Saito, Rintaro

写真a

Affiliation

Graduate School of Media and Governance (Shonan Fujisawa)

Position

Project Professor (Non-tenured)

Related Websites

Career 【 Display / hide

  • 2000.04
    -
    2002.03

    理化学研究所, ゲノム科学総合研究センター, 研究員

  • 2002.04
    -
    2011.01

    慶應義塾大学, 環境情報学部, 専任講師(有期)

  • 2011.02
    -
    2014.01

    University of California, San Diego, Department of Medicine, Visiting Assistant Professor

  • 2014.03
    -
    2017.07

    University of California, San Diego, Department of Medicine, Associate Project Scientist

  • 2017.09
    -
    Present

    慶應義塾大学, 政策・メディア研究科, 特任教授

Academic Background 【 Display / hide

  • 2000.03

    Keio University, 政策・メディア研究科

    Graduate School, Completed, Doctoral course

Academic Degrees 【 Display / hide

  • 学術, Keio University, 2000.03

 

Research Areas 【 Display / hide

  • Life / Health / Medical informatics

 

Books 【 Display / hide

  • 実験医学「Cytoscapeによる細胞内インタラクトームの解析」

    SAITO Rintaro, ONO, Keiichiro, 羊土社, 2013.08

    Scope: 2291-2297

  • 機能性non-coding RNA「バイオインフォマティクスを用いたnon-coding RNA予測の様々な試み」

    SAITO RINTARO, クバプロ, 2006.04

    Scope: 171-187

  • 人工知能学事典「モチーフ抽出」

    SAITO RINTARO, 共立出版, 2005.12

    Scope: 17-4

  • バイオインフォマティクスの基礎-ゲノム解析プログラミングを中心に

    TOMITA, Masaru, SAITO Rintaro, サイエンス社, 2005.07

  • ゲノムネットワーク「ゲノムワイドデータの精製」

    SAITO RINTARO, Suzuki Harukazu, Tomita Masaru, 共立出版, 2004.12

    Scope: III 8

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Papers 【 Display / hide

  • Urinary metabolome analyses of patients with acute kidney injury using capillary electrophoresis-mass spectrometry

    Saito R., Hirayama A., Akiba A., Kamei Y., Kato Y., Ikeda S., Kwan B., Pu M., Natarajan L., Shinjo H., Akiyama S., Tomita M., Soga T., Maruyama S.

    Metabolites (Metabolites)  11 ( 10 )  2021.10

     View Summary

    Acute kidney injury (AKI) is defined as a rapid decline in kidney function. The associated syndromes may lead to increased morbidity and mortality, but its early detection remains difficult. Using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS), we analyzed the urinary metabolomic profile of patients admitted to the intensive care unit (ICU) after invasive surgery. Urine samples were collected at six time points: before surgery, at ICU admission and 6, 12, 24 and 48 h after. First, urine samples from 61 initial patients (non-AKI: 23, mild AKI: 24, severe AKI: 14) were measured, followed by the measurement of urine samples from 60 additional patients (non-AKI: 40, mild AKI: 20). Glycine and ethanolamine were decreased in patients with AKI compared with non-AKI patients at 6–24 h in the two groups. The linear statistical model constructed at each time point by machine learning achieved the best performance at 24 h (median AUC: 89%, cross-validated) for the 1st group. When cross-validated between the two groups, the AUC showed the best value of 70% at 12 h. These results identified metabolites and time points that show patterns specific to subjects who develop AKI, paving the way for the development of better biomarkers.

  • Quality Assessment of Untargeted Analytical Data in a Large-Scale Metabolomic Study

    Saito R, Sugimoto M, Hirayama A, Soga T, Tomita M, Takebayashi T

    J Clin Med (Journal of Clinical Medicine)  10 ( 9 )  2021.04

    Joint Work

     View Summary

    Large-scale metabolomic studies have become common, and the reliability of the peak data produced by the various instruments is an important issue. However, less attention has been paid to the large number of uncharacterized peaks in untargeted metabolomics data. In this study, we tested various criteria to assess the reliability of 276 and 202 uncharacterized peaks that were detected in a gathered set of 30 plasma and urine quality control samples, respectively, using capillary electrophoresis-time-of-flight mass spectrometry (CE-TOFMS). The linear relationship between the amounts of pooled samples and the corresponding peak areas was one of the criteria used to select reliable peaks. We used samples from approximately 3000 participants in the Tsuruoka Metabolome Cohort Study to investigate patterns of the areas of these uncharacterized peaks among the samples and clustered the peaks by combining the patterns and differences in the migration times. Our assessment pipeline removed substantial numbers of unreliable or redundant peaks and detected 35 and 74 reliable uncharacterized peaks in plasma and urine, respectively, some of which may correspond to metabolites involved in important physiological processes such as disease progression. We propose that our assessment pipeline can be used to help establish large-scale untargeted clinical metabolomic studies.

  • Identification of pathognomonic purine synthesis biomarkers by metabolomic profiling of adolescents with obesity and type 2 diabetes

    Concepcion J., Chen K., Saito R., Gangoiti J., Mendez E., Nikita M.E., Barshop B.A., Natarajan L., Sharma K., Kim J.J., Kim J.J.

    PLoS ONE (PLoS ONE)  15 ( 6 June )  2020.06

     View Summary

    © 2020 Concepcion et al. The incidence of type 2 diabetes is increasing more rapidly in adolescents than in any other age group. We identified and compared metabolite signatures in obese children with type 2 diabetes (T2D), obese children without diabetes (OB), and healthy, age- and gendermatched normal weight controls (NW) by measuring 273 analytes in fasting plasma and 24- hour urine samples from 90 subjects by targeted LC-MS/MS. Diabetic subjects were within 2 years of diagnosis in an attempt to capture early-stage disease prior to declining renal function. We found 22 urine metabolites that were uniquely associated with T2D when compared to OB and NW groups. The metabolites most significantly elevated in T2D youth included members of the betaine pathway, nucleic acid metabolism, and branched-chain amino acids (BCAAs) and their catabolites. Notably, the metabolite pattern in OB and T2D groups differed between urine and plasma, suggesting that urinary BCAAs and their intermediates behaved as a more specific biomarker for T2D, while plasma BCAAs associated with the obese, insulin resistant state independent of diabetes status. Correlative analysis of metabolites in the T2D signature indicated that betaine metabolites, BCAAs, and aromatic amino acids were associated with hyperglycemia, but BCAA acylglycine derivatives and nucleic acid metabolites were linked to insulin resistance. Of major interest, we found that urine levels of succinylaminoimidazole carboxamide riboside (SAICA-riboside) were increased in diabetic youth, identifying urine SAICA-riboside as a potential biomarker for T2D.

  • The NASA twins study: A multidimensional analysis of a year-long human spaceflight

    Garrett-Bakelman F.E., Darshi M., Green S.J., Gur R.C., Lin L., Macias B.R., McKenna M.J., Meydan C., Mishra T., Nasrini J., Piening B.D., Rizzardi L.F., Sharma K., Siamwala J.H., Taylor L., Vitaterna M.H., Afkarian M., Afshinnekoo E., Ahadi S., Ambati A., Arya M., Bezdan D., Callahan C.M., Chen S., Choi A.M.K., Chlipala G.E., Contrepois K., Covington M., Crucian B.E., De Vivo I., Dinges D.F., Ebert D.J., Feinberg J.I., Gandara J.A., George K.A., Goutsias J., Grills G.S., Hargens A.R., Heer M., Hillary R.P., Hoofnagle A.N., Hook V.Y.H., Jenkinson G., Jiang P., Keshavarzian A., Laurie S.S., Lee-McMullen B., Lumpkins S.B., MacKay M., Maienschein-Cline M.G., Melnick A.M., Moore T.M., Nakahira K., Patel H.H., Pietrzyk R., Rao V., Saito R., Salins D.N., Schilling J.M., Sears D.D., Sheridan C.K., Stenger M.B., Tryggvadottir R., Urban A.E., Vaisar T., Van Espen B., Zhang J., Ziegler M.G., Zwart S.R., Charles J.B., Kundrot C.E., Scott G.B.I., Bailey S.M., Basner M., Feinberg A.P., Lee S.M.C., Mason C.E., Mignot E., Rana B.K., Smith S.M., Snyder M.P., Turek F.W.

    Science (Science)  364 ( 6436 )  2019

    ISSN  00368075

     View Summary

    © 2017 The Authors. To understand the health impact of long-duration spaceflight, one identical twin astronaut was monitored before, during, and after a 1-year mission onboard the International Space Station; his twin served as a genetically matched ground control. Longitudinal assessments identified spaceflight-specific changes, including decreased body mass, telomere elongation, genome instability, carotid artery distension and increased intimamedia thickness, altered ocular structure, transcriptional and metabolic changes, DNA methylation changes in immune and oxidative stress-related pathways, gastrointestinal microbiota alterations, and some cognitive decline postflight. Although average telomere length, global gene expression, and microbiome changes returned to near preflight levels within 6 months after return to Earth, increased numbers of short telomeres were observed and expression of some genes was still disrupted. These multiomic, molecular, physiological, and behavioral datasets provide a valuable roadmap of the putative health risks for future human spaceflight.

  • Distinct gene signatures predict insulin resistance in young mice with high fat diet-induced obesity

    Chen, K., Jih, A., Osborn, O., Kavaler, S. T., Fu, W., Sasik, R., Saito, R. and Kim, J. J.

    Physiol Genomics (Physiological Genomics)  50 ( 3 ) 144 - 157 2018.03

    ISSN  1531-2267

     View Summary

    Highly inbred C57BL/6 mice show wide variation in their degree of insulin resistance in response to diet-induced obesity even though they are almost genetically identical. Here we employed transcriptional profiling by RNA sequencing (RNA-Seq) of visceral adipose tissue (VAT) and liver in young mice to determine how gene expression patterns correlate with the later development of high-fat diet (HFD)-induced insulin resistance in adulthood. To accomplish this goal, we partially removed and banked tissues from pubertal mice. Mice subsequently received HFD followed by metabolic phenotyping to identify two well-defined groups of mice with either severe or mild insulin resistance. The remaining tissues were collected at study termination. We then applied RNA-Seq to generate transcriptome profiles associated with worsened insulin resistance before and after the initiation of HFD. We found 244 up- and 109 downregulated genes in VAT of the most insulin-resistant mice even before HFD exposure. Downregulated genes included serine protease inhibitor, major urinary protein, and complement genes; upregulated genes represented mostly muscle constituents. These gene families were also differentially expressed in VAT of mice with high or low insulin resistance after HFD. Inflammatory genes predicted insulin resistance in liver, but not in VAT. In contrast, when we compared VAT of all mice before and after HFD, differentially expressed genes were predominantly composed of immune response genes. These data show a distinct set of gene transcripts in young mice correlates with the severity of insulin resistance in adulthood, providing insight into the pathogenesis of insulin resistance in early life.

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Papers, etc., Registered in KOARA 【 Display / hide

Presentations 【 Display / hide

  • Multi-omics and Systems Analysis Reveal a Novel Role of MDM2 in Diabetic Nephropathy

    Saito R, Rocanin-Arjo A, You Y-H, Darshi M, Van Espen B, Miyamoto S, Pham J, Pu M, Romoli S, Natarajan L, Ju W, Kretzler M, Nelson R, Ono K, Thomasova D, Mulay SR, Ideker T, D'Agati, Beyret E, Izpisua Belmonte JC, Anders HJ, Sharma K

    ISN's Forefronts Symposium on the Metabolome and Microbiome in Kidney Disease, 2016, Oral Presentation(general)

  • Computational Prediction and Experimental Analyses of Proteins That Bridge Metabolite Markers of Human Diabetic Nephropathy

    Saito R, Rocanin-Arjo A, You Y-H, Darshi M, Van Espen B, Pu M, Romoli S, Natarajan L, Ju W, Kretzler M, Nelson R, Ono K, Thomasova D, Mulay S, Belmonte JC, Anders HJ, Sharma K

    ASN Kidney Week 2015 Annual Meeting, 2015, Oral Presentation(general)

  • A travel guide to Cytoscape plugins

    Saito R, Smoot ME, Ono K, Ruscheinski J, Wang P-L, Lotia S, Pico AR, Bader GD, Ideker T.

    Cytoscape Retreat, 2012, Oral Presentation(general)

  • Comprehensive Analysis of Domain-Domain Interactions Using In Vitro Virus

    Saito R, Ozawa Y, Fujimori S, Matsui M, Ushiama S, Kashima H, Yanagawa H, Miyamoto-Sato E, Tomita M

    Genome Informatics Workshop 2007 (GIW2007) (Singapore) , 2007, Oral Presentation(general)

  • IVV データを用いたモチーフ間相互作用ネットワークの構築と創薬への応用

    斎藤輪太郎, 宮本悦子, 柳川弘志, 冨田勝

    分子生物学会2006フォーラム シンポジウム Sp2K「ゲノムネットワーク解析に基づく新しい創薬,診断,治療戦略」 (名古屋) , 2006, Oral Presentation(general)

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Research Projects of Competitive Funds, etc. 【 Display / hide

  • Development of AI for the discovery of novel kidney disease genes using metabolomics and network biology

    2019.04
    -
    2022.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, 齋藤 輪太郎, Grant-in-Aid for Scientific Research (C), Principal Investigator

  • 人々を軸にあらゆる情報をオープンに活用する基盤「PeOPLe」によるライフイノベーションの創出

    2018
    -
    2022

    国立研究開発法人科学技術振興機構, 産学共創プラットフォーム共同研究推進プログラム, 武林亨, Co-investigator

  • Discovery and Validation of Biomarkers for Diabetic Nephropathy

    2015.10
    -
    2018.03

    JDRF, JDRF Network Grant, Kumar Sharma, No Setting, Co-investigator

  • 分子間相互作用ネットワ―クのクラスタリングアルゴリズムの開発

    2008

    慶應義塾大学, Keio Gijuku Academic Development Funds, Research grant, Principal Investigator

  • In vitro virus法による転写因子複合体の大規模解析

    2006
    -
    2008

    文部科学省, ゲノムネットワークプロジェクト, 柳川弘志, Co-investigator

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Intellectual Property Rights, etc. 【 Display / hide

  • タンパク質間相互作用の評価方法

    Application No.: 2001-397762  2001 

    Patent, Joint

 

Courses Taught 【 Display / hide

  • GENOMIC MOLECULAR BIOLOGY 2

    2021

  • GENOMIC MOLECULAR BIOLOGY 1

    2021

  • GENOMIC MOLECULAR BIOLOGY 2

    2020

  • GENOMIC MOLECULAR BIOLOGY 1

    2020

  • GENOMIC MOLECULAR BIOLOGY 2

    2019

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Courses Previously Taught 【 Display / hide

  • ゲノム分子生物学1

    Keio University, 2018, Spring Semester

  • ゲノム分子生物学2

    Keio University, 2018, Autumn Semester, Lecture

  • 生命情報解析

    慶應義塾大学環境情報学部, 2010

  • バイオインフォマティクスアルゴリズム

    慶應義塾大学政策・メディア研究科, 2010

  • ゲノム解析プログラミング

    慶應義塾大学環境情報学部, 2010

 

Memberships in Academic Societies 【 Display / hide

  • 日本分子生物学会

     
  • 日本バイオインフォマティクス学会

     

Committee Experiences 【 Display / hide

  • 2018

    生命医薬情報学連合大会プログラム委員, 日本バイオインフォマティクス学会

  • 2011
    -
    2014

    Collaboration consultant, Cytoscape instructor, National Resource for Network Biology / The San Diego Center for Systems Biology

  • 2010

    Programme committee member, The International Conference on Bioinformatics (InCoB2010)

  • 2008
    -
    2010

    Programme committee member, Genome Informatics Workshop (GIW)