Tohoku University Tohoku Medical Megabank Organization (Seizo Koshiba)
2022.08.12

1.   Nakai Taku, Saigusa Daisuke, Iwamura Yuma, et al. Esterification promotes the intracellular accumulation of roxadustat, an activator of hypoxia-inducible factors, to extend its effective duration. Biochemical Pharmacology. 2022; 197 : 114939. doi:10.1016/j.bcp.2022.114939  
2.   Sugawara Junichi, Ishikuro Mami, Obara Taku, et al. Maternal Baseline Characteristics and Perinatal Outcomes: The Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study. Journal of epidemiology. 2022; 32 (2): 69-79. doi:10.2188/jea.JE20200338  
3.   Uchida Yasuo, Higuchi Tomoya, Shirota Matsuyuki, et al. Identification and Validation of Combination Plasma Biomarker of Afamin, Fibronectin and Sex Hormone-Binding Globulin to Predict Pre-eclampsia. Biological and Pharmaceutical Bulletin. 2021; 44 (6): 804-815. doi:10.1248/bpb.b20-01043  
4.   Horie Yuta, Suzuki Takafumi, Inoue Jin, et al. Molecular basis for the disruption of Keap1–Nrf2 interaction via Hinge & Latch mechanism. Communications Biology. 2021; 4 (1): 576. doi:10.1038/s42003-021-02100-6  
5.   Uruno Akira, Saigusa Daisuke, Suzuki Takafumi, et al. Nrf2 plays a critical role in the metabolic response during and after spaceflight. Communications Biology. 2021; 4 (1): 1381. doi:10.1038/s42003-021-02904-6  
6.   Saigusa Daisuke, Matsukawa Naomi, Hishinuma Eiji, Koshiba Seizo. Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics. Drug Metabolism and Pharmacokinetics. 2021; 37 : 100373. doi:10.1016/j.dmpk.2020.11.008  
7.   Ogishima Soichi, Nagaie Satoshi, Mizuno Satoshi, et al. dbTMM: an integrated database of large-scale cohort, genome and clinical data for the Tohoku Medical Megabank Project. Human Genome Variation. 2021; 8 (1): 44. doi:10.1038/s41439-021-00175-5  
8.   Hozawa Atsushi, Tanno Kozo, Nakaya Naoki, et al. Study profile of the tohoku medical megabank community-based cohort study. Journal of Epidemiology. 2021; 31 (1): 65-76. doi:10.2188/jea.JE20190271  
9.   Saigusa Daisuke, Hishinuma Eiji, Matsukawa Naomi, et al. Comparison of Kit-Based Metabolomics with Other Methodologies in a Large Cohort, towards Establishing Reference Values. Metabolites. 2021; 11 (10): 652. doi:10.3390/metabo11100652  
10.   Sakaue Saori, Kanai Masahiro, Tanigawa Yosuke, et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nature Genetics. 2021; 53 (10): 1415-1424. doi:10.1038/s41588-021-00931-x  
11.   Tadaka Shu, Hishinuma Eiji, Komaki Shohei, et al. jMorp updates in 2020: large enhancement of multi-omics data resources on the general Japanese population. Nucleic Acids Research. 2021; 49 (D1): D536-D544. doi:10.1093/nar/gkaa1034  
12.   Yamauchi Takafumi, Ochi Daisuke, Matsukawa Naomi, et al. Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis. Scientific Reports. 2021; 11 (1): 17777. doi:10.1038/s41598-021-97342-z  
13.   Sakurai-Yageta Mika, Kumada Kazuki, Gocho Chinatsu, et al. Japonica Array NEO with increased genome-wide coverage and abundant disease risk SNPs. The Journal of Biochemistry. 2021; 170 (3): 399-410. doi:10.1093/jb/mvab060  
14.   Hishinuma Eiji, Shimada Muneaki, Matsukawa Naomi, et al. Wide-Targeted Metabolome Analysis Identifies Potential Biomarkers for Prognosis Prediction of Epithelial Ovarian Cancer. Toxins. 2021; 13 (7): 461. doi:10.3390/toxins13070461  
15.   Akiyama Tomoyuki, Saigusa Daisuke, Hyodo Yuki, et al. Metabolic Profiling of the Cerebrospinal Fluid in Pediatric Epilepsy. Acta Medica Okayama. 2020; 74 (1): 65-72. doi:10.18926/AMO/57955  
16.   Yokose Takahiro, Kabe Yasuaki, Matsuda Atsushi, et al. O-Glycan-Altered Extracellular Vesicles: A Specific Serum Marker Elevated in Pancreatic Cancer. Cancers. 2020; 12 (9): 2469. doi:10.3390/cancers12092469  
17.   Koshiba Seizo, Motoike Ikuko N., Saigusa Daisuke, et al. Identification of critical genetic variants associated with metabolic phenotypes of the Japanese population. Communications Biology. 2020; 3 (1): 662. doi:10.1038/s42003-020-01383-5  
18.   Suzuki Takafumi, Uruno Akira, Yumoto Akane, et al. Nrf2 contributes to the weight gain of mice during space travel. Communications Biology. 2020; 3 (1): 496. doi:10.1038/s42003-020-01227-2  
19.   Kuriyama Shinichi, Metoki Hirohito, Kikuya Masahiro, et al. Cohort Profile: Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study (TMM BirThree Cohort Study): rationale, progress and perspective. International Journal of Epidemiology. 2020; 49 (1): 18-19m. doi:10.1093/ije/dyz169  
20.   Kasai Takuma, Ono Shunsuke, Koshiba Seizo, et al. Amino-acid selective isotope labeling enables simultaneous overlapping signal decomposition and information extraction from NMR spectra. Journal of Biomolecular NMR. 2020; 74 (2-3): 125-137. doi:10.1007/s10858-019-00295-9  
21.   Shirota Matsuyuki, Saigusa Daisuke, Yamashita Riu, et al. Longitudinal plasma amino acid profiling with maternal genomic background throughout human pregnancy. Medical Mass Spectrometry. 2020; 4 (1): 36-49. doi:10.24508/mms.2020.06.001  
22.   Oikawa Yoshitsugu, Izumi Rumiko, Koide Masashi, et al. Mitochondrial dysfunction underlying sporadic inclusion body myositis is ameliorated by the mitochondrial homing drug MA-5. PLOS ONE. 2020; 15 (12): e0231064. doi:10.1371/journal.pone.0231064  
23.   Nagai Koshi, Uranbileg Baasanjav, Chen Zhen, et al. 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. Rapid Communications in Mass Spectrometry. 2020; 34 (S1): rcm.8551. doi:10.1002/rcm.8551  
24.   Tsuboi Akito, Matsui Hiroyuki, Shiraishi Naru, et al. Design and Progress of Oral Health Examinations in the Tohoku Medical Megabank Project. The Tohoku Journal of Experimental Medicine. 2020; 251 (2): 97-115. doi:10.1620/tjem.251.97  
25.   Takahashi Yuta, Ueki Masao, Yamada Makoto, et al. Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection. Translational Psychiatry. 2020; 10 (1): 157. doi:10.1038/s41398-020-0831-9  
26.   Sugawara Junichi, Ochi Daisuke, Yamashita Riu, et al. Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy. BMJ Open. 2019; 9 (2): e025939. doi:10.1136/bmjopen-2018-025939  
27.   Wada Yoichi, Kikuchi Atsuo, Arai-Ichinoi Natsuko, et al. Biallelic GALM pathogenic variants cause a novel type of galactosemia. Genetics in Medicine. 2019; 21 (6): 1286-1294. doi:10.1038/s41436-018-0340-x  
28.   Yamaguchi-Kabata Yumi, Yasuda Jun, Uruno Akira, et al. Estimating carrier frequencies of newborn screening disorders using a whole-genome reference panel of 3552 Japanese individuals. Human Genetics. 2019; 138 (4): 389-409. doi:10.1007/s00439-019-01998-7  
29.   Tadaka Shu, Katsuoka Fumiki, Ueki Masao, et al. 3.5KJPNv2: an allele frequency panel of 3552 Japanese individuals including the X chromosome. Human Genome Variation. 2019; 6 (1): 28. doi:10.1038/s41439-019-0059-5  
30.   Sato Tomonori, Kawasaki Yoshihide, Maekawa Masamitsu, et al. Value of global metabolomics in association with diagnosis and clinicopathological factors of renal cell carcinoma. International Journal of Cancer. 2019; 145 (2): 484-493. doi:10.1002/ijc.32115  
31.   Saigusa Daisuke, Matsukawa Naomi, Tadaka Shu, et al. Metabolome Analysis of Human Plasma by GC-MS/MS in a Large-scale Cohort. Proteome Letters. 2019; 4 (1): 31-40. doi:10.14889/jpros.4.1_31  
32.   Yasuda Jun, Kinoshita Kengo, Katsuoka Fumiki, et al. Genome analyses for the Tohoku Medical Megabank Project towards establishment of personalized healthcare. The Journal of Biochemistry. 2019; 165 (2): 139-158. doi:10.1093/jb/mvy096  
33.   Koshiba Seizo, Motoike Ikuko, Saigusa Daisuke, et al. Omics research project on prospective cohort studies from the Tohoku Medical Megabank Project. Genes to Cells. 2018; 23 (6): 406-417. doi:10.1111/gtc.12588  
34.   Yamaguchi-Kabata Yumi, Yasuda Jun, Tanabe Osamu, et al. Evaluation of reported pathogenic variants and their frequencies in a Japanese population based on a whole-genome reference panel of 2049 individuals. Journal of Human Genetics. 2018; 63 (2): 213-230. doi:10.1038/s10038-017-0347-1  
35.   Tadaka Shu, Saigusa Daisuke, Motoike Ikuko N., et al. jMorp: Japanese Multi Omics Reference Panel. Nucleic Acids Research. 2018; 46 (D1): D551-D557. doi:10.1093/nar/gkx978  
36.   Sato Kota, Saigusa Daisuke, Saito Ritsumi, et al. Metabolomic changes in the mouse retina after optic nerve injury. Scientific Reports. 2018; 8 (1): 11930. doi:10.1038/s41598-018-30464-z  
37.   Kuriyama Shinichi, Yaegashi Nobuo, Nagami Fuji, et al. The Tohoku Medical Megabank Project: Design and Mission. Journal of Epidemiology. 2016; 26 (9): 493-511. doi:10.2188/jea.JE20150268  
38.   Saigusa Daisuke, Okamura Yasunobu, Motoike Ikuko N., et al. Establishment of Protocols for Global Metabolomics by LC-MS for Biomarker Discovery. PLOS ONE. 2016; 11 (8): e0160555. doi:10.1371/journal.pone.0160555  
39.   Koshiba Seizo, Motoike Ikuko, Kojima Kaname, et al. The structural origin of metabolic quantitative diversity. Scientific Reports. 2016; 6 (1): 31463. doi:10.1038/srep31463  
40.   Kasai Takuma, Koshiba Seizo, Yokoyama Jun, Kigawa Takanori. Stable isotope labeling strategy based on coding theory. Journal of Biomolecular NMR. 2015; 63 (2): 213-221. doi:10.1007/s10858-015-9978-8  
41.   Tochio Naoya, Umehara Takashi, Nakabayashi Kazuhiko, et al. Solution structures of the DNA-binding domains of immune-related zinc-finger protein ZFAT. Journal of Structural and Functional Genomics. 2015; 16 (2): 55-65. doi:10.1007/s10969-015-9196-3  
42.   Koizumi Taichi, Terada Tohru, Nakajima Ken-ichiro, et al. Identification of key neoculin residues responsible for the binding and activation of the sweet taste receptor. Scientific Reports. 2015; 5 (1): 12947. doi:10.1038/srep12947