Key components of the eight classes of type IV secretion systems involved in bacterial conjugation or protein secretion

Conjugation of DNA through a type IV secretion system (T4SS) drives horizontal gene transfer. Yet little is known on the diversity of these nanomachines. We previously found that T4SS can be divided in eight classes based on the phylogeny of the only ubiquitous protein of T4SS (VirB4). Here, we use an ab initio approach to identify protein families systematically and specifically associated with VirB4 in each class. We built profiles for these proteins and used them to scan 2262 genomes for the presence of T4SS. Our analysis led to the identification of thousands of occurrences of 116 protein families for a total of 1623 T4SS. Importantly, we could identify almost always in our profiles the essential genes of well-studied T4SS. This allowed us to build a database with the largest number of T4SS described to date. Using profile-profile alignments, we reveal many new cases of homology between components of distant classes of T4SS. We mapped these similarities on the T4SS phylogenetic tree and thus obtained the patterns of acquisition and loss of these protein families in the history of T4SS. The identification of the key VirB4-associated proteins paves the way toward experimental analysis of poorly characterized T4SS classes.Funding. Spanish Ministry of Economy [BFU2011-26608]; European Seventh Framework Program [282004/FP7-HEALTH.2011, 612146/FP7-ICT-2013]; European Research Council Grant [EVOMOBILOME no. 281605]. Source of open access funding: European Research Council grant to the PI

Fastq sample dataset

<p>sample data for data carpentry genomics</p> <p>https://github.com/plijnzaad/elixir-cloud-genomics/</p

Extracting Dynamical Correlations and Identifying Key Residues for Allosteric Communication in Proteins by correlationplus

Erratum in Correction to "Extracting Dynamical Correlations and Identifying Key Residues for Allosteric Communication in Proteins by correlationplus". Tekpinar M, Neron B, Delarue M. J Chem Inf Model. 2021 Nov 22;61(11):5720. doi: 10.1021/acs.jcim.1c01333. Epub 2021 Nov 10. PMID: 34756028International audienceIntegrons are flexible gene-exchanging platforms that contain multiple cassettes encoding accessory genes whose order is shuffled by a specific integrase. Integrons embedded within mobile genetic elements often contain multiple antibiotic resistance genes that they spread among nosocomial pathogens and contribute to the current antibiotic resistance crisis. However, most integrons are presumably sedentary and encode a much broader diversity of functions. IntegronFinder is a widely used software to identify novel integrons in bacterial genomes, but has aged and lacks some useful functionalities to handle very large datasets of draft genomes or metagenomes. Here, we present IntegronFinder version 2. We have updated the code, improved its efficiency and usability, adapted the output to incomplete genome data, and added a few novel functions. We describe these changes and illustrate the relevance of the program by analyzing the distribution of integrons across more than 20,000 fully sequenced genomes. We also take full advantage of its novel capabilities to analyze close to 4000 Klebsiella pneumoniae genomes for the presence of integrons and antibiotic resistance genes within them. Our data show that K. pneumoniae has a large diversity of integrons and the largest mobile integron in our database of plasmids. The pangenome of these integrons contains a total of 165 different gene families with most of the largest families being related with resistance to numerous types of antibiotics. IntegronFinder is a free and open-source software available on multiple public platforms

Assessment of the interplay between scaffold geometry, induced shear stresses and cell proliferation within a packed bed perfusion bioreactor

International audienceBy favoring cell proliferation and differentiation, perfusion bioreactors proved efficient at optimizing cell culture. The aim of this study was to quantify cell proliferation within a perfusion bioreactor and correlate it to the wall shear stress (WSS) distribution by combining 3-D imaging and computational fluid dynamics simulations.NIH-3T3 fibroblasts were cultured onto a scaffold model made of impermeable polyacetal spheres or Polydimethylsiloxane cubes. After 1, 2, and 3 weeks of culture, constructs were analyzed by micro-computed tomography (mu CT) and quantification of cell proliferation was assessed. After 3 weeks, the volume of cells was found four times higher in the stacking of spheres than in the stacking of cube.3D-mu CT reconstruction of bioreactors was used as input for the numerical simulations. Using a lattice-Boltzmann method, we simulated the fluid flow within the bioreactors. We retrieved the WSS distribution (PDF) on the scaffolds surface at the beginning of cultivation and correlated this distribution to the local presence of cells after 3 weeks of cultivation. We found that the WSS distributions strongly differ between spheres and cubes even if the porosity and the specific wetted area of the stackings were very similar. The PDF is narrower and the mean WSS is lower for cubes (11 mPa) than for spheres (20 mPa). For the stacking of spheres, the relative occupancy of the surface sites by cells is maximal when WSS is greater than 20 mPa. For cubes, the relative occupancy is maximal when the WSS is lower than 10 mPa. The discrepancies between spheres and cubes are attributed to the more numerous sites in stacking of spheres that may induce 3-D (multi-layered) proliferation

MacSyFinder v2: Improved modelling and search engine to identify molecular systems in genomes

ABSTRACT Complex cellular functions are usually encoded by a set of genes in one or a few organized genetic loci in microbial genomes. MacSyFinder uses these properties to model and then annotate cellular functions in microbial genomes. This is done by integrating the identification of each individual gene at the level of the molecular system. We hereby present a major release of MacSyFinder (Macromolecular System Finder), MacSyFinder version 2 (v2). This new version is coded in Python 3 (>= 3.7). The code was improved and rationalized to facilitate future maintainability. Several new features were added to allow more flexible modelling of the systems. We introduce a more intuitive and comprehensive search engine to identify all the best candidate systems and sub-optimal ones that respect the models’ constraints. We also introduce the novel macsydata companion tool that enables the easy installation and broad distribution of the models developed for MacSyFinder (macsy-models) from GitHub repositories. Finally, we have updated, improved, and made available MacSyFinder popular models for this novel version: TXSScan to identify protein secretion systems, TFFscan to identify type IV filaments, CONJscan to identify conjugative systems, and CasFinder to identify CRISPR associated proteins

MacSyFinder: a program to mine genomes for molecular systems with an application to CRISPR-Cas systems.

Biologists often wish to use their knowledge on a few experimental models of a given molecular system to identify homologs in genomic data. We developed a generic tool for this purpose.Macromolecular System Finder (MacSyFinder) provides a flexible framework to model the properties of molecular systems (cellular machinery or pathway) including their components, evolutionary associations with other systems and genetic architecture. Modelled features also include functional analogs, and the multiple uses of a same component by different systems. Models are used to search for molecular systems in complete genomes or in unstructured data like metagenomes. The components of the systems are searched by sequence similarity using Hidden Markov model (HMM) protein profiles. The assignment of hits to a given system is decided based on compliance with the content and organization of the system model. A graphical interface, MacSyView, facilitates the analysis of the results by showing overviews of component content and genomic context. To exemplify the use of MacSyFinder we built models to detect and class CRISPR-Cas systems following a previously established classification. We show that MacSyFinder allows to easily define an accurate "Cas-finder" using publicly available protein profiles.MacSyFinder is a standalone application implemented in Python. It requires Python 2.7, Hmmer and makeblastdb (version 2.2.28 or higher). It is freely available with its source code under a GPLv3 license at https://github.com/gem-pasteur/macsyfinder. It is compatible with all platforms supporting Python and Hmmer/makeblastdb. The "Cas-finder" (models and HMM profiles) is distributed as a compressed tarball archive as Supporting Information

Identification and analysis of integrons and cassette arrays in bacterial genomes

International audienceIntegrons recombine gene arrays and favor the spread of antibiotic resistance. Their broader roles in bacterial adaptation remain mysterious, partly due to lack of computational tools. We made a program – IntegronFinder – to identify integrons with high accuracy and sensitivity. IntegronFinder is available as a standalone program and as a web application. It searches for attC sites using covari-ance models, for integron-integrases using HMM profiles , and for other features (promoters, attI site) using pattern matching. We searched for integrons, integron-integrases lacking attC sites, and clusters of attC sites lacking a neighboring integron-integrase in bacterial genomes. All these elements are especially frequent in genomes of intermediate size. They are missing in some key phyla, such as-Proteobacteria, which might reflect selection against cell lineages that acquire integrons. The similarity between attC sites is proportional to the number of cassettes in the integron, and is particularly low in clusters of attC sites lacking integron-integrases. The latter are unexpectedly abundant in genomes lacking integron-integrases or their remains, and have a large novel pool of cassettes lacking homologs in the databases. They might represent an evolutionary step between the acquisition of genes within inte-grons and their stabilization in the new genome

Antisense transcriptional interference mediates condition-specific gene repression in budding yeast

International audiencePervasive transcription generates many unstable non-coding transcripts in budding yeast. The transcription of such noncoding RNAs, in particular an-tisense RNAs (asRNAs), has been shown in a few examples to repress the expression of the associated mRNAs. Yet, such mechanism is not known to commonly contribute to the regulation of a given class of genes. Using a mutant context that stabilized pervasive transcripts, we observed that the least expressed mRNAs during the exponential phase were associated with high levels of asRNAs. These asR-NAs also overlapped their corresponding gene promoters with a much higher frequency than average. Interrupting antisense transcription of a subset of genes corresponding to quiescence-enriched mRNAs restored their expression. The underlying mechanism acts in cis and involves several chro-matin modifiers. Our results convey that transcription interference represses up to 30% of the 590 least expressed genes, which includes 163 genes with quiescence-enriched mRNAs. We also found that pervasive transcripts constitute a higher fraction of the transcriptome in quiescence relative to the exponential phase, consistent with gene expression itself playing an important role to suppress pervasive transcription. Accordingly, the HIS1 asRNA, normally only present in quiescence, is expressed in exponential phase upon HIS1 mRNA transcription interruption