MAGNETO: An Automated Workflow for Genome-Resolved Metagenomics.

Metagenome-assembled genomes (MAGs) represent individual genomes recovered from metagenomic data. MAGs are extremely useful to analyze uncultured microbial genomic diversity, as well as to characterize associated functional and metabolic potential in natural environments. Recent computational developments have considerably improved MAG reconstruction but also emphasized several limitations, such as the nonbinning of sequence regions with repetitions or distinct nucleotidic composition. Different assembly and binning strategies are often used; however, it still remains unclear which assembly strategy, in combination with which binning approach, offers the best performance for MAG recovery. Several workflows have been proposed in order to reconstruct MAGs, but users are usually limited to single-metagenome assembly or need to manually define sets of metagenomes to coassemble prior to genome binning. Here, we present MAGNETO, an automated workflow dedicated to MAG reconstruction, which includes a fully-automated coassembly step informed by optimal clustering of metagenomic distances, and implements complementary genome binning strategies, for improving MAG recovery. MAGNETO is implemented as a Snakemake workflow and is available at: https://gitlab.univ-nantes.fr/bird_pipeline_registry/magneto. IMPORTANCE Genome-resolved metagenomics has led to the discovery of previously untapped biodiversity within the microbial world. As the development of computational methods for the recovery of genomes from metagenomes continues, existing strategies need to be evaluated and compared to eventually lead to standardized computational workflows. In this study, we compared commonly used assembly and binning strategies and assessed their performance using both simulated and real metagenomic data sets. We propose a novel approach to automate coassembly, avoiding the requirement for a priori knowledge to combine metagenomic information. The comparison against a previous coassembly approach demonstrates a strong impact of this step on genome binning results, but also the benefits of informing coassembly for improving the quality of recovered genomes. MAGNETO integrates complementary assembly-binning strategies to optimize genome reconstruction and provides a complete reads-to-genomes workflow for the growing microbiome research community.

Integrated pseudotime analysis of human pre-implantation embryo single-cell transcriptomes reveals the dynamics of lineage specification.

Understanding lineage specification during human pre-implantation development is a gateway to improving assisted reproductive technologies and stem cell research. Here we employ pseudotime analysis of single-cell RNA sequencing (scRNA-seq) data to reconstruct early mouse and human embryo development. Using time-lapse imaging of annotated embryos, we provide an integrated, ordered, and continuous analysis of transcriptomics changes throughout human development. We reveal that human trophectoderm/inner cell mass transcriptomes diverge at the transition from the B2 to the B3 blastocyst stage, just before blastocyst expansion. We explore the dynamics of the fate markers IFI16 and GATA4 and show that they gradually become mutually exclusive upon establishment of epiblast and primitive endoderm fates, respectively. We also provide evidence that NR2F2 marks trophectoderm maturation, initiating from the polar side, and subsequently spreads to all cells after implantation. Our study pinpoints the precise timing of lineage specification events in the human embryo and identifies transcriptomics hallmarks and cell fate markers.

Gap-134, a Connexin43 activator, prevents age-related development of ventricular fibrosis in Scn5a+/- mice.

Down-regulation of Connexin43 (Cx43) has often been associated with the development of cardiac fibrosis. We showed previously that Scn5a heterozygous knockout mice (Scn5a+/-), which mimic familial progressive cardiac conduction defect, exhibit an age-dependent decrease of Cx43 expression and phosphorylation concomitantly with activation of TGF-ß pathway and fibrosis development in the myocardium between 45 and 60 weeks of age. The aim of this study was to investigate whether Gap-134 prevents Cx43 down-regulation with age and fibrosis development in Scn5a+/- mice. We observed in 60-week-old Scn5a+/- mouse heart a Cx43 expression and localization remodeling correlated with fibrosis. Chronic administration of a potent and selective gap junction modifier, Gap-134 (danegaptide), between 45 and 60 weeks, increased Cx43 expression and phosphorylation on serine 368 and prevented Cx43 delocalization. Furthermore, we found that Gap-134 prevented fibrosis despite the persistence of the conduction defects and the TGF-ß canonical pathway activation. In conclusion, the present study demonstrates that the age-dependent decrease of Cx43 expression is involved in the ventricular fibrotic process occurring in Scn5a+/- mice. Finally, our study suggests that gap junction modifier, such as Gap-134, could be an effective anti-fibrotic agent in the context of age-dependent fibrosis in progressive cardiac conduction disease.

Structure and co-occurrence patterns in microbial communities under acute environmental stress reveal ecological factors fostering resilience.

Understanding the factors that modulate bacterial community assembly in natural soils is a longstanding challenge in microbial community ecology. In this work, we compared two microbial co-occurrence networks representing bacterial soil communities from two different sections of a pH, temperature and humidity gradient occurring along a western slope of the Andes in the Atacama Desert. In doing so, a topological graph alignment of co-occurrence networks was used to determine the impact of a shift in environmental variables on OTUs taxonomic composition and their relationships. We observed that a fraction of association patterns identified in the co-occurrence networks are persistent despite large environmental variation. This apparent resilience seems to be due to: (1) a proportion of OTUs that persist across the gradient and maintain similar association patterns within the community and (2) bacterial community ecological rearrangements, where an important fraction of the OTUs come to fill the ecological roles of other OTUs in the other network. Actually, potential functional features suggest a fundamental role of persistent OTUs along the soil gradient involving nitrogen fixation. Our results allow identifying factors that induce changes in microbial assemblage configuration, altering specific bacterial soil functions and interactions within the microbial communities in natural environments.

Parallel derivation of isogenic human primed and naive induced pluripotent stem cells.

Induced pluripotent stem cells (iPSCs) have considerably impacted human developmental biology and regenerative medicine, notably because they circumvent the use of cells of embryonic origin and offer the potential to generate patient-specific pluripotent stem cells. However, conventional reprogramming protocols produce developmentally advanced, or primed, human iPSCs (hiPSCs), restricting their use to post-implantation human development modeling. Hence, there is a need for hiPSCs resembling preimplantation naive epiblast. Here, we develop a method to generate naive hiPSCs directly from somatic cells, using OKMS overexpression and specific culture conditions, further enabling parallel generation of their isogenic primed counterparts. We benchmark naive hiPSCs against human preimplantation epiblast and reveal remarkable concordance in their transcriptome, dependency on mitochondrial respiration and X-chromosome status. Collectively, our results are essential for the understanding of pluripotency regulation throughout preimplantation development and generate new opportunities for disease modeling and regenerative medicine.

Genome-wide analysis of host mRNA translation during hepatitis C virus infection.

In the model of Huh-7.5.1 hepatocyte cells infected by the JFH1 hepatitis C virus (HCV) strain, transcriptomic and proteomic studies have revealed modulations of pathways governing mainly apoptosis and cell cycling. Differences between transcriptomic and proteomic studies pointed to regulations occurring at the posttranscriptional level, including the control of mRNA translation. In this study, we investigated at the genome-wide level the translational regulation occurring during HCV infection. Sucrose gradient ultracentrifugation followed by microarray analysis was used to identify translationally regulated mRNAs (mRNAs associated with ribosomes) from JFH1-infected and uninfected Huh-7.5.1 cells. Translationally regulated mRNAs were found to correspond to genes enriched in specific pathways, including vesicular transport and posttranscriptional regulation. Interestingly, the strongest translational regulation was found for mRNAs encoding proteins involved in pre-mRNA splicing, mRNA translation, and protein folding. Strikingly, these pathways were not previously identified, through transcriptomic studies, as being modulated following HCV infection. Importantly, the observed changes in host mRNA translation were directly due to HCV replication rather than to HCV entry, since they were not observed in JFH1-infected Huh-7.5.1 cells treated with a potent HCV NS3 protease inhibitor. Overall, this study highlights the need to consider, beyond transcriptomic or proteomic studies, the modulation of host mRNA translation as an important aspect of HCV infection.

Meta-analysis of muscle transcriptome data using the MADMuscle database reveals biologically relevant gene patterns.

BACKGROUND: DNA microarray technology has had a great impact on muscle research and microarray gene expression data has been widely used to identify gene signatures characteristic of the studied conditions. With the rapid accumulation of muscle microarray data, it is of great interest to understand how to compare and combine data across multiple studies. Meta-analysis of transcriptome data is a valuable method to achieve it. It enables to highlight conserved gene signatures between multiple independent studies. However, using it is made difficult by the diversity of the available data: different microarray platforms, different gene nomenclature, different species studied, etc. DESCRIPTION: We have developed a system tool dedicated to muscle transcriptome data. This system comprises a collection of microarray data as well as a query tool. This latter allows the user to extract similar clusters of co-expressed genes from the database, using an input gene list. Common and relevant gene signatures can thus be searched more easily. The dedicated database consists in a large compendium of public data (more than 500 data sets) related to muscle (skeletal and heart). These studies included seven different animal species from invertebrates (Drosophila melanogaster, Caenorhabditis elegans) and vertebrates (Homo sapiens, Mus musculus, Rattus norvegicus, Canis familiaris, Gallus gallus). After a renormalization step, clusters of co-expressed genes were identified in each dataset. The lists of co-expressed genes were annotated using a unified re-annotation procedure. These gene lists were compared to find significant overlaps between studies. CONCLUSIONS: Applied to this large compendium of data sets, meta-analyses demonstrated that conserved patterns between species could be identified. Focusing on a specific pathology (Duchenne Muscular Dystrophy) we validated results across independent studies and revealed robust biomarkers and new pathways of interest. The meta-analyses performed with MADMuscle show the usefulness of this approach. Our method can be applied to all public transcriptome data.

MADGene: retrieval and processing of gene identifier lists for the analysis of heterogeneous microarray datasets.

UNLABELLED: MADGene is a software environment comprising a web-based database and a java application. This platform aims at unifying gene identifiers (ids) and performing gene set analysis. MADGene allows the user to perform inter-conversion of clone and gene ids over a large range of nomenclatures relative to 17 species. We propose a set of 23 functions to facilitate the analysis of gene sets and we give two microarray applications to show how MADGene can be used to conduct meta-analyses. AVAILABILITY: The MADGene resources are freely available online from http://www.madtools.org, a website dedicated to the analysis and annotation of DNA microarray data.

Nutritional programming in the rat is linked to long-lasting changes in nutrient sensing and energy homeostasis in the hypothalamus.

BACKGROUND: Nutrient deficiency during perinatal development is associated with an increased risk to develop obesity, diabetes and hypertension in the adulthood. However, the molecular mechanisms underlying the developmental programming of the metabolic syndrome remain largely unknown. METHODOLOGY/PRINCIPAL FINDINGS: Given the essential role of the hypothalamus in the integration of nutritional, endocrine and neuronal cues, here we have analyzed the profile of the hypothalamus transcriptome in 180 days-old rats born to dams fed either a control (200 g/kg) or a low-protein (80 g/kg) diet through pregnancy and lactation. From a total of 26 209 examined genes, 688 were up-regulated and 309 down-regulated (P<0.003) by early protein restriction. Further bioinformatic analysis of the data revealed that perinatal protein restriction permanently alters the expression of two gene clusters regulating common cellular processes. The first one includes several gate keeper genes regulating insulin signaling and nutrient sensing. The second cluster encompasses a functional network of nuclear receptors and co-regulators of transcription involved in the detection and use of lipid nutrients as fuel which, in addition, link temporal and nutritional cues to metabolism through their tight interaction with the circadian clock. CONCLUSIONS/SIGNIFICANCE: Collectively, these results indicate that the programming of the hypothalamic circuits regulating energy homeostasis is a key step in the development of obesity associated with malnutrition in early life and provide a valuable resource for further investigating the role of the hypothalamus in the programming of the metabolic syndrome.

Molecular risk stratification in advanced heart failure patients.

Risk stratification in advanced heart failure (HF) is crucial for the individualization of therapeutic strategy, in particular for heart transplantation and ventricular assist device implantation. We tested the hypothesis that cardiac gene expression profiling can distinguish between HF patients with different disease severity. We obtained tissue samples from both left (LV) and right (RV) ventricle of explanted hearts of 44 patients undergoing cardiac transplantation or ventricular assist device placement. Gene expression profiles were obtained using an in-house microarray containing 4217 muscular organ-relevant genes. Based on their clinical status, patients were classified into three HF-severity groups: deteriorating (n= 12), intermediate (n= 19) and stable (n= 13). Two-class statistical analysis of gene expression profiles of deteriorating and stable patients identified a 170-gene and a 129-gene predictor for LV and RV samples, respectively. The LV molecular predictor identified patients with stable and deteriorating status with a sensitivity of 88% and 92%, and a specificity of 100% and 96%, respectively. The RV molecular predictor identified patients with stable and deteriorating status with a sensitivity of 100% and 96%, and a specificity of 100% and 100%, respectively. The molecular prediction was reproducible across biological replicates in LV and RV samples. Gene expression profiling has the potential to reproducibly detect HF patients with highest HF severity with high sensitivity and specificity. In addition, not only LV but also RV samples could be used for molecular risk stratification with similar predictive power.

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

Microarray 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.

A dynamic, web-accessible resource to process raw microarray scan data into consolidated gene expression values: importance of replication.

We propose a freely accessible web-based pipeline, which processes raw microarray scan data to obtain experimentally consolidated gene expression values. The tool MADSCAN, which stands for MicroArray Data Suites of Computed ANalysis, makes a practical choice among the numerous methods available for filtering, normalizing and scaling of raw microarray expression data in a dynamic and automatic way. Different statistical methods have been adapted to extract reliable information from replicate gene spots as well as from replicate microarrays for each biological situation under study. A carefully constructed experimental design thus allows to detect outlying expression values and to identify statistically significant expression values, together with a list of quality controls with proposed threshold values. The integrated processing procedure described here, based on multiple measurements per gene, is decisive for reliably monitoring subtle gene expression changes typical for most biological events.