A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets

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.

Digital Twins : High Resolution Disease Models for Optimized Diagnosis and Treatment

“…In our disease model, we showed that neutralization of an UR which was predicted to target many different cell types over multiple developmental stages had a higher potential to prevent further disease-associated changes. Furthermore, the ranking was supported in samples from AD, UC, and CD [106], which confirms its relevance to other diseases as well. However, further studies will be needed to validate the potential of such high-ranked URs as therapeutic targets.…”

“…We therefore reason that PDGF-BB should be a more effective target for treatment than IL-4. The importance of URs shared between multiple cell types and disease stages was further supported by a meta-analysis of non-lesional and lesional biopsies from patients with atopic dermatitis (AD), ulcerative colitis (UC), and Crohn's disease (CD) [106]. These analyses showed how known therapeutic targets and genes related to inflammation was found as top-predicted URs in MCDMs of both non-lesional and lesional samples, which were reasoned to represent different disease stages for each respective disease [106].…”

“…Node size corresponds to the significance of the predicted interaction. The figure is modified with consent from[106].…”

The Digital Twin in Medicine: A Key to the Future of Healthcare?

“…In an artificial pancreas model for patients with type 1 diabetes, mathematical models of human glucose metabolism and data algorithms that simulate insulin delivery are customized into the patient-specific DT model, which can continuously calculate insulin requirements and regulate blood insulin concentrations ( 37 , 38 ). Li et al presented a scalable framework for modeling and prioritizing disease genes and drug targets among dynamic changes in the DTs for seasonal allergic rhinitis ( 39 ).…”

Molecular interaction networks and drug development: Novel approach to drug target identification and drug repositioning

“…The strategy for this type of study is illustrated in Figure 10. 26 Drug toxicities can also be predicted using interactomebased strategies. Just as disease modules connote a subnetwork of proteins and pathways that govern disease phenotype, drug toxicity modules provide the same kind of mechanistic insight into pathways that govern adverse drug effects.…”