Metagenomic analysis of structure and function of bacterial community in tobacco field soils.

Autor: SHEN Xiaoqing, GAO Huajun, TUO Yangyang, GENG Zhaoliang, CAI Bin, LI Hongli, WANG Yan
Zdroj: Tobacco Science & Technology; 2024, Vol. 57 Issue 1, p46-57, 12p
Abstrakt: To study the structure and function of soil microbial community in healthy and susceptible tobacco fields, one healthy and two susceptible(to black shank, bacterial wilt and root rot) tobacco fields with continuous cropping for 10 years in Liangshan Yi Autonomous Prefecture were selected as research subjects, and the soil bacterial community structure was analyzed by metagenomic technology. The results showed that there were significant differences in soil bacterial community structure between the healthy and susceptible tobacco fields. The dominant phyla with relative abundance greater than 4% in tobacco field soils were Actinobacteria, Proteobacteria, Acidobacteria, and Chloroflexi. The relative abundance of the dominant beneficial genera Streptomyces, Bradyrhizobium, and Mycobacterium was significantly higher in the healthy tobacco fields than in the susceptible tobacco fields. The relative abundance of the potential pathogen Rhodanobacter was significantly higher in the susceptible tobacco fields than in the healthy tobacco fields. Protease activity was significantly higher in the healthy tobacco fields than in the susceptible tobacco fields. The relative abundance of key enzyme genes in the carbon metabolism pathway was higher in the healthy tobacco fields than in the susceptible tobacco fields. In the nitrogen metabolism pathway, the abundance of enzyme genes during denitrification was lower in the healthy tobacco fields than in the susceptible tobacco fields. Therefore, there are differences in the bacterial community composition, enzyme activity and abundance of key enzyme genes in the process of carbon and nitrogen metabolism in the healthy and susceptible tobacco fields to varying degrees. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index