Abstract
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.
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References
Baker, M. (2011). qPCR: quicker and easier but don’t be sloppy. Nat Methods 8, 207–212.
Bardgett, R.D., and van der Putten, W.H. (2014). Belowground biodiversity and ecosystem functioning. Nature 515, 505–511.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
He, Z., Deng, Y., and Zhou, J. (2012). Development of functional gene microarrays for microbial community analysis. Curr Opin Biotech 23, 49–55.
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.
Ito, K., and Murphy, D. (2013). Application of ggplot2 to pharmacometric graphics. CPT Pharmacomet Syst Pharmacol 2, e79.
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.
Kolde, R., Kolde, M.R. (2015). Package ‘pheatmap’.
Kuypers, M.M.M., Marchant, H.K., and Kartal, B. (2018). The microbial nitrogen-cycling network. Nat Rev Micro 16, 263–276.
Lalitha, S. (2000). Primer premier 5. Biotech Software & Internet Report: The Computer Software. J Sci 1, 270–272.
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.
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.
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.
Lim, B.L., Yeung, P., Cheng, C., and Hill, J.E. (2007). Distribution and diversity of phytate-mineralizing bacteria. ISME J 1, 321–330.
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.
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.
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.
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.
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.
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.
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.
Ragot, S.A., Kertesz, M.A., and Bünemann, E.K. (2015). phoD alkaline phosphatase gene diversity in soil. Appl Environ Microbiol 81, 7281–7289.
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.
Saunders, N.A. (2013). Real-time PCR Arrays. Real-time PCR: Advanced Technologies and Applications. INBUNDEN Engelska, 2013-07-01.
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.
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.
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].
Stevenson, F.J., and Cole, M.A. (1999). Cycles of soils: carbon, nitrogen, phosphorus, sulfur, micronutrients. Quarterly Rev Biol 61, 554.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Weinstock, G.M. (2012). Genomic approaches to studying the human microbiota. Nature 489, 250–256.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Acknowledgements
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.
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QMEC: A tool for high-throughput quantitative assessment of microbial functional potential in C, N, P, and S biogeochemical cycling
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Zheng, B., Zhu, Y., Sardans, J. et al. QMEC: a tool for high-throughput quantitative assessment of microbial functional potential in C, N, P, and S biogeochemical cycling. Sci. China Life Sci. 61, 1451–1462 (2018). https://doi.org/10.1007/s11427-018-9364-7
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DOI: https://doi.org/10.1007/s11427-018-9364-7