4.7 Article

Close relationship between the gene abundance and activity of soil extracellular enzyme: Evidence from a vegetation restoration chronosequence

Journal

SOIL BIOLOGY & BIOCHEMISTRY
Volume 177, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.soilbio.2022.108929

Keywords

Soil microbes; Extracellular enzyme; Enzyme -encoding gene; Nutrient availability; Enzyme production

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Extracellular enzymes (EEs) play vital roles in soil organic matter transformations. The study finds that EE gene abundance is positively correlated with EE activity. The direct gene-enzyme link suggests the importance of gene-informed Earth system models in predicting soil carbon dynamics.
Extracellular enzymes (EEs) play vital roles in the transformations of soil organic matter, yet the regulators of EE activity remain unclear. Inducible EE production predicts that EE activity is determined by microbial nutrient status. Alternatively, EE production can be constitutive (continuous and baseline-level expression of EE gene regardless of the nutrient status), and in this case EE gene abundance potentially affects EE activity. Here, we examined whether EE gene abundance was a key regulator of EE activity along a vegetation restoration chronosequence. For the four hydrolytic EEs related to microbial acquisition of carbon, nitrogen and phosphorus, their gene abundance ratios (obtained from metagenomic sequencing) were positively related to corresponding activity ratios (R2 = 0.43-0.94). These relationships were maintained in the structural equation modeling when the effects of microbial nutrient status and soil property were considered. The gene-enzyme link was further confirmed by the positive relationship between the absolute abundance of phoD gene (obtained from qPCR analysis) and the alkaline phosphomonoesterase activity. Overall, our results suggest that constitutive EE production may contribute significantly to soil EE activity. The direct gene-enzyme link highlights the development of gene-informed Earth system models to better predict the soil carbon dynamics under global change.

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