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Refining a steroidogenic model: an analysis of RNA-seq datasets from insect prothoracic glands.

ABSTRACT: BACKGROUND:The prothoracic gland (PG), the principal steroidogenic organ of insects, has been proposed as a model for steroid hormone biosynthesis and regulation. RESULTS:To validate the robustness of the model, we present an analysis of accumulated transcriptomic data from PGs of two model species, Drosophila melanogaster and Bombyx mori. We identify that the common core components of the model in both species are encoded by nine genes. Five of these are Halloween genes whose expression differs substantially between the PGs of these species. CONCLUSIONS:We conclude that the PGs can be a model for steroid hormone synthesis and regulation within the context of mitochondrial cholesterol transport and steroid biosynthesis but beyond these core mechanisms, gene expression in insect PGs is too diverse to fit in a context-specific model and should be analysed within a species-specific framework.

SUBMITTER: Moulos P

PROVIDER: S-EPMC6045881 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Refining a steroidogenic model: an analysis of RNA-seq datasets from insect prothoracic glands.

Moulos Panagiotis P Alexandratos Alexandros A Nellas Ioannis I Dedos Skarlatos G SG

BMC genomics 20180713 1

<h4>Background</h4>The prothoracic gland (PG), the principal steroidogenic organ of insects, has been proposed as a model for steroid hormone biosynthesis and regulation.<h4>Results</h4>To validate the robustness of the model, we present an analysis of accumulated transcriptomic data from PGs of two model species, Drosophila melanogaster and Bombyx mori. We identify that the common core components of the model in both species are encoded by nine genes. Five of these are Halloween genes whose exp ...[more]

PMID: 30005604

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Project description:High-throughput RNA sequencing (RNA-seq) has become an instrumental assay for the analysis of multiple aspects of an organism's transcriptome. Further, the analysis of a biological specimen's associated microbiome can also be performed using RNA-seq data and this application is gaining interest in the scientific community. There are many existing bioinformatics tools designed for analysis and visualization of transcriptome data. Despite the availability of an array of next generation sequencing (NGS) analysis tools, the analysis of RNA-seq data sets poses a challenge for many biomedical researchers who are not familiar with command-line tools. Here we present RNA CoMPASS, a comprehensive RNA-seq analysis pipeline for the simultaneous analysis of transcriptomes and metatranscriptomes from diverse biological specimens. RNA CoMPASS leverages existing tools and parallel computing technology to facilitate the analysis of even very large datasets. RNA CoMPASS has a web-based graphical user interface with intrinsic queuing to control a distributed computational pipeline. RNA CoMPASS was evaluated by analyzing RNA-seq data sets from 45 B-cell samples. Twenty-two of these samples were derived from lymphoblastoid cell lines (LCLs) generated by the infection of naïve B-cells with the Epstein Barr virus (EBV), while another 23 samples were derived from Burkitt's lymphomas (BL), some of which arose in part through infection with EBV. Appropriately, RNA CoMPASS identified EBV in all LCLs and in a fraction of the BLs. Cluster analysis of the human transcriptome component of the RNA CoMPASS output clearly separated the BLs (which have a germinal center-like phenotype) from the LCLs (which have a blast-like phenotype) with evidence of activated MYC signaling and lower interferon and NF-kB signaling in the BLs. Together, this analysis illustrates the utility of RNA CoMPASS in the simultaneous analysis of transcriptome and metatranscriptome data. RNA CoMPASS is freely available at http://rnacompass.sourceforge.net/.

Dataset Information

Refining a steroidogenic model: an analysis of RNA-seq datasets from insect prothoracic glands.

Publications

Refining a steroidogenic model: an analysis of RNA-seq datasets from insect prothoracic glands.

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