used as a reference sample while the other sample is subjected to a selective treatment. Knockdown of a specific gene by the shRNA vector can lead to three possible cellular responses to this treatment. First the cells can remain unaffected identical to control cells, second the cells can become more sensitive to the treatment and third the cells can acquire resistance to the treatment. As a consequence of the differential response to the treatment, the relative number of cells that harbor a specific shRNA can increase, decrease or remain the same. The relative abundance of each shRNA cassette can be determined by the isolation of the shRNA cassettes from the population, labeling of the barcode identifiers with different fluorescent dyes and subsequent hybridization to DNA microarrays representing all shRNA sequences. By comparing the relative abundance of all shRNAs against the reference population, shRNAs responsible for the three possible phenotypes can be identified. The shRNA barcode technique can be used for different screening approaches, most notably the identification of genes that are involved in drug resistance. For instance, we have used the barcoding approach to identify genes that are involved in the resistance to the cytotoxic effects of Nutlin-3, a drug that activates p53 by acting as an inhibitor of MDM2 [13]. This led to the identification of 53BP1, a p53 binding protein, as a critical modulator of the effect of Nutlin-3. More recently, we found the tumor suppressor PTEN as a gene, which upon decreased expression confers resistance to trastuzumab in breast cancer [12]. Significantly, the PI3K pathway, which is negatively regulated by PTEN, was also shown to be a major regulator of trastuzumab sensitivity in the clinic, underscoring the utility of the in vitro genetic screens to identify drug modulators. In the first screen applying the NKI shRNA library, Berns et al. used BJtsLT cells to identify 5 new players in the p53 pathway [11]. This screen did not take advantage of the barcode technology. Rather, it was performed by the conventional method of isolating and expanding colonies that are resistant to p53- mediated growth arrest. Subsequent isolation of the shRNA inserts and sequence analysis was required to identify the shRNA responsible for the bypass of the p53 mediated cell cycle arrest. This method is rather laborious and such an approach may not uncover all active shRNAs in a library. Moreover, due to the labour-intensive nature of the screen, only part of the 24,000 vector NKI shRNA library was covered in this initial approach. Here we describe the screening of the entire NKI shRNA library for modulators of p53 activity using the barcode technology. We find three additional genes whose suppression causes resistance to p53-dependent proliferation arrest. Results Screening of the BJtsLT cells To identify shRNA vectors that can modulate the activity of the p53 pathway, we performed a shRNA bar code screen in BJtsLT cells infected with the NKI shRNA library (See Figure 1a). These cells proliferate at 32uC but enter into a p53-dependent proliferation arrest at 39uC [11]. The infected cells were cultured for three days at 32uC to allow retroviral integration and for gene knockdown to become effective. At day 4 the cells were split into two populations; one was kept at 32uC and the other was shifted to 39uC. The BJtsLT at 32uC were cultured until the population reached confluency and genomic DNA was isolated. The BJtsLT cells grown at 39uC cease proliferation unless a shRNA is expressed that inactivates the p53-dependent anti-proliferative response. Such cells will continue to proliferate and give rise to a colony. In addition to infection with the NKI shRNA library, we shRNA Screen Modulators of p53 also used a shRNA vector targeting p53 as a positive control for colony outgrowth of the BJtsLT cells at 39uC. After 3 weeks of culturing at 39uC, the control plates infected with p53 shRNA vector contained large numbers of colonies consisting of rapidly prolifer- ating cells (data not shown). The plates infected with the shRNA library also contained several colonies. Colonies from the library- infected plates were pooled and genomic DNA was isolated. The shRNA cassettes were recovered from the genomic DNA using PCR. The recovered shRNA inserts from both the control population and the colonies that continued proliferation at 39uC following infection with the shRNA library, were used to hybridize DNA microarrays containing all 24,000 19-mer sequences of the NKI shRNA library. The hybridization was performed for each of the replicate experiments, after which the results were combined to increase the statistical significance of the enriched shRNA vectors identified. Those shRNA vectors that confer resistance to the p53 mediated cell cycle arrest are enriched in the population cultured at 39uC and can be detected as outliers in a MA-plot representation of the barcode microarray experiment (figure 1b). From this MA-plot we generated a ‘‘hit list’’ of shRNA vectors that are specifically enriched in cell cultured at 39uC. The following considerations were used to produce this hit list (summarized in figure 1c). We excluded all shRNAs that had an intensity (A-value) lower than 10, as spots with a relative low intensity are likely to be ‘‘noise’’ and as a consequence can have aberrantly high ‘‘M’’ ratios. In addition we only included shRNAs if their ratio (M = 2log ratio cy5/cy3) was .0.75, this effectively means the top 100 most enriched shRNAs on the micro-array. To shorten the list of shRNA vectors to be validated, we first asked where shRNA vectors targeting known components of the p53 pathway were positioned in the top 100. In total, five shRNA vectors targeting known components of the p53 pathway were present in the top 100 and their distribution was as follows (Table 1): one shRNA vector targeting p53 (position #8), two shRNA vectors targeting p21cip1 (#7 & #37) and two shRNA vectors targeting 53BP1 (#5 & #36). As we identified shRNA vectors targeting 53BP1 and p21cip1 at positions 36 and 37 on the hit list, we decided to individually test all shRNA vectors on positions 1–37 in a second round selection. Included in this set are vectors targeting 3 out of 5 genes that were previously identified and validated by Berns et al., (2004): HDAC4 (#2) KIA0828 (#13), HTATIP (#25). The identification of these shRNA vectors provides further support for the notion that the barcode method enables both fast and reliable screening of shRNA libraries. In addition, we selected 3 genes that were represented by two independent shRNAs in the top 100 (of which one is present in the 37 already selected shRNAs), bringing the total number of shRNAs to be tested to 42. We included these 3 additional genes in the validation, because if two independent shRNAs targeting the same transcript are enriched in the shRNA screen, this gives higher level of confidence to that specific hit. This is because it is less likely to be ‘‘off target’’ when two independent shRNAs yield the same phenotype and such off target effects of shRNAs are a common problem in these types of genetic screens [18,19]. The other three genes, ARNTL [20], RBCK1 [21] and TNIP1 [22] have not been linked to p53 before. To prove that the shRNA barcode technique specifically identifies shRNAs that are enriched in the experiment we also tested 30 randomly-selected shRNAs not enriched in the experiment. All shRNAs were re-tested in the BJtsLT cells. Cells were infected with individual shRNAs, shifted to 39uC and incubated for 3 weeks. When colonies were observed the cells were fixed and stained. As expected, none of the 30 randomly-selected shRNAs was able to produce colonies at 39uC (data not shown). PLoS ONE | www.plosone.org 2 March 2009 | Volume 4 | Issue 3 | e4798