AMYPdb: A database dedicated to amyloid precursor proteins

<p>Abstract</p> <p>Background</p> <p>Misfolding and aggregation of proteins into ordered fibrillar structures is associated with a number of severe pathologies, including Alzheimer's disease, prion diseases, and type II diabetes. The rapid accumulation of knowledge about the sequences and structures of these proteins allows using of <it>in silico </it>methods to investigate the molecular mechanisms of their abnormal conformational changes and assembly. However, such an approach requires the collection of accurate data, which are inconveniently dispersed among several generalist databases.</p> <p>Results</p> <p>We therefore created a free online knowledge database (AMYPdb) dedicated to amyloid precursor proteins and we have performed large scale sequence analysis of the included data. Currently, AMYPdb integrates data on 31 families, including 1,705 proteins from nearly 600 organisms. It displays links to more than 2,300 bibliographic references and 1,200 3D-structures. A Wiki system is available to insert data into the database, providing a sharing and collaboration environment. We generated and analyzed 3,621 amino acid sequence patterns, reporting highly specific patterns for each amyloid family, along with patterns likely to be involved in protein misfolding and aggregation.</p> <p>Conclusion</p> <p>AMYPdb is a comprehensive online database aiming at the centralization of bioinformatic data regarding all amyloid proteins and their precursors. Our sequence pattern discovery and analysis approach unveiled protein regions of significant interest. AMYPdb is freely accessible <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>.</p

Regulation of epithelial plasticity by miR-424 and miR-200 in a new prostate cancer metastasis model

Using an in vivo cycling strategy, we selected metastatic cancer cells from the lymph nodes (LN) of mice bearing orthotopic DU145 human prostate tumors. Repeated rounds of metastatic selection (LN1–LN4) progressively increased the epithelial phenotype, resulting in a new model of tumor cell mesenchymal-epithelial transition (MET). DU145-LN4 showed increased cell-cell adhesions, higher expression of multiple epithelial markers, such as E-cadherin, EpCAM and cytokeratin 18, and reduced expression of mesenchymal markers such as vimentin. The MET in DU145-LN4 cells was accompanied by increased expression of the miR-200 family, and antimiRs to miR-200c and miR-141 induced an EMT. MET also correlated with the loss of miR-424. Ectopic transient and stable miR-424 expression induced EMT, with reduced epithelial marker expression and increased cell scattering. Our model provides evidence for spontaneous MET in vivo. We show that this cellular plasticity can be mediated through the combined action of miR-424 and the miR-200 family

A Novel Network Integrating a miRNA-203/SNAI1 Feedback Loop which Regulates Epithelial to Mesenchymal Transition

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

TECRL, a new life‐threatening inherited arrhythmia gene associated with overlapping clinical features of both LQTS and CPVT

Genetic causes of many familial arrhythmia syndromes remain elusive. In this study, whole-exome sequencing (WES) was carried out on patients from three different families that presented with life-threatening arrhythmias and high risk of sudden cardiac death (SCD). Two French Canadian probands carried identical homozygous rare variant in TECRL gene (p.Arg196Gln), which encodes the trans-2,3-enoyl-CoA reductase-like protein. Both patients had cardiac arrest, stress-induced atrial and ventricular tachycardia, and QT prolongation on adrenergic stimulation. A third patient from a consanguineous Sudanese family diagnosed with catecholaminergic polymorphic ventricular tachycardia (CPVT) had a homozygous splice site mutation (c.331+1G&gt;A) in TECRL Analysis of intracellular calcium ([Ca(2+)]i) dynamics in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) generated from this individual (TECRLHom-hiPSCs), his heterozygous but clinically asymptomatic father (TECRLHet-hiPSCs), and a healthy individual (CTRL-hiPSCs) from the same Sudanese family, revealed smaller [Ca(2+)]i transient amplitudes as well as elevated diastolic [Ca(2+)]i in TECRLHom-hiPSC-CMs compared with CTRL-hiPSC-CMs. The [Ca(2+)]i transient also rose markedly slower and contained lower sarcoplasmic reticulum (SR) calcium stores, evidenced by the decreased magnitude of caffeine-induced [Ca(2+)]i transients. In addition, the decay phase of the [Ca(2+)]i transient was slower in TECRLHom-hiPSC-CMs due to decreased SERCA and NCX activities. Furthermore, TECRLHom-hiPSC-CMs showed prolonged action potentials (APs) compared with CTRL-hiPSC-CMs. TECRL knockdown in control human embryonic stem cell-derived CMs (hESC-CMs) also resulted in significantly longer APs. Moreover, stimulation by noradrenaline (NA) significantly increased the propensity for triggered activity based on delayed afterdepolarizations (DADs) in TECRLHom-hiPSC-CMs and treatment with flecainide, a class Ic antiarrhythmic drug, significantly reduced the triggered activity in these cells. In summary, we report that mutations in TECRL are associated with inherited arrhythmias characterized by clinical features of both LQTS and CPVT Patient-specific hiPSC-CMs recapitulated salient features of the clinical phenotype and provide a platform for drug screening evidenced by initial identification of flecainide as a potential therapeutic. These findings have implications for diagnosis and treatment of inherited cardiac arrhythmias

Predicting missing expression values in gene regulatory networks using a discrete logic modeling optimization guided by network stable states

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

M@IA: a modular open-source application for microarray workflow and integrative datamining.

International audienceMicroarray technology is a widely used approach to gene expression analysis. Many tools for microarray management and data analysis have been developed, and recently new methods have been proposed for deciphering biological pathways by integrating microarray data with other data sources. However, to improve microarray analysis and provide meaningful gene interaction networks, integrated software solutions are still needed. Therefore, we developed M@IA, an environment for DNA microarray data analysis allowing gene network reconstruction. M@IA is a microarray integrated application which includes all of the steps of a microarray study, from MIAME-compliant raw data storage and processing gene expression analysis. Furthermore, M@IA allows automatic gene annotation based on ontology, metabolic/signalling pathways, protein interaction, miRNA and transcriptional factor associations, as well as integrative analysis of gene interaction networks. Statistical and graphical methods facilitate analysis, yielding new hypotheses on gene expression data. To illustrate our approach, we applied M@IA modules to microarray data taken from an experiment on liver tissue. We integrated differentially expressed genes with additional biological information, thus identifying new molecular interaction networks that are associated with fibrogenesis. M@IA is a new application for microarray management and data analysis, offering functional insights into microarray data by the combination of gene expression data and biological knowledge annotation based on interactive graphs. M@IA is an interactive multi-user interface based on a flexible modular architecture and it is freely available for academic users at http://maia.genouest.org

Integrative study of the aggregation of amyloid peptide, PHF6 a test case.

Poster présenté par C. Garnie

The AnnotSV webserver in 2023: updated visualization and ranking

International audienceMuch of the human genetics variant repertoire is composed of single nucleotide variants (SNV) and small insertion/deletions (indel) but structural variants (SV) remain a major part of our modified DNA. SV detection has often been a complex question to answer either because of the necessity to use different technologies (array CGH, SNP array, Karyotype, Optical Genome Mapping…) to detect each category of SV or to get an appropriate resolution (Whole Genome Sequencing). Thanks to the deluge of pangenomic analysis, Human geneticists are accumulating SV and their interpretation remains time consuming and challenging. The AnnotSV webserver (https://www.lbgi.fr/AnnotSV/) aims at being an efficient tool to (i) annotate and interpret SV potential pathogenicity in the context of human diseases, (ii) recognize potential false positive variants from all the SV identified and (iii) visualize the patient variants repertoire. The most recent developments in the AnnotSV webserver are: (i) updated annotations sources and ranking, (ii) three novel output formats to allow diverse utilization (analysis, pipelines), as well as (iii) two novel user interfaces including an interactive circos view