Bayesian network highlights the contributing factors for efficient arsenic phytoextraction by Pteris vittata in a contaminated field

Abstract

Phytoextraction is a low-cost and eco-friendly method for removing pollutants, such as arsenic (As), from contaminated soil. One of the most studied As hyperaccumulators for soil remediation include Pteris vittata. Although phytoextraction using plant-assisted microbes has been considered a promising soil remediation method, microbial harnessing has not been achieved due to the complex and difficult to understand interactions between microbes and plants. This problem can possibly be addressed with a multi-omics approach using a Bayesian network. However, limited studies have used Bayesian networks to analyze plant-microbe interactions. Therefore, to understand this complex interaction and to facilitate efficient As phytoextraction using microbial inoculants, we conducted field cultivation experiments at two sites with different total As contents (62 and 8.9 mg/kg). Metabolome and microbiome data were obtained from rhizosphere soil samples using nuclear magnetic resonance and high-throughput sequencing, respectively, and a Bayesian network was applied to the obtained multi-omics data. In a highly As-contaminated site, inoculation with Pseudomonas sp. strain m307, which is an arsenite-oxidizing microbe having multiple copies of the arsenite oxidase gene, increased As concentration in the shoots of P. vittata to 157.5 mg/kg under this treatment; this was 1.5-fold higher than that of the other treatments. Bayesian network demonstrated that strain m307 contributed to As accumulation in P. vittata. Furthermore, the network showed that microbes belonging to the MND1 order positively contributed to As accumulation in P. vittata. Based on the ecological characteristics of MND1, it was suggested that the rhizosphere of P. vittata inoculated with strain m307 was under low-nitrogen conditions. Strain m307 may have induced low-nitrogen conditions via arsenite oxidation accompanied by nitrate reduction, potentially resulting in microbial iron reduction or the prevention of microbial iron oxidation. These conditions may have enhanced the bioavailability of arsenate, leading to increased As accumulation in P. vittata.

Keywords: Bayesian network; Field experiment; Metabolome; Microbiome; Multi-omics; Pteris vittata.