Tohoku University Tohoku Medical Megabank Organization (Seizo Koshiba)
2024.04.19

1.   Kowalik Marta Anna, Taguchi Keiko, Serra Marina, et al. Metabolic reprogramming in Nrf2-driven proliferation of normal rat hepatocytes. Hepatology. 2024; 79 (4): 829-843. doi:10.1097/HEP.0000000000000568  
2.   Harada Sei, Iida Miho, Miyagawa Naoko, et al. Study Profile of the Tsuruoka Metabolomics Cohort Study (TMCS). Journal of Epidemiology. 2024; : JE20230192. doi:10.2188/jea.JE20230192  
3.   Sato Mitsuharu, Hishinuma Eiji, Matsukawa Naomi, et al. Dietary habits and plasma lipid concentrations in a general Japanese population. Metabolomics. 2024; 20 (2): 34. doi:10.1007/s11306-024-02087-1  
4.   Tadaka Shu, Kawashima Junko, Hishinuma Eiji, et al. jMorp: Japanese Multi-Omics Reference Panel update report 2023. Nucleic Acids Research. 2024; 52 (D1): D622-D632. doi:10.1093/nar/gkad978  
5.   Hishinuma Eiji, Shimada Muneaki, Matsukawa Naomi, et al. Identification of predictive biomarkers for endometrial cancer diagnosis and treatment response monitoring using plasma metabolome profiling. Cancer & Metabolism. 2023; 11 (1): 16. doi:10.1186/s40170-023-00317-z  
6.   Sato Shiho, Yu Zhiqian, Sakai Mai, et al. Decreased β‐hydroxybutyrate and ketogenic amino acid levels in depressed human adults. European Journal of Neuroscience. 2023; 57 (6): 1018-1032. doi:10.1111/ejn.15931  
7.   Miyoshi Keitaro, Hishinuma Eiji, Matsukawa Naomi, et al. Global Proteomics for Identifying the Alteration Pathway of Niemann–Pick Disease Type C Using Hepatic Cell Models. International Journal of Molecular Sciences. 2023; 24 (21): 15642. doi:10.3390/ijms242115642  
8.   Fujino Mitsunori, Morito Naoki, Hayashi Takuto, et al. Transcription factor c-Maf deletion improves streptozotocin-induced diabetic nephropathy by directly regulating Sglt2 and Glut2. JCI Insight. 2023; 8 (6): . doi:10.1172/jci.insight.163306  
9.   Sato Shuichi, Imaeda Takao, Mugikura Shunji, et al. Association Between Olfactory Test Data with Multiple Levels of Odor Intensity and Suspected Cognitive Impairment: A Cross-Sectional Study. Journal of Alzheimer's Disease. 2023; 95 (4): 1469-1480. doi:10.3233/JAD-230318  
10.   Sugawara Yuka, Hirakawa Yosuke, Nagasu Hajime, et al. Genome-wide association study of the risk of chronic kidney disease and kidney-related traits in the Japanese population: J-Kidney-Biobank. Journal of Human Genetics. 2023; 68 (2): 55-64. doi:10.1038/s10038-022-01094-1  
11.   Hishinuma Eiji, Shimada Muneaki, Matsukawa Naomi, et al. Identification of predictive biomarkers for diagnosis and radiation sensitivity of uterine cervical cancer using wide‐targeted metabolomics. Journal of Obstetrics and Gynaecology Research. 2023; 49 (8): 2109-2117. doi:10.1111/jog.15709  
12.   Saito Yoshie, Sato Keigo, Jinno Shinji, et al. Effect of Nicotinamide Mononucleotide Concentration in Human Milk on Neurodevelopmental Outcome: The Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study. Nutrients. 2023; 16 (1): 145. doi:10.3390/nu16010145  
13.   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  
14.   Yu Zhiqian, Matsukawa Naomi, Saigusa Daisuke, et al. Plasma metabolic disturbances during pregnancy and postpartum in women with depression. iScience. 2022; 25 (12): 105666. doi:10.1016/j.isci.2022.105666  
15.   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  
16.   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  
17.   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  
18.   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  
19.   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  
20.   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  
21.   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  
22.   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  
23.   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  
24.   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  
25.   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  
26.   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  
27.   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  
28.   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  
29.   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  
30.   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  
31.   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  
32.   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  
33.   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  
34.   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  
35.   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  
36.   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  
37.   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  
38.   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  
39.   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  
40.   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  
41.   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  
42.   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  
43.   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  
44.   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  
45.   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  
46.   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  
47.   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  
48.   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  
49.   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  
50.   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  
51.   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  
52.   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  
53.   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  
54.   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  
55.   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