Model Checking to Assess T-Helper Cell Plasticity

Computational modeling constitutes a crucial step toward the functional understanding of complex cellular networks. In particular, logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks. In this context, signaling input components are generally meant to convey external stimuli, or environmental cues. In response to such external signals, cells acquire specific gene expression patterns modeled in terms of attractors (e.g., stable states). The capacity for cells to alter or reprogram their differentiated states upon changes in environmental conditions is referred to as cell plasticity. In this article, we present a multivalued logical framework along with computational methods recently developed to efficiently analyze large models. We mainly focus on a symbolic model checking approach to investigate switches between attractors subsequent to changes of input conditions. As a case study, we consider the cellular network regulating the differentiation of T-helper (Th) cells, which orchestrate many physiological and pathological immune responses. To account for novel cellular subtypes, we present an extended version of a published model of Th cell differentiation. We then use symbolic model checking to analyze reachability properties between Th subtypes upon changes of environmental cues. This allows for the construction of a synthetic view of Th cell plasticity in terms of a graph connecting subtypes with arcs labeled by input conditions. Finally, we explore novel strategies enabling specific Th cell polarizing or reprograming events.LabEx MemoLife, Ecole Normale Supérieure, FCT grants: (PEst-OE/EEI/LA0021/2013, IF/01333/2013), Ph.D.program of the Agence National de Recherche sur Le Sida (ANRS), European Research Council consolidator grant

TSLP-activated dendritic cells induce human T follicular helper cell differentiation through OX40-ligand.

T follicular helper cells (Tfh) are important regulators of humoral responses. Human Tfh polarization pathways have been thus far associated with Th1 and Th17 polarization pathways. How human Tfh cells differentiate in Th2-skewed environments is unknown. We show that thymic stromal lymphopoietin (TSLP)-activated dendritic cells (DCs) promote human Tfh differentiation from naive CD4 T cells. We identified a novel population, distinct from Th2 cells, expressing IL-21 and TNF, suggestive of inflammatory cells. TSLP-induced T cells expressed CXCR5, CXCL13, ICOS, PD1, BCL6, BTLA, and SAP, among other Tfh markers. Functionally, TSLP-DC-polarized T cells induced IgE secretion by memory B cells, and this depended on IL-4Rα. TSLP-activated DCs stimulated circulating memory Tfh cells to produce IL-21 and CXCL13. Mechanistically, TSLP-induced Tfh differentiation depended on OX40-ligand, but not on ICOS-ligand. Our results delineate a pathway of human Tfh differentiation in Th2 environments

Multivariate study of human CD4 T cell cytokine diversity : generation and association with breast cancer subtypes

Aujourd’hui, plusieurs niveaux de complexité ont émergé dans l’étude des phénotypes T CD4 auxiliaires. 1) le nombre important de cytokines différentes pouvant être secrétées par les lymphocytes T CD4. 2) la multiplicité de signaux pouvant agir durant la différenciation des T CD4 pour spécifier leur profile de sécrétion cytokinique. 3) l’association de ces différents profils de cytokines à des pathologies complexes. Au cours de mon doctorat je me suis concentré sur ces trois niveaux de complexité en étudiant la génération de la diversité cytokinique T CD4 et ses associations aux différents sous types de cancer du sein en utilisant des analyses multivariées et des modèles statistiques. Tout d’abord, j’ai pu construire le premier modèle multivarié de la différentiation T CD4 reliant 37 signaux venant de cellules dendritiques à 18 cytokines T CD4. Utilisant ce modèle pour dériver des prédictions, j’ai pu trouver un nouveau rôle à l’IL-12p70 en tant qu’inducteur de différenciation Th17, mais également comme inducteur spécifique d’IL-17F mais pas d’IL-17A lorsqu’il est combiné à l’IL-1. Ensuite, j’ai étudié l’association de ces cytokines T CD4 avec les différents sous types de cancer du sein connus. J’ai pu trouver que les cytokines Th17 étaient préférentiellement associées avec les cancers du sein dits triple négatifs (TNBC). J’ai pu mettre en évidence qu’une forte signature Th17 était associée à une meilleure survie. De plus, en combinant cette signature Th17 à des scores utilisés pour définir le pronostic clinique, tel que l’index pronostic de Nottingham, j’ai pu proposer une nouvelle et meilleure stratification de la survie de ces patients.Today several levels of complexity have emerged in the field of T helper cytokines: 1) the important number of distinct cytokines that Th cell can secrete in various combinations; 2) The multiplicity of signals that can act during Th differentiation to define the Th cytokine secretion profiles 3) The associations of these T helper secretion profiles with complex diseases. During my PhD I focused on these three levels of complexity and study the generation of T helper cytokine diversity and its association to breast cancer subtypes using multivariate analysis and statistical modeling. First, I was able to build the first statistical model linking 37 dendritic cell derived signals to 18 T helper cytokines. Using this model to derive in silico predictions, I was able to found a new role for IL-12p70 as a promoter of Th17 differentiation and as a main differential inducer of IL-17F independently of Il-17A in presence of IL-1. Then, studying the associations of the Th cytokine diversity with the different subtypes of human breast cancers, I found that Th17 cytokines were preferentially associated to Triple Negative Breast Cancer (TNBC). I found that TNBC patients with a high Th17 signature had a better survival. In addition, I showed that Th17 can be combined to clinical prognosis assessment scores, such as the Nottingham Prognosis Index, to better stratify TNBC patients in relevant subgroups for survival prognosis assessment

Analyses multivariées de la génération de la diversité des cytokines des cellules T CD4 et association de cette diversité aux différents sous types de cancer du sein

Today several levels of complexity have emerged in the field of T helper cytokines: 1) the important number of distinct cytokines that Th cell can secrete in various combinations; 2) The multiplicity of signals that can act during Th differentiation to define the Th cytokine secretion profiles 3) The associations of these T helper secretion profiles with complex diseases. During my PhD I focused on these three levels of complexity and study the generation of T helper cytokine diversity and its association to breast cancer subtypes using multivariate analysis and statistical modeling. First, I was able to build the first statistical model linking 37 dendritic cell derived signals to 18 T helper cytokines. Using this model to derive in silico predictions, I was able to found a new role for IL-12p70 as a promoter of Th17 differentiation and as a main differential inducer of IL-17F independently of Il-17A in presence of IL-1. Then, studying the associations of the Th cytokine diversity with the different subtypes of human breast cancers, I found that Th17 cytokines were preferentially associated to Triple Negative Breast Cancer (TNBC). I found that TNBC patients with a high Th17 signature had a better survival. In addition, I showed that Th17 can be combined to clinical prognosis assessment scores, such as the Nottingham Prognosis Index, to better stratify TNBC patients in relevant subgroups for survival prognosis assessment.Aujourd’hui, plusieurs niveaux de complexité ont émergé dans l’étude des phénotypes T CD4 auxiliaires. 1) le nombre important de cytokines différentes pouvant être secrétées par les lymphocytes T CD4. 2) la multiplicité de signaux pouvant agir durant la différenciation des T CD4 pour spécifier leur profile de sécrétion cytokinique. 3) l’association de ces différents profils de cytokines à des pathologies complexes. Au cours de mon doctorat je me suis concentré sur ces trois niveaux de complexité en étudiant la génération de la diversité cytokinique T CD4 et ses associations aux différents sous types de cancer du sein en utilisant des analyses multivariées et des modèles statistiques. Tout d’abord, j’ai pu construire le premier modèle multivarié de la différentiation T CD4 reliant 37 signaux venant de cellules dendritiques à 18 cytokines T CD4. Utilisant ce modèle pour dériver des prédictions, j’ai pu trouver un nouveau rôle à l’IL-12p70 en tant qu’inducteur de différenciation Th17, mais également comme inducteur spécifique d’IL-17F mais pas d’IL-17A lorsqu’il est combiné à l’IL-1. Ensuite, j’ai étudié l’association de ces cytokines T CD4 avec les différents sous types de cancer du sein connus. J’ai pu trouver que les cytokines Th17 étaient préférentiellement associées avec les cancers du sein dits triple négatifs (TNBC). J’ai pu mettre en évidence qu’une forte signature Th17 était associée à une meilleure survie. De plus, en combinant cette signature Th17 à des scores utilisés pour définir le pronostic clinique, tel que l’index pronostic de Nottingham, j’ai pu proposer une nouvelle et meilleure stratification de la survie de ces patients

A Quantitative Multivariate Model of Human Dendritic Cell-T Helper Cell Communication

International audienceCell-cell communication involves a large number of molecular signals that function as words of a complex language whose grammar remains mostly unknown. Here, we describe an integrative approach involving (1) protein-level measurement of multiple communication signals coupled to output responses in receiving cells and (2) mathematical modeling to uncover input-output relationships and interactions between signals. Using human dendritic cell (DC)-T helper (Th) cell communication as a model, we measured 36 DC-derived signals and 17 Th cytokines broadly covering Th diversity in 428 observations. We developed a data-driven, computationally validated model capturing 56 already described and 290 potentially novel mechanisms of Th cell specification. By predicting context-dependent behaviors, we demonstrate a new function for IL-12p70 as an inducer of Th17 in an IL-1 signaling context. This work provides a unique resource to decipher the complex combinatorial rules governing DC-Th cell communication and guide their manipulation for vaccine design and immunotherapies

PD-L1 and ICOSL discriminate human Secretory and Helper dendritic cells in cancer, allergy and autoimmunity

International audienceAbstract Dendritic cells (DC) are traditionally classified according to their ontogeny and their ability to induce T cell response to antigens, however, the phenotypic and functional state of these cells in cancer does not necessarily align to the conventional categories. Here we show, by using 16 different stimuli in vitro that activated DCs in human blood are phenotypically and functionally dichotomous, and pure cultures of type 2 conventional dendritic cells acquire these states (termed Secretory and Helper) upon appropriate stimuli. PD-L1highICOSLlow Secretory DCs produce large amounts of inflammatory cytokines and chemokines but induce very low levels of T helper (Th) cytokines following co-culturing with T cells. Conversely, PD-L1lowICOSLhigh Helper DCs produce low levels of secreted factors but induce high levels and a broad range of Th cytokines. Secretory DCs bear a single-cell transcriptomic signature indicative of mature migratory LAMP3+ DCs associated with cancer and inflammation. Secretory DCs are linked to good prognosis in head and neck squamous cell carcinoma, and to response to checkpoint blockade in Melanoma. Hence, the functional dichotomy of DCs we describe has both fundamental and translational implications in inflammation and immunotherapy