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Bacterial communities are fundamental for many health-related, ecological, environmental, and biotechnological processes. With proteins constituting a large share of bacterial biomass and being responsible for metabolic activity, metaproteomics has become increasingly popular for characterizing bacterial communities. However, high sample complexity impairs the detection of low-abundant proteins, and a sample-specific database is necessary for accurate protein identification. Here, we present a targeted metaproteomic approach to quantify bacterial community composition at the family level using the taxonomic marker protein GroEL (synonyms: Cpn60 and Hsp60), which is conserved and abundant in almost all bacteria. We refer to our method as GroEL-taxoproteomics. GroEL-taxoproteomics is based on high-resolution mass spectrometry of extracted proteins and identification of GroEL-derived peptides in the data via a Galaxy workflow and a subsequent Python-based analysis script. Its advantage is that it can be performed with a pre-computed and extendable sample-independent database. GroEL-taxoproteomics was validated by applying it to raw metaproteome data from synthetic microbial communities and real human gut samples. In addition, we experimentally determined the relative detection limit of GroEL-taxoproteomics for detecting low-abundant bacterial taxa. To reduce sample complexity and improve GroEL identification while simultaneously decreasing the instrument time, proteins from crude extracts were pre-separated by SDS-PAGE. The gel bands corresponding to GroEL's molecular weight size of approximately 60 kDa were excised and analyzed by mass spectrometry followed by the GroEL-taxoproteomics workflow. Our data shows that GroEL-taxoproteomics enables fast and low-effort analysis of bacterial communities at good taxonomic resolution. Thus, GroEL-taxoproteomics allows protein-based community analyses at a large scale.

Profiling of bacterial populations in mixed or enriched communities is a common task in microbial ecology. While 16S rRNA-based sequencing is a generally accepted and functional approach it is dependent on amplification primers and cannot reveal incorporation of isotopically labeled substrates into specific populations. We here explore the hypothesis that protein mass spectrometry of taxonomic marker proteins can complement traditional population analyses. Therefore, we here describe an approach to quantify bacterial community compositions at the family level using the taxonomic marker protein GroEL (synonyms: Cpn60 and Hsp60), which is conserved and abundant in almost all bacteria. We refer to our method as GroEL-taxoproteomics. GroEL-taxoproteomics is based on high-resolution mass spectrometry of extracted proteins and identification of GroEL-derived peptides in the data via a Galaxy workflow and a subsequent Python-based analysis script. Its advantage is that it can be performed with a curated and extendable sample-independent database and that GroEL can be pre-separated by SDS-PAGE to reduce sample complexity, improving GroEL identification while simultaneously decreasing the instrument time. GroEL-taxoproteomics was validated by applying it to a comprehensive raw data set obtained with a metaproteome approach from synthetic microbial communities and real human gut samples. In addition, we experimentally determined the relative detection limit of GroEL-taxoproteomics for detecting low-abundant bacterial taxa. Our data shows that GroEL-taxoproteomics enables fast and low-effort profiling of bacterial communities at good taxonomic resolution. Thus, GroEL-taxoproteomics allows protein-based community analyses at a large scale.

Profiling bacterial populations in mixed communities is a common task in microbiology. Sequencing of 16S small subunit ribosomal-RNA (16S rRNA) gene amplicons is a widely accepted and functional approach but relies on amplification primers and cannot quantify isotope incorporation. Tandem mass spectrometry proteotyping is an effective alternative for taxonomically profiling microorganisms. We suggest that targeted proteotyping approaches can complement traditional population analyses. Therefore, we describe an approach to assess bacterial community compositions at the family level using the taxonomic marker protein GroEL, which is ubiquitously found in bacteria, except a few obligate intracellular species. We refer to our method as GroEL-proteotyping. GroEL-proteotyping is based on high-resolution tandem mass spectrometry of GroEL peptides and identification of GroEL-derived taxa via a Galaxy workflow and a subsequent Python-based analysis script. Its advantage is that it can be performed with a curated and extendable sample-independent database and that GroEL can be pre-separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) to reduce sample complexity, improving GroEL identification while simultaneously decreasing the instrument time. GroEL-proteotyping was validated by employing it on a comprehensive raw dataset obtained through a metaproteome approach from synthetic microbial communities as well as real human gut samples. Our data show that GroEL-proteotyping enables fast and straightforward profiling of highly abundant taxa in bacterial communities at reasonable taxonomic resolution.