containing 0.5 mg of siRNA, followed by transfection by electroporation using Nucleofector II Device (Amaxa). The transfected cells were seeded in either a 96-well or a 384-well tissue culture plate, as required by the experiment. For non- specific control, scrambled, non-targeting siRNA was used. The fluorescent probe siGLO red (Dharmacon) was used to monitor the efficiency of siRNA uptake, and the efficiency of protein knock-down was determined by western blot analysis (Figure S3B). Additional validation for SMARTPool siRNA experiments were performed using a minimum of four independent duplex siRNA for each gene (Figure S10). Pathway identification Pathway analysis was conducted first by a priori identification of protein/pathway relationships and KegArray, yielding from which we included DNA damage response, glutathione metabolism, the TOR pathway, proteasome, and DNA repair pathways. For a systematic analysis on the MMS hits, KegArray (with default settings) was used (http://genome.jp/download/), yielding Notch signaling, ATPase, basal transcription, ribosome, proteasome and glutathione metabolism. These pathways were examined for the number of hits identified in the pathway versus the number of total pathway components. Pathways that we included from prior knowledge only were not top pathways retrieved by KegArray due to their relatively small size or completeness in the KEGG database in fly. Interactome analysis Protein interactome data were obtained from IntAct [27] (http://www.ebi.ac.uk/intact/), the Database of Interacting Proteins [28] (DIP, http://dip.doe-mbi.ucla.edu/) and the Bio- molecular Interaction Network Database [29] (BIND, http:// bond.unleashedinformatics.com/). Data from the three databases were combined into a single interactome, using the CG number of each gene as the identifier of the protein. Interactomes were visualized using Cytoscape 2.6 (http://www.cytoscape.org/). Pathway nodes were created external to Cytoscape by renaming all nodes representing protein in the pathways with the name of the pathway. If a protein was found in multiple pathways, it is represented in all relevant pathway nodes. Interactions between pathways were trimmed if the only interaction between them also existed within both pathways. For instance, if a protein:protein interaction occurs between two members of the NER pathway, and these two proteins also exist in the BER pathway, then the interaction is more likely to be pathway specific and not cross-talk between pathways and was therefore removed. Connectivity analyses Four measurements of connectivity were made: (1) Counting the number of pairs of MMS survival proteins that directly interact in the interaction network, (2) computing the average geodesic distance (i.e., the number of edges in a shortest path) between each pair of MMS survival proteins in the interaction network [51], (3) the global efficiency of the network [51], and (4) the clustering coefficient [51]. The number of direct interactions provides an intuitive measure of the connectivity of a subnetwork, while the average distance measures the global connectivity of the sub- network. Global efficiency provides a similar measure to average distance, but allows for disconnected components. The global efficiency was measured by NðN1{1Þ Pi, j~1::N, i=j d1ij, where n is the number of vertices in the network and dij is the geodesic distance between vertices vi and vj. The clustering coefficient is a measure of local connectivity of the network. For each vertex vi, let gi be the subnetwork that consists of direct neighbours of vi (excluding vi Damage Survival Pathways itself) and the edges between them. The clustering coefficient of vi was measured by the total number of edges in gi divided by the maximum number of edges that could possibly exist in gi. The clustering coefficient of a network is given by the average of the clustering coefficient of each vertex, with a high clustering coefficient indicating a distinction between a real network from a random one. To assess the statistical significance of each measurement, the same number of proteins or proteins pairs, as appropriate, were randomly sampled from the PPI or PPI subnetwork. This sampling was repeated 1,000 times to estimate a p-value that the difference could be expected by chance. Statistical analyses To determine if knock-down of genes resulted in increased sensitivity to MMS, raw data obtained from the viability assays was normalized and statistically analyzed as described previously [18]. A T-test was performed between normalized quadruplicates to determine the significant difference between treated and untreated wells. Percent control survival with MMS treatment was then estimated for each experimental gene knock-down as described previously [18], and a second T-test was performed on the percent of viability as compared with luciferase control within each plate as described before [18]. From these analyses, significant hits were selected as death hits if there was no greater than 55% viability in treated wells as compared to untreated if a p- value of less than 0.05 resulted from at least one of the T-tests. For those genes with an essential phenotype of less than 40% viability of non-targeting dsRNA, more stringent requirements were made on the viability effect after MMS treatment, such that 30–40% viability when untreated needed 15% viability after MMS treatment, 20–30% needed 10% after treatment, 10–20% needed 5% after treatment, and 0–10% was too dead to determine if MMS had an effect. Genes were considered ‘‘trend death hits’’ if they exhibited less than 55% viability after treatment but did not have a p-value with significance or if they exhibited 55–65% viability after MMS treatment with a significant p-value. Knock- down of genes resulting in resistance to MMS were confirmed as those with greater than 85% viability after treatment as compared to untreated wells, with one of the two p-values of less than 0. 0001 and viability after no treatment to be at least 60% that of non- targeting knock-down. To determine if a gene was essential for viability, a T-test was performed on untreated wells comparing values for non-targeting dsRNA against luciferase and dsRNA targeting the gene. If the targeting dsRNA resulted in less than 70% viability of luciferase with a p-value of less than 0.00001, the gene was deemed essential. Comparisons between predicted and observed numbers of MMS survival genes between yeast and Drosophila studies were done by a standard G-test [52]. The G-test is equivalent to a contingency Chi-square test but allows for classes with zero events. To determine protein enrichment within a pathway where there is an unbalanced distribution of data between the numbers of proteins within a pathway compared to the total number of genes within a genome, a Fisher’s Exact test was employed [53]. For connectivity measurements, except in the case of clustering coefficient, p-values were estimated using a Z-test, given that the connectivity measurement for the random subnetworks approxi- mately follows a normal distribution (Table S6). The p-value for the clustering coefficient measurement was estimated by simply counting the frequency that the clustering coefficient of a randomly sampled sub-network was at least as high as that of the real sub-network. PLoS Genetics | www.plosgenetics.org 14 June 2009 | Volume 5 | Issue 6 | e1000527