A comprehensive resource of interacting protein regions for refining human transcription factor networks - PubMed (original) (raw)

. 2010 Feb 24;5(2):e9289.

doi: 10.1371/journal.pone.0009289.

Shigeo Fujimori, Masamichi Ishizaka, Naoya Hirai, Kazuyo Masuoka, Rintaro Saito, Yosuke Ozawa, Katsuya Hino, Takanori Washio, Masaru Tomita, Tatsuhiro Yamashita, Tomohiro Oshikubo, Hidetoshi Akasaka, Jun Sugiyama, Yasuo Matsumoto, Hiroshi Yanagawa

Affiliations

A comprehensive resource of interacting protein regions for refining human transcription factor networks

Etsuko Miyamoto-Sato et al. PLoS One. 2010.

Abstract

Large-scale data sets of protein-protein interactions (PPIs) are a valuable resource for mapping and analysis of the topological and dynamic features of interactome networks. The currently available large-scale PPI data sets only contain information on interaction partners. The data presented in this study also include the sequences involved in the interactions (i.e., the interacting regions, IRs) suggested to correspond to functional and structural domains. Here we present the first large-scale IR data set obtained using mRNA display for 50 human transcription factors (TFs), including 12 transcription-related proteins. The core data set (966 IRs; 943 PPIs) displays a verification rate of 70%. Analysis of the IR data set revealed the existence of IRs that interact with multiple partners. Furthermore, these IRs were preferentially associated with intrinsic disorder. This finding supports the hypothesis that intrinsically disordered regions play a major role in the dynamics and diversity of TF networks through their ability to structurally adapt to and bind with multiple partners. Accordingly, this domain-based interaction resource represents an important step in refining protein interactions and networks at the domain level and in associating network analysis with biological structure and function.

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Conflict of interest statement

Competing Interests: Some of the authors are company employees of BioIT Business Development Unit, Fujitsu Limited; Production Solution Business Unit, Production Solution Division, Solutions and Services Department, Solution Section, Fujitsu Advanced Engineering Limited; Special Suite Team, Custom Primer Production Department, Invitrogen Japan K.K.; and Automation, QIAGEN K.K.. But the companies did not fund the study of this work, and no patents or commercial products were newly produced in this work.

Figures

Figure 1

Figure 1. Toward the production of a comprehensive IR data set using IVV mRNA display technology.

(A) Schematic of in vitro parallel auto-selection with IVV for large-scale analysis of PPIs and IRs. Individual steps (left) and expression of bait proteins (right) are indicated. This system is based on a modified high-throughput version of in vitro selection using IVV , consisting of four major steps (left side): (i) the preparation of mRNA templates that encode bait proteins and a randomly primed prey IVV library; (ii) in vitro parallel co-translation of bait and prey proteins and the formation of prey IVV as protein-RNA fusion molecules linked through puromycin and released from the ribosome; (iii) in vitro parallel selection, including RT-PCR and sequencing; and (iv) identification of PPIs and IRs by IST analysis (Figure S2). In IVV selection, ISTs are obtained as interaction fragmented sequences from a randomly primed prey IVV library. Bait protein expression was confirmed following in vitro translation by western blotting with an anti-T7 antibody (right side; ‘Confirmation of in vitro bait expression’). Lanes M and N indicate the molecular weight markers and negative control, respectively. Other lane numbers indicate bait protein IDs (Table S1). Expression was detected by 10–15% SDS-PAGE followed by protein staining. (B) Verification of PPIs (IRs) obtained following IVV selection by an in vitro C-terminal labeling pull-down assay . Twelve representative examples of reciprocal pull-down assays are shown. ‘Pull down’ and ‘Pull down (reciprocal)’ indicate that assays were performed with the same and reciprocal combination of bait and prey (compared with the selection results), respectively. Binding was detected by 10–15% SDS-PAGE followed by protein staining. Also see Figure S4A. The bottom table summarizes the data obtained by the IVV selection approach for three classes (classes 1, 2, and 3; see also Supporting Data III in Text S1). The IVV core data set (Core) is defined as the proteins belonging to classes 1 and 2. The rows indicate the number of interaction sequence tags (ISTs), the number of interaction regions (IRs), the number of protein-protein interactions (PPIs), and the number of interactions verified by pull-down assay (pull down OK). The numbers of known PPIs overlapping with LC PPIs and known domains/motifs overlapping with the Pfam data (Supporting Data V in Text S1) are given in parentheses.

Figure 2

Figure 2. Validation of IR data obtained following IVV selection.

(A) Left: IST density of IRs on the 3D protein structures of AP-1. ISTs obtained as prey in selections using FOS and JUN were mapped onto the 3D structure of AP-1 (PDB: 1A02, chain F and J; . Right: Contact regions of AP-1. All amino acids of one protein within 4.0 Å of the other protein are colored blue. IST densities are ranked and colored on a scale of 1 to >5 according to the number of ISTs at each amino acid position. (B) Left: MAX interacting regions in FTH1. Twenty-four ISTs derived from FTH1, obtained using MAX as bait, were mapped onto the 3D structure of FTH1 (PDB:1FHA; [56]). Right: Pull-down assay to evaluate the MAX/FTH interaction. ‘IR’ and ‘full’ correspond to the assays performed with the IR (region: 124.176) and full-length FTH, respectively, as bait. Full-length MAX was used as prey. (C) Left: SMAD2 interacting regions in RHOA. An IST derived from RHOA, obtained using SMAD2 as bait, was mapped onto the 3-D structure of RHOA (PDB: 1OW3, chain B; [57]). Right: Pull-down assay to evaluate the SMAD2/RHOA interaction. ‘IR’ and ‘full’ correspond to the assays performed with the IR (region: 38..63) and full-length RHOA, respectively, as bait. The (522..1401) region of SMAD2 was used as prey.

Figure 3

Figure 3. A TF network at the IR level developed using IVV data.

(A) Graphic expression of the PPI network at the IR level. Interacting interfaces of the proteins, determined as IRs by IVV experiments, are drawn on the graph as diamond-shape nodes (IR nodes). Broken and solid lines indicate ‘intra-’ and ‘inter-’ protein edges, respectively. The graph contains 1,572 nodes (842 IR nodes and 730 protein nodes) and 842 intra-protein edges. Note that overlapping IRs are merged into a single node in the constructed network. Also see Figure S12. (B) An example of an underlying network graph at the IR level. Graphical expression of the FOS network at the protein level (upper). PPIs are simply expressed by nodes indicating proteins and edges that connect them. Graphical expression of the FOS network at the IR level (lower). A leucine zipper region of the FOS protein exclusively interacts with leucine zipper regions of other proteins (JUN, JUNB, JUND and ATF2). In addition, a region distinct from the leucine zipper in the FOS protein interacts with SMAD2.

Figure 4

Figure 4. Analysis of the rates of disordered regions.

The proportions of intrinsically ordered and disordered regions in 13 datasets consisting of IR (7 datasets) and Protein (6 datasets) were analyzed by DISOPRED2 as follows: IR (IR-level data); IVV Core; IVV (class 1); IVV (class 2); Pfam hit (a set of IRs hit by Pfam search); Multiple partners (IRs obtained from multiple bait proteins); Single partners (IRs obtained from a single bait protein) and Refseq (random regions) or Proteins (protein-level data); IVV Core; LC (a set of known interacting partners for 50 bait proteins); ‘Transcription regulator activity’ (a set of proteins for which GO:0030528 is assigned); ‘Transcription cofactor activity’ (a set of proteins for which the GO:0003712 is assigned); ‘Transcription factor activity’ (a set of proteins for which GO:0003700 is assigned); and All RefSeq: all human RefSeqs. The dataset of random regions was created by random selection of protein regions (n = 10000) from the human RefSeq that together correspond to the same length distribution as that of detected IRs. Information about the assignment of GO identifiers for proteins can be obtained from the Gene Ontology Web site (

http://www.geneontology.org

).

Figure 5

Figure 5. IR properties and the number of known interaction partners.

Counts of LCI for each prey gene (protein) were plotted for two datasets: prey proteins having IRs obtained from multiple bait proteins (multiple partners), and proteins having IRs obtained from a single bait protein (single partner).

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