Results from a multi-laboratory ocean metaproteomic intercomparison effects of LC-MS acquisition and data analysis procedures

Author(s)
Mak A. Saito, Jaclyn K. Saunders, Matthew R. McIlvin, Erin M. Bertrand, John A. Breier, Margaret Mars Brisbin, Sophie M. Colston, Jaimee R. Compton, Tim J. Griffin, W. Judson Hervey, Robert L. Hettich, Pratik D. Jagtap, Michael Janech, Rod Johnson, Rick Keil, Hugo Kleikamp, Dagmar Leary, Lennart Martens, J. Scott P. McCain, Eli Moore, Subina Mehta, Dawn M. Moran, Jaqui Neibauer, Benjamin A. Neely, Michael V. Jakuba, Jim Johnson, Megan Duffy, Gerhard J. Herndl, Richard Giannone, Ryan Mueller, Brook L. Nunn, Martin Pabst, Samantha Peters, Andrew Rajczewski, Elden Rowland, Brian Searle, Tim Van Den Bossche, Gary J. Vora, Jacob R. Waldbauer, Haiyan Zheng, Zihao Zhao
Abstract

Metaproteomics is an increasingly popular methodology that provides information regarding the metabolic functions of specific microbial taxa and has potential for contributing to ocean ecology and biogeochemical studies. A blinded multi-laboratory intercomparison was conducted to assess comparability and reproducibility of taxonomic and functional results and their sensitivity to methodological variables. Euphotic zone samples from the Bermuda Atlantic Time-series Study (BATS) in the North Atlantic Ocean collected by in situ pumps and the autonomous underwater vehicle (AUV) Clio were distributed with a paired metagenome, and one-dimensional (1D) liquid chromatographic data-dependent acquisition mass spectrometry analysis was stipulated. Analysis of mass spectra from seven laboratories through a common bioinformatic pipeline identified a shared set of 1056 proteins from 1395 shared peptide constituents. Quantitative analyses showed good reproducibility: pairwise regressions of spectral counts between laboratories yielded R2 values averaged 0.62 ± 0.11, and a Sørensen similarity analysis of the top 1000 proteins revealed 70 %–80 % similarity between laboratory groups. Taxonomic and functional assignments showed good coherence between technical replicates and different laboratories. A bioinformatic intercomparison study, involving 10 laboratories using eight software packages, successfully identified thousands of peptides within the complex metaproteomic datasets, demonstrating the utility of these software tools for ocean metaproteomic research. Lessons learned and potential improvements in methods were described. 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.

Organisation(s)
Functional and Evolutionary Ecology
External organisation(s)
Woods Hole Oceanographic Institution, University of Georgia, Dalhousie University, University of Texas, Brownsville, U.S. Naval Research Laboratory, University of Minnesota, Twin Cities, Oak Ridge National Laboratory , College of Charleston, Arizona State University, University of Washington, Delft University of Technology, Ghent University , Vlaams Instituut voor Biotechnologie, Massachusetts Institute of Technology, United States Geological Survey, National Institute of Standards and Technology, Gaithersburg, Oregon State University, Ohio State University, University of Chicago, University of Medicine and Dentistry of New Jersey
Journal
Biogeosciences
Volume
21
Pages
4889-4908
No. of pages
20
ISSN
1726-4170
DOI
https://doi.org/10.5194/bg-21-4889-2024
Publication date
11-2024
Peer reviewed
Yes
Austrian Fields of Science 2012
106021 Marine biology
ASJC Scopus subject areas
Ecology, Evolution, Behavior and Systematics, Earth-Surface Processes
Sustainable Development Goals
SDG 14 - Life Below Water
Portal url
https://ucrisportal.univie.ac.at/en/publications/274daa24-fb2b-49d6-9560-3220a7c9924d