An international team of researchers has developed advanced computer models, or “digital twins,” of diseases that can identify dynamic genome- and cellulome-wide, disease-associated changes in cells across time. Developed with the goal of improving diagnosis and treatment, the research, published in Genome Medicine, underlines the complexity of disease and the necessity of using the right treatment at the right time. The scientists, headed by Mikael Benson, PhD, at Linköping University, and Karolinska Institutet, reported on the development of one model to identify the most important disease protein in hay fever.

In their published paper, titled, “A dynamic single cell‑based framework for digital twins to prioritize disease genes and drug targets,” the investigators concluded, “We propose that our framework allows organization and prioritization of UR [upstream regulator] genes for biomarker and drug discovery. This may have far-reaching clinical implications, including identification of biomarkers for personalized treatment, new drug candidates, and time-dependent personalized prescriptions of drug combinations.”

For some complex diseases, medication may be ineffective in 40–70% of the patients. But why is a drug effective against a certain illness in some individuals, but not in others? One reason is that diseases are seldom caused by a single fault that can be easily treated. And as the authors noted, “Characterization and prioritization of pathogenic mechanisms in complex diseases, such as allergies and autoimmunity, are challenging because each disease may involve altered expression of thousands of genes across multiple cell types.” Moreover, disease processes often evolve over long periods, so treatment timing is also important. “On top of this complexity, those alterations may differ across different between time points of a disease process,” the team continued. So we are often not aware of disease development until symptoms appear, and diagnosis and treatment are thus often delayed, which may contribute to insufficient medical efficacy.

Digital twins were first developed in the engineering sector, with the aim of computationally modeling complex systems such as airplanes or cities, the authors continued. “The medical counterpart, digital twins of patients, has been proposed as a solution for integrating the wide range of data relevant for human diseases, in order to improve prediction, prevention, and treatment.” Examples of medical digital twins do already exist, including the artificial lung, which models lung function based on ventilator measurements, and the artificial pancreas, which optimizes insulin therapy for type 1 diabetes patients based on continuous blood glucose measurements. However, as the authors pointed out, these are early examples, and “ … solutions for characterizing, organizing and prioritizing molecular changes on dynamic cellulome- and genome-wide scales are needed for diagnostic and therapeutic purposes.”

In their newly reported study, the investigators aimed to bridge the gap between disease complexity and health care by constructing computational disease models of altered gene interactions across many cell types at different time points. The long-term goal is to develop such computational models into digital twins of individual patients’ diseases, which might be used to tailor medication for personalized therapy. Ideally, each twin could be matched with and treated with thousands of drugs on the computer, before actual treatment on the patient begins. “Broadly speaking, a digital twin has been defined as an in silico model that brings together the technology to map, monitor, and control real-world entities by continually receiving and integrating data from the physical twin to provide an up-to-date digital representation of the physical entity,” the team noted.

For their research, the investigators started by developing methods to construct digital twins of patients with seasonal allergic rhinitis (SAR, or hay fever). They used single-cell RNA sequencing to determine all gene activity in each of thousands of individual peripheral blood mononuclear cells (PMBCs) isolated from individuals with hay fever and challenged with pollen. Since these interactions between genes and cell types may differ between different time points in the same patient, the researchers measured gene activity at different time points before and after stimulating the white blood cells with pollen. Samples from sixteen patients with SAR and 14 control volunteers who didn’t suffer from hay fever, were included in the study. Samples were taken outside the pollen season, when the SAR participants were not symptomatic.

“Here, we aimed to address these challenges using time-series scRNA-seq analysis of allergen-challenged PBMC from patients with SAR,” they wrote. “This approach may be optimal for modeling the dynamics of a complex disease process because the environmental trigger (pollen allergens) is known and absent outside of the pollen season when the patients are asymptomatic. Thus, the specific response process can be modeled in vitro by stimulating PBMC from SAR patients with a standardized dose of allergen outside of the pollen season.”

In order to construct computer models of all the data, the researchers used network analyses. Networks can be used to describe and analyze complex systems. For example, a football team could be analyzed as a network based on the passes between the players. The player that passes most to other players during the whole match may be most important in that network. Similar principles were applied to construct the computer models, or “twins,” as well as to identify the most important disease protein.

Summarizing their approach, the authors wrote, “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.”

Their analyses showed that multiple proteins and signaling cascades were important in seasonal allergies, and that these varied greatly across cell types and at different stages of the disease. “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,” the team stated. “Instead of a single-UR gene in each MNM, we found multiple URs dispersed across the cell types.­­” Benson added, “We can see that these are extremely complicated changes that occur in different phases of a disease. The variation between different time points means that you have to treat the patient with the right medicine at the right time.”

Finally, the researchers identified the most important protein in the twin model of hay fever. They showed that inhibiting this protein, called PDGF-BB, in experiments with cells was more effective than using a known allergy drug directed against another protein, called IL-4.

The study also demonstrated that the methods developed could potentially be applied to help give the right treatment at the right time in other immunological diseases, like rheumatism or inflammatory bowel diseases. The authors concluded, “We propose that time series MNMs provide a scalable strategy for modeling and analyzing the dynamics of cellulome- and genome-wide changes in digital twins.”

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