In silico identification of Drosophila melanogaster genes encoding RNA polymerase subunits

Remote homology identification of the Drosophila melanogaster ortholog of the RNA Polymerase I subunit Rpa34/POLR1G

Highly conserved orthologous proteins are easily identified by sequence homology alone, whereas poorly conserved orthologs require additional structural information to be identified. All Drosophila orthologs of RNA polymerase I, II, and III subunits-except one-have been identified by sequence homology. Here, we identified CG11076 as the missing Rpa34/POLR1G ortholog in Drosophila . Remote homology detection and secondary structure analysis showed that CG11076 is predicted to have high structural conservation with Rpa34/POLR1G, and phylogenetic analysis demonstrated that these proteins are closely related. Our work underscores the importance of utilizing both sequence and structure to identify highly divergent orthologous proteins in different species.

A new metabolic model of Drosophila melanogaster and the integrative analysis of Parkinson's disease

High conservation of the disease-associated genes between flies and humans facilitates the common use of Drosophila melanogaster to study metabolic disorders under controlled laboratory conditions. However, metabolic modeling studies are highly limited for this organism. We here report a comprehensively curated genome-scale metabolic network model of Drosophila using an orthology-based approach. The gene coverage and metabolic information of the draft model derived from a reference human model were expanded via Drosophila-specific KEGG and MetaCyc databases, with several curation steps to avoid metabolic redundancy and stoichiometric inconsistency. Furthermore, we performed literature-based curations to improve gene-reaction associations, subcellular metabolite locations, and various metabolic pathways. The performance of the resulting Drosophila model (8,230 reactions, 6,990 metabolites, and 2,388 genes), iDrosophila1 (https://github.com/SysBioGTU/iDrosophila), was assessed using flux balance analysis in comparison with the other currently available fly models leading to superior or comparable results. We also evaluated the transcriptome-based prediction capacity of iDrosophila1, where differential metabolic pathways during Parkinson's disease could be successfully elucidated. Overall, iDrosophila1 is promising to investigate system-level metabolic alterations in response to genetic and environmental perturbations.