References of "Le Béchec, Antony 40000413"
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See detailmiR-661 expression in SNAI1-induced epithelial to mesenchymal transition contributes to breast cancer cell invasion by targeting Nectin-1 and StarD10 messengers
Vetter, G.; Saumet, A.; Moes, Michèle UL et al

in Oncogene (2016), 35(5), 670

Epithelial to mesenchymal transition (EMT) is a key step toward metastasis. MCF7 breast cancer cells conditionally expressing the EMT master regulator SNAI1 were used to identify early expressed microRNAs ... [more ▼]

Epithelial to mesenchymal transition (EMT) is a key step toward metastasis. MCF7 breast cancer cells conditionally expressing the EMT master regulator SNAI1 were used to identify early expressed microRNAs (miRNAs) and their targets that may contribute to the EMT process. Potential targets of miRNAs were identified by matching lists of in silico predicted targets and of inversely expressed mRNAs. MiRNAs were ranked based on the number of predicted hits, highlighting miR-661, a miRNA with so far no reported role in EMT. MiR-661 was found required for efficient invasion of breast cancer cells by destabilizing two of its predicted mRNA targets, the cell-cell adhesion protein Nectin-1 and the lipid transferase StarD10, resulting, in turn, in the downregulation of epithelial markers. Reexpression of Nectin-1 or StarD10 lacking the 3'-untranslated region counteracted SNAI1-induced invasion. Importantly, analysis of public transcriptomic data from a cohort of 295 well-characterized breast tumor specimen revealed that expression of StarD10 is highly associated with markers of luminal subtypes whereas its loss negatively correlated with the EMT-related, basal-like subtype. Collectively, our non-a priori approach revealed a nonpredicted link between SNAI1-triggered EMT and the down-regulation of Nectin-1 and StarD10 through the up-regulation of miR-661, which may contribute to the invasion of breast cancer cells and poor disease outcome. [less ▲]

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See detailMMP13 mutations are the cause of recessive metaphyseal dysplasia, Spahr type
Le Béchec, Antony UL; Bonafe, Luisa; Liang, Jinlong et al

in American Journal of Medical Genetics. Part A (2014), 164(5), 1175-1179

Metaphyseal dysplasia, Spahr type (MDST; OMIM 250400) was described in 1961 based on the observation of four children in one family who had rickets-like metaphyseal changes but normal blood chemistry and ... [more ▼]

Metaphyseal dysplasia, Spahr type (MDST; OMIM 250400) was described in 1961 based on the observation of four children in one family who had rickets-like metaphyseal changes but normal blood chemistry and moderate short stature. Its molecular basis and nosologic status remained unknown. We followed up on those individuals and diagnosed the disorder in an additional member of the family. We used exome sequencing to ascertain the underlying mutation and explored its consequences on three-dimensional models of the affected protein. The MDST phenotype is associated with moderate short stature and knee pain in adults, while extra-skeletal complications are not observed. The sequencing showed that MDST segregated with a c.619T>G single nucleotide transversion in MMP13. The predicted non-conservative amino acid substitution, p. Trp207Gly, disrupts a crucial hydrogen bond in the calcium-binding region of the catalytic domain of the matrix metalloproteinase, MMP13. The MDST phenotype is associated with recessive MMP13 mutations, confirming the importance of this metalloproteinase in the metaphyseal growth plate. Dominant MMP13 mutations have been associated with metaphyseal anadysplasia (OMIM 602111), while a single child homozygous for a MMP13 mutation had been previously diagnosed as "recessive metaphyseal anadysplasia," that we conclude is the same nosologic entity as MDST. Molecular confirmation of MDST allows distinction of it from dominant conditions (e.g., metaphyseal dysplasia, Schmid type; OMIM # 156500) and from more severe multi-system conditions (such as cartilage-hair hypoplasia; OMIM # 250250) and to give precise recurrence risks and prognosis. [less ▲]

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See detailOn different aspects of network analysis in systems biology
Chaiboonchoe, Amphun; Jurkowski, Wiktor UL; Pellet, Johann et al

in Systems Biology (2013), 1

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations ... [more ▼]

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations in disease. Regulatory and signalling pathways involve DNA, RNA, proteins and metabolites as key elements to coordinate most aspects of cellular functioning. Cellular processes depend on the structure and dynamics of gene regulatory networks and can be studied by employing a network representation of molecular interactions. This chapter describes several types of biological networks, how combination of different analytic approaches can be used to study diseases, and provides a list of selected tools for network visualization and analysis. It also introduces protein-protein interaction networks, gene regulatory networks, signalling networks and metabolic networks to illustrate concepts underlying network representation of cellular processes and molecular interactions. It finally discusses how the level of accuracy in inferring functional relationships influences the choice of methods applied for the analysis of a particular biological network type. © Springer Science+Business Media Dordrecht 2013. All rights are reserved. [less ▲]

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See detailNetwork analysis for systems biology
Chaiboonchoe, A.; Jurkowski, Wiktor UL; Pellet, J. et al

in Prokop, Aleš; Csukás (Eds.) Springer book in Systems Biology, Vol.1: Systems Biology:, Integrative Biology and Simulation Tools (2013)

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations ... [more ▼]

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations in disease. Regulatory and signalling pathways, which involve DNA, RNA proteins and metabolites as key elements, coordinate most aspects of cellular functioning. Cellular processes, which are dependent on the structure and dynamics of gene regulatory networks, can be studied by employing a network representation of molecular interactions. In this chapter we describe several types of networks and how combination of different analytic approaches can be used to study diseases. We provide a list of selected tools for visualization and network analysis. We introduce protein-protein interaction networks, gene regulatory networks, signalling networks and metabolic networks. We then define concepts underlying network representation of cellular processes and molecular interactions. We finally discuss how the level of accuracy in inferring functional relationships influences the choice of methods applied for the analysis of a particular network type. [less ▲]

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See detailFAM111A mutations result in hypoparathyroidism and impaired skeletal development
Unger, Sheila; Górna, Maria W.; Le Béchec, Antony UL et al

in American Journal of Human Genetics (2013), 92(6), 990-995

Kenny-Caffey syndrome (KCS) and the similar but more severe osteocraniostenosis (OCS) are genetic conditions characterized by impaired skeletal development with small and dense bones, short stature, and ... [more ▼]

Kenny-Caffey syndrome (KCS) and the similar but more severe osteocraniostenosis (OCS) are genetic conditions characterized by impaired skeletal development with small and dense bones, short stature, and primary hypoparathyroidism with hypocalcemia. We studied five individuals with KCS and five with OCS and found that all of them had heterozygous mutations in FAM111A. One mutation was identified in four unrelated individuals with KCS, and another one was identified in two unrelated individuals with OCS; all occurred de novo. Thus, OCS and KCS are allelic disorders of different severity. FAM111A codes for a 611 amino acid protein with homology to trypsin-like peptidases. Although FAM111A has been found to bind to the large T-antigen of SV40 and restrict viral replication, its native function is unknown. Molecular modeling of FAM111A shows that residues affected by KCS and OCS mutations do not map close to the active site but are clustered on a segment of the protein and are at, or close to, its outer surface, suggesting that the pathogenesis involves the interaction with as yet unidentified partner proteins rather than impaired catalysis. FAM111A appears to be crucial to a pathway that governs parathyroid hormone production, calcium homeostasis, and skeletal development and growth. © 2013 The American Society of Human Genetics. [less ▲]

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See detailPredicting missing expression values in gene regulatory networks using a discrete logic modeling optimization guided by network stable states
Crespo, Isaac UL; Krishna, Abhimanyu UL; Le Béchec, Antony UL et al

in Nucleic Acids Research (2013), 41(1), 8

The development of new high-throughput technologies enables us to measure genome-wide transcription levels, protein abundance, metabolite concentration, etc. Nevertheless, these experimental data are ... [more ▼]

The development of new high-throughput technologies enables us to measure genome-wide transcription levels, protein abundance, metabolite concentration, etc. Nevertheless, these experimental data are often noisy and incomplete, which hinders data analysis, modeling and prediction. Here, we propose a method to predict expression values of genes involved in stable cellular phenotypes from the expression values of the remaining genes in a literature-based gene regulatory network. The consistency between predicted and known stable states from experimental data is used to guide an iterative network pruning that contextualizes the network to the biological conditions under which the expression data were obtained. Using the contextualized network and the property of network stability we predict gene expression values missing from experimental data. The prediction method assumes a Boolean model to compute steady states of networks and an evolutionary algorithm to iteratively prune the networks. The evolutionary algorithm samples the probability distribution of positive feedback loops or positive circuits and individual interactions within the subpopulation of the best-pruned networks at each iteration. The resulting expression inference is based not only on previous knowledge about local connectivity but also on a global network property (stability), providing robustness in the predictions. [less ▲]

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See detailA Novel Network Integrating a miRNA-203/SNAI1 Feedback Loop which Regulates Epithelial to Mesenchymal Transition.
Moes, Michèle UL; Le Béchec, Antony UL; Crespo, Isaac UL et al

in PLoS ONE (2012), 7(4), 35440

Background: The majority of human cancer deaths are caused by metastasis. The metastatic dissemination is initiated by the breakdown of epithelial cell homeostasis. During this phenomenon, referred to as ... [more ▼]

Background: The majority of human cancer deaths are caused by metastasis. The metastatic dissemination is initiated by the breakdown of epithelial cell homeostasis. During this phenomenon, referred to as epithelial to mesenchymal transition (EMT), cells change their genetic and trancriptomic program leading to phenotypic and functional alterations. The challenge of understanding this dynamic process resides in unraveling regulatory networks involving master transcription factors (e.g. SNAI1/2, ZEB1/2 and TWIST1) and microRNAs. Here we investigated microRNAs regulated by SNAI1 and their potential role in the regulatory networks underlying epithelial plasticity. Results: By a large-scale analysis on epithelial plasticity, we highlighted miR-203 and its molecular link with SNAI1 and the miR-200 family, key regulators of epithelial homeostasis. During SNAI1-induced EMT in MCF7 breast cancer cells, miR-203 and miR-200 family members were repressed in a timely correlated manner. Importantly, miR-203 repressed endogenous SNAI1, forming a double negative miR203/SNAI1 feedback loop. We integrated this novel miR203/SNAI1 with the known miR200/ZEB feedback loops to construct an a priori EMT core network. Dynamic simulations revealed stable epithelial and mesenchymal states, and underscored the crucial role of the miR203/SNAI1 feedback loop in state transitions underlying epithelial plasticity. Conclusion: By combining computational biology and experimental approaches, we propose a novel EMT core network integrating two fundamental negative feedback loops, miR203/SNAI1 and miR200/ZEB. Altogether our analysis implies that this novel EMT core network could function as a switch controlling epithelial cell plasticity during differentiation and cancer progression. [less ▲]

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See detailTime-resolved analysis of transcriptional events during SNAI1-triggered epithelial to mesenchymal transition
Vetter, G.; Le Béchec, Antony UL; Muller, J. et al

in Biochemical and Biophysical Research Communications (2009), 385(4), 485-91

The transcription regulator SNAI1 triggers a transcriptional program leading to epithelial to mesenchymal transition (EMT), providing epithelial cells with mesenchymal features and invasive properties ... [more ▼]

The transcription regulator SNAI1 triggers a transcriptional program leading to epithelial to mesenchymal transition (EMT), providing epithelial cells with mesenchymal features and invasive properties during embryonic development and tumor progression. To identify early transcriptional changes occurring during SNAI1-induced EMT, we performed a time-resolved genome-scale study using human breast carcinoma cells conditionally expressing SNAI1. The approach we developed for microarray data analysis, allowed identifying three distinct EMT stages and the temporal classification of genes. Importantly, we identified unexpected, biphasic expression profiles of EMT-associated genes, supporting their pivotal role during this process. Finally, we established early EMT gene networks by identifying transcription factors and their potential targets which may orchestrate early events of EMT. Collectively, our work provides a framework for the identification and future systematic analysis of novel genes which contribute to SNAI1-triggered EMT. [less ▲]

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