ADAMTS12 is a stromal modulator in chronic liver disease.

Adamalysins, a family of metalloproteinases containing a disintegrin and metalloproteinases (ADAMs) and ADAM with thrombospondin motifs (ADAMTSs), belong to the matrisome and play important roles in various biological and pathological processes, such as development, immunity and cancer. Using a liver cancer dataset from the International Cancer Genome Consortium, we developed an extensive in silico screening that identified a cluster of adamalysins co-expressed in livers from patients with hepatocellular carcinoma (HCC). Within this cluster, ADAMTS12 expression was highly associated with recurrence risk and poorly differentiated HCC signatures. We showed that ADAMTS12 was expressed in the stromal cells of the tumor and adjacent fibrotic tissues of HCC patients, and more specifically in activated stellate cells. Using a mouse model of carbon tetrachloride-induced liver injury, we showed that Adamts12 was strongly and transiently expressed after a 24 h acute treatment, and that fibrosis was exacerbated in Adamts12-null mice submitted to carbon tetrachloride-induced chronic liver injury. Using the HSC-derived LX-2 cell line, we showed that silencing of ADAMTS12 resulted in profound changes of the gene expression program. In particular, genes previously reported to be induced upon HSC activation, such as PAI-1, were mostly down-regulated following ADAMTS12 knock-down. The phenotype of these cells was changed to a less differentiated state, showing an altered actin network and decreased nuclear spreading. These phenotypic changes, together with the down-regulation of PAI-1, were offset by TGF-ß treatment. The present study thus identifies ADAMTS12 as a modulator of HSC differentiation, and a new player in chronic liver disease.

Phylogenetic inference of the emergence of sequence modules and protein-protein interactions in the ADAMTS-TSL family.

Numerous computational methods based on sequences or structures have been developed for the characterization of protein function, but they are still unsatisfactory to deal with the multiple functions of multi-domain protein families. Here we propose an original approach based on 1) the detection of conserved sequence modules using partial local multiple alignment, 2) the phylogenetic inference of species/genes/modules/functions evolutionary histories, and 3) the identification of co-appearances of modules and functions. Applying our framework to the multidomain ADAMTS-TSL family including ADAMTS (A Disintegrin-like and Metalloproteinase with ThromboSpondin motif) and ADAMTS-like proteins over nine species including human, we identify 45 sequence module signatures that are associated with the occurrence of 278 Protein-Protein Interactions in ancestral genes. Some of these signatures are supported by published experimental data and the others provide new insights (e.g. ADAMTS-5). The module signatures of ADAMTS ancestors notably highlight the dual variability of the propeptide and ancillary regions suggesting the importance of these two regions in the specialization of ADAMTS during evolution. Our analyses further indicate convergent interactions of ADAMTS with COMP and CCN2 proteins. Overall, our study provides 186 sequence module signatures that discriminate distinct subgroups of ADAMTS and ADAMTSL and that may result from selective pressures on novel functions and phenotypes.

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.

Constructing xenobiotic maps of metabolism to predict enzymes catalyzing metabolites capable of binding to DNA.

BACKGROUND: The liver plays a major role in the metabolic activation of xenobiotics (drugs, chemicals such as pollutants, pesticides, food additives...). Among environmental contaminants of concern, heterocyclic aromatic amines (HAA) are xenobiotics classified by IARC as possible or probable carcinogens (2A or 2B). There exist little information about the effect of these HAA in humans. While HAA is a family of more than thirty identified chemicals, the metabolic activation and possible DNA adduct formation have been fully characterized in human liver for only a few of them (MeIQx, PhIP, A[Formula: see text]C). RESULTS: We have developed a modeling approach in order to predict all the possible metabolites of a xenobiotic and enzymatic profiles that are linked to the production of metabolites able to bind DNA. Our prediction of metabolites approach relies on the construction of an enriched and annotated map of metabolites from an input metabolite.The pipeline assembles reaction prediction tools (SyGMa), sites of metabolism prediction tools (Way2Drug, SOMP and Fame 3), a tool to estimate the ability of a xenobotics to form DNA adducts (XenoSite Reactivity V1), and a filtering procedure based on Bayesian framework. This prediction pipeline was evaluated using caffeine and then applied to HAA. The method was applied to determine enzymes profiles associated with the maximization of metabolites derived from each HAA which are able to bind to DNA. The classification of HAA according to enzymatic profiles was consistent with their chemical structures. CONCLUSIONS: Overall, a predictive toxicological model based on an in silico systems biology approach opens perspectives to estimate the genotoxicity of various chemical classes of environmental contaminants. Moreover, our approach based on enzymes profile determination opens the possibility of predicting various xenobiotics metabolites susceptible to bind to DNA in both normal and physiopathological situations.

Integrin-αV-mediated activation of TGF-ß regulates anti-tumour CD8 T cell immunity and response to PD-1 blockade.

TGF-ß is secreted in the tumour microenvironment in a latent, inactive form bound to latency associated protein and activated by the integrin αV subunit. The activation of latent TGF-ß by cancer-cell-expressed αV re-shapes the tumour microenvironment, and this could affect patient responses to PD-1-targeting therapy. Here we show, using multiplex immunofluorescence staining in cohorts of anti-PD-1 and anti-PD-L1-treated lung cancer patients, that decreased expression of cancer cell αV is associated with improved immunotherapy-related, progression-free survival, as well as with an increased density of CD8+CD103+ tumour-infiltrating lymphocytes. Mechanistically, tumour αV regulates CD8 T cell recruitment, induces CD103 expression on activated CD8+ T cells and promotes their differentiation to granzyme B-producing CD103+CD69+ resident memory T cells via autocrine TGF-ß signalling. Thus, our work provides the underlying principle of targeting cancer cell αV for more efficient PD-1 checkpoint blockade therapy.

ADAM and ADAMTS Proteins, New Players in the Regulation of Hepatocellular Carcinoma Microenvironment.

The tumor microenvironment plays a major role in tumor growth, invasion and resistance to chemotherapy, however understanding how all actors from microenvironment interact together remains a complex issue. The tumor microenvironment is classically represented as three closely connected components including the stromal cells such as immune cells, fibroblasts, adipocytes and endothelial cells, the extracellular matrix (ECM) and the cytokine/growth factors. Within this space, proteins of the adamalysin family (ADAM for a disintegrin and metalloproteinase; ADAMTS for ADAM with thrombospondin motifs; ADAMTSL for ADAMTS-like) play critical roles by modulating cell-cell and cell-ECM communication. During last decade, the implication of adamalysins in the development of hepatocellular carcinoma (HCC) has been supported by numerous studies however the functional characterization of most of them remain unsettled. In the present review we propose both an overview of the literature and a meta-analysis of adamalysins expression in HCC using data generated by The Cancer Genome Atlas (TCGA) Research Network.

Integration of miRNA-regulatory networks in hepatic stellate cells identifies TIMP3 as a key factor in chronic liver disease.

BACKGROUND & AIMS: Activation of hepatic stellate cells (HSC) is a critical process involved in liver fibrosis. Several miRNAs are implicated in gene regulation during this process but their exact and respective contribution is still incompletely understood. Here we propose an integrative approach of miRNA-regulatory networks to predict new targets. METHODS: miRNA regulatory networks in activated HSCs were built using lists of validated miRNAs and the CyTargetLinker tool. The resulting graphs were filtered according to public transcriptomic data and the reduced graphs were analysed through GO annotation. A miRNA network regulating the expression of TIMP3 was further studied in human liver samples, isolated hepatic cells and mouse model of liver fibrosis. RESULTS: Within the up-regulated miRNAs, we identified a subnetwork of five miRNAs (miR-21-5p, miR-222-3p, miR-221-3p miR-181b-5p and miR-17-5p) that target TIMP3. We demonstrated that TIMP3 expression is inversely associated with inflammatory activity and IL1-ß expression in vivo. We further showed that IL1-ß inhibits TIMP3 expression in HSC-derived LX-2 cells. Using data from The Cancer Genome Atlas (TCGA), we showed that, in hepatocellular carcinoma (HCC), TIMP3 expression is associated with survival (P < .001), while miR-221 (P < .05), miR-222 (P < .01) and miR-181b (P < .01) are markers for a poor prognosis. CONCLUSIONS: Several miRNAs targeting TIMP3 are up-regulated in activated HSCs and down-regulation of TIMP3 expression is associated with inflammatory activity in liver fibrosis and poor prognosis in HCC. The regulatory network including specific miRNAs and TIMP3 is therefore central for the evolution of chronic liver disease.

A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma.

BACKGROUND: Integrating genome-wide gene expression patient profiles with regulatory knowledge is a challenging task because of the inherent heterogeneity, noise and incompleteness of biological data. From the computational side, several solvers for logic programs are able to perform extremely well in decision problems for combinatorial search domains. The challenge then is how to process the biological knowledge in order to feed these solvers to gain insights in a biological study. It requires formalizing the biological knowledge to give a precise interpretation of this information; currently, very few pathway databases offer this possibility. RESULTS: The presented work proposes an automatic pipeline to extract automatically regulatory knowledge from pathway databases and generate novel computational predictions related to the state of expression or activity of biological molecules. We applied it in the context of hepatocellular carcinoma (HCC) progression, and evaluate the precision and the stability of these computational predictions. Our working base is a graph of 3383 nodes and 13,771 edges extracted from the KEGG database, in which we integrate 209 differentially expressed genes between low and high aggressive HCC across 294 patients. Our computational model predicts the shifts of expression of 146 initially non-observed biological components. Our predictions were validated at 88% using a larger experimental dataset and cross-validation techniques. In particular, we focus on the protein complexes predictions and show for the first time that NFKB1/BCL-3 complexes are activated in aggressive HCC. In spite of the large dimension of the reconstructed models, our analyses over the computational predictions discover a well constrained region where KEGG regulatory knowledge constrains gene expression of several biomolecules. These regions can offer interesting windows to perturb experimentally such complex systems. CONCLUSION: This new pipeline allows biologists to develop their own predictive models based on a list of genes. It facilitates the identification of new regulatory biomolecules using knowledge graphs and predictive computational methods. Our workflow is implemented in an automatic python pipeline which is publicly available at https://github.com/LokmaneChebouba/key-pipeand contains as testing data all the data used in this paper.

MiR-146a is over-expressed and controls IL-6 production in cystic fibrosis macrophages.

Cystic fibrosis (CF) is an inherited disease that is characterised by susceptibility to bacterial infections and chronic lung inflammation. Recently, it was suggested that macrophages contribute to impaired host defence and excessive inflammatory responses in CF. Indeed, dysfunction attributed to CF macrophages includes decreased bacterial killing and exaggerated inflammatory responses. However, the mechanisms behind such defects have only been partially defined. MicroRNAs (miRNAs) have emerged as key regulators of several macrophage functions, including their activation, differentiation and polarisation. The goal of this study was to investigate whether miRNA dysregulation underlies the functional abnormalities of CF macrophages. MiRNA profiling of macrophages was performed, with 22 miRNAs identified as differentially expressed between CF and non-CF individuals. Among these, miR-146a was associated with significant enrichment of validated target genes involved in responses to microorganisms and inflammation. As miR-146a dysregulation has been reported in several human inflammatory diseases, we analysed the impact of increased miR-146a expression on inflammatory responses of CF macrophages. These data show that inhibition of miR-146a in lipopolysaccharide-stimulated CF macrophages results in increased interleukin-6 production, which suggests that miR-146a overexpression in CF is functional, to restrict inflammatory responses.

In silico prediction of Heterocyclic Aromatic Amines metabolism susceptible to form DNA adducts in humans.

Heterocyclic Aromatic Amines (HAAs) are environmental and food contaminants that are classified as probable or possible carcinogens by the International Agency for Research on Cancer. Thirty different HAAs have been identified. However the metabolism of only three of them have been fully characterized in human hepatocytes: AαC (2-amino-9H-pyrido[2,3-b]indole), MeIQx (2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline) and PhIP (2-amino-1-methyl-6-phenyl-imidazo[4,5-b]pyridine). In this study, we use an integrative approach to accurately predict the biotransformation of 30 HAAs into DNA reactive and non DNA reactive compounds. We first build predicted metabolites networks by iterating a knowledge-based expert system of prediction of metabolic reactions based on fingerprint similarities. Next, we combine several methods for predicting Sites Of Metabolism (SOM) in order to reduce the metabolite reaction graphs and to predict the metabolites reactive with DNA. We validate the method by comparing the experimental versus predicted data for the known AαC, MeIQx and PhIP metabolism. 28 of the 30 experimentally determined metabolites are well predicted and 9 of the 10 metabolites known to form DNA adducts are predicted with a high probability to be reactive with DNA. Applying our approach to the 27 unknown HAAs, we generate maps for the metabolic biotransformation of each HAA, including new metabolites with a high-predicted DNA reactivity, which can be further explored through an user-friendly and interactive web interface.

Correction: Proteomic screening identifies the zonula occludens protein ZO-1 as a new partner for ADAM12 in invadopodia-like structures.

[This corrects the article DOI: 10.18632/oncotarget.25106.].

Proteomic screening identifies the zonula occludens protein ZO-1 as a new partner for ADAM12 in invadopodia-like structures.

The epithelial mesenchymal transition (EMT) is a key process for cancer cell invasion and migration. This complex program whereby epithelial tumor cells loose polarity and acquire mesenchymal phenotype is driven by the regulation of cell-cell adhesion and cell-substrate interactions. We recently described the association of ADAM12 with EMT and we now use immunoprecipitation and proteomic approaches to identify interacting partners for ADAM12 during EMT. We identify twenty proteins that are involved in molecular mechanisms associated with adhesion/invasion processes. Integrative network analyses point out the zonula occludens protein ZO-1, as a new potential partner for ADAM12. In silico screening demonstrates that ZO-1 and ADAM12 are coexpressed in breast cancer cell lines sharing EMT signature. We validate the interaction between ZO-1 and ADAM12 in invasive breast cancer cell lines and show that ZO-1 and ADAM12 co-localize in actin- and cortactin-rich structures. Silencing either ADAM12 or ZO-1 inhibits gelatin degradation demonstrating that both proteins are required for matrix degradation. We further show that matrix metalloprotease 14, known to mediate degradation of collagen in invadopodia-like structures interacts with ZO-1. Depletion of PKCε that regulates the recruitment of ADAM12 and ZO-1 to cell membranes induces a decrease in ADAM12 and ZO-1 at invadopodia-like structures and degradation activity. Together our data provide evidence for a new interaction between ADAM12, a mesenchymal marker induced during TGF-ß-dependent EMT and ZO-1, a scaffolding protein expressed in tight junctions of epithelial cells, both proteins being redistributed at the invadopodia-like structures of mesenchymal invasive cells to promote PKCε-dependent matrix degradation.

Molecular and tissue alterations of collagens in fibrosis.

The collagen network is altered in fibrotic diseases associated with extracellular matrix (ECM) biosynthesis and remodeling. This mini-review focuses on the quantitative and qualitative modifications of collagens occurring at the molecular and tissue levels in fibrosis. They result from changes in collagen expression, biosynthesis, enzymatic cross-linking and degradation by several protease families. These molecular modifications, which are mostly regulated by TGF-ß, are associated with altered collagen organization at the tissue level, leading to a fibrotic signature that can be analyzed by Second Harmonic Generation (SHG) microscopy.

A modeling approach to evaluate the balance between bioactivation and detoxification of MeIQx in human hepatocytes.

BACKGROUND: Heterocyclic aromatic amines (HAA) are environmental and food contaminants that are potentially carcinogenic for humans. 2-Amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx) is one of the most abundant HAA formed in cooked meat. MeIQx is metabolized by cytochrome P450 1A2 in the human liver into detoxificated and bioactivated products. Once bioactivated, MeIQx metabolites can lead to DNA adduct formation responsible for further genome instability. METHODS: Using a computational approach, we developed a numerical model for MeIQx metabolism in the liver that predicts the MeIQx biotransformation into detoxification or bioactivation pathways according to the concentration of MeIQx. RESULTS: Our results demonstrate that (1) the detoxification pathway predominates, (2) the ratio between detoxification and bioactivation pathways is not linear and shows a maximum at 10 µM of MeIQx in hepatocyte cell models, and (3) CYP1A2 is a key enzyme in the system that regulates the balance between bioactivation and detoxification. Our analysis suggests that such a ratio could be considered as an indicator of MeIQx genotoxicity at a low concentration of MeIQx. CONCLUSIONS: Our model permits the investigation of the balance between bioactivation (i.e., DNA adduct formation pathway through the prediction of potential genotoxic compounds) and detoxification of MeIQx in order to predict the behaviour of this environmental contaminant in the human liver. It highlights the importance of complex regulations of enzyme competitions that should be taken into account in any further multi-organ models.

Geometric analysis of pathways dynamics: Application to versatility of TGF-ß receptors.

We propose a new geometric approach to describe the qualitative dynamics of chemical reactions networks. By this method we identify metastable regimes, defined as low dimensional regions of the phase space close to which the dynamics is much slower compared to the rest of the phase space. These metastable regimes depend on the network topology and on the orders of magnitude of the kinetic parameters. Benchmarking of the method on a computational biology model repository suggests that the number of metastable regimes is sub-exponential in the number of variables and equations. The dynamics of the network can be described as a sequence of jumps from one metastable regime to another. We show that a geometrically computed connectivity graph restricts the set of possible jumps. We also provide finite state machine (Markov chain) models for such dynamic changes. Applied to signal transduction models, our approach unravels dynamical and functional capacities of signalling pathways, as well as parameters responsible for specificity of the pathway response. In particular, for a model of TGFß signalling, we find that the ratio of TGFBR2 to TGFBR1 receptors concentrations can be used to discriminate between metastable regimes. Using expression data from the NCI60 panel of human tumor cell lines, we show that aggressive and non-aggressive tumour cell lines function in different metastable regimes and can be distinguished by measuring the relative concentrations of receptors of the two types.

In silico characterization of the interaction between LSKL peptide, a LAP-TGF-beta derived peptide, and ADAMTS1.

Metalloproteases involved in extracellular matrix remodeling play a pivotal role in cell response by regulating the bioavailability of cytokines and growth factors. Recently, the disintegrin and metalloprotease, ADAMTS1 has been demonstrated to be able to activate the transforming growth factor TGF-ß, a major factor in fibrosis and cancer. The KTFR sequence from ADAMTS1 is responsible for the interaction with the LSKL peptide from the latent form of TGF-ß, leading to its activation. While the atomic details of the interaction site can be the basis of the rational design of efficient inhibitory molecules, the binding mode of interaction is totally unknown. In this study, we show that recombinant fragments of human ADAMTS1 containing KTFR sequence keep the ability to bind the latent form of TGF-ß. The recombinant fragment with the best affinity is modeled to investigate the binding mode of LSKL peptide with ADAMTS1 at the atomic level. Using a combined approach with molecular docking and multiple independent molecular dynamics (MD) simulations, we provide the binding mode of LSKL peptide with ADAMTS1. The MD simulations starting with the two lowest energy model predicted by molecular docking shows stable interactions characterized by 3 salt bridges (K3-NH3(+) with E626-COO(-); L4-COO(-) with K619-NH3(+); L1-NH3(+) with E624-COO(-)) and 2 hydrogen bonds (S2-OH with E623-COO(-); L4-NH with E623-COO(-)). The knowledge of this interaction mechanism paves the way to the design of more potent and more specific inhibitors against the inappropriate activation of TGF-ß by ADAMTS1 in liver diseases.

TGFß Signaling Intersects with CD103 Integrin Signaling to Promote T-Lymphocyte Accumulation and Antitumor Activity in the Lung Tumor Microenvironment.

Homing of CD8(+) T lymphocytes to the tumor microenvironment is an important step for mounting a robust antitumor immune response. TGFß is responsible for CD103 (αEß7) integrin induction in activated intraepithelial CD8(+) T lymphocytes. However, the interplay between TGFß and CD103 and their contribution to T-cell infiltration and antitumor activity remain unknown. Here, we used viable human lung tumor slices and autologous tumor antigen-specific T-lymphocyte clones to provide evidence that CD103 is directly involved in T-lymphocyte recruitment within epithelial tumor islets and intratumoral early T-cell signaling. Moreover, TGFß enhanced CD103-dependent T-cell adhesion and signaling, whereas it inhibited leukocyte function-associated antigen (LFA)-1 (αLß2) integrin expression and LFA-1-mediated T-lymphocyte functions. Mechanistic investigations revealed that TGFß bound to its receptors (TGFBR), which promoted the recruitment and phosphorylation of integrin-linked kinase (ILK) by TGFBR1. We further show that ILK interacted with the CD103 intracellular domain, resulting in protein kinase B (PKB)/AKT activation, thereby initiating integrin inside-out signaling. Collectively, our findings suggest that the abundance of TGFß in the tumor microenvironment may in fact engage with integrin signaling pathways to promote T-lymphocyte antitumor functions, with potential implications for T-cell-based immunotherapies for cancer. Cancer Res; 76(7); 1757-69. ©2016 AACR.

The probiotic Propionibacterium freudenreichii as a new adjuvant for TRAIL-based therapy in colorectal cancer.

TNF-Related Apoptosis-Inducing Ligand (TRAIL) is a well-known apoptosis inducer, which activates the extrinsic death pathway. TRAIL is pro-apoptotic on colon cancer cells, while not cytotoxic towards normal healthy cells. However, its clinical use is limited by cell resistance to cell death which occurs in approximately 50% of cancer cells. Short Chain Fatty Acids (SCFA) are also known to specifically induce apoptosis of cancer cells. In accordance, we have shown that food grade dairy propionibacteria induce intrinsic apoptosis of colon cancer cells, via the production and release of SCFA (propionate and acetate) acting on mitochondria. Here, we investigated possible synergistic effect between Propionibacterium freudenreichii and TRAIL. Indeed, we hypothesized that acting on both extrinsic and intrinsic death pathways may exert a synergistic pro-apoptotic effect. Whole transcriptomic analysis demonstrated that propionibacterial supernatant or propionibacterial metabolites (propionate and acetate), in combination with TRAIL, increased pro-apoptotic gene expression (TRAIL-R2/DR5) and decreased anti-apoptotic gene expression (FLIP, XIAP) in HT29 human colon cancer cells. The revealed synergistic pro-apoptotic effect, depending on both death receptors (TRAIL-R1/DR4, TRAIL-R2/DR5) and caspases (caspase-8, -9 and -3) activation, was lethal on cancer cells but not on normal human intestinal epithelial cells (HIEC), and was inhibited by Bcl-2 expression. Finally, milk fermented by P. freudenreichii induced HT29 cells apoptosis and enhanced TRAIL cytotoxic activity, as did P. freudenreichii DMEM culture supernatants or its SCFA metabolites. These results open new perspectives for food grade P. freudenreichii-containing products in order to potentiate TRAIL-based cancer therapy in colorectal cancer.

Increasing 3D Matrix Rigidity Strengthens Proliferation and Spheroid Development of Human Liver Cells in a Constant Growth Factor Environment.

Mechanical forces influence the growth and shape of virtually all tissues and organs. Recent studies show that increased cell contractibility, growth and differentiation might be normalized by modulating cell tensions. Particularly, the role of these tensions applied by the extracellular matrix during liver fibrosis could influence the hepatocarcinogenesis process. The objective of this study is to determine if 3D stiffness could influence growth and phenotype of normal and transformed hepatocytes and to integrate extracellular matrix (ECM) stiffness to tensional homeostasis. We have developed an appropriate 3D culture model: hepatic cells within three-dimensional collagen matrices with varying rigidity. Our results demonstrate that the rigidity influenced the cell phenotype and induced spheroid clusters development whereas in soft matrices, Huh7 transformed cells were less proliferative, well-spread and flattened. We confirmed that ERK1 played a predominant role over ERK2 in cisplatin-induced death, whereas ERK2 mainly controlled proliferation. As compared to 2D culture, 3D cultures are associated with epithelial markers expression. Interestingly, proliferation of normal hepatocytes was also induced in rigid gels. Furthermore, biotransformation activities are increased in 3D gels, where CYP1A2 enzyme can be highly induced/activated in primary culture of human hepatocytes embedded in the matrix. In conclusion, we demonstrated that increasing 3D rigidity could promote proliferation and spheroid developments of liver cells demonstrating that 3D collagen gels are an attractive tool for studying rigidity-dependent homeostasis of the liver cells embedded in the matrix and should be privileged for both chronic toxicological and pharmacological drug screening.

The Disintegrin and Metalloprotease ADAM12 Is Associated with TGF-ß-Induced Epithelial to Mesenchymal Transition.

The increased expression of the Disintegrin and Metalloprotease ADAM12 has been associated with human cancers, however its role remain unclear. We have previously reported that ADAM12 expression is induced by the transforming growth factor, TGF-ß and promotes TGF-ß-dependent signaling through interaction with the type II receptor of TGF-ß. Here we explore the implication of ADAM12 in TGF-ß-mediated epithelial to mesenchymal transition (EMT), a key process in cancer progression. We show that ADAM12 expression is correlated with EMT markers in human breast cancer cell lines and biopsies. Using a non-malignant breast epithelial cell line (MCF10A), we demonstrate that TGF-ß-induced EMT increases expression of the membrane-anchored ADAM12L long form. Importantly, ADAM12L overexpression in MCF10A is sufficient to induce loss of cell-cell contact, reorganization of actin cytoskeleton, up-regulation of EMT markers and chemoresistance. These effects are independent of the proteolytic activity but require the cytoplasmic tail and are specific of ADAM12L since overexpression of ADAM12S failed to induce similar changes. We further demonstrate that ADAM12L-dependent EMT is associated with increased phosphorylation of Smad3, Akt and ERK proteins. Conversely, inhibition of TGF-ß receptors or ERK activities reverses ADAM12L-induced mesenchymal phenotype. Together our data demonstrate that ADAM12L is associated with EMT and contributes to TGF-ß-dependent EMT by favoring both Smad-dependent and Smad-independent pathways.