Neural network-assisted humanisation of COVID-19 hamster transcriptomic data reveals matching severity states in human disease.

COVID-19 Cross-species analysis Deep learning Disease state matching Hamster model Single-cell RNA-seq

Journal

EBioMedicine
ISSN: 2352-3964
Titre abrégé: EBioMedicine
Pays: Netherlands
ID NLM: 101647039

Informations de publication

Date de publication:
31 Aug 2024
Historique:
received: 05 04 2024
revised: 12 08 2024
accepted: 13 08 2024
medline: 25 9 2024
pubmed: 25 9 2024
entrez: 24 9 2024
Statut: aheadofprint

Résumé

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 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. 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. This work was supported by German Federal Ministry of Education and Research, (BMBF) grant IDs: 01ZX1304B, 01ZX1604B, 01ZX1906A, 01ZX1906B, 01KI2124, 01IS18026B and German Research Foundation (DFG) grant IDs: 14933180, 431232613.

Sections du résumé

BACKGROUND 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 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 RESULTS
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 CONCLUSIONS
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.
FUNDING BACKGROUND
This work was supported by German Federal Ministry of Education and Research, (BMBF) grant IDs: 01ZX1304B, 01ZX1604B, 01ZX1906A, 01ZX1906B, 01KI2124, 01IS18026B and German Research Foundation (DFG) grant IDs: 14933180, 431232613.

Identifiants

DOI: 10.1016/j.ebiom.2024.105312 PMID: 39317638
pubmed: 39317638
pii: S2352-3964(24)00348-7
doi: 10.1016/j.ebiom.2024.105312
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105312

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests MS received funding from Pfizer Inc. for a project related to pneumococcal vaccination. MS receives funding from Owkin for a project not related to this research. MW reports grants and personal fees from Biotest, grants and personal fees from Pantherna, grants and personal fees from Vaxxilon, personal fees from Aptarion, personal fees from Astra Zeneca, personal fees from Chiesi, personal fees from Insmed, personal fees from Gilead, outside the submitted work. GN reports grants from Biotest AG outside the submitted work. Unrelated to this work, Freie Universität Berlin has filed a patent application (PCT/EP2022/051215) for SARS-CoV-2 vaccines. JT is named as inventor on this application and receives remuneration in accordance with German law (“Gesetz über Arbeitnehmererfindungen”). Freie Universität Berlin is collaborating with RocketVax Inc. for further development of SARS-CoV-2 vaccines and receives funding for research. The other authors declare no competing interest.

Auteurs

Vincent D Friedrich (VD)

University of Leipzig, Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig, Germany; Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Leipzig, Germany.

Peter Pennitz (P)

Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Berlin, Germany.

Emanuel Wyler (E)

Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany.

Julia M Adler (JM)

Freie Universität Berlin, Institut für Virologie, Berlin, Germany.

Dylan Postmus (D)

Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Virology, Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany; Liverpool School of Tropical Medicine, Department of Tropical Disease Biology, Liverpool, United Kingdom.

Kristina Müller (K)

University of Leipzig, Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig, Germany.

Luiz Gustavo Teixeira Alves (LG)

Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany.

Julia Prigann (J)

Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Virology, Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany; Gladstone Institutes, San Francisco, USA.

Fabian Pott (F)

Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Virology, Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.

Daria Vladimirova (D)

Freie Universität Berlin, Institut für Virologie, Berlin, Germany.

Thomas Hoefler (T)

Freie Universität Berlin, Institut für Virologie, Berlin, Germany.

Cengiz Goekeri (C)

Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Berlin, Germany; Cyprus International University, Faculty of Medicine, Nicosia, Cyprus.

Markus Landthaler (M)

Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany; Humboldt-Universität zu Berlin, Institut fuer Biologie, Berlin, Germany.

Christine Goffinet (C)

Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Virology, Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany; Liverpool School of Tropical Medicine, Department of Tropical Disease Biology, Liverpool, United Kingdom.

Antoine-Emmanuel Saliba (AE)

Faculty of Medicine, Institute of Molecular Infection Biology (IMIB), University of Würzburg, Würzburg, Germany; Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz Center for Infection Research (HZI), Würzburg, Germany.

Markus Scholz (M)

University of Leipzig, Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig, Germany; University of Leipzig, Faculty of Mathematics and Computer Science, Leipzig, Germany.

Martin Witzenrath (M)

Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Berlin, Germany; German Center for Lung Research (DZL), Berlin, Germany.

Jakob Trimpert (J)

Freie Universität Berlin, Institut für Virologie, Berlin, Germany.

Holger Kirsten (H)

University of Leipzig, Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig, Germany. Electronic address: holger.kirsten@imise.uni-leipzig.de.

Geraldine Nouailles (G)

Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Berlin, Germany. Electronic address: geraldine.nouailles@charite.de.

Classifications MeSH