Changes in proteolytic bacteria in paddy soils in response to organic management

Proteolytic bacterial communities, which mineralize organic nitrogen, play a key role in agricultural systems. In this study, alkaline metalloprotease (apr) gene fragments from proteolytic bacteria were investigated in bulk and rhizosphere paddy soil from four fields under organic management (for 2, 3, 5, and 9 years), and from one field under conventional management (for 2 years). We analyzed the abundance and structure of the proteolytic bacterial communities using real-time quantitative PCR and denaturing gradient gel electrophoresis. Our results showed that the abundance of proteolytic bacteria ranged from 1.57?×?10⁸ to 8.02?×?10⁸?copies/g of soil. In addition, the abundance of the proteolytic bacteria in the paddy soils under organic management was significantly higher than those in the paddy soil under conventional management. Moreover, the gene copy numbers in the rhizosphere soils were significantly higher than those in the bulk soils. The abundance of proteolytic bacteria tended to increase with the duration of organic management, with the highest abundance being found in the soil that had been under organic management for 5 years. However, the proteolytic bacteria communities in the paddy soils were not significantly affected by management practices. Phylogeny analysis showed that all gel bands obtained represented genes from Pseudomonas. Additionally, correlation analysis and canonical correspondence analysis showed that C/N, C, and N were important factors that influenced the abundance and community structure of the proteolytic bacteria. These results suggest that proteolytic bacteria are indicators in organic management systems, depolymerize organic N and hence maintain soil sustainability.

Abbreviations: CM: conventional management; OM: organic management; DGGE: denaturing gradient gel electrophoresis; qPCR: real-time quantitative PCR detecting system; COFCC: China organic food certification center; CCA: canonical correspondence analysis