Network Embedding Across Multiple Tissues and Data Modalities Elucidates the Context of Host Factors Important for COVID-19 Infection - Hu, Yue; Rehawi, Ghalia; Moyon, Lambert; Gerstner, Nathalie; Ogris, Christoph; Knauer-Arloth, Janine; Bittner, Florian; Marsico, Annalisa; Mueller, Nikola S. (Frontiers in Genetics, 2022)
COVID-19 is a heterogeneous disease caused by SARS-CoV-2. Apart from infections of the lungs, the disease can spread throughout the body and damage many other tissues, leading to multi-organ failure in severe cases. The highly variable symptom-severity is influenced by genetic predispositions and pre-existing diseases which have not been investigated in a large-scale multi-modal manner. We present a holistic analysis framework, setting previously reported COVID-19-genes in context with pre-pandemic data, such as gene expression patterns across multiple tissues, poly-genetic predispositions and patient diseases, which are putative comorbidities of COVID-19. Firstly, we generate a multi-modal network using the prior-based network inference method KiMONo. We then embed the network to generate a meaningful lower-dimensional representation of the data. The input data is obtained via the Genotype-Tissue Expression project (GTEx), containing expression data from a range of tissues with genomic and phenotypic information of over 900 patients and 50 tissues. The generated network consists of nodes, i.e.: genes and polygenic risk scores (PRS) for several diseases/phenotypes, as well as for COVID-19 severity and hospitalization, and links between them if they are statistically associated in a regularized linear model by feature selection. Applying network embedding on the generated multi-modal network allows us to perform efficient network analysis by identifying nodes close by in a lower-dimensional space that correspond to entities which are statistically linked. By determining the similarity between COVID-19 genes and other nodes through the embedding, we identify disease associations to tissues, like the brain and gut. We also find strong associations between COVID-19 genes and various diseases such as ischemic heart disease, cerebrovascular disease and hypertension. Moreover, we find evidence linking PTPN6 to a range of comorbidities along with the genetic predisposition of COVID-19, suggesting that this kinase is a central player in the severe cases of COVID-19. In conclusion, our holistic network inference coupled with network embedding of multi-modal data enables the contextualization of COVID-19 associated genes with respect to tissues, disease states and genetic risk factors. Such contextualization can be exploited to further elucidate the biological importance of known and novel genes for the severity of the disease in patients.