Science China Life Sciences

, Volume 61, Issue 12, pp 1451–1462 | Cite as

QMEC: a tool for high-throughput quantitative assessment of microbial functional potential in C, N, P, and S biogeochemical cycling

  • Bangxiao Zheng
  • Yongguan Zhu
  • Jordi Sardans
  • Josep PeñuelasEmail author
  • Jianqiang SuEmail author
Cover Article


Microorganisms are major drivers of elemental cycling in the biosphere. Determining the abundance of microbial functional traits involved in the transformation of nutrients, including carbon (C), nitrogen (N), phosphorus (P) and sulfur (S), is critical for assessing microbial functionality in elemental cycling. We developed a high-throughput quantitative-PCR-based chip, Quantitative microbial element cycling (QMEC), for assessing and quantifying the genetic potential of microbiota to mineralize soil organic matter and to release C, N, P and S. QMEC contains 72 primer pairs targeting 64 microbial functional genes for C, N, P, S and methane metabolism. These primer pairs were characterized by high coverage (average of 18–20 phyla covered per gene) and sufficient specificity (>70% match rate) with a relatively low detection limit (7–102 copies per run). QMEC was successfully applied to soil and sediment samples, identifying significantly different structures, abundances and diversities of the functional genes (P<0.05). QMEC was also able to determine absolute gene abundance. QMEC enabled the simultaneous qualitative and quantitative determination of 72 genes from 72 samples in one run, which is promising for comprehensively investigating microbially mediated ecological processes and biogeochemical cycles in various environmental contexts including those of the current global change.


microbial genes functional potential high-throughput qPCR elemental cycling biogeochemical cycle ecological process 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB15020402, XDB15020302) and the Natural Science Foundation of China (41571130063, 41430858). Josep Peñuelas and Jianqiang Su acknowledge the financial support from the European Research Council Synergy Grant ERC-SyG-2013-610028 IMBALANCE-P.

Supplementary material

11427_2018_9364_MOESM1_ESM.pdf (3 mb)
QMEC: A tool for high-throughput quantitative assessment of microbial functional potential in C, N, P, and S biogeochemical cycling


  1. Baker, M. (2011). qPCR: quicker and easier but don’t be sloppy. Nat Methods 8, 207–212.CrossRefGoogle Scholar
  2. Bardgett, R.D., and van der Putten, W.H. (2014). Belowground biodiversity and ecosystem functioning. Nature 515, 505–511.CrossRefGoogle Scholar
  3. Blazewicz, S.J., Barnard, R.L., Daly, R.A., and Firestone, M.K. (2013). Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses. ISME J 7, 2061–2068.CrossRefPubMedPubMedCentralGoogle Scholar
  4. Chapin, F.S., 3rd., Zavaleta, E.S., Eviner, V.T., Naylor, R.L., Vitousek, P. M., Reynolds, H.L., Hooper, D.U., Lavorel, S., Sala, O.E., Hobbie, S.E., et al. (2000). Consequences of changing biodiversity. Nature 405, 234–242.CrossRefGoogle Scholar
  5. Chen, Q., An, X., Li, H., Su, J., Ma, Y., and Zhu, Y.G. (2016). Long-term field application of sewage sludge increases the abundance of antibiotic resistance genes in soil. Environ Int 92–93, 1–10.Google Scholar
  6. Chen, Y., Gelfond, J.A.L., McManus, L.M., and Shireman, P.K. (2009). Reproducibility of quantitative RT-PCR array in miRNA expression profiling and comparison with microarray analysis. BMC Genomics 10, 407.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Church, M.J., Wai, B., Karl, D.M., and DeLong, E.F. (2010). Abundances of crenarchaeal amoA genes and transcripts in the Pacific Ocean. Environ Microbiol 12, 679–688.CrossRefPubMedPubMedCentralGoogle Scholar
  8. De Wilde, B., Lefever, S., Dong, W., Dunne, J., Husain, S., Derveaux, S., Hellemans, J., and Vandesompele, J. (2014). Target enrichment using parallel nanoliter quantitative PCR amplification. BMC Genomics 15, 184.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Deng, Y., He, Z., Xiong, J., Yu, H., Xu, M., Hobbie, S.E., Reich, P.B., Schadt, C.W., Kent, A., Pendall, E., et al. (2016). Elevated carbon dioxide accelerates the spatial turnover of soil microbial communities. Glob Change Biol 22, 957–964.CrossRefGoogle Scholar
  10. Elser, J.J., Sterner, R.W., Gorokhova, E., Fagan, W.F., Markow, T.A., Cotner, J.B., Harrison, J.F., Hobbie, S.E., Odell, G.M., and Weider, L.W. (2000). Biological stoichiometry from genes to ecosystems. Ecol Lett 3, 540–550.CrossRefGoogle Scholar
  11. Feng, W., Liang, J., Hale, L.E., Jung, C.G., Chen, J., Zhou, J., Xu, M., Yuan, M., Wu, L., Bracho, R., et al. (2017). Enhanced decomposition of stable soil organic carbon and microbial catabolic potentials by longterm field warming. Glob Change Biol 23, 4765–4776.CrossRefGoogle Scholar
  12. Frias-Lopez, J., Shi, Y., Tyson, G.W., Coleman, M.L., Schuster, S.C., Chisholm, S.W., and Delong, E.F. (2008). Microbial community gene expression in ocean surface waters. Proc Natl Acad Sci USA 105, 3805–3810.CrossRefPubMedGoogle Scholar
  13. Gaby, J.C., and Buckley, D.H. (2017). The use of degenerate primers in qPCR analysis of functional genes can cause dramatic quantification bias as revealed by investigation of nifH primer performance. Microb Ecol 74, 701–708.CrossRefPubMedGoogle Scholar
  14. Gifford, S.M., Sharma, S., Rinta-Kanto, J.M., and Moran, M.A. (2011). Quantitative analysis of a deeply sequenced marine microbial metatranscriptome. ISME J 5, 461–472.CrossRefPubMedGoogle Scholar
  15. Graham, E.B., Knelman, J.E., Schindlbacher, A., Siciliano, S., Breulmann, M., Yannarell, A., Beman, J., Abell, G., Philippot, L., and Prosser, J. (2016). Microbes as engines of ecosystem function: when does community structure enhance predictions of ecosystem processes? Front Microbiol 7, 214.PubMedPubMedCentralGoogle Scholar
  16. Hazen, T.C., Dubinsky, E.A., DeSantis, T.Z., Andersen, G.L., Piceno, Y.M., Singh, N., Jansson, J.K., Probst, A., Borglin, S.E., Fortney, J.L., et al. (2010). Deep-sea oil plume enriches indigenous oil-degrading bacteria. Science 330, 204–208.CrossRefPubMedGoogle Scholar
  17. He, Z., Deng, Y., Van Nostrand, J.D., Tu, Q., Xu, M., Hemme, C.L., Li, X., Wu, L., Gentry, T.J., Yin, Y., et al. (2010). GeoChip 3.0 as a highthroughput tool for analyzing microbial community composition, structure and functional activity. ISME J 4, 1167–1179.CrossRefPubMedGoogle Scholar
  18. He, Z., Deng, Y., and Zhou, J. (2012). Development of functional gene microarrays for microbial community analysis. Curr Opin Biotech 23, 49–55.CrossRefPubMedGoogle Scholar
  19. Hwangbo, H., Park, R.D., Kim, Y.W., Rim, Y.S., Park, K.H., Kim, T.H., Suh, J.S., and Kim, K.Y. (2003). 2-Ketogluconic acid production and phosphate solubilization by Enterobacter intermedium. Curr Microbiol 47, 87–92.CrossRefPubMedGoogle Scholar
  20. Ito, K., and Murphy, D. (2013). Application of ggplot2 to pharmacometric graphics. CPT Pharmacomet Syst Pharmacol 2, e79.CrossRefGoogle Scholar
  21. Katsuyama, C., Kondo, N., Suwa, Y., Yamagishi, T., Itoh, M., Ohte, N., Kimura, H., Nagaosa, K., and Kato, K. (2008). Denitrification activity and relevant bacteria revealed by nitrite reductase gene fragments in soil of temperate mixed forest. Microb Environ 23, 337–345.CrossRefGoogle Scholar
  22. Kolde, R., Kolde, M.R. (2015). Package ‘pheatmap’.Google Scholar
  23. Kuypers, M.M.M., Marchant, H.K., and Kartal, B. (2018). The microbial nitrogen-cycling network. Nat Rev Micro 16, 263–276.CrossRefGoogle Scholar
  24. Lalitha, S. (2000). Primer premier 5. Biotech Software & Internet Report: The Computer Software. J Sci 1, 270–272.CrossRefGoogle Scholar
  25. Langille, M.G.I., Zaneveld, J., Caporaso, J.G., McDonald, D., Knights, D., Reyes, J.A., Clemente, J.C., Burkepile, D.E., Vega Thurber, R.L., Knight, R., et al. (2013). Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol 31, 814–821.CrossRefPubMedPubMedCentralGoogle Scholar
  26. Larkin, M.A., Blackshields, G., Brown, N.P., Chenna, R., McGettigan, P. A., McWilliam, H., Valentin, F., Wallace, I.M., Wilm, A., Lopez, R., et al. (2007). Clustal W and clustal X version 2.0. Bioinformatics 23, 2947–2948.CrossRefPubMedGoogle Scholar
  27. Liang, Y., Van Nostrand, J.D., Deng, Y., He, Z., Wu, L., Zhang, X., Li, G., and Zhou, J. (2011). Functional gene diversity of soil microbial communities from five oil-contaminated fields in China. ISME J 5, 403–413.CrossRefPubMedGoogle Scholar
  28. Lim, B.L., Yeung, P., Cheng, C., and Hill, J.E. (2007). Distribution and diversity of phytate-mineralizing bacteria. ISME J 1, 321–330.CrossRefPubMedGoogle Scholar
  29. Looft, T., Johnson, T.A., Allen, H.K., Bayles, D.O., Alt, D.P., Stedtfeld, R. D., Sul, W.J., Stedtfeld, T.M., Chai, B., Cole, J.R., et al. (2012). In-feed antibiotic effects on the swine intestinal microbiome. Proc Natl Acad Sci USA 109, 1691–1696.CrossRefPubMedGoogle Scholar
  30. Lu, Z., Deng, Y., Van Nostrand, J.D., He, Z., Voordeckers, J., Zhou, A., Lee, Y.J., Mason, O.U., Dubinsky, E.A., Chavarria, K.L., et al. (2012). Microbial gene functions enriched in the Deepwater Horizon deep-sea oil plume. ISME J 6, 451–460.CrossRefPubMedGoogle Scholar
  31. Lueders, T., and Friedrich, M.W. (2003). Evaluation of PCR amplification bias by terminal restriction fragment length polymorphism analysis of small-subunit rRNA and mcrA genes by using defined template mixtures of methanogenic pure cultures and soil DNA extracts. Appl Environ Microbiol 69, 320–326.CrossRefPubMedPubMedCentralGoogle Scholar
  32. Mamanova, L., Coffey, A.J., Scott, C.E., Kozarewa, I., Turner, E.H., Kumar, A., Howard, E., Shendure, J., and Turner, D.J. (2010). Targetenrichment strategies for next-generation sequencing. Nat Methods 7, 111–118.CrossRefPubMedGoogle Scholar
  33. Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., Minchin, P.R., O’hara, R., Simpson, G.L., Solymos, P., Stevens, M.H.H., and Wagner, H. (2013). Package ‘vegan’. Community ecology package, version 2.Google Scholar
  34. Penuelas, J., Poulter, B., Sardans, J., Ciais, P., van der Velde, M., Bopp, L., Boucher, O., Godderis, Y., Hinsinger, P., Llusia, J., et al. (2013). Human-induced nitrogen-phosphorus imbalances alter natural and managed ecosystems across the globe. Nat Commun 4, 2934.CrossRefGoogle Scholar
  35. Petersen, D.G., Blazewicz, S.J., Firestone, M., Herman, D.J., Turetsky, M., and Waldrop, M. (2012). Abundance of microbial genes associated with nitrogen cycling as indices of biogeochemical process rates across a vegetation gradient in Alaska. Environ Microbiol 14, 993–1008.CrossRefGoogle Scholar
  36. Ragot, S.A., Kertesz, M.A., and Bünemann, E.K. (2015). phoD alkaline phosphatase gene diversity in soil. Appl Environ Microbiol 81, 7281–7289.CrossRefPubMedPubMedCentralGoogle Scholar
  37. Ragot, S.A., Kertesz, M.A., Meszaros, E., Frossard, E., and Bunemann, E. K. (2017). Soil phoD and phoX alkaline phosphatase gene diversity responds to multiple environmental factors. FEMS Microbiol Ecol 93, pii: fiw212.Google Scholar
  38. Saunders, N.A. (2013). Real-time PCR Arrays. Real-time PCR: Advanced Technologies and Applications. INBUNDEN Engelska, 2013-07-01.Google Scholar
  39. Schmidt, T.M., DeLong, E.F., and Pace, N.R. (1991). Analysis of a marine picoplankton community by 16S rRNA gene cloning and sequencing. J Bacteriol 173, 4371–4378.CrossRefPubMedPubMedCentralGoogle Scholar
  40. Sebastian, M., and Ammerman, J.W. (2009). The alkaline phosphatase PhoX is more widely distributed in marine bacteria than the classical PhoA. ISME J 3, 563–572.CrossRefPubMedGoogle Scholar
  41. Sievert, C., Parmer, C., Hocking, T., Chamberlain, S., Ram, K., Corvellec, M., and Despouy, P. (2016). plotly: create interactive web graphics via Plotly’s JavaScript graphing library. [Software].Google Scholar
  42. Stevenson, F.J., and Cole, M.A. (1999). Cycles of soils: carbon, nitrogen, phosphorus, sulfur, micronutrients. Quarterly Rev Biol 61, 554.Google Scholar
  43. Su, J.Q., Wei, B., Ou-Yang, W.Y., Huang, F.Y., Zhao, Y., Xu, H.J., and Zhu, Y.G. (2015). Antibiotic resistome and its association with bacterial communities during sewage sludge composting. Environ Sci Technol 49, 7356–7363.CrossRefPubMedGoogle Scholar
  44. Trivedi, P., He, Z., Van Nostrand, J.D., Albrigo, G., Zhou, J., and Wang, N. (2012). Huanglongbing alters the structure and functional diversity of microbial communities associated with citrus rhizosphere. ISME J 6, 363–383.CrossRefGoogle Scholar
  45. Tu, Q., Yu, H., He, Z., Deng, Y., Wu, L., Van Nostrand, J.D., Zhou, A., Voordeckers, J., Lee, Y.J., Qin, Y., et al. (2014). GeoChip 4: a functional gene-array-based high-throughput environmental technology for microbial community analysis. Mol Ecol Resour 14, 914–928.Google Scholar
  46. van der Heijden, M.G.A., Bardgett, R.D., and van Straalen, N.M. (2008). The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol Lett 11, 296–310.CrossRefGoogle Scholar
  47. Vanwonterghem, I., Jensen, P.D., Ho, D.P., Batstone, D.J., and Tyson, G.W. (2014). Linking microbial community structure, interactions and function in anaerobic digesters using new molecular techniques. Curr Opin Biotech 27, 55–64.CrossRefPubMedGoogle Scholar
  48. Wang, F., Zhou, H., Meng, J., Peng, X., Jiang, L., Sun, P., Zhang, C., Van Nostrand, J.D., Deng, Y., He, Z., et al. (2009). GeoChip-based analysis of metabolic diversity of microbial communities at the Juan de Fuca Ridge hydrothermal vent. Proc Natl Acad Sci USA 106, 4840–4845.CrossRefPubMedGoogle Scholar
  49. Wang, H., Ji, G., Bai, X., and He, C. (2015). Assessing nitrogen transformation processes in a trickling filter under hydraulic loading rate constraints using nitrogen functional gene abundances. Bioresource Tech 177, 217–223.CrossRefGoogle Scholar
  50. Wang, L., Zhang, Y., Luo, X., Zhang, J., and Zheng, Z. (2016). Effects of earthworms and substrate on diversity and abundance of denitrifying genes ( nir S and nir K) and denitrifying rate during rural domestic wastewater treatment. Bioresource Tech 212, 174–181.CrossRefGoogle Scholar
  51. Wei, W., Isobe, K., Nishizawa, T., Zhu, L., Shiratori, Y., Ohte, N., Koba, K., Otsuka, S., and Senoo, K. (2015). Higher diversity and abundance of denitrifying microorganisms in environments than considered previously. ISME J 9, 1954–1965.CrossRefPubMedPubMedCentralGoogle Scholar
  52. Weinstock, G.M. (2012). Genomic approaches to studying the human microbiota. Nature 489, 250–256.CrossRefPubMedPubMedCentralGoogle Scholar
  53. Wuchter, C., Abbas, B., Coolen, M.J.L., Herfort, L., van Bleijswijk, J., Timmers, P., Strous, M., Teira, E., Herndl, G.J., Middelburg, J.J., et al. (2006). Archaeal nitrification in the ocean. Proc Natl Acad Sci USA 103, 12317–12322.CrossRefPubMedGoogle Scholar
  54. Yergeau, E., Kang, S., He, Z., Zhou, J., and Kowalchuk, G.A. (2007). Functional microarray analysis of nitrogen and carbon cycling genes across an Antarctic latitudinal transect. ISME J 1, 163–179.CrossRefPubMedPubMedCentralGoogle Scholar
  55. Yoshida, M., Ishii, S., Fujii, D., Otsuka, S., and Senoo, K. (2012). Identification of active denitrifiers in rice paddy soil by DNA-and RNAbased analyses. Microb Environ 27, 456–461.CrossRefGoogle Scholar
  56. Zarraonaindia, I., Smith, D.P., and Gilbert, J.A. (2013). Beyond the genome: community-level analysis of the microbial world. Biol & Philos 28, 261–282.CrossRefGoogle Scholar
  57. Zhang, X., Liu, W., Schloter, M., Zhang, G., Chen, Q., Huang, J., Li, L., Elser, J.J., and Han, X. (2013). Response of the abundance of key soil microbial nitrogen-cycling genes to multi-factorial global changes. PLoS ONE 8, e76500.CrossRefPubMedPubMedCentralGoogle Scholar
  58. Zheng, B., Hao, X., Ding, K., Zhou, G., Chen, Q., Zhang, J., and Zhu, Y. (2017). Long-term nitrogen fertilization decreased the abundance of inorganic phosphate solubilizing bacteria in an alkaline soil. Sci Rep 7, 42284.CrossRefPubMedPubMedCentralGoogle Scholar
  59. Zhou, J., He, Z., Yang, Y., Deng, Y., Tringe, S.G., and Alvarez-Cohen, L. (2015). High-throughput metagenomic technologies for complex microbial community analysis: open and closed formats. MBio 6, pii: e03-388-14.Google Scholar
  60. Zhou, J., Kang, S., Schadt, C.W., and Garten Jr., C.T. (2008). Spatial scaling of functional gene diversity across various microbial taxa. Proc Natl Acad Sci USA 105, 7768–7773.CrossRefPubMedGoogle Scholar
  61. Zhu, Y., Zhao, Y., Li, B., Huang, C., Zhang, S., Yu, S., Chen, Y., Zhang, T., Gillings, M.R., and Su, J. (2017). Continental-scale pollution of estuaries with antibiotic resistance genes. Nat Microbiol 2, 16270.CrossRefPubMedGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Urban Environment and Health, Institute of Urban EnvironmentChinese Academy of SciencesXiamenChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Consejo Superior de Investigaciones Científicas (CSIC), Global Ecology UnitCentre for Ecological Research and Forestry Applications (CREAF)-CSIC-Universitat Autonoma de Barcelona (UAB)BellaterraSpain
  4. 4.CREAF, Cerdanyola del VallèsBarcelonaSpain
  5. 5.State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijingChina

Personalised recommendations