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. 2024 Feb 26:15:1321309.
doi: 10.3389/fimmu.2024.1321309. eCollection 2024.

An integrative mechanistic model of thymocyte dynamics

Victoria Kulesh  1   2 , Kirill Peskov  1   2   3   4 , Gabriel Helmlinger  5 , Gennady Bocharov  2   6   7
Affiliations

An integrative mechanistic model of thymocyte dynamics

Victoria Kulesh et al. Front Immunol. .

Abstract

Background: The thymus plays a central role in shaping human immune function. A mechanistic, quantitative description of immune cell dynamics and thymic output under homeostatic conditions and various patho-physiological scenarios are of particular interest in drug development applications, e.g., in the identification of potential therapeutic targets and selection of lead drug candidates against infectious diseases.

Methods: We here developed an integrative mathematical model of thymocyte dynamics in human. It incorporates mechanistic features of thymocyte homeostasis as well as spatial constraints of the thymus and considerations of age-dependent involution. All model parameter estimates were obtained based on published physiological data of thymocyte dynamics and thymus properties in mouse and human. We performed model sensitivity analyses to reveal potential therapeutic targets through an identification of processes critically affecting thymic function; we further explored differences in thymic function across healthy subjects, multiple sclerosis patients, and patients on fingolimod treatment.

Results: We found thymic function to be most impacted by the egress, proliferation, differentiation and death rates of those thymocytes which are most differentiated. Model predictions also showed that the clinically observed decrease in relapse risk with age, in multiple sclerosis patients who would have discontinued fingolimod therapy, can be explained mechanistically by decreased thymic output with age. Moreover, we quantified the effects of fingolimod treatment duration on thymic output.

Conclusions: In summary, the proposed model accurately describes, in mechanistic terms, thymic output as a function of age. It may be further used to perform predictive simulations of clinically relevant scenarios which combine specific patho-physiological conditions and pharmacological interventions of interest.

Keywords: immune system; mechanistic model; thymic involution; thymopoiesis; thymus.

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Conflict of interest statement

Author KP is an employee and shareholder of Modeling & Simulation Decisions FZ - LLC. Author GH is an employee of Biorchestra US, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that this study received funding from Modeling & Simulation Decisions FZ - LLC. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
Scheme of the mechanistic model describing human thymocyte dynamics. (A) Model scheme, thymocyte homeostasis; (B) Model development steps describing thymus involution.
Figure 2
Figure 2
Global sensitivity analysis on a physiologically plausible set of parameters using a PRCC-based method.
Figure 3
Figure 3
Validation of the thymocyte dynamics model. (A) Thymus wet weight [solid line and shaded area represents the model predicted mean with 95% CI uncertainty band, respectively, and symbols and error bars stand for the mean and 95% CI of clinical data (6)]; (B) Relative volume of thymic cortex, normalized to initial value, derived from the modified function in Equation 7 [solid line and shaded area represents the model predicted mean with 95% CI uncertainty band, respectively, and symbols and error bars stand for the mean and 95% CI of clinical data (6)]; (C) Relative proportions of DN, DP and SP cells [solid line and shaded area represents the model predicted mean with 95% CI uncertainty band, respectively, symbols stand for clinical data (–33) and black solid line with error bars represents moving average with 95% CI (10 years step)]; (D) Absolute values of total thymocyte numbers [solid line and shaded area represents the model predicted mean with 95% CI uncertainty band, respectively, and symbols and error bars stand for mean and 95% CI of clinical data derived from (25, 6); see also Supplementary Table 1 ].
Figure 4
Figure 4
Predictions of the thymocyte dynamics model. (A) Absolute values of all thymocyte counts and of cortex and medulla thymocytes (solid line and shaded area represents the model predicted mean with 95% CI uncertainty band, respectively); (B) Absolute values of DN, DP and SP thymocyte populations (solid line and shaded area represents the model predicted mean with 95% CI uncertainty band, respectively); (C) Predicted mean of thymic output for healthy subjects, subjects with early-onset thymus involution, and subjects with early-onset thymus involution and blocked thymocytes egress (dashed line – thymic output threshold for fingolimod discontinuation); (D) Predicted mean of SP cell counts, expressed as changes from baseline, for healthy subjects, subjects with early-onset thymus involution, and subjects with early-onset thymus involution and blocked thymocytes egress; (E) Predicted thymic output rebound after thymocyte egress restoration in 20-year old subjects with early-onset thymus involution.
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The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Academic leadership program Priority 2030 proposed by the Federal State Autonomous Educational Institution of Higher Education I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), the Ministry of Science and Higher Education of the Russian Federation (Agreement 075-10-2021-093, Project MMD-RND-2266) and Modeling & Simulation Decisions FZ - LLC, Dubai, UAE. GB was supported by the Moscow Center of Fundamental and Applied Mathematics (agreement with the Ministry of Education and Science of the Russian Federation No. 075-15-2022-286). VK and KP were supported by the Russian Science Foundation (Grant Number 23-71-10051).