Biology subjects

Vladimirova, D.

Publications and source records attributed to Vladimirova, D..

5 recordsLinked to original sources

bioRxiv2024

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

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

immunology↗

bioRxiv2023

Antigenic cartography using variant-specific hamster sera reveals substantial antigenic variation among Omicron subvariants

SARS-CoV-2 has developed substantial antigenic variability. As the majority of the population now has pre-existing immunity due to infection or vaccination, the use of experimentally generated animal immune sera can be valuable for measuring antigenic differences between virus variants. Here, we immunized Syrian hamsters by two successive infections with one of eight SARS-CoV-2 variants. Their sera were titrated against 14 SARS-CoV-2 variants and the resulting titers visualized using antigenic cartography. The antigenic map shows a condensed cluster containing all pre-Omicron variants (D614G, Alpha, Delta, Beta, Mu, and an engineered B.1+E484K variant), and a considerably more distributed positioning among a selected panel of Omicron subvariants (BA.1, BA.2, BA.4/5, the BA.5 descendants BF.7 and BQ.1.18; the BA.2.75 descendant BN.1.3.1; and the BA.2-derived recombinant XBB.2). Some Omicron subvariants were as antigenically distinct from each other as the wildtype is from the Omicron BA.1 variant. The results highlight the potential of using variant-specifically infected hamster sera for the continued antigenic characterisation of SARS-CoV-2.

immunology↗

bioRxiv2020

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

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.

immunology↗

bioRxiv2020

A SARS-CoV-2 neutralizing antibody protects from lung pathology in a COVID-19 hamster model

The emergence of SARS-CoV-2 led to pandemic spread of coronavirus disease 2019 (COVID-19), manifesting with respiratory symptoms and multi-organ dysfunction. Detailed characterization of virus-neutralizing antibodies and target epitopes is needed to understand COVID-19 pathophysiology and guide immunization strategies. Among 598 human monoclonal antibodies (mAbs) from ten COVID-19 patients, we identified 40 strongly neutralizing mAbs. The most potent mAb CV07-209 neutralized authentic SARS-CoV-2 with IC50 of 3.1 ng/ml. Crystal structures of two mAbs in complex with the SARS-CoV-2 receptor-binding domain at 2.55 and 2.70 [A] revealed a direct block of ACE2 attachment. Interestingly, some of the near-germline SARS-CoV-2 neutralizing mAbs reacted with mammalian self-antigens. Prophylactic and therapeutic application of CV07-209 protected hamsters from SARS-CoV-2 infection, weight loss and lung pathology. Our results show that non-self-reactive virus-neutralizing mAbs elicited during SARS-CoV-2 infection are a promising therapeutic strategy.

immunology↗

bioRxiv2020

Age-dependent progression of SARS-CoV-2 infection in Syrian hamsters

In late 2019, an outbreak of a severe respiratory disease caused by an emerging coronavirus, SARS-CoV-2, resulted in high morbidity and mortality in infected humans1. Complete understanding of COVID-19, the multi-faceted disease caused by SARS-CoV-2, requires suitable small animal models, as does the development and evaluation of vaccines and antivirals2. Because age-dependent differences of COVID-19 were identified in humans3, we compared the course of SARS-CoV-2 infection in young and aged Syrian hamsters. We show that virus replication in the upper and lower respiratory tract was independent of the age of the animals. However, older hamsters exhibited more pronounced and consistent weight loss. In situ hybridization in the lungs identified viral RNA in bronchial epithelium, alveolar epithelial cells type I and II, and macrophages. Histopathology revealed clear age-dependent differences, with young hamsters launching earlier and stronger immune cell influx than aged hamsters. The latter developed conspicuous alveolar and perivascular edema, indicating vascular leakage. In contrast, we observed rapid lung recovery at day 14 after infection only in young hamsters. We propose that comparative assessment in young versus aged hamsters of SARS-CoV-2 vaccines and treatments may yield valuable information as this small-animal model appears to mirror age-dependent differences in human patients.

microbiology↗