Container-aided integrative QTL and RNA-seq analysis of Collaborative Cross mice supports distinct sex-oriented molecular modes of response in obesity.

BACKGROUND: The Collaborative Cross (CC) mouse population is a valuable resource to study the genetic basis of complex traits, such as obesity. Although the development of obesity is influenced by environmental factors, underlying genetic mechanisms play a crucial role in the response to these factors. The interplay between the genetic background and the gene expression pattern can provide further insight into this response, but we lack robust and easily reproducible workflows to integrate genomic and transcriptomic information in the CC mouse population. RESULTS: We established an automated and reproducible integrative workflow to analyse complex traits in the CC mouse genetic reference panel at the genomic and transcriptomic levels. We implemented the analytical workflow to assess the underlying genetic mechanisms of host susceptibility to diet induced obesity and integrated these results with diet induced changes in the hepatic gene expression of susceptible and resistant mice. Hepatic gene expression differs significantly between obese and non-obese mice, with a significant sex effect, where male and female mice exhibit different responses and coping mechanisms. CONCLUSION: Integration of the data showed that different genes but similar pathways are involved in the genetic susceptibility and disturbed in diet induced obesity. Genetic mechanisms underlying susceptibility to high-fat diet induced obesity are different in female and male mice. The clear distinction we observed in the systemic response to the high-fat diet challenge and to obesity between male and female mice points to the need for further research into distinct sex-related mechanisms in metabolic disease.

Role of the early secretory pathway in SARS-CoV-2 infection.

Similar to other RNA viruses, SARS-CoV-2 must (1) enter a target/host cell, (2) reprogram it to ensure its replication, (3) exit the host cell, and (4) repeat this cycle for exponential growth. During the exit step, the virus hijacks the sophisticated machineries that host cells employ to correctly fold, assemble, and transport proteins along the exocytic pathway. Therefore, secretory pathway-mediated assemblage and excretion of infective particles represent appealing targets to reduce the efficacy of virus biogenesis, if not to block it completely. Here, we analyze and discuss the contribution of the molecular machines operating in the early secretory pathway in the biogenesis of SARS-CoV-2 and their relevance for potential antiviral targeting. The fact that these molecular machines are conserved throughout evolution, together with the redundancy and tissue specificity of their components, provides opportunities in the search for unique proteins essential for SARS-CoV-2 biology that could also be targeted with therapeutic objectives. Finally, we provide an overview of recent evidence implicating proteins of the early secretory pathway as potential antiviral targets with effective therapeutic applications.

ANASTASIA: An Automated Metagenomic Analysis Pipeline for Novel Enzyme Discovery Exploiting Next Generation Sequencing Data.

Metagenomic analysis of environmental samples provides deep insight into the enzymatic mixture of the corresponding niches, capable of revealing peptide sequences with novel functional properties exploiting the high performance of next-generation sequencing (NGS) technologies. At the same time due to their ever increasing complexity, there is a compelling need for ever larger computational configurations to ensure proper bioinformatic analysis, and fine annotation. With the aiming to address the challenges of such an endeavor, we have developed a novel web-based application named ANASTASIA (automated nucleotide aminoacid sequences translational plAtform for systemic interpretation and analysis). ANASTASIA provides a rich environment of bioinformatic tools, either publicly available or novel, proprietary algorithms, integrated within numerous automated algorithmic workflows, and which enables versatile data processing tasks for (meta)genomic sequence datasets. ANASTASIA was initially developed in the framework of the European FP7 project HotZyme, whose aim was to perform exhaustive analysis of metagenomes derived from thermal springs around the globe and to discover new enzymes of industrial interest. ANASTASIA has evolved to become a stable and extensible environment for diversified, metagenomic, functional analyses for a range of applications overarching industrial biotechnology to biomedicine, within the frames of the ELIXIR-GR project. As a showcase, we report the successful in silico mining of a novel thermostable esterase termed "EstDZ4" from a metagenomic sample collected from a hot spring located in Krisuvik, Iceland.

KENeV: A web-application for the automated reconstruction and visualization of the enriched metabolic and signaling super-pathways deriving from genomic experiments.

Gene expression analysis, using high throughput genomic technologies,has become an indispensable step for the meaningful interpretation of the underlying molecular complexity, which shapes the phenotypic manifestation of the investigated biological mechanism. The modularity of the cellular response to different experimental conditions can be comprehended through the exploitation of molecular pathway databases, which offer a controlled, curated background for statistical enrichment analysis. Existing tools enable pathway analysis, visualization, or pathway merging but none integrates a fully automated workflow, combining all above-mentioned modules and destined to non-programmer users. We introduce an online web application, named KEGG Enriched Network Visualizer (KENeV), which enables a fully automated workflow starting from a list of differentially expressed genes and deriving the enriched KEGG metabolic and signaling pathways, merged into two respective, non-redundant super-networks. The final networks can be downloaded as SBML files, for further analysis, or instantly visualized through an interactive visualization module. In conclusion, KENeV (available online at http://www.grissom.gr/kenev) provides an integrative tool, suitable for users with no programming experience, for the functional interpretation, at both the metabolic and signaling level, of differentially expressed gene subsets deriving from genomic experiments.