A combined bioinformatics and LC-MS based approach for the development and benchmarking of a comprehensive database for CNS proteins in Lymnaea stagnalis

Abstract

AbstractApplications of key technologies in biomedical research, such as qRT-PCR or LC-MS based proteomics, are generating large biological (-omics) data sets which are useful for the identification and quantification of biomarkers involved in molecular mechanisms of any research area of interest. Genome, transcriptome and proteome databases are already available for a number of model organisms including vertebrates and invertebrates. However, there is insufficient information available for protein sequences of certain invertebrates, such as the great pond snail Lymnaea stagnalis, a model organism that has been used highly successfully in elucidating evolutionarily conserved mechanisms of learning and memory, ageing and age-related as well as amyloid-β induced memory decline. In this investigation, we used a bioinformatics approach to designing and benchmarking a comprehensive CNS proteomics database (LymCNS-PDB) for the identification of proteins from the Central Nervous System (CNS) of Lymnaea stagnalis by LC-MS based proteomics. LymCNS-PDB was created by using the Trinity TransDecoder bioinformatics tool to translate amino acid sequences from mRNA transcript assemblies obtained from an existing published Lymnaea stagnalis transcriptomics database. The blast-style MMSeq2 software was used to match all translated sequences to sequences for molluscan proteins (including Lymnaea stagnalis and other molluscs) available from UniProtKB. LymCNS-PDB, which contains 9,628 identified matched proteins, was then benchmarked by performing LC-MS based proteomics analysis with proteins isolated from the CNS of Lymnaea stagnalis. MS/MS analysis using the LymCNS-PDB database led to the identification of 3,810 proteins while only 982 proteins were identified by using a non-specific Molluscan database. LymCNS-PDB provides a valuable tool that will enable us to perform quantitative proteomics analysis to identify a plethora of protein interactomes involved in several CNS functions in Lymnaea stagnalis including learning and memory, aging-related memory decline and others.