齋藤 輪太郎 (サイトウ リンタロウ)

Saito, Rintaro

写真a

所属(所属キャンパス)

政策・メディア研究科 (湘南藤沢)

職名

特任教授(有期)

HP

経歴 【 表示 / 非表示

  • 2000年04月
    -
    2002年03月

    理化学研究所, ゲノム科学総合研究センター, 研究員

  • 2002年04月
    -
    2011年01月

    慶應義塾大学, 環境情報学部, 専任講師(有期)

  • 2011年02月
    -
    2014年01月

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

  • 2014年03月
    -
    2017年07月

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

  • 2017年09月
    -
    継続中

    慶應義塾大学, 政策・メディア研究科, 特任教授

学歴 【 表示 / 非表示

  • 2000年03月

    慶應義塾, 政策・メディア研究科

    大学院, 修了, 博士

学位 【 表示 / 非表示

  • 学術, 慶應義塾, 2000年03月

 

研究分野 【 表示 / 非表示

  • 生命・健康・医療情報学

 

著書 【 表示 / 非表示

  • 実験医学「Cytoscapeによる細胞内インタラクトームの解析」

    斎藤 輪太郎, 大野圭一朗, 羊土社, 2013年08月

    担当範囲: 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」

    斎藤 輪太郎, クバプロ, 2006年04月

    担当範囲: 171-187

  • 人工知能学事典「モチーフ抽出」

    斎藤 輪太郎, 共立出版, 2005年12月

    担当範囲: 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

    冨田勝(監) 斎藤 輪太郎(著), サイエンス社, 2005年07月

  • ゲノムネットワーク「ゲノムワイドデータの精製」

    斎藤 輪太郎, 鈴木 治和, 冨田 勝, 共立出版, 2004年12月

    担当範囲: III 8

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論文 【 表示 / 非表示

  • 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年10月

     概要を見る

    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.

    Journal of Clinical Medicine (Journal of Clinical Medicine)  10 ( 9 )  2021年04月

    共著

     概要を見る

    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年06月

     概要を見る

    © 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年

    ISSN  00368075

     概要を見る

    © 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., Fu W., Sasik R., Saito R., Kim J.

    Physiological Genomics (Physiological Genomics)  50 ( 3 ) 144 - 157 2018年03月

    ISSN  1531-2267

     概要を見る

    © 2018 American Physiological Society. All rights reserved. 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 upand 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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KOARA(リポジトリ)収録論文等 【 表示 / 非表示

研究発表 【 表示 / 非表示

  • 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年, 口頭(一般)

  • 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年, 口頭(一般)

  • 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年, 口頭(一般)

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

    斎藤 輪太郎

    Genome Informatics Workshop 2007 (GIW2007) (Singapore) , 2007年, 口頭(一般)

  • 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年, 口頭(一般)

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競争的資金等の研究課題 【 表示 / 非表示

  • メタボロミクスとネットワーク生物学を用いた新規腎臓病関連遺伝子予測AIの開発

    2019年04月
    -
    2022年03月

    文部科学省・日本学術振興会, 科学研究費助成事業, 齋藤 輪太郎, 基盤研究(C), 補助金,  代表

  • 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年
    -
    2022年

    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, 分担

  • Discovery and Validation of Biomarkers for Diabetic Nephropathy

    2015年10月
    -
    2018年03月

    JDRF, JDRF Network Grant, Kumar Sharma, 分担

  • 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年

    慶應義塾大学, 慶應義塾学事振興資金, 補助金,  代表

  • In vitro virus法による転写因子複合体の大規模解析

    2006年
    -
    2008年

    文部科学省, ゲノムネットワークプロジェクト, 柳川弘志, 分担

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知的財産権等 【 表示 / 非表示

  • タンパク質間相互作用の評価方法

    特願: 2001-397762  2001年 

    特許, 共同

 

担当授業科目 【 表示 / 非表示

  • ゲノム分子生物学2

    2021年度

  • ゲノム分子生物学1

    2021年度

  • ゲノム分子生物学2

    2020年度

  • ゲノム分子生物学1

    2020年度

  • ゲノム分子生物学2

    2019年度

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担当経験のある授業科目 【 表示 / 非表示

  • ゲノム分子生物学1

    慶應義塾, 2018年度, 春学期

  • ゲノム分子生物学2

    慶應義塾, 2018年度, 秋学期, 講義

  • 生命情報解析

    慶應義塾大学環境情報学部, 2010年度

  • 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年度

  • 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年度

 

所属学協会 【 表示 / 非表示

  • 日本分子生物学会

     
  • 日本バイオインフォマティクス学会

     

委員歴 【 表示 / 非表示

  • 2018年

    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年
    -
    2014年

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

  • 2010年

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

  • 2008年
    -
    2010年

    Programme committee member, Genome Informatics Workshop (GIW)