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Friedrich, Vincent D., Pennitz, Peter, Wyler, Emanuel, Adler, Julia M., Postmus, Dylan, Müller, Kristina, Teixeira Alves, Luiz Gustavo, Prigann, Julia, Pott, Fabian, Vladimirova, Daria, Hoefler, Thomas, Goekeri, Cengiz, Landthaler, Markus, Goffinet, Christine, Saliba, Antoine-Emmanuel, Scholz, Markus, Witzenrath, Martin, Trimpert, Jakob, Kirsten, Holger and Nouailles, Geraldine (2024) 'Neural network-assisted humanisation of COVID-19 hamster transcriptomic data reveals matching severity states in human disease'. EBioMedicine, Vol 180, e105312.
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Abstract
Background
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.
Methods
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.
Findings
Integration of data from patients with COVID-19 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 patients with severe COVID-19, 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.
Interpretation
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.
| Item Type: | Article |
|---|---|
| Subjects: | QS Anatomy > QS 124 Comparative anatomy of humans and animals WC Communicable Diseases > Virus Diseases > Viral Respiratory Tract Infections. Respirovirus Infections > WC 506 COVID-19 |
| Faculty: Department: | Biological Sciences > Department of Tropical Disease Biology |
| Digital Object Identifer (DOI): | https://doi.org/10.1016/j.ebiom.2024.105312 |
| SWORD Depositor: | JISC Pubrouter |
| Depositing User: | JISC Pubrouter |
| Date Deposited: | 09 Oct 2024 11:30 |
| Last Modified: | 09 Oct 2024 11:30 |
| URI: | https://archive.lstmed.ac.uk/id/eprint/25397 |
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