Soil Metabolomics Predict Microbial Taxa as Biomarkers of Moisture Status in Soils from a Tidal Wetland

被引:7
作者
RoyChowdhury, Taniya [1 ,3 ]
Bramer, Lisa M. [1 ]
Brown, Joseph [1 ,4 ]
Kim, Young-Mo [1 ]
Zink, Erika [1 ]
Metz, Thomas O. [1 ]
McCue, Lee Ann [1 ]
Diefenderfer, Heida L. [2 ]
Bailey, Vanessa [1 ]
机构
[1] Pacific Northwest Natl Lab, Earth & Biol Sci Directorate, Richland, WA 99352 USA
[2] Pacific Northwest Natl Lab, Energy & Environm Directorate, Sequim, WA 98382 USA
[3] Woodwell Climate Res Ctr, Falmouth, MA 02540 USA
[4] Univ Utah, Dept Human Genet, Salt Lake City, UT 84112 USA
关键词
16S rRNA gene; soil metabolomics; random forest classification; wetland microbiome; COMMUNITY RESPONSE; METHANE PRODUCTION; SP-NOV; ACIDOBACTERIA; DIVERSITY; OXIDATION; OSMOLYTES; BACTERIUM; SEDIMENT; DYNAMICS;
D O I
10.3390/microorganisms10081653
中图分类号
Q93 [微生物学];
学科分类号
071005 ; 100705 ;
摘要
We present observations from a laboratory-controlled study on the impacts of extreme wetting and drying on a wetland soil microbiome. Our approach was to experimentally challenge the soil microbiome to understand impacts on anaerobic carbon cycling processes as the system transitions from dryness to saturation and vice-versa. Specifically, we tested for impacts on stress responses related to shifts from wet to drought conditions. We used a combination of high-resolution data for small organic chemical compounds (metabolites) and biological (community structure based on 16S rRNA gene sequencing) features. Using a robust correlation-independent data approach, we further tested the predictive power of soil metabolites for the presence or absence of taxa. Here, we demonstrate that taking an untargeted, multidimensional data approach to the interpretation of metabolomics has the potential to indicate the causative pathways selecting for the observed bacterial community structure in soils.
引用
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页数:16
相关论文
共 73 条
[1]  
Alexander M., 1977, Introduction to soil microbiology.
[2]   Systems-Level Metabolic Flux Profiling Elucidates a Complete, Bifurcated Tricarboxylic Acid Cycle in Clostridium acetobutylicum [J].
Amador-Noguez, Daniel ;
Feng, Xiao-Jiang ;
Fan, Jing ;
Roquet, Nathaniel ;
Rabitz, Herschel ;
Rabinowitz, Joshua D. .
JOURNAL OF BACTERIOLOGY, 2010, 192 (17) :4452-4461
[3]   Methanogenesis in oxygenated soils is a substantial fraction of wetland methane emissions [J].
Angle, Jordan C. ;
Morin, Timothy H. ;
Solden, Lindsey M. ;
Narrowe, Adrienne B. ;
Smith, Garrett J. ;
Borton, Mikayla A. ;
Rey-Sanchez, Camilo ;
Daly, Rebecca A. ;
Mirfenderesgi, Golnazalsdat ;
Hoyt, David W. ;
Riley, William J. ;
Miller, Christopher S. ;
Bohrer, Gil ;
Wrighton, Kelly C. .
NATURE COMMUNICATIONS, 2017, 8
[4]  
ANTHONY C, 1975, SCI PROG, V62, P167
[5]  
Aronesty E., 2013, OPEN BIOINFORMATICS, V7, P1, DOI [10.2174/1875036201307010001, DOI 10.2174/1875036201307010001]
[6]   Changing precipitation pattern alters soil microbial community response to wet-up under a Mediterranean-type climate [J].
Barnard, Romain L. ;
Osborne, Catherine A. ;
Firestone, Mary K. .
ISME JOURNAL, 2015, 9 (04) :946-957
[7]   Iterative random forests to discover predictive and stable high-order interactions [J].
Basu, Sumanta ;
Kumbier, Karl ;
Brown, James B. ;
Yu, Bin .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2018, 115 (08) :1943-1948
[8]  
BICKNELL B, 1980, J GEN MICROBIOL, V117, P89
[9]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[10]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32