Recon3D enables a three-dimensional view of gene variation in human metabolism

Elizabeth Brunk; Swagatika Sahoo; Daniel C. Zielinski; Ali Altunkaya; Andreas Dräger; Nathan Mih; Francesco Gatto; Avlant Nilsson; German Andres Preciat Gonzalez; Maike Kathrin Aurich; Andreas Prlic; Anand Sastry; Anna D. Danielsdottir; Almut Heinken; Alberto Noronha; Peter W. Rose; Stephen K. Burley; Ronan M.T. Fleming; Jens Nielsen; Ines Thiele; Bernhard O. Palsson

doi:10.1038/nbt.4072

Recon3D enables a three-dimensional view of gene variation in human metabolism

Elizabeth Brunk
, Swagatika Sahoo
, Daniel C. Zielinski
, Ali Altunkaya
, Andreas Dräger
, Nathan Mih
, Francesco Gatto
, Avlant Nilsson
, German Andres Preciat Gonzalez
, Maike Kathrin Aurich
, Andreas Prlic
, Anand Sastry
, Anna D. Danielsdottir
, Almut Heinken
, Alberto Noronha
, Peter W. Rose
, Stephen K. Burley
, Ronan M.T. Fleming
, Jens Nielsen
, Ines Thiele

Bernhard O. Palsson

University of California, San Diego
Technical University of Denmark
University of Luxembourg
Indian Institute of Technology Madras
San Diego Supercomputer Center
Arizona State University
University of Tübingen
Chalmers University of Technology
Rutgers Cancer Institute of New Jersey
Leiden Academic Centre for Drug Research
Department of Pediatrics

Research output: Contribution to a Journal (Peer & Non Peer) › Article › peer-review

545 Citations (Scopus)

Abstract

Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.

Original language	English
Pages (from-to)	272-281
Number of pages	10
Journal	Nature Biotechnology
Volume	36
Issue number	3
DOIs	https://doi.org/10.1038/nbt.4072
Publication status	Published - 1 Mar 2018
Externally published	Yes

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Brunk, E., Sahoo, S., Zielinski, D. C., Altunkaya, A., Dräger, A., Mih, N., Gatto, F., Nilsson, A., Preciat Gonzalez, G. A., Aurich, M. K., Prlic, A., Sastry, A., Danielsdottir, A. D., Heinken, A., Noronha, A., Rose, P. W., Burley, S. K., Fleming, R. M. T., Nielsen, J., ... Palsson, B. O. (2018). Recon3D enables a three-dimensional view of gene variation in human metabolism. Nature Biotechnology, 36(3), 272-281. https://doi.org/10.1038/nbt.4072

@article{6a161496d1144bf4aff2e528ced9df6f,

title = "Recon3D enables a three-dimensional view of gene variation in human metabolism",

abstract = "Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17\% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.",

author = "Elizabeth Brunk and Swagatika Sahoo and Zielinski, \{Daniel C.\} and Ali Altunkaya and Andreas Dr{\"a}ger and Nathan Mih and Francesco Gatto and Avlant Nilsson and \{Preciat Gonzalez\}, \{German Andres\} and Aurich, \{Maike Kathrin\} and Andreas Prlic and Anand Sastry and Danielsdottir, \{Anna D.\} and Almut Heinken and Alberto Noronha and Rose, \{Peter W.\} and Burley, \{Stephen K.\} and Fleming, \{Ronan M.T.\} and Jens Nielsen and Ines Thiele and Palsson, \{Bernhard O.\}",

year = "2018",

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doi = "10.1038/nbt.4072",

language = "English",

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Brunk, E, Sahoo, S, Zielinski, DC, Altunkaya, A, Dräger, A, Mih, N, Gatto, F, Nilsson, A, Preciat Gonzalez, GA, Aurich, MK, Prlic, A, Sastry, A, Danielsdottir, AD, Heinken, A, Noronha, A, Rose, PW, Burley, SK, Fleming, RMT, Nielsen, J, Thiele, I & Palsson, BO 2018, 'Recon3D enables a three-dimensional view of gene variation in human metabolism', Nature Biotechnology, vol. 36, no. 3, pp. 272-281. https://doi.org/10.1038/nbt.4072

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AU - Brunk, Elizabeth

AU - Sahoo, Swagatika

AU - Zielinski, Daniel C.

AU - Altunkaya, Ali

AU - Dräger, Andreas

AU - Mih, Nathan

AU - Gatto, Francesco

AU - Nilsson, Avlant

AU - Preciat Gonzalez, German Andres

AU - Aurich, Maike Kathrin

AU - Prlic, Andreas

AU - Sastry, Anand

AU - Danielsdottir, Anna D.

AU - Heinken, Almut

AU - Noronha, Alberto

AU - Rose, Peter W.

AU - Burley, Stephen K.

AU - Fleming, Ronan M.T.

AU - Nielsen, Jens

AU - Thiele, Ines

AU - Palsson, Bernhard O.

PY - 2018/3/1

Y1 - 2018/3/1

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AB - Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.

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U2 - 10.1038/nbt.4072

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SP - 272

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JO - Nature Biotechnology

JF - Nature Biotechnology

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ER -

Recon3D enables a three-dimensional view of gene variation in human metabolism

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