Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Community proteogenomics reveals the systemic impact of phosphorus availability on microbial functions in tropical soil


Phosphorus is a scarce nutrient in many tropical ecosystems, yet how soil microbial communities cope with growth-limiting phosphorus deficiency at the gene and protein levels remains unknown. Here, we report a metagenomic and metaproteomic comparison of microbial communities in phosphorus-deficient and phosphorus-rich soils in a 17-year fertilization experiment in a tropical forest. The large-scale proteogenomics analyses provided extensive coverage of many microbial functions and taxa in the complex soil communities. A greater than fourfold increase in the gene abundance of 3-phytase was the strongest response of soil communities to phosphorus deficiency. Phytase catalyses the release of phosphate from phytate, the most recalcitrant phosphorus-containing compound in soil organic matter. Genes and proteins for the degradation of phosphorus-containing nucleic acids and phospholipids, as well as the decomposition of labile carbon and nitrogen, were also enhanced in the phosphorus-deficient soils. In contrast, microbial communities in the phosphorus-rich soils showed increased gene abundances for the degradation of recalcitrant aromatic compounds, transformation of nitrogenous compounds and assimilation of sulfur. Overall, these results demonstrate the adaptive allocation of genes and proteins in soil microbial communities in response to shifting nutrient constraints.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Impact of phosphorus availability on the phosphorus, carbon, nitrogen and sulfur cycles of the Gigante soil communities.
Fig. 2: Taxonomic distribution of key phosphorus-acquisition enzymes.
Fig. 3: Classification and evolution of phytase genes.
Fig. 4: Proteogenomics comparison of phosphorus-deficient and phosphorus-rich soils.
Fig. 5: Comparison of enzyme activities.


  1. Condron, L. M., Turner, B. L. & Cade-Menun, B. J. in Phosphorus: Agriculture and the Environment (eds Sims, J. & Sharpley, A.) 87–121 (American Society of Agronomy, Madison, 2005).

  2. Sharma, S. B., Sayyed, R. Z., Trivedi, M. H. & Gobi, T. A. Phosphate solubilizing microbes: sustainable approach for managing phosphorus deficiency in agricultural soils. Springerplus 2, 587 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Turner, B. L. & Wright, S. J. The response of microbial biomass and hydrolytic enzymes to a decade of nitrogen, phosphorus, and potassium addition in a lowland tropical rain forest. Biogeochemistry 117, 115–130 (2014).

    CAS  Article  Google Scholar 

  4. Quesada, C. A. et al. Soils of Amazonia with particular reference to the RAINFOR sites. Biogeosciences 8, 1415–1440 (2011).

    CAS  Article  Google Scholar 

  5. Wright, S. J. et al. Potassium, phosphorus, or nitrogen limit root allocation, tree growth, or litter production in a lowland tropical forest. Ecology 92, 1616–1625 (2011).

    Article  PubMed  Google Scholar 

  6. Wurzburger, N. & Wright, S. J. Fine-root responses to fertilization reveal multiple nutrient limitation in a lowland tropical forest. Ecology 96, 2137–2146 (2015).

    Article  PubMed  Google Scholar 

  7. Santiago, L. S. et al. Tropical tree seedling growth responses to nitrogen, phosphorus and potassium addition. J. Ecol. 100, 309–316 (2012).

    CAS  Article  Google Scholar 

  8. Mayor, J. R., Wright, S. J. & Turner, B. L. Species-specific responses of foliar nutrients to long-term nitrogen and phosphorus additions in a lowland tropical forest. J. Ecol. 102, 36–44 (2014).

    CAS  Article  Google Scholar 

  9. Liu, L., Gundersen, P., Zhang, T. & Mo, J. M. Effects of phosphorus addition on soil microbial biomass and community composition in three forest types in tropical China. Soil. Biol. Biochem. 44, 31–38 (2012).

    Article  Google Scholar 

  10. Cassman, N. A. et al. Plant and soil fungal but not soil bacterial communities are linked in long-term fertilized grassland. Sci. Rep. 6, 23680 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. Jonasson, S., Michelsen, A., Schmidt, I. K. & Nielsen, E. V. Responses in microbes and plants to changed temperature, nutrient, and light regimes in the Arctic. Ecology 80, 1828–1843 (1999).

    Article  Google Scholar 

  12. Rinnan, R., Michelsen, A., Baath, E. & Jonasson, S. Fifteen years of climate change manipulations alter soil microbial communities in a subarctic heath ecosystem. Glob. Chang. Biol. 13, 28–39 (2007).

    Article  Google Scholar 

  13. Koyama, A., Wallenstein, M. D., Simpson, R. T. & Moore, J. C. Carbon-degrading enzyme activities stimulated by increased nutrient availability in Arctic tundra soils. PLoS. ONE 8, e77212 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. Mack, M. C., Schuur, E. A., Bret-Harte, M. S., Shaver, G. R. & Chapin, F. S. Ecosystem carbon storage in Arctic tundra reduced by long-term nutrient fertilization. Nature 431, 440–443 (2004).

  15. Tveit, A., Schwacke, R., Svenning, M. M. & Urich, T. Organic carbon transformations in high-Arctic peat soils: key functions and microorganisms. ISME J. 7, 299–311 (2013).

    CAS  Article  PubMed  Google Scholar 

  16. Hultman, J. et al. Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes. Nature 521, 208–212 (2015).

    CAS  Article  PubMed  Google Scholar 

  17. Butterfield, C. N. et al. Proteogenomic analyses indicate bacterial methylotrophy and archaeal heterotrophy are prevalent below the grass root zone. PeerJ 4, e2687 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Xue, K. et al. Tundra soil carbon is vulnerable to rapid microbial decomposition under climate warming. Nat. Clim. Change 6, 595–600 (2016).

  19. Jorquera, M. A. et al. Identification of beta-propeller phytase-encoding genes in culturable Paenibacillus and Bacillus spp. from the rhizosphere of pasture plants on volcanic soils. FEMS Microbiol. Ecol. 75, 163–172 (2011).

    CAS  Article  PubMed  Google Scholar 

  20. Mullaney, E. J. & Ullah, A. H. The term phytase comprises several different classes of enzymes. Biochem. Biophys. Res. Commun. 312, 179–184 (2003).

    CAS  Article  PubMed  Google Scholar 

  21. Lim, B. L., Yeung, P., Cheng, C. & Hill, J. E. Distribution and diversity of phytate-mineralizing bacteria. ISME J. 1, 321–330 (2007).

    CAS  Article  PubMed  Google Scholar 

  22. Turner, B. L. et al. Seasonal changes and treatment effects on soil inorganic nutrients following a decade of fertilizer addition in a lowland tropical forest. Soil. Sci. Soc. Am. J. 77, 1357–1369 (2013).

    CAS  Article  Google Scholar 

  23. Turner, B. L., Yavitt, J. B., Harms, K. E., Garcia, M. N. & Wright, S. J. Seasonal changes in soil organic matter after a decade of nutrient addition in a lowland tropical forest. Biogeochemistry 123, 221–235 (2015).

    CAS  Article  Google Scholar 

  24. Turner, B. L., Wells, A. & Condron, L. M. Soil organic phosphorus transformations along a coastal dune chronosequence under New Zealand temperate rain forest. Biogeochemistry 121, 595–611 (2014).

    CAS  Article  Google Scholar 

  25. Turner, B. L. & Engelbrecht, B. M. J. Soil organic phosphorus in lowland tropical rain forests. Biogeochemistry 103, 297–315 (2011).

    CAS  Article  Google Scholar 

  26. Turner, B. L. Resource partitioning for soil phosphorus: a hypothesis. J. Ecol. 96, 698–702 (2008).

    CAS  Article  Google Scholar 

  27. Funk, J. L. & Vitousek, P. M. Resource-use efficiency and plant invasion in low-resource systems. Nature 446, 1079–1081 (2007).

    CAS  Article  PubMed  Google Scholar 

  28. Cleveland, C. C. & Liptzin, D. C:N:P stoichiometry in soil: is there a “Redfield ratio” for the microbial biomass? Biogeochemistry 85, 235–252 (2007).

    Article  Google Scholar 

  29. Sterner, R. W. & Elser, J. J. Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere (Princeton Univ. Press, Princeton, 2002).

  30. Zechmeister-Boltenstern, S. et al. The application of ecological stoichiometry to plant–microbial–soil organic matter transformations. Ecol. Monogr. 85, 133–155 (2015).

    Article  Google Scholar 

  31. Kaspari, M. et al. Multiple nutrients limit litterfall and decomposition in a tropical forest. Ecol. Lett. 11, 35–43 (2008).

    PubMed  Google Scholar 

  32. Tilman, D. Resource Competition and Community Structure (Princeton Univ. Press, Princeton, 1982).

  33. Bloom, A. J., Chapin, F. S. & Mooney, H. A. Resource limitation in plants—an economic analogy. Annu. Rev. Ecol. Syst. 16, 363–392 (1985).

    Article  Google Scholar 

  34. Allison, S. D. & Vitousek, P. M. Responses of extracellular enzymes to simple and complex nutrient inputs. Soil. Biol. Biochem. 37, 937–944 (2005).

    CAS  Article  Google Scholar 

  35. Gleeson, S. K. & Tilman, D. Plant allocation and the multiple limitation hypothesis. Am. Nat. 139, 1322–1343 (1992).

    Article  Google Scholar 

  36. Hurt, R. A. et al. Improved yield of high molecular weight DNA coincides with increased microbial diversity access from iron oxide cemented sub-surface clay environments. PLoS ONE 9, e102826 (2014).

  37. Haider, B. et al. Omega: an overlap-graph de novo assembler for metagenomics. Bioinformatics 30, 2717–2722 (2014).

    CAS  Article  PubMed  Google Scholar 

  38. Varghese, N. J. et al. Microbial species delineation using whole genome sequences. Nucleic Acids Res. 43, 6761–6771 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    CAS  Article  PubMed  Google Scholar 

  40. Chai, J., Kora, G., Ahn, T. H., Hyatt, D. & Pan, C. Functional phylogenomics analysis of bacteria and archaea using consistent genome annotation with UniFam. BMC Evolut. Biol. 14, 207 (2014).

    Article  Google Scholar 

  41. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  Article  PubMed  Google Scholar 

  42. Jonsson, V., Osterlund, T., Nerman, O. & Kristiansson, E. Statistical evaluation of methods for identification of differentially abundant genes in comparative metagenomics. BMC Genom. 17, 78 (2016).

    Article  Google Scholar 

  43. Zhang, Z. H. et al. A comparative study of techniques for differential expression analysis on RNA-Seq data. PLoS. ONE 9, e103207 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Rapaport, F. et al. Comprehensive evaluation of differential gene expression analysis methods for RNA-Seq data. Genome Biol. 14, R95 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    CAS  Article  Google Scholar 

  46. Mosier, A. C. et al. Elevated temperature alters proteomic responses of individual organisms within a biofilm community. ISME J. 9, 180–194 (2015).

    CAS  Article  PubMed  Google Scholar 

  47. Hyatt, D., LoCascio, P. F., Hauser, L. J. & Uberbacher, E. C. Gene and translation initiation site prediction in metagenomic sequences. Bioinformatics 28, 2223–2230 (2012).

    CAS  Article  PubMed  Google Scholar 

  48. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. Kang, D. D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3, e1165 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Wu, Y. W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).

    CAS  Article  PubMed  Google Scholar 

  51. Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. Segata, N., Bornigen, D., Morgan, X. C. & Huttenhower, C. PhyloPhlAn is a new method for improved phylogenetic and taxonomic placement of microbes. Nat. Commun. 4, 2304 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Letunic, I. & Bork, P. Interactive Tree Of Life v2: online annotation and display of phylogenetic trees made easy. Nucleic Acids Res. 39, W475–W478 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  54. Oyserman, B. O., Noguera, D. R., del Rio, T. G., Tringe, S. G. & McMahon, K. D. Metatranscriptomic insights on gene expression and regulatory controls in Candidatus Accumulibacter phosphatis. ISME J. 10, 810–822 (2016).

    CAS  Article  PubMed  Google Scholar 

  55. Davis, M. P., van Dongen, S., Abreu-Goodger, C., Bartonicek, N. & Enright, A. J. Kraken: a set of tools for quality control and analysis of high-throughput sequence data. Methods 63, 41–49 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. Li, Z. et al. Diverse and divergent protein post-translational modifications in two growth stages of a natural microbial community. Nat. Commun. 5, 4405 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Wang, Y., Ahn, T. H., Li, Z. & Pan, C. Sipros/ProRata: a versatile informatics system for quantitative community proteomics. Bioinformatics 29, 2064–2065 (2013).

    CAS  Article  PubMed  Google Scholar 

  58. Guo, X. et al. Sipros ensemble improves database searching and filtering for complex metaproteomics. Bioinformatics (2017).

  59. Li, Z. et al. Integrated proteomics and metabolomics suggests symbiotic metabolism and multimodal regulation in a fungal-endobacterial system. Environ. Microbiol. 19, 1041–1053 (2017).

    CAS  Article  PubMed  Google Scholar 

  60. Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).

    CAS  Article  PubMed  Google Scholar 

  61. Lazar, C., Gatto, L., Ferro, M., Bruley, C. & Burger, T. Accounting for the multiple natures of missing values in label-free quantitative proteomics data sets to compare imputation strategies. J. Proteome Res. 15, 1116–1125 (2016).

  62. Gee, G. W. & Or, D. in Methods of Soil Analysis: Part 4—Physical Methods SSSA Book Series 5.4 (eds Dane, J. H. & Topp, G. C.) 255–294 (Soil Science Society of America, Madison, 2002).

  63. Loeppert, R. L. & Inskeep, W. P. in Methods of Soil Analysis: Part 3 Chemical Methods SSSA Book Series 5.3 (ed. Sparks, D. L.) 639–654 (Soil Science Society of America, Madison, 1996).

  64. Beck, T. et al. An inter-laboratory comparison of ten different ways of measuring soil microbial biomass C. Soil. Biol. Biochem. 29, 1023–1032 (1997).

    CAS  Article  Google Scholar 

  65. Brookes, P. C., Landman, A., Pruden, G. & Jenkinson, D. S. Chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil. Biol. Biochem. 17, 837–842 (1985).

    CAS  Article  Google Scholar 

  66. Tfaily, M. M. et al. Sequential extraction protocol for organic matter from soils and sediments using high resolution mass spectrometry. Anal. Chim. Acta 972, 54–61 (2017).

    CAS  Article  PubMed  Google Scholar 

  67. Kujawinski, E. B., Longnecker, K., Barott, K. L., Weber, R. J. M. & Kido Soule, M. C. Microbial community structure affects marine dissolved organic matter composition. Front. Mar. Sci. 3, 45 (2016).

  68. Tfaily, M. M. et al. Advanced solvent based methods for molecular characterization of soil organic matter by high-resolution mass spectrometry. Anal. Chem. 87, 5206–5215 (2015).

    CAS  Article  PubMed  Google Scholar 

Download references


This work was supported by Laboratory Directed Research and Development funding from Oak Ridge National Laboratory (ORNL). The authors acknowledge R. Hurt of ORNL’s Biosciences Division for assistance with DNA extractions from tropical soils and J. Phillips of ORNL’s Environmental Sciences Division for soil characterization. The metagenomic sequencing was conducted by the US Department of Energy (DOE) Joint Genome Institute (JGI). The Fourier transform ion cyclotron resonance MS analyses were performed by the Environmental Molecular Sciences Laboratory (EMSL). The JGI and EMSL are DOE Office of Science User Facilities sponsored by the Office of Biological and Environmental Research. This research used resources of the Oak Ridge Leadership Computing Facility. The ORNL and JGI are supported by the Office of Science of the US DOE under contract numbers DE-AC05-00OR22725 and DE-AC02-05CH11231, respectively.

Author information

Authors and Affiliations



C.P. and M.A.M. designed the project. M.A.M., C.P., S.J.W. and B.L.T. performed the soil sampling. Q.Y., T.C.H., S.G.T. and C.P. performed the metagenomics. Z.L. and X.G. performed the metaproteomics. Q.Y. and M.A.M. performed the enzyme assay. M.M.T., L.P.-T. and Y.S. performed the soil organic matter analysis. Q.Y. and C.P. analysed the meta-omics results. B.L.T., S.J.W. and M.A.M. assisted in interpretation of the results. The manuscript was drafted by Q.Y. and C.P. and revised by all authors.

Corresponding author

Correspondence to Chongle Pan.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figures 1 and 2

Life Sciences Reporting Summary

Supplementary Data 1

Summary of the community proteogenomic results

Supplementary Data 2

Differential analysis of gene abundances by EC numbers and GO terms in the Gigante soil metagenomes

Supplementary Data 3

Assembly and pathway analysis of the near-complete genomes

Supplementary Data 4

Differential analysis of protein abundances by EC numbers and GO terms in the Gigante soil metaproteomes

Supplementary Data 5

Measurement of soil properties

Supplementary Data 6

Measurement of soil organic matter by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yao, Q., Li, Z., Song, Y. et al. Community proteogenomics reveals the systemic impact of phosphorus availability on microbial functions in tropical soil. Nat Ecol Evol 2, 499–509 (2018).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing