-based cohort study to assess the long-term impact of the Great East Japan Earthquake on disaster victims and gene-environment interactions on the incidence of major diseases, such as cancer and cardiovascular diseases.

Methods

We asked participants to join our cohort in the health check-up settings and assessment center based settings. Inclusion criteria were aged 20 years or over and living in Miyagi or Iwate Prefecture. We obtained information on lifestyle, effect of disaster, blood, and urine information (Type 1 survey), and some detailed measurements (Type 2 survey), such as carotid echography and calcaneal ultrasound bone mineral density. All participants agreed to measure genome information and to distribute their information widely.

Results

As a result, 87,865 gave their informed consent to join our study. Participation rate at health check-up site was about 70%. The participants in the Type 1 survey were more likely to have psychological distress than those in the Type 2 survey, and women were more likely to have psychological distress than men. Additionally, coastal residents were more likely to have higher degrees of psychological distress than inland residents, regardless of sex.

Conclusion

This cohort comprised a large sample size and it contains information on the natural disaster, genome information, and metabolome information. This cohort also had several detailed measurements. Using this cohort enabled us to clarify the long-term effect of the disaster and also to establish personalized prevention based on genome, metabolome, and other omics information.

Maternal Baseline Characteristics and Perinatal Outcomes: The Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study

Background

The Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study was launched in 2013 to evaluate the complex interactions of genetic and environmental factors in multifactorial diseases. The present study describes the maternal baseline profile and perinatal data of participating mothers and infants.

Methods

Expectant mothers living in Miyagi Prefecture were recruited from obstetric facilities or affiliated centers between 2013 and 2017. Three sets of self-administered questionnaires were collected, and the medical records were reviewed to obtain precise information about each antenatal visit and each delivery. Biospecimens, including blood, urine, umbilical cord blood, and breast milk, were collected for the study biobank. The baseline maternal sociodemographic characteristics, results of screening tests, and obstetric outcomes were analyzed according to the maternal age group.

Results

A total of 23,406 pregnancies involving 23,730 fetuses resulted in 23,143 live births. Younger maternal participants had a tendency toward a higher incidence of threatened abortion and threatened premature labor, while older age groups exhibited a significantly higher rate of low lying placenta, placenta previa, gestational diabetes, and hypertensive disorders of pregnancy.

Conclusions

The present study clearly shows the distribution of maternal baseline characteristics and the range of perinatal outcomes according to maternal age group. This cohort study can provide strategic information for creating breakthroughs in the pathophysiology of perinatal, developmental, and noncommunicable diseases by collaborative data visiting or sharing.

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.

Genome analyses for the Tohoku Medical Megabank Project towards establishment of personalized healthcare

Personalized healthcare (PHC) based on an individual's genetic make-up is one of the most advanced, yet feasible, forms of medical care. The Tohoku Medical Megabank (TMM) Project aims to combine population genomics, medical genetics and prospective cohort studies to develop a critical infrastructure for the establishment of PHC. To date, a TMM CommCohort (adult general population) and a TMM BirThree Cohort (birth+three-generation families) have conducted recruitments and baseline surveys. Genome analyses as part of the TMM Project will aid in the development of a high-fidelity whole-genome Japanese reference panel, in designing custom single-nucleotide polymorphism (SNP) arrays specific to Japanese, and in estimation of the biological significance of genetic variations through linked investigations of the cohorts. Whole-genome sequencing from >3,500 unrelated Japanese and establishment of a Japanese reference genome sequence from long-read data have been done. We next aim to obtain genotype data for all TMM cohort participants (>150,000) using our custom SNP arrays. These data will help identify disease-associated genomic signatures in the Japanese population, while genomic data from TMM BirThree Cohort participants will be used to improve the reference genome panel. Follow-up of the cohort participants will allow us to test the genetic markers and, consequently, contribute to the realization of PHC.

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.

Biallelic GALM pathogenic variants cause a novel type of galactosemia

PURPOSE

Galactosemia is caused by metabolic disturbances at various stages of galactose metabolism, including deficiencies in enzymes involved in the Leloir pathway (GALT, GALK1, and GALE). Nevertheless, the etiology of galactosemia has not been identified in a subset of patients. This study aimed to explore the causes of unexplained galactosemia.

METHODS

Trio-based exome sequencing and/or Sanger sequencing was performed in eight patients with unexplained congenital galactosemia. In vitro enzymatic assays and immunoblot assays were performed to confirm the pathogenicity of the variants.

RESULTS

The highest blood galactose levels observed in each patient were 17.3-41.9 mg/dl. Bilateral cataracts were observed in two patients. In all eight patients, we identified biallelic variants (p.Arg82*, p.Ile99Leufs*46, p.Gly142Arg, p.Arg267Gly, and p.Trp311*) in the GALM encoding galactose mutarotase, which catalyzes epimerization between β- and α-D-galactose in the first step of the Leloir pathway. GALM enzyme activities were undetectable in lymphoblastoid cell lines established from two patients. Immunoblot analysis showed the absence of the GALM protein in the patients' peripheral blood mononuclear cells. In vitro GALM expression and protein stability assays revealed altered stabilities of the variant GALM proteins.

CONCLUSION

Biallelic GALM pathogenic variants cause galactosemia, suggesting the existence of type IV galactosemia.

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.

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.

Detection of novel metabolite for Roxadustat doping by global metabolomics

Roxadustat (FG-4592, Rox) is a stabilizer for hypoxia-inducible transcription factors (HIFs), which induce production of the erythroid growth factor erythropoietin, and has been listed by the World Anti-Doping Agency as a prohibited substance for athletes since 2011. Although the detection technologies for Rox and its glucuronide-conjugated metabolite (Rox-Gluc) have been developed exploiting triple quadrupole mass spectrometry (MS/MS), the production of metabolites from Rox in the human body remains to be clarified. Here, we established a protocol for the detection of unknown metabolites in plasma and urine samples from Rox-doping mice by global metabolomics using an ultra high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS). We identified methylated Rox (Rox-Methyl), a novel metabolite, and Rox-Gluc in mouse urine by principal component analysis and orthogonal partial least squares discriminant analysis based on detected features by UHPLC-QTOF/MS analysis. The estimated pharmacokinetic parameters of Rox-Methyl and Rox-Gluc in mouse plasma showed similar profiles to that of Rox and both compounds showed similar biological activities. Of note, Rox-Methyl showed shorter half-life than Rox-Gluc in vivo, implying the easy escape from anti-doping screen. These results demonstrate that the global metabolomics method introduced in this study will contribute to the identification and detection of HIF-analog doping.

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

Metabolomics is a promising avenue for biomarker discovery. Although the quality of metabolomic analyses, especially global metabolomics (G-Met) using mass spectrometry (MS), largely depends on the instrumentation, potential bottlenecks still exist at several basic levels in the metabolomics workflow. Therefore, we established a precise protocol initially for the G-Met analyses of human blood plasma to overcome some these difficulties. In our protocol, samples are deproteinized in a 96-well plate using an automated liquid-handling system, and conducted either using a UHPLC-QTOF/MS system equipped with a reverse phase column or a LC-FTMS system equipped with a normal phase column. A normalization protocol of G-Met data was also developed to compensate for intra- and inter-batch differences, and the variations were significantly reduced along with our normalization, especially for the UHPLC-QTOF/MS data with a C18 reverse-phase column for positive ions. Secondly, we examined the changes in metabolomic profiles caused by the storage of EDTA-blood specimens to identify quality markers for the evaluation of the specimens’ pre-analytical conditions. Forty quality markers, including lysophospholipids, dipeptides, fatty acids, succinic acid, amino acids, glucose, and uric acid were identified by G-Met for the evaluation of plasma sample quality and established the equation of calculating the quality score. We applied our quality markers to a small-scale study to evaluate the quality of clinical samples. The G-Met protocols and quality markers established here should prove useful for the discovery and development of biomarkers for a wider range of diseases.

The structural origin of metabolic quantitative diversity

Relationship between structural variants of enzymes and metabolic phenotypes in human population was investigated based on the association study of metabolite quantitative traits with whole genome sequence data for 512 individuals from a population cohort. We identified five significant associations between metabolites and non-synonymous variants. Four of these non-synonymous variants are located in enzymes involved in metabolic disorders, and structural analyses of these moderate non-synonymous variants demonstrate that they are located in peripheral regions of the catalytic sites or related regulatory domains. In contrast, two individuals with larger changes of metabolite levels were also identified, and these individuals retained rare variants, which caused non-synonymous variants located near the catalytic site. These results are the first demonstrations that variant frequency, structural location, and effect for phenotype correlate with each other in human population, and imply that metabolic individuality and susceptibility for diseases may be elicited from the moderate variants and much more deleterious but rare variants.

The Tohoku Medical Megabank Project: Design and Mission

The Great East Japan Earthquake (GEJE) and resulting tsunami of March 11, 2011 gave rise to devastating damage on the Pacific coast of the Tohoku region. The Tohoku Medical Megabank Project (TMM), which is being conducted by Tohoku University Tohoku Medical Megabank Organization (ToMMo) and Iwate Medical University Iwate Tohoku Medical Megabank Organization (IMM), has been launched to realize creative reconstruction and to solve medical problems in the aftermath of this disaster. We started two prospective cohort studies in Miyagi and Iwate Prefectures: a population-based adult cohort study, the TMM Community-Based Cohort Study (TMM CommCohort Study), which will recruit 80 000 participants, and a birth and three-generation cohort study, the TMM Birth and Three-Generation Cohort Study (TMM BirThree Cohort Study), which will recruit 70 000 participants, including fetuses and their parents, siblings, grandparents, and extended family members. The TMM CommCohort Study will recruit participants from 2013 to 2016 and follow them for at least 5 years. The TMM BirThree Cohort Study will recruit participants from 2013 to 2017 and follow them for at least 4 years. For children, the ToMMo Child Health Study, which adopted a cross-sectional design, was also started in November 2012 in Miyagi Prefecture. An integrated biobank will be constructed based on the two prospective cohort studies, and ToMMo and IMM will investigate the chronic medical impacts of the GEJE. The integrated biobank of TMM consists of health and clinical information, biospecimens, and genome and omics data. The biobank aims to establish a firm basis for personalized healthcare and medicine, mainly for diseases aggravated by the GEJE in the two prefectures. Biospecimens and related information in the biobank will be distributed to the research community. TMM itself will also undertake genomic and omics research. The aims of the genomic studies are: 1) to construct an integrated biobank; 2) to return genomic research results to the participants of the cohort studies, which will lead to the implementation of personalized healthcare and medicine in the affected areas in the near future; and 3) to contribute the development of personalized healthcare and medicine worldwide. Through the activities of TMM, we will clarify how to approach prolonged healthcare problems in areas damaged by large-scale disasters and how useful genomic information is for disease prevention.

Stable isotope labeling strategy based on coding theory

We describe a strategy for stable isotope-aided protein nuclear magnetic resonance (NMR) analysis, called stable isotope encoding. The basic idea of this strategy is that amino-acid selective labeling can be considered as "encoding and decoding" processes, in which the information of amino acid type is encoded by the stable isotope labeling ratio of the corresponding residue and it is decoded by analyzing NMR spectra. According to the idea, the strategy can diminish the required number of labelled samples by increasing information content per sample, enabling discrimination of 19 kinds of non-proline amino acids with only three labeled samples. The idea also enables this strategy to combine with information technologies, such as error detection by check digit, to improve the robustness of analyses with low quality data. Stable isotope encoding will facilitate NMR analyses of proteins under non-ideal conditions, such as those in large complex systems, with low-solubility, and in living cells.

Solution structures of the DNA-binding domains of immune-related zinc-finger protein ZFAT

ZFAT is a transcriptional regulator, containing eighteen C₂H₂-type zinc-fingers and one AT-hook, involved in autoimmune thyroid disease, apoptosis, and immune-related cell survival. We determined the solution structures of the thirteen individual ZFAT zinc-fingers (ZF) and the tandemly arrayed zinc-fingers in the regions from ZF2 to ZF5, by NMR spectroscopy. ZFAT has eight uncommon bulged-out helix-containing zinc-fingers, and six of their structures (ZF4, ZF5, ZF6, ZF10, ZF11, and ZF13) were determined. The distribution patterns of the putative DNA-binding surface residues are different among the ZFAT zinc-fingers, suggesting the distinct DNA sequence preferences of the N-terminal and C-terminal zinc-fingers. Since ZFAT has three to five consecutive tandem zinc-fingers, which may cooperatively function as a unit, we also determined two tandemly arrayed zinc-finger structures, between ZF2 to ZF4 and ZF3 to ZF5. Our NMR spectroscopic analysis detected the interaction between ZF4 and ZF5, which are connected by an uncommon linker sequence, KKIK. The ZF4–ZF5 linker restrained the relative structural space between the two zinc-fingers in solution, unlike the other linker regions with determined structures, suggesting the involvement of the ZF4–ZF5 interfinger linker in the regulation of ZFAT function.