CASCADE: a novel quasi all paths-based network analysis algorithm for clustering biological interactions

<p>Abstract</p> <p>Background</p> <p>Quantitative characterization of the topological characteristics of protein-protein interaction (PPI) networks can enable the elucidation of biological functional modules. Here, we present a novel clustering methodology for PPI networks wherein the biological and topological influence of each protein on other proteins is modeled using the probability distribution that the series of interactions necessary to link a pair of distant proteins in the network occur within a time constant (the occurrence probability).</p> <p>Results</p> <p>CASCADE selects representative nodes for each cluster and iteratively refines clusters based on a combination of the occurrence probability and graph topology between every protein pair. The CASCADE approach is compared to nine competing approaches. The clusters obtained by each technique are compared for enrichment of biological function. CASCADE generates larger clusters and the clusters identified have <it>p</it>-values for biological function that are approximately 1000-fold better than the other methods on the yeast PPI network dataset. An important strength of CASCADE is that the percentage of proteins that are discarded to create clusters is much lower than the other approaches which have an average discard rate of 45% on the yeast protein-protein interaction network.</p> <p>Conclusion</p> <p>CASCADE is effective at detecting biologically relevant clusters of interactions.</p

A novel functional module detection algorithm for protein-protein interaction networks

BACKGROUND: The sparse connectivity of protein-protein interaction data sets makes identification of functional modules challenging. The purpose of this study is to critically evaluate a novel clustering technique for clustering and detecting functional modules in protein-protein interaction networks, termed STM. RESULTS: STM selects representative proteins for each cluster and iteratively refines clusters based on a combination of the signal transduced and graph topology. STM is found to be effective at detecting clusters with a diverse range of interaction structures that are significant on measures of biological relevance. The STM approach is compared to six competing approaches including the maximum clique, quasi-clique, minimum cut, betweeness cut and Markov Clustering (MCL) algorithms. The clusters obtained by each technique are compared for enrichment of biological function. STM generates larger clusters and the clusters identified have p-values that are approximately 125-fold better than the other methods on biological function. An important strength of STM is that the percentage of proteins that are discarded to create clusters is much lower than the other approaches. CONCLUSION: STM outperforms competing approaches and is capable of effectively detecting both densely and sparsely connected, biologically relevant functional modules with fewer discards

An interactive retrieval system for clinical trial studies with context-dependent protocol elements.

A well-defined protocol for a clinical trial guarantees a successful outcome report. When designing the protocol, most researchers refer to electronic databases and extract protocol elements using a keyword search. However, state-of-the-art database systems only offer text-based searches for user-entered keywords. In this study, we present a database system with a context-dependent and protocol-element-selection function for successfully designing a clinical trial protocol. To do this, we first introduce a database for a protocol retrieval system constructed from individual protocol data extracted from 184,634 clinical trials and 13,210 frame structures of clinical trial protocols. The database contains a variety of semantic information that allows the filtering of protocols during the search operation. Based on the database, we developed a web application called the clinical trial protocol database system (CLIPS; available at https://corus.kaist.edu/clips). This system enables an interactive search by utilizing protocol elements. To enable an interactive search for combinations of protocol elements, CLIPS provides optional next element selection according to the previous element in the form of a connected tree. The validation results show that our method achieves better performance than that of existing databases in predicting phenotypic features

Identification and validation of Triamcinolone and Gallopamil as treatments for early COVID-19 via an in silico repurposing pipeline

SARS-CoV-2, the causative virus of COVID-19 continues to cause an ongoing global pandemic. Therapeutics are still needed to treat mild and severe COVID-19. Drug repurposing provides an opportunity to deploy drugs for COVID-19 more rapidly than developing novel therapeutics. Some existing drugs have shown promise for treating COVID-19 in clinical trials. This in silico study uses structural similarity to clinical trial drugs to identify two drugs with potential applications to treat early COVID-19. We apply in silico validation to suggest a possible mechanism of action for both. Triamcinolone is a corticosteroid structurally similar to Dexamethasone. Gallopamil is a calcium channel blocker structurally similar to Verapamil. We propose that both these drugs could be useful to treat early COVID-19 infection due to the proximity of their targets within a SARS-CoV-2-induced protein-protein interaction network to kinases active in early infection, and the APOA1 protein which is linked to the spread of COVID-19.Comment: 32 pages, 4 figure

Identification of SARS-CoV-2-induced pathways reveals drug repurposing strategies.

The global outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) necessitates the rapid development of new therapies against coronavirus disease 2019 (COVID-19) infection. Here, we present the identification of 200 approved drugs, appropriate for repurposing against COVID-19. We constructed a SARS-CoV-2-induced protein network, based on disease signatures defined by COVID-19 multiomics datasets, and cross-examined these pathways against approved drugs. This analysis identified 200 drugs predicted to target SARS-CoV-2-induced pathways, 40 of which are already in COVID-19 clinical trials, testifying to the validity of the approach. Using artificial neural network analysis, we classified these 200 drugs into nine distinct pathways, within two overarching mechanisms of action (MoAs): viral replication (126) and immune response (74). Two drugs (proguanil and sulfasalazine) implicated in viral replication were shown to inhibit replication in cell assays. This unbiased and validated analysis opens new avenues for the rapid repurposing of approved drugs into clinical trials

Semantic integration to identify overlapping functional modules in protein interaction networks

<p>Abstract</p> <p>Background</p> <p>The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms.</p> <p>Results</p> <p>We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches.</p> <p>Conclusion</p> <p>The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.</p

Multi-agent modeling of the South Korean avian influenza epidemic

<p>Abstract</p> <p>Background</p> <p>Several highly pathogenic avian influenza (AI) outbreaks have been reported over the past decade. South Korea recently faced AI outbreaks whose economic impact was estimated to be 6.3 billion dollars, equivalent to nearly 50% of the profit generated by the poultry-related industries in 2008. In addition, AI is threatening to cause a human pandemic of potentially devastating proportions. Several studies show that a stochastic simulation model can be used to plan an efficient containment strategy on an emerging influenza. Efficient control of AI outbreaks based on such simulation studies could be an important strategy in minimizing its adverse economic and public health impacts.</p> <p>Methods</p> <p>We constructed a spatio-temporal multi-agent model of chickens and ducks in poultry farms in South Korea. The spatial domain, comprised of 76 (37.5 km × 37.5 km) unit squares, approximated the size and scale of South Korea. In this spatial domain, we introduced 3,039 poultry flocks (corresponding to 2,231 flocks of chickens and 808 flocks of ducks) whose spatial distribution was proportional to the number of birds in each province. The model parameterizes the properties and dynamic behaviors of birds in poultry farms and quarantine plans and included infection probability, incubation period, interactions among birds, and quarantine region.</p> <p>Results</p> <p>We conducted sensitivity analysis for the different parameters in the model. Our study shows that the quarantine plan with well-chosen values of parameters is critical for minimize loss of poultry flocks in an AI outbreak. Specifically, the aggressive culling plan of infected poultry farms over 18.75 km radius range is unlikely to be effective, resulting in higher fractions of unnecessarily culled poultry flocks and the weak culling plan is also unlikely to be effective, resulting in higher fractions of infected poultry flocks.</p> <p>Conclusions</p> <p>Our results show that a prepared response with targeted quarantine protocols would have a high probability of containing the disease. The containment plan with an aggressive culling plan is not necessarily efficient, causing a higher fraction of unnecessarily culled poultry farms. Instead, it is necessary to balance culling with other important factors involved in AI spreading. Better estimations for the containment of AI spreading with this model offer the potential to reduce the loss of poultry and minimize economic impact on the poultry industry.</p

Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data

We present a network framework for analyzing multi-level regulation in higher eukaryotes based on systematic integration of various high-throughput datasets. The network, namely the integrated regulatory network, consists of three major types of regulation: TF→gene, TF→miRNA and miRNA→gene. We identified the target genes and target miRNAs for a set of TFs based on the ChIP-Seq binding profiles, the predicted targets of miRNAs using annotated 3′UTR sequences and conservation information. Making use of the system-wide RNA-Seq profiles, we classified transcription factors into positive and negative regulators and assigned a sign for each regulatory interaction. Other types of edges such as protein-protein interactions and potential intra-regulations between miRNAs based on the embedding of miRNAs in their host genes were further incorporated. We examined the topological structures of the network, including its hierarchical organization and motif enrichment. We found that transcription factors downstream of the hierarchy distinguish themselves by expressing more uniformly at various tissues, have more interacting partners, and are more likely to be essential. We found an over-representation of notable network motifs, including a FFL in which a miRNA cost-effectively shuts down a transcription factor and its target. We used data of C. elegans from the modENCODE project as a primary model to illustrate our framework, but further verified the results using other two data sets. As more and more genome-wide ChIP-Seq and RNA-Seq data becomes available in the near future, our methods of data integration have various potential applications

Identification of potential pan-coronavirus therapies using a computational drug repurposing platform.

In the past 20 years, there have been several infectious disease outbreaks in humans for which the causative agent has been a zoonotic coronavirus. Novel infectious disease outbreaks, as illustrated by the current coronavirus disease 2019 (COVID-19) pandemic, demand a rapid response in terms of identifying effective treatments for seriously ill patients. The repurposing of approved drugs from other therapeutic areas is one of the most practical routes through which to approach this. Here, we present a systematic network-based drug repurposing methodology, which interrogates virus-human, human protein-protein and drug-protein interactome data. We identified 196 approved drugs that are appropriate for repurposing against COVID-19 and 102 approved drugs against a related coronavirus, severe acute respiratory syndrome (SARS-CoV). We constructed a protein-protein interaction (PPI) network based on disease signatures from COVID-19 and SARS multi-omics datasets. Analysis of this PPI network uncovered key pathways. Of the 196 drugs predicted to target COVID-19 related pathways, 44 (hypergeometric p-value: 1.98e-04) are already in COVID-19 clinical trials, demonstrating the validity of our approach. Using an artificial neural network, we provide information on the mechanism of action and therapeutic value for each of the identified drugs, to facilitate their rapid repurposing into clinical trials