Microbial functional gene patterns related to soil greenhouse gas emissions in oil contaminated areas

Abstract

Linking microbial community structure to physiology and ecological processes is a critical focus of microbial ecology. To understand the microbial functional gene patterns related to soil greenhouse gas [carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O)] emissions under oil contamination, we used functional gene array (GeoChip 5.0) analysis and network methods to investigate the feedback responses of soil microbial functional gene patterns and identify keystone genes in Shengli Oilfield, China. The microbial functional gene number, relative abundance and diversity involved in carbon degradation and nitrogen cycling decreased consistently with the reduced CO2 and N2O flux in oil contaminated soils, whereas the gene number and relative abundance of methane-production related genes increased with contamination. Functional molecular ecological networks were built based on random matrix theory, where network structures and properties showed significantly variation between oil contaminated and uncontaminated soils (P<0.05). Network nodes, connectivity and complexity all reduced under oil contamination. The sensitive and the highest connective genes in the network were identified as keystone genes, based on Mann-Whitney U tests and network analysis. Our findings improved the understanding of the microbe-mediated mechanisms affecting soil greenhouse gas emissions.