Abstract
Abstract
Background
Medical digital twins are computational disease models for drug discovery and treatment. Unresolved problems include how to organize and prioritize between disease-associated changes in digital twins, on cellulome- and genome-wide scales. We present a dynamic framework that can be used to model such changes and thereby prioritize upstream regulators (URs) for biomarker- and drug discovery.
Methods
We started with seasonal allergic rhinitis (SAR) as a disease model, by analyses of in vitro allergen-stimulated peripheral blood mononuclear cells (PBMC) from SAR patients. Time-series a single-cell RNA-sequencing (scRNA-seq) data of these cells were used to construct multicellular network models (MNMs) at each time point of molecular interactions between cell types. We hypothesized that predicted molecular interactions between cell types in the MNMs could be traced to find an UR gene, at an early time point. We performed bioinformatic and functional studies of the MNMs to develop a scalable framework to prioritize UR genes. This framework was tested on a single-cell and bulk-profiling data from SAR and other inflammatory diseases.
Results
Our scRNA-seq-based time-series MNMs of SAR showed thousands of differentially expressed genes (DEGs) across multiple cell types, which varied between time points. Instead of a single-UR gene in each MNM, we found multiple URs dispersed across the cell types. Thus, at each time point, the MNMs formed multi-directional networks. The absence of linear hierarchies and time-dependent variations in MNMs complicated the prioritization of URs. For example, the expression and functions of Th2 cytokines, which are approved drug targets in allergies, varied across cell types, and time points. Our analyses of bulk- and single-cell data from other inflammatory diseases also revealed multi-directional networks that showed stage-dependent variations. We therefore developed a quantitative approach to prioritize URs: we ranked the URs based on their predicted effects on downstream target cells. Experimental and bioinformatic analyses supported that this kind of ranking is a tractable approach for prioritizing URs.
Conclusions
We present a scalable framework for modeling dynamic changes in digital twins, on cellulome- and genome-wide scales, to prioritize UR genes for biomarker and drug discovery.
Funder
National Institutes of Health
American Heart Association
VINNOVA
European Commission
Swedish Cancer Society
Joanna Cocozza Foundation
National Research Foundation of Korea (NRF) grant funded by the Korea government
Austrian Science Funds
Vetenskapsrådet
Linköping University
Publisher
Springer Science and Business Media LLC
Subject
Genetics (clinical),Genetics,Molecular Biology,Molecular Medicine
Reference67 articles.
1. U.S. Food and Drug Administration. Paving the way for personalized medicine: FDA’s role in a new era of medical product development. Pers Med. 2014:1–74. (FDA’s Emerg Role).
2. Tao F, Qi Q. Make more digital twins. Nature. 2019:573:490–1. (Nature Publishing Group).
3. Björnsson B, Borrebaeck C, Elander N, Gasslander T, Gawel DR, Gustafsson M, et al. Digital twins to personalize medicine. Genome Med. 2019;12:4. (BioMed Central Ltd.).
4. Laubenbacher R, Sluka JP, Glazier JA. Using digital twins in viral infection. Science (80- ). American Association for the Advancement of Science. 2021;371:1105–6.
5. Zhou C, Chase JG, Knopp J, Sun Q, Tawhai M, Möller K, et al. Virtual patients for mechanical ventilation in the intensive care unit. Comput Methods Prog Biomed. 2021;199:105912 Elsevier Ireland Ltd.
Cited by
10 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献