Neural Network-Assisted Humanization of COVID-19 Hamster Transcriptomic Data Reveals Matching Interspecies Severity States

Résumé fourni par la source

The recent coronavirus disease 2019 (COVID-19) pandemic has highlighted the need for appropriate models to evaluate therapeutic options applicable in a clinical setting. Though animal models are particularly valuable to study host-pathogen interactions the translation of experimental data to humans remain challenging. High throughput approaches like single-cell transcriptomics (scRNAseq) have the capability to dissect molecular and cellular changes between species, however novel methodology robustly linking and evaluating interspecies changes remains lacking. Here, we introduce a neural network using variational autoencoders capable of mapping temporal disease states of two hamster models to human COVID-19 severity ranks for individual cell types isolated from peripheral blood. Quantifying similarities confirmed that the transcriptional state of most Syrian hamster cell types best matched that of patients with moderate disease progression. Roborovski hamsters which develop fatal outcomes upon SARS-CoV-2 infection, showed highest similarities in neutrophils to severe COVID-19 patient neutrophils. Transcriptome-wide analysis and candidate gene expression revealed immune response similarities between hamsters and humans particularly in monocytes and neutrophils. Disease-linked pathways across species highlighted interferon responses and inhibition of viral replication. Our demonstrated generalizable neural network-supported workflow is applicable to other diseases, enhancing the identification of animal models with shared pathomechanisms.

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