Modeling the effector - regulatory T cell cross-regulation reveals the intrinsic character of relapses in Multiple Sclerosis

Mathematical modeling in autoimmune diseases: from theory to clinical application

The research & development (R&D) of novel therapeutic agents for the treatment of autoimmune diseases is challenged by highly complex pathogenesis and multiple etiologies of these conditions. The number of targeted therapies available on the market is limited, whereas the prevalence of autoimmune conditions in the global population continues to rise. Mathematical modeling of biological systems is an essential tool which may be applied in support of decision-making across R&D drug programs to improve the probability of success in the development of novel medicines. Over the past decades, multiple models of autoimmune diseases have been developed. Models differ in the spectra of quantitative data used in their development and mathematical methods, as well as in the level of "mechanistic granularity" chosen to describe the underlying biology. Yet, all models strive towards the same goal: to quantitatively describe various aspects of the immune response. The aim of this review was to conduct a systematic review and analysis of mathematical models of autoimmune diseases focused on the mechanistic description of the immune system, to consolidate existing quantitative knowledge on autoimmune processes, and to outline potential directions of interest for future model-based analyses. Following a systematic literature review, 38 models describing the onset, progression, and/or the effect of treatment in 13 systemic and organ-specific autoimmune conditions were identified, most models developed for inflammatory bowel disease, multiple sclerosis, and lupus (5 models each). ≥70% of the models were developed as nonlinear systems of ordinary differential equations, others - as partial differential equations, integro-differential equations, Boolean networks, or probabilistic models. Despite covering a relatively wide range of diseases, most models described the same components of the immune system, such as T-cell response, cytokine influence, or the involvement of macrophages in autoimmune processes. All models were thoroughly analyzed with an emphasis on assumptions, limitations, and their potential applications in the development of novel medicines.

Excitable dynamics of flares and relapses in autoimmune diseases

Many autoimmune disorders exhibit flares in which symptoms erupt and then decline, as exemplified by multiple sclerosis (MS) in its relapsing-remitting form. Existing mathematical models of autoimmune flares often assume regular oscillations, failing to capture the stochastic and non-periodic nature of flare-ups. We suggest that autoimmune flares are driven by excitable dynamics triggered by stochastic events auch as stress, infection and other factors. Our minimal model, involving autoreactive and regulatory T-cells, demonstrates this concept. Autoimmune response initiates antigen-induced expansion through positive feedback, while regulatory cells counter the autoreactive cells through negative feedback. The model explains the decrease in MS relapses during pregnancy and the subsequent surge postpartum, based on lymphocyte dynamics. Additionally, it identifies potential therapeutic targets, predicting significant reduction in relapse rate from mild adjustments of regulatory T cell activity or production. These findings indicate that excitable dynamics may underlie flare-ups across various autoimmune disorders, potentially informing treatment strategies.

The effect of chemotaxis on T-cell regulatory dynamics

Autoimmune diseases, such as Multiple Sclerosis, are often modelled through the dynamics of T-cell interactions. However, the spatial aspect of such diseases, and how dynamics may result in spatially heterogeneous outcomes, is often overlooked. We consider the effects of diffusion and chemotaxis on T-cell regulatory dynamics using a three-species model of effector and regulator T-cell populations, along with a chemical signalling agent. While diffusion alone cannot lead to instability and spatial patterning, the inclusion of chemotaxis can result in multiple forms of instability that produce highly complicated spatiotemporal behaviour. The parameter regimes in which different instabilities occur are determined through linear stability analysis and the full dynamics is explored through numerical simulation.

Could Mathematics be the Key to Unlocking the Mysteries of Multiple Sclerosis?

Multiple sclerosis (MS) is an autoimmune, neurodegenerative disease that is driven by immune system-mediated demyelination of nerve axons. While diseases such as cancer, HIV, malaria and even COVID have realised notable benefits from the attention of the mathematical community, MS has received significantly less attention despite the increasing disease incidence rates, lack of curative treatment, and long-term impact on patient well-being. In this review, we highlight existing, MS-specific mathematical research and discuss the outstanding challenges and open problems that remain for mathematicians. We focus on how both non-spatial and spatial deterministic models have been used to successfully further our understanding of T cell responses and treatment in MS. We also review how agent-based models and other stochastic modelling techniques have begun to shed light on the highly stochastic and oscillatory nature of this disease. Reviewing the current mathematical work in MS, alongside the biology specific to MS immunology, it is clear that mathematical research dedicated to understanding immunotherapies in cancer or the immune responses to viral infections could be readily translatable to MS and might hold the key to unlocking some of its mysteries.

Modeling the interaction between donor-derived regulatory T cells and effector T cells early after allogeneic hematopoietic stem cell transplantation

While allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a potential curative therapy against hematological malignancies, modulation of donor T cell alloreactivity is required to enhance the graft-versus-leukemia (GVL) effect and control graft-versus-host-disease (GVHD) after allo-HSCT. Donor-derived regulatory CD4⁺CD25⁺Foxp3⁺ T cells (Tregs) play a central role in establishing of immune tolerance after allo-HSCT. They could be a key target to be modulated for increasing the GVL effect and control of GVHD. We constructed an ordinary differential equation model incorporating bidirectional interactions between Tregs and effector CD4⁺ T cells (Teffs) as a mechanism for control of Treg cell concentration. The goal is to elucidate how the interaction between Tregs and Teffs is modulated in order to get insights into fine tuning of alloreactivity after allo-HSCT. The model was calibrated with respect to published Treg and Teff recovery data after allo-HSCT. The calibrated model exhibits perfect or near-perfect adaptation to stepwise perturbations between Treg and Teff interactions, as seen in Treg cell populations when patients with relapsed malignancy were treated with anti-CTLA-4 (cytotoxic T lymphocyte-associated antigen 4). In addition, the model predicts observed shifts of Tregs and Teffs concentrations after co-stimulatory receptor IL-2R or TNFR2 blockade with allo-HSCT. The present results suggest simultaneous blockades of co-stimulatory and co-inhibitory receptors as a potential treatment for enhancing the GVL effect after allo-HSCT without developing GVHD.

Computational modeling of the immune response in multiple sclerosis using epimod framework

BACKGROUND

Multiple Sclerosis (MS) represents nowadays in Europe the leading cause of non-traumatic disabilities in young adults, with more than 700,000 EU cases. Although huge strides have been made over the years, MS etiology remains partially unknown. Furthermore, the presence of various endogenous and exogenous factors can greatly influence the immune response of different individuals, making it difficult to study and understand the disease. This becomes more evident in a personalized-fashion when medical doctors have to choose the best therapy for patient well-being. In this optics, the use of stochastic models, capable of taking into consideration all the fluctuations due to unknown factors and individual variability, is highly advisable.

RESULTS

We propose a new model to study the immune response in relapsing remitting MS (RRMS), the most common form of MS that is characterized by alternate episodes of symptom exacerbation (relapses) with periods of disease stability (remission). In this new model, both the peripheral lymph node/blood vessel and the central nervous system are explicitly represented. The model was created and analysed using Epimod, our recently developed general framework for modeling complex biological systems. Then the effectiveness of our model was shown by modeling the complex immunological mechanisms characterizing RRMS during its course and under the DAC administration.

CONCLUSIONS

Simulation results have proven the ability of the model to reproduce in silico the immune T cell balance characterizing RRMS course and the DAC effects. Furthermore, they confirmed the importance of a timely intervention on the disease course.

The Potential of Computational Modeling to Predict Disease Course and Treatment Response in Patients with Relapsing Multiple Sclerosis

As of today, 20 disease-modifying drugs (DMDs) have been approved for the treatment of relapsing multiple sclerosis (MS) and, based on their efficacy, they can be grouped into moderate-efficacy DMDs and high-efficacy DMDs. The choice of the drug mostly relies on the judgment and experience of neurologists and the evaluation of the therapeutic response can only be obtained by monitoring the clinical and magnetic resonance imaging (MRI) status during follow up. In an era where therapies are focused on personalization, this study aims to develop a modeling infrastructure to predict the evolution of relapsing MS and the response to treatments. We built a computational modeling infrastructure named Universal Immune System Simulator (UISS), which can simulate the main features and dynamics of the immune system activities. We extended UISS to simulate all the underlying MS pathogenesis and its interaction with the host immune system. This simulator is a multi-scale, multi-organ, agent-based simulator with an attached module capable of simulating the dynamics of specific biological pathways at the molecular level. We simulated six MS patients with different relapsing-remitting courses. These patients were characterized based on their age, sex, presence of oligoclonal bands, therapy, and MRI lesion load at the onset. The simulator framework is made freely available and can be used following the links provided in the availability section. Even though the model can be further personalized employing immunological parameters and genetic information, we generated a few simulation scenarios for each patient based on the available data. Among these simulations, it was possible to find the scenarios that realistically matched the real clinical and MRI history. Moreover, for two patients, the simulator anticipated the timing of subsequent relapses, which occurred, suggesting that UISS may have the potential to assist MS specialists in predicting the course of the disease and the response to treatment.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome as a Hyper-Regulated Immune System Driven by an Interplay Between Regulatory T Cells and Chronic Human Herpesvirus Infections

Autoimmunity and chronic viral infections are recurrent clinical observations in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), a complex disease with an unknown cause. Given these observations, the regulatory CD4+ T cells (Tregs) show promise to be good candidates for the underlying pathology due to their capacity to suppress the immune responses against both self and microbial antigens. Here, we discussed the overlooked role of these cells in the chronicity of Human Herpes Virus 6 (HHV6), Herpes Simplex 1 (HSV1), and Epstein–Barr virus (EBV), as often reported as triggers of ME/CFS. Using simulations of the cross-regulation model for the dynamics of Tregs, we illustrated that mild infections might lead to a chronically activated immune responses under control of Tregs if the responding clone has a high autoimmune potential. Such infections promote persistent inflammation and possibly fatigue. We then hypothesized that ME/CFS is a condition characterized by a predominance of this type of infections under control of Tregs. In contrast, healthy individuals are hypothesized to trigger immune responses of a virus-specific clone with a low autoimmune potential. According to this hypothesis, simple model simulations of the CD4+ T-cell repertoire could reproduce the increased density and percentages of Tregs observed in patients suffering from the disease, when compared to healthy controls. A deeper analysis of Tregs in the pathogenesis of ME/CFS will help to assess the validity of this hypothesis.

Evaluating a Multiscale Mechanistic Model of the Immune System to Predict Human Immunogenicity for a Biotherapeutic in Phase 1

A mechanistic model of the immune response was evaluated for its ability to predict anti-drug antibody (ADA) and their impact on pharmacokinetics (PK) and pharmacodynamics (PD) for a biotherapeutic in a phase 1 clinical trial. Observed ADA incidence ranged from 33 to 67% after single doses and 27-50% after multiple doses. The model captured the single dose incidence well; however, there was overprediction after multiple dosing. The model was updated to include a T-regulatory (Treg) cell mediated tolerance, which reduced the overprediction (relative decrease in predicted incidence rate of 21.5-59.3% across multidose panels) without compromising the single dose predictions (relative decrease in predicted incidence rate of 0.6-13%). The Treg-adjusted model predicted no ADA impact on PK or PD, consistent with the observed data. A prospective phase 2 trial was simulated, including co-medication effects in the form of corticosteroid-induced immunosuppression. Predicted ADA incidences were 0-10%, depending on co-medication dosage. This work demonstrates the utility in applying an integrated, iterative modeling approach to predict ADA during different stages of clinical development.

Dynamics and heterogeneity of brain damage in multiple sclerosis

Multiple Sclerosis (MS) is an autoimmune disease driving inflammatory and degenerative processes that damage the central nervous system (CNS). However, it is not well understood how these events interact and evolve to evoke such a highly dynamic and heterogeneous disease. We established a hypothesis whereby the variability in the course of MS is driven by the very same pathogenic mechanisms responsible for the disease, the autoimmune attack on the CNS that leads to chronic inflammation, neuroaxonal degeneration and remyelination. We propose that each of these processes acts more or less severely and at different times in each of the clinical subgroups. To test this hypothesis, we developed a mathematical model that was constrained by experimental data (the expanded disability status scale [EDSS] time series) obtained from a retrospective longitudinal cohort of 66 MS patients with a long-term follow-up (up to 20 years). Moreover, we validated this model in a second prospective cohort of 120 MS patients with a three-year follow-up, for which EDSS data and brain volume time series were available. The clinical heterogeneity in the datasets was reduced by grouping the EDSS time series using an unsupervised clustering analysis. We found that by adjusting certain parameters, albeit within their biological range, the mathematical model reproduced the different disease courses, supporting the dynamic CNS damage hypothesis to explain MS heterogeneity. Our analysis suggests that the irreversible axon degeneration produced in the early stages of progressive MS is mainly due to the higher rate of myelinated axon degeneration, coupled to the lower capacity for remyelination. However, and in agreement with recent pathological studies, degeneration of chronically demyelinated axons is not a key feature that distinguishes this phenotype. Moreover, the model reveals that lower rates of axon degeneration and more rapid remyelination make relapsing MS more resilient than the progressive subtype. Therefore, our results support the hypothesis of a common pathogenesis for the different MS subtypes, even in the presence of genetic and environmental heterogeneity. Hence, MS can be considered as a single disease in which specific dynamics can provoke a variety of clinical outcomes in different patient groups. These results have important implications for the design of therapeutic interventions for MS at different stages of the disease.

Multiple Sclerosis (MS) is an autoimmune disease in which inflammatory and degenerative processes damage the brain. We tested the hypothesis that the variability in disease progression and the clinical heterogeneity observed in MS is driven by a single mechanism, namely the autoimmune attack on the CNS that provokes this chronic inflammation and degeneration. As such, it is the difference in the intensity of these processes at distinct times that is responsible for establishing each of the disease subtypes defined to date. Mathematical models of brain damage and disease course were generated that were fitted to clinical data. We found that the phenotypes of the different MS subtypes were reproduced by the model, supporting the concept of a common pathogenesis and thus, that of a single disease in which specific dynamics can produce a variety of clinical outcomes in different groups of patients. These results are likely to be helpful when designing new therapies for this disease.

Programmed Death Ligand-1 on Microglia Regulates Th1 Differentiation via Nitric Oxide in Experimental Autoimmune Encephalomyelitis

Microglia are considered to be potential antigen-presenting cells and have the ability to present antigen under pathological conditions. Nevertheless, whether and how microglia are involved in immune regulation are largely unknown. Here, we investigated the suppressive activity of microglia during experimental autoimmune encephalomyelitis (EAE) induced by myelin oligodendrocyte glycoprotein, with the goal of understanding their role in regulating the T cell reaction. Using flow cytometric analysis, we found that microglia were characterized by increased cell number and up-regulated programmed death ligand-1 (PD-L1) at the peak phase of EAE. Meanwhile, both the CD4(+) T cells and microglia that infiltrated the central nervous system expressed higher levels of PD1, the receptor for PD-L1, accompanied by a decline of Th1 cells. In an ex vivo co-culture system, microglia from EAE mice inhibited the proliferation of antigen-specific CD4(+) T cells and the differentiation of Th1 cells, and this was significantly inhibited by PD-L1 blockade. Further, microglia suppressed Th1 cells via nitric oxide (NO), the production of which was dependent on PD-L1. Thus, these data suggest a scenario in which microglia are involved in the regulation of EAE by suppressing Th1-cell differentiation via the PD-L1-NO pathway.

Physiological Dynamics in Demyelinating Diseases: Unraveling Complex Relationships through Computer Modeling

Despite intense research, few treatments are available for most neurological disorders. Demyelinating diseases are no exception. This is perhaps not surprising considering the multifactorial nature of these diseases, which involve complex interactions between immune system cells, glia and neurons. In the case of multiple sclerosis, for example, there is no unanimity among researchers about the cause or even which system or cell type could be ground zero. This situation precludes the development and strategic application of mechanism-based therapies. We will discuss how computational modeling applied to questions at different biological levels can help link together disparate observations and decipher complex mechanisms whose solutions are not amenable to simple reductionism. By making testable predictions and revealing critical gaps in existing knowledge, such models can help direct research and will provide a rigorous framework in which to integrate new data as they are collected. Nowadays, there is no shortage of data; the challenge is to make sense of it all. In that respect, computational modeling is an invaluable tool that could, ultimately, transform how we understand, diagnose, and treat demyelinating diseases.

Agent based modeling of the effects of potential treatments over the blood-brain barrier in multiple sclerosis

Multiple sclerosis is a disease of the central nervous system that involves the destruction of the insulating sheath of axons, causing severe disabilities. Since the etiology of the disease is not yet fully understood, the use of novel techniques that may help to understand the disease, to suggest potential therapies and to test the effects of candidate treatments is highly advisable. To this end we developed an agent based model that demonstrated its ability to reproduce the typical oscillatory behavior observed in the most common form of multiple sclerosis, relapsing-remitting multiple sclerosis. The model has then been used to test the potential beneficial effects of vitamin D over the disease. Many scientific studies underlined the importance of the blood-brain barrier and of the mechanisms that influence its permeability on the development of the disease. In the present paper we further extend our previously developed model with a mechanism that mimics the blood-brain barrier behavior. The goal of our work is to suggest the best strategies to follow for developing new potential treatments that intervene in the blood-brain barrier. Results suggest that the best treatments should potentially prevent the opening of the blood-brain barrier, as treatments that help in recovering the blood-brain barrier functionality could be less effective.

A Population Approach to Characterize Interferon Beta-1b Effect on Contrast Enhancing Lesions in Patients With Relapsing Remitting Multiple Sclerosis

In patients with relapsing-remitting multiple sclerosis (RRMS), interferon beta-1b (IFNβ-1b) reduces the occurrence of contrast enhancing lesions (CELs) on magnetic resonance imaging (MRI). Questions remain on the stability of IFNβ-1b effect over time and its action beyond the reduction of CELs. In this study, we described the IFNβ-1b effect by a mixed effects model, quantifying the interpatient variability associated with its parameters. Using a negative binomial distribution model as a natural history model, the effect of IFNβ-1b was evaluated using different mathematical functions of time. IFNβ-1b produced a decrease in the expected CEL numbers, inhibiting the formation of new CELs but did not promote the resolution of the already-formed ones. Based on the final selected model, simulations were carried out to optimize the combined IFNβ-1b-corticosteroid therapy as a proof-of-concept. In summary, we provide evidence on the dynamics of CELs under IFNβ-1b treatment that can be used to monitor the effects of therapies in MS.

Agent-based modeling of the immune system: NetLogo, a promising framework

Several components that interact with each other to evolve a complex, and, in some cases, unexpected behavior, represents one of the main and fascinating features of the mammalian immune system. Agent-based modeling and cellular automata belong to a class of discrete mathematical approaches in which entities (agents) sense local information and undertake actions over time according to predefined rules. The strength of this approach is characterized by the appearance of a global behavior that emerges from interactions among agents. This behavior is unpredictable, as it does not follow linear rules. There are a lot of works that investigates the immune system with agent-based modeling and cellular automata. They have shown the ability to see clearly and intuitively into the nature of immunological processes. NetLogo is a multiagent programming language and modeling environment for simulating complex phenomena. It is designed for both research and education and is used across a wide range of disciplines and education levels. In this paper, we summarize NetLogo applications to immunology and, particularly, how this framework can help in the development and formulation of hypotheses that might drive further experimental investigations of disease mechanisms.

Mechanisms Underlying CD4+ Treg Immune Regulation in the Adult: From Experiments to Models

To maintain immunological balance the organism has to be tolerant to self while remaining competent to mount an effective immune response against third-party antigens. An important mechanism of this immune regulation involves the action of regulatory T-cell (Tregs). In this mini-review, we discuss some of the known and proposed mechanisms by which Tregs exert their influence in the context of immune regulation, and the contribution of mathematical modeling for these mechanistic studies. These models explore the mechanisms of action of regulatory T cells, and include hypotheses of multiple signals, delivered through simultaneous antigen-presenting cell (APC) conjugation; interaction of feedback loops between APC, Tregs, and effector cells; or production of specific cytokines that act on effector cells. As the field matures, and competing models are winnowed out, it is likely that we will be able to quantify how tolerance-inducing strategies, such as CD4-blockade, affect T-cell dynamics and what mechanisms explain the observed behavior of T-cell based tolerance.

Agent based modeling of Treg-Teff cross regulation in relapsing-remitting multiple sclerosis

Background

Multiple sclerosis (MS) is a disease of central nervous system that causes the removal of fatty myelin sheath from axons of the brain and spinal cord. Autoimmunity plays an important role in this pathology outcome and body's own immune system attacks on the myelin sheath causing the damage. The etiology of the disease is partially understood and the response to treatment cannot easily be predicted.

Results

We presented the results obtained using 8 genetically predisposed randomly chosen individuals reproducing both the absence and presence of malfunctions of the Teff-Treg cross-balancing mechanisms at a local level. For simulating the absence of a local malfunction we supposed that both Teff and Treg populations had similar maximum duplication rates. Results presented here suggest that presence of a genetic predisposition is not always a sufficient condition for developing the disease. Other conditions such as a breakdown of the mechanisms that regulate and allow peripheral tolerance should be involved.

Conclusions

The presented model allows to capture the essential dynamics of relapsing-remitting MS despite its simplicity. It gave useful insights that support the hypothesis of a breakdown of Teff-Treg cross balancing mechanisms.

Predicting relapsing-remitting dynamics in multiple sclerosis using discrete distribution models: a population approach

BACKGROUND

Relapsing-remitting dynamics are a hallmark of autoimmune diseases such as Multiple Sclerosis (MS). A clinical relapse in MS reflects an acute focal inflammatory event in the central nervous system that affects signal conduction by damaging myelinated axons. Those events are evident in T1-weighted post-contrast magnetic resonance imaging (MRI) as contrast enhancing lesions (CEL). CEL dynamics are considered unpredictable and are characterized by high intra- and inter-patient variability. Here, a population approach (nonlinear mixed-effects models) was applied to analyse of CEL progression, aiming to propose a model that adequately captures CEL dynamics.

METHODS AND FINDINGS

We explored several discrete distribution models to CEL counts observed in nine MS patients undergoing a monthly MRI for 48 months. All patients were enrolled in the study free of immunosuppressive drugs, except for intravenous methylprednisolone or oral prednisone taper for a clinical relapse. Analyses were performed with the nonlinear mixed-effect modelling software NONMEM 7.2. Although several models were able to adequately characterize the observed CEL dynamics, the negative binomial distribution model had the best predictive ability. Significant improvements in fitting were observed when the CEL counts from previous months were incorporated to predict the current month's CEL count. The predictive capacity of the model was validated using a second cohort of fourteen patients who underwent monthly MRIs during 6-months. This analysis also identified and quantified the effect of steroids for the relapse treatment.

CONCLUSIONS

The model was able to characterize the observed relapsing-remitting CEL dynamic and to quantify the inter-patient variability. Moreover, the nature of the effect of steroid treatment suggested that this therapy helps resolve older CELs yet does not affect newly appearing active lesions in that month. This model could be used for design of future longitudinal studies and clinical trials, as well as for the evaluation of new therapies.

FOXP3 subcellular localization predicts recurrence in oral squamous cell carcinoma

Forkhead box protein P3 (FOXP3) expression in tumor infiltrating CD4(+)T cells is generally associated with an intrinsic capacity to suppress tumor immunity. Based on this notion, different studies have evaluated the prognostic value of this maker in cancer but contradictory results have been found. Indeed, even within the same cancer population, the presence of CD4(+)FOXP3(+)T cells has been associated,with either a poor or a good prognosis, or no correlation has beenfound. Here, we demonstrate,in patients with oral squamous cell carcinoma (OSCC), that what really represents a prognostic parameter is not the overall expression of FOXP3 but its intracellular localization.While overallFOXP3 expression in tumor infiltrating CD4(+)T cells does not correlate with tumor recurrence, its intracellular localization within the CD4 cells does: nuclear FOXP3 (nFOXP3) is associated with tumor recurrence within 3 years, while cytoplasmicFOXP3 (cFOXP3) is associated with a lower likelihood of recurrence. Thus, we propose elevated levels of the cFOXP3/nFOXP3 ratio within tumor infiltrating CD4(+) T cells as a predictor of OSCC recurrence.

Systems biology as a comparative approach to understand complex gene expression in neurological diseases

Systems biology interdisciplinary approaches have become an essential analytical tool that may yield novel and powerful insights about the nature of human health and disease. Complex disorders are known to be caused by the combination of genetic, environmental, immunological or neurological factors. Thus, to understand such disorders, it becomes necessary to address the study of this complexity from a novel perspective. Here, we present a review of integrative approaches that help to understand the underlying biological processes involved in the etiopathogenesis of neurological diseases, for example, those related to autism and autism spectrum disorders (ASD) endophenotypes. Furthermore, we highlight the role of systems biology in the discovery of new biomarkers or therapeutic targets in complex disorders, a key step in the development of personalized medicine, and we demonstrate the role of systems approaches in the design of classifiers that can shorten the time for behavioral diagnosis of autism.

Dynamic cross-regulation of antigen-specific effector and regulatory T cell subpopulations and microglia in brain autoimmunity

Background

Multiple Sclerosis (MS) is considered a T-cell-mediated autoimmune disease with a prototypical oscillatory behavior, as evidenced by the presence of clinical relapses. Understanding the dynamics of immune cells governing the course of MS, therefore, has many implications for immunotherapy. Here, we used flow cytometry to analyze the time-dependent behavior of antigen-specific effector (T_eff) and regulatory (T_reg) T cells and microglia in mice model of MS, Experimental Autoimmune Encephalomyelitis (EAE), and compared the observations with a mathematical cross-regulation model of T-cell dynamics in autoimmune disease.

Results

We found that T_eff and T_reg cells specific to myelin olygodendrocyte glycoprotein (MOG) developed coupled oscillatory dynamics with a 4- to 5-day period and decreasing amplitude that was always higher for the T_eff populations, in agreement with the mathematical model. Microglia activation followed the oscillations of MOG-specific T_eff cells in the secondary lymphoid organs, but they were activated before MOG-specific T-cell peaks in the CNS. Finally, we assessed the role of B-cell depletion induced by anti-CD20 therapy in the dynamics of T cells in an EAE model with more severe disease after therapy. We observed that B-cell depletion decreases T_eff expansion, although its oscillatory behavior persists. However, the effect of B cell depletion was more significant in the T_reg population within the CNS, which matched with activation of microglia and worsening of the disease. Mathematical modeling of T-cell cross-regulation after anti-CD20 therapy suggests that B-cell depletion may influence the dynamics of T cells by fine-tuning their activation.

Conclusions

The oscillatory dynamics of T-cells have an intrinsic origin in the physiological regulation of the adaptive immune response, which influences both disease phenotype and response to immunotherapy.

Data integration and systems biology approaches for biomarker discovery: challenges and opportunities for multiple sclerosis

New "omic" technologies and their application to systems biology approaches offer new opportunities for biomarker discovery in complex disorders, including multiple sclerosis (MS). Recent studies using massive genotyping, DNA arrays, antibody arrays, proteomics, glycomics, and metabolomics from different tissues (blood, cerebrospinal fluid, brain) have identified many molecules associated with MS, defining both susceptibility and functional targets (e.g., biomarkers). Such discoveries involve many different levels in the complex organizational hierarchy of humans (DNA, RNA, protein, etc.), and integrating these datasets into a coherent model with regard to MS pathogenesis would be a significant step forward. Given the dynamic and heterogeneous nature of MS, validating biomarkers is mandatory. To develop accurate markers of disease prognosis or therapeutic response that are clinically useful, combining molecular, clinical, and imaging data is necessary. Such an integrative approach would pave the way towards better patient care and more effective clinical trials that test new therapies, thus bringing the paradigm of personalized medicine in MS one step closer.