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

Applications of key technologies in biomedical research, such as qRT-PCR or LC-MS-based proteomics, are generating large biological (-omics) datasets which are useful for the identification and quantification of biomarkers in 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 memory function and dysfunction. Here, we used a bioinformatics approach to designing and benchmarking a comprehensive central nervous system (CNS) proteomics database (LymCNS-PDB) for the identification of proteins from the CNS of Lymnaea 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 a published Lymnaea transcriptomics database. The blast-style MMSeq2 software was used to match all translated sequences to UniProtKB sequences for molluscan proteins, including those from Lymnaea and other molluscs. LymCNS-PDB contains 9628 identified matched proteins that were benchmarked by performing LC-MS-based proteomics analysis with proteins isolated from the Lymnaea CNS. MS/MS analysis using the LymCNS-PDB database led to the identification of 3810 proteins. 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 of protein interactomes involved in several CNS functions in Lymnaea, including learning and memory and age-related memory decline.