An integrative modeling framework reveals plasticity of TGF-β signaling

A probabilistic Boolean model on hair follicle cell fate regulation by TGF-β

Hair follicles (HFs) are mini skin organs that undergo cyclic growth. Various signals regulate HF cell fate decisions jointly. Recent experimental results suggest that transforming growth factor beta (TGF-β) exhibits a dual role in HF cell fate regulation that can be either anti- or pro-apoptosis. To understand the underlying mechanisms of HF cell fate control, we develop a novel probabilistic Boolean network (pBN) model on the HF epithelial cell gene regulation dynamics. First, the model is derived from literature, then refined using single-cell RNA sequencing data. Using the model, we both explore the mechanisms underlying HF cell fate decisions and make predictions that could potentially guide future experiments: 1) we propose that a threshold-like switch in the TGF-β strength may necessitate the dual roles of TGF-β in either activating apoptosis or cell proliferation, in cooperation with bone morphogenetic protein (BMP) and tumor necrosis factor (TNF) and at different stages of a follicle growth cycle; 2) our model shows concordance with the high-activator-low-inhibitor theory of anagen initiation; 3) we predict that TNF may be more effective in catagen initiation than TGF-β, and they may cooperate in a two-step fashion; 4) finally, predictions of gene knockout and overexpression reveal the roles in HF cell fate regulations of each gene. Attractor and motif analysis from the associated Boolean networks reveal the relations between the topological structure of the gene regulation network and the cell fate regulation mechanism. A discrete spatial model equipped with the pBN illustrates how TGF-β and TNF cooperate in initiating and driving the apoptosis wave during catagen.

Discrete modeling for integration and analysis of large-scale signaling networks

Most biological processes are orchestrated by large-scale molecular networks which are described in large-scale model repositories and whose dynamics are extremely complex. An observed phenotype is a state of this system that results from control mechanisms whose identification is key to its understanding. The Biological Pathway Exchange (BioPAX) format is widely used to standardize the biological information relative to regulatory processes. However, few modeling approaches developed so far enable for computing the events that control a phenotype in large-scale networks.

Here we developed an integrated approach to build large-scale dynamic networks from BioPAX knowledge databases in order to analyse trajectories and to identify sets of biological entities that control a phenotype. The Cadbiom approach relies on the guarded transitions formalism, a discrete modeling approach which models a system dynamics by taking into account competition and cooperation events in chains of reactions. The method can be applied to every BioPAX (large-scale) model thanks to a specific package which automatically generates Cadbiom models from BioPAX files.

The Cadbiom framework was applied to the BioPAX version of two resources (PID, KEGG) of the Pathway Commons database and to the Atlas of Cancer Signalling Network (ACSN). As a case-study, it was used to characterize sets of biological entities implicated in the epithelial-mesenchymal transition. Our results highlight the similarities between the PID and ACSN resources in terms of biological content, and underline the heterogeneity of usage of the BioPAX semantics limiting the fusion of models that require curation. Causality analyses demonstrate the smart complementarity of the databases in terms of combinatorics of controllers that explain a phenotype. From a biological perspective, our results show the specificity of controllers for epithelial and mesenchymal phenotypes that are consistent with the literature and identify a novel signature for intermediate states.

The computation of sets of biological entities implicated in phenotypes is hampered by the complex nature of controllers acting in competitive or cooperative combinations. These biological mechanisms are underlied by chains of reactions involving interactions between biomolecules (DNA, RNA, proteins, lipids, complexes, etc.), all of which form complex networks. Hence, the identification of controllers relies on computational methods for dynamical systems, which require the biological information about the interactions to be translated into a formal language. The BioPAX standard is a reference ontology associated with a description language to describe biological mechanisms, which satisfies the Linked Open Data initiative recommendations for data interoperability. Although it has been widely adopted by the community to describe biological pathways, no computational method is able of studying the dynamics of the networks described in the BioPAX large-scale resources. To solve this issue, our Cadbiom framework was designed to automatically transcribe the biological systems knowledge of large-scale BioPAX networks into discrete models. The framework then identifies the trajectories that explain a biological phenotype (e.g., all the biomolecules that are activated to induce the expression of a gene).

Here, we created Cadbiom models from three biological pathway databases (KEGG, PID and ACSN). The comparative analysis of these models highlighted the diversity of molecules in sets of biological entities that can explain a same phenotype. The application of our framework to the search of biomolecules regulating the epithelial-mesenchymal transition not only confirmed known pathways in the control of epithelial or mesenchymal cell markers but also highlighted new pathways for transient states.

Identification via Numerical Computation of Transcriptional Determinants of a Cell Phenotype Decision Making

Complex cellular processes, such as phenotype decision making, are exceedingly difficult to analyze experimentally, due to the multiple-layer regulation of gene expression and the intercellular variability referred to as biological noise. Moreover, the heterogeneous experimental approaches used to investigate distinct macromolecular species, and their intrinsic differential time-scale dynamics, add further intricacy to the general picture of the physiological phenomenon. In this respect, a computational representation of the cellular functions of interest can be used to extract relevant information, being able to highlight meaningful active markers within the plethora of actors forming an active molecular network. The multiscale power of such an approach can also provide meaningful descriptions for both population and single-cell level events. To validate this paradigm a Boolean and a Markov model were combined to identify, in an objective and user-independent manner, a signature of genes recapitulating epithelial to mesenchymal transition in-vitro. The predictions of the model are in agreement with experimental data and revealed how the expression of specific molecular markers is related to distinct cell behaviors. The presented method strengthens the evidence of a role for computational representation of active molecular networks to gain insight into cellular physiology and as a general approach for integrating in-silico/in-vitro study of complex cell population dynamics to identify their most relevant drivers.

Interaction of the transforming growth factor-β and Notch signaling pathways in the regulation of granulosa cell proliferation

The Notch and transforming growth factor (TGF)-β signalling pathways play an important role in granulosa cell proliferation. However, the mechanisms underlying the cross-talk between these two signalling pathways are unknown. Herein we demonstrated a functional synergism between Notch and TGF-β signalling in the regulation of preantral granulosa cell (PAGC) proliferation. Activation of TGF-β signalling increased hairy/enhancer-of-split related with YRPW motif 2 gene (Hey2) expression (one of the target genes of the Notch pathway) in PAGCs, and suppression of TGF-β signalling by Smad3 knockdown reduced Hey2 expression. Inhibition of the proliferation of PAGCs by N-[N-(3,5-difluorophenacetyl)-l-alanyl]-S-phenylglycine t-butylester (DAPT), an inhibitor of Notch signalling, was rescued by both the addition of ActA and overexpression of Smad3, indicating an interaction between the TGF-β and Notch signalling pathways. Co-immunoprecipitation (CoIP) and chromatin immunoprecipitation (ChIP) assays were performed to identify the point of interaction between the two signalling pathways. CoIP showed direct protein-protein interaction between Smad3 and Notch2 intracellular domain (NICD2), whereas ChIP showed that Smad3 could be recruited to the promoter regions of Notch target genes as a transcription factor. Therefore, the findings of the present study support the idea that nuclear Smad3 protein can integrate with NICD2 to form a complex that acts as a transcription factor to bind specific DNA motifs in Notch target genes, such as Hey1 and Hey2, and thus participates in the transcriptional regulation of Notch target genes, as well as regulation of the proliferation of PAGCs.

Deciphering Epithelial-Mesenchymal Transition Regulatory Networks in Cancer through Computational Approaches

Epithelial–mesenchymal transition (EMT), the process by which epithelial cells can convert into motile mesenchymal cells, plays an important role in development and wound healing but is also involved in cancer progression. It is increasingly recognized that EMT is a dynamic process involving multiple intermediate or “hybrid” phenotypes rather than an “all-or-none” process. However, the role of EMT in various cancer hallmarks, including metastasis, is debated. Given the complexity of EMT regulation, computational modeling has proven to be an invaluable tool for cancer research, i.e., to resolve apparent conflicts in experimental data and to guide experiments by generating testable hypotheses. In this review, we provide an overview of computational modeling efforts that have been applied to regulation of EMT in the context of cancer progression and its associated tumor characteristics. Moreover, we identify possibilities to bridge different modeling approaches and point out outstanding questions in which computational modeling can contribute to advance our understanding of pathological EMT.

Characterization of miRNAs involved in response to poly(I:C) in porcine airway epithelial cells

MicroRNAs (miRNA) have been implicated in a variety of pathological conditions including infectious diseases. Knowledge of the miRNAs affected by poly(I:C), a synthetic analog of viral double-stranded RNA, in porcine airway epithelial cells (PAECs) contributes to understanding the mechanisms of swine viral respiratory diseases, which bring enormous economic loss worldwide every year. In this study, we used high throughput sequencing to profile miRNA expression in PAECs treated with poly(I:C) as compared to the untreated control. This approach revealed 23 differentially expressed miRNAs (DEMs), five of which have not been implicated in viral infection before. Nineteen of the 23 miRNAs were down-regulated including members of the miR-17-92 cluster, a well-known polycistronic oncomir and extensively involved in viral infection in humans. Target genes of DEMs, predicted using bioinformatic methods and validated by luciferase reporter analysis on two representative DEMs, were significantly enriched in several pathways including transforming growth factor-β signaling. A large quantity of sequence variations (isomiRs) were found including a substitution at position 5, which was verified to redirect miRNAs to a new spectrum of targets by luciferase reporter assay together with bioinformatics analysis. Twelve novel porcine miRNAs conserved in other species were identified by homology analysis together with cloning verification. Furthermore, the expression analysis revealed the potential importance of three novel miRNAs in porcine immune response to viruses. Overall, our data contribute to clarifying the mechanisms underlying the host immune response against respiratory viruses in pigs, and enriches the repertoire of porcine miRNAs.

Inhibition of CD1d-mediated antigen presentation by the transforming growth factor-β/Smad signalling pathway

CD1d-mediated lipid antigen presentation activates a subset of innate immune lymphocytes called invariant natural killer T (NKT) cells that, by virtue of their potent cytokine production, bridge the innate and adaptive immune systems. Transforming growth factor (TGF-β) is a known immune modulator that can activate the mitogen-activated protein kinase p38; we have previously shown that p38 is a negative regulator of CD1d-mediated antigen presentation. Several studies implicate a role for TGF-β in the activation of p38. Therefore, we hypothesized that TGF-β would impair antigen presentation by CD1d. Indeed, a dose-dependent decrease in CD1d-mediated antigen presentation and impairment of lipid antigen processing was observed in response to TGF-β treatment. However, it was found that this inhibition was not through p38 activation. Instead, Smads 2, 3 and 4, downstream elements of the TGF-β canonical signalling pathway, contributed to the observed effects. In marked contrast to that observed with CD1d, TGF-β was found to enhance MHC class II-mediated antigen presentation. Overall, these results suggest that the canonical TGF-β/Smad pathway negatively regulates an important arm of the host's innate immune responses - CD1d-mediated lipid antigen presentation to NKT cells.

Heightened inflammation in severe malaria is associated with decreased IL-10 expression levels and neutrophils

Dysregulation of the cytokine network in severe malaria owing to variations in factors like parasite load, strains and host factors is well documented but the key cytokines that are dysregulated remain poorly elucidated. Longitudinal changes in cytokine levels in an individual with parasitemia and disease resolution is likely to identify the key cytokines. We have analyzed the mRNA expression of cytokines over a 7-d period in severe (SM) and uncomplicated (UM) Plasmodium falciparum malaria. We found up-regulated expression of TNF-α, IL-1β, IFN-γ and TGF-β in SM, with decreased expression of IL-10 on d 0. Further, we observed a negative correlation of IL-10 expression with parasitemia and pro-inflammatory cytokines, suggesting IL-10 to be the key cytokine in tilting the balance to an inflammatory response. Longitudinal analysis revealed that the key cytokines associated with disease were TNF-α, IL-1β, IFN-γ, IL-12α, RANTES and TGF-β, while TNF-α, IL-10 and TGF-β discriminated between SM and UM. A higher neutrophil count in SM and its positive association with parasite density and IL-1β and IL-8 provides support for neutrophils in inflammation in malaria. Our findings suggest subversion of anti-inflammatory response in SM by parasite factors towards an exaggerated pro-inflammatory response with involvement of neutrophils, the classical inflammatory cells.