A data-driven Boolean model explains memory subsets and evolution in CD8+ T cell exhaustion

Geena V. Ildefonso, Stacey D. Finley

Preprint posted on 15 March 2023

Building upon a published mathematical model of T-cell exhaustion (Bolouri et al., 2020) to study the dynamics that give rise to CD8+ T-cell exhaustion.

Selected by Benjamin Dominik Maier

Categories: systems biology


T-cell exhaustion

While antigen stimulation is required for the proliferation and differentiation of naïve T-cells, a chronic stimulation leads to a stepwise and progressive loss of T-cell functions called T-cell exhaustion (TCE). This T-cell dysfunction occurs in chronic infections or cancer and is caused by a failure to clear antigens which leads to a prolonged exposure of T-cells to antigens. This chronic activation of T-cells results in the upregulation of inhibitory receptors like PD-1 suppressing T-cell activation and proliferation. Due to the inhibition, they become dysfunctional, losing their ability to produce (enough) cytokines and killing infected or cancerous cells.

Understanding of CD8+ T-cell differentiation and TCE

Recent advances allowed researchers to identify the key transcription factors responsible for CD8+ T-cell differentiation (Wherry et al., 2015; Seo et al., 2021) and led to the development of two models of TCE (Henning et al., 2018; Chen et al., 2018). According to the linear model, a gradual change in the transcriptional profile regulated by different signal strengths and durations results in a linear progression towards terminally differentiated CD8+ T-cells, white the circular model assumes that there are (multiple) oscillatory changes in the gene expression before T-cells reach their terminal state.

Figure 1: Proposed CD8+ T cell differentiation models result in unique gene expression patterns over time. (A) In the circular model of CD8+ T cell differentiation, naïve T cells (TN) cycle between memory (TM) and exhausted (TE) intermediates before reaching a terminal differentiated state (TT). (B) In the linear model, CD8+ T cells differentiate depending on the gradual acquisition of memory- or exhausted-associated genes. Figure and caption taken from Ildefonso & Finley (2023) bioRXiv, which was published under a Attribution-NoDerivatives 4.0 International License (CC BY-ND 4.0).


Due to the limited mechanistic understanding of the gene regulatory networks causing TCE, it is not yet possible to therapeutically prevent or reverse TCE. Additionally, Abdel-Hakeem et al. (2021) found that exhausted T-cells remain exhausted when they are no longer exposed to antigen stimulation suggesting that even after long-term remission from cancer, T-cells do not recover.

Literature derived, data-driven mathematical model of CD8+ T cell gene regulation

In this preprint, Ildefonso and Finley adapted a published Boolean model of TCE which was constructed using manual literature curation of key molecular interactions associated with TCE and public gene expression data of various states of T-cell differentiation (Bolouri et al., 2020). Using expression clustering and time course analysis, Bolouri et al. found that the expression of network genes can be used as a marker to classify genes as associated with effector function and irreversible exhaustion (EE) or the pro-memory and proliferative state (PP).

Boolean models

A boolean model is a type of mathematical model used to study complex systems. It uses a set of binary variables (True = 1, False = 0) and a set of logical rules (Boolean functions) that describe the interactions between the variables, to represent the state of the system. By updating the variable values according to these rules, the model can simulate the behaviour of the system over time. In comparison to rule-based and ordinary differential equation (ODE)-based mathematical models, logical Boolean models are simpler and more computationally efficient, making them ideal for studying large-scale biological networks with discrete variables and interactions. While ODE models predict system behaviour quantitatively, Boolean models are better suited for identifying key regulatory interactions and analysing system behaviour qualitatively.

Key Findings

Population-level dynamic show early activation of effector exhausted associated genes

Ildefonso and Finley investigated the dynamic interplay and the transcriptional changes during CD8+ T-cell differentiation and TCE. Therefore, they sampled 10,000 trajectories each representing a single cell from the model upon stimulation of the T-cell receptor and the interleukin network. When comparing the activation of EE and PP associated genes over time, they found that EE-associated genes exhibited a rapid-onset with an early peak dominating over PP-associated genes. Subsequently, more PP-genes were activated before both groups behaved similarly at low activation levels for the rest of the time course. The observation that cells reaching a terminal state early display high levels of EE-genes as opposed to PP-genes suggests that T-cell exhaustion may be caused by early transition events.

Identification of eight distinct end states of T-cell exhaustion

Next, the authors studied the different gene expression profiles and network structures regulating terminal T-cell differentiation resulting from the initial common set of network interactions. Clustering the terminal states revealed eight distinct networks with different combinations of PP and EE genes. Six of them were associated with T-cell exhaustion (99.9% of terminal cells), while two were associated with pro-memory T-cells (0.1%). Their respective transcriptional profiles were in line with previously described experimental observations.

Circular and linear model of differentiation

From each cluster, one trajectory (single cell) was extracted to study the changes of transcriptional profiles over time. Thus, the authors could determine which specific gene combinations result in the distinct terminal networks and whether their activation pattern follows the linear or the circular model. For five of the six exhausted T-cell profiles, an oscillatory pattern (circular model) between PP and EE states was observed before cells were reaching their terminal state. Both pro-memory state profiles as well as one T-cell exhaustion profile displayed a progressive gradual increase in gene expression suggesting that they follow the linear model.

NFATC1 repression of PD1 favours terminal pro-memory state

To study how the model behaves under perturbed conditions, the authors repressed a known activator of programmed cell death protein 1 (PD-1). PD-1 is a key receptor of T-cell exhaustion and a well-established cancer drug target. When simulating the model, the authors observed that the early dynamics of the cell population were mostly driven by high activation of memory-associated PP genes as opposed to EE genes earlier. Looking at long-term activation, low and similar activation profiles were again observed for both gene groups. Generally, cells took more time before reaching a terminal state and the majority of terminal states was characterised as pro-memory T-cells (54%) as compared to 0.1% for the wildtype. Moreover, the common modulators and the network architecture of the resulting terminal states were different compared to the wildtype ones. The two terminally exhausted states were caused by an alternative route allowing for PD-1 activation and a high expression of a tumour suppressor gene known to activate receptors inhibiting immune responses, respectively. With the exception of one pro-memory T-cell state, all perturbation profiles were following the circular model when studying individual trajectories.


The developed model helps to better understand the two proposed models of T-cell exhaustion and the specific gene profiles driving their dynamics. The model can be used as a framework for hypothesis generation and to predict T-cell differentiation (dynamics and terminal states) for cells with altered gene regulatory networks to ultimately computationally test and predict potential therapeutic targets.

In the future, one could transform the model into a system of ordinary differential equations (ODE) to study the dynamics continuously and more quantitatively (see Wittmann et al., 2009). Some genes, signalling cascades and readouts which were not represented in the model might be added to capture the cross-talk and account for CD4+ and NK-cells modulating Cd8+ T-cell exhaustion (Waggoner et al., 2011). Finally, transforming the model into a weak background model (see classification from Froehlich et al., 2015) could help to better represent network rewiring.

What I liked about this preprint

When I first read the manuscript, I was surprised how much insight the authors were able to generate from a fairly small and simple boolean model (e.g. no cross-talk, just key gene regulatory interactions, …) and how well their results agree with known experimental observations. Nowadays, many scientists (including me) often aim for bigger, more detailed models of biological processes and we do not question enough whether this is needed or beneficial. It reminds me a lot of Occam’s razor, the principle of parsimony in philosophy, suggesting that the simplest explanation is usually the best one.

It is always delightful to see authors incorporate and adapt previously published models to answer their own research questions especially given that many mathematical models are not reproducible – sometimes not even for the respective authors (Tiwari et al., 2021) despite community standards (Hucka et al., 2003; Le Novère et al., 2006).

Moreover, as my PhD project aims at developing (whole-cell) context-specific executable models of signalling processes, it is always helpful to learn how other researchers approach similar questions and to draw inspiration from their methods.


Abdel-Hakeem, M. S., Manne, S., Beltra, J.-C., Stelekati, E., Chen, Z., Nzingha, K., Ali, M.-A., Johnson, J. L., Giles, J. R., Mathew, D., Greenplate, A. R., Vahedi, G., & Wherry, E. J. (2021). Epigenetic scarring of exhausted T cells hinders memory differentiation upon eliminating chronic antigenic stimulation. Nature Immunology, 22(8),  1008–1019.

Bolouri, H., Young, M., Beilke, J., Johnson, R., Fox, B., Huang, L., … Ratushny, A. (2020). Integrative network modeling reveals mechanisms underlying T cell exhaustion. Scientific Reports, 10(1), 1915.

Chen, Y., Zander, R., Khatun, A., Schauder, D. M., & Cui, W. (2018). Transcriptional and Epigenetic Regulation of Effector and Memory CD8 T Cell Differentiation. Frontiers in Immunology, 9.

Cornberg, M., Kenney, L., Chen, A., Kim, S.-K., Dienes, H., Waggoner, S., … Selin, L. (2013). Clonal Exhaustion as a Mechanism to Protect Against Severe Immunopathology and Death from an Overwhelming CD8 T Cell Response. Frontiers in Immunology, 4.

Fröhlich, H., Bahamondez, G., Götschel, F., & Korf, U. (2015). Dynamic Bayesian Network Modeling of the Interplay between EGFR and Hedgehog Signaling. PLOS ONE, 10(11), e0142646).

He, X., & Xu, C. (2020). PD-1: A Driver or Passenger of T Cell Exhaustion? Molecular Cell, 77(5), 930–931.

Henning, A. N., Roychoudhuri, R., & Restifo, N. P. (2018). Epigenetic control of CD8+ T cell differentiation. Nature Reviews Immunology, 18(5), 340–356.

Hucka, M., Finney, A., Sauro, H. M., Bolouri, H., Doyle, J. C., Kitano, H., … and the rest of the SBML Forum: (2003). The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics, 19(4), 524–531.

Montacchiesi, G., & Pace, L. (2022). Epigenetics and CD8+ T cell memory*. Immunological Reviews, 305(1), 77–89.

Le Novère, N., Bornstein, B., Broicher, A., Courtot, M., Donizelli, M., Dharuri, H., … Hucka, M. (2006). BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucleic Acids Research, 34(Database issue), D689-91.

Seo, W., Jerin, C., & Nishikawa, H. (2021). Transcriptional regulatory network for the establishment of CD8+ T cell exhaustion. Experimental & Molecular Medicine, 53(2), 202–209.

Tiwari, K., Kananathan, S., Roberts, M. G., Meyer, J. P., Sharif Shohan, M. U., Xavier, A., … Malik-Sheriff, R. S. (2021). Reproducibility in systems biology modelling. Molecular Systems Biology, 17(2), e9982.

Waggoner, S. N., Cornberg, M., Selin, L. K., & Welsh, R. M. (2011). Natural killer cells act as rheostats modulating antiviral T cells. Nature, 481(7381), 394–398).

Wherry, E. J., & Kurachi, M. (2015). Molecular and cellular insights into T cell exhaustion. Nature Reviews Immunology, 15(8), 486–499.

Wittmann, D. M., Krumsiek, J., Saez-Rodriguez, J., Lauffenburger, D. A., Klamt, S., & Theis, F. J. (2009). Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling. BMC Systems Biology, 3(1).

Tags: boolean model, cd8+ t-cells, t-cell exhaustion, transcriptional profiles

Posted on: 10 April 2023 , updated on: 11 April 2023


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Author's response

The author team shared

Thank you for your interest in our pre-print!

Q1: When analysing the progression of T-cell states, you decided to look at representative trajectories of single cells (Fig. 4 & 7). Have you analysed the variability between trajectories of cells within the same group?

Great question. The results for each group analysis for the fractional activation profiles of PP and EE genes are now included in supplemental materials. The overall dynamics of each cell gene pattern profile for WT groups are comparable with each other; there is minimal variability seen. With the PD1 blockade, the model-predicted groups presented similar trends within each end state group; however, the timing and gene pattern activations have more variability between cells. This was interesting to see because experimentally it has been shown that some treatments to target PD1 have variable response (Ahn et al., 2018; Wei et al., 2013), though the cells still terminally differentiate into an exhausted state. We hypothesize that the oscillatory dynamics resulting from the PD1 blockade results in more dynamic gene pattern switching over the time course before reaching the terminally differentiated state.

Q2: Can you speculate about whether your results are specific to CD8+ T-cell exhaustion or would you expect a similar mechanism when it comes to CD4+ T-cell, B-cell and/or NK-cell exhaustion?

Although exhaustion in CD8+ T cells has been fairly well characterized, in-depth analyses of exhaustion in other immune types, including CD4+, B cells and NK cells, are lacking. Recent work in CD4+ T cells suggests that CD4+ T cells can become exhausted and contribute to promoting the antitumor immune response, even after checkpoint blockades (Miggelbrink et al., 2021). The framework for current understanding of CD4+ T cell exhaustion is generated from the more extensively studied CD8+ T cell exhaustion, both composed of similar suggested exhaustion markers, CTL4A, PD1, LAG3, and TIM3. We would hypothesize that CD4+ and CD8+ mechanisms of T cell exhaustion may be similar; however, their execution mechanisms (e.g., timing and expression of particular genes) are different. In regard to B cells, they have the ability to undergo exhaustion; however, the markers and characteristics are widely different from CD8+ T cells. NK cells also have the ability to undergo exhaustion in response to chronic infections and cancer, exhibiting a similar exhausted status with T cells, displaying poor effector function. A notable difference between NK and T cell exhaustion is expression of inhibitory receptors NKG2A, TIM3, and CD96, driving exhaustion. We would not expect similar mechanisms for B and NK cells; however, the results in this study could be adapted and expanded to study the mechanisms of B and NK cell exhaustion.

Q3: Cornberg et al. (2013) suggested that T-cell exhaustion is not only a dysfunctional state exploited by tumours as an immune evasion strategy but also serves an immunoprotective role against severe immunopathology and death. How can this be reflected in a potential immunotherapy and what are the implications on preventing/reversing T-cell exhaustion?

That is correct. T cell exhaustion is not only a dysfunctional state but is suggested to be beneficial, depending on the context. It has recently been shown that PD1, a known marker of T cell exhaustion is required for optimal memory development (Pauken et al., 2020), suggesting that in order to achieve positive results from potential immunotherapy, it is crucial to understand the interplay of T cell exhaustion and memory markers.

Q4: Several researchers question whether PD-1 is really a driver of T-cell exhaustion or whether it is just a common passenger based on studies that indicate that “PD-1 primarily affects effector function of T-cells but does not induce an exhaustion program at the early stage of T cell activation” (He and Xu, 2020). I wondered whether your model could be adapted to study future research questions outlined in He and Xu (2020), namely:

a) Investigate whether transient PD-1 signalling could control effector function rather than induce exhaustion

In our study, in silico PD1 blockade suggests that PD1 signaling controls effector function to promote optimal terminal memory instead of T cell exhaustion solely. We can compare the oscillatory dynamics as transient (or short-lived) expression for PD1, which we show is able to better control effector function rather than induce exhaustion at the early stages after T cell activation.

b) Study the synergistic effect of PD-1 and other co-inhibitory receptors (i.a. TIM3 and LAG3)

The model is modular, which allows for straightforward addition of other rules representing additional genes and their activation or inhibition interactions. This is a great point of a potential future study to examine the synergy between PD1 and other co-inhibitory receptors and their effect on downstream gene expression.

Q5: Even though it is widely agreed that epigenetic regulatory molecules play a crucial role for TCE, the relationship and mechanism between transcription factors and epigenetic regulatory molecules remains poorly understood. Could your model be adapted so that one could reveal the functional interaction between these two components?

Yes! Very important point, a recent review (Henning et al., 2018) evaluated the contributions of both epigenetic and transcription factors to understand CD8+ T cell exhaustion. Historically, Boolean models have been exclusively utilized to study gene regulatory networks, because of the switch like behavior that gene expression in cells display (Schwab et al., 2020). Epigenetic regulatory molecules have binding kinetics that cannot be captured with Boolean modeling. In order to model the functional interaction between epigenetic and transcription factors, we would need to take a multi-scale modeling approach. Epigenetic and transcription factors individually are on different scales; therefore, ordinary differential equation (ODE) modeling may be better suited to be able to incorporate the dynamic binding affinities involved at the epigenetic level with changes at the transcriptional level.

Q6: How would you experimentally validate your results and what wet lab experiment/data would be needed to further improve the model?

It would be interesting to experimentally validate the predicted results of the in silico PD1 blockade intervention. We test the impact of PD1 activation through repression of one of the activators of PD1, NFATC1, which interestingly resulted in oscillatory dynamics of PD1 activation over time. Experimentally, one way to test this prediction would be to use either an siRNA or shRNA for NFATC1, which would either knockout or knockdown NFATC1, respectively, and examine the expression levels of PD1 over time. Alternatively, it may be possible to target NFATC1 using gene editing approaches such as CRISPR. Since NFATC1 is an activator of PD1, it would be interesting to examine experimentally if we see similar oscillatory dynamics of PD1 expression and possibly use the avenue of targeting PD1 through NFATC1 therapeutically.


Ahn, E., Araki, K., Hashimoto, M., Li, W., Riley, J.L., Cheung, J., Sharpe, A.H., Freeman, G.J., Irving, B.A., and Ahmed, R. (2018). Role of PD-1 during effector CD8 T cell differentiation. Proc. Natl. Acad. Sci. U. S. A. 115, 4749–4754.

Henning, A.N., Roychoudhuri, R., and Restifo, N.P. (2018). Epigenetic control of CD8+ T cell differentiation. Nat. Rev. Immunol. 2018 185 18, 340–356.

Miggelbrink, A.M., Jackson, J.D., Lorrey, S.J., Srinivasan, E.S., Waibl-Polania, J., Wilkinson, D.S., and Fecci, P.E. (2021). CD4 T-Cell Exhaustion: Does It Exist and What Are Its Roles in Cancer? Clin. Cancer Res. 27, 5742.

Pauken, K.E., Godec, J., Odorizzi, P.M., Brown, K.E., Yates, K.B., Ngiow, S.F., Burke, K.P., Maleri, S., Grande, S.M., Francisco, L.M., et al. (2020). The PD-1 Pathway Regulates Development and Function of Memory CD8+ T Cells following Respiratory Viral Infection. Cell Rep. 31, 107827.

Schwab, J.D., Kühlwein, S.D., Ikonomi, N., Kühl, M., and Kestler, H.A. (2020). Concepts in Boolean network modeling: What do they all mean? Comput. Struct. Biotechnol. J. 18, 571–582.

Wei, F., Zhong, S., Ma, Z., Kong, H., Medvec, A., Ahmed, R., Freeman, G.J., Krogsgaard, M., and Riley, J.L. (2013). Strength of PD-1 signaling differentially affects T-cell effector functions. Proc. Natl. Acad. Sci. U. S. A. 110, E2480–E2489.


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