Sarah K. Wooller, A. Anagnostopoulou, B. Kuropka, Michael Crossley, P. Benjamin, F. Pearl, I. Kemenes, G. Kemenes, Murat Eravci
{"title":"结合生物信息学和LC-MS为基础的方法开发和基准的综合数据库淋巴中枢神经系统蛋白。","authors":"Sarah K. Wooller, A. Anagnostopoulou, B. Kuropka, Michael Crossley, P. Benjamin, F. Pearl, I. Kemenes, G. Kemenes, Murat Eravci","doi":"10.1242/jeb.243753","DOIUrl":null,"url":null,"abstract":"Applications 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 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 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 Lymnaea and other molluscs. LymCNS-PDB contains 9,628 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 3,810 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.","PeriodicalId":22458,"journal":{"name":"THE EGYPTIAN JOURNAL OF EXPERIMENTAL BIOLOGY","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A combined bioinformatics and LC-MS based approach for the development and benchmarking of a comprehensive database of Lymnaea CNS proteins.\",\"authors\":\"Sarah K. Wooller, A. Anagnostopoulou, B. Kuropka, Michael Crossley, P. Benjamin, F. Pearl, I. Kemenes, G. Kemenes, Murat Eravci\",\"doi\":\"10.1242/jeb.243753\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Applications 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 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 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 Lymnaea and other molluscs. LymCNS-PDB contains 9,628 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 3,810 proteins. Only 982 proteins were identified by using a non-specific molluscan database. 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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) data sets 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 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 Lymnaea and other molluscs. LymCNS-PDB contains 9,628 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 3,810 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.