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174 results on '"Furuno, M"'

1. The Transcriptional Landscape of the Mammalian Genome

Author

Carninci, P., Kasukawa, T., Katayama, S., Gough, J., Frith, M. C., Maeda, N., Oyama, R., Ravasi, T., Lenhard, B., Wells, C., Kodzius, R., Shimokawa, K., Bajic, V. B., Brenner, S. E., Batalov, S., Forrest, A. R. R., Zavolan, M., Davis, M. J., Wilming, L. G., Aidinis, V., Allen, J. E., Ambesi-Impiombato, A., Apweiler, R., Aturaliya, R. N., Bailey, T. L., Bansal, M., Baxter, L., Beisel, K. W., Bersano, T., Bono, H., Chalk, A. M., Chiu, K. P., Choudhary, V., Christoffels, A., Clutterbuck, D. R., Crowe, M. L., Dalla, E., Dalrymple, B. P., de Bono, B., Della Gatta, G., di Bernardo, D., Down, T., Engstrom, P., Fagiolini, M., Faulkner, G., Fletcher, C. F., Fukushima, T., Furuno, M., Futaki, S., Gariboldi, M., Georgii-Hemming, P., Gingeras, T. R., Gojobori, T., Green, R. E., Gustincich, S., Harbers, M., Hayashi, Y., Hensch, T. K., Hirokawa, N., Hill, D., Huminiecki, L., Iacono, M., Ikeo, K., Iwama, A., Ishikawa, T., Jakt, M., Kanapin, A., Katoh, M., Kawasawa, Y., Kelso, J., Kitamura, H., Kitano, H., Kollias, G., Krishnan, S. P. T., Kruger, A., Kummerfeld, S. K., Kurochkin, I. V., Lareau, L. F., Lazarevic, D., Lipovich, L., Liu, J., Liuni, S., McWilliam, S., Babu, M. Madan, Madera, M., Marchionni, L., Matsuda, H., Matsuzawa, S., Miki, H., Mignone, F., Miyake, S., Morris, K., Mottagui-Tabar, S., Mulder, N., Nakano, N., Nakauchi, H., Ng, P., Nilsson, R., Nishiguchi, S., Nishikawa, S., Nori, F., Ohara, O., Okazaki, Y., Orlando, V., Pang, K. C., Pavan, W. J., Pavesi, G., Pesole, G., Petrovsky, N., Piazza, S., Reed, J., Reid, J. F., Ring, B. Z., Ringwald, M., Rost, B., Ruan, Y., Salzberg, S. L., Sandelin, A., Schneider, C., Schöbach, C., Sekiguchi, K., Semple, C. A. M., Seno, S., Sessa, L., Sheng, Y., Shibata, Y., Shimada, H., Shimada, K., Silva, D., Sinclair, B., Sperling, S., Stupka, E., Sugiura, K., Sultana, R., Takenaka, Y., Taki, K., Tammoja, K., Tan, S. L., Tang, S., Taylor, M. S., Tegner, J., Teichmann, S. A., Ueda, H. R., van Nimwegen, E., Verardo, R., Wei, C. L., Yagi, K., Yamanishi, H., Zabarovsky, E., Zhu, S., Zimmer, A., Hide, W., Bult, C., Grimmond, S. M., Teasdale, R. D., Liu, E. T., Brusic, V., Quackenbush, J., Wahlestedt, C., Mattick, J. S., Hume, D. A., Kai, C., Sasaki, D., Tomaru, Y., Fukuda, S., Kanamori-Katayama, M., Suzuki, M., Aoki, J., Arakawa, T., Iida, J., Imamura, K., Itoh, M., Kato, T., Kawaji, H., Kawagashira, N., Kawashima, T., Kojima, M., Kondo, S., Konno, H., Nakano, K., Ninomiya, N., Nishio, T., Okada, M., Plessy, C., Shibata, K., Shiraki, T., Suzuki, S., Tagami, M., Waki, K., Watahiki, A., Okamura-Oho, Y., Suzuki, H., and Kawai, J.

Published
2005

2. Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Author

Grapotte M., Saraswat M., Bessiere C., Menichelli C., Ramilowski J. A., Severin J., Hayashizaki Y., Itoh M., Tagami M., Murata M., Kojima-Ishiyama M., Noma S., Noguchi S., Kasukawa T., Hasegawa A., Suzuki H., Nishiyori-Sueki H., Frith M. C., Abugessaisa I., Aitken S., Aken B. L., Alam I., Alam T., Alasiri R., Alhendi A. M. N., Alinejad-Rokny H., Alvarez M. J., Andersson R., Arakawa T., Araki M., Arbel T., Archer J., Archibald A. L., Arner E., Arner P., Asai K., Ashoor H., Astrom G., Babina M., Baillie J. K., Bajic V. B., Bajpai A., Baker S., Baldarelli R. M., Balic A., Bansal M., Batagov A. O., Batzoglou S., Beckhouse A. G., Beltrami A. P., Beltrami C. A., Bertin N., Bhattacharya S., Bickel P. J., Blake J. A., Blanchette M., Bodega B., Bonetti A., Bono H., Bornholdt J., Bttcher M., Bougouffa S., Boyd M., Breda J., Brombacher F., Brown J. B., Bult C. J., Burroughs A. M., Burt D. W., Busch A., Caglio G., Califano A., Cameron C. J., Cannistraci C. V., Carbone A., Carlisle A. J., Carninci P., Carter K. W., Cesselli D., Chang J. -C., Chen J. C., Chen Y., Chierici M., Christodoulou J., Ciani Y., Clark E. L., Coskun M., Dalby M., Dalla E., Daub C. O., Davis C. A., de Hoon M. J. L., de Rie D., Denisenko E., Deplancke B., Detmar M., Deviatiiarov R., Di Bernardo D., Diehl A. D., Dieterich L. C., Dimont E., Djebali S., Dohi T., Dostie J., Drablos F., Edge A. S. B., Edinger M., Ehrlund A., Ekwall K., Elofsson A., Endoh M., Enomoto H., Enomoto S., Faghihi M., Fagiolini M., Farach-Carson M. C., Faulkner G. J., Favorov A., Fernandes A. M., Ferrai C., Forrest A. R. R., Forrester L. M., Forsberg M., Fort A., Francescatto M., Freeman T. C., Frith M., Fukuda S., Funayama M., Furlanello C., Furuno M., Furusawa C., Gao H., Gazova I., Gebhard C., Geier F., Geijtenbeek T. B. H., Ghosh S., Ghosheh Y., Gingeras T. R., Gojobori T., Goldberg T., Goldowitz D., Gough J., Greco D., Gruber A. J., Guhl S., Guigo R., Guler R., Gusev O., Gustincich S., Ha T. J., Haberle V., Hale P., Hallstrom B. M., Hamada M., Handoko L., Hara M., Harbers M., Harrow J., Harshbarger J., Hase T., Hashimoto K., Hatano T., Hattori N., Hayashi R., Herlyn M., Hettne K., Heutink P., Hide W., Hitchens K. J., Sui S. H., 't Hoen P. A. C., Hon C. C., Hori F., Horie M., Horimoto K., Horton P., Hou R., Huang E., Huang Y., Hugues R., Hume D., Ienasescu H., Iida K., Ikawa T., Ikemura T., Ikeo K., Inoue N., Ishizu Y., Ito Y., Ivshina A. V., Jankovic B. R., Jenjaroenpun P., Johnson R., Jorgensen M., Jorjani H., Joshi A., Jurman G., Kaczkowski B., Kai C., Kaida K., Kajiyama K., Kaliyaperumal R., Kaminuma E., Kanaya T., Kaneda H., Kapranov P., Kasianov A. S., Katayama T., Kato S., Kawaguchi S., Kawai J., Kawaji H., Kawamoto H., Kawamura Y. I., Kawasaki S., Kawashima T., Kempfle J. S., Kenna T. J., Kere J., Khachigian L., Kiryu H., Kishima M., Kitajima H., Kitamura T., Kitano H., Klaric E., Klepper K., Klinken S. P., Kloppmann E., Knox A. J., Kodama Y., Kogo Y., Kojima M., Kojima S., Komatsu N., Komiyama H., Kono T., Koseki H., Koyasu S., Kratz A., Kukalev A., Kulakovskiy I., Kundaje A., Kunikata H., Kuo R., Kuo T., Kuraku S., Kuznetsov V. A., Kwon T. J., Larouche M., Lassmann T., Law A., Le-Cao K. -A., Lecellier C. -H., Lee W., Lenhard B., Lennartsson A., Li K., Li R., Lilje B., Lipovich L., Lizio M., Lopez G., Magi S., Mak G. K., Makeev V., Manabe R., Mandai M., Mar J., Maruyama K., Maruyama T., Mason E., Mathelier A., Matsuda H., Medvedeva Y. A., Meehan T. F., Mejhert N., Meynert A., Mikami N., Minoda A., Miura H., Miyagi Y., Miyawaki A., Mizuno Y., Morikawa H., Morimoto M., Morioka M., Morishita S., Moro K., Motakis E., Motohashi H., Mukarram A. K., Mummery C. L., Mungall C. J., Murakawa Y., Muramatsu M., Nagasaka K., Nagase T., Nakachi Y., Nakahara F., Nakai K., Nakamura K., Nakamura Y., Nakazawa T., Nason G. P., Nepal C., Nguyen Q. H., Nielsen L. K., Nishida K., Nishiguchi K. M., Nishiyori H., Nitta K., Notredame C., Ogishima S., Ohkura N., Ohno H., Ohshima M., Ohtsu T., Okada Y., Okada-Hatakeyama M., Okazaki Y., Oksvold P., Orlando V., Ow G. S., Ozturk M., Pachkov M., Paparountas T., Parihar S. P., Park S. -J., Pascarella G., Passier R., Persson H., Philippens I. H., Piazza S., Plessy C., Pombo A., Ponten F., Poulain S., Poulsen T. M., Pradhan S., Prezioso C., Pridans C., Qin X. -Y., Quackenbush J., Rackham O., Ramilowski J., Ravasi T., Rehli M., Rennie S., Rito T., Rizzu P., Robert C., Roos M., Rost B., Roudnicky F., Roy R., Rye M. B., Sachenkova O., Saetrom P., Sai H., Saiki S., Saito M., Saito A., Sakaguchi S., Sakai M., Sakaue S., Sakaue-Sawano A., Sandelin A., Sano H., Sasamoto Y., Sato H., Saxena A., Saya H., Schafferhans A., Schmeier S., Schmidl C., Schmocker D., Schneider C., Schueler M., Schultes E. A., Schulze-Tanzil G., Semple C. A., Seno S., Seo W., Sese J., Sheng G., Shi J., Shimoni Y., Shin J. W., SimonSanchez J., Sivertsson A., Sjostedt E., Soderhall C., Laurent G. S., Stoiber M. H., Sugiyama D., Summers K. M., Suzuki A. M., Suzuki K., Suzuki M., Suzuki N., Suzuki T., Swanson D. J., Swoboda R. K., Taguchi A., Takahashi H., Takahashi M., Takamochi K., Takeda S., Takenaka Y., Tam K. T., Tanaka H., Tanaka R., Tanaka Y., Tang D., Taniuchi I., Tanzer A., Tarui H., Taylor M. S., Terada A., Terao Y., Testa A. C., Thomas M., Thongjuea S., Tomii K., Triglia E. T., Toyoda H., Tsang H. G., Tsujikawa M., Uhlen M., Valen E., van de Wetering M., van Nimwegen E., Velmeshev D., Verardo R., Vitezic M., Vitting-Seerup K., von Feilitzen K., Voolstra C. R., Vorontsov I. E., Wahlestedt C., Wasserman W. W., Watanabe K., Watanabe S., Wells C. A., Winteringham L. N., Wolvetang E., Yabukami H., Yagi K., Yamada T., Yamaguchi Y., Yamamoto M., Yamamoto Y., Yamanaka Y., Yano K., Yasuzawa K., Yatsuka Y., Yo M., Yokokura S., Yoneda M., Yoshida E., Yoshida Y., Yoshihara M., Young R., Young R. S., Yu N. Y., Yumoto N., Zabierowski S. E., Zhang P. G., Zucchelli S., Zwahlen M., Chatelain C., Brehelin L., Grapotte, M., Saraswat, M., Bessiere, C., Menichelli, C., Ramilowski, J. A., Severin, J., Hayashizaki, Y., Itoh, M., Tagami, M., Murata, M., Kojima-Ishiyama, M., Noma, S., Noguchi, S., Kasukawa, T., Hasegawa, A., Suzuki, H., Nishiyori-Sueki, H., Frith, M. C., Abugessaisa, I., Aitken, S., Aken, B. L., Alam, I., Alam, T., Alasiri, R., Alhendi, A. M. N., Alinejad-Rokny, H., Alvarez, M. J., Andersson, R., Arakawa, T., Araki, M., Arbel, T., Archer, J., Archibald, A. L., Arner, E., Arner, P., Asai, K., Ashoor, H., Astrom, G., Babina, M., Baillie, J. K., Bajic, V. B., Bajpai, A., Baker, S., Baldarelli, R. M., Balic, A., Bansal, M., Batagov, A. O., Batzoglou, S., Beckhouse, A. G., Beltrami, A. P., Beltrami, C. A., Bertin, N., Bhattacharya, S., Bickel, P. J., Blake, J. A., Blanchette, M., Bodega, B., Bonetti, A., Bono, H., Bornholdt, J., Bttcher, M., Bougouffa, S., Boyd, M., Breda, J., Brombacher, F., Brown, J. B., Bult, C. J., Burroughs, A. M., Burt, D. W., Busch, A., Caglio, G., Califano, A., Cameron, C. J., Cannistraci, C. V., Carbone, A., Carlisle, A. J., Carninci, P., Carter, K. W., Cesselli, D., Chang, J. -C., Chen, J. C., Chen, Y., Chierici, M., Christodoulou, J., Ciani, Y., Clark, E. L., Coskun, M., Dalby, M., Dalla, E., Daub, C. O., Davis, C. A., de Hoon, M. J. L., de Rie, D., Denisenko, E., Deplancke, B., Detmar, M., Deviatiiarov, R., Di Bernardo, D., Diehl, A. D., Dieterich, L. C., Dimont, E., Djebali, S., Dohi, T., Dostie, J., Drablos, F., Edge, A. S. B., Edinger, M., Ehrlund, A., Ekwall, K., Elofsson, A., Endoh, M., Enomoto, H., Enomoto, S., Faghihi, M., Fagiolini, M., Farach-Carson, M. C., Faulkner, G. J., Favorov, A., Fernandes, A. M., Ferrai, C., Forrest, A. R. R., Forrester, L. M., Forsberg, M., Fort, A., Francescatto, M., Freeman, T. C., Frith, M., Fukuda, S., Funayama, M., Furlanello, C., Furuno, M., Furusawa, C., Gao, H., Gazova, I., Gebhard, C., Geier, F., Geijtenbeek, T. B. H., Ghosh, S., Ghosheh, Y., Gingeras, T. R., Gojobori, T., Goldberg, T., Goldowitz, D., Gough, J., Greco, D., Gruber, A. J., Guhl, S., Guigo, R., Guler, R., Gusev, O., Gustincich, S., Ha, T. J., Haberle, V., Hale, P., Hallstrom, B. M., Hamada, M., Handoko, L., Hara, M., Harbers, M., Harrow, J., Harshbarger, J., Hase, T., Hashimoto, K., Hatano, T., Hattori, N., Hayashi, R., Herlyn, M., Hettne, K., Heutink, P., Hide, W., Hitchens, K. J., Sui, S. H., 't Hoen, P. A. C., Hon, C. C., Hori, F., Horie, M., Horimoto, K., Horton, P., Hou, R., Huang, E., Huang, Y., Hugues, R., Hume, D., Ienasescu, H., Iida, K., Ikawa, T., Ikemura, T., Ikeo, K., Inoue, N., Ishizu, Y., Ito, Y., Ivshina, A. V., Jankovic, B. R., Jenjaroenpun, P., Johnson, R., Jorgensen, M., Jorjani, H., Joshi, A., Jurman, G., Kaczkowski, B., Kai, C., Kaida, K., Kajiyama, K., Kaliyaperumal, R., Kaminuma, E., Kanaya, T., Kaneda, H., Kapranov, P., Kasianov, A. S., Katayama, T., Kato, S., Kawaguchi, S., Kawai, J., Kawaji, H., Kawamoto, H., Kawamura, Y. I., Kawasaki, S., Kawashima, T., Kempfle, J. S., Kenna, T. J., Kere, J., Khachigian, L., Kiryu, H., Kishima, M., Kitajima, H., Kitamura, T., Kitano, H., Klaric, E., Klepper, K., Klinken, S. P., Kloppmann, E., Knox, A. J., Kodama, Y., Kogo, Y., Kojima, M., Kojima, S., Komatsu, N., Komiyama, H., Kono, T., Koseki, H., Koyasu, S., Kratz, A., Kukalev, A., Kulakovskiy, I., Kundaje, A., Kunikata, H., Kuo, R., Kuo, T., Kuraku, S., Kuznetsov, V. A., Kwon, T. J., Larouche, M., Lassmann, T., Law, A., Le-Cao, K. -A., Lecellier, C. -H., Lee, W., Lenhard, B., Lennartsson, A., Li, K., Li, R., Lilje, B., Lipovich, L., Lizio, M., Lopez, G., Magi, S., Mak, G. K., Makeev, V., Manabe, R., Mandai, M., Mar, J., Maruyama, K., Maruyama, T., Mason, E., Mathelier, A., Matsuda, H., Medvedeva, Y. A., Meehan, T. F., Mejhert, N., Meynert, A., Mikami, N., Minoda, A., Miura, H., Miyagi, Y., Miyawaki, A., Mizuno, Y., Morikawa, H., Morimoto, M., Morioka, M., Morishita, S., Moro, K., Motakis, E., Motohashi, H., Mukarram, A. K., Mummery, C. L., Mungall, C. J., Murakawa, Y., Muramatsu, M., Nagasaka, K., Nagase, T., Nakachi, Y., Nakahara, F., Nakai, K., Nakamura, K., Nakamura, Y., Nakazawa, T., Nason, G. P., Nepal, C., Nguyen, Q. H., Nielsen, L. K., Nishida, K., Nishiguchi, K. M., Nishiyori, H., Nitta, K., Notredame, C., Ogishima, S., Ohkura, N., Ohno, H., Ohshima, M., Ohtsu, T., Okada, Y., Okada-Hatakeyama, M., Okazaki, Y., Oksvold, P., Orlando, V., Ow, G. S., Ozturk, M., Pachkov, M., Paparountas, T., Parihar, S. P., Park, S. -J., Pascarella, G., Passier, R., Persson, H., Philippens, I. H., Piazza, S., Plessy, C., Pombo, A., Ponten, F., Poulain, S., Poulsen, T. M., Pradhan, S., Prezioso, C., Pridans, C., Qin, X. -Y., Quackenbush, J., Rackham, O., Ramilowski, J., Ravasi, T., Rehli, M., Rennie, S., Rito, T., Rizzu, P., Robert, C., Roos, M., Rost, B., Roudnicky, F., Roy, R., Rye, M. B., Sachenkova, O., Saetrom, P., Sai, H., Saiki, S., Saito, M., Saito, A., Sakaguchi, S., Sakai, M., Sakaue, S., Sakaue-Sawano, A., Sandelin, A., Sano, H., Sasamoto, Y., Sato, H., Saxena, A., Saya, H., Schafferhans, A., Schmeier, S., Schmidl, C., Schmocker, D., Schneider, C., Schueler, M., Schultes, E. A., Schulze-Tanzil, G., Semple, C. A., Seno, S., Seo, W., Sese, J., Sheng, G., Shi, J., Shimoni, Y., Shin, J. W., Simonsanchez, J., Sivertsson, A., Sjostedt, E., Soderhall, C., Laurent, G. S., Stoiber, M. H., Sugiyama, D., Summers, K. M., Suzuki, A. M., Suzuki, K., Suzuki, M., Suzuki, N., Suzuki, T., Swanson, D. J., Swoboda, R. K., Taguchi, A., Takahashi, H., Takahashi, M., Takamochi, K., Takeda, S., Takenaka, Y., Tam, K. T., Tanaka, H., Tanaka, R., Tanaka, Y., Tang, D., Taniuchi, I., Tanzer, A., Tarui, H., Taylor, M. S., Terada, A., Terao, Y., Testa, A. C., Thomas, M., Thongjuea, S., Tomii, K., Triglia, E. T., Toyoda, H., Tsang, H. G., Tsujikawa, M., Uhlen, M., Valen, E., van de Wetering, M., van Nimwegen, E., Velmeshev, D., Verardo, R., Vitezic, M., Vitting-Seerup, K., von Feilitzen, K., Voolstra, C. R., Vorontsov, I. E., Wahlestedt, C., Wasserman, W. W., Watanabe, K., Watanabe, S., Wells, C. A., Winteringham, L. N., Wolvetang, E., Yabukami, H., Yagi, K., Yamada, T., Yamaguchi, Y., Yamamoto, M., Yamamoto, Y., Yamanaka, Y., Yano, K., Yasuzawa, K., Yatsuka, Y., Yo, M., Yokokura, S., Yoneda, M., Yoshida, E., Yoshida, Y., Yoshihara, M., Young, R., Young, R. S., Yu, N. Y., Yumoto, N., Zabierowski, S. E., Zhang, P. G., Zucchelli, S., Zwahlen, M., Chatelain, C., Brehelin, L., Institute of Biotechnology, Biosciences, Institut de Génétique Moléculaire de Montpellier (IGMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), RIKEN Center for Integrative Medical Sciences [Yokohama] (RIKEN IMS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), National Institute of Advanced Industrial Science and Technology (AIST), SANOFI Recherche, University of British Columbia (UBC), Experimental Immunology, Infectious diseases, AII - Infectious diseases, Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), and Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)

Subjects
0301 basic medicine, General Physics and Astronomy, Genome, Mice, 0302 clinical medicine, Transcription (biology), Promoter Regions, Genetic, Transcription Initiation, Genetic, 0303 health sciences, Multidisciplinary, 1184 Genetics, developmental biology, physiology, High-Throughput Nucleotide Sequencing, Neurodegenerative Diseases, 222 Other engineering and technologies, Genomics, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], humanities, Enhancer Elements, Genetic, Microsatellite Repeat, Transcription Initiation Site, Sequence motif, Transcription Initiation, Human, Enhancer Elements, Neural Networks, Science, 610 Medicine & health, Computational biology, Biology, Article, General Biochemistry, Genetics and Molecular Biology, Promoter Regions, 03 medical and health sciences, Computer, Deep Learning, Tandem repeat, Genetic, Clinical Research, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Machine learning, Genetics, Animals, Humans, Polymorphism, Enhancer, Transcriptomics, Gene, A549 Cell, 030304 developmental biology, Polymorphism, Genetic, Neurodegenerative Disease, Base Sequence, Animal, Genome, Human, Human Genome, Computational Biology, Promoter, General Chemistry, 113 Computer and information sciences, Cap analysis gene expression, 030104 developmental biology, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, Cardiovascular and Metabolic Diseases, A549 Cells, Minion, Generic health relevance, 3111 Biomedicine, Neural Networks, Computer, 610 Medizin und Gesundheit, 030217 neurology & neurosurgery, FANTOM consortium, Microsatellite Repeats
Abstract

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism., Nature Communications, 12 (1), ISSN:2041-1723

Published
2020

5. Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs

Author

Okazaki, Y., Furuno, M., Kasukawa, T., Adachi, J., Bono, H., Kondo, S., Nikaido, I., Osato, N., Saito, R., Suzuki, H., Yamanaka, I., Kiyosawa, H., Yagi, K., Tomaru, Y., Hasegawa, Y., Nogami, A., Schonbach, C., Gojobori, T., Baldarelli, R., Hill, D. P., Bult, C., Hume, D. A., Quackenbush, J., Schriml, L. M., Kanapin, A., Matsuda, H., Batalov, S., Beisel, K. W., Blake, J. A., Bradt, D., Brusic, V., Chothia, C., Corbani, L. E., Cousins, S., Dalla, E., Dragani, T. A., Fletcher, C. F., Forrest, A., Frazer, K. S., Gaasterland, T., Gariboldi, M., Gissi, C., Godzik, A., Gough, J., Grimmond, S., Gustincich, S., Hirokawa, N., Jackson, I. J., Jarvis, E. D., Kanai, A., Kawaji, H., Kawasawa, Y., Kedzierski, R. M., King, B. L., Konagaya, A., Kurochkin, I. V., Lee, Y., Lenhard, B., Lyons, P. A., Maglott, D. R., Maltais, L., Marchionni, L., McKenzie, L., Miki, H., Nagashima, T., Numata, K., Okido, T., Pavan, W. J., Pertea, G., Pesole, G., Petrovsky, N., Pillai, R., Pontius, J. U., Qi, D., Ramachandran, S., Ravasi, T., Reed, J. C., Reed, D. J., Reid, J., Ring, B. Z., Ringwald, M., Sandelin, A., Schneider, C., Semple, C. A. M., Setou, M., Shimada, K., Sultana, R., Takenaka, Y., Taylor, M. S., Teasdale, R. D., Tomita, M., Verardo, R., Wagner, L., Wahlestedt, C., Wang, Y., Watanabe, Y., Wells, C., Wilming, L. G., Wynshaw-Boris, A., Yanagisawa, M., Yang, I., Yang, L., Yuan, Z., Zavolan, M., Zhu, Y., Zimmer, A., Carninci, P., Hayatsu, N., Hirozane-Kishikawa, T., Konno, H., Nakamura, M., Sakazume, N., Sato, K., Shiraki, T., Waki, K., Kawai, J., Aizawa, K., Arakawa, T., Fukuda, S., Hara, A., Hashizume, W., Imotani, K., Ishii, Y., Itoh, M., Kagawa, I., Miyazaki, A., Sakai, K., Sasaki, D., Shibata, K., Shinagawa, A., Yasunishi, A., Yoshino, M., Waterston, R., Lander, E. S., Rogers, J., Birney, E., and Hayashizaki, Y.

Subjects
Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): The FANTOM Consortium and the RIKEN Genome Exploration Research Group Phase I & II Team*; FANTOM Consortium:; Y. Okazaki [1, 2]; M. Furuno [1]; T. Kasukawa [1, 3]; J. [...]

Published
2002
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8. Data Descriptor: FANTOM5 CAGE profiles of human and mouse samples

Author

Noguchi, S, Arakawa, T, Fukuda, S, Furuno, M, Hasegawa, A, Hori, F, Ishikawa-Kato, S, Kaida, K, Kaiho, A, Kanamori-Katayama, M, Kawashima, T, Kojima, M, Kubosaki, A, Manabe, R-I, Murata, M, Nagao-Sato, S, Nakazato, K, Ninomiya, N, Nishiyori-Sueki, H, Noma, S, Saijyo, E, Saka, A, Sakai, M, Simon, C, Suzuki, N, Tagami, M, Watanabe, S, Yoshida, S, Arner, P, Axton, RA, Babina, M, Baillie, JK, Barnett, TC, Beckhouse, AG, Blumenthal, A, Bodega, B, Bonetti, A, Briggs, J, Brombacher, F, Carlisle, AJ, Clevers, HC, Davis, CA, Detmar, M, Dohi, T, Edge, ASB, Edinger, M, Ehrlund, A, Ekwall, K, Endoh, M, Enomoto, H, Eslami, A, Fagiolini, M, Fairbairn, L, Farach-Carson, MC, Faulkner, GJ, Ferrai, C, Fisher, ME, Forrester, LM, Fujita, R, Furusawa, J-I, Geijtenbeek, TB, Gingeras, T, Goldowitz, D, Guhl, S, Guler, R, Gustincich, S, Ha, TJ, Hamaguchi, M, Hara, M, Hasegawa, Y, Herlyn, M, Heutink, P, Hitchens, KJ, Hume, DA, Ikawa, T, Ishizu, Y, Kai, C, Kawamoto, H, Kawamura, YI, Kempfle, JS, Kenna, TJ, Kere, J, Khachigian, LM, Kitamura, T, Klein, S, Klinken, SP, Knox, AJ, Kojima, S, Koseki, H, Koyasu, S, Lee, W, Lennartsson, A, Mackay-sim, A, Mejhert, N, Mizuno, Y, Morikawa, H, Morimoto, M, Moro, K, Morris, KJ, Motohashi, H, Mummery, CL, Nakachi, Y, Nakahara, F, Nakamura, T, Nakamura, Y, Nozaki, T, Ogishima, S, Ohkura, N, Ohno, H, Ohshima, M, Okada-Hatakeyama, M, Okazaki, Y, Orlando, V, Ovchinnikov, DA, Passier, R, Patrikakis, M, Pombo, A, Pradhan-Bhatt, S, Qin, X-Y, Rehli, M, Rizzu, P, Roy, S, Sajantila, A, Sakaguchi, S, Sato, H, Satoh, H, Savvi, S, Saxena, A, Schmidl, C, Schneider, C, Schulze-Tanzil, GG, Schwegmann, A, Sheng, G, Shin, JW, Sugiyama, D, Sugiyama, T, Summers, KM, Takahashi, N, Takai, J, Tanaka, H, Tatsukawa, H, Tomoiu, A, Toyoda, H, van de Wetering, M, van den Berg, LM, Verardo, R, Vijayan, D, Wells, CA, Winteringham, LN, Wolvetang, E, Yamaguchi, Y, Yamamoto, M, Yanagi-Mizuochi, C, Yoneda, M, Yonekura, Y, Zhang, PG, Zucchelli, S, Abugessaisa, I, Arner, E, Harshbarger, J, Kondo, A, Lassmann, T, Lizio, M, Sahin, S, Sengstag, T, Severin, J, Shimoji, H, Suzuki, M, Suzuki, H, Kawai, J, Kondo, N, Itoh, M, Daub, CO, Kasukawa, T, Kawaji, H, Carninci, P, Forrest, ARR, Hayashizaki, Y, Noguchi, S, Arakawa, T, Fukuda, S, Furuno, M, Hasegawa, A, Hori, F, Ishikawa-Kato, S, Kaida, K, Kaiho, A, Kanamori-Katayama, M, Kawashima, T, Kojima, M, Kubosaki, A, Manabe, R-I, Murata, M, Nagao-Sato, S, Nakazato, K, Ninomiya, N, Nishiyori-Sueki, H, Noma, S, Saijyo, E, Saka, A, Sakai, M, Simon, C, Suzuki, N, Tagami, M, Watanabe, S, Yoshida, S, Arner, P, Axton, RA, Babina, M, Baillie, JK, Barnett, TC, Beckhouse, AG, Blumenthal, A, Bodega, B, Bonetti, A, Briggs, J, Brombacher, F, Carlisle, AJ, Clevers, HC, Davis, CA, Detmar, M, Dohi, T, Edge, ASB, Edinger, M, Ehrlund, A, Ekwall, K, Endoh, M, Enomoto, H, Eslami, A, Fagiolini, M, Fairbairn, L, Farach-Carson, MC, Faulkner, GJ, Ferrai, C, Fisher, ME, Forrester, LM, Fujita, R, Furusawa, J-I, Geijtenbeek, TB, Gingeras, T, Goldowitz, D, Guhl, S, Guler, R, Gustincich, S, Ha, TJ, Hamaguchi, M, Hara, M, Hasegawa, Y, Herlyn, M, Heutink, P, Hitchens, KJ, Hume, DA, Ikawa, T, Ishizu, Y, Kai, C, Kawamoto, H, Kawamura, YI, Kempfle, JS, Kenna, TJ, Kere, J, Khachigian, LM, Kitamura, T, Klein, S, Klinken, SP, Knox, AJ, Kojima, S, Koseki, H, Koyasu, S, Lee, W, Lennartsson, A, Mackay-sim, A, Mejhert, N, Mizuno, Y, Morikawa, H, Morimoto, M, Moro, K, Morris, KJ, Motohashi, H, Mummery, CL, Nakachi, Y, Nakahara, F, Nakamura, T, Nakamura, Y, Nozaki, T, Ogishima, S, Ohkura, N, Ohno, H, Ohshima, M, Okada-Hatakeyama, M, Okazaki, Y, Orlando, V, Ovchinnikov, DA, Passier, R, Patrikakis, M, Pombo, A, Pradhan-Bhatt, S, Qin, X-Y, Rehli, M, Rizzu, P, Roy, S, Sajantila, A, Sakaguchi, S, Sato, H, Satoh, H, Savvi, S, Saxena, A, Schmidl, C, Schneider, C, Schulze-Tanzil, GG, Schwegmann, A, Sheng, G, Shin, JW, Sugiyama, D, Sugiyama, T, Summers, KM, Takahashi, N, Takai, J, Tanaka, H, Tatsukawa, H, Tomoiu, A, Toyoda, H, van de Wetering, M, van den Berg, LM, Verardo, R, Vijayan, D, Wells, CA, Winteringham, LN, Wolvetang, E, Yamaguchi, Y, Yamamoto, M, Yanagi-Mizuochi, C, Yoneda, M, Yonekura, Y, Zhang, PG, Zucchelli, S, Abugessaisa, I, Arner, E, Harshbarger, J, Kondo, A, Lassmann, T, Lizio, M, Sahin, S, Sengstag, T, Severin, J, Shimoji, H, Suzuki, M, Suzuki, H, Kawai, J, Kondo, N, Itoh, M, Daub, CO, Kasukawa, T, Kawaji, H, Carninci, P, Forrest, ARR, and Hayashizaki, Y

Abstract

In the FANTOM5 project, transcription initiation events across the human and mouse genomes were mapped at a single base-pair resolution and their frequencies were monitored by CAGE (Cap Analysis of Gene Expression) coupled with single-molecule sequencing. Approximately three thousands of samples, consisting of a variety of primary cells, tissues, cell lines, and time series samples during cell activation and development, were subjected to a uniform pipeline of CAGE data production. The analysis pipeline started by measuring RNA extracts to assess their quality, and continued to CAGE library production by using a robotic or a manual workflow, single molecule sequencing, and computational processing to generate frequencies of transcription initiation. Resulting data represents the consequence of transcriptional regulation in each analyzed state of mammalian cells. Non-overlapping peaks over the CAGE profiles, approximately 200,000 and 150,000 peaks for the human and mouse genomes, were identified and annotated to provide precise location of known promoters as well as novel ones, and to quantify their activities.

Published
2017

9. The statistical geometry of transcriptome divergence in cell-type evolution and cancer

Author

Liang, C, Alam, I, Albanese, D, Altschuler, G, Andersson, R, Arakawa, T, Archer, J, Arner, E, Arner, P, Babina, M, Baillie, K, Bajic, V, Baker, S, Balic, A, Balwierz, P, Beckhouse, A, Bertin, N, Blake, Ja, Blumenthal, A, Bodega, B, Bonetti, A, Briggs, J, Brombacher, F, Burroughs, M, Califano, A, Cannistraci, C, Carbajo, D, Carninci, P, Chen, Yang, Chierici, M, Ciani, Y, Clevers, H, Dalla, Emiliano, Daub, C, Davis, C, De Hoon, M, De Lima Morais, D, Dermar, M, Diehl, A, Dimont, E, Dohl, T, Drabros, F, Edge, A, Edinger, M, Ekwall, K, Endoh, M, Enomoto, H, Fagiolini, M, Fairbairn, L, Fang, H, Farach Carson, Mc, Faulkner, G, Favorov, A, Fisher, M, Forrest, A, Francescatto, M, Freeman, T, Frith, M, Fujita, R, Fukuda, S, Furlanello, C, Furuno, M, Furusawa, J, Geijtenbeek, Tb, Gibson, A, Gingeras, T, Goldowithz, D, Gough, J, Guhl, S, Guler, R, Gustincich, Stefano, Ha, T, Haberle, V, Hamaguchi, M, Hara, M, Harbers, M, Harshbarger, J, Hasegawa, A, Hasegawa, Y, Hashimoto, T, Hayashizaki, Y, Herlyn, M, Heutink, P, Hide, W, Hitchens, K, Ho Sui, S, Hofmann, O, Hoof, I, Hori, F, Hume, D, Huminiecki, L, Iida, K, Ikawa, T, Ishizu, Y, Itoh, M, Jankovic, B, Jia, H, Jorgensen, M, Joshi, A, Jurman, G, Kaczkowski, B, Kai, C, Kaida, K, Kaiho, A, Kajiyama, K, Kanamori Katayama, M, Kasianov, A, Kasukawa, T, Katayama, S, Kato Ishikawa, S, Kawaguchi, S, Kawai, J, Kawaji, H, Kawamoto, H, Kawamura, Y, Kawashima, T, Kempfle, J, Kenna, T, Kere, J, Khachigian, L, Kitamura, T, Klinken, P, Knox, A, Kojima, M, Kojima, S, Kondo, N, Koseki, H, Koyasu, S, Krampitz, S, Kubosaki, A, Kulakovskiy, I, Kwon, At, Laros, J, Lassmann, T, Lenhard, B, Lennartsson, A, Li, K, Lilji, B, Lipovich, L, Lizio, M, Mackay Sim, A, Makeev, V, Manabe, R, Mar, J, Marchand, B, Mathelier, A, Medvedeva, Y, Meehan, Tf, Mejhert, N, Meynert, A, Mizuno, Y, Morikawa, H, Morimoto, M, Moro, K, Motakis, E, Motohashi, H, Mummery, C, Mungall, Cj, Murata, M, Nagao Sato, S, Nakachi, Y, Nakahara, F, Nakamura, T, Nakamura, Y, Nakazato, K, Ninomiya Fukuda, N, Nishiyori Sueki, H, Noma, S, Nozaki, T, Ogishima, S, Ohkura, N, Ohmiya, H, Ohno, H, Ohshima, M, Okada Hatakeyama, M, Okazaki, Y, Orlando, V, Ovchinnikov, D, Pain, A, Passier, R, Persson, H, Piazza, Silvano, Plessy, C, Pradhan Bhatt, S, Prendergast, J, Rackham, O, Ramilowski, J, Rashid, M, Ravasi, T, Rehli, M, Rizzu, P, Roncador, M, Roy, S, Rye, M, Saijyo, E, Sajantila, A, Saka, A, Sakaguchi, S, Sakai, M, Sandelin, A, Sato, H, Satoh, H, Suzana, S, Alka, S, Schaefer, U, Schmeier, S, Schmidl, C, Schneider, C, Schultes, Ea, Schulze Tanzil, G, Schwegmann, A, Semple, C, Sengstag, T, Severin, J, Sheng, G, Shimoji, H, Shimoni, Y, Shin, J, Simon, C, Sugiyama, D, Sugiyama, T, Summers, K, Suzuki, H, Suzuki, M, Suzuki, N, Swoboda, R, Hoen P, T, Tagami, M, Takahashi, N, Takai, J, Tanaka, H, Tatsukawa, H, Tatum, Z, Taylor, M, Thompson, M, Toyoda, H, Toyoda, T, Valen, E, Van De Wetering, M, Van Den Berg, L, Van Nimwegen, E, Verardo, R, Vijayan, D, Vitezic, M, Vorontzov, I, Wasserman, W, Watanabe, S, Wells, C, Winteringham, L, Wolvetang, E, Wood, Ej, Yamaguchi, Y, Yamamoto, M, Yoneda, M, Yonekura, Y, Yoshida, Shin'Ichirou, Young, R, Zabierowski, Se, Zhang, P, Zhao, X, Zucchelli, Silvia, Forrest, Ar, Wagner, Gp, Hubrecht Institute for Developmental Biology and Stem Cell Research, AII - Amsterdam institute for Infection and Immunity, Infectious diseases, and Experimental Immunology

Subjects
Cell type, General Physics and Astronomy, rna-seq data, phylogenetic networks, Biology, ENCODE, General Biochemistry, Genetics and Molecular Biology, Divergence, Transcriptome, Models, Settore BIO/13 - Biologia Applicata, Neoplasms, Humans, Genetics, Models, Statistical, Multidisciplinary, Statistical model, General Chemistry, Statistical, Biological Evolution, Body plan, Tree structure, Evolutionary biology, Cancer cell
Abstract

In evolution, body plan complexity increases due to an increase in the number of individualized cell types. Yet, there is very little understanding of the mechanisms that produce this form of organismal complexity. One model for the origin of novel cell types is the sister cell-type model. According to this model, each cell type arises together with a sister cell type through specialization from an ancestral cell type. A key prediction of the sister cell-type model is that gene expression profiles of cell types exhibit tree structure. Here we present a statistical model for detecting tree structure in transcriptomic data and apply it to transcriptomes from ENCODE and FANTOM5. We show that transcriptomes of normal cells harbour substantial amounts of hierarchical structure. In contrast, cancer cell lines have less tree structure, suggesting that the emergence of cancer cells follows different principles from that of evolutionary cell-type origination.

Published
2015

10. Differential roles of epigenetic changes and Foxp3 expression in regulatory T cell-specific transcriptional regulation

Author

Morikawa, H, Ohkura, N, Vandenbon, A, Itoh, M, Nagao Sato, S, Kawaji, H, Lassmann, T, Carninci, P, Hayashizaki, Y, Forrest, Ar, Standley, Dm, Date, H, Sakaguchi, S, FANTOM Consortium (Forrest AR, Rehli, M, Baillie, Jk, de Hoon MJ, Haberle, V, Kulakovskiy, Iv, Lizio, M, Andersson, R, Mungall, Cj, Meehan, Tf, Schmeier, S, Bertin, N, Jørgensen, M, Dimont, E, Arner, E, Schmidl, C, Schaefer, U, Medvedeva, Ya, Plessy, C, Vitezic, M, Severin, J, Semple, Ca, Ishizu, Y, Francescatto, M, Alam, I, Albanese, D, Altschuler, Gm, Archer, Ja, Arner, P, Babina, M, Baker, S, Balwierz, Pj, Beckhouse, Ag, Pradhan Bhatt, S, Blake, Ja, Blumenthal, A, Bodega, B, Bonetti, A, Briggs, J, Brombacher, F, Burroughs, Am, Califano, A, Cannistraci, Cv, Carbajo, D, Chen, Y, Chierici, M, Ciani, Y, Clevers, Hc, Dalla, E, Davis, Ca, Deplancke, B, Detmar, M, Diehl, Ad, Dohi, T, Drabløs, F, Edge, As, Edinger, M, Ekwall, K, Endoh, M, Enomoto, H, Fagiolini, M, Fairbairn, L, Fang, H, Farach Carson MC, Faulkner, Gj, Favorov, Av, Fisher, Me, Frith, Mc, Fujita, R, Fukuda, S, Furlanello, C, Furuno, M, Furusawa, J, Geijtenbeek, Tb, Gibson, A, Gingeras, T, Goldowitz, D, Gough, J, Guhl, S, Guler, R, Gustincich, Stefano, Ha, Tj, Hamaguchi, M, Hara, M, Harbers, M, Harshbarger, J, Hasegawa, A, Hasegawa, Y, Hashimoto, T, Herlyn, M, Hitchens, Kj, Ho Sui SJ, Hofmann, Om, Hoof, I, Hori, F, Huminiecki, L, Iida, K, Ikawa, T, Jankovic, Br, Jia, H, Joshi, A, Jurman, G, Kaczkowski, B, Kai, C, Kaida, K, Kaiho, A, Kajiyama, K, Kanamori Katayama, M, Kasianov, As, Kasukawa, T, Katayama, S, Kato, S, Kawaguchi, S, Kawamoto, H, Kawamura, Yi, Kawashima, T, Kempfle, Js, Kenna, Tj, Kere, J, Khachigian, Lm, Kitamura, T, Klinken, Sp, Knox, Aj, Kojima, M, Kojima, S, Kondo, N, Koseki, H, Koyasu, S, Krampitz, S, Kubosaki, A, Kwon, At, Laros, Jf, Lee, W, Lennartsson, A, Li, K, Lilje, B, Lipovich, L, Mackay Sim, A, Manabe, R, Mar, Jc, Marchand, B, Mathelier, A, Mejhert, N, Meynert, A, Mizuno, Y, Morais, Da, Morimoto, M, Moro, K, Motakis, E, Motohashi, H, Mummery, Cl, Murata, M, Nakachi, Y, Nakahara, F, Nakamura, T, Nakamura, Y, Nakazato, K, van Nimwegen, E, Ninomiya, N, Nishiyori, H, Noma, S, Nozaki, T, Ogishima, S, Ohmiya, H, Ohno, H, Ohshima, M, Okada Hatakeyama, M, Okazaki, Y, Orlando, V, Ovchinnikov, Da, Pain, A, Passier, R, Patrikakis, M, Persson, H, Piazza, S, Prendergast, Jg, Rackham, Oj, Ramilowski, Ja, Rashid, M, Ravasi, T, Rizzu, P, Roncador, M, Roy, S, Rye, Mb, Saijyo, E, Sajantila, A, Saka, A, Sakai, M, Sato, H, Satoh, H, Savvi, S, Saxena, A, Schneider, C, Schultes, Ea, Schulze Tanzil GG, Schwegmann, A, Sengstag, T, Sheng, G, Shimoji, H, Shimoni, Y, Shin, Jw, Simon, C, Sugiyama, D, Sugiyama, T, Suzuki, M, Swoboda, Rk, 't Hoen PA, Tagami, M, Takahashi, N, Takai, J, Tanaka, H, Tatsukawa, H, Tatum, Z, Thompson, M, Toyoda, H, Toyoda, T, Valen, E, van de Wetering, M, van den Berg LM, Verardo, R, Vijayan, D, Vorontsov, Ie, Wasserman, Ww, Watanabe, S, Wells, Ca, Winteringham, Ln, Wolvetang, E, Wood, Ej, Yamaguchi, Y, Yamamoto, M, Yoneda, M, Yonekura, Y, Yoshida, S, Zabierowski, Se, Zhang, Pg, Zhao, X, Zucchelli, S, Summers, Km, Suzuki, H, Daub, Co, Kawai, J, Heutink, P, Hide, W, Freeman, Tc, Lenhard, B, Bajic, Vb, Taylor, Ms, Makeev, Vj, Sandelin, A, Hume, Da, Hayashizaki, Y., AII - Amsterdam institute for Infection and Immunity, Infectious diseases, Experimental Immunology, and Hubrecht Institute for Developmental Biology and Stem Cell Research

Subjects
Transcription, Genetic, Regulatory T cell, T-Lymphocytes, Down-Regulation, chemical and pharmacologic phenomena, Biology, Inbred C57BL, T-Lymphocytes, Regulatory, Epigenesis, Genetic, Mice, Genetic, Settore BIO/13 - Biologia Applicata, medicine, Transcriptional regulation, Animals, Epigenetics, Gene, Inbred BALB C, Genetics, Regulation of gene expression, Mice, Inbred BALB C, Multidisciplinary, Binding Sites, FOXP3, hemic and immune systems, Forkhead Transcription Factors, DNA Methylation, Biological Sciences, Regulatory, Cap analysis gene expression, Mice, Inbred C57BL, medicine.anatomical_structure, Gene Expression Regulation, DNA methylation, Transcription, Epigenesis
Abstract

Naturally occurring regulatory T (Treg) cells, which specifically express the transcription factor forkhead box P3 (Foxp3), are engaged in the maintenance of immunological self-tolerance and homeostasis. By transcriptional start site cluster analysis, we assessed here how genome-wide patterns of DNA methylation or Foxp3 binding sites were associated with Treg-specific gene expression. We found that Treg-specific DNA hypomethylated regions were closely associated with Treg up-regulated transcriptional start site clusters, whereas Foxp3 binding regions had no significant correlation with either up- or down-regulated clusters in nonactivated Treg cells. However, in activated Treg cells, Foxp3 binding regions showed a strong correlation with down-regulated clusters. In accordance with these findings, the above two features of activation-dependent gene regulation in Treg cells tend to occur at different locations in the genome. The results collectively indicate that Treg-specific DNA hypomethylation is instrumental in gene up-regulation in steady state Treg cells, whereas Foxp3 down-regulates the expression of its target genes in activated Treg cells. Thus, the two events seem to play distinct but complementary roles in Treg-specific gene expression.

Published
2014

12. The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line

Author

FANTOM Consortium, Suzuki H, Forrest AR, van Nimwegen E, Daub CO, Balwierz PJ, Irvine KM, Lassmann T, Ravasi T, Hasegawa Y, de Hoon MJ, Katayama S, Schroder K, Carninci P, Tomaru Y, Kanamori-Katayama M, Kubosaki A, Akalin A, Ando Y, Arner E, Asada M, Asahara H, Bailey T, Bajic VB, Bauer D, Beckhouse AG, Bertin N, Bjxf6rkegren J, Brombacher F, Bulger E, Chalk AM, Chiba J, Cloonan N, Dawe A, Dostie J, Engstrxf6m PG, Essack M, Faulkner GJ, Fink JL, Fredman D, Fujimori K, Furuno M, Gojobori T, Gough J, and Grimmond SM

Published
2009

13. Analysis of the mouse transcriptome based on functional annotation of 60, 770 full-length cDNAs

Author

Okazaki, Y., Furuno, M., Kasukawa, T., Adachi, J., Bono, H., Kondo, S., Nikaido, I., Osato, N., Saito, R., Suzuki, H., Yamanaka, I., Kiyosawa, H., Yagi, K., Tomaru, Y., Hasegawa, Y., Nogami, A., Schonbach, C., and Lenhard, Boris

Subjects
genom miša, transkriptom miša
Abstract

Only a small proportion of the mouse genome is transcribed into mature messenger RNA transcripts. There is an international collaborative effort to identify all full-length mRNA transcripts from the mouse, and to ensure that each is represented in a physical collection of clones. Here we report the manual annotation of 60, 770 full-length mouse complementary DNA sequences. These are clustered into 33, 409 'transcriptional units', contributing 90.1% of a newly established mouse transcriptome database. Of these transcriptional units, 4, 258 are new protein-coding and 11, 665 are new non-coding messages, indicating that non-coding RNA is a major component of the transcriptome. 41% of all transcriptional units showed evidence of alternative splicing. In protein-coding transcripts, 79% of splice variations altered the protein product. Whole-transcriptome analyses resulted in the identification of 2, 431 sense-antisense pairs. The present work, completely supported by physical clones, provides the most comprehensive survey of a mammalian transcriptome so far, and is a valuable resource for functional genomics

Published
2002

15. Clusters of internally primed transcripts reveal novel long noncoding RNAs

Author

Blake, J, Hancock, J, Pavan, B, Stubbs, L, Furuno, M, Pang, KC, Ninomiya, N, Fukuda, S, Frith, MC, Bult, C, Kai, C, Kawai, J, Carninci, P, Hayashizaki, Y, Mattick, JS, Suzuki, H, Blake, J, Hancock, J, Pavan, B, Stubbs, L, Furuno, M, Pang, KC, Ninomiya, N, Fukuda, S, Frith, MC, Bult, C, Kai, C, Kawai, J, Carninci, P, Hayashizaki, Y, Mattick, JS, and Suzuki, H

Abstract

Non-protein-coding RNAs (ncRNAs) are increasingly being recognized as having important regulatory roles. Although much recent attention has focused on tiny 22- to 25-nucleotide microRNAs, several functional ncRNAs are orders of magnitude larger in size. Examples of such macro ncRNAs include Xist and Air, which in mouse are 18 and 108 kilobases (Kb), respectively. We surveyed the 102,801 FANTOM3 mouse cDNA clones and found that Air and Xist were present not as single, full-length transcripts but as a cluster of multiple, shorter cDNAs, which were unspliced, had little coding potential, and were most likely primed from internal adenine-rich regions within longer parental transcripts. We therefore conducted a genome-wide search for regional clusters of such cDNAs to find novel macro ncRNA candidates. Sixty-six regions were identified, each of which mapped outside known protein-coding loci and which had a mean length of 92 Kb. We detected several known long ncRNAs within these regions, supporting the basic rationale of our approach. In silico analysis showed that many regions had evidence of imprinting and/or antisense transcription. These regions were significantly associated with microRNAs and transcripts from the central nervous system. We selected eight novel regions for experimental validation by northern blot and RT-PCR and found that the majority represent previously unrecognized noncoding transcripts that are at least 10 Kb in size and predominantly localized in the nucleus. Taken together, the data not only identify multiple new ncRNAs but also suggest the existence of many more macro ncRNAs like Xist and Air.

Published
2006

16. Transcript annotation in FANTOM3: Mouse gene catalog based on physical cDNAs

Author

Maeda, N, Kasukawa, T, Oyama, R, Gough, J, Frith, M, Engstrom, PG, Lenhard, B, Aturaliya, RN, Batalov, S, Beisel, KW, Bult, CJ, Fletcher, CF, Forrest, ARR, Furuno, M, Hill, D, Itoh, M, Kanamori-Katayama, M, Katayama, S, Katoh, M, Kawashima, T, Quackenbush, J, Ravasi, T, Ring, BZ, Shibata, K, Sugiura, K, Takenaka, Y, Teasdale, RD, Wells, CA, Zhu, Y, Kai, C, Kawai, J, Hume, DA, Carninci, P, Hayashizaki, Y, Maeda, N, Kasukawa, T, Oyama, R, Gough, J, Frith, M, Engstrom, PG, Lenhard, B, Aturaliya, RN, Batalov, S, Beisel, KW, Bult, CJ, Fletcher, CF, Forrest, ARR, Furuno, M, Hill, D, Itoh, M, Kanamori-Katayama, M, Katayama, S, Katoh, M, Kawashima, T, Quackenbush, J, Ravasi, T, Ring, BZ, Shibata, K, Sugiura, K, Takenaka, Y, Teasdale, RD, Wells, CA, Zhu, Y, Kai, C, Kawai, J, Hume, DA, Carninci, P, and Hayashizaki, Y

Abstract

The international FANTOM consortium aims to produce a comprehensive picture of the mammalian transcriptome, based upon an extensive cDNA collection and functional annotation of full-length enriched cDNAs. The previous dataset, FANTOM2, comprised 60,770 full-length enriched cDNAs. Functional annotation revealed that this cDNA dataset contained only about half of the estimated number of mouse protein-coding genes, indicating that a number of cDNAs still remained to be collected and identified. To pursue the complete gene catalog that covers all predicted mouse genes, cloning and sequencing of full-length enriched cDNAs has been continued since FANTOM2. In FANTOM3, 42,031 newly isolated cDNAs were subjected to functional annotation, and the annotation of 4,347 FANTOM2 cDNAs was updated. To accomplish accurate functional annotation, we improved our automated annotation pipeline by introducing new coding sequence prediction programs and developed a Web-based annotation interface for simplifying the annotation procedures to reduce manual annotation errors. Automated coding sequence and function prediction was followed with manual curation and review by expert curators. A total of 102,801 full-length enriched mouse cDNAs were annotated. Out of 102,801 transcripts, 56,722 were functionally annotated as protein coding (including partial or truncated transcripts), providing to our knowledge the greatest current coverage of the mouse proteome by full-length cDNAs. The total number of distinct non-protein-coding transcripts increased to 34,030. The FANTOM3 annotation system, consisting of automated computational prediction, manual curation, and final expert curation, facilitated the comprehensive characterization of the mouse transcriptome, and could be applied to the transcriptomes of other species.

Published
2006

33. LOW-TEMPERATURE EPITAXY AND IN-SITU DOPING OF SILICON FILMS

Author

KIRCHER, R., FURUNO, M., MUROTA, J., ONO, S., KIRCHER, R., FURUNO, M., MUROTA, J., and ONO, S.

Abstract

The B2H6- and PH3- partial pressure dependence of the doping concentration of in-situ doped silicon films, deposited in the temperature range between 600 and 750°C by low pressure chemical vapor deposition has been investigated for two different substrate orientations. In addition, the influence of B2H6and PH3and their partial pressure on the silicon growth rate has been studied. It is found that the doping concentration can be controlled over a wide range, and that in the presence of PH3as well as B2H6the silicon growth is reduced. In the case of boron doping, the in-situ doping has been used to fabricate delta-doped silicon layers.

Published
1992
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37. Roquesite from the Akenobe tin-polymetallic deposits, southwest Japan.

Author

Murao S., Furuno M., Murao S., and Furuno M.

Abstract

Roquesite, CuInS2, grains were analysed by EPMA. From the narrow range of the chemical compositions, and the texture of roquesite, together with the chemical composition of coexisting sphalerite, it is concluded that the roquesite exsolved from a Cu-Zn-Fe-In-S phase., Roquesite, CuInS2, grains were analysed by EPMA. From the narrow range of the chemical compositions, and the texture of roquesite, together with the chemical composition of coexisting sphalerite, it is concluded that the roquesite exsolved from a Cu-Zn-Fe-In-S phase.

43. Periodontal tissue susceptibility to glycaemic control in type 2 diabetes.

Author

Inoue M, Sakanaka A, Katakami N, Furuno M, Nishizawa H, Omori K, Taya N, Ishikawa A, Mayumi S, Tanaka Isomura E, Takeuchi H, Amano A, Shimomura I, Fukusaki E, and Kuboniwa M

Subjects
Humans, Male, Female, Middle Aged, Aged, Glycated Hemoglobin metabolism, Glycated Hemoglobin analysis, Hypoglycemic Agents therapeutic use, Glycation End Products, Advanced, Glycated Serum Albumin, Serum Albumin analysis, C-Peptide blood, Ankle Brachial Index, Disease Susceptibility, Diabetes Mellitus, Type 2 complications, Diabetes Mellitus, Type 2 blood, Glycemic Control, Blood Glucose metabolism, Periodontitis complications, Periodontitis blood, Periodontitis therapy
Abstract

Aim: To assess the direct effect of intensive glycaemic control on periodontal tissues in patients with diabetes mellitus., Materials and Methods: Twenty-nine patients with type 2 diabetes were enrolled and hospitalized to receive a 2-week intensive glycaemic control regimen. We observed and analysed the systemic and oral disease indicators before and after treatment and clarified the indicators related to periodontal inflammation., Results: A significant reduction in glycaemic and periodontal parameters, including glycated albumin levels and periodontal inflamed surface area (PISA), was observed after treatment. The changes in PISA per tooth, indicative of periodontal healing, exhibited a bimodal distribution; the patients were divided into two groups on this basis. Correlations were observed between the changes in PISA per tooth and fasting plasma glucose, acetoacetic acid, and beta-hydroxybutyrate levels in the PISA-improved group. Significantly lower levels of C-peptide, coefficient of variation of R-R interval, and ankle-brachial pressure index were observed before treatment in the PISA non-improved group., Conclusions: Glycaemic control treatment can effectively improve periodontitis in patients with type 2 diabetes, even in the absence of specific periodontal treatments. However, the periodontal responsiveness to glycaemic control treatment depends on the systemic condition of the patient., (© 2024 The Author(s). Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)

Published
2024
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44. New insights into the characteristic flavor components of traditional sour beers such as Lambic and Flanders Red Ale beers.

Author

Onishi K, Furuno M, Mori A, and Fukusaki E

Subjects
Humans, Fermentation, Flavoring Agents analysis, Flavoring Agents chemistry, Liquid-Liquid Extraction methods, Beer analysis, Taste, Gas Chromatography-Mass Spectrometry, Odorants analysis
Abstract

In recent years, the demand for beers with a variety of flavors has increased considerably owing to the diversification of consumer preferences. Sour beer is characterized by a sour taste unlike normal beer flavor, and previous studies on sour beer have been primarily focused on addressing issues, such as inconsistent product quality and long production time, and on the associated microorganisms. Scientific knowledge regarding the characteristic flavor of sour beer and flavor components is limited. Therefore, in this study, we aimed to clarify the characteristic sensory attributes of sour beer and the component profiles that explain these attributes. Component analysis was performed on 10 traditional sour beers (eight Flanders Red Ales and two Lambics), using untargeted gas chromatography-mass spectrometry with liquid-liquid extraction, liquid chromatography-mass spectrometry targeting amines and anionic compounds. Further, sensory evaluation was conducted by well-trained panelists via quantitative descriptive analysis. Orthogonal partial least squares regression analysis was also conducted to investigate candidate flavor components. Thus, 261 components were identified and our methods could explain the flavor attributes of the examined samples. Comprehensive component profiling data also showed that differences in fermentation method, barrel aging duration, and blending ratio affected beer flavor. Further, Lambics were found to be characterized by citrus and phenolic aroma, while Flanders Red Ales were characterized by solvent-like aroma, sourness complexity, full bodied, graininess, astringency, and bitterness. These findings may serve as a basis for addressing issues related to sour beer production and may facilitate process design for obtaining targeted sour beer flavors., (Copyright © 2024 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.)

Published
2024
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45. xcore: an R package for inference of gene expression regulators.

Author

Migdał M, Arakawa T, Takizawa S, Furuno M, Suzuki H, Arner E, Winata CL, and Kaczkowski B

Subjects
Animals, Chromatin Immunoprecipitation methods, Protein Binding, Gene Expression, Binding Sites, Transcription Factors genetics, Transcription Factors metabolism, Chromatin Immunoprecipitation Sequencing
Abstract

Background: Elucidating the Transcription Factors (TFs) that drive the gene expression changes in a given experiment is a common question asked by researchers. The existing methods rely on the predicted Transcription Factor Binding Site (TFBS) to model the changes in the motif activity. Such methods only work for TFs that have a motif and assume the TF binding profile is the same in all cell types., Results: Given the wealth of the ChIP-seq data available for a wide range of the TFs in various cell types, we propose that gene expression modeling can be done using ChIP-seq "signatures" directly, effectively skipping the motif finding and TFBS prediction steps. We present xcore, an R package that allows TF activity modeling based on ChIP-seq signatures and the user's gene expression data. We also provide xcoredata a companion data package that provides a collection of preprocessed ChIP-seq signatures. We demonstrate that xcore leads to biologically relevant predictions using transforming growth factor beta induced epithelial-mesenchymal transition time-courses, rinderpest infection time-courses, and embryonic stem cells differentiated to cardiomyocytes time-course profiled with Cap Analysis Gene Expression., Conclusions: xcore provides a simple analytical framework for gene expression modeling using linear models that can be easily incorporated into differential expression analysis pipelines. Taking advantage of public ChIP-seq databases, xcore can identify meaningful molecular signatures and relevant ChIP-seq experiments., (© 2023. The Author(s).)

Published
2023
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46. Salivary metabolic signatures of carotid atherosclerosis in patients with type 2 diabetes hospitalized for treatment.

Author

Sakanaka A, Katakami N, Furuno M, Nishizawa H, Omori K, Taya N, Ishikawa A, Mayumi S, Inoue M, Tanaka Isomura E, Amano A, Shimomura I, Fukusaki E, and Kuboniwa M

Abstract

Atherosclerosis is a life-threatening disease associated with morbidity and mortality in patients with type 2 diabetes (T2D). This study aimed to characterize a salivary signature of atherosclerosis based on evaluation of carotid intima-media thickness (IMT) to develop a non-invasive predictive tool for diagnosis and disease follow-up. Metabolites in saliva and plasma samples collected at admission and after treatment from 25 T2D patients hospitalized for 2 weeks to undergo medical treatment for diabetes were comprehensively profiled using metabolomic profiling with gas chromatography-mass spectrometry. Orthogonal partial least squares analysis, used to explore the relationships of IMT with clinical markers and plasma and salivary metabolites, showed that the top predictors for IMT included salivary allantoin and 1,5-anhydroglucitol (1,5-AG) at both the baseline examination at admission and after treatment. Furthermore, though treatment induced alterations in salivary levels of allantoin and 1,5-AG, it did not modify the association between IMT and these metabolites ( p interaction > 0.05), and models with these metabolites combined yielded satisfactory diagnostic accuracy for the high IMT group even after treatment (area under curve = 0.819). Collectively, this salivary metabolite combination may be useful for non-invasive identification of T2D patients with a higher atherosclerotic burden in clinical settings., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Sakanaka, Katakami, Furuno, Nishizawa, Omori, Taya, Ishikawa, Mayumi, Inoue, Tanaka Isomura, Amano, Shimomura, Fukusaki and Kuboniwa.)

Published
2022
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47. Molecular epidemiology of methicillin-resistant Staphylococcus aureus isolated from pigs in Japan.

Author

Ozawa M, Furuya Y, Akama R, Harada S, Matsuda M, Abo H, Shirakawa T, Kawanishi M, Yoshida E, Furuno M, Fukuhara H, Kasuya K, and Shimazaki Y

Subjects
Animals, Anti-Bacterial Agents pharmacology, Dust, Humans, Japan epidemiology, Microbial Sensitivity Tests veterinary, Molecular Epidemiology, Multilocus Sequence Typing veterinary, Sus scrofa, Swine, Methicillin-Resistant Staphylococcus aureus, Staphylococcal Infections epidemiology, Staphylococcal Infections veterinary, Swine Diseases epidemiology
Abstract

Methicillin-resistant Staphylococcus aureus (MRSA) is the leading cause of infection in hospitalized patients and can be prevalent in humans and various animal species. In European countries, MRSA isolates belonging to clonal complex 398 have been detected at high rates in pigs. However, the prevalence of MRSA in pigs and farm environments in Japan remains unclear. MRSA isolates were obtained from pigs in slaughterhouses, diseased pigs on farms, imported breeding pigs, and farm dust. We conducted whole-genome sequencing (WGS) and analyzed the molecular epidemiological relationship between these MRSA isolates using core genome multilocus sequence typing (cgMLST). The prevalence rates of MRSA among pigs in slaughterhouses, diseased pigs on farms, imported breeding pigs, and farm dust were 5.2 %, 3.4 %, 28.8 %, and 0.06 %, respectively. ST 398 isolates that classified as ST398/t034 were isolated from pigs from all sources. The results of cgMLST showed that ST398/t034 isolates originating from domestic pigs clustered into the same cluster as the isolates from imported breeding pigs. However, some clusters only included isolates of domestic pig origin. Most MRSA isolates in this study carried resistance genes for aminoglycosides, β-lactams, macrolides, tetracyclines, and zinc. None of the MRSA isolates in this study harbored Panton-Valentine leukocidin toxin genes. Molecular epidemiological analysis suggested a relationship between isolates from slaughter pigs and imported breeding pigs and the presence of MRSA isolates of domestic origin. However, more data are needed for elucidation of the origin of these MRSA variants in the pig industry in Japan., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Published
2022
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48. Evaluation of change in metabolome caused by comprehensive diabetes treatment: A prospective observational study of diabetes inpatients with gas chromatography/mass spectrometry-based non-target metabolomic analysis.

Author

Taya N, Katakami N, Omori K, Arakawa S, Hosoe S, Watanabe H, Takahara M, Miyashita K, Nishizawa H, Matsuoka TA, Furuno M, Bamba T, Iida J, Fukusaki E, and Shimomura I

Subjects
Adult, Aged, Amino Acids blood, Case-Control Studies, Cluster Analysis, Diabetes Mellitus, Type 2 drug therapy, Fasting blood, Female, Glycemic Control methods, Humans, Inpatients statistics & numerical data, Male, Middle Aged, Monosaccharides blood, Principal Component Analysis, Prospective Studies, Young Adult, Diabetes Mellitus, Type 2 blood, Gas Chromatography-Mass Spectrometry, Glycemic Control statistics & numerical data, Metabolome, Metabolomics methods
Abstract

Aims/introduction: Diabetes patients develop a variety of metabolic abnormalities in addition to hyperglycemia. However, details regarding change in various metabolites after comprehensive diabetes treatment remain unknown. This study aimed to identify the short-term change in metabolome in inpatients who were subject to comprehensive diabetes treatment, using gas chromatography/mass spectrometry-based non-target metabolomics techniques., Materials and Methods: Participants of the present study were randomly recruited from the patients with type 2 diabetes hospitalized due to problems with glycemic control (n = 31) and volunteers without diabetes (n = 30), both of whom were aged between 20 and 75 years. A metabolomic analysis of fasting plasma samples on the 2nd (pre-treatment) and 16th hospital (post-treatment) day with gas chromatography/mass spectrometry using a multiple reaction monitoring mode was carried out., Results: A principal component analysis showed that metabolome of fasting plasma was different between individuals with and without diabetes. The metabolome of fasting plasma in diabetes patients after treatment was different from that of pre-treatment, as well as individuals without diabetes. Many amino acids (proline, glycine, serine, threonine, methionine, pyroglutamic acid, glutamine and lysine) were significantly increased by >10% after administering the inpatient diabetes treatment. A hierarchical clustering analysis showed that in the case of patients with markedly decreased monosaccharide levels and increased 1,5-anhydroglucitol, the levels of amino acids increased more significantly., Conclusions: After a 2-week comprehensive treatment, the plasma levels of various amino acids increased in conjunction with the reduction in monosaccharide levels in poorly controlled type 2 diabetes patients., (© 2021 The Authors. Journal of Diabetes Investigation published by Asian Association for the Study of Diabetes (AASD) and John Wiley & Sons Australia, Ltd.)

Published
2021
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49. Spliceostatin A interaction with SF3B limits U1 snRNP availability and causes premature cleavage and polyadenylation.

Author

Yoshimoto R, Chhipi-Shrestha JK, Schneider-Poetsch T, Furuno M, Burroughs AM, Noma S, Suzuki H, Hayashizaki Y, Mayeda A, Nakagawa S, Kaida D, Iwasaki S, and Yoshida M

Subjects
Female, HeLa Cells, Humans, Phosphoproteins chemistry, Phosphoproteins metabolism, Polyadenylation drug effects, Pyrans chemistry, RNA Splicing drug effects, RNA Splicing Factors chemistry, RNA Splicing Factors metabolism, Ribonucleoprotein, U1 Small Nuclear chemistry, Ribonucleoprotein, U1 Small Nuclear metabolism, Spiro Compounds chemistry, Tumor Cells, Cultured, Phosphoproteins antagonists & inhibitors, Pyrans pharmacology, RNA Splicing Factors antagonists & inhibitors, RNA, Long Noncoding metabolism, Ribonucleoprotein, U1 Small Nuclear antagonists & inhibitors, Spiro Compounds pharmacology
Abstract

RNA splicing, a highly conserved process in eukaryotic gene expression, is seen as a promising target for anticancer agents. Splicing is associated with other RNA processing steps, such as transcription and nuclear export; however, our understanding of the interaction between splicing and other RNA regulatory mechanisms remains incomplete. Moreover, the impact of chemical splicing inhibition on long non-coding RNAs (lncRNAs) has been poorly understood. Here, we demonstrate that spliceostatin A (SSA), a chemical splicing modulator that binds to the SF3B subcomplex of the U2 small nuclear ribonucleoprotein particle (snRNP), limits U1 snRNP availability in splicing, resulting in premature cleavage and polyadenylation of MALAT1, a nuclear lncRNA, as well as protein-coding mRNAs. Therefore, truncated transcripts are exported into the cytoplasm and translated, resulting in aberrant protein products. Our work demonstrates that active recycling of the splicing machinery maintains homeostasis of RNA processing beyond intron excision., Competing Interests: Declaration of interests There are no conflicts to declare., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Published
2021
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50. Saliva and Plasma Reflect Metabolism Altered by Diabetes and Periodontitis.

Author

Sakanaka A, Kuboniwa M, Katakami N, Furuno M, Nishizawa H, Omori K, Taya N, Ishikawa A, Mayumi S, Tanaka Isomura E, Shimomura I, Fukusaki E, and Amano A

Abstract

Periodontitis is an inflammatory disorder caused by disintegration of the balance between the periodontal microbiome and host response. While growing evidence suggests links between periodontitis and various metabolic disorders including type 2 diabetes (T2D), non-alcoholic liver disease, and cardiovascular disease (CVD), which often coexist in individuals with abdominal obesity, factors linking periodontal inflammation to common metabolic alterations remain to be fully elucidated. More detailed characterization of metabolomic profiles associated with multiple oral and cardiometabolic traits may provide better understanding of the complexity of oral-systemic crosstalk and its underlying mechanism. We performed comprehensive profiling of plasma and salivary metabolomes using untargeted gas chromatography/mass spectrometry to investigate multivariate covariation with clinical markers of oral and systemic health in 31 T2D patients with metabolic comorbidities and 30 control subjects. Orthogonal partial least squares (OPLS) results enabled more accurate characterization of associations among 11 oral and 25 systemic clinical outcomes, and 143 salivary and 78 plasma metabolites. In particular, metabolites that reflect cardiometabolic changes were identified in both plasma and saliva, with plasma and salivary ratios of (mannose + allose):1,5-anhydroglucitol achieving areas under the curve of 0.99 and 0.92, respectively, for T2D diagnosis. Additionally, OPLS analysis of periodontal inflamed surface area (PISA) as the numerical response variable revealed shared and unique responses of metabolomic and clinical markers to PISA between healthy and T2D groups. When combined with linear regression models, we found a significant correlation between PISA and multiple metabolites in both groups, including threonate, cadaverine and hydrocinnamate in saliva, as well as lactate and pentadecanoic acid in plasma, of which plasma lactate showed a predominant trend in the healthy group. Unique metabolites associated with PISA in the T2D group included plasma phosphate and salivary malate, while those in the healthy group included plasma gluconate and salivary adenosine. Remarkably, higher PISA was correlated with altered hepatic lipid metabolism in both groups, including higher levels of triglycerides, aspartate aminotransferase and alanine aminotransferase, leading to increased risk of cardiometabolic disease based on a score summarizing levels of CVD-related biomarkers. These findings revealed the potential utility of saliva for evaluating the risk of metabolic disorders without need for a blood test, and provide evidence that disrupted liver lipid metabolism may underlie the link between periodontitis and cardiometabolic disease., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Sakanaka, Kuboniwa, Katakami, Furuno, Nishizawa, Omori, Taya, Ishikawa, Mayumi, Tanaka Isomura, Shimomura, Fukusaki and Amano.)

Published
2021
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