Direct in vivo investigation of mammalian metabolism is complicated by the distinct metabolic functions of different tissues. We present a computational method that successfully describes the tissue specificity of human metabolism on a large scale. By integrating tissue-specific gene- and protein-expression data with an existing comprehensive reconstruction of the global human metabolic network, we predict tissue-specific metabolic activity in ten human tissues. This reveals a central role for post-transcriptional regulation in shaping tissue-specific metabolic activity profiles. The predicted tissue specificity of genes responsible for metabolic diseases and tissue-specific differences in metabolite exchange with biofluids extend markedly beyond tissue-specific differences manifest in enzyme-expression data, and are validated by large-scale mining of tissue-specificity data. Our results establish a computational basis for the genome-wide study of normal and abnormal human metabolism in a tissue-specific manner.
%0 Journal Article
%1 Shlomi2008Networkbased
%A Shlomi, Tomer
%A Cabili, Moran N.
%A Herrgard, Markus J.
%A Palsson, Bernhard Ø.
%A Ruppin, Eytan
%B Nat Biotech
%C 1 School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel. 2 These authors contributed equally to this work.
%D 2008
%I Nature Publishing Group
%J Nature biotechnology
%K human metabolic-networks reconstruction transcriptional-regulation
%N 9
%P 1003--1010
%R 10.1038/nbt.1487
%T Network-based prediction of human tissue-specific metabolism.
%U http://dx.doi.org/10.1038/nbt.1487
%V 26
%X Direct in vivo investigation of mammalian metabolism is complicated by the distinct metabolic functions of different tissues. We present a computational method that successfully describes the tissue specificity of human metabolism on a large scale. By integrating tissue-specific gene- and protein-expression data with an existing comprehensive reconstruction of the global human metabolic network, we predict tissue-specific metabolic activity in ten human tissues. This reveals a central role for post-transcriptional regulation in shaping tissue-specific metabolic activity profiles. The predicted tissue specificity of genes responsible for metabolic diseases and tissue-specific differences in metabolite exchange with biofluids extend markedly beyond tissue-specific differences manifest in enzyme-expression data, and are validated by large-scale mining of tissue-specificity data. Our results establish a computational basis for the genome-wide study of normal and abnormal human metabolism in a tissue-specific manner.
@article{Shlomi2008Networkbased,
abstract = {Direct in vivo investigation of mammalian metabolism is complicated by the distinct metabolic functions of different tissues. We present a computational method that successfully describes the tissue specificity of human metabolism on a large scale. By integrating tissue-specific gene- and protein-expression data with an existing comprehensive reconstruction of the global human metabolic network, we predict tissue-specific metabolic activity in ten human tissues. This reveals a central role for post-transcriptional regulation in shaping tissue-specific metabolic activity profiles. The predicted tissue specificity of genes responsible for metabolic diseases and tissue-specific differences in metabolite exchange with biofluids extend markedly beyond tissue-specific differences manifest in enzyme-expression data, and are validated by large-scale mining of tissue-specificity data. Our results establish a computational basis for the genome-wide study of normal and abnormal human metabolism in a tissue-specific manner.},
added-at = {2018-12-02T16:09:07.000+0100},
address = {[1] School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel. [2] These authors contributed equally to this work.},
author = {Shlomi, Tomer and Cabili, Moran N. and Herrg\r{a}rd, Markus J. and Palsson, Bernhard {\O}. and Ruppin, Eytan},
biburl = {https://www.bibsonomy.org/bibtex/24433aafc0924d5fb432e1cd67ab066cb/karthikraman},
booktitle = {Nat Biotech},
citeulike-article-id = {3196658},
citeulike-linkout-0 = {http://dx.doi.org/10.1038/nbt.1487},
citeulike-linkout-1 = {http://dx.doi.org/10.1038/nbt.1487},
citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/18711341},
citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=18711341},
day = 17,
doi = {10.1038/nbt.1487},
interhash = {f0c2811d3a287ab70f33aba9c4239f56},
intrahash = {4433aafc0924d5fb432e1cd67ab066cb},
issn = {1546-1696},
journal = {Nature biotechnology},
keywords = {human metabolic-networks reconstruction transcriptional-regulation},
month = sep,
number = 9,
pages = {1003--1010},
pmid = {18711341},
posted-at = {2010-12-03 10:48:11},
priority = {2},
publisher = {Nature Publishing Group},
timestamp = {2018-12-02T16:09:07.000+0100},
title = {Network-based prediction of human tissue-specific metabolism.},
url = {http://dx.doi.org/10.1038/nbt.1487},
volume = 26,
year = 2008
}