Microbial regulation of the soil carbon cycle: evidence from gene–enzyme relationships

ISME Journal - Tập 10 Số 11 - Trang 2593-2604 - 2016
Pankaj Trivedi1, Manuel Delgado‐Baquerizo1, Chanda Trivedi1, Hang‐Wei Hu2, Ian C. Anderson1, Thomas C. Jeffries1, Jizhong Zhou3,4,5, Brajesh K. Singh6,1
1Hawkesbury Institute for the Environment, Western Sydney University , Penrith South, New South Wales, Australia
2Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, Victoria, Australia
3Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
4Institute for Environmental Genomics and Department of Botany and Microbiology, The University of Oklahoma , Norman, OK, USA
5State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China
6Global Centre for Land Based Innovation, Western Sydney University, Penrith South, New South Wales, Australia

Tóm tắt

Abstract

A lack of empirical evidence for the microbial regulation of ecosystem processes, including carbon (C) degradation, hinders our ability to develop a framework to directly incorporate the genetic composition of microbial communities in the enzyme-driven Earth system models. Herein we evaluated the linkage between microbial functional genes and extracellular enzyme activity in soil samples collected across three geographical regions of Australia. We found a strong relationship between different functional genes and their corresponding enzyme activities. This relationship was maintained after considering microbial community structure, total C and soil pH using structural equation modelling. Results showed that the variations in the activity of enzymes involved in C degradation were predicted by the functional gene abundance of the soil microbial community (R2>0.90 in all cases). Our findings provide a strong framework for improved predictions on soil C dynamics that could be achieved by adopting a gene-centric approach incorporating the abundance of functional genes into process models.

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