Tissue Specificity of Human Disease Module

Genes carrying mutations associated with genetic diseases are present in all human cells; yet, clinical manifestations of genetic diseases are usually highly tissue-specific. Although some disease genes are expressed only in selected tissues, the expression patterns of disease genes alone cannot explain the observed tissue specificity of human diseases. Here we hypothesize that for a disease to manifest itself in a particular tissue, a whole functional subnetwork of genes (disease module) needs to be expressed in that tissue. Driven by this hypothesis, we conducted a systematic study of the expression patterns of disease genes within the human interactome. We find that genes expressed in a specific tissue tend to be localized in the same neighborhood of the interactome. By contrast, genes expressed in different tissues are segregated in distinct network neighborhoods. Most important, we show that it is the integrity and the completeness of the expression of the disease module that determines disease manifestation in selected tissues. This approach allows us to construct a disease-tissue network that confirms known and predicts unexpected disease-tissue associations

Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19

The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug's targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs that had been experimentally screened in VeroE6 cells, and the list of drugs under clinical trial, that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that while most algorithms offer predictive power for these ground truth data, no single method offers consistently reliable outcomes across all datasets and metrics. This prompted us to develop a multimodal approach that fuses the predictions of all algorithms, showing that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We find that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based actions that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development

Endophenotype Network Models: Common Core of Complex Diseases

Historically, human diseases have been differentiated and categorized based on the organ system in which they primarily manifest. Recently, an alternative view is emerging that emphasizes that different diseases often have common underlying mechanisms and shared intermediate pathophenotypes, or endo(pheno)types. Within this framework, a specific disease’s expression is a consequence of the interplay between the relevant endophenotypes and their local, organ-based environment. Important examples of such endophenotypes are inflammation, fibrosis, and thrombosis and their essential roles in many developing diseases. In this study, we construct endophenotype network models and explore their relation to different diseases in general and to cardiovascular diseases in particular. We identify the local neighborhoods (module) within the interconnected map of molecular components, i.e., the subnetworks of the human interactome that represent the inflammasome, thrombosome, and fibrosome. We find that these neighborhoods are highly overlapping and significantly enriched with disease-associated genes. In particular they are also enriched with differentially expressed genes linked to cardiovascular disease (risk). Finally, using proteomic data, we explore how macrophage activation contributes to our understanding of inflammatory processes and responses. The results of our analysis show that inflammatory responses initiate from within the cross-talk of the three identified endophenotypic modules

A DIseAse MOdule Detection (DIAMOnD) Algorithm Derived from a Systematic Analysis of Connectivity Patterns of Disease Proteins in the Human Interactome

The observation that disease associated proteins often interact with each other has fueled the development of network-based approaches to elucidate the molecular mechanisms of human disease. Such approaches build on the assumption that protein interaction networks can be viewed as maps in which diseases can be identified with localized perturbation within a certain neighborhood. The identification of these neighborhoods, or disease modules, is therefore a prerequisite of a detailed investigation of a particular pathophenotype. While numerous heuristic methods exist that successfully pinpoint disease associated modules, the basic underlying connectivity patterns remain largely unexplored. In this work we aim to fill this gap by analyzing the network properties of a comprehensive corpus of 70 complex diseases. We find that disease associated proteins do not reside within locally dense communities and instead identify connectivity significance as the most predictive quantity. This quantity inspires the design of a novel Disease Module Detection (DIAMOnD) algorithm to identify the full disease module around a set of known disease proteins. We study the performance of the algorithm using well-controlled synthetic data and systematically validate the identified neighborhoods for a large corpus of diseases

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<p>(A) Illustration of how the algorithm can be modified to give the initial seed proteins a higher weight α = 2 by (virtually) doubling the seed proteins while keeping their interactions. Tuning α results in different sets of detected proteins. (B,C) Comparing the performance for varying values of α in synthetic shells (B) and connectivity significance (C) modules, respectively. The best results are obtained for α = 3. (C) The performance may also saturate for α larger than a certain value. For a given disease α can be tuned to optimize the results. Performance of DIAMOnD with respect to different values of α is shown for ulcerative colitis (D) and nutritional and metabolic diseases (E). These plots suggest that at α = 2 the number of true positives is maximal. (F) Overall, α ∼10 results in the best performance of DIAMOnD across 70 diseases. The individual values may vary considerably, however, suggesting an individual optimization for best results.</p

List of the 70 considered diseases.

<p>List of the 70 considered diseases.</p

Biological evaluation of DIAMOnD.

<p>(A) Validation of the DIAMOnD genes based on GeneOntology terms (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004120#sec012" target="_blank">Materials & Methods</a>). (B) The significance of the similarity between DIAMOnD genes and seed genes suggests a cutoff of ∼200 DIAMOnD genes. (C) Network representation of the <i>lysosomal storage diseases</i> module. (D,E) Summary of the validation for all 70 disease modules based on GeneOntology (D) and biological pathways (E). (F) Fraction of seed proteins that are contained in the LCC of the DIAMOnD module for varying iteration steps. The distributions show the values obtained from 70 diseases. By introducing DIAMOnD proteins, previously disconnected seed proteins become part of the LCC.</p

Comparison between DIAMOnD and Random Walk (RW).

<p>(A,B) Average recovery rates of DIAMOnD and the reference RW algorithm when removing 50% (100 nodes) of 100 generated <i>shells</i> (A) and <i>connectivity</i> (B) modules. (C) Comparison of the biological evidence for proteins identified by DIAMOnD and RW for <i>lysosomal storage diseases</i>. (D) Overlap between identified proteins and immediate neighbors of seed proteins. In contrast to RW, DIAMOnD includes a considerable number of proteins without first-order interactions to seed genes. (E) Comparison of the performance of DIAMOnD and RW across 70 diseases with respect to non-specific disease data. (F) Degree distributions of the identified proteins. DIAMonD proteins are characterized by the absence of hubs.</p

Topological properties of disease proteins within the Interactome.

<p>(A) Proteins associated with the same phenotype tend to localize in specific neighborhoods of the Interactome, indicating the approximate location of the corresponding <i>disease modules</i>. Topological network communities are highly interconnected groups of nodes. (B) Distribution of the fraction of disease proteins within the largest connected component (LCC) for 70 diseases. Only 10%-30% of the disease proteins are part of the LCC. (C) LCC size of proteins associated with <i>lysosomal storage disease</i> compared to random expectation. Out of 45 disease proteins, 24 (53%) are part of the LCC (<i>z</i>-score = 23.42, empirical <i>p</i>-value < 10^–6). (D) Significance of the LCC sizes as measured by the <i>z</i>-score for all 70 considered diseases. The whiskers indicate the minimum, 25^th, 50^th, 75^th percentile and maximum across all diseases. Overall, 70% of the diseases show significant clustering (<i>z</i>-score>1.6). (E) LCC z-score distribution in noisy networks in which a fraction <i>f</i> of all links is randomized by either link removal or rewiring. (F) We applied three representative community detection algorithms to explore the extent to which <i>topological</i> modules correspond to <i>disease</i> modules. Only 1%-5% of the communities detected by the different methods are significantly enriched with disease proteins, none of which includes a significant fraction of all disease proteins. (G) Comparison of the distribution of the local modularity <i>R</i> for disease proteins and proteins randomly selected from the Interactome. (H) Distribution of the connectivity significance of disease proteins and randomly selected proteins. (I) Connectivity significance of disease proteins as a function of the fraction <i>f</i> of links removed from the network. The red bars denote the mean and the standard deviation as measured across 70 diseases, yellow bars show random expectation obtained from the same number of randomly distributed genes. (J) Local modularity of disease proteins and randomly selected proteins when a fraction <i>f</i> of the links is removed from the network. (K) Illustration of the local modularity <i>R</i>.</p