Abstract:Despite high sequence similarity between pandemic and seasonal influenza viruses, there is extreme variation in host pathogenicity from one viral strain to the next. Identifying the underlying mechanisms of variability in pathogenicity is a critical task for understanding influenza virus infection and effective management of highly pathogenic influenza virus disease. We applied a network-based modeling approach to identify critical functions related to influenza virus pathogenicity using large transcriptomic a… Show more
“…These results demonstrate that average s -betweenness, but not necessarily average harmonic s -closeness, considers the complexity of the hypergraph and provides superior performance over graph metrics with regards to identifying biologically important genes. This aligns with prior work in which betweenness centrality computed for vertices in a graph identifies important genes in a network [ 1 , 2 , 27 , 32 ]. Fig.…”
Section: Resultssupporting
confidence: 80%
“…In order to compare our hypergraph approach to more common graph approaches we employed the CLR graph methodology that we have used previously to enrich for important genes in a network [ 1 , 2 , 27 ]. The CLR algorithm was run on the matrix of transcriptomics -fold change values using parameters spline = 3 and bins = 10 (as used in the original CLR manuscript [ 28 ]) and the resulting matrix was filtered for all mutual information values .…”
Section: Resultsmentioning
confidence: 99%
“…Previous work [ 1 , 2 , 27 , 32 ] had demonstrated that graph metrics can be used to identify important genes in association graphs, and so we set out to determine if hypergraphs provided an improvement over graphs. To assess performance of (hyper)graphs derived from our large viral infection gene expression data set, we identified three gene sets related to virus/pathogen infection and performed an enrichment analysis of our ranked lists compared to these gene sets.…”
Background
Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets.
Results
We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality.
Conclusions
Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.
“…These results demonstrate that average s -betweenness, but not necessarily average harmonic s -closeness, considers the complexity of the hypergraph and provides superior performance over graph metrics with regards to identifying biologically important genes. This aligns with prior work in which betweenness centrality computed for vertices in a graph identifies important genes in a network [ 1 , 2 , 27 , 32 ]. Fig.…”
Section: Resultssupporting
confidence: 80%
“…In order to compare our hypergraph approach to more common graph approaches we employed the CLR graph methodology that we have used previously to enrich for important genes in a network [ 1 , 2 , 27 ]. The CLR algorithm was run on the matrix of transcriptomics -fold change values using parameters spline = 3 and bins = 10 (as used in the original CLR manuscript [ 28 ]) and the resulting matrix was filtered for all mutual information values .…”
Section: Resultsmentioning
confidence: 99%
“…Previous work [ 1 , 2 , 27 , 32 ] had demonstrated that graph metrics can be used to identify important genes in association graphs, and so we set out to determine if hypergraphs provided an improvement over graphs. To assess performance of (hyper)graphs derived from our large viral infection gene expression data set, we identified three gene sets related to virus/pathogen infection and performed an enrichment analysis of our ranked lists compared to these gene sets.…”
Background
Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets.
Results
We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality.
Conclusions
Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.
“…EGFR was found as a pivotal regulator of thrombin-mediated inflammation. It also plays a role in changes from lethal to non-lethal influenza infections [ 56 , 62 ]. EGFR showed decreased signaling in infected hiPSC-CMs.…”
Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease 2019; COVID-19) is associated with adverse outcomes in patients with cardiovascular disease (CVD). The aim of the study was to characterize the interaction between SARS-CoV-2 and Angiotensin-Converting Enzyme 2 (ACE2) functional networks with a focus on CVD. Methods: Using the network medicine approach and publicly available datasets, we investigated ACE2 tissue expression and described ACE2 interaction networks that could be affected by SARS-CoV-2 infection in the heart, lungs and nervous system. We compared them with changes in ACE-2 networks following SARS-CoV-2 infection by analyzing public data of human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). This analysis was performed using the Network by Relative Importance (NERI) algorithm, which integrates protein-protein interaction with co-expression networks. We also performed miRNA-target predictions to identify which miRNAs regulate ACE2-related networks and could play a role in the COVID19 outcome. Finally, we performed enrichment analysis for identifying the main COVID-19 risk groups. Results: We found similar ACE2 expression confidence levels in respiratory and cardiovascular systems, supporting that heart tissue is a potential target of SARS-CoV-2. Analysis of ACE2 interaction networks in infected hiPSC-CMs identified multiple hub genes with corrupted signaling which can be responsible for cardiovascular symptoms. The most affected genes were EGFR (Epidermal Growth Factor Receptor), FN1 (Fibronectin 1), TP53, HSP90AA1, and APP (Amyloid Beta Precursor Protein), while the most affected interactions were associated with MAST2 and CALM1 (Calmodulin 1). Enrichment analysis revealed multiple diseases associated with the interaction networks of ACE2, especially cancerous diseases, obesity, hypertensive disease, Alzheimer’s disease, non-insulin-dependent diabetes mellitus, and congestive heart failure. Among affected ACE2-network components connected with the SARS-Cov-2 interactome, we identified AGT (Angiotensinogen), CAT (Catalase), DPP4 (Dipeptidyl Peptidase 4), CCL2 (C-C Motif Chemokine Ligand 2), TFRC (Transferrin Receptor) and CAV1 (Caveolin-1), associated with cardiovascular risk factors. We described for the first time miRNAs which were common regulators of ACE2 networks and virus-related proteins in all analyzed datasets. The top miRNAs regulating ACE2 networks were miR-27a-3p, miR-26b-5p, miR-10b-5p, miR-302c-5p, hsa-miR-587, hsa-miR-1305, hsa-miR-200b-3p, hsa-miR-124-3p, and hsa-miR-16-5p. Conclusion: Our study provides a complete mechanistic framework for investigating the ACE2 network which was validated by expression data. This framework predicted risk groups, including the established ones, thus providing reliable novel information regarding the complexity of signaling pathways affected by SARS-CoV-2. It also identified miRNAs that could be used in personalized diagnosis in COVID-19.
“…EGFR was found as a pivotal regulator of thrombin-mediated inflammation. It also plays a role in changes from lethal versus non-lethal influenza infections [56,62]. EGFR showed decreased signalling in infected hiPSC-CMs.…”
Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease 2019; is associated with adverse outcome in patients with cardiovascular disease (CVD).Aim: To characterize the interaction between SARS-CoV-2 and Angiotensin Converting Enzyme 2 (ACE2) functional networks with focus on CVD.Methods: Using bioinformatic tools, network medicine approaches and publicly available datasets, we investigated ACE2 tissue expression and described ACE2 interaction network which could be affected by SARS-CoV-2 infection. We identified top ACE2 interactors, including miRNAs which are shared regulators between the ACE2, virus-infection related proteins and heart interaction networks, using lung and nervous system networks as a reference. We also identified main SARS-CoV-2 risk groups and performed drug predictions for them.Results: We found the same range of ACE2 expression confidence in respiratory and cardiovascular systems (averaging 4.48 and 4.64, respectively). Analysing the complete ACE2 interaction network, we identified 11 genes (ACE2, DPP4, ANPEP, CCL2, TFRC, MEP1A, ADAM17, FABP2, NPC1, CLEC4M, TMPRSS2) associated with virus-infection related processes. Previously described genes associated with cardiovascular risk factors DPP4, CCL2 and ANPEP were extensively connected with top regulators of ACE2 network, including ACE, INS and KNG1. Enrichment analysis revealed several disease phenotypes associated with interaction networks of ACE2, heart tissue, and virus-infection related protein, with the strongest associations with the following diseases (in decreasing rank order): obesity, hypertensive disease, noninsulin dependent diabetes mellitus, congestive heart failure, and coronary artery disease. We described for the first time microRNAs-miR (miR-302c-5p, miR-1305, miR-587, miR-26b-5p, and mir-27a-3p), which were common regulators of the three networks: ACE2, heart tissue and virus-infection related proteins.Conclusion: Our study provides novel information regarding the complexity of signaling pathways affected by SARS-CoV-2 and proposes predictive tools as miR towards personalized diagnosis and therapy in COVID-19. Additionally, our study provides a list of miRNAs with biomarker potential in prediction of adverse outcome in patients with COVID-19 and CVD.
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