Using Bioinformatics-driven Diffusion Model to Analyze theDegradation Capacity of Soil Microbiome against Heavy Metal CdPollution

Introduction: The soil microbiome has enormous potential for degrading heavy metal Cd pollution, but microscale mass transfer processes and gene expression heterogeneity intricately regulate its functional activity. Traditional analytical methods often struggle to reveal degradation mechanisms due to the fragmentation of metagenome-assembled genomes (MAGs) and the spatial heterogeneity of biofilms. Methods: This study constructed a bioinformatics-driven dynamic diffusion model to systematically analyze the microbiome's ability to degrade heavy metal Cd. This approach integrated a three-dimensional rhizosphere pore structure reconstructed by X-ray micro-CT with time-series metatranscriptome data, using partial differential equations to describe the dynamic balance between heavy metal Cd transport and microbial degradation. A Bayesian dynamic linear model was also introduced to achieve bidirectional coupling between gene activity and physical mass transfer processes by real-time updating of the expression weights of key degradation genes, such as czcA and merA. Results: Quantitative analysis of the model revealed that siderophores and sulfides were the most effective pathways for Cd²⁺ conversion, reaching conversion rates of 78.2% and 89.4%, respectively. Pseudomonas and Thiobacillus were the most contributing degrading bacteria. At the gene level, czcA expression peaked within 24 hours (82.4 FPKM) and competed with organic acid secretion. merA expression continued to rise, reaching a peak at 48 hours (102.4 FPKM), and was tightly coupled to respiration. Model validation demonstrated a prediction error as low as 0.248 at a depth of 200 μm and a structural similarity of 0.808 across the time scale. Discussion: Iron reduction and sulfur precipitation are the most efficient blocking pathways, and Pseudomonas and Thiobacillus are the most critical degrading bacterial genera. The metabolic division of labor strategy of the microbial community is manifested in the early reliance on czcA-mediated ion efflux and organic acid chelation to achieve a rapid emergency response, and in the later stage, persistent detoxification through the merA-ETC system. The time-gradient defense strategy enables microorganisms to optimize energy utilization and maximize survival under heavy metal stress. This provides theoretical support for the patent development of related bioremediation technologies. Conclusion: This study reveals a temporal gradient defense strategy for microbial communities to cope with heavy metal Cd stress: an early, rapid emergency response relies on czcAmediated ion efflux and organic acid chelation, followed by sustained reductive detoxification via the merA-electron transport chain (ETC) system. This study not only confirms that iron reduction and sulfur precipitation are the most effective blocking pathways but, more importantly, establishes a dynamic modeling paradigm that integrates multi-omics data with physical laws, providing a theoretical basis and targeted regulatory strategies for the precise remediation of heavy metal Cd-contaminated soils.