11/08/2024 | Press release | Distributed by Public on 11/09/2024 03:02
Bender, S. J., Moran, D. M., McIlvin, M. R., Zheng, H., McCrow, J. P., Badger, J., DiTullio, G. R., Allen, A. E., and Saito, M. A.: Colony formation in Phaeocystis antarctica: connecting molecular mechanisms with iron biogeochemistry, Biogeosciences, 15, 4923-4942, https://doi.org/10.5194/bg-15-4923-2018, 2018.
Bergauer, K., Fernandez-Guerra, A., Garcia, J. A., Sprenger, R. R., Stepanauskas, R., Pachiadaki, M. G., Jensen, O. N., and Herndl, G. J.: Organic matter processing by microbial communities throughout the Atlantic water column as revealed by metaproteomics, P. Natl. Acad. Sci. USA, 115, E400-E408, https://doi.org/10.1073/pnas.1708779115, 2018.
Bertrand, E. M., Moran, D. M., McIlvin, M. R., Hoffman, J. M., Allen, A. E., and Saito, M. A.: Methionine synthase interreplacement in diatom cultures and communities: Implications for the persistence of B12 use by eukaryotic phytoplankton, Limnol. Oceanogr., 58, 1431-1450, 2013.
Breier, J. A., Jakuba, M. V., Saito, M. A., Dick, G. J., Grim, S. L., Chan, E. W., McIlvin, M. R., Moran, D. M., Alanis, B. A., and Allen, A. E.: Revealing ocean-scale biochemical structure with a deep-diving vertical profiling autonomous vehicle, Science Robotics, 5, eabc7104, https://doi.org/10.1126/scirobotics.abc7104, 2020.
Buchfink, B., Xie, C., and Huson, D. H.: Fast and sensitive protein alignment using DIAMOND, Nat. Methods, 12, 59-60, 2015.
Carlson, C. A., Morris, R., Parsons, R., Treusch, A. H., Giovannoni, S. J., and Vergin, K.: Seasonal dynamics of SAR11 populations in the euphotic and mesopelagic zones of the northwestern Sargasso Sea, ISME J., 3, 283-295, 2009.
Casey, J. R., Lomas, M. W., Mandecki, J., and Walker, D. E.: Prochlorococcus contributes to new production in the Sargasso Sea deep chlorophyll maximum, Geophys. Res. Lett., 34, 2007.
Cohen, N. R., McIlvin, M. R., Moran, D. M., Held, N. A., Saunders, J. K., Hawco, N. J., Brosnahan, M., DiTullio, G. R., Lamborg, C., and McCrow, J. P.: Dinoflagellates alter their carbon and nutrient metabolic strategies across environmental gradients in the central Pacific Ocean, Nat. Microbiol., 6, 173-186, 2021.
Cohen, N. R., Krinos, A. I., Kell, R. M., Chmiel, R. J., Moran, D. M., McIlvin, M. R., Lopez, P. Z., Barth, A., Stone, J., Alanis, B. A., Chan, E. W., Breier, J. A., Jakuba, M. V., Johnson, R., Alexander, H., and Saito, M. A.: Microeukaryote metabolism across the western North Atlantic Ocean revealed through autonomous underwater profiling, Nat. Commun., 15, 7325, https://doi.org/10.1038/s41467-024-51583-4, 2023.
Coleman, M. L. and Chisholm, S. W.: Ecosystem-specific selection pressures revealed through comparative population genomics, P. Natl. Acad. Sci. USA, 107, 18634-18639, 2010.
Conway, J. R., Lex, A., and Gehlenborg, N.: UpSetR: an R package for the visualization of intersecting sets and their properties, Bioinformatics, 33, 2938-2940, https://doi.org/10.1093/bioinformatics/btx364, 2017.
Dai, C., Füllgrabe, A., Pfeuffer, J., Solovyeva, E. M., Deng, J., Moreno, P., Kamatchinathan, S., Kundu, D. J., George, N., and Fexova, S.: A proteomics sample metadata representation for multiomics integration and big data analysis, Nat. Commun., 12, 5854, https://doi.org/10.1038/s41467-021-26111-3, 2021.
Deutsch, E. W., Bandeira, N., Sharma, V., Perez-Riverol, Y., Carver, J. J., Kundu, D. J., García-Seisdedos, D., Jarnuczak, A. F., Hewapathirana, S., Pullman, B. S., Wertz, J., Sun, Z., Kawano, S., Okuda, S., Watanabe, Y., Hermjakob, H., MacLean, B., MacCoss, M. J., Zhu, Y., Ishihama, Y., and Vizcaíno, J. A.: The ProteomeXchange consortium in 2020: enabling "big data" approaches in proteomics, Nucleic Acids Res., 48, D1145-D1152, https://doi.org/10.1093/nar/gkz984, 2019.
Falkowski, P. G., Fenchel, T., and Delong, E. F.: The microbial engines that drive Earth's biogeochemical cycles, Science, 320, 1034-1039, 2008.
Fuchsman, C. A., Palevsky, H. I., Widner, B., Duffy, M., Carlson, M. C., Neibauer, J. A., Mulholland, M. R., Keil, R. G., Devol, A. H., and Rocap, G.: Cyanobacteria and cyanophage contributions to carbon and nitrogen cycling in an oligotrophic oxygen-deficient zone, ISME J., 13, 2714-2726, 2019.
Georges, A. A., El-Swais, H., Craig, S. E., Li, W. K., and Walsh, D. A.: Metaproteomic analysis of a winter to spring succession in coastal northwest Atlantic Ocean microbial plankton, ISME J., 8, 1301-1313, 2014.
Hawley, A. K., Brewer, H. M., Norbeck, A. D., Paša-Tolić, L., and Hallam, S. J.: Metaproteomics reveals differential modes of metabolic coupling among ubiquitous oxygen minimum zone microbes, P. Natl. Acad. Sci. USA, 111, 11395-11400, 2014.
Held, N. A., Sutherland, K. M., Webb, E. A., McIlvin, M. R., Cohen, N. R., Devaux, A. J., Hutchins, D. A., Waterbury, J. B., Hansel, C. M., and Saito, M. A.: Mechanisms and heterogeneity of in situ mineral processing by the marine nitrogen fixer Trichodesmium revealed by single-colony metaproteomics, ISME Communications, 1, 1-9, 2021.
Hulstaert, N., Shofstahl, J., Sachsenberg, T., Walzer, M., Barsnes, H., Martens, L., and Perez-Riverol, Y.: ThermoRawFileParser: modular, scalable, and cross-platform RAW file conversion, J. Proteome Res., 19, 537-542, 2019.
Hyatt, D., Chen, G.-L., LoCascio, P. F., Land, M. L., Larimer, F. W., and Hauser, L. J.: Prodigal: prokaryotic gene recognition and translation initiation site identification, BMC Bioinformatics, 11, 1-11, 2010.
Jagtap, P. D., Hoopmann, M. R., Neely, B. A., Harvey, A., Käll, L., Perez-Riverol, Y., Abajorga, M. K., Thomas, J. A., Weintraub, S. T., and Palmblad, M.: The Association of Biomolecular Resource FacilitiesProteome Informatics Research Group Study on Metaproteomics(iPRG-2020), J. Biomol. Tech., 34, 3fc1f5fe.a058bad4, https://doi.org/10.7171/3fc1f5fe.a058bad4, 2023.
Johnson, R. J., Bates, N., Lethaby, P. J., Smith, D., and Lomas, M. W.: Discrete bottle samples collected at the Bermuda Atlantic Time-series Study (BATS) site in the Sargasso Sea from October 1988 through December 2023, Biological and Chemical Oceanography Data Management Office (BCO-DMO) [data set], https://doi.org/10.26008/1912/bco-dmo.3782.6, 2024.
Joy-Warren, H. L., Alderkamp, A.-C., van Dijken, G. L., J Jabre, L., Bertrand, E. M., Baldonado, E. N., Glickman, M. W., Lewis, K. M., Middag, R., and Seyitmuhammedov, K.: Springtime phytoplankton responses to light and iron availability along the western Antarctic Peninsula, Limnol. Oceanogr., 67, 800-815, 2022.
Kanehisa, M., Sato, Y., and Morishima, K.: BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences, J. Mol. Biol., 428, 726-731, 2016.
Keller, A., Nesvizhskii, A. I., Kolker, E., and Aebersold, R.: An explanation of the Peptide Prophet algorithm developed, Anal. Chem, 74, 5383-5392, 2002.
Kim, S. and Pevzner, P. A.: MS-GF+makes progress towards a universal database search tool for proteomics, Nat. Commun., 5, 5277, https://doi.org/10.1038/ncomms6277, 2014.
Kiweler, M., Looso, M., and Graumann, J.: MARMoSET-extracting publication-ready mass spectrometry metadata from RAW files, Molecular & Cellular Proteomics, 18, 1700-1702, 2019.
Kleiner, M.: Metaproteomics: much more than measuring gene expression in microbial communities, Msystems, 4, e00115-19, https://doi.org/10.1128/mSystems.00115-19, 2019.
Kleiner, M., Thorson, E., Sharp, C. E., Dong, X., Liu, D., Li, C., and Strous, M.: Assessing species biomass contributions in microbial communities via metaproteomics, Nat. Commun., 8, 1558, https://doi.org/10.1038/s41467-017-01544-x, 2017.
Leary, D. H., Li, R. W., Hamdan, L. J., Hervey IV, W. J., Lebedev, N., Wang, Z., Deschamps, J. R., Kusterbeck, A. W., and Vora, G. J.: Integrated metagenomic and metaproteomic analyses of marine biofilm communities, Biofouling, 30, 1211-1223, 2014.
Malmstrom, R. R., Coe, A., Kettler, G. C., Martiny, A. C., Frias-Lopez, J., Zinser, E. R., and Chisholm, S. W.: Temporal dynamics of Prochlorococcus ecotypes in the Atlantic and Pacific oceans, ISME J., 4, 1252-1264, 2010.
McCain, J. S. P. and Bertrand, E. M.: Prediction and consequences of cofragmentation in metaproteomics, J. Proteome Res., 18, 3555-3566, 2019.
McCain, J. S. P., Allen, A. E., and Bertrand, E. M.: Proteomic traits vary across taxa in a coastal Antarctic phytoplankton bloom, ISME J., 16, 569-579, 2022.
McIlvin, M. R. and Saito, M. A.: Online Nanoflow Two-Dimension Comprehensive Active Modulation Reversed Phase-Reversed Phase Liquid Chromatography High-Resolution Mass Spectrometry for Metaproteomics of Environmental and Microbiome Samples, J. Proteome Res., 20, 4589-4597, 2021.
McIlvin, M. and Saito, M. A.: Informatics Component: Results from a Multi-Laboratory Ocean Metaproteomic Intercomparison: Effects of LC-MS Acquisition and Data Analysis Procedures, Pride PXD044234 [data set], https://doi.org/10.6019/PXD044234, 2024.
Mikan, M. P., Harvey, H. R., Timmins-Schiffman, E., Riffle, M., May, D. H., Salter, I., Noble, W. S., and Nunn, B. L.: Metaproteomics reveal that rapid perturbations in organic matter prioritize functional restructuring over taxonomy in western Arctic Ocean microbiomes, ISME J., 14, 39-52, 2020.
Moore, E. K., Nunn, B. L., Goodlett, D. R., and Harvey, H. R.: Identifying and tracking proteins through the marine water column: Insights into the inputs and preservation mechanisms of protein in sediments, Geochim. Cosmochim. Ac., 83, 324-359, 2012.
Moran, M. A., Kujawinski, E. B., Schroer, W. F., Amin, S. A., Bates, N. R., Bertrand, E. M., Braakman, R., Brown, C. T., Covert, M. W., Doney, S. C., and Dyhrman, S. T.: Microbial metabolites in the marine carbon cycle, Nat. Microbiol., 7, 508-523, 2022.
Morris, R. M., Nunn, B. L., Frazar, C., Goodlett, D. R., Ting, Y. S., and Rocap, G.: Comparative metaproteomics reveals ocean-scale shifts in microbial nutrient utilization and energy transduction, ISME J., 4, 673-685, 2010.
Mueller, R. S. and Pan, C.: Sample handling and mass spectrometry for microbial metaproteomic analyses, in: Methods in Enzymology, vol. 531, Elsevier, 289-303, https://doi.org/10.1016/B978-0-12-407863-5.00015-0, 2013.
Nesvizhskii, A. I., Keller, A., Kolker, E., and Aebersold, R.: A statistical model for identifying proteins by tandem mass spectrometry, Anal. Chem., 75, 4646-4658, 2003.
Participants of the Ocean Metaproteome Intercomparison Consortium: Results from a Multi-Laboratory Ocean Metaproteomic Intercomparison: Effects of LC-MS Acquisition and Data Analysis Procedures, Pride PXD043218 [data set], https://doi.org/10.6019/PXD043218, 2024.
Piehowski, P. D., Petyuk, V. A., Orton, D. J., Xie, F., Moore, R. J., Ramirez-Restrepo, M., Engel, A., Lieberman, A. P., Albin, R. L., and Camp, D. G.: Sources of technical variability in quantitative LC-MS proteomics: human brain tissue sample analysis, J. Proteome Res., 12, 2128-2137, 2013.
Pietilä, S., Suomi, T., and Elo, L. L.: Introducing untargeted data-independent acquisition for metaproteomics of complex microbial samples, ISME Commun., 12, 7305, https://doi.org/10.1038/s43705-022-00137-0, 2022.
Ram, R. J., VerBerkmoes, N. C., Thelen, M. P., Tyson, G. W., Baker, B. J., Blake, R. C., Shah, M., Hettich, R. L., and Banfield, J. F.: Community proteomics of a natural microbial biofilm, Science, 308, 1915-1920, 2005.
Saito, M. A. and Cohen, N.: Scaffold-derived metaproteomic exclusive and total spectral counts associated with proteins from samples taken during R/V Atlantic Explorer cruise AE1913 from the Sargasso Sea to Northeast US shelf waters in June of 2019, MBLWHOI Library [data set], https://doi.org/10.26008/1912/bco-dmo.934706.1, 2024.
Saito, M. A., McIlvin, M. R., Moran, D. M., Goepfert, T. J., DiTullio, G. R., Post, A. F., and Lamborg, C. H.: Multiple nutrien t stresses at intersecting Pacific Ocean biomes detected by protein biomarkers, Science, 345, 1173-1177, 2014.
Saito, M. A., Dorsk, A., Post, A. F., McIlvin, M. R., Rappé, M. S., DiTullio, G. R., and Moran, D. M.: Needles in the blue sea: Sub-species specificity in targeted protein biomarker analyses within the vast oceanic microbial metaproteome, Proteomics, 15, 3521-3531, 2015.
Saito, M. A., Bertrand, E. M., Duffy, M. E., Gaylord, D. A., Held, N. A., Hervey IV, W. J., Hettich, R. L., Jagtap, P. D., Janech, M. G., and Kinkade, D. B.: Progress and challenges in ocean metaproteomics and proposed best practices for data sharing, J. Proteome Res., 18, 1461-1476, 2019.
Saito, M. A., McIlvin, M. R., Moran, D. M., Santoro, A. E., Dupont, C. L., Rafter, P. A., Saunders, J. K., Kaul, D., Lamborg, C. H., and Westley, M.: Abundant nitrite-oxidizing metalloenzymes in the mesopelagic zone of the tropical Pacific Ocean, Nat. Geosci., 13, 355-362, 2020.
Saunders, J. K., Gaylord, D. A., Held, N. A., Symmonds, N., Dupont, C. L., Shepherd, A., Kinkade, D. B., and Saito, M. A.: METATRYP v 2.0: Metaproteomic least common ancestor analysis for taxonomic inference using specialized sequence assemblies-standalone software and web servers for marine microorganisms and coronaviruses, J. Proteome Res., 19, 4718-4729, 2020.
Scanlan, D. J., Silman, N. J., Donald, K. M., Wilson, W. H., Carr, N. G., Joint, I., and Mann, N. H.: An immunological approach to detect phosphate stress in populations and single cells of photosynthetic picoplankton, Appl. Environ. Microbiol., 63, 2411-2420, 1997.
Schiebenhoefer, H., Van Den Bossche, T., Fuchs, S., Renard, B. Y., Muth, T., and Martens, L.: Challenges and promise at the interface of metaproteomics and genomics: an overview of recent progress in metaproteogenomic data analysis, Expert Rev. Proteomic., 16, 375-390, 2019.
Sørensen, T.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish common, Kongelige Danske Videnskabernes Selskab, 5, 1-34, 1948.
Sowell, S. M., Wilhelm, L. J., Norbeck, A. D., Lipton, M. S., Nicora, C. D., Barofsky, D. F., Carlson, C. A., Smith, R. D., and Giovanonni, S. J.: Transport functions dominate the SAR11 metaproteome at low-nutrient extremes in the Sargasso Sea, ISME J., 3, 93-105, 2009.
Stewart, H. I., Grinfeld, D., Giannakopulos, A., Petzoldt, J., Shanley, T., Garland, M., Denisov, E., Peterson, A. C., Damoc, E., Zeller, M., Arrey, T. N., Pashkova, A., Renuse, S., Hakimi, A., Kühn, A., Biel, M., Kreutzmann, A., Hagedorn, B., Colonius, I., Schütz, A., Stefes, A., Dwivedi, A., Mourad, D., Hoek, M., Reitemeier, B., Cochems, P., Kholomeev, A., Ostermann, R., Quiring, G., Ochmann, M., Möhring, S., Wagner, A., Petker, A., Kanngiesser, S., Wiedemeyer, M., Balschun, W., Hermanson, D., Zabrouskov, V., Makarov, A. A., and Hock, C.: Parallelized Acquisition of Orbitrap and Astral Analyzers Enables High-Throughput Quantitative Analysis, Anal. Chem., 95, 15656-15664, https://doi.org/10.1021/acs.analchem.3c02856, 2023.
Tagliabue, A.: "Oceans are hugely complex';: modelling marine microbes is key to climate forecasts, Nature, 623, 250-252, https://doi.org/10.1038/d41586-023-03425-4, 2023.
Timmins-Schiffman, E., May, D. H., Mikan, M., Riffle, M., Frazar, C., Harvey, H. R., Noble, W. S., and Nunn, B. L.: Critical decisions in metaproteomics: achieving high confidence protein annotations in a sea of unknowns, ISME J., 11, 309-314, 2017.
Ustick, L. J., Larkin, A. A., Garcia, C. A., Garcia, N. S., Brock, M. L., Lee, J. A., Wiseman, N. A., Moore, J. K., and Martiny, A. C.: Metagenomic analysis reveals global-scale patterns of ocean nutrient limitation, Science, 372, 287-291, 2021.
Van Den Bossche, T., Kunath, B. J., Schallert, K., Schäpe, S. S., Abraham, P. E., Armengaud, J., Arntzen, M. Ø., Bassignani, A., Benndorf, D., and Fuchs, S.: Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows, Nat. Commun., 12, 1-15, https://doi.org/10.1038/s41467-021-27542-8, 2021.
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., and Bright, J.: SciPy 1.0: fundamental algorithms for scientific computing in Python, Nat. Methods, 17, 261-272, 2020.
Waskom, M. L.: Seaborn: statistical data visualization, J. Open Source Softw., 6, 3021, https://doi.org/10.21105/joss, 2021.
Williams, T. J., Long, E., Evans, F., DeMaere, M. Z., Lauro, F. M., Raftery, M. J., Ducklow, H., Grzymski, J. J., Murray, A. E., and Cavicchioli, R.: A metaproteomic assessment of winter and summer bacterioplankton from Antarctic Peninsula coastal surface waters, ISME J., 6, 1883-1900, 2012.
Wilmes, P. and Bond, P. L.: Metaproteomics: studying functional gene expression in microbial ecosystems, Trends Microbiol., 14, 92-97, 2006.
Wilmes, P., Andersson, A. F., Lefsrud, M. G., Wexler, M., Shah, M., Zhang, B., Hettich, R. L., Bond, P. L., VerBerkmoes, N. C., and Banfield, J. F.: Community proteogenomics highlights microbial strain-variant protein expression within activated sludge performing enhanced biological phosphorus removal, ISME J., 2, 853-864, 2008.
Worden, A. Z., Follows, M. J., Giovannoni, S. J., Wilken, S., Zimmerman, A. E., and Keeling, P. J.: Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes, Science, 347, 1257594, https://doi.org/10.1126/science.1257594, 2015.
Wu, M., McCain, J. S. P., Rowland, E., Middag, R., Sandgren, M., Allen, A. E., and Bertrand, E. M.: Manganese and iron deficiency in Southern Ocean Phaeocystis antarctica populations revealed through taxon-specific protein indicators, Nat. Commun., 10, 3582, https://doi.org/10.1038/s41467-019-11426-z, 2019.