Toward an understanding of the protein interaction network of the human liver - PubMed (original) (raw)

doi: 10.1038/msb.2011.67.

Keke Huo, Lixin Ma, Liujun Tang, Dong Li, Xiaobi Huang, Yanzhi Yuan, Chunhua Li, Wei Wang, Wei Guan, Hui Chen, Chaozhi Jin, Juncheng Wei, Wanqiao Zhang, Yongsheng Yang, Qiongming Liu, Ying Zhou, Cuili Zhang, Zhihao Wu, Wangxiang Xu, Ying Zhang, Tao Liu, Donghui Yu, Yaping Zhang, Liang Chen, Dewu Zhu, Xing Zhong, Lixin Kang, Xiang Gan, Xiaolan Yu, Qi Ma, Jing Yan, Li Zhou, Zhongyang Liu, Yunping Zhu, Tao Zhou, Fuchu He, Xiaoming Yang

Affiliations

Toward an understanding of the protein interaction network of the human liver

Jian Wang et al. Mol Syst Biol. 2011.

Erratum in

Abstract

Proteome-scale protein interaction maps are available for many organisms, ranging from bacteria, yeast, worms and flies to humans. These maps provide substantial new insights into systems biology, disease research and drug discovery. However, only a small fraction of the total number of human protein-protein interactions has been identified. In this study, we map the interactions of an unbiased selection of 5026 human liver expression proteins by yeast two-hybrid technology and establish a human liver protein interaction network (HLPN) composed of 3484 interactions among 2582 proteins. The data set has a validation rate of over 72% as determined by three independent biochemical or cellular assays. The network includes metabolic enzymes and liver-specific, liver-phenotype and liver-disease proteins that are individually critical for the maintenance of liver functions. The liver enriched proteins had significantly different topological properties and increased our understanding of the functional relationships among proteins in a liver-specific manner. Our data represent the first comprehensive description of a HLPN, which could be a valuable tool for understanding the functioning of the protein interaction network of the human liver.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1

Figure 1

Construction of the proteome-scale HLPN. (A) Strategy of high-throughput Y2H screening. (B, C) Distribution of the GO categories of cellular component (CC) and molecular function (MF). From inside to outside, the rings represent all of the human proteins (16 022 with CC and 15 259 with MF annotations), human liver proteins (12 066 with CC and 10 863 with MF annotations), Y2H matrix proteins (4553 with CC and 4374 with MF annotations) and HLPN proteins (2342 with CC and 2252 with MF annotations). Each section reflects the percentage of proteins assigned to the given GO category. (D) GST pull-down assay. Bacteria-expressed GST or GST-tagged proteins were immobilized on glutathione-Sepharose 4B beads, and the beads were subsequently incubated with the Myc- or Flag-tagged proteins expressed in the HEK293T cell lysates. The proteins were detected using the indicated antibodies. (E) Co-IP assay. Flag- or Myc-tagged plasmids were transfected into HEK293T cells. Immunoprecipitations were performed using anti-Myc or anti-Flag antibodies and protein A/G-agarose. The lysates and immunoprecipitates were detected using the indicated antibodies. Myc-, GST- and Flag-fusions mean the fusion proteins with Myc, GST or Flag tags. (F) Luciferase reporter gene assays of SMAD3. Data are presented as mean values±s.d. (_n_=3). The results are representative of three independent experiments.

Figure 2

Figure 2

Network views of the HLPN. (A) Visualization of the HLPN. Yellow circles, selected Y2H proteins; red lines, interactions identified by library screening; blue lines, interactions identified by array screening; green lines, overlapping interactions of library and array screening; bold edges, interactions reported in the HPRD. (B) Distribution of the GO categories of biological processes for metabolic enzymes and their partners in the HLPN. The sections reflect the percentage of proteins assigned to the given GO category. (C) The ROS subnetwork. Blue circles, ROS proteins; red lines, data from the HPRD. (D) The liver-phenotype proteins network. Bold edges, interactions reported in the HPRD. (E) Liver-disease-associated proteins network. Red circles, HCC; cyan circles, cholangiocellular carcinoma; blue circles, liver fibrosis; yellow circles, other liver-disease proteins; gray circles, proteins without human liver disease annotation in the Library of Molecular Associations database (

http://www.medicalgenomics.org/databases/loma

); red lines: interactions among liver-disease proteins; yellow lines, interactions among non-liver-disease proteins; green lines, other interactions.

Figure 3

Figure 3

GIT2 is a negative regulator of the NF-κB signaling pathway. (A) Confirmation of the interaction between GIT2 and IKBKG by the GST pull-down assay. (B) GIT2 enhances the TNFAIP3-dependent deubiquitination of IKBKG. HEK293 cells were transfected with the indicated plasmids. Cell extracts were immunoprecipitated with anti-Flag antibody and detected by western blotting. (C) Knockdown of GIT2 by siRNA impairs the TNFAIP3-dependent deubiquitination of IKBKG. HEK293 cells were cotransfected with the indicated plasmids or the GIT2 siRNA. Cell extracts were immunoprecipitated with an anti-Flag antibody and detected by western blotting. (D) Knockdown of GIT2 impairs the TNFAIP3-mediated inhibition of NF-κB activity. HEK293 cells were cotransfected with the indicated plasmids. One day after transfection, the cells were stimulated with TNF-α for 6 h. Relative reporter activity was measured. Data are presented as mean values±s.d. (_n_=3). The results are representative of three independent experiments.

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