Wooller Sarah, Anagnostopoulou Aikaterini, Kuropka Benno, Crossley Michael, Benjamin Paul R, Pearl Frances, Kemenes Ildikó, Kemenes György, Eravci Murat
Bioinformatics Group, School of Life Sciences, University of Sussex, Brighton, BN1 9QG, UK.
Sussex Neuroscience, School of Life Sciences, University of Sussex, Brighton, BN1 9QG, UK.
J Exp Biol. 2022 Apr 1;225(7). doi: 10.1242/jeb.243753. Epub 2022 Apr 12.
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.
关键技术在生物医学研究中的应用，如基于qRT-PCR或LC-MS的蛋白质组学，正在生成大量生物学（组学）数据集，这些数据集有助于在任何感兴趣的研究领域中识别和定量生物标志物。包括脊椎动物和无脊椎动物在内的许多模式生物的基因组、转录组和蛋白质组数据库已经可用。然而，某些无脊椎动物的蛋白质序列信息不足，例如大蜗牛静水椎实螺，这是一种在阐明记忆功能和功能障碍的进化保守机制方面非常成功的模式生物。在这里，我们使用生物信息学方法设计并评估了一个全面的中枢神经系统（CNS）蛋白质组学数据库（LymCNS-PDB），用于通过基于LC-MS的蛋白质组学从静水椎实螺的中枢神经系统中鉴定蛋白质。LymCNS-PDB是通过使用Trinity TransDecoder生物信息学工具从已发表的静水椎实螺转录组学数据库获得的mRNA转录本组装中翻译氨基酸序列而创建的。使用blast风格的MMSeq2软件将所有翻译后的序列与软体动物蛋白质的UniProtKB序列进行匹配，包括来自静水椎实螺和其他软体动物的序列。LymCNS-PDB包含9628个已鉴定的匹配蛋白质，这些蛋白质通过对从静水椎实螺中枢神经系统分离的蛋白质进行基于LC-MS的蛋白质组学分析进行了评估。使用LymCNS-PDB数据库进行的MS/MS分析鉴定出3810种蛋白质。使用非特异性软体动物数据库仅鉴定出982种蛋白质。LymCNS-PDB提供了一个有价值的工具，将使我们能够对参与静水椎实螺几种中枢神经系统功能（包括学习和记忆以及与年龄相关的记忆衰退）的蛋白质相互作用组进行定量蛋白质组学分析。
