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.

Effects of NRF2 polymorphisms on safety and efficacy of bardoxolone methyl: subanalysis of TSUBAKI study.

BACKGROUND: In the TSUBAKI study, bardoxolone methyl significantly increased measured and estimated glomerular filtration rates (GFR) in patients with multiple forms of chronic kidney disease (CKD), including Japanese patients with type 2 diabetes and stage 3-4 CKD. Since bardoxolone methyl targets the nuclear factor erythroid 2-related factor 2 pathway, this exploratory analysis of the TSUBAKI study investigated the impact of the regulatory single nucleotide polymorphism, rs6721961, on the effects of bardoxolone methyl. METHODS: Japanese patients aged 20-79 years with type 2 diabetes and stage 3-4 CKD were randomized to bardoxolone methyl 5-15 mg/day (titrated as tolerated) or placebo for 16 weeks. Genotype frequency, clinical characteristics, renal function, and adverse events were primarily assessed. RESULTS: Of 104 patients (bardoxolone methyl n = 55, placebo n = 49); 57% were genotype C/C, 32% C/A and 12% A/A. The frequency of the A/A genotype was higher among patients with diabetic kidney disease than in the general Japanese population (~ 5%). Measured and estimated GFRs increased from baseline in all genotypes receiving bardoxolone methyl. There were no significant differences between genotypes for safety parameters, including blood pressure, bodyweight, and levels of B-type natriuretic peptide, or in the type and frequency of adverse events, suggesting that the efficacy and safety of bardoxolone methyl are unaffected by the rs6721961 polymorphism-617 (C→A) genotype. CONCLUSIONS: Our approach of combining genome analysis with clinical trials for an investigational drug provides important and useful clues for exploring the efficacy and safety of the drug. TRIAL REGISTRATION: ClinicalTrials.gov; NCT02316821.

Familial Paget's disease of bone with ocular manifestations and a novel TNFRSF11A duplication variant (72dup27).

INTRODUCTION: Paget's disease of bone (PDB) is a skeletal disorder characterized by disorganized bone remodeling due to abnormal osteoclasts. Tumor necrosis factor receptor superfamily member 11A (TNFRSF11A) gene encodes the receptor activator of nuclear factor kappa B (RANK), which has a critical role in osteoclast function. There are five types of rare PDB and related osteolytic disorders due to TNFRSF11A tandem duplication variants so far, including familial expansile osteolysis (84dup18), expansile skeletal hyperphosphatasia (84dup15), early-onset familial PDB (77dup27), juvenile PDB (87dup15), and panostotic expansile bone disease (90dup12). MATERIALS AND METHODS: We reviewed a Japanese family with PDB, and performed whole-genome sequencing to identify a causative variant. RESULTS: This family had bone symptoms, hyperphosphatasia, hearing loss, tooth loss, and ocular manifestations such as angioid streaks or early-onset glaucoma. We identified a novel duplication variant of TNFRSF11A (72dup27). Angioid streaks were recognized in Juvenile Paget's disease due to loss-of-function variants in the gene TNFRSF11B, and thought to be specific for this disease. However, the novel recognition of angioid streaks in our family raised the possibility of occurrence even in bone disorders due to TNFRSF11A duplication variants and the association of RANKL-RANK signal pathway as the pathogenesis. Glaucoma has conversely not been reported in any case of Paget's disease. It is not certain whether glaucoma is coincidental or specific for PDB with 72dup27. CONCLUSION: Our new findings might suggest a broad spectrum of phenotypes in bone disorders with TNFRSF11A duplication variants.

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.

The association between ERK inhibitor sensitivity and molecular characteristics in colorectal cancer.

The mitogen-activated protein kinase (MAPK) pathway plays an important role in the colorectal cancer (CRC) progression, being supposed to be activated by the gene mutations, such as BRAF or KRAS. Although the inhibitors of extracellular signal-regulated kinase (ERK) have demonstrated efficacy in the cells with the BRAF or KRAS mutations, a clinical response is not always associated with the molecular signature. The patient-derived organoids (PDO) have emerged as a powerful in vitro model system to study cancer, and it has been widely applied for the drug screening. The present study aims to analyze the association between the molecular characteristics which analyzed by next-generation sequencing (NGS) and sensitivity to the ERK inhibitor (i.e., SCH772984) in PDO derived from CRC specimens. A drug sensitivity test for the SCH772984 was conducted using 14 CRC cell lines, and the results demonstrated that the sensitivity was in agreement with the BRAF mutation, but was not completely consistent with the KRAS status. In the drug sensitivity test for PDO, 6 out of 7 cases with either BRAF or KRAS mutations showed sensitivity to the SCH772984, while 5 out of 6 cases of both BRAF and KRAS wild-types were resistant. The results of this study suggested that the molecular status of the clinical specimens are likely to represent the sensitivity in the PDOs but is not necessarily absolutely overlapping. PDO might be able to complement the limitations of the gene panel and have the potential to provide a novel precision medicine.

A genotype imputation method for de-identified haplotype reference information by using recurrent neural network.

Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of haplotypes for thousands of individuals, which is known as a haplotype reference panel. In general, more accurate imputation results were obtained using a larger size of haplotype reference panel. Most of the existing genotype imputation methods explicitly require the haplotype reference panel in precise form, but the accessibility of haplotype data is often limited, due to the requirement of agreements from the donors. Since de-identified information such as summary statistics or model parameters can be used publicly, imputation methods using de-identified haplotype reference information might be useful to enhance the quality of imputation results under the condition where the access of the haplotype data is limited. In this study, we proposed a novel imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network (RNN). The model parameters are presented in the form of de-identified information from which the restoration of the genotype data at the individual-level is almost impossible. We demonstrated that the proposed method provides comparable imputation accuracy when compared with the existing imputation methods using haplotype datasets from the 1000 Genomes Project (1KGP) and the Haplotype Reference Consortium. We also considered a scenario where a subset of haplotypes is made available only in de-identified form for the haplotype reference panel. In the evaluation using the 1KGP dataset under the scenario, the imputation accuracy of the proposed method is much higher than that of the existing imputation methods. We therefore conclude that our RNN-based method is quite promising to further promote the data-sharing of sensitive genome data under the recent movement for the protection of individuals' privacy.

Impacts of NRF2 activation in non-small-cell lung cancer cell lines on extracellular metabolites.

Aberrant activation of NRF2 is as a critical prognostic factor that drives the malignant progression of various cancers. Cancer cells with persistent NRF2 activation heavily rely on NRF2 activity for therapeutic resistance and aggressive tumorigenic capacity. To clarify the metabolic features of NRF2-activated lung cancers, we conducted targeted metabolomic (T-Met) and global metabolomic (G-Met) analyses of non-small-cell lung cancer (NSCLC) cell lines in combination with exome and transcriptome analyses. Exome analysis of 88 cell lines (49 adenocarcinoma, 14 large cell carcinoma, 15 squamous cell carcinoma and 10 others) identified non-synonymous mutations in the KEAP1, NRF2 and CUL3 genes. Judging from the elevated expression of NRF2 target genes, these mutations are expected to result in the constitutive stabilization of NRF2. Out of the 88 cell lines, 52 NSCLC cell lines (29 adenocarcinoma, 10 large cell carcinoma, 9 squamous cell carcinoma and 4 others) were subjected to T-Met analysis. Classification of the 52 cell lines into three groups according to the NRF2 target gene expression enabled us to draw typical metabolomic signatures induced by NRF2 activation. From the 52 cell lines, 18 NSCLC cell lines (14 adenocarcinoma, 2 large cell carcinoma, 1 squamous cell carcinoma and 1 others) were further chosen for G-Met and detailed transcriptome analyses. G-Met analysis of their culture supernatants revealed novel metabolites associated with NRF2 activity, which may be potential diagnostic biomarkers of NRF2 activation. This study also provides useful information for the exploration of new metabolic nodes for selective toxicity towards NRF2-activated NSCLC.

Estimating carrier frequencies of newborn screening disorders using a whole-genome reference panel of 3552 Japanese individuals.

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.

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.

Construction of full-length Japanese reference panel of class I HLA genes with single-molecule, real-time sequencing.

Human leukocyte antigen (HLA) is a gene complex known for its exceptional diversity across populations, importance in organ and blood stem cell transplantation, and associations of specific alleles with various diseases. We constructed a Japanese reference panel of class I HLA genes (ToMMo HLA panel), comprising a distinct set of HLA-A, HLA-B, HLA-C, and HLA-H alleles, by single-molecule, real-time (SMRT) sequencing of 208 individuals included in the 1070 whole-genome Japanese reference panel (1KJPN). For high-quality allele reconstruction, we developed a novel pipeline, Primer-Separation Assembly and Refinement Pipeline (PSARP), in which the SMRT sequencing and additional short-read data were used. The panel consisted of 139 alleles, which were all extended from known IPD-IMGT/HLA sequences, contained 40 with novel variants, and captured more than 96.5% of allelic diversity in 1KJPN. These newly available sequences would be important resources for research and clinical applications including high-resolution HLA typing, genetic association studies, and analyzes of cis-regulatory elements.

GATA2 haploinsufficiency accelerates EVI1-driven leukemogenesis.

Chromosomal rearrangements between 3q21 and 3q26 induce inappropriate EVI1 expression by recruiting a GATA2-distal hematopoietic enhancer (G2DHE) to the proximity of the EVI1 gene, leading to myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). The acquisition of G2DHE by the EVI1 gene reciprocally deprives this enhancer of 1 of the 2 GATA2 alleles, resulting in a loss-of-function genetic reduction in GATA2 abundance. Because GATA2 haploinsufficiency is strongly associated with MDS and AML, we asked whether EVI1 misexpression and GATA2 haploinsufficiency both contributed to the observed leukemogenesis by using a 3q21q26 mouse model that recapitulates the G2DHE-driven EVI1 misexpression, but in this case, it was coupled to a Gata2 heterozygous germ line deletion. Of note, the Gata2 heterozygous deletion promoted the EVI1-provoked leukemic transformation, resulting in early onset of leukemia. The 3q21q26 mice suffered from leukemia in which B220+ cells and/or Gr1+ leukemic cells occupied their bone marrows. We found that the B220+Gr1-c-Kit+ population contained leukemia-initiating cells and supplied Gr1+ leukemia cells in the 3q21q26 leukemia. When Gata2 expression levels in the B220+Gr1-c-Kit+ cells were decreased as a result of Gata2 heterozygous deletion or spontaneous phenomenon, myeloid differentiation of the B220+Gr1-c-Kit+ cells was suppressed, and the cells acquired induced proliferation as well as B-lymphoid-primed characteristics. Competitive transplantation analysis revealed that Gata2 heterozygous deletion confers selective advantage to EVI1-expressing leukemia cell expansion in recipient mice. These results demonstrate that both the inappropriate stimulation of EVI1 and the loss of 1 allele equivalent of Gata2 expression contribute to the acceleration of leukemogenesis.

Biobank Establishment and Sample Management in the Tohoku Medical Megabank Project.

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

Identification of somatic genetic alterations in ovarian clear cell carcinoma with next generation sequencing.

Ovarian clear cell carcinoma (OCCC) is the most refractory subtype of ovarian cancer and more prevalent in Japanese than Caucasians (25% and 5% of all ovarian cancer, respectively). The aim of this study is to discover the genomic alterations that may cause OCCC and effective molecular targets for chemotherapy. Paired genomic DNAs of 48 OCCC tissues and corresponding noncancerous tissues were extracted from formalin-fixed, paraffin embedded specimens collected between 2007 and 2015 at Tohoku University Hospital. All specimens underwent exome sequencing and the somatic genetic alterations were identified. We divided the cases into three clusters based on the mutation spectra. Clinical characteristics such as age of onset and endometriosis are similar among the clusters but one cluster shows mutations related to APOBEC activation, indicating its contribution to subset of OCCC cases. There are three hypermutated cases (showing 12-fold or higher somatic mutations than the other 45 cases) and they have germline and somatic mismatch repair gene alterations. The frequently mutated genes are ARID1A (66.7%), PIK3CA (50%), PPP2R1A (18.8%), and KRAS (16.7%). Somatic mutations important for selection of chemotherapeutic agents, such as BRAF, ERBB2, PDGFRB, PGR, and KRAS are found in 27.1% of OCCC cases, indicating clinical importance of exome analysis for OCCC. Our study suggests that the genetic instability caused by either mismatch repair defect or activation of APOBEC play critical roles in OCCC carcinogenesis.

Regional genetic differences among Japanese populations and performance of genotype imputation using whole-genome reference panel of the Tohoku Medical Megabank Project.

BACKGROUND: 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. RESULTS: We found that the 1KJPN population was more similar to other Japanese populations, Nagahama (south-central Japan) and Aki (Shikoku Island), than to East Asian populations in the 1000 Genomes Project other than JPT, suggesting that the large-scale collection (more than 1000) of Japanese genomes from the Miyagi region covered many of the genetic variations of Japanese in mainland Japan. Moreover, 1KJPN outperformed the phase 3 reference panel of the 1000 Genomes Project (1KGPp3) for Japanese samples, and IKJPN showed similar imputation rates for the TMM and other Japanese samples for SNPs with minor allele frequencies (MAFs) higher than 1%. CONCLUSIONS: 1KJPN covered most of the variants found in the samples from areas of the Japanese mainland outside the Miyagi region, implying 1KJPN is representative of the Japanese population's genomes. 1KJPN and successive reference panels are useful genome reference panels for the mainland Japanese population. Importantly, the addition of whole genome sequences not included in the 1KJPN panel improved imputation efficiencies for SNPs with MAFs under 1% for samples from most regions of the Japanese archipelago.

Evaluation of reported pathogenic variants and their frequencies in a Japanese population based on a whole-genome reference panel of 2049 individuals.

Clarifying allele frequencies of disease-related genetic variants in a population is important in genomic medicine; however, such data is not yet available for the Japanese population. To estimate frequencies of actionable pathogenic variants in the Japanese population, we examined the reported pathological variants in genes recommended by the American College of Medical Genetics and Genomics (ACMG) in our reference panel of genomic variations, 2KJPN, which was created by whole-genome sequencing of 2049 individuals of the resident cohort of the Tohoku Medical Megabank Project. We searched for pathogenic variants in 2KJPN for 57 autosomal ACMG-recommended genes responsible for 26 diseases and then examined their frequencies. By referring to public databases of pathogenic variations, we identified 143 reported pathogenic variants in 2KJPN for the 57 ACMG recommended genes based on a classification system. At the individual level, 21% of the individuals were found to have at least one reported pathogenic allele. We then conducted a literature survey to review the variants and to check for evidence of pathogenicity. Our results suggest that a substantial number of people have reported pathogenic alleles for the ACMG genes, and reviewing variants is indispensable for constructing the information infrastructure of genomic medicine for the Japanese population.

Identification of somatic mutations in postmortem human brains by whole genome sequencing and their implications for psychiatric disorders.

AIM: Somatic mutations in the human brain are hypothesized to contribute to the functional diversity of brain cells as well as the pathophysiology of neuropsychiatric diseases. However, there are still few reports on somatic mutations in non-neoplastic human brain tissues. This study attempted to unveil the landscape of somatic mutations in the human brain. METHODS: We explored the landscape of somatic mutations in human brain tissues derived from three individuals with no neuropsychiatric diseases by whole-genome deep sequencing at a depth of around 100. The candidate mutations underwent multi-layered filtering, and were validated by ultra-deep target amplicon sequencing at a depth of around 200 000. RESULTS: Thirty-one somatic mutations were identified in the human brain, demonstrating the utility of whole-genome sequencing of bulk brain tissue. The mutations were enriched in neuron-expressed genes, and two-thirds of the identified somatic single nucleotide variants in the brain tissues were cytosine-to-thymine transitions, half of which were in CpG dinucleotides. CONCLUSION: Our developed filtering and validation approaches will be useful to identify somatic mutations in the human brain. The vulnerability of neuron-expressed genes to mutational events suggests their potential relevance to neuropsychiatric diseases.

Small Maf deficiency recapitulates the liver phenotypes of Nrf1- and Nrf2-deficient mice.

Nrf1 and Nrf2 (NF-E2-related factors 1 and 2, respectively) are transcription factors that belong to the Cap'n'collar (CNC) family and play critical roles in various tissues, including the liver. Liver-specific Nrf1 knockout mice show hepatic steatosis, accompanied by dysregulation of various metabolic genes. Nrf2 knockout mice show impairment in the induction of antioxidant and xenobiotic-metabolizing enzyme genes. Although it has been shown that small Maf (sMaf) proteins act as obligatory partners of CNC proteins, their precise contributions to the function of CNC proteins remain unclear especially in the context of adult liver functions. To address this issue, we generated mice that conditionally lack expression of all sMaf proteins in the liver. The liver-specific sMaf-deficient mice develop hepatic steatosis and dysregulation of genes involved in lipid and amino acid metabolism and proteasomal subunit expression. Importantly, the gene expression profiles in the sMaf-deficient livers share a strong similarity with those in Nrf1-deficient livers. In addition, the basal expression levels of a number of Nrf2 target genes were diminished in the sMaf-deficient livers. These results provide the first genetic evidence that sMaf proteins are indispensable for liver functions as heterodimeric partners for Nrf1 and Nrf2.

Discovery of an NRF1-specific inducer from a large-scale chemical library using a direct NRF1-protein monitoring system.

NRF1 (NF-E2-p45-related factor 1) plays an important role in the regulation of genes encoding proteasome subunits, a cystine transporter, and lipid-metabolizing enzymes. Global and tissue-specific disruptions of the Nrf1 gene in mice result in embryonic lethality and spontaneous development of severe tissue defects, respectively, suggesting NRF1 plays a critical role in vivo. Mechanistically, the continuous degradation of the NRF1 protein by the proteasome is regarded as a major regulatory nexus of NRF1 activity. To develop NRF1-specific inducers that act to overcome the phenotypes related to the lack of NRF1 activity, we constructed a novel NRF1ΔC-Luc fusion protein reporter and developed cell lines that stably express the reporter in Hepa1c1c7 cells for use in high-throughput screening. In screening of a chemical library with this reporter system, we identified two hit compounds that significantly induced luciferase activity. Through an examination of a series of derivatives of one of the hit compounds, we identified T1-20, which induced a 70-fold increase in luciferase activity. T1-20 significantly increased the level of NRF1 protein in the mouse liver, indicating that the compound is also functional in vivo. Thus, these results show the successful identification of the first small chemical compounds which specifically and significantly induce NRF1.

Transcription factor NF-E2-related factor 1 impairs glucose metabolism in mice.

Nrf1 (NF-E2-related factor 1) is a basic region leucine zipper-type transcription factor belonging to the CNC (cap-'n'-collar) family. Major pathophysiological contribution of Nrf1 remains unclear. As single nucleotide polymorphism rs3764400 in 5'-flanking region of NRF1 gene appears to associate with obesity, in this study, we focused on the Nrf1 function on metabolism. We found that the risk C allele of rs3764400 increased NRF1 gene transcriptional activity compared with the T allele in hepatoma cell lines. Therefore, we newly established Nrf1 transgenic (Nrf1-Tg) mouse lines and examined roles that Nrf1 plays on the obesity and metabolism. Unexpectedly, Nrf1 over-expression repressed bodyweight gain in both lean and diet-induced obesity mice. Of note, Nrf1-Tg mice showed rise in blood glucose levels; Nrf1 strongly reduced glucose infusion rate in euglycemic-hyperinsulinemic clamp test and increased blood glucose levels in insulin tolerance test, indicating that Nrf1 induces insulin resistance in mice. Nrf1 repressed insulin-regulated glycolysis-related gene expression and gave rise to loss of glucose-6-phosphate and fructose-6-phosphate contents in liver. Consistently, Nrf1 heterozygote improved impaired glucose regulations in diet-induced obesity model. These results showed that Nrf1 contributes to metabolic regulation, which gain-of-function develops diabetes mellitus in mice.

Validation of multiple single nucleotide variation calls by additional exome analysis with a semiconductor sequencer to supplement data of whole-genome sequencing of a human population.

BACKGROUND: Validation of single nucleotide variations in whole-genome sequencing is critical for studying disease-related variations in large populations. A combination of different types of next-generation sequencers for analyzing individual genomes may be an efficient means of validating multiple single nucleotide variations calls simultaneously. RESULTS: Here, we analyzed 12 independent Japanese genomes using two next-generation sequencing platforms: the Illumina HiSeq 2500 platform for whole-genome sequencing (average depth 32.4×), and the Ion Proton semiconductor sequencer for whole exome sequencing (average depth 109×). Single nucleotide polymorphism (SNP) calls based on the Illumina Human Omni 2.5-8 SNP chip data were used as the reference. We compared the variant calls for the 12 samples, and found that the concordance between the two next-generation sequencing platforms varied between 83% and 97%. CONCLUSIONS: Our results show the versatility and usefulness of the combination of exome sequencing with whole-genome sequencing in studies of human population genetics and demonstrate that combining data from multiple sequencing platforms is an efficient approach to validate and supplement SNP calls.