A first-ever mechanistic disease model of IgG4-related disease identifies high-potential therapy targets
Milad Ghomlaghi (1), Wanbing Zhao (2), Hui Gao (3), Cuiping Pan (4), Yan Ge (2), Ruimin Sun (2), Di Sun (2), Xiaoqiang Hou (2), Wei Ye (2), Dimitris Christodoulou (1), Shirley Wen (2), Atefeh Kazeroonian (1)
(1) differentia biotech, (2) AliveX biotech, (3) Department of Rheumatology and Immunology, Peking University International Hospital, Beijing, China, (4) Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, China
Objectives: IgG4-related disease (IgG4-RD) is a rare and newly discovered chronic inflammatory disease regulated by the immune system that affects 1/100,000 people per year. It is believed that immune disorders, genetics, infection, hypersensitivity and environmental factors are related to the occurrence of the disease. The hallmarks of IgG4-RD include changes in levels of serum IgG4 and IgE, plasma cells and follicular helper T (Tfh) cells. Currently, mechanisms underlying the occurrence and progression of IgG4-RD are not fully understood. This lack of mechanistic understanding together with high inter-patient variability, have hindered development of biomarkers for early diagnosis as well as specific treatments for IgG4-RD[1,2,3].
Here, we aimed at developing a mechanistic disease model for IgG4-RD to provide a mechanistic understanding of the disease pathology and to guide identification of successful therapy targets.
Methods: We collected PBMC samples from eight IgG4-RD patients and generated single-cell RNA sequencing, flow cytometry and cytokine profiling data. We first investigated the transcriptomic landscape of patient samples in comparison with healthy controls, and identified cell types, proteins and inter-cellular communication patterns most relevant to IgG4-RD. We then employed our in-house knowledge graph to extract all the interactions between these cell types and cytokines, and accordingly developed an inter-cellular model topology for IgG4-RD.
Using CERENA[4] and AMICI[5] packages, we developed an ordinary differential equations (ODE) model based on 1st and 2nd-order moment equations[6]. We employed a multi-start gradient-based optimization algorithm implemented in PESTO[7] to estimate the model parameters using cytokine profiling, flow cytometry and scRNA-seq data from patients. Finally, we used this model to simulate therapy strategies for IgG4-RD, by in-silico perturbation of potential therapeutic targets in the model.
Results: Through transcriptomics analysis, we captured changes in the immune system composition between IgG4-RD patient and healthy controls: we found enrichment of plasma B cell and mDC populations, as well as reduction of pDC population in IgG4-RD patients. In addition, patients showed enriched cell-cell communication in terms of overall interaction strength as well as specific patterns such as enhanced IL6 and IL10 signaling. Motivated by these findings, we developed a first-of-its-kind inter-cellular disease model for IgG4-RD, comprising of more than thirty immune cell types and cytokines and more than 120 kinetic parameters. The interactions between model components mirror both direct cell contact, as well as cytokine-mediated, activation and inhibition. Our model captures the interplay between Tfh and activated B cells that is considered one of the dominant mechanisms underlying IgG4-RD.
Our disease model accurately reproduces the cell type percentages as well as the cytokine concentration data of patients. The estimated parameter values imply that although Tfh plays an important role in IgG4-RD, its rate of differentiation from activated CD4 T cells is much lower than other CD4 T subsets, such as Th2, iTreg and CD4_CTL. In terms of cytokine-mediated mechanisms, our results suggest that IFN1, IL21 and IL6 have similar contributions to the differentiation of Tfh from CD4 T cells, while IL4 is the dominant activating factor for B cells.
Conclusion: Our knowledge-empowered modeling and simulation platform enabled us to develop a first-ever mechanistic disease model for this rare autoimmune disease. Model-generated insights about the importance of various cytokines in the activation and inhibition of CD4 T and B cells enabled us to propose high potential therapy targets for IgG4-RD. Currently, we are incorporating cell population and cytokine concentration data of individual patients to estimate patient-specific parameter values to inform individual therapy targets/strategies and enable personalized medicine. This is of utmost importance in IgG4-RD given the high inter-patient variability. Our model can guide specification of desirable drug properties through perturbation analyses for the proposed cytokine targets. This model can also be utilized for QSP modeling by linking the proposed cytokine targets to the disease endpoint, such as the IgG4 serum level. In this way, our disease model can enhance success rates in the later stages of drug development for IgG4-RD.
References:
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