SETDB1 modulates the TGFß response in Duchenne muscular dystrophy myotubes.

Overactivation of the transforming growth factor-ß (TGFß) signaling in Duchenne muscular dystrophy (DMD) is a major hallmark of disease progression, leading to fibrosis and muscle dysfunction. Here, we investigated the role of SETDB1 (SET domain, bifurcated 1), a histone lysine methyltransferase involved in muscle differentiation. Our data show that, following TGFß induction, SETDB1 accumulates in the nuclei of healthy myotubes while being already present in the nuclei of DMD myotubes where TGFß signaling is constitutively activated. Transcriptomics revealed that depletion of SETDB1 in DMD myotubes leads to down-regulation of TGFß target genes coding for secreted factors involved in extracellular matrix remodeling and inflammation. Consequently, SETDB1 silencing in DMD myotubes abrogates the deleterious effect of their secretome on myoblast differentiation by impairing myoblast pro-fibrotic response. Our findings indicate that SETDB1 potentiates the TGFß-driven fibrotic response in DMD muscles, providing an additional axis for therapeutic intervention.

The cytoplasmic fraction of the histone lysine methyltransferase Setdb1 is essential for embryonic stem cells.

The major lysine methyltransferase (KMT) Setdb1 is essential for self-renewal and viability of mouse embryonic stem cells (mESCs). Setdb1 was primarily known to methylate the lysine 9 of histone 3 (H3K9) in the nucleus, where it regulates chromatin functions. However, Setdb1 is also massively localized in the cytoplasm, including in mESCs, where its role remains elusive. Here, we show that the cytoplasmic Setdb1 (cSetdb1) is essential for the survival of mESCs. Yeast two-hybrid analysis revealed that cSetdb1 interacts with several regulators of mRNA stability and protein translation machinery, such as the ESCs-specific E3 ubiquitin ligase and mRNA silencer Trim71/Lin41. We found that cSetdb1 is required for the integrity of Trim71 complex(es) involved in mRNA metabolism and translation. cSetdb1 modulates the abundance of mRNAs and the rate of newly synthesized proteins. Altogether, our data uncovered the cytoplasmic post-transcriptional regulation of gene expression mediated by a key epigenetic regulator.

Glucose controls co-translation of structurally related mRNAs via the mTOR and eIF2 pathways in human pancreatic beta cells.

Pancreatic beta cell response to glucose is critical for the maintenance of normoglycemia. A strong transcriptional response was classically described in rodent models but, interestingly, not in human cells. In this study, we exposed human pancreatic beta cells to an increased concentration of glucose and analysed at a global level the mRNAs steady state levels and their translationalability. Polysome profiling analysis showed an early acute increase in protein synthesis and a specific translation regulation of more than 400 mRNAs, independently of their transcriptional regulation. We clustered the co-regulated mRNAs according to their behaviour in translation in response to glucose and discovered common structural and sequence mRNA features. Among them mTOR- and eIF2-sensitive elements have a predominant role to increase mostly the translation of mRNAs encoding for proteins of the translational machinery. Furthermore, we show that mTOR and eIF2α pathways are independently regulated in response to glucose, participating to a translational reshaping to adapt beta cell metabolism. The early acute increase in the translation machinery components prepare the beta cell for further protein demand due to glucose-mediated metabolism changes.

The network makeup artist (NORMA-2.0): distinguishing annotated groups in a network using innovative layout strategies.

Motivation: Network biology is a dominant player in today's multi-omics era. Therefore, the need for visualization tools which can efficiently cope with intra-network heterogeneity emerges. Results: NORMA-2.0 is a web application which uses efficient layouts to group together areas of interest in a network. In this version, NORMA-2.0 utilizes three different strategies to make such groupings as distinct as possible while it preserves all of the properties from its first version where one can handle multiple networks and annotation files simultaneously. Availability and implementation: The web resource is available at http://norma.pavlopouloslab.info/. The source code is freely available at https://github.com/PavlopoulosLab/NORMA.

Bipartite graphs in systems biology and medicine: a survey of methods and applications.

The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.

From multiple pathogenicity islands to a unique organized pathogenicity archipelago.

Pathogenicity islands are sets of successive genes in a genome that determine the virulence of a bacterium. In a growing number of studies, bacterial virulence appears to be determined by multiple islands scattered along the genome. This is the case in a family of seven plant pathogens and a human pathogen that, under KdgR regulation, massively secrete enzymes such as pectinases that degrade plant cell wall. Here we show that their multiple pathogenicity islands form together a coherently organized, single "archipelago" at the genome scale. Furthermore, in half of the species, most genes encoding secreted pectinases are expressed from the same DNA strand (transcriptional co-orientation). This genome architecture favors DNA conformations that are conducive to genes spatial co-localization, sometimes complemented by co-orientation. As proteins tend to be synthetized close to their encoding genes in bacteria, we propose that this architecture would favor the efficient funneling of pectinases at convergent points within the cell. The underlying functional hypothesis is that this convergent funneling of the full blend of pectinases constitutes a crucial strategy for successful degradation of the plant cell wall. Altogether, our work provides a new approach to describe and predict, at the genome scale, the full virulence complement.

Analysis tools for the interplay between genome layout and regulation.

BACKGROUND: Genome layout and gene regulation appear to be interdependent. Understanding this interdependence is key to exploring the dynamic nature of chromosome conformation and to engineering functional genomes. Evidence for non-random genome layout, defined as the relative positioning of either co-functional or co-regulated genes, stems from two main approaches. Firstly, the analysis of contiguous genome segments across species, has highlighted the conservation of gene arrangement (synteny) along chromosomal regions. Secondly, the study of long-range interactions along a chromosome has emphasised regularities in the positioning of microbial genes that are co-regulated, co-expressed or evolutionarily correlated. While one-dimensional pattern analysis is a mature field, it is often powerless on biological datasets which tend to be incomplete, and partly incorrect. Moreover, there is a lack of comprehensive, user-friendly tools to systematically analyse, visualise, integrate and exploit regularities along genomes. RESULTS: Here we present the Genome REgulatory and Architecture Tools SCAN (GREAT:SCAN) software for the systematic study of the interplay between genome layout and gene expression regulation. GREAT: SCAN is a collection of related and interconnected applications currently able to perform systematic analyses of genome regularities as well as to improve transcription factor binding sites (TFBS) and gene regulatory network predictions based on gene positional information. CONCLUSIONS: We demonstrate the capabilities of these tools by studying on one hand the regular patterns of genome layout in the major regulons of the bacterium Escherichia coli. On the other hand, we demonstrate the capabilities to improve TFBS prediction in microbes. Finally, we highlight, by visualisation of multivariate techniques, the interplay between position and sequence information for effective transcription regulation.

GREAT: a web portal for Genome Regulatory Architecture Tools.

GREAT (Genome REgulatory Architecture Tools) is a novel web portal for tools designed to generate user-friendly and biologically useful analysis of genome architecture and regulation. The online tools of GREAT are freely accessible and compatible with essentially any operating system which runs a modern browser. GREAT is based on the analysis of genome layout -defined as the respective positioning of co-functional genes- and its relation with chromosome architecture and gene expression. GREAT tools allow users to systematically detect regular patterns along co-functional genomic features in an automatic way consisting of three individual steps and respective interactive visualizations. In addition to the complete analysis of regularities, GREAT tools enable the use of periodicity and position information for improving the prediction of transcription factor binding sites using a multi-view machine learning approach. The outcome of this integrative approach features a multivariate analysis of the interplay between the location of a gene and its regulatory sequence. GREAT results are plotted in web interactive graphs and are available for download either as individual plots, self-contained interactive pages or as machine readable tables for downstream analysis. The GREAT portal can be reached at the following URL https://absynth.issb.genopole.fr/GREAT and each individual GREAT tool is available for downloading.

A pigeonpea gene confers resistance to Asian soybean rust in soybean.

Asian soybean rust (ASR), caused by the fungus Phakopsora pachyrhizi, is one of the most economically important crop diseases, but is only treatable with fungicides, which are becoming less effective owing to the emergence of fungicide resistance. There are no commercial soybean cultivars with durable resistance to P. pachyrhizi, and although soybean resistance loci have been mapped, no resistance genes have been cloned. We report the cloning of a P. pachyrhizi resistance gene CcRpp1 (Cajanus cajan Resistance against Phakopsora pachyrhizi 1) from pigeonpea (Cajanus cajan) and show that CcRpp1 confers full resistance to P. pachyrhizi in soybean. Our findings show that legume species related to soybean such as pigeonpea, cowpea, common bean and others could provide a valuable and diverse pool of resistance traits for crop improvement.

Characterisation and analysis of the Aegilops sharonensis transcriptome, a wild relative of wheat in the Sitopsis section.

Aegilops sharonensis Eig (Sharon goatgrass) is a wild diploid relative of wheat within the Sitopsis section of Aegilops. This species represents an untapped reservoir of genetic diversity for traits of agronomic importance, especially as a source of novel disease resistance. To gain a foothold in this genetic resource, we sequenced the cDNA from leaf tissue of two geographically distinct Ae. sharonensis accessions (1644 and 2232) using the 454 Life Sciences platform. We compared the results of two different assembly programs using different parameter sets to generate 13 distinct assemblies in an attempt to maximize representation of the gene space in de novo transcriptome assembly. The most sensitive assembly (71,029 contigs; N50 674 nts) retrieved 18,684 unique best reciprocal BLAST hits (BRBH) against six previously characterised grass proteomes while the most specific assembly (30,609 contigs; N50 815 nts) retrieved 15,687 BRBH. We combined these two assemblies into a set of 62,243 non-redundant sequences and identified 139 belonging to plant disease resistance genes of the nucleotide binding leucine-rich repeat class. Based on the non-redundant sequences, we predicted 37,743 single nucleotide polymorphisms (SNP), equivalent to one per 1,142 bp. We estimated the level of heterozygosity as 1.6% in accession 1644 and 30.1% in 2232. The Ae. sharonensis leaf transcriptome provides a rich source of sequence and SNPs for this wild wheat relative. These sequences can be used with existing monocot genome sequences and EST sequence collections (e.g. barley, Brachypodium, wheat, rice, maize and Sorghum) to assist with genetic and physical mapping and candidate gene identification in Ae. sharonensis. These resources provide an initial framework to further build on and characterise the genetic and genomic structure of Ae. sharonensis.

A combined computational-experimental approach predicts human microRNA targets.

A new paradigm of gene expression regulation has emerged recently with the discovery of microRNAs (miRNAs). Most, if not all, miRNAs are thought to control gene expression, mostly by base pairing with miRNA-recognition elements (MREs) found in their messenger RNA (mRNA) targets. Although a large number of human miRNAs have been reported, many of their mRNA targets remain unknown. Here we used a combined bioinformatics and experimental approach to identify important rules governing miRNA-MRE recognition that allow prediction of human miRNA targets. We describe a computational program, "DIANA-microT", that identifies mRNA targets for animal miRNAs and predicts mRNA targets, bearing single MREs, for human and mouse miRNAs.