De novoassembled databases enable species-specific protein-based stable isotope probing of microbiomes without prior knowledge of the community composition

Abstract

Background:Protein-based stable isotope probing (Protein-SIP) is a powerful approach that can directly link individual taxa to activity and substrate assimilation, elucidating metabolic pathways and trophic relationships within microbiomes. In Protein-SIP, peptides and corresponding taxa are identified by database matches. Thus, database quality is crucial for accurate Protein-SIP analyses. For samples with unknown community composition, Protein-SIP usually employs broad-spectrum or metagenome-derived databases. However, broad-spectrum databases require advanced post-processing strategies and metagenome-derived databases can be costly to acquire. Further, both can inflate database size, negatively impacting peptide identification.Results:Here, an approach in which we use de novo assembled databases for Protein-SIP on microbiomes with unknown community compositions is introduced. This approach enables databases to be directly generated from the mass spectrometry raw data using de novo peptide sequencing. We then use the mass spectrometric data from labeled cultures to quantify isotope incorporation into specific peptides. We benchmark our approach against the canonical approach in which a sample-matching database is generated by DNA sequencing on three different datasets: 1) a proteome analysis from a defined microbial community containing 13C-labeled E. coli cells, 2) time-course data of an anammox-dominated continuous reactor after feeding with 13C-labeled bicarbonate, and 3) a model of the human distal gut simulating a high-protein and high-fiber diet cultivated in either 2H2O or H218O. Our results show that de novo database assembly is applicable to different isotopes and detects similar amounts of labeled peptides compared to sample-matching databases. Furthermore, we show that peptide-centric Protein-SIP allows species-specific resolution, enabling the assessment of activity related to individual biological processes. Finally, we provide access to our modular Python pipeline to assist the de novo assembly of databases and subsequent peptide-centric Protein-SIP data analysis (https://git.ufz.de/meb/denovo-sip).Conclusions:De novo assembled databases enable species-specific Protein-SIP of microbiomes without prior knowledge of the community composition.