Metagenomic analysis reveals distinct patterns of denitrification gene abundance across soil moisture, nitrate gradients

Wiley - Tập 21 Số 4 - Trang 1255-1266 - 2019
Sarah Nadeau1, Constance A. Roco2, Spencer J. Debenport3, Todd R. Anderson4, K. Hofmeister5, M. Todd Walter1, James P. Shapleigh2
1Department of Biological & Environmental Engineering, Cornell University, Ithaca, NY, USA
2Department of Microbiology, Cornell University, Ithaca, NY, USA
3Soil and Crop Sciences Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
4Division of Natural Sciences and Mathematics, Keuka College, Keuka Park, NY, USA
5Department of Natural Resources, Cornell University, Ithaca, NY, USA

Tóm tắt

SummaryThis study coupled a landscape‐scale metagenomic survey of denitrification gene abundance in soils with in situ denitrification measurements to show how environmental factors shape distinct denitrification communities that exhibit varying denitrification activity. Across a hydrologic gradient, the distribution of total denitrification genes (nap/nar + nirK/nirS + cNor/qNor + nosZ) inferred from metagenomic read abundance exhibited no consistent patterns. However, when genes were considered independently, nirS, cNor and nosZ read abundance was positively associated with areas of higher soil moisture, higher nitrate and higher annual denitrification rates, whereas nirK and qNor read abundance was negatively associated with these factors. These results suggest that environmental conditions, in particular soil moisture and nitrate, select for distinct denitrification communities that are characterized by differential abundance of genes encoding apparently functionally redundant proteins. In contrast, taxonomic analysis did not identify notable variability in denitrifying community composition across sites. While the capacity to denitrify was ubiquitous across sites, denitrification genes with higher energetic costs, such as nirS and cNor, appear to confer a selective advantage in microbial communities experiencing more frequent soil saturation and greater nitrate inputs. This study suggests metagenomics can help identify denitrification hotspots that could be protected or enhanced to treat non‐point source nitrogen pollution.

Từ khóa


Tài liệu tham khảo

10.2134/jeq2002.1017

10.1016/j.agee.2014.03.026

10.1016/j.jhydrol.2014.12.043

AndrewsS.(2010) FastQC: a quality control tool for high throughput sequence data. URLhttp://www.bioinformatics.babraham.ac.uk/projects/fastqc.

10.1128/mSphere.00327-17

10.1023/A:1015775225913

10.5194/hess-18-3279-2014

10.1038/nmeth.3176

10.1191/0309133305pp450ra

10.1038/s41396-018-0063-7

10.1038/nrmicro.2017.87

Firestone M.K., 1989, Exchange of Trace Gases between Terrestrial Ecosystems and the Atmosphere, 7

Fry J.A., 2011, Completion of the 2006 national land cover database for the conterminous United States, Photogramm Eng Remote Sensing, 77, 858

10.1641/0006-3568(2003)053[0341:TNC]2.0.CO;2

10.1126/science.1136674

10.1371/journal.pone.0114118

10.3389/fmicb.2016.00214

10.1128/AEM.03069-09

Groffman P.M.(2015) Terrestrial denitrification: a tale of misery woe and the frontiers of environmental science. Horn Point Laboratory Seminar University of Maryland Center for Environmental Science.

10.1007/s10533-008-9277-5

10.1016/j.tim.2017.07.003

10.7717/peerj.616

10.1111/j.1462-2920.2006.01081.x

10.2489/jswc.71.4.289

10.1038/ismej.2009.152

10.1186/s12859-016-1278-0

10.1016/j.soilbio.2014.03.021

10.1371/journal.pcbi.1004075

10.2134/jeq2008.0406

10.1038/ismej.2017.82

10.1002/hyp.1494

10.14806/ej.17.1.200

10.1007/s10021-003-0161-9

10.1021/acs.est.8b02133

MRLC. (2012) Multi‐Resolution land cover characteristics consortium. URLhttp://www.mrlc.gov.

10.1186/s13059-016-0997-x

10.3389/fmicb.2013.00279

10.1038/s41598-017-16520-0

Randall A.D. Snavely D.S. Holecek T.J. andWaller R.M.(1988) Alternative sources of large seasonal ground‐water supplies in the headwaters of the Susquehanna river basin New York USGS Water‐Resources Investigations Report 85‐4127.

R Core Team. (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing Vienna Austria. URLhttp://www.R-project.org/

10.1038/461472a

10.1111/1462-2920.13250

10.1073/pnas.1211238109

Shapleigh J.P., 2012, The Prokaryotes: Prokaryotic Physiology and Biochemistry

10.1016/j.agee.2009.03.001

10.1126/science.1259855

Swader F.N., 1972, Harford Teaching and Research Center. Physical Resources I: The Soil Resources, 72

10.1007/s10533-008-9276-6

10.1029/2003WR002473

10.1002/hyp.5030

10.1021/acs.est.6b06521

10.1111/j.1758-2229.2011.00311.x

10.1046/j.1365-2958.2002.02875.x

Thông tin
Thông tin xuất bản

Wiley

Tập 21 Số 4

1255-1266

Thông tin tác giả