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Title: Enhancing the Prioritization of Disease-Causing Genes through Tissue Specific Protein Interaction Networks}
Article:
Abstract:
The prioritization of candidate disease-causing genes is a fundamental challenge in the post-genomic era. Current state of the art methods exploit a protein-protein interaction( PPI) network for this task. They are based on the observation that genes causing phenotypically-similar diseases tend to lie close to one another in a PPI network. However, to date, these methods have used a static picture of human PPIs, while diseases impact specific tissues in which the PPI networks may be dramatically different. Here, for the first time, we perform a large-scale assessment of the contribution of tissue-specific information to gene prioritization. By integrating tissue-specific gene expression data with PPI information, we construct tissue-specific PPI networks for 60 tissues and investigate their prioritization power. We find that tissue-specific PPI networks considerably improve the prioritization results compared to those obtained using a generic PPI network. Furthermore, they allow predicting novel disease-tissue associations, pointing to sub-clinical tissue effects that may escape early detection.
Introduction:
A fundamental challenge in human health is elucidating the molecular basis of hereditary diseases. Contemporary methods for discovering disease-causing genes usually consist of two steps: first, genome-wide association studies identify genomic intervals that are linked to a disease of interest. Second, the genes within these intervals are examined for their causal relation to the disease[1]–[3]. Experimentally verifying that a gene is associated with a disease is an expensive and time-consuming process, calling for the prioritization of candidate causal genes. A plethora of computational methods were developed to meet this challenge. These methods are often based on system-wide data such as protein interaction networks[4]–[9], gene expression[8],[10]–[12], sequence similarity of genes[13],[14], functional similarity and annotation[8],[12],[13] and more( for a review on these methods see[15],[16]). Many state of the art algorithms for the gene prioritization problem use protein interaction or functional linkage networks[8],[17],[18], exploiting the tendency of genes causing similar diseases to lie close to one another in the network[16],[19],[20]. However, these methods do not take into account the fact that the vast majority of genetic disorders tend to manifest only in a single or a few tissues[20]–[22]. Typically, the same data sets are used to prioritize genes for a liver disease or a brain disease, even though the molecular landscapes of a Hepatocyte and a neuron are quite dissimilar. In this work, we incorporate tissue-specific gene expression data into the prioritization process and demonstrate its impact on the prioritization results. The integration is achieved by constructing tissue-specific protein-protein interaction( PPI) networks and employing them in the prioritization. The rationale behind this approach is that many disorders involve a disruption of the ‘molecular fabric’ of different, healthy tissues. From a protein interaction network point of view, this disruption can be often characterized as a perturbation of a gene, corresponding to node removal, or the perturbation of an interaction between two gene products, corresponding to an edge removal[23]. In the context of genetic disorders, even though the underlying harmful mutation exists in all the cells of our body, it most often wreaks havoc only in a few tissues. This tissue selectivity is likely to emerge due to differences in the functionality of the mutated protein within these tissues, its tissue-specific interacting proteins, its abundance and the abundance of its interactors. Thus, the hypothesis underlying the current work is that a tissue specific network, which better represents the actual disease-related tissue, is likely to yield more accurate prioritizations of the diseases it manifests. The concept of tissue-specific protein interactions is relatively unexplored. Bossi and Lehner[24] analyzed human PPIs in a tissue-specific context, showing that many housekeeping proteins interact with highly tissue-specific proteins, which in turn implies that housekeeping proteins may have tissue-specific roles. Emig and Albrecht[25] expanded this analysis to identify functional differences between tissues, showing that tissue-specific protein interactions are often involved in transmembrane transport and receptor activation. Lin et al[26] analyzed the topological properties of housekeeping and tissue specific proteins within the generic( non tissue-specific) PPI network. Waldman et al.[27] analyzed translation efficiency in humans using PPIs. Using tissue specific PPI networks, they showed that proteins whose genes are translated more efficiently in a specific tissue tend to have more connections within this tissue as compared to other proteins in the same tissue. Lopes et al.[28] created unweighted tissue-specific networks for several separate PPI databases. They used these networks to analyze host-pathogen interactions in a tissue-specific manner. Finally, a proof-of-concept work by Jiang et al.[29] combined five tissue-specific networks taken from Bossi and Lehner[24] to prioritize candidate genes for type 2 Diabetes. Of note, the lack of tissue specific PPI networks stands in marked difference from the existence of many tissue- and cell-specific variants of other types of biological networks, such as regulatory networks[30]–[32], functional linkage networks[33],[34] and metabolic networks[35]–[37]. The current study is the first large-scale study that aims to enhance the accuracy of existing network-based gene prioritization algorithms by taking into account tissue-specific information. This is achieved by constructing tissue specific PPI networks and utilizing them for gene prioritization instead of the standard, generic PPI network. First, we examine the hypothesis that a gene is likely to be expressed in a healthy tissue for its mutation to clinically manifest in that tissue. Indeed, a large majority( 71–83%) of the known disease-causing genes are significantly expressed in the corresponding disease-associated tissue. However, not all disease-associated genes are significantly expressed in the tissues where the disease is manifested. Interestingly, as shown below, we find that most of the remaining genes either have a low expression level across all tissues, or are involved in mediating a response to external stimulus or being involved in multi-cellular developmental processes, and as such are not expected to have high expression under steady-state conditions in the adult tissue. Focusing on the cases where the disease-related gene is expressed in the associated tissue, we show that integrating tissue specific expression information into a gene prioritization scheme markedly improves its prediction accuracy. Specifically, we generate tissue-specific PPI networks for 60 healthy human tissues using gene expression data from those tissues[38]. We then apply the same candidate prioritization algorithm for both the original and the tissue-specific PPI networks, and compare the performance of each in a cross-validation setting. We find that the tissue-specific variant of the algorithm yields higher area under the receiver-operator characteristics( ROC) curve( AUC) and gives the correct gene a higher ranking than the original variant more often than not. Finally, we extend our method to infer new disease-tissue associations.
Discussion:
In the current study we aimed to infer disease causing genes using tissue-specific PPI networks. Most previous studies that used these networks to infer causal genes were based on generic PPI networks and ignored differences between tissues[19]. Nevertheless, this generic approach may be limited as there are significant differences in expression patterns between tissues, both with respect to mRNA as well as in protein levels[38],[42]. These differences imply that different tissues have different active PPI networks: a specific interaction may take place in some tissues while not in others, based on the expression distribution of the interacting proteins[24]. Moreover, these differences may explain why, in many cases, a disease may affect a specific tissue and not others: the same protein may be active in specific tissues and inactive in others, or can have different function in different tissues based on its different neighbors in the different networks. Following these observations, we decided to examine the utility of building and incorporating tissue specific PPI networks in our analysis. Adding tissue specificity information for various diseases[40] we were able to perform a tissue specific inference of disease causing genes. We used the PRINCE algorithm for gene prioritization and contrasted between generic and tissue specific PPIs. We found that the tissue specific approach enhances the performance of the algorithm. In our analysis we used two different methods for tissue specific PPI networks construction that yield different gene prioritization performance. We observed that better results were obtained when modifying the weights of the networks edges( using the ERW method) compared to following the more drastic approach of removing lowly-expressed proteins from the network( using the NR method). There may be several explanations for these differences. First, it may be related to PRINCE algorithm. A global network-based algorithm such as PRINCE is expected to be less successful when applied to a more disconnected network, such as those generated by the NR approach. Moreover, even for other algorithms that are based on local inference which is not propagated, ERW may be proven more appropriate. NR is a very strict method, eliminating every unexpressed protein, while ERW assigns a continuous value for the interaction based on the expression of the two interacting proteins. Thus, the former is likely to be less robust to noisy data such as gene expression[43]–[45]). One might suggest that there is no need to generate tissue specific PPI networks for tissue specific prioritization. Rather, one might use the generic PRINCE and then, in a post-processing manner, assign the lowest possible ranks to the lowly-expressed genes in the tissue being investigated. While such an attenuation approach performs poorly when applied to the entire gene-disease association set( AUC = 0. 755, Figure S3, ‘Unexpressed genes attenuated’), it improves over the standard PRINCE when its application is restricted to associations that involve genes that are expressed in the relevant tissue. As shown in Figure S5, for MAS>40% this approach yields an AUC of 0. 897, albeit significantly smaller than the attenuated version of TS-ERW( AUC = 0. 905, Wilcoxon sign rank P-value = 0. 02 for rw = 0. 1). Interestingly, as a preprocessing step for the tissue specific PRINCE algorithm, we found that a considerable fraction of disease genes are not expressed in the tissue associated with the disease. There may be several explanations for this observation. First, it may reflect an error in measurements, either of the expression microarray or the computational inference of disease-tissue association. Nevertheless, such a substantial fraction of genes is more likely to reflect a true biological observation. For example, a protein may be active although having lower mRNA levels. Posttranscriptional modifications or higher translational efficiency may also result in higher protein levels or longer protein half-lives[46],[47]. In addition to putative differences between mRNA and protein levels, obviously, there may be proteins who perform their function in relatively low levels Indeed, we found that many of the genes unexpressed at their disease's assigned tissue also have low overall expression levels, suggesting that these genes might still be expressed at functional level in the diseased tissue. Another possibility may be that the damage to the tissue was caused by a disruption of the protein function within the tissue in earlier developmental stages. Supporting this hypothesis we found that lowly-expressed disease causing genes are enriched with developmental annotations such as multicellular development processes( GO:0007275) and organ development( GO:0048513), and with stimulus response annotations( GO:0051716). Hence, the protein may not be active in the adult tissues( as manifested by its expression pattern), but a mutation in the genes may alter normal development of the tissue or may prevent the normal response of the tissue to stress or other stimuli, resulting in a disease. Finally, due to the complexity and the dependencies between tissues in a multi-tissue organism, a mutation in a protein active in one tissue may result in clinical pathology in another tissue. For example, Vitamin D – dependent rickets 1A( MIM: 264700) is primarily a bone disorder, but it is caused by a mutation in the gene CYP27B1, which is active in the kidney and participates in the hydroxylation of Vitamin D into its active form, Calcitriol[48]. Overall, the role of lowly expressed genes in causing disease in a given tissue is a rather complex one and deserves a separate analysis that is beyond the current scope. Some limitations of the current analysis should be mentioned. First, a direct tissue specific measure of protein abundance would be more adequate than mRNA levels as a measure for the presence and hence the activation and functionality of a protein in a tissue. However, despite the best efforts of the scientific community, compendiums of human tissue-specific protein abundance levels across multiple tissues are not nearly as comprehensive as the mRNA expression dataset we use, both in tissue scope, gene coverage and quantitative resolution[42]. In addition, the mutual expression of two possibly interacting proteins does not guarantee that the interaction will take place, and there are other factors that also should be considered such as, most prominently, the proteins' phosphorylation state. Nevertheless, even given these limitations, our tissue specific approach performs better than the generic approach. As large scale data on tissue specific protein abundance and cellular localization will become available, it will be interesting to repeat the analysis reported here to see whether it yields better predictions, as may be expected. In recent years, PPI networks were shown to be a powerful tool in many fields of molecular biology, such as predicting protein annotation and more[19],[49],[50]. We hope that the results of this study will encourage future studies to utilize tissue-specific PPI networks to further increase their predictive and explanatory power.
Identifying the genes causing genetic disease is a key challenge in human health , and a crucial step on the road for developing novel diagnostics and treatments . Modern discovery methods involve genome-wide association studies that reveal regions of the genome where the causal gene is likely to reside , and then prioritizing the candidate genes within these regions and experimentally examining the most promising candidates' potential influence on the disease . Many computational methods were developed to automatically prioritize candidate genes . Some of the most successful methods use a biological network of interacting genes or proteins as an input . However , these networks – and subsequently , these methods – do not take into account the differences between tissues . In other words , a heart disease is analyzed using the same network as a skin disease . We constructed tissue-specific protein interaction networks and explored their effect on an existing prioritization algorithm by comparing the algorithm's performance on the tissue-specific networks and the generic network . We find that integrating tissue-specific data indeed leads to better prioritization . We also used the prioritization results of different tissues in order to suggest new disease-tissue associations .