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Tracking nitrogen allocation to proteome biosynthesis in a marine microbial community

Abstract

Microbial growth in many environments is limited by nitrogen availability, yet there is limited understanding of how complex communities compete for and allocate this resource. Here we develop a broadly applicable approach to track biosynthetic incorporation of 15N-labelled nitrogen substrates into microbial community proteomes, enabling quantification of protein turnover and N allocation to specific cellular functions in individual taxa. Application to oligotrophic ocean surface water identifies taxa-specific substrate preferences and a distinct subset of protein functions undergoing active biosynthesis. The cyanobacterium Prochlorococcus is the most effective competitor for acquisition of ammonium and urea and shifts its proteomic allocation of N over the day/night cycle. Our approach reveals that infrastructure and protein-turnover functions comprise substantial biosynthetic demand for N in Prochlorococcus and a range of other microbial taxa. The direct interrogation of the proteomic underpinnings of N limitation with 15N-tracking proteomics illuminates how nutrient stress differentially influences metabolism in co-existing microbes.

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Fig. 1: Proteomic tracking of nitrogen assimilation.
Fig. 2: Nitrogen substrate use by taxon and coarse functional type.
Fig. 3: Net synthesis versus turnover of proteins.
Fig. 4: Proteome allocation by Prochlorococcus.
Fig. 5: Proteome allocation by Pelagibacter and eukaryotic phytoplankton.

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Data availability

Proteomic mass spectral data are available through ProteomeXchange via the MassIVE repository (massive.ucsd.edu) under accession MSV000089118 and via the PRIDE repository (ebi.ac.uk/pride) under accession PXD038614. FASTA sequence databases used for peptide identification are available via Mendeley Data at data.mendeley.com/datasets/7226j6cpp3/1.

Code availability

Code for the cPIE isotope-tracking proteomics analysis pipeline is available at github.com/waldbauerlab.

References

  1. Moore, C. M. et al. Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701–710 (2013).

    Article  CAS  Google Scholar 

  2. Shenhav, L. & Zeevi, D. Resource conservation manifests in the genetic code. Science 370, 683–687 (2020).

    Article  CAS  PubMed  Google Scholar 

  3. Ustick, L. J. et al. Metagenomic analysis reveals global-scale patterns of ocean nutrient limitation. Science 372, 287–291 (2021).

    Article  CAS  PubMed  Google Scholar 

  4. Dlugosch, L. et al. Nitrogen availability drives gene length of dominant prokaryotes and diversity of genes acquiring nitrogen-species in oceanic system. Preprint at bioRxiv https://doi.org/10.1101/2021.01.10.426031 (2022).

  5. Read, R. W. et al. Nitrogen cost minimization is promoted by structural changes in the transcriptome of N-deprived Prochlorococcus cells. ISME J. 11, 2267–2278 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Muratore, D. et al. Complex marine microbial communities partition metabolism of scarce resources over the diel cycle. Nat. Ecol. Evol. 6, 218–229 (2022).

    Article  PubMed  Google Scholar 

  7. Saito, M. A. et al. Multiple nutrient stresses at intersecting Pacific Ocean biomes detected by protein biomarkers. Science 345, 1173–1177 (2014).

    Article  CAS  PubMed  Google Scholar 

  8. Szul, M. J., Dearth, S. P., Campagna, S. R. & Zinser, E. R. Carbon fate and flux in Prochlorococcus under nitrogen limitation. mSystems 4, e00254–18 (2019).

  9. Ofaim, S., Sulheim, S., Almaas, E., Sher, D. & Segrè, D. Dynamic allocation of carbon storage and nutrient-dependent exudation in a revised genome-scale model of Prochlorococcus. Front. Genet. 12, 586293 (2021).

  10. Roth-Rosenberg, D., Aharonovich, D., Omta, A. W., Follows, M. J. & Sher, D. Dynamic macromolecular composition and high exudation rates in Prochlorococcus. Limnol. Oceanogr. 66, 1759–1773 (2021).

    Article  CAS  Google Scholar 

  11. Buchanan, P. J., Aumont, O., Bopp, L., Mahaffey, C. & Tagliabue, A. Impact of intensifying nitrogen limitation on ocean net primary production is fingerprinted by nitrogen isotopes. Nat. Commun. 12, 6214 (2021).

  12. Goldberg, S. J., Nelson, C. E., Viviani, D. A., Shulse, C. N. & Church, M. J. Cascading influence of inorganic nitrogen sources on DOM production, composition, lability and microbial community structure in the open ocean. Environ. Microbiol. 19, 3450–3464 (2017).

    Article  CAS  PubMed  Google Scholar 

  13. Rii, Y. M., Bidigare, R. R. & Church, M. J. Differential responses of eukaryotic phytoplankton to nitrogenous nutrients in the North Pacific Subtropical Gyre. Front. Mar. Sci. 5, 92 (2018).

    Article  Google Scholar 

  14. Shilova, I. N. et al. Differential effects of nitrate, ammonium, and urea as N sources for microbial communities in the North Pacific Ocean. Limnol. Oceanogr. 62, 2550–2574 (2017).

    Article  CAS  Google Scholar 

  15. Aldunate, M. et al. Nitrogen assimilation in picocyanobacteria inhabiting the oxygen-deficient waters of the eastern tropical North and South Pacific. Limnol. Oceanogr. 65, 437–453 (2020).

    Article  CAS  Google Scholar 

  16. Kirchman, D., K’nees, E. & Hodson, R. Leucine incorporation and its potential as a measure of protein synthesis by bacteria in natural aquatic systems. Appl. Environ. Microbiol. 49, 599–607 (1985).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Simon, M. & Azam, F. Protein content and protein synthesis rates of planktonic marine bacteria. Mar. Ecol. Prog. Ser. 51, 201–213 (1989).

    Article  CAS  Google Scholar 

  18. Church, M. J., Hutchins, D. A. & Ducklow, H. W. Limitation of bacterial growth by dissolved organic matter and iron in the Southern Ocean. Appl. Environ. Microbiol. 66, 455–466 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Berthelot, H. et al. NanoSIMS single cell analyses reveal the contrasting nitrogen sources for small phytoplankton. ISME J. 13, 651–662 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Berthelot, H., Duhamel, S., L’helguen, S., Maguer, J.-F. & Cassar, N. Inorganic and organic carbon and nitrogen uptake strategies of picoplankton groups in the northwestern Atlantic Ocean. Limnol. Oceanogr. 66, 3682–3696 (2021).

    Article  CAS  Google Scholar 

  21. Deng, W. et al. Potential competition between marine heterotrophic prokaryotes and autotrophic picoplankton for nitrogen substrates. Limnol. Oceanogr. 66, 3338–3355 (2021).

    Article  CAS  Google Scholar 

  22. Kieft, B. et al. Phytoplankton exudates and lysates support distinct microbial consortia with specialized metabolic and ecophysiological traits. Proc. Natl Acad. Sci. USA 118, e2101178118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Waldbauer, J., Zhang, L., Rizzo, A. & Muratore, D. diDO-IPTL: a peptide-labeling strategy for precision quantitative proteomics. Anal. Chem. 89, 11498–11504 (2017).

    Article  CAS  PubMed  Google Scholar 

  24. Waldbauer, J. R. et al. Nitrogen sourcing during viral infection of marine cyanobacteria. Proc. Natl Acad. Sci. USA 116, 15590–15595 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wan, X. S. et al. Phytoplankton–nitrifier interactions control the geographic distribution of nitrite in the upper ocean. Glob. Biogeochem. Cycles 35, e2021GB007072 (2021).

  26. Ianiri, H. L., Shen, Y., Broek, T. A. B. & McCarthy, M. D. Bacterial sources and cycling dynamics of amino acids in high and low molecular weight dissolved organic nitrogen in the ocean. Mar. Chem. 241, 104104 (2022).

    Article  CAS  Google Scholar 

  27. HOT-DOGS application. University of Hawai’i at Mānoa https://hahana.soest.hawaii.edu/hot/hot-dogs/interface.html (2022).

  28. Walker, M. C. & van der Donk, W. A. The many roles of glutamate in metabolism. J. Ind. Microbiol. Biotechnol. 43, 419–430 (2016).

    Article  CAS  PubMed  Google Scholar 

  29. Evans, C., Gómez-Pereira, P. R., Martin, A. P., Scanlan, D. J. & Zubkov, M. V. Photoheterotrophy of bacterioplankton is ubiquitous in the surface oligotrophic ocean. Prog. Oceanogr. 135, 139–145 (2015).

    Article  Google Scholar 

  30. Björkman, K. M., Church, M. J., Doggett, J. K. & Karl, D. M. Differential assimilation of inorganic carbon and leucine by Prochlorococcus in the oligotrophic North Pacific Subtropical Gyre. Front. Microbiol. 6, 1401 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Duhamel, S., Van Wambeke, F., Lefevre, D., Benavides, M. & Bonnet, S. Mixotrophic metabolism by natural communities of unicellular cyanobacteria in the western tropical South Pacific Ocean. Environ. Microbiol. 20, 2743–2756 (2018).

    Article  CAS  PubMed  Google Scholar 

  32. Hunt, D. E. et al. Relationship between abundance and specific activity of bacterioplankton in open ocean surface waters. Appl. Environ. Microbiol. 79, 177–184 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Noell, S. E. et al. SAR11 cells rely on enzyme multifunctionality to metabolize a range of polyamine compounds. mBio 12, e01091–21 (2021).

  34. Malmstrom, R. R., Kiene, R. P., Cottrell, M. T. & Kirchman, D. L. Contribution of SAR11 bacteria to dissolved dimethylsulfoniopropionate and amino acid uptake in the North Atlantic Ocean. Appl. Environ. Microbiol. 70, 4129–4135 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Lahtvee, P. J., Seiman, A., Arike, L., Adamberg, K. & Vilu, R. Protein turnover forms one of the highest maintenance costs in Lactococcus lactis. Microbiology 160, 1501–1512 (2014).

    Article  CAS  PubMed  Google Scholar 

  36. Karlsen, J., Asplund-Samuelsson, J., Jahn, M., Vitay, D. & Hudson, E. P. Slow protein turnover explains limited protein-level response to diurnal transcriptional oscillations in cyanobacteria. Front. Microbiol. 12, 657379 (2021).

  37. Murphy, C. D. et al. Photoinactivation of photosystem II in Prochlorococcus and Synechococcus. PLoS ONE 12, e0168991 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Waldbauer, J. R., Rodrigue, S., Coleman, M. L. & Chisholm, S. W. Transcriptome and proteome dynamics of a light–dark synchronized bacterial cell cycle. PLoS ONE 7, e43432 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Hauser, T., Popilka, L., Hartl, F. U. & Hayer-Hartl, M. Role of auxiliary proteins in Rubisco biogenesis and function. Nat. Plants 1, 15065 (2015).

    Article  CAS  PubMed  Google Scholar 

  40. Hayer-Hartl, M., Bracher, A. & Hartl, F. U. The GroEL–GroES chaperonin machine: a nano-cage for protein folding. Trends Biochem. Sci. 41, 62–76 (2016).

    Article  CAS  PubMed  Google Scholar 

  41. Halder, R., Nissley, D. A., Sitarik, I. & O’Brien, E. P. Subpopulations of soluble, misfolded proteins commonly bypass chaperones: how it happens at the molecular level. Preprint at bioRxiv https://doi.org/10.1101/2021.08.18.456736 (2022).

  42. Steinberg, D. K., Goldthwait, S. A. & Hansell, D. A. Zooplankton vertical migration and the active transport of dissolved organic and inorganic nitrogen in the Sargasso Sea. Deep Sea Res. Part I Oceanogr. Res. Pap. 49, 1445–1461 (2002).

    Article  CAS  Google Scholar 

  43. Mori, M. et al. From coarse to fine: the absolute Escherichia coli proteome under diverse growth conditions. Mol. Syst. Biol. 17, e9536 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Sowell, S. M. et al. Transport functions dominate the SAR11 metaproteome at low-nutrient extremes in the Sargasso Sea. ISME J. 3, 93–105 (2008).

    Article  PubMed  Google Scholar 

  45. Hanson, B. T., Hewson, I. & Madsen, E. L. Metaproteomic survey of six aquatic habitats: discovering the identities of microbial populations active in biogeochemical cycling. Microb. Ecol. 67, 520–539 (2014).

    Article  PubMed  Google Scholar 

  46. Norris, N., Levine, N. M., Fernandez, V. I. & Stocker, R. Mechanistic model of nutrient uptake explains dichotomy between marine oligotrophic and copiotrophic bacteria. PLoS Comput. Biol. 17, e1009023 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ollison, G. A., Hu, S. K., Mesrop, L. Y., Delong, E. F. & Caron, D. A. Come rain or shine: depth not season shapes the active protistan community at Station ALOHA in the North Pacific Subtropical Gyre. Deep Sea Res. Part I Oceanogr. Res. Pap. 170, 103494 (2021).

    Article  Google Scholar 

  48. Sibbald, S. J., Hopkins, J. F., Filloramo, G. V. & Archibald, J. M. Ubiquitin fusion proteins in algae: implications for cell biology and the spread of photosynthesis. BMC Genomics 20, 1–13 (2019).

    Article  Google Scholar 

  49. Langer, G., Jan de Nooijer, L., Oetjen Biogeosciences, K. & Wegener, A. On the role of the cytoskeleton in coccolith morphogenesis: the effect of cytoskeleton inhibitors. J. Phycol. 46, 1252–1256 (2010).

    Article  CAS  Google Scholar 

  50. Durak, G. M., Brownlee, C. & Wheeler, G. L. The role of the cytoskeleton in biomineralisation in haptophyte algae. Sci. Rep. 7, 15409 (2017).

  51. Linschooten, C. et al. Role of the light–dark cycle and medium composition on the production of coccoliths by Emiliania huxleyi (Haptophyceae). J. Phycol. 27, 82–86 (1991).

    Article  Google Scholar 

  52. Müller, M. N., Antia, A. N. & LaRoche, J. Influence of cell cycle phase on calcification in the coccolithophore Emiliania huxleyi. Limnol. Oceanogr. 53, 506–512 (2008).

    Article  Google Scholar 

  53. Eiler, A., Hayakawa, D. H., Church, M. J., Karl, D. M. & Rappé, M. S. Dynamics of the SAR11 bacterioplankton lineage in relation to environmental conditions in the oligotrophic North Pacific Subtropical Gyre. Environ. Microbiol. 11, 2291–2300 (2009).

    Article  CAS  PubMed  Google Scholar 

  54. Pasulka, A. L., Landry, M. R., Taniguchi, D. A. A., Taylor, A. G. & Church, M. J. Temporal dynamics of phytoplankton and heterotrophic protists at station ALOHA. Deep Sea Res. Part II Top. Stud. Oceanogr. 93, 44–57 (2013).

    Article  CAS  Google Scholar 

  55. Ramm, E. et al. A review of the importance of mineral nitrogen cycling in the plant-soil-microbe system of permafrost-affected soils—changing the paradigm. Environ. Res. Lett. 17, 013004 (2022).

    Article  CAS  Google Scholar 

  56. Karl, D. M. & Church, M. J. Microbial oceanography and the Hawaii Ocean Time-series programme. Nat. Rev. Microbiol. 12, 699–713 (2014).

    Article  CAS  PubMed  Google Scholar 

  57. Erde, J., Loo, R. R. O. & Loo, J. A. Enhanced FASP (eFASP) to increase proteome coverage and sample recovery for quantitative proteomic experiments. J. Proteome Res. 13, 1885–1895 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Vislova, A., Sosa, O. A., Eppley, J. M., Romano, A. E. & DeLong, E. F. Diel oscillation of microbial gene transcripts declines with depth in oligotrophic ocean waters. Front. Microbiol. 10, 2191 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Ottesen, E. A. et al. Multispecies diel transcriptional oscillations in open ocean heterotrophic bacterial assemblages. Science 345, 207–212 (2014).

    Article  CAS  PubMed  Google Scholar 

  60. Eng, J. K., McCormack, A. L. & Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass. Spectrom. 5, 976–989 (1994).

    Article  CAS  PubMed  Google Scholar 

  61. Käll, L., Canterbury, J. D., Weston, J., Noble, W. S. & MacCoss, M. J. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat. Methods 4, 923–925 (2007).

    Article  PubMed  Google Scholar 

  62. Fan, K. T. et al. Proteome scale-protein turnover analysis using high resolution mass spectrometric data from stable-isotope labeled plants. J. Proteome Res. 15, 851–867 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Heinrich, P. et al. Correcting for natural isotope abundance and tracer impurity in MS-, MS/MS- and high-resolution-multiple-tracer-data from stable isotope labeling experiments with IsoCorrectoR. Sci. Rep. 8, 17910 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Sing, T., Sander, O., Beerenwinkel, N. & Lengauer, T. ROCR: visualizing classifier performance in R. Bioinformatics 21, 3940–3941 (2005).

    Article  CAS  PubMed  Google Scholar 

  66. Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).

  67. Singh, R. G. et al. Unipept 4.0: functional analysis of metaproteome data. J. Proteome Res. 18, 606–615 (2019).

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Acknowledgements

We are grateful to: the Hawaii Ocean Time-series programme (supported by US National Science Foundation award no. 1756517), especially T. Clemente; the chief scientist of HOT-311, F. Santiago-Mandujano; the marine technicians of the University of Hawaii Ocean Technology Group; and the captain and crew of R/V Kilo Moana for making these experiments possible. Thanks to L. Zhang for maintenance of the biogeochemical proteomics facility at UChicago and members of the Waldbauer and Coleman labs for helpful discussions and support. We are also grateful to L. Kelly for comments that improved the manuscript. This work was supported by the Simons Foundation (grant no. 402971 to J.R.W.).

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Contributions

Conceived and designed the experiments: A.E.Z. and J.R.W. Performed the experiments: A.E.Z., G.E.G. and J.R.W. Analysed the data: A.E.Z., J.C.P., M.L.C. and J.R.W. Contributed materials/analysis tools: A.E.Z., J.C.P., M.L.C. and J.R.W. Wrote the paper: A.E.Z., M.L.C. and J.R.W.

Corresponding author

Correspondence to Jacob R. Waldbauer.

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Nature Microbiology thanks Barbara Bayer and Xavier Mayali for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 cPIE 15N-tracking proteomics pipeline.

Workflow for peptide atom% 15N determination with cPIE (classified Peptide Isotope Enrichment) pipeline.

Extended Data Fig. 2 Peptide 15N incorporation rates.

Histogram of 15N incorporation rates from peptide timecourses (gray; total n = 9062) and fitted t-distribution (solid black line) used to determine peptide enrichment thresholds. Using the t-distribution as a null, the false discovery rate for 15N-enriched peptides (incorporation rate >0.898 atom% / h; n = 862) is constrained to 4.76%.

Extended Data Fig. 3 Taxonomic composition of inoculum and incubation proteomes.

Proteome taxonomic composition in initial inocula and after 11 hours incubation with added substrates or no-amendment control. Only peptides associated with complete 15N incorporation rate timecourses are included (n = 1605). Similar trends were observed when considering intensity, number of spectra, or number of distinct peptides (cf. Extended Data Fig. 4A & B).

Extended Data Fig. 4 15N incorporation and data quantity.

(A) and (B) Taxonomic composition of 15N-enriched peptides in each N amendment, characterized by either MS/MS spectra (A) or MS1 signal intensity (B) associated with detected 15N-enriched peptides. (C) and (D) Relationship between 15N incorporation rate and quantity of MS data for peptide timecourses (n = 9062), expressed as either total MS/MS spectra (C) or total MS1 signal intensity (D). Dotted and dashed horizonal lines indicate the lower bound of incorporation rate for 15N-enriched peptides and the upper bound for non-enriched peptides, respectively; intermediate peptides fall in between the two lines. Datapoints are colored by taxon as in (A) and (B).

Extended Data Fig. 5 Peptide 15N incorporation categories by N amendment and incubation period.

Total peptide timecourses detected and 15N enrichment categories by nitrogen amendment and incubation period. Fraction of enriched peptides in the ammonium amendment increased from 21.4% in the day/dusk incubation to 26.6% in the night/dawn incubation, while enriched peptides in the urea amendment increased from 14.3% in day/dusk to 24.3% during night/dawn; changes in the other amendments between the two incubation periods were negligible.

Extended Data Fig. 6 Distribution of peptide 15N enrichment rates by taxon.

Histograms of peptide 15N enrichment rates by taxon in the 15N-ammonium (A) and -urea (B) treatments. Dashed lines indicate the threshold enrichment rate (>0.898 atom% h−1) for 15N-enriched peptides.

Extended Data Fig. 7 Prochlorococcus 15N proteome allocation across N substrate additions.

Prochlorococcus 15N allocation to proteome functional categories across all substrate amendments (n = 2850 timecourses from 469 distinct peptides).

Extended Data Fig. 8 Prochlorococcus proteome expression changes across N substrate additions.

Changes in Prochlorococcus peptide abundances during the incubations across all natural-abundance substrate amendments (n = 880 timecourses from 143 distinct peptides). In contrast to the 15N incorporation patterns (Extended Data Fig. 7), peptide abundance changes (including day/night differences) were consistent across all amendments. This uniformity of protein expression patterns, irrespective of whether an amended substrate was being incorporated or not, is further evidence of the absence of a fertilization effect in these experiments and the representativeness of these results for in situ activity.

Extended Data Fig. 9 Pelagibacter transporter 15N incorporation.

Incorporation of 15N into subgroups of transporters in Pelagibacter (n = 1583 timecourses from 258 distinct peptides). No subgroup of transporters appeared more highly labeled than others in either experiment.

Extended Data Fig. 10 Turnover of cytoskeletal proteins in Isochrysidales.

Incorporation of 15N (solid lines) and relative abundance change (dashed lines) into four cytoskeleton (actin/tubulin) peptides of Isochrysidales (coccolithophorids) during the day/dusk and night/dawn ammonium-amended incubations. That 15N incorporation is observed despite a decline in abundance of these peptides suggests these proteins are turned over rapidly, likely as part of cytoskeletal remodeling during coccolith export to the cell surface.

Supplementary information

Supplementary Information

Supplementary Fig. 1 and Tables 1 and 2.

Reporting Summary

Supplementary Data 1

Amino acid sequences, metadata, 15N incorporation rate (atom% h−1), rate of relative abundance change (log2 h−1), and taxonomic and functional annotations for 1,740 peptides with timecourses in at least one natural abundance or 15N nitrogen amendment.

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Zimmerman, A.E., Podowski, J.C., Gallagher, G.E. et al. Tracking nitrogen allocation to proteome biosynthesis in a marine microbial community. Nat Microbiol 8, 498–509 (2023). https://doi.org/10.1038/s41564-022-01303-9

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