Abstract
Metaproteomics is an increasingly popular methodology that provides information regarding the metabolic functions of specific microbial taxa and can be used to assess environmental stressors and adaptation and has potential for contributing to ocean ecology and biogeochemical studies. To increase community confidence and enable future large-scale studies, a multi-laboratory intercomparison was conducted to demonstrate comparability and reproducibility of taxonomic and functional results. Euphotic zone samples from the Bermuda Atlantic Time-Series Study in the North Atlantic Ocean were collected using in situ pumps and the AUV Clio and were subdivided and distributed to nine independent labs. A paired metagenome was sequenced to construct a reference sequence database for the subsequent metaproteomic data analysis. One-dimensional liquid chromatographic data-dependent acquisition mass spectrometry analyses were stipulated in the study design. Analysis of mass spectra from seven laboratories through a common informatic pipeline identified a shared set of 1056 proteins from 1395 shared peptides constituents. Quantitative analyses showed good reproducibility: pairwise regressions of spectral counts between laboratories yielded R2 values ranging from 0.43 to 0.83, and a S繪rensen similarity analysis of the top 1,000 proteins revealed 70-80% similarity between most laboratory groups. Taxonomic and functional assignments showed good coherence between both technical replicates and different laboratories, with the two major taxa being Prochlorococcus and Pelagibacter. An additional informatic intercomparison study, involving 10 laboratories and conducted using 8 distinct software packages representing the most frequently employed search engines, successfully identified thousands of peptides within the complex metaproteomic datasets, demonstrating the utility of these software tools for ocean research. Future efforts could examine reproducibility in deeper metaproteomes, examine accuracy in targeted absolute quantitation analyses, and develop standards for data output formats to improve data interoperability. Together, these results demonstrate the reproducibility of metaproteomic analyses and their suitability for microbial oceanography research including integration into global scale ocean surveys and ocean biogeochemical models.
