Identification of trans-golgi network proteins in Arabidopsis thaliana root tissue.
Identification of trans-golgi network proteins in Arabidopsis thaliana root tissue.
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DOI：
10.1021/pr4008464
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发表时间：
2014-02-07
影响因子：
4.4
通讯作者：
Lilley, Kathryn S.
中科院分区：
文献类型：
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作者：
Groen, Arnoud J.;Sancho-Andres, Gloria;Breckels, Lisa M.;Gatto, Laurent;Aniento, Fernando;Lilley, Kathryn S.
关键词：
Knowledge of protein subcellular localization assists in the elucidation of protein function and understanding of different biological mechanisms that occur at discrete subcellular niches. Organelle-centric proteomics enables localization of thousands of proteins simultaneously. Although such techniques have successfully allowed organelle protein catalogues to be achieved, they rely on the purification or significant enrichment of the organelle of interest, which is not achievable for many organelles. Incomplete separation of organelles leads to false discoveries, with erroneous assignments. Proteomics methods that measure the distribution patterns of specific organelle markers along density gradients are able to assign proteins of unknown localization based on comigration with known organelle markers, without the need for organelle purification. These methods are greatly enhanced when coupled to sophisticated computational tools. Here we apply and compare multiple approaches to establish a high-confidence data set of Arabidopsis root tissue trans-Golgi network (TGN) proteins. The method employed involves immunoisolations of the TGN, coupled to probability-based organelle proteomics techniques. Specifically, the technique known as LOPIT (localization of organelle protein by isotope tagging), couples density centrifugation with quantitative mass-spectometry-based proteomics using isobaric labeling and targeted methods with semisupervised machine learning methods. We demonstrate that while the immunoisolation method gives rise to a significant data set, the approach is unable to distinguish cargo proteins and persistent contaminants from full-time residents of the TGN. The LOPIT approach, however, returns information about many subcellular niches simultaneously and the steady-state location of proteins. Importantly, therefore, it is able to dissect proteins present in more than one organelle and cargo proteins en route to other cellular destinations from proteins whose steady-state location favors the TGN. Using this approach, we present a robust list of Arabidopsis TGN proteins.
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DOI：
10.1073/pnas.0506958103
发表时间：
2006-04-25
影响因子：
11.1
作者：
Dunkley, TPJ;Hester, S;Lilley, KS
通讯作者：
Lilley, KS
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
5.6
作者：
Chen, Y;Shin, YK;Bassham, DC
通讯作者：
Bassham, DC
影响因子：
64.5
作者：
Foster, LJ;de Hoog, CL;Mann, M
通讯作者：
Mann, M
影响因子：
16.8
作者：
通讯作者：
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