Computational approaches toward gene annotation are a formidable challenge, now that many genome sequences have been determined. Each gene has its own function, but complicated cellular functions are achieved by sets of genes. Therefore, sets of genes with strong functional relationships must be identified. For this purpose, the similarities of gene expression patterns and gene sequences have been separately utilized, although the combined information will provide a better solution.

The protein coding sequences of the human reference genome GRCh38, RefSeq mRNA and UniProt protein databases are sometimes inconsistent with each other, due to polymorphisms in the human population, but the overall landscape of the discordant sequences has not been clarified. In this study, we comprehensively listed the discordant bases and regions between the GRCh38, RefSeq and UniProt reference sequences, based on the genomic coordinates of GRCh38. We observed that the RefSeq sequences are more likely to represent the major alleles than GRCh38 and UniProt, by assigning the alternative allele frequencies of the discordant bases. Since some reference sequences have minor alleles, functional and structural annotations may be performed based on rare alleles in the human population, thereby biasing these analyses. Some of the differences between the RefSeq and GRCh38 account for biological differences due to known RNA-editing sites. The definitions of the coding regions are frequently complicated by possible micro-exons within introns and by SNVs with large alternative allele frequencies near exon-intron boundaries. The mRNA or protein regions missing from GRCh38 were mainly due to small deletions, and these sequences need to be identified. Taken together, our results clarify overall consistency and remaining inconsistency between the reference sequences.

Interpretation of binding modes of protein-small ligand complexes from 3D structure data is essential for understanding selective ligand recognition by proteins. It is often performed by visual inspection and sometimes largely depends on a priori knowledge about typical interactions such as hydrogen bonds and π-π stacking. Because it can introduce some biases due to scientists' subjective perspectives, more objective viewpoints considering a wide range of interactions are required.

NLDB (Natural Ligand DataBase; URL: http://nldb.hgc.jp ) is a database of automatically collected and predicted 3D protein-ligand interactions for the enzymatic reactions of metabolic pathways registered in KEGG. Structural information about these reactions is important for studying the molecular functions of enzymes, however a large number of the 3D interactions are still unknown. Therefore, in order to complement such missing information, we predicted protein-ligand complex structures, and constructed a database of the 3D interactions in reactions. NLDB provides three different types of data resources; the natural complexes are experimentally determined protein-ligand complex structures in PDB, the analog complexes are predicted based on known protein structures in a complex with a similar ligand, and the ab initio complexes are predicted by docking simulations. In addition, NLDB shows the known polymorphisms found in human genome on protein structures. The database has a flexible search function based on various types of keywords, and an enrichment analysis function based on a set of KEGG compound IDs. NLDB will be a valuable resource for experimental biologists studying protein-ligand interactions in specific reactions, and for theoretical researchers wishing to undertake more precise simulations of interactions.

Pancreatic cancer is among the most lethal malignancies worldwide. This study aimed to identify a novel prognostic biomarker, facilitating treatment selection, using mass spectrometry (MS)-based proteomic analysis with formalin-fixed paraffin-embedded (FFPE) tissue.

Epigenome-wide association studies, which searches for blood-based DNA methylation signatures associated with environmental exposures and/or disease susceptibilities, is a promising approach to a better understanding of the molecular aetiology of common diseases. To carry out large-scale epigenome-wide association studies while avoiding false negative detection, an efficient strategy to determine target CpG sites for microarray-based or sequencing-based DNA methylation profiling is essentially needed. Here, we propose and validate a hypothesis that a strategy focusing on CpG sites with high DNA methylation level variability may attain an improved efficacy. Through whole-genome bisulfite sequencing of purified blood cells collected from > 100 apparently healthy subjects, we identified ~2.0 million inter-individually variable CpG sites as potential targets. The efficacy of our strategy was estimated to be 3.7-fold higher than that of the most frequently used strategy. Our catalogue of inter-individually variable CpG sites will accelerate the discovery of clinically relevant DNA methylation biomarkers in future epigenome-wide association studies.

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.

The accuracy of previous genetic studies in predicting polygenic psychiatric phenotypes has been limited mainly due to the limited power in distinguishing truly susceptible variants from null variants and the resulting overfitting. A novel prediction algorithm, Smooth-Threshold Multivariate Genetic Prediction (STMGP), was applied to improve the genome-based prediction of psychiatric phenotypes by decreasing overfitting through selecting variants and building a penalized regression model. Prediction models were trained using a cohort of 3685 subjects in Miyagi prefecture and validated with an independently recruited cohort of 3048 subjects in Iwate prefecture in Japan. Genotyping was performed using HumanOmniExpressExome BeadChip Arrays. We used the target phenotype of depressive symptoms and simulated phenotypes with varying complexity and various effect-size distributions of risk alleles. The prediction accuracy and the degree of overfitting of STMGP were compared with those of state-of-the-art models (polygenic risk scores, genomic best linear-unbiased prediction, summary-data-based best linear-unbiased prediction, BayesR, and ridge regression). In the prediction of depressive symptoms, compared with the other models, STMGP showed the highest prediction accuracy with the lowest degree of overfitting, although there was no significant difference in prediction accuracy. Simulation studies suggested that STMGP has a better prediction accuracy for moderately polygenic phenotypes. Our investigations suggest the potential usefulness of STMGP for predicting polygenic psychiatric conditions while avoiding overfitting.

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.

Alzheimer's disease (AD) has no cure, but early detection and risk prediction could allow earlier intervention. Genetic risk factors may differ between ethnic populations. To discover novel susceptibility loci of AD in the Japanese population, we conducted a genome-wide association study (GWAS) with 3962 AD cases and 4074 controls. Out of 4,852,957 genetic markers that passed stringent quality control filters, 134 in nine loci, including APOE and SORL1, were convincingly associated with AD. Lead SNPs located in seven novel loci were genotyped in an independent Japanese AD case-control cohort. The novel locus FAM47E reached genome-wide significance in a meta-analysis of association results. This is the first report associating the FAM47E locus with AD in the Japanese population. A trans-ethnic meta-analysis combining the results of the Japanese data sets with summary statistics from stage 1 data of the International Genomics of Alzheimer's Project identified an additional novel susceptibility locus in OR2B2. Our data highlight the importance of performing GWAS in non-European populations.

Macrophage recognition and phagocytosis of crystals is critical for the associated fibrosis and cancer. Of note, multi-walled carbon nanotubes (MWCNTs), the highly representative products of nanotechnology, induce macrophage NLRP3 inflammasome activation and cause asbestosis-like pathogenesis. However, it remains largely unknown how macrophages efficiently recognize MWCNTs on their cell surfaces. Here, we identify by a targeted screening of phagocyte receptors the phosphatidylserine receptors T cell immunoglobulin mucin 4 (Tim4) and Tim1 as the pattern-recognition receptors for carbon crystals. Docking simulation studies reveal spatiotemporally stable interfaces between aromatic residues in the extracellular IgV domain of Tim4 and one-dimensional carbon crystals. Further, CRISPR-Cas9-mediated deletion of Tim4 and Tim1 reveals that Tim4, but not Tim1, critically contributes to the recognition of MWCNTs by peritoneal macrophages and to granuloma development in a mouse model of direct mesothelium exposure to MWCNTs. These results suggest that Tim4 recognizes MWCNTs through aromatic interactions and mediates phagocytosis leading to granulomas.

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.

Cytochrome P450 1A2 (CYP1A2), which accounts for approximately 13% of the total hepatic cytochrome content, catalyzes the metabolic reactions of approximately 9% of frequently used drugs, including theophylline and olanzapine. Substantial inter-individual differences in enzymatic activity have been observed among patients, which could be caused by genetic polymorphisms. Therefore, we functionally characterized 21 novel CYP1A2 variants identified in 4773 Japanese individuals by determining the kinetic parameters of phenacetin O-deethylation. Our results showed that most of the evaluated variants exhibited decreased or no enzymatic activity, which may be attributed to potential structural alterations. Notably, the Leu98Gln, Gly233Arg, Ser380del Gly454Asp, and Arg457Trp variants did not exhibit quantifiable enzymatic activity. Additionally, three-dimensional (3D) docking analyses were performed to further understand the underlying mechanisms behind variant pharmacokinetics. Our data further suggest that despite mutations occurring on the protein surface, accumulating interactions could result in the impairment of protein function through the destabilization of binding regions and changes in protein folding. Therefore, our findings provide additional information regarding rare CYP1A2 genetic variants and how their underlying effects could clarify discrepancies noted in previous phenotypical studies. This would allow the improvement of personalized therapeutics and highlight the importance of identifying and characterizing rare variants.

Epithelial ovarian cancer (EOC) is a fatal gynecologic cancer, and its poor prognosis is mainly due to delayed diagnosis. Therefore, biomarker identification and prognosis prediction are crucial in EOC. Altered cell metabolism is a characteristic feature of cancers, and metabolomics reflects an individual's current phenotype. In particular, plasma metabolome analyses can be useful for biomarker identification. In this study, we analyzed 624 metabolites, including uremic toxins (UTx) in plasma derived from 80 patients with EOC using ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Compared with the healthy control, we detected 77 significantly increased metabolites and 114 significantly decreased metabolites in EOC patients. Especially, decreased concentrations of lysophosphatidylcholines and phosphatidylcholines and increased concentrations of triglycerides were observed, indicating a metabolic profile characteristic of EOC patients. After calculating the parameters of each metabolic index, we found that higher ratios of kynurenine to tryptophan correlates with worse prognosis in EOC patients. Kynurenine, one of the UTx, can affect the prognosis of EOC. Our results demonstrated that plasma metabolome analysis is useful not only for the diagnosis of EOC, but also for predicting prognosis with the variation of UTx and evaluating response to chemotherapy.

NF-E2-related factor 2 (NRF2) is a transcription factor that regulates cytoprotective gene expression in response to oxidative and electrophilic stresses. NRF2 activity is mainly controlled by Kelch-like ECH-associated protein 1 (KEAP1). Constitutive NRF2 activation by NRF2 mutations or KEAP1 dysfunction results in a poor prognosis for esophageal squamous cell carcinoma (ESCC) through the activation of cytoprotective functions. However, the detailed contributions of NRF2 to ESCC initiation or promotion have not been clarified. Here, we investigated the fate of NRF2-activated cells in the esophageal epithelium.

Fanconi anemia is a rare recessive disease characterized by multiple congenital abnormalities, progressive bone marrow failure, and a predisposition to malignancies. It results from mutations in one of the 22 known FANC genes. The number of Japanese Fanconi anemia patients with a defined genetic diagnosis was relatively limited. In this study, we reveal the genetic subtyping and the characteristics of mutated FANC genes in Japan and clarify the genotype-phenotype correlations. We studied 117 Japanese patients and successfully subtyped 97% of the cases. FANCA and FANCG pathogenic variants accounted for the disease in 58% and 25% of Fanconi anemia patients, respectively. We identified one FANCA and two FANCG hot spot mutations, which are found at low percentages (0.04-0.1%) in the whole-genome reference panel of 3,554 Japanese individuals (Tohoku Medical Megabank). FANCB was the third most common complementation group and only one FANCC case was identified in our series. Based on the data from the Tohoku Medical Megabank, we estimate that approximately 2.6% of Japanese are carriers of disease-causing FANC gene variants, excluding missense mutations. This is the largest series of subtyped Japanese Fanconi anemia patients to date and the results will be useful for future clinical management.

Genotype imputation from single-nucleotide polymorphism (SNP) genotype data using a haplotype reference panel consisting of thousands of unrelated individuals from populations of interest can help to identify strongly associated variants in genome-wide association studies. The Tohoku Medical Megabank (TMM) project was established to support the development of precision medicine, together with the whole-genome sequencing of 1070 human genomes from individuals in the Miyagi region (Northeast Japan) and the construction of the 1070 Japanese genome reference panel (1KJPN). Here, we investigated the performance of 1KJPN for genotype imputation of Japanese samples not included in the TMM project and compared it with other population reference panels.

ATTED-II (http://atted.jp) is a gene coexpression database for a wide variety of experimental designs, such as prioritizations of genes for functional identification and analyses of the regulatory relationships among genes. Here, we report updates of ATTED-II focusing on two new features: condition-specific coexpression and homologous coexpression with rice. To analyze a broad range of biological phenomena, it is important to collect data under many diverse experimental conditions, but the meaning of coexpression can become ambiguous under these conditions. One approach to overcome this difficulty is to calculate the coexpression for each set of conditions with a clear biological meaning. With this viewpoint, we prepared five sets of experimental conditions (tissue, abiotic stress, biotic stress, hormones and light conditions), and users can evaluate the coexpression by employing comparative gene lists and switchable gene networks. We also developed an interactive visualization system, using the Cytoscape web system, to improve the network representation. As the second update, rice coexpression is now available. The previous version of ATTED-II was specifically developed for Arabidopsis, and thus coexpression analyses for other useful plants have been difficult. To solve this problem, we extended ATTED-II by including comparison tables between Arabidopsis and rice. This representation will make it possible to analyze the conservation of coexpression among flowering plants. With the ability to investigate condition-specific coexpression and species conservation, ATTED-II can help researchers to clarify the functional and regulatory networks of genes in a broad array of plant species.

Voltage-sensitive potassium ion channels are essential for life, but the molecular basis of their ion conduction is not well understood. In particular, the impact of ion concentration on ion conduction has not been fully studied. We performed several micro-second molecular dynamics simulations of the pore domain of the Kv1.2 potassium channel in KCl solution at four different ion concentrations, and scrutinized each of the conduction events, based on graphical representations of the simulation trajectories. As a result, we observed that the conduction mechanism switched with different ion concentrations: at high ion concentrations, potassium conduction occurred by Hodgkin and Keynes' knock-on mechanism, where the association of an incoming ion with the channel is tightly coupled with the dissociation of an outgoing ion, in a one-step manner. On the other hand, at low ion concentrations, ions mainly permeated by a two-step association/dissociation mechanism, in which the association and dissociation of ions were not coupled, and occurred in two distinct steps. We also found that this switch was triggered by the facilitated association of an ion from the intracellular side within the channel pore and by the delayed dissociation of the outermost ion, as the ion concentration increased.

Dihydropyrimidine dehydrogenase (DPD), encoded by the DPYD gene, is the rate-limiting enzyme in 5-fluorouracil (5-FU) degradation. In Caucasians, four DPYD risk variants are recognized to be responsible for interindividual variations in the development of 5-FU toxicity. However, these risk variants have not been identified in Asian populations. Recently, 41 DPYD allelic variants, including 15 novel single nucleotide variants, were identified in 3,554 Japanese individuals by analyzing their whole-genome sequences; however, the effects of these variants on DPD enzymatic activity remain unknown. In the present study, an in vitro analysis was performed on 41 DPD allelic variants and three DPD risk variants to elucidate the changes in enzymatic activity. Wild-type and 44 DPD-variant proteins were heterologously expressed in 293FT cells. DPD expression levels and dimerization of DPD were determined by immunoblotting after SDS-PAGE and blue native PAGE, respectively. The enzymatic activity of DPD was evaluated by quantification of dihydro-5-FU, a metabolite of 5-FU, using high-performance liquid chromatography-tandem mass spectrometry. Moreover, we used 3D simulation modeling to analyze the effect of amino acid substitutions on the conformation of DPD. Among the 41 DPD variants, seven exhibited drastically decreased intrinsic clearance (CL int ) compared to the wild-type protein. Moreover, R353C and G926V exhibited no enzymatic activity, and the band patterns observed in the immunoblots after blue native PAGE indicated that DPD dimerization is required for its enzymatic activity. Our data suggest that these variants may contribute to the significant inter-individual variability observed in the pharmacokinetics and pharmacodynamics of 5-FU. In our study, nine DPD variants exhibited drastically decreased or no enzymatic activity due to dimerization inhibition or conformational changes in each domain. Especially, the rare DPYD variants, although at very low frequencies, may serve as important pharmacogenomic markers associated with the severe 5-FU toxicity in Japanese population.