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Unified Workflow for the Rapid and In-Depth Characterization of Bacterial Proteomes.
Abele, Miriam; Doll, Etienne; Bayer, Florian P; Meng, Chen; Lomp, Nina; Neuhaus, Klaus; Scherer, Siegfried; Kuster, Bernhard; Ludwig, Christina.
Afiliação
  • Abele M; Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), TUM School of Life Sciences, Technical University of Munich, Freising, Germany; Division of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Doll E; Division of Microbial Ecology, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Bayer FP; Division of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Meng C; Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Lomp N; Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Neuhaus K; Division of Microbial Ecology, TUM School of Life Sciences, Technical University of Munich, Freising, Germany; Core Facility Microbiome, ZIEL - Institute for Food & Health, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Scherer S; Division of Microbial Ecology, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Kuster B; Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), TUM School of Life Sciences, Technical University of Munich, Freising, Germany; Division of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Ludwig C; Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), TUM School of Life Sciences, Technical University of Munich, Freising, Germany. Electronic address: tina.ludwig@tum.de.
Mol Cell Proteomics ; 22(8): 100612, 2023 08.
Article em En | MEDLINE | ID: mdl-37391045
Bacteria are the most abundant and diverse organisms among the kingdoms of life. Due to this excessive variance, finding a unified, comprehensive, and safe workflow for quantitative bacterial proteomics is challenging. In this study, we have systematically evaluated and optimized sample preparation, mass spectrometric data acquisition, and data analysis strategies in bacterial proteomics. We investigated workflow performances on six representative species with highly different physiologic properties to mimic bacterial diversity. The best sample preparation strategy was a cell lysis protocol in 100% trifluoroacetic acid followed by an in-solution digest. Peptides were separated on a 30-min linear microflow liquid chromatography gradient and analyzed in data-independent acquisition mode. Data analysis was performed with DIA-NN using a predicted spectral library. Performance was evaluated according to the number of identified proteins, quantitative precision, throughput, costs, and biological safety. With this rapid workflow, over 40% of all encoded genes were detected per bacterial species. We demonstrated the general applicability of our workflow on a set of 23 taxonomically and physiologically diverse bacterial species. We could confidently identify over 45,000 proteins in the combined dataset, of which 30,000 have not been experimentally validated before. Our work thereby provides a valuable resource for the microbial scientific community. Finally, we grew Escherichia coli and Bacillus cereus in replicates under 12 different cultivation conditions to demonstrate the high-throughput suitability of the workflow. The proteomic workflow we present in this manuscript does not require any specialized equipment or commercial software and can be easily applied by other laboratories to support and accelerate the proteomic exploration of the bacterial kingdom.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Proteoma / Proteômica Tipo de estudo: Prognostic_studies Idioma: En Revista: Mol Cell Proteomics Assunto da revista: BIOLOGIA MOLECULAR / BIOQUIMICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Alemanha

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Proteoma / Proteômica Tipo de estudo: Prognostic_studies Idioma: En Revista: Mol Cell Proteomics Assunto da revista: BIOLOGIA MOLECULAR / BIOQUIMICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Alemanha