Neural Network-Assisted Humanization of COVID-19 Hamster scRNAseq Data Reveals Matching Severity States in Human Disease (preprint)

Translating findings from animal models to human disease is essential for dissecting disease mechanisms, developing and testing precise therapeutic strategies. The coronavirus disease 2019 (COVID-19) pandemic has highlighted this need, particularly for models showing disease severity-dependent immune responses. Single-cell transcriptomics (scRNAseq) is well poised to reveal similarities and differences between species at the molecular and cellular level with unprecedented resolution. However, computational methods enabling detailed matching are still scarce. Here, we provide a structured scRNAseq-based approach that we applied to scRNAseq from blood leukocytes originating from humans and hamsters affected with moderate or severe COVID-19. Integration of COVID-19 patient data with two hamster models that develop moderate (Syrian hamster, Mesocricetus auratus) or severe (Roborovski hamster, Phodopus roborovskii) disease revealed that most cellular states are shared across species. A neural network-based analysis using variational autoencoders quantified the overall transcriptomic similarity across species and severity levels, showing highest similarity between neutrophils of Roborovski hamsters and severe COVID-19 patients, while Syrian hamsters better matched patients with moderate disease, particularly in classical monocytes. We further used transcriptome-wide differential expression analysis to identify which disease stages and cell types display strongest transcriptional changes. Consistently, hamsters response to COVID-19 was most similar to humans in monocytes and neutrophils. Disease-linked pathways found in all species specifically related to interferon response or inhibition of viral replication. Analysis of candidate genes and signatures supported the results. Our structured neural network-supported workflow could be applied to other diseases, allowing better identification of suitable animal models with similar pathomechanisms across species. Key PointsO_LINeural networks can successfully match disease states between animal models and humans using single-cell data as shown for COVID-19 C_LIO_LIModerately diseased patients best matched Syrian hamster cells; severely diseased patients best matched Roborovski hamster neutrophils C_LI

Single-cell-resolved interspecies comparison identifies a shared inflammatory axis and a dominant neutrophil-endothelial program in severe COVID-19 (preprint)

Key issues for research of COVID-19 pathogenesis are the lack of biopsies from patients and of samples at the onset of infection. To overcome these hurdles, hamsters were shown to be useful models for studying this disease. Here, we further leveraged the model to molecularly survey the disease progression from time-resolved single-cell RNA-sequencing data collected from healthy and SARS-CoV-2-infected Syrian and Roborovski hamster lungs. We compared our data to human COVID-19 studies, including BALF, nasal swab, and post-mortem lung tissue, and identified a shared axis of inflammation dominated by macrophages, neutrophils, and endothelial cells, which we show to be transient in Syrian and terminal in Roborovski hamsters. Our data suggest that, following SARS-CoV-2 infection, commitment to a type 1 or type 3-biased immunity determines moderate versus severe COVID-19 outcomes, respectively.

Post-COVID syndrome is associated with capillary alterations, macrophage infiltration and distinct transcriptomic signatures in skeletal muscles (preprint)

The SARS-CoV-2 pandemic not only resulted in millions of acute infections worldwide, but also caused innumerable cases of post-infectious syndromes, colloquially referred to as long COVID. Due to the heterogeneous nature of symptoms and scarcity of available tissue samples, little is known about the underlying mechanisms. We present an in-depth analysis of skeletal muscle biopsies obtained from eleven patients suffering from enduring fatigue and post-exertional malaise after an infection with SARS-CoV-2. Compared to two independent historical control cohorts, patients with post-COVID exertion intolerance had fewer capillaries, thicker capillary basement membranes and increased numbers of CD169+ macrophages. SARS-CoV-2 RNA could not be detected in the muscle tissues, but transcriptomic analysis revealed distinct gene signatures compared to the two control cohorts, indicating immune dysregulations and altered metabolic pathways. We hypothesize that the initial viral infection may have caused immune-mediated structural changes of the microvasculature, potentially explaining the exercise-dependent fatigue and muscle pain.

Tissue protective role of Ganetespib in SARS-CoV-2-infected Syrian golden hamsters (preprint)

The emergence of new SARS-CoV-2 variants, capable of escaping the humoral immunity acquired by the available vaccines, together with waning immunity and vaccine hesitancy, challenges the efficacy of the vaccination strategy in fighting COVID-19. Improved therapeutic strategies are therefore urgently needed to better intervene particularly in severe cases of the disease. They should aim at controlling the hyper-inflammatory state generated upon infection, at reducing lung tissue pathology and endothelial damages, along with viral replication. Previous research has pointed a possible role for the chaperone HSP90 in SARS-CoV-2 replication and COVID-19 pathogenesis. Pharmacological intervention through HSP90 inhibitors was shown to be beneficial in the treatment of inflammatory diseases, infections and reducing replication of diverse viruses. In this study, we analyzed the effects of the potent HSP90 inhibitor Ganetespib in vitro on alveolar epithelial cells and alveolar macrophages to characterize its effects on cell activation and viral replication. Additionally, to evaluate its efficacy in controlling systemic inflammation and the viral burden after infection in vivo, a Syrian hamster model was used. In vitro, Ganetespib reduced viral replication on AECs in a dose-dependent manner and lowered significantly the expression of pro-inflammatory genes, in both AECs and alveolar macrophages. In vivo, administration of Ganetespib led to an overall improvement of the clinical condition of infected animals, with decreased systemic inflammation, reduced edema formation and lung tissue pathology. Altogether, we show that Ganetespib could be a potential medicine to treat moderate and severe cases of COVID-19.

De Novo Whole Genome Assembly of the Roborovski Dwarf Hamster (Phodopus roborovskii) Genome, an Animal Model for Severe/Critical COVID-19 (preprint)

The Roborovski dwarf hamster Phodopus roborovskii belongs to the Phodopus genus, one of seven within Cricetinae subfamily. Like other rodents such as mice, rats or ferrets, hamsters can be important animal models for a range of diseases. Whereas the Syrian hamster from the genus Mesocricetus is now widely used as a model for mild to moderate COVID-19, Roborovski dwarf hamster show a severe to lethal course of disease upon infection with the novel human coronavirus SARS-CoV-2.