Integration of RT-qPCR analysis and grey situation decision-making model for evaluating the effects of plant growth regulators on the gene expression in rice seedlings under thiocyanate exposure

Abstract

Thiocyanate (SCN−) present in irrigation water can have negative effects on plant growth and crop yields. Addition of plant growth regulators (PGRs) can alleviate toxic stress to plants. In the current study, we established a grey situation decision-making model (GSDM) to integrate the data of RT-qPCR analysis for screening the optimal addition of PGRs to minimise pollution stress. The effects of PGRs (i.e., jasmonic acid [JA], indole-3-acetic acid [IAA] and sodium hydrosulfide [NaHS]) on the abundance of IAA oxidation and conjugation-related genes in rice seedlings under potassium thiocyanate (KSCN) exposure was examined. The results obtained from RT-qPCR analysis can roughly present the mitigating effects of IAA, JA, and NaHS on rice seedlings under KSCN stress. Integration of RT-qPCR analysis and GSDM further quantified the regulatory effects of PGRs. Simulation results showed that the effect of NaHS on the gene expression at KSCN exposure is apparently better than that of JA and IAA. Our study provides a new simple, efficient, and cheap approach to identify the optimal plant growth regulators under the stress of environmental pollution.