template DNA for the quantitative analysis was PCR-amplified from each selected sequence. In vitro pull-down assay Pull-down assay using the C-terminal fluorescence labeling technique was performed as previously described [21], with some modifications. The bait Jun and prey proteins were translated in vitro separately. Only the preys were fluorescence-labeled in in vitro translation. The bait and preys were incubated with affinity beads, and captured prey proteins were electrophoresed and analyzed by using a Molecular Imager FX (Bio-Rad). Details are given in Methods S1 online. Surface plasmon resonance analysis SPR analysis was performed on a BIACORE3000 instrument with a CM4 sensor chip conjugating anti-GST mAb (Biacore). GST-fused candidate proteins were employed as ligands. Full-length mouse Jun fused with a His-tag was employed as an analyte. The experiments were performed under two conditions of analyte concentration (500 nM and 250 nM). Details are given in Methods S1 online. Results Design of a transcription-factor tiling (TFT) array and sample labeling In affinity selection of protein interactions from randomly fragmented cDNA libraries, relatively short cDNA fragments encoding specific binding regions are often obtained. In order to detect these fragments, we adopted a tiling array strategy for the design of custom oligo DNA microarrays as follows: 1) Oligonucle- otide probes of 50-mer in length were used. This is the preferred length for microarray probes, because shorter probes result in low sensitivity and longer probes produce non-specific signals [25]. 2) There should be no gaps between the probes. A contiguous linear series of data is required to recognize a signal peak in the algorithm for tiling array analysis (in this case, at least 4 data points are needed in a search window), so the probes must be densely arranged. 3) mRNA sequences were employed for the tiling array. Only coding regions are required for the purpose of protein-interaction analysis, so other genomic sequences, e.g., introns, control regions and non- coding RNAs, were not employed. In this study, we constructed TFT arrays containing ,1,600 ORF sequences of known and predicted mouse transcription-regulatory factors (334,372 oligonu- Tiling Arrays for mRNA Display cleotides) to analyze cDNA fragments from IVV screening for Jun (a transcription factor)-interactors [21]. We also improved the method for labeling of cDNA samples. Usually, double-stranded DNA samples for a tiling array analysis are labeled by using random primers [26]. However, cDNA fragments selected from a randomly fragmented cDNA library [21] seem to be too short for efficient labeling by random priming. Indeed, in a test analysis with a TFT array using the random priming labeling method, we failed to detect all of the previously detected candidates (data not shown). Therefore we employed another labeling procedure [24], in which sense-strand labeled RNAs were produced by one-step in vitro transcription using a SP6 promoter attached to cDNA fragments from IVV screening. Identification of selected candidates using the TFT array From the 5th-round DNA library of the IVV screening in the presence and absence of a bait Jun protein, called bait (+) and bait (2) screening, respectively [21], we obtained labeled RNAs and hybridized them onto the tiling array. First, the ratios of the signal intensities from the experiments in the presence and absence of bait were calculated. The ratio data are presented in Data S1 online as a GFF formatted file. Next, we searched for signal peaks in the data, as described in Methods. Only reproducible signal peaks were collected (Table S1 online); the total number of peaks was 647 on 545 mRNA sequences (some of the mRNA sequences included multiple peaks). To distinguish between true positives and false positives, specific enrichment of the selected candidates was validated by real-time PCR. Among the top 10 percent of the peaks (64 regions), specific enrichment of 35 peaks was confirmed in the screening (white graph in Figure 2A; the signal intensity and peak data of the 35 candidates are presented in Figure S1 online). The data indicate that the appropriate threshold for distinguishing between true positives and noise in the microarray signal is a signal ratio of 3,4. The 35 candidates identified in the present study include all of the 20 Jun-interactors identified in our previous studies using conventional cloning and sequencing (Table 1) [21,22]. Further- more, the 35 candidates include eight well-known Jun-associated proteins, i.e., c-Maf, Fos, Jun, Atf7, Atf4, Jdp2, Atf3 and Fosl2 (Table 1), which is double the number in the previous study, in which four known Jun-interactors were obtained (white graphs of Figure 2B) [21,22]. In other words, 15 proteins including four known Jun-interactors were newly detected using the TFT arrays. Figure 2. Data from the tiling array. (A) The top 10% candidates were confirmed by real-time PCR. White and gray indicate numbers of enriched and non-enriched candidates, respectively. (B) Numbers of known (white) and newly selected (gray) proteins from conventional sequencing and the TFT arrays. doi:10.1371/journal.pone.0001646.g002 PLoS ONE | www.plosone.org 3 February 2008 | Volume 3 | Issue 2 | e1646