Abundance correlations of carbon fixation and pollutant degradation functions in oil-contaminated soils: Insights derived from integrated metagenomic and quantitative PCR analyses
Year: 2025
Type: article
Abstract: Microbial communities in petroleum-contaminated soils degrade hydrocarbons while harboring underexplored CO 2 fixation potential. This study analyzes 44 soil samples from six Chinese petroleum-contaminated sites with varying pollution gradients and physicochemical properties. Metagenomic analysis identified dominant carbon fixation pathways (reverse Tricarboxylic Acid (rTCA) cycle and the Dicarboxylate/4-Hydroxybutyrate (DC/4-HB) cycle) co-occurring with upregulated hydrocarbon degradation genes (alkane 1-monooxygenase ( alkB ) and benzoate 1,2-dioxygenase). Gene co-occurrence analysis demonstrated strong positive correlations (r = 0.798) between alkane degradation genes (e.g., alkB ) and carbon fixation genes (e.g., cbbL_red ), with polycyclic aromatic hydrocarbon (PAH) degradation genes exhibited moderate associations. Quantitative PCR validation across sites confirmed these relationships. The results uncover a novel metabolic coupling mechanism, where hydrocarbon degradation coincides with enhanced carbon fixation, advancing our understanding of microbial adaptation in contaminated ecosystems. This study provides foundational insights for designing eco-friendly bioremediation strategies leveraging microbial dual metabolic capabilities. • Analyzed 44 samples from 6 oil-contaminated sites in China with distinct physicochemical properties and pollution levels. • Utilized metagenomic sequencing and qPCR to reveal synergy between carbon fixation and pollution reduction gene abundances. • Validated positive correlations between alkB and PAH_RHDαGN, and specific carbon fixation genes. • Uncovered the potential coupling mechanism between carbon fixation and pollutant degradation pathways.
Authors: Jiayu Song, Yakui Chen, Yilei Han, Xingchun Li, Zheng Liu, Ziyi Peng, Aimin Fu, Jinman Ren, Diannan Lu, Chunmao Chen
Language: English
Subfield: Pollution
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,350
Awards: G1724216446, G2905645628, G3150262668
OpenAlex ID: W4409446965
