A Virtual Culture of CD4+ T Lymphocytes

CD4+ T Cell Regulatory Network Underlies the Decrease in Th1 and the Increase in Anergic and Th17 Subsets in Severe COVID-19

In this model we use a dynamic and multistable Boolean regulatory network to provide a mechanistic explanation of the lymphopenia and dysregulation of CD4+ T cell subsets in COVID-19 and provide therapeutic targets. Using a previous model, the cytokine micro-environments found in mild, moderate, and severe COVID-19 with and without TGF-β and IL-10 was we simulated. It shows that as the severity of the disease increases, the number of antiviral Th1 cells decreases, while the the number of Th1-like regulatory and exhausted cells and the proportion between Th1 and Th1R cells increases. The addition of the regulatory cytokines TFG-β and IL-10 makes the Th1 attractor unstable and favors the Th17 and regulatory subsets. This is associated with the contradictory signals in the micro-environment that activate SOCS proteins that block the signaling pathways. Furthermore, it determined four possible therapeutic targets that increase the Th1 compartment in severe COVID-19: the activation of the IFN-γ pathway, or the inhibition of TGF-β or IL-10 pathways or SOCS1 protein; from these, inhibiting SOCS1 has the lowest number of predicted collateral effects. Finally, a tool is provided that allows simulations of specific cytokine environments and predictions of CD4 T cell subsets and possible interventions, as well as associated secondary effects.

A multi-approach and multi-scale platform to model CD4+ T cells responding to infections

Immune responses rely on a complex adaptive system in which the body and infections interact at multiple scales and in different compartments. We developed a modular model of CD4+ T cells, which uses four modeling approaches to integrate processes at three spatial scales in different tissues. In each cell, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models. Cell population dynamics are described by an agent-based model and systemic cytokine concentrations by ordinary differential equations. A Monte Carlo simulation algorithm allows information to flow efficiently between the four modules by separating the time scales. Such modularity improves computational performance and versatility and facilitates data integration. We validated our technology by reproducing known experimental results, including differentiation patterns of CD4+ T cells triggered by different combinations of cytokines, metabolic regulation by IL2 in these cells, and their response to influenza infection. In doing so, we added multi-scale insights to single-scale studies and demonstrated its predictive power by discovering switch-like and oscillatory behaviors of CD4+ T cells that arise from nonlinear dynamics interwoven across three scales. We identified the inflamed lymph node’s ability to retain naive CD4+ T cells as a key mechanism in generating these emergent behaviors. We envision our model and the generic framework encompassing it to serve as a tool for understanding cellular and molecular immunological problems through the lens of systems immunology.

CD4+ T cells are a key part of the adaptive immune system. They differentiate into different phenotypes to carry out different functions. They do so by secreting molecules called cytokines to regulate other immune cells. Multi-scale modeling can potentially explain their emergent behaviors by integrating biological phenomena occurring at different spatial (intracellular, cellular, and systemic), temporal, and organizational scales (signal transduction, gene regulation, metabolism, cellular behaviors, and cytokine transport). We built a computational platform by combining disparate modeling frameworks (compartmental ordinary differential equations, agent-based modeling, Boolean network modeling, and constraint-based modeling). We validated the platform’s ability to predict CD4+ T cells’ emergent behaviors by reproducing their differentiation patterns, metabolic regulation, and population dynamics in response to influenza infection. We then used it to predict and explain novel switch-like and oscillatory behaviors for CD4+ T cells. On the basis of these results, we believe that our multi-approach and multi-scale platform will be a valuable addition to the systems immunology toolkit. In addition to its immediate relevance to CD4+ T cells, it also has the potential to become the foundation of a virtual immune system.

Modeling the Th17 and Tregs Paradigm: Implications for Cancer Immunotherapy

CD4 + T cell differentiation is governed by gene regulatory and metabolic networks, with both networks being highly interconnected and able to adapt to external stimuli. Th17 and Tregs differentiation networks play a critical role in cancer, and their balance is affected by the tumor microenvironment (TME). Factors from the TME mediate recruitment and expansion of Th17 cells, but these cells can act with pro or anti-tumor immunity. Tregs cells are also involved in tumor development and progression by inhibiting antitumor immunity and promoting immunoevasion. Due to the complexity of the underlying molecular pathways, the modeling of biological systems has emerged as a promising solution for better understanding both CD4 + T cell differentiation and cancer cell behavior. In this review, we present a context-dependent vision of CD4 + T cell transcriptomic and metabolic network adaptability. We then discuss CD4 + T cell knowledge-based models to extract the regulatory elements of Th17 and Tregs differentiation in multiple CD4 + T cell levels. We highlight the importance of complementing these models with data from omics technologies such as transcriptomics and metabolomics, in order to better delineate existing Th17 and Tregs bifurcation mechanisms. We were able to recompilate promising regulatory components and mechanisms of Th17 and Tregs differentiation under normal conditions, which we then connected with biological evidence in the context of the TME to better understand CD4 + T cell behavior in cancer. From the integration of mechanistic models with omics data, the transcriptomic and metabolomic reprograming of Th17 and Tregs cells can be predicted in new models with potential clinical applications, with special relevance to cancer immunotherapy.

Leveraging Prior Knowledge to Recover Characteristic Immune Regulatory Motifs in Gulf War Illness

Potentially linked to the basic physiology of stress response, Gulf War Illness (GWI) is a debilitating condition presenting with complex immune, endocrine and neurological symptoms. Here we interrogate the immune response to physiological stress by measuring 16 blood-borne immune markers at 8 time points before, during and after maximum exercise challenge in n = 12 GWI veterans and n = 11 healthy veteran controls deployed to the same theater. Immune markers were combined into functional sets and the dynamics of their joint expression described as classical rate equations. These empirical networks were further informed structurally by projection onto prior knowledge networks mined from the literature. Of the 49 literature-informed immune signaling interactions, 21 were found active in the combined exercise response data. However, only 4 signals were common to both subject groups while 7 were uniquely active in GWI and 10 uniquely active in healthy veterans. Feedforward mediation of IL-23 and IL-17 by IL-6 and IL-10 emerged as distinguishing control elements that were characteristically active in GWI versus healthy subjects. Simulated restructuring of the regulatory circuitry in GWI as a result of applying an IL-6 receptor antagonist in combination with either a Th1 (IL-2, IFNγ, and TNFα) or IL-23 receptor antagonist predicted a partial rescue of immune response elements previously associated with illness severity. Overall, results suggest that pharmacologically altering the topology of the immune response circuitry identified as active in GWI can inform on strategies that while not curative, may nonetheless deliver a reduction in symptom burden. A lasting and more complete remission in GWI may therefore require manipulation of a broader physiology, namely one that includes endocrine oversight of immune function.

An Integrative Network Modeling Approach to T CD4 Cell Activation

The adaptive immune response is initiated by the interaction of the T cell antigen receptor/CD3 complex (TCR) with a cognate peptide bound to a MHC molecule. This interaction, along with the activity of co-stimulatory molecules and cytokines in the microenvironment, enables cells to proliferate and produce soluble factors that stimulate other branches of the immune response for inactivation of infectious agents. The intracellular activation signals are reinforced, amplified and diversified by a complex network of biochemical interactions, and includes the activity of molecules that modulate the activation process and stimulate the metabolic changes necessary for fulfilling the cell energy demands. We present an approach to the analysis of the main early signaling events of T cell activation by proposing a concise 46-node hybrid Boolean model of the main steps of TCR and CD28 downstream signaling, encompassing the activity of the anergy factor Ndrg1, modulation of activation by CTLA-4, and the activity of the nutrient sensor AMPK as intrinsic players of the activation process. The model generates stable states that reflect the overcoming of activation signals and induction of anergy by the expression of Ndrg1 in the absence of co-stimulation. The model also includes the induction of CTLA-4 upon activation and its competition with CD28 for binding to the co-stimulatory CD80/86 molecules, leading to stable states that reflect the activation arrest. Furthermore, the model integrates the activity of AMPK to the general pathways driving differentiation to functional cell subsets (Th1, Th2, Th17, and Treg). Thus, the network topology incorporates basic mechanism associated to activation, regulation and induction of effector cell phenotypes. The model puts forth a conceptual framework for the integration of functionally relevant processes in the analysis of the T CD4 cell function.

From Discrete to Continuous Modeling of Lymphocyte Development and Plasticity in Chronic Diseases

The molecular events leading to differentiation, development, and plasticity of lymphoid cells have been subject of intense research due to their key roles in multiple pathologies, such as lymphoproliferative disorders, tumor growth maintenance and chronic diseases. The emergent roles of lymphoid cells and the use of high-throughput technologies have led to an extensive accumulation of experimental data allowing the reconstruction of gene regulatory networks (GRN) by integrating biochemical signals provided by the microenvironment with transcriptional modules of lineage-specific genes. Computational modeling of GRN has been useful for the identification of molecular switches involved in lymphoid specification, prediction of microenvironment-dependent cell plasticity, and analyses of signaling events occurring downstream the activation of antigen recognition receptors. Among most common modeling strategies to analyze the dynamical behavior of GRN, discrete dynamic models are widely used for their capacity to capture molecular interactions when a limited knowledge of kinetic parameters is present. However, they are less powerful when modeling complex systems sensitive to biochemical gradients. To compensate it, discrete models may be transformed into regulatory networks that includes state variables and parameters varying within a continuous range. This approach is based on a system of differential equations dynamics with regulatory interactions described by fuzzy logic propositions. Here, we discuss the applicability of this method on modeling of development and plasticity processes of adaptive lymphocytes, and its potential implications in the study of pathological landscapes associated to chronic diseases.

Shenmai injection improves the postoperative immune function of papillary thyroid carcinoma patients by inhibiting differentiation into Treg cells via miR-103/GPER1 axis

Shenmai injection (SMI) is increasingly used in tumor combination therapy, devoting to enhancing anti-tumor effects and reducing the toxicity of chemotherapy drugs. This study aimed to explore the role of SMI in papillary thyroid carcinoma (PTC) treatment. Flow cytometry was used to examine Treg cells percentage in CD4 + T cells. The expression of RNA and protein was analyzed by quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot, respectively. Inducers were used to stimulate CD4 + T cells to differentiate into Treg cells. The interaction between miR-103 and G protein-coupled estrogen receptor 1 (GPER1) was confirmed with the dual luciferase assays. Cell transfection and recombinant plasmids were used to achieve endogenous expression. Compared with patients not treated with ¹³¹ I, the Treg cells percentage and Foxp3 expression were clearly increased in patients with ¹³¹ I radiotherapy, just the opposite in SMI combination therapy. SMI inhibited the differentiation of CD4 + T cells into Treg cells. Aberrant expression of miR-103 and GPER1 induced by ¹³¹ I was reversed by SMI and ¹³¹ I combination therapy. GPER1 was negatively regulated by miR-103 and SMI inhibits the differentiation of CD4 + T cells into Treg cells via miR-103/GPER1 axis, which improves the postoperative immunological function of PTC patients with ¹³¹ I radiotherapy.

Diverse continuum of CD4+ T-cell states is determined by hierarchical additive integration of cytokine signals

During cell differentiation, progenitor cells integrate signals from their environment that guide their development into specialized phenotypes. The ways by which cells respond to complex signal combinations remain difficult to analyze and model. To gain additional insight into signal integration, we systematically mapped the response of CD4⁺ T cells to a large number of input cytokine combinations that drive their differentiation. We find that, in response to varied input combinations, cells differentiate into a continuum of cell fates as opposed to a limited number of discrete phenotypes. Input cytokines hierarchically influence the cell population, with TGFβ being most dominant followed by IL-6 and IL-4. Mathematical modeling explains these results using additive signal integration within hierarchical groups of input cytokine combinations and correctly predicts cell population response to new input conditions. These findings suggest that complex cellular responses can be effectively described using a segmented linear approach, providing a framework for prediction of cellular responses to new cytokine combinations and doses, with implications to fine-tuned immunotherapies.

Disruption of circulating CD4+ T-lymphocyte subpopulations in psoriasis patients is ameliorated by narrow-band UVB therapy

Narrow-band UVB (NB-UVB) therapy is widely used in the treatment of psoriasis; however, its precise mechanism is still unclear. To investigate the circulating CD4(+) T-lymphocyte subpopulations in psoriasis patients before and after NB-UVB, thus providing new insights into the mechanism of NB-UVB in the treatment of psoriasis. We performed NB-UVB treatments for psoriasis patients (n = 30) and used flow cytometry, real-time PCR, and ELISA for the detection of circulating CD4(+) T-lymphocyte subpopulations. The results were compared with healthy controls (n = 20) as well. We found increased circulating T helper 1 (Th1) and Th17 cell levels as well as decreased circulating regulatory T cells (Treg) levels compared to healthy controls. Additionally, there was a positive correlation between the percentage of circulating Th17 cells and Psoriasis Area and Severity Index (PASI) score. Furthermore, the percentage of circulating Th17 cells was negatively correlated with the Treg cells which led to an imbalance of Th17/Treg. NB-UVB therapy significantly reduced circulating Th1and Th17 cell levels while increasing Treg cell levels. These findings indicate that the overexpression of Th1 and Th17 cells together with the imbalance of Th17/Treg cells may play an important role in the pathogenesis of psoriasis. The mechanism of NB-UVB in the treatment of psoriasis may be through the inhibition of Th1 and Th17 cell immune response as well as the promotion of Treg cell immune response, thus ameliorating the disorder of circulating CD4(+) T-lymphocyte subsets.

A dynamical model of the regulatory network controlling lymphopoiesis

Due to the large number of diseases associated to a malfunction of the hematopoietic system, there is an interest in knowing the molecular mechanisms controlling the differentiation of blood cell lineages. However, the structure and dynamical properties of the underlying regulatory network controlling this process is not well understood. This manuscript presents a regulatory network of 81 nodes, representing several types of molecules that regulate each other during the process of lymphopoiesis. The regulatory interactions were inferred mostly from published experimental data. However, 15 out of 159 regulatory interactions are predictions arising from the present study. The network is modelled as a continuous dynamical system, in the form of a set of differential equations. The dynamical behaviour of the model describes the differentiation process from the common lymphocyte precursor (CLP) to several mature B and T cell types; namely, plasma cell (PC), cytotoxic T lymphocyte (CTL), T helper 1 (Th1), Th2, Th17, and T regulatory (Treg) cells. The model qualitatively recapitulates key cellular differentiation events, being able to represent the directional and branched nature of lymphopoiesis, going from a multipotent progenitor to fully differentiated cell types.

A Minimal Regulatory Network of Extrinsic and Intrinsic Factors Recovers Observed Patterns of CD4+ T Cell Differentiation and Plasticity

CD4+ T cells orchestrate the adaptive immune response in vertebrates. While both experimental and modeling work has been conducted to understand the molecular genetic mechanisms involved in CD4+ T cell responses and fate attainment, the dynamic role of intrinsic (produced by CD4+ T lymphocytes) versus extrinsic (produced by other cells) components remains unclear, and the mechanistic and dynamic understanding of the plastic responses of these cells remains incomplete. In this work, we studied a regulatory network for the core transcription factors involved in CD4+ T cell-fate attainment. We first show that this core is not sufficient to recover common CD4+ T phenotypes. We thus postulate a minimal Boolean regulatory network model derived from a larger and more comprehensive network that is based on experimental data. The minimal network integrates transcriptional regulation, signaling pathways and the micro-environment. This network model recovers reported configurations of most of the characterized cell types (Th0, Th1, Th2, Th17, Tfh, Th9, iTreg, and Foxp3-independent T regulatory cells). This transcriptional-signaling regulatory network is robust and recovers mutant configurations that have been reported experimentally. Additionally, this model recovers many of the plasticity patterns documented for different T CD4+ cell types, as summarized in a cell-fate map. We tested the effects of various micro-environments and transient perturbations on such transitions among CD4+ T cell types. Interestingly, most cell-fate transitions were induced by transient activations, with the opposite behavior associated with transient inhibitions. Finally, we used a novel methodology was used to establish that T-bet, TGF-β and suppressors of cytokine signaling proteins are keys to recovering observed CD4+ T cell plastic responses. In conclusion, the observed CD4+ T cell-types and transition patterns emerge from the feedback between the intrinsic or intracellular regulatory core and the micro-environment. We discuss the broader use of this approach for other plastic systems and possible therapeutic interventions.

A Mathematical Framework for Understanding Four-Dimensional Heterogeneous Differentiation of CD4+ T Cells

At least four distinct lineages of CD4+ T cells play diverse roles in the immune system. Both in vivo and in vitro, naïve CD4+ T cells often differentiate into a variety of cellular phenotypes. Previously, we developed a mathematical framework to study heterogeneous differentiation of two lineages governed by a mutual-inhibition motif. To understand heterogeneous differentiation of CD4+ T cells involving more than two lineages, we present here a mathematical framework for the analysis of multiple stable steady states in dynamical systems with multiple state variables interacting through multiple mutual-inhibition loops. A mathematical model for CD4+ T cells based on this framework can reproduce known properties of heterogeneous differentiation involving multiple lineages of this cell differentiation system, such as heterogeneous differentiation of TH1-TH2, TH1-TH17 and iTReg-TH17 under single or mixed types of differentiation stimuli. The model shows that high concentrations of differentiation stimuli favor the formation of phenotypes with co-expression of lineage-specific master regulators.

Computational modeling of heterogeneity and function of CD4+ T cells

The immune system is composed of many different cell types and hundreds of intersecting molecular pathways and signals. This large biological complexity requires coordination between distinct pro-inflammatory and regulatory cell subsets to respond to infection while maintaining tissue homeostasis. CD4+ T cells play a central role in orchestrating immune responses and in maintaining a balance between pro- and anti- inflammatory responses. This tight balance between regulatory and effector reactions depends on the ability of CD4+ T cells to modulate distinct pathways within large molecular networks, since dysregulated CD4+ T cell responses may result in chronic inflammatory and autoimmune diseases. The CD4+ T cell differentiation process comprises an intricate interplay between cytokines, their receptors, adaptor molecules, signaling cascades and transcription factors that help delineate cell fate and function. Computational modeling can help to describe, simulate, analyze, and predict some of the behaviors in this complicated differentiation network. This review provides a comprehensive overview of existing computational immunology methods as well as novel strategies used to model immune responses with a particular focus on CD4+ T cell differentiation.

MiR-142-3p attenuates the migration of CD4⁺ T cells through regulating actin cytoskeleton via RAC1 and ROCK2 in arteriosclerosis obliterans

The migration of CD4⁺ T cells plays an important role in arteriosclerosis obliterans (ASO). However, the molecular mechanisms involved in CD4⁺ T cell migration are still unclear. The current study is aimed to determine the expression change of miR-142-3p in CD4⁺ T cells from patients with ASO and investigate its role in CD4⁺ T cell migration as well the potential mechanisms involved. We identified by qRT-PCR and in situ hybridization that the expression of miR-142-3p in CD4⁺ T cells was significantly down-regulated in patients with ASO. Chemokine (C-X-C motif) ligand 12 (CXCL12), a common inflammatory chemokine under the ASO condition, was able to down-regulate the expression of miR-142-3p in cultured CD4⁺ T cells. Up-regulation of miR-142-3p by lentivirus-mediated gene transfer had a strong inhibitory effect on CD4⁺ T cell migration both in cultured human cells in vitro and in mouse aortas and spleens in vivo. RAC1 and ROCK2 were identified to be the direct target genes in human CD4⁺ T cells, which are further confirmed by dual luciferase assay. MiR-142-3p had strong regulatory effects on actin cytoskeleton as shown by the actin staining in CD4⁺ T cells. The results suggest that the expression of miR-142-3p is down-regulated in CD4⁺ T cells from patients with ASO. The down-regulation of miR-142-3p could increase the migration of CD4⁺ T cells to the vascular walls by regulation of actin cytoskeleton via its target genes, RAC1 and ROCK2.