HIV Dependency Factors (HDFs) are a class of human proteins that are essential for HIV replication, but are not lethal to the host cell when silenced. Three previous genome-wide RNAi experiments identified HDF sets with little overlap. We combine data from these three studies with a human protein interaction network to predict new HDFs, using an intuitive algorithm called SinkSource and four other algorithms published in the literature. Our algorithm achieves high precision and recall upon cross validation, as do the other methods. A number of HDFs that we predict are known to interact with HIV proteins. They belong to multiple protein complexes and biological processes that are known to be manipulated by HIV. We also demonstrate that many predicted HDF genes show significantly different programs of expression in early response to SIV infection in two non-human primate species that differ in AIDS progression. Our results suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development. More generally, if multiple genome-wide gene-level studies have been performed at independent labs to study the same biological system or phenomenon, our methodology is applicable to interpret these studies simultaneously in the context of molecular interaction networks and to ask if they reinforce or contradict each other.
Conventional high-throughput antiviral discovery often targets the activities of specific viral enzymes. These approaches have been ineffective in stemming the emergence of drug-resistant variants, especially in the face of rapidly-mutating RNA viruses. One powerful yet under-explored avenue is the evolutionarily resilient nature of host proteins. Viral pathogens are parasitic in nature owing to their limited genomes. In principle, disruptions to host-pathogen interactions would impede the propagation of pathogens. The recent identification of HIV dependency factors (HDFs) or “host cellular factors” highlights this point [1], [2], [3]. HDFs represent a class of host proteins that are essential for HIV replication, but are not lethal to the host cell when silenced. By measuring levels of viral protein expression or production of infectious viral particles in human cells after knocking down individual genes using RNA interference (RNAi), these studies search for human genes that are required by HIV. Such studies have also been performed for other viruses and bacteria pathogenic to humans [4], [5], [6], [7], [8]. HDFs not only provide critical insights into HIV pathogenesis by helping to identify potential mechanisms for manipulation of host pathways, but may also have the potential to serve as therapeutic targets. The studies conducted by Brass et al. [1], Konig et al. [2], and Zhou et al. [3] identified 275,296, and 375 HDFs, respectively. The Brass and Konig sets had an overlap of 13 proteins, the Konig and Zhou sets had an overlap of 10 proteins, while the Brass and Zhou sets had 17 common proteins. One potential reason for the small overlap is that the experiments were performed in different cell lines; the Brass and Zhou studies used HeLa cells while the Konig study used HEK293T cells. The small overlaps could also arise from differences in the HIV strains used, the assay time post-infection, the procedures used to measure infection, and other approaches used to analyze experimental data [9], [10]. Although the three siRNA screens showed little overlap at the level of individual genes, Bushman et al. [10] found that similar Gene Ontology (GO) terms were enriched in the three gene sets. Interestingly, Konig et al. noted that 64 HDFs reported by Brass et al. directly interacted (via a physical interaction between proteins) with a confirmed HDF in their study. In support of this observation, Bushman et al. constructed a network of protein-protein interactions among HIV proteins and 2,410 host cell genes identified in the three siRNA screens and six other HIV-related studies. Dense clusters within this network contained multiple proteins identified in two or more siRNA screens and were enriched in processes and complexes such as the proteasome and the mediator complex, which are known to be associated with HIV replication. In a related study, Wuchty et al. [11] found that HDFs and human proteins that interact with HIV also appeared in dense clusters. The proposed that such protein groups may serve as “infection gateways” that enable the virus to control specific human cellular processes. They also noted that transcription factors and protein kinases mediated indirect interactions between HDFs and viral proteins. Macpherson et al. [12] performed a complementary analysis. Starting from known human-HIV protein-protein interactions (PPIs), they used biclustering to identify sets of human proteins that participated in the same types of interactions with HIV proteins. They evaluated the functional information in each bicluster and further grouped the human proteins in biclusters into higher-level subsystems. By overlapping these subsystems with HDFs, they characterized host systems that were perturbed by HIV-1 infection and identified patterns of human-HIV PPIs that correlated to these perturbations. We took these analyses as our starting point, since they suggested that the three siRNA genomic screens may be incomplete and that there are potentially many HDFs yet to be discovered. In particular, we hypothesized that the proximity of experimentally-detected HDFs within the human protein-protein interaction (PPI) network can be fruitfully exploited by machine-learning algorithms to predict novel HDFs. We treated the computational problem of predicting HDFs as an instance of semi-supervised learning: we combined HDFs identified by Brass et al. , Konig et al. , or Zhou et al. (positive examples formed by the union of these three sets) with non-HDFs (negative examples, see “Data and Algorithms” for details) in the context of a human PPI network. The other proteins in this network constituted the unknown examples. We used an intuitive graph-theoretic approach that we call SinkSource and other algorithms published in the literature [13], [14], [15] to predict undiscovered HDFs. Our results, along with those of other studies [10], [11], [12], suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development.
Figure 1 displays the results of two-fold cross validation for the six algorithms tested on four datasets. Two-fold cross validation involves splitting the positive and negative examples into two halves, and using each half to make predictions for the genes in the other half. We used two-fold cross validation since we felt it better mimics our state of knowledge of HDFs than the more commonly used five-fold or 10-fold cross validations. We averaged the results over 10 independent runs for each algorithm-dataset combination. For each algorithm, it is evident from Figure 1 (a) that the area under the precision-recall curve (AUPRC) value for the BKZ dataset is larger than the values for the B, K, or Z datasets. It is also clear that these results are robust to the randomization inherent in cross validation: the largest standard deviation in the AUPRC values is 0. 033 (as indicated by the error bars in Figure 1 (a) and data in Table S1). Figure 1 (b) displays the precision-recall curve for SinkSource on the four datasets and Figure 1 (c) shows the results for SinkSource+. The results for SinkSource+ were obtained with an internal parameter λ set to a value of 1 (see “Other Algorithms” for the role played by this parameter in the SinkSource+ algorithm). In each figure, we observed that the curve for the BKZ dataset dominated the other three curves at most values of recall. This result is consistent with the expectation that the Brass, Konig, and Zhou studies did not discover all true HDFs, and that combining the three sets provides a better coverage of the true HDF universe. We also noted that the variation in precision (indicated by the error bars in Figure 1 (b) and Figure 1 (c) ) decreases with increasing recall, suggesting that high confidence predictions are more subject to variation than low confidence predictions. Finally, Figure 1 (d) compares the performance of all seven algorithms on the BKZ dataset. Three of the algorithms that do not use negative examples (Local+, SinkSource+, and Functional Flow with 1 and with 7 phases) achieved higher precision values than the other algorithms for values of recall less than 20%. However, SinkSource has the best performance for values of recall greater than 20%. PRINCE, the fourth algorithm that did not use negative examples, had uniformly lower precision than SinkSource+. Its precision was superior to that of SinkSource for values of recall less than 10%. To obtain the results for PRINCE, we used 0. 8 for the value of an internal parameter α, since PRINCE achieved the highest precision values for this setting of α (see “Other Algorithms” for the role played by this parameter in the SinkSource+ algorithm). Furthermore, the precisions of the algorithms that do not use negative examples dropped considerably beyond a recall of 20% (beyond 10% in the case of PRINCE). We believe that this performance drop is caused by an undue influence of positive examples, resulting in many false positives. The performance of FunctionalFlow did not vary much with an increase in the number of phases (see Figure S1). The performance of SinkSource+ was independent of the parameter λ (see Figure S2), as was the performance of PRINCE with respect to the parameter α (see Figure S3). We also noted that the AUPRC values for the BKZ dataset were 0. 67 for Local, Local+, and for FunctionalFlow with 7 phases, 0. 65 for PRINCE, 0. 69 for SinkSource+, 0. 73 for SinkSource, and 0. 74 for Hopfield. There is a difference of 11% between the AUPRCs of the worst performing algorithms (0. 67) and the best performing algorithm (0. 74). The results for weighted versions of the network did not substantially differ from those for the unweighted network (see Figure S4 and Table S2). The SinkSource algorithm achieved a precision of 81% at 20% recall. The precision dropped only to 70% at a recall of 60%. The corresponding precisions for SinkSource+ were 85% and 60%. Although the Hopfield network algorithm achieved an AUPRC of 0. 74, we observed that the smallest recall value attained by the algorithm was 60%, since the algorithm assigned a confidence of either 1 or −1 to a large number of predictions. We concluded that the Hopfield network algorithm was not a good choice for prioritizing predictions for further experimental analysis. It is surprising that the very simple guilt-by-association algorithms (Local+ and FunctionalFlow with one phase) perform nearly as well as more sophisticated methods (FunctionalFlow with 7 phases, Hopfield, PRINCE, and SinkSource) that attempt to optimize predictions by taking into account constraints imposed by the entire protein interaction network. However, across 10 runs of cross validation, both Local+ and FunctionalFlow with one phase showed higher variation in precision and recall than the other algorithms (see Figure S5). Therefore, these two algorithms are likely to be more susceptible to missing or erroneous information. Based on these results, we concluded that SinkSource+ and SinkSource were the two best algorithms for predicting HDFs. When high precision is required, SinkSource+ is superior to SinkSource. Thus, the predictions made by SinkSource+ might be the most suitable as the basis for detailed experimental studies of candidate HDFs. In the rest of the paper, we focus on the results obtained by the SinkSource+ and SinkSource algorithms. We compared how many predictions SinkSource+ and SinkSource made at confidence values that correspond to approximately 80% precision after cross validation. SinkSource+ achieved a precision of 85% (and a recall of 20%) at a confidence of 0. 5. The corresponding numbers for SinkSource were a confidence of 0. 71 at a precision of 81% (and a recall of 20%). To further compare the two algorithms, we computed the overlaps in their predictions for different cutoffs on the confidence values. Specifically, we computed the k highest confidence genes predicted by SinkSource+ and the k highest-confidence genes predicted by SinkSource, and measured the Jaccard coefficient of the pair of gene sets, for different values of k in increments of 100. Figure S6 demonstrates that the overlap between the predictions of the two algorithms is at least 0. 34 up to the first 2000 predictions, with peaks at around 300 and 1000 predictions. These results are consistent with the relatively low recall (20–40%) predicted for the two algorithms at this level of precision. The data suggest that approximately half of the predictions may be ranked differently by the two algorithms. Predictions made by SinkSource+ for different values of the parameter λ did not vary much in their ranking (see Figures S7 and S8). On the basis of these comparisons, we identified a set of high confidence predictions composed of the 1000 top-ranked predictions from SinkSource+ and from SinkSource respectively. These two sets contained 606 predictions in common and comprised a total of 1394 proteins in addition to the 908 BKZ HDFs. At the confidence levels of the 1000 SinkSource and SinkSource+ predictions, the precisions with two-fold cross validation are 88% and 81% respectively, suggesting that these predictions are relatively reliable. The corresponding recalls with two-fold validation are roughly 17% and 15% respectively, suggesting that these predictions are quite conservative. In the rest of the paper, we use the phrases “BKZ HDFs”, “SS+ predicted HDFs”, and “SS predicted HDFs” to distinguish between the HDFs identified by one or more of the three siRNA screens [1], [2], [3], the HDFs predicted by SinkSource+, and the HDFs predicted by SinkSource, respectively. We extensively evaluated the predicted HDFs by comparing them to each other and to BKZ HDFs in terms of their functional annotations, interactions with HIV proteins, clustering with the PPI network, and role in disease pathogenesis. We based these evaluations on additional datasets that we did not use for predicting HDFs. Specifically, the new datasets we used were (i) Gene Ontology (GO) annotations for human proteins, (ii) interactions between HIV and human proteins, and (iii) gene expression data from two non-human primate species following infection with SIV. Hence, the analyses described below constitute independent evaluation of the relevance of our predictions to HIV infection and disease progression. We summarized the functional roles of predicted HDFs by asking which GO terms were enriched in the HDFs, and whether any terms were considerably enriched in predicted HDFs but not in BKZ HDFs. We used the FuncAssociate software [23] for this purpose, since it can take ordered lists of genes as input, in which case it finds and utilizes the set of top-ranked genes displaying the greatest enrichment. FuncAssociate adjusts for multiple hypotheses testing by computing an experiment-wise p-value. Note that FuncAssociate operates solely on the ranked list of genes and the GO annotations. It does not utilize a network. (See “Methods” for details.) We invoked FuncAssociate with three inputs: (a) the unordered set of BKZ HDFs, (b) the SS+ predicted HDFs, ordered by confidence, and (c) the SS predicted HDFs, also ordered by confidence. We used default values of all other parameters used by FuncAssociate. FuncAssociate reported 52 GO terms as being enriched in BKZ HDFs with an adjusted p-value of 0. 05 or less and 199 GO terms as enriched in SS+ predicted HDFs. We identified three classes of terms (see Table S3). We note that FuncAssociate may report many related terms as enriched, due to the hierarchical nature of GO. Therefore, we also manually inspected the directed acyclic graph connecting the enriched terms in order to make the observations below. The trends were similar for the HDFs predicted by SinkSource (data not shown). Therefore, we compared the FuncAssociate results for SS+ predicted HDFs and for SS predicted HDFs in a similar manner. We only considered GO terms enriched with an adjusted p-value of 0. 05 or less. As shown in Table S4,280 GO terms were enriched in both sets of predictions, 182 GO terms were enriched only in SinkSource+ predictions, and 25 GO terms were enriched only in SinkSource predictions. The 280 common terms were related to processes such as RNA splicing (GO: 0008380), translation initiation (GO: 0003743), and oxidative phosphorylation (GO: 0003743) and complexes such as the proteasome (GO: 0003743), the kinetochore (GO: 0000776), and the nuclear pore (GO: 0005643); we discuss their relevance to HIV when we discuss clusters in the PPI network below (See “PPI Clusters Spanned by BKZ HDFs and Predicted HDFs Are Exploited by HIV”). The 182 GO terms enriched only in SinkSource+ predictions included the Ndc80 complex and MIS12/MIND type complex (mentioned above), apoptosis (including its induction and regulation) (GO: 0006915, GO: 0006917, and GO: 0042981), and specializations of terms enriched in both sets of predictions. Among the 25 GO terms enriched only in SinkSource predictions, there were 12 GO terms whose specializations or near neighbors (in the GO directed acyclic graph) were enriched in SinkSource+ predictions. Each of the remaining 13 GO terms enriched only in SinkSource predictions were closely related to the assembly of glycosylphosphatidylinositol (GPI) anchors (GO: 0006506). Based on these results, we concluded that, for the most part, similar functions were enriched in HDFs predicted by SinkSource+ and by SinkSource. Bushman et al. observed that each of the Brass, Konig, and Zhou HDF sets were statistically significantly enriched with human proteins that interact with HIV proteins (as reported in the NCBI HIV interaction database [25]). We hypothesized that predicted HDFs might be significantly enriched with HIV interactors. Accordingly, for each algorithm, we selected the k top ranking predictions made by that algorithm, for different values of k starting at 100 and in increments of 100, computed the overlap of each set of predictions with the human proteins that interact with HIV, estimated the statistical significance of the overlap using the one-sided version of Fisher' s exact test, and adjusted the p-values to account for testing multiple hypotheses [26]. The overlap fraction for SS+ predicted HDFs peaked at 26% (79 of the top 300 predicted HDFs interact with HIV proteins, p-value 2. 1×10−7), better than the BKZ HDFs of which 20% (109 proteins, p-value 9. 11×10−6) interacted with HIV proteins. The trend for SS predicted HDFs was mixed: the overlap ratio was as high as 17. 5% (70 of the top 400 predictions interact with HIV proteins), slightly less than the BKZ HDFs, but in no case was the enrichment statistically significant. These results suggest that SinkSource+ HDF predictions are dominated by proteins that lie close to BKZ and HIV proteins in the joint HIV-human PPI network, whereas the SinkSource predictions are dispersed further away. We discuss specific SS+ predicted HDFs that interact with HIV in the context of MCODE clusters below. The cross validation analysis suggested that HDFs are not randomly located in the human PPI network. Rather, HDFs are closer to each other within the PPI network than to the negative examples. Therefore, in order to better understand how BKZ HDFs and SS+ predicted HDFs are related to each other, we computed the subnetwork of PPIs spanned by these two sets of genes. We applied a modified version of the well-known MCODE [27] graph clustering algorithm to this sub-network (see “Modifying MCODE to Compute PPI Clusters”). The network contained 1,562 proteins and 30,855 PPIs. MCODE identified 41 clusters of varying sizes containing a total of 829 proteins and 16,721 PPIs. Table 1 contains statistics on the 10 clusters with the largest number of PPIs computed by MCODE. Using the one-sided version of Fisher' s exact test, we checked the overlap of each of the 42 clusters with BKZ HDFs. Only eight clusters had overlaps that were statistically significant, as shown in Table S5. Table S6 contains a list of BKZ HDFs and HDFs predicted by SinkSource+, annotated with MCODE cluster membership and information on interaction with HIV proteins. Table S7 lists the human PPIs in each MCODE cluster. We computed GO terms enriched in all clusters. Table 2 contains statistics on highly enriched GO terms in the 10 most highly-connected clusters discovered by MCODE. Among the top 10 clusters, only clusters #1, #4, #7, #8, and #9 have statistically significant overlaps with BKZ HDFs (see Table S5). The fraction of BKZ HDFs is small in clusters #1, #4, and #9, so we reasoned that any functions enriched in these clusters would not be overly influenced by annotations of BKZ HDFs. In contrast, more than half the proteins in clusters #7 and #8 are BKZ HDFs; the functions enriched in these clusters are likely to annotate a number of BKZ HDFs. We now discuss the enriched functions in all clusters in Table 2. We focus our discussion on selected predicted HDFs contained within these clusters and present the support in the literature for the relevance of these HDFs to HIV pathogenesis. Since HDFs play a critical role in HIV replication [1], [2], [3], we hypothesized that some of them may have value as prognostic markers of HIV pathogenesis and of AIDS development and progression. We anticipated that both experimentally-detected (BKZ) and predicted HDFs would satisfy this hypothesis. To explore this question, we combined BKZ HDFs and predicted HDFs with DNA microarray data from a study detailing the host response to simian immunodeficiency virus (SIV) infection in African green monkeys (AGMs) and pigtailed macaques (PTMs). AGMs are natural reservoirs of SIV that do not develop AIDS, while PTMs are non-natural hosts that develop AIDS when infected with SIV. The virus replicates to the same viral load in both of these hosts. Lederer et al. [56] performed a longitudinal transcriptomic analysis comparing AGMs to PTMs. They analyzed the host response in the setting of acute SIV infection with the same primary isolate (SIVagm. sab92018). They studied three different tissues: blood, colon, and lymph nodes. They collected samples at 10 days and 45 days post-viral inoculation and compared each sample to a sample from the same animal pre-inoculation. For each day-tissue combination, they performed an analysis of three AGMs and three PTMs using rhesus macaque (Macaca mulatta) oligonucleotide microarrays. The probes in this microarray were based on the human Reference Sequence (RefSeq) collection. Thus, there is a direct mapping from these probes to human gene identifiers. For each tissue (blood, colon, lymph node) and day (10 and 45 post infection) combination, we performed a separate ANOVA analysis, using the host system as factor, to identify genes that are differentially expressed between AGMs and PTMs. Such differentially expressed genes could potentially serve as diagnostic markers of AIDS development and progression. We constructed six lists (three tissues×two time points) of genes that were differentially expressed between AGMs and PTMs to a statistically-significant extent (p≤0. 05). We used the one-sided version of Fisher' s exact test to determine if BKZ HDFs had a significant intersection with each of these six lists. We repeated this test with the top k predicted HDFs, for values of k starting at 100 and in increments of 100. We used the method of Benjamini and Hochberg [26] to correct for testing multiple hypotheses. Figure 2 displays plots of the fraction of BKZ HDFs or of predicted HDFs that are also differentially-expressed to a significant extent in the AGM-PTM comparison; Figures S9 and S10 plot the corresponding p-values. Note that the plot for BKZ HDFs is a horizontal line since changing the score cutoff for predictions has no effect on BKZ HDFs. Three notable trends emerged from this analysis. First, for many tissue-day combinations, the overlap fraction for predicted HDFs was larger than the overlap fraction for BKZ HDFs. These trends were most noteworthy in day 10 lymph nodes, where the overlap ratio for predicted HDFs was larger than that for BKZ HDFs over the entire range of prediction confidence values. In particular, in day 10 lymph nodes, the overlap fraction of SS+ predicted HDFs peaked at 0. 26 (53 of the top 203 predicted HDFs were also differentially-expressed in day 10 lymph nodes, p-value 0. 01). The largest overlap for SS predicted HDFs was also 0. 26 (26 of the top 100 predicted HDFs, an insignificant p-value of 0. 07). In contrast, the overlap ratio for BKZ HDFs with genes differentially expressed in day 10 lymph nodes was 0. 19 (p-value, 0. 59). Second, none of the overlaps of BKZ HDFs with differentially-expressed genes were statistically significant, for any tissue-day combination. In contrast, p-values for HDFs predicted by each algorithm were statistically significant (red points in Figure 2 and Figures S9 and S10) in day 10 lymph nodes, across a wide range of prediction confidences. Third, no statistically significant overlaps appeared for predicted HDFs in blood or colon samples at any time point or in day 45 samples from lymph nodes. We re-estimated the significance of these results after randomizing the gene expression data, by permuting each gene' s p-values independently. This process retained the distribution of p-values for each gene, but randomized the associations between p-values and tissue-day combinations. We repeated the overlap analysis for predicted HDFs with each of 10,000 randomized gene expression data sets, for a total of 60,000 randomized tissue-day combinations. We observed only one randomized dataset for which any overlap ratio was at least as large as 0. 26, the largest overlap ratio between HDFs predicted by SinkSource+ and genes differentially expressed in day 10 lymph nodes. Thus, the p-value of the observed overlap ratio was 1. 7×10−5. For predictions made by SinkSource, we obtained a p-value of 8. 3×10−5, for the largest observed overlap of 0. 26. Thus, we concluded that the predicted HDFs have a significant overlap with genes that are differentially expressed between AGMs and PTMs in day 10 lymph nodes, indicating that many predicted HDFs show considerably different programs of expression in the two species in response to SIV infection, especially in early time points. These data suggest that the algorithms have identified a highly responsive subset of potential HDFs, and provide strong experimental support for the prediction that these proteins are in fact HDFs. This result further suggests that viral manipulation of these host factors in lymph nodes soon after infection may have an effect on long-term pathological outcome. We used FuncAssociate to perform GO enrichment analysis on predicted HDFs that were also differentially expressed between AGMs and PTMs in day 10 lymph nodes. The terms we found were almost identical to those reported in the PPI clusters (data not shown). In summary, these results suggest that not only are HDFs critical for viral replication and infection, they may have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development. We have used network-based approaches to predict HIV dependency factors (HDFs). Upon two-fold cross-validation, we found that combining the three experimental data sets yielded much higher precision and recall than using each data set on its own. A number of the algorithms we compared achieved both high precision and recall on cross validation. Our results suggest that global optimization techniques such as SinkSource and SinkSource+ perform slightly better than the simple guilt-by-association rule [57]. Furthermore, SinkSource+ and SinkSource had the most consistent and reliable performance. Software implementing the function prediction algorithms is available at http: //bioinformatics. cs. vt. edu/~murali/software/gain. We also observed that estimating the reliability of PPIs did not confer an advantage; in fact, the cross validation results worsened slightly with edge weights (Table S2). The decrease in performance is likely to be a combination of the close proximity of HDFs within the PPI network and the high reliability of PPIs that HDFs are involved in, since the corresponding biological processes are well studied. We found that the HDFs predicted by SinkSource+ were significantly enriched in proteins that interact with HIV proteins. On the other hand, SinkSource predicted a set of HDFs that were not significantly enriched in HIV-interacting proteins. We computed clusters within the subgraph of the PPI network that encompassed the BKZ HDFs and HDFs predicted by SinkSource+. These clusters were enriched in host cellular complexes and pathways known to be that are known to be manipulated by HIV and perturbed during HIV infection such as the spliceosome, the microtubule network, the proteasome, the mitochondrion, and nuclear import and export. Finally, we integrated BKZ HDFs and predicted HDFs with gene expression data from a non-human primate study detailing the host response to SIV infection in non-human primates that do not develop AIDS (African green monkeys) and those that do (pigtailed macaques) [56]. We found that up to 26% of predicted HDFs are differentially expressed, when we compared their gene expression profiles in macaques to their profiles in African green monkeys. This differential expression of HDFs was time- and tissue-specific, being strongest in lymph nodes 10 days post-inoculation. These HDFs are excellent candidates for studying transcriptional programs relevant to AIDS progression in humans. Our results support three conclusions. First, existing genomic screens are incomplete and many HDFs are yet to be discovered. The HDFs predicted by SinkSource+ may include many proteins required for HIV replication that could not have been uncovered experimentally because the predictions were not constrained to non-essential human proteins. Second, HDFs are clustered in the human PPI network and belong to cellular pathways or protein complexes that play a critical role in HIV pathogenesis and AIDS progression. Third, many HDF genes show differential expression during AIDS development in non-human primates. Thus, HDFs may play an important role in the control of initial infection and eventual pathological outcome. It will be valuable to integrate other HIV-relevant functional genomic data with PPI networks to improve the quality and robustness of HDF prediction. Modeling the impact on off-target effects of siRNAs on false positive HDFs is also important. To date, experiments that have detected HDFs have been performed in cell lines. Approaches such as ours may help to prioritize HDFs for further experimental study in more disease-relevant models such as non-human primates. Ultimately, we anticipate that future extensions of our work may provide multiple new targets and strategies for combating HIV in humans. Our approach is general purpose and can be applied to interpret other genome wide gene-level studies. In particular, if independent labs have conducted multiple studies to study the same biological system or phenomenon, we provide a methodology to interpret them simultaneously within the context of molecular interaction networks. Our approach can be used to ask if the studies reinforce or contradict each other and to prioritize new genes for further experimental analysis.
We downloaded all the HDF and PPI data used in this study between August and December 2008. We downloaded functional annotation data in December 2010. We used Entrez Gene IDs in all analyses. We modeled the human protein interaction network as an undirected graph G = (V, E), consisting of a set V of nodes (i. e. , proteins) and a set E of edges (i. e. , interactions). We used wuv to denote the weight of the edge, computed as described earlier. We partitioned V into three subsets and V− as follows: V+ was the set of HDFs (positive examples), V− was the set of human proteins orthologous to essential mouse proteins (negative examples), and V0 was the remaining set of nodes (unknown examples). For each node v∈V0, our goal was to assess whether v should be a member of V+ or V−. We did so by computing a function that is “smooth” over G. Specifically, we set r (v) = 1 for every node v∈V+, r (v) = 0 for every node v∈V−, and required that r minimize the functionMinimizing S (G, r) enforces the smoothness of r in the sense that the larger the weight of an edge (u, v), the closer in value r (u) and r (v) must be. The function S (G, r) is minimized when, for each node v∈V0, (1) where Nv is the set of neighbors of node v [64]. The right-hand side of this equation can be split into two parts: one corresponding to contributions to r (v) from neighbors in V0 and the second to a constant contribution from neighbors in V+ and V−. Let r0 denote the vector of values taken by the function r at the nodes in V0. Let M denote the square matrix, where, for every. We see that r0 satisfies the equations r0 = Mr0+c, where c is a vector denoting contributions from V+ and V−. We computed r0 by initializing it to 0 for each node and repeatedly applying the operation r0 = M r0+c. This process is known to converge [64], yielding a value of r0 = (I−M) −1c, where I is the identity matrix. The matrix M is sparse, being the adjacency matrix of a PPI network. Therefore, this iterative approach is efficient in practice. We implemented six other algorithms for the purpose of comparison. The first two algorithms use both positive and negative examples. The other four algorithms do not use negative examples for making predictions, avoiding the uncertainties associated with choosing an accurate set of negative examples. We used both types of algorithms in order to assess the impact of our choice of negative examples on the cross validation results. Table 5 summarizes these algorithms. Although SinkSource+, Local+, FunctionalFlow, and PRINCE do not use negative examples when making predictions, we used negative examples when computing the performance of these algorithms on cross validation in order to count the number of true negatives and false positives. A number of approaches are available for computing GO terms enriched in lists of genes [23], [65], [66], [67]. Since BKZ HDFs are unordered while predicted HDFs can be ranked by confidence, we used the FuncAssociate software [23], which can take both unordered and ordered lists of genes as input. For an ordered list of genes, FuncAssociate analyses each one of the list' s prefixes, and reports results for the prefix with the smallest p-value. It asks if the genes annotated by each GO term have surprisingly low ranks in the ranked list. The final p-value computed by FuncAssociate can be informally interpreted as the probability that a given overlap between a GO term and a ranked list of genes could be observed if the genes were ranked randomly. Note that FuncAssociate operates solely on the ranked list of genes and the GO annotations. It does not utilize a network. Details on how FuncAssociate operates are provided at http: //llama. mshri. on. ca/FuncAssociate_Methods. html. To determine enriched GO functions in each cluster computed by MCODE, we did not associate any weights with the proteins, since MCODE had already incorporated protein weights. We used an in-house implementation of the Ontologizer [68] to compute enriched GO terms. We chose the Ontologizer because it accounts for annotation dependencies that arise from GO' s true path rule. We retained only those functions for which the p-value is at most 0. 05, after accounting for multiple hypothesis testing using the method of Benjamini and Hochberg [26]. We modified MCODE to multiply internally-computed node weights with externally-defined node weights. For our application, we supplied the SinkSource+-derived confidence as the weight of a predicted HDF. For every BKZ HDF, we defined its weight as 1. By imposing these externally-defined weights, we aimed to bias MCODE towards finding dense subgraphs in the vicinity of BKZ and SS+ predicted HDFs. Therefore, we included all SS+ predictions together with their confidence levels in the network and used the ability of MCODE to utilize the confidence levels to identify high confidence clusters.
Medicines to cure infectious diseases usually target proteins in the pathogens. Since pathogens have short life cycles, the targeted proteins can rapidly evolve and make the medicines ineffective, especially in viruses such as HIV. However, since viruses have very small genomes, they must exploit the cellular machinery of the host to propagate. Therefore, disrupting the activity of selected host proteins may impede viruses. Three recent experiments have discovered hundreds of such proteins in human cells that HIV depends upon. Surprisingly, these three sets have very little overlap. In this work, we demonstrate that this discrepancy can be explained by considering physical interactions between the human proteins in these studies. Moreover, we exploit these interactions to predict new dependency factors for HIV. Our predictions show very significant overlaps with human proteins that are known to interact with HIV proteins and with human cellular processes that are known to be subverted by the virus. Most importantly, we show that proteins predicted by us may play a prominent role in affecting HIV-related disease progression in lymph nodes. Therefore, our predictions constitute a powerful resource for experimentalists who desire to discover new human proteins that can control the spread of HIV.
systems biology
immunodeficiency viruses
data mining
virology
signal processing
biology
computational biology
microbiology
host-pathogen interaction
genetics and genomics
engineering
Network-Based Prediction and Analysis of HIV Dependency Factors