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33S nuclear magnetic resonance (NMR) spectroscopy is limited by inherently low NMR sensitivity because of the quadrupolar moment and low gyromagnetic ratio of the 33S nucleus. We have developed a 10 mm 33S cryogenic NMR probe, which is operated at 9–26 K with a cold preamplifier and a cold rf switch operated at 60 K. The 33S NMR sensitivity of the cryogenic probe is as large as 9.8 times that of a conventional 5 mm broadband NMR probe. The 33S cryogenic probe was applied to biological samples such as human urine, bile, chondroitin sulfate, and scallop tissue. We demonstrated that the system can detect and determine sulfur compounds having SO42- anions and –SO3- groups using the 33S cryogenic probe, as the 33S nuclei in these groups are in highly symmetric environments. The NMR signals for other common sulfur compounds such as cysteine are still undetectable by the 33S cryogenic probe, as the 33S nuclei in these compounds are in asymmetric environments. If we shorten the rf pulse width or decrease the rf coil diameter, we should be able to detect the NMR signals for these compounds. [ABSTRACT FROM AUTHOR]

Describes the solution structure of the mouse enhancer of rudimentary (ER) protein determined by using heteronuclear nuclear magnetic resonance spectroscopy. Role of the regulatory or enzymatic activity of ER in pyrimidine biosynthesis and the cell cycle; Dimer or trimer behavior of ER in solution.

The BolA-like proteins are widely conserved from prokaryotes to eukaryotes. The BolA-like proteins seem to be involved in cell proliferation or cell-cycle regulation, but the molecular function is still unknown. Here we determined the structure of a mouse BolA-like protein. The overall topology is αββααβα, in which β1 and β2 are antiparallel, and β3 is parallel to β2. This fold is similar to the class II KH fold, except for the absence of the GXXG loop, which is well conserved in the KH fold. The conserved residues in the BolA-like proteins are assembled on the one side of the protein. [ABSTRACT FROM AUTHOR]

The first step towards realizing personalized healthcare is to catalog the genetic variations in a population. Since the dissemination of individual-level genomic information is strictly controlled, it will be useful to construct population-level allele frequency panels with easy-to-use interfaces. In the Tohoku Medical Megabank Project, we sequenced nearly 4000 individuals from a Japanese population and constructed an allele frequency panel of 3552 individuals after removing related samples. The panel is called the 3.5KJPNv2. It was constructed by using a standard pipeline including the 1KGP and gnomAD algorithms to reduce technical biases and to allow comparisons to other populations. Our database is the first large-scale panel providing the frequencies of variants present on the X chromosome and on the mitochondria in the Japanese population. All the data are available on our original database at https://jmorp.megabank.tohoku.ac.jp. Population genetics: large database of Japanese gene variations constructed A new database provides information on the frequency of genetic variations within 3552 Japanese individuals, and facilitates comparisons with other populations. The reference panel, constructed by Kengo Kinoshita of Tohoku University, Sendai, and colleagues in Japan is also the first large-scale database to provide genetic variation frequency information on the X chromosome and mitochondrial DNA in the Japanese population. The methods used to sequence the genetic data are similar to those used in other large databases, allowing comparisons with other populations. The population size and methods used to compile the database overcome limitations in previous Japanese reference panels. This and similar databases that catalog genetic variations within populations can improve efforts towards personalizing healthcare and contribute to the study of human population genetics. The database is publicly available online. [ABSTRACT FROM AUTHOR]

Presents a letter to the editor about nuclear magnetic resonance assignment of the hypothetical ENTH-VHS domain At3g16270 from arabidopsis thaliana.

Background: Dementia 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., Methods: This 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., Results: A 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)., Conclusion: Amino acids are protectively associated with cognitive decline, whereas ketone metabolites are associated with higher odds of cognitive decline.

Purpose: The purpose of this study was to investigate biologically meaningful endotypes of open-angle glaucoma (OAG) by applying unsupervised machine learning to plasma metabolites., Methods: This retrospective longitudinal cohort study enrolled consecutive patients aged ≥20 years with OAG at Tohoku University Hospital from January 2017 to January 2020. OAG was confirmed based on comprehensive ophthalmic examinations. Among the 523 patients with OAG with available clinical metabolomic data, 173 patients were longitudinally followed up for ≥2 years, with available data from ≥5 reliable visual field (VF) tests without glaucoma surgery. We collected fasting blood samples and clinical data at enrollment and nuclear magnetic resonance spectroscopy to profile 45 plasma metabolites in a targeted approach. After computing a distance matrix of preprocessed metabolites with Pearson distance, gap statistics determined the optimal number of OAG endotypes. Its risk factors, clinical presentations, metabolomic profiles, and progression rate of sector-based VF loss were compared across endotypes., Results: Five distinct OAG endotypes were identified. The highest-risk endotype (endotype B) showed a significant faster progression of central VF loss (P = 0.007). Compared with patients with other endotypes, those with endotype B were more likely to have a high prevalence of dyslipidemia, cold extremities, oxidative stress, and low OAG genetic risk scores. Pathway analysis of metabolomic profiles implicated altered fatty acid and ketone body metabolism in this endotype, with 34 differentially enriched pathways (false discovery rate [FDR] < 0.05)., Conclusions: Integrated metabolomic profiles identified five distinct etiologic endotypes of OAG, suggesting pathological mechanisms related with a high-risk group of central vision loss progression in the Japanese population.

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., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

(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.

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.

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.

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.

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 15 N 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.

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.

Incidence rates of Mendelian diseases vary among ethnic groups, and frequencies of variant types of causative genes also vary among human populations. In this study, we examined to what extent we can predict population frequencies of recessive disorders from genomic data, and explored better strategies for variant interpretation and classification. We used a whole-genome reference panel from 3552 general Japanese individuals constructed by the Tohoku Medical Megabank Organization (ToMMo). Focusing on 32 genes for 17 congenital metabolic disorders included in newborn screening (NBS) in Japan, we identified reported and predicted pathogenic variants through variant annotation, interpretation, and multiple ways of classifications. The estimated carrier frequencies were compared with those from the Japanese NBS data based on 1,949,987 newborns from a previous study. The estimated carrier frequency based on genomic data with a recent guideline of variant interpretation for the PAH gene, in which defects cause hyperphenylalaninemia (HPA) and phenylketonuria (PKU), provided a closer estimate to that by the observed incidence than the other methods. In contrast, the estimated carrier frequencies for SLC25A13, which causes citrin deficiency, were much higher compared with the incidence rate. The results varied greatly among the 11 NBS diseases with single responsible genes; the possible reasons for departures from the carrier frequencies by reported incidence rates were discussed. Of note, (1) the number of pathogenic variants increases by including additional lines of evidence, (2) common variants with mild effects also contribute to the actual frequency of patients, and (3) penetrance of each variant remains unclear.

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.

(33)S nuclear magnetic resonance (NMR) spectroscopy is limited by inherently low NMR sensitivity because of the quadrupolar moment and low gyromagnetic ratio of the (33)S nucleus. We have developed a 10 mm (33)S cryogenic NMR probe, which is operated at 9-26 K with a cold preamplifier and a cold rf switch operated at 60 K. The (33)S NMR sensitivity of the cryogenic probe is as large as 9.8 times that of a conventional 5 mm broadband NMR probe. The (33)S cryogenic probe was applied to biological samples such as human urine, bile, chondroitin sulfate, and scallop tissue. We demonstrated that the system can detect and determine sulfur compounds having SO(4)(2-) anions and -SO(3)(-) groups using the (33)S cryogenic probe, as the (33)S nuclei in these groups are in highly symmetric environments. The NMR signals for other common sulfur compounds such as cysteine are still undetectable by the (33)S cryogenic probe, as the (33)S nuclei in these compounds are in asymmetric environments. If we shorten the rf pulse width or decrease the rf coil diameter, we should be able to detect the NMR signals for these compounds.

Human RNA helicase II/Gu alpha (RH-II/Gu alpha) and RNA helicase II/Gu beta (RH-II/Gu beta) are paralogues that share the same domain structure, consisting of the DEAD box helicase domain (DEAD), the helicase conserved C-terminal domain (helicase&#95;C), and the GUCT domain. The N-terminal regions of the RH-II/Gu proteins, including the DEAD domain and the helicase&#95;C domain, unwind double-stranded RNAs. The C-terminal tail of RH-II/Gu alpha, which follows the GUCT domain, folds a single RNA strand, while that of RH-II/Gu beta does not, and the GUCT domain is not essential for either the RNA helicase or foldase activity. Thus, little is known about the GUCT domain. In this study, we have determined the solution structure of the RH-II/Gu beta GUCT domain. Structural calculations using NOE-based distance restraints and residual dipolar coupling-based angular restraints yielded a well-defined structure with beta-alpha-alpha-beta-beta-alpha-beta topology in the region for K585-A659, while the Pfam HMM algorithm defined the GUCT domain as G571-E666. This structure-based domain boundary revealed false positives in the sequence homologue search using the HMM definition. A structural homology search revealed that the GUCT domain has the RRM fold, which is typically found in RNA-interacting proteins. However, it lacks the surface-exposed aromatic residues and basic residues on the beta-sheet that are important for the RNA recognition in the canonical RRM domains. In addition, the overall surface of the GUCT domain is fairly acidic, and thus the GUCT domain is unlikely to interact with RNA molecules. Instead, it may interact with proteins via its hydrophobic surface around the surface-exposed tryptophan., ((c) 2008 Wiley-Liss, Inc.)

The BolA-like proteins are widely conserved from prokaryotes to eukaryotes. The BolA-like proteins seem to be involved in cell proliferation or cell-cycle regulation, but the molecular function is still unknown. Here we determined the structure of a mouse BolA-like protein. The overall topology is alphabetabetaalphaalphabetaalpha, in which beta(1) and beta(2) are antiparallel, and beta(3) is parallel to beta(2). This fold is similar to the class II KH fold, except for the absence of the GXXG loop, which is well conserved in the KH fold. The conserved residues in the BolA-like proteins are assembled on the one side of the protein.