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.

A live attenuated vaccine confers superior mucosal and systemic immunity to SARS-CoV-2 variants (preprint)

Vaccines are a cornerstone in COVID-19 pandemic management. Here, we compare immune responses to and preclinical efficacy of the mRNA vaccine BNT162b2, an adenovirus-vectored spike vaccine, and the live-attenuated-virus vaccine candidate sCPD9 after single and double vaccination in Syrian hamsters. All regimens containing sCPD9 showed superior efficacy. The robust immunity elicited by sCPD9 was evident in a wide range of immune parameters after challenge with heterologous SARS-CoV-2 including rapid viral clearance, reduced tissue damage, fast differentiation of pre-plasmablasts, strong systemic and mucosal humoral responses, and rapid recall of memory T cells from lung tissue. Our results demonstrate that use of live-attenuated vaccines may offer advantages over available COVID-19 vaccines, specifically when applied as booster, and may provide a solution for containment of the COVID-19 pandemic.

Insights into standards of care: dexamethasone and antibodies against COVID-19 in hamster models (preprint)

Rationale: In face of the ongoing SARS-CoV-2 pandemic, effective and well-understood treatment options are still scarce. While vaccines have proven instrumental in fighting SARS-CoV-2, their efficacy is challenged by vaccine hesitancy, novel variants and short-lasting immunity. Therefore, understanding and optimization of therapeutic options remains essential. Objectives: We aimed at generating a deeper understanding on how currently used drugs, specifically dexamethasone and anti-SARS-CoV-2 antibodies, affect SARS-CoV-2 infection and host responses. Possible synergistic effects of both substances are investigated to evaluate combinatorial treatments. Methods: By using two COVID-19 hamster models, pulmonary immune responses were analyzed to characterize effects of treatment with either dexamethasone, anti-SARS-CoV-2 spike monoclonal antibody or a combination of both. scRNA sequencing was employed to reveal transcriptional response to treatment on a single cell level. Measurements and main results: Dexamethasone treatment resulted in similar or increased viral loads compared to controls. Anti-SARS-CoV-2 antibody treatment alone or combined with dexamethasone successfully reduced pulmonary viral burden. Dexamethasone exhibited strong anti-inflammatory effects and prevented fulminant disease in a severe COVID-19-like disease model. Combination therapy showed additive benefits with both anti-viral and anti-inflammatory potency. Bulk and single-cell transcriptomic analyses confirmed dampened inflammatory cell recruitment into lungs upon dexamethasone treatment and identified a candidate subpopulation of neutrophils specifically responsive to dexamethasone. Conclusions: Our analyses i) confirm the anti-inflammatory properties and indicate possible modes of action for dexamethasone, ii) validate anti-viral effects of anti-SARS-CoV-2 antibody treatment, and iii) reveal synergistic effects of a combination therapy and can thus inform more effective COVID-19 therapies.

Longitudinal omics in Syrian hamsters integrated with human data unravel cellular effector responses to moderate COVID-19 (preprint)

In COVID-19, immune responses are key in determining disease severity. However, cellular mechanisms at the onset of inflammatory lung injury in SARS-CoV-2 infection, particularly involving endothelial cells, remain ill-defined. Using Syrian hamsters as model for moderate COVID-19, we conducted a detailed longitudinal analysis of systemic and pulmonary cellular responses, and corroborated it with datasets from COVID-19 patients. Monocyte-derived macrophages in lungs exerted the earliest and strongest transcriptional response to infection, including induction of pro-inflammatory genes, while epithelial cells showed weak activation. Without evidence for productive infection, endothelial cells reacted, depending on cell subtypes, by strong and early expression of anti-viral, pro-inflammatory, and T cell recruiting genes. Recruitment of cytotoxic T cells as well as emergence of IgM antibodies preceded viral clearance at day 5 post infection. Investigating SARS-CoV-2 infected Syrian hamsters can thus identify cell type-specific effector functions, provide detailed insights into pathomechanisms of COVID-19, and inform therapeutic strategies.

Longitudinal omics in Syrian hamsters integrated with human data unravel complexity of moderate immune responses to SARS-CoV-2 (preprint)

In COVID-19, the immune response largely determines disease severity and is key to therapeutic strategies. Cellular mechanisms contributing to inflammatory lung injury and tissue repair in SARS-CoV-2 infection, particularly endothelial cell involvement, remain ill-defined. We performed detailed spatiotemporal analyses of cellular and molecular processes in SARS-CoV-2 infected Syrian hamsters. Comparison of hamster single-cell sequencing and proteomics with data sets from COVID-19 patients demonstrated inter-species concordance of cellular and molecular host-pathogen interactions. In depth vascular and pulmonary compartment analyses (i) supported the hypothesis that monocyte-derived macrophages dominate inflammation, (ii) revealed endothelial inflammation status and T-cell attraction, and (iii) showed that CD4+ and CD8+ cytotoxic T-cell responses precede viral elimination. Using the Syrian hamster model of self-limited moderate COVID-19, we defined the specific roles of endothelial and epithelial cells, among other myeloid and non-myeloid lung cell subtypes, for determining the disease course.