美國(guó)加州大學(xué)圣地亞哥分校和一個(gè)國(guó)際協(xié)會(huì)大學(xué)的研究人員在早期開(kāi)拓性工作的基礎(chǔ)上,，制作出如同谷歌地圖一樣,，迄今為止最全面的虛擬重建人體新陳代謝模型——“偵察 2”（Recon 2）。“偵察 2”可鑒定疾病的成因,，并對(duì)諸如癌癥,、糖尿病，甚至精神和神經(jīng)退行性疾病等提出新的具有針對(duì)性的治療方法,。該研究成果在線刊登在最新期的《自然·生物技術(shù)》（Nature Biotechnology)上,。
新陳代謝可將食物轉(zhuǎn)換成能量和人體所需的分子，而代謝失衡是潛在的致病因素,?？茖W(xué)家以人類(lèi)基因組和系統(tǒng)生物學(xué)為基礎(chǔ)，借助高效率計(jì)算機(jī)建立了廣闊可交互的新陳代謝信息數(shù)據(jù)庫(kù),。加州大學(xué)圣地亞哥分校雅各布工程學(xué)院生物工程教授伯恩哈德·保爾松表示,，在不斷改進(jìn)原來(lái)“偵察 1”的基礎(chǔ)上,，開(kāi)發(fā)出的“偵察 2”可使生物醫(yī)學(xué)人員在研究人體新陳代謝的網(wǎng)絡(luò)中比以往更精確,，以了解特定的代謝途徑是如何出現(xiàn)偏差并造成疾病的。
將“偵察 2”比喻為谷歌地圖,，是因?yàn)樗心芰?fù)雜的細(xì)節(jié)合并成一張單一的,、互動(dòng)的地圖。例如,，研究人員觀察癌腫瘤的生長(zhǎng)代謝如何進(jìn)展時(shí),，可以在該地圖上放大個(gè)人代謝反應(yīng)的細(xì)致圖像，也可以縮小來(lái)看看與其他代謝間的關(guān)系,。就像谷歌地圖匯集大量數(shù)據(jù),，把圖片、地址,、街道和交通流量融匯成用于導(dǎo)航的綜合工具,。該模型匯編了大量公開(kāi)發(fā)表的文獻(xiàn)資料和既有的代謝過(guò)程模式。
“偵察 2”可以向研究人員多角度展示人體代謝網(wǎng)絡(luò),，提供必要的背景數(shù)據(jù),。人體代謝網(wǎng)絡(luò)最有前景的應(yīng)用之一，是能夠確定特定基因表達(dá)及其代謝途徑的靶向給藥,。借助“偵察 2”,，研究人員能夠使用現(xiàn)有的基因數(shù)據(jù)庫(kù)和整個(gè)代謝網(wǎng)絡(luò)圖，找到影響癌細(xì)胞生長(zhǎng)的特定代謝途徑,，然后通過(guò)虛擬實(shí)驗(yàn),，驗(yàn)證哪些藥物能夠修復(fù)代謝失衡類(lèi)疾病。
“偵察 2”無(wú)疑將加速個(gè)性化診斷和治療的發(fā)展,。未來(lái),，醫(yī)生可為患者開(kāi)發(fā)出個(gè)人代謝網(wǎng)絡(luò)的虛擬模型，并針對(duì)各種疾病，包括糖尿病,、癌癥和神經(jīng)退行性疾病確定最有效的治療方案,。(生物谷Bioon.com)
doi:10.1038/nbt.2488
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A community-driven global reconstruction of human metabolism
Ines Thiele, Neil Swainston, Ronan M T Fleming, Andreas Hoppe, Swagatika Sahoo, Maike K Aurich, Hulda Haraldsdottir, Monica L Mo, Ottar Rolfsson, Miranda D Stobbe, Stefan G Thorleifsson, Rasmus Agren, Christian Blling, Sergio Bordel, Arvind K Chavali, Paul Dobson, Warwick B Dunn, Lukas Endler,David Hala, Michael Hucka, Duncan Hull, Daniel Jameson, Neema Jamshidi, Jon J Jonsson, Nick Juty, Sarah Keating, Intawat Nookaew, Nicolas Le Novère, Naglis Malys, Alexander Mazein, Jason A Papin, Nathan D Price, Evgeni Selkov Sr, Martin I Sigurdsson, Evangelos Simeonidis,Nikolaus Sonnenschein, Kieran Smallbone,Anatoly Sorokin,Johannes H G M van Beek, Dieter Weichart,Igor Goryanin,Jens Nielsen, Hans V Westerhoff,Douglas B Kell,Pedro Mendes & Bernhard Palsson.
Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ~2× more reactions and ~1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type–specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.
