Detection of Highly Poisonous Nerium oleander Using Quantitative Real-Time PCR with Specific Primers

Abstract

Nerium oleander is one of the most poisonous plants, and its accidental ingestion has frequently occurred in humans and livestock. It is vital to develop a rapid and accurate identification method for the timely rescue of oleander-poisoned patients and the investigation of poisoning cases. In this study, a specific and highly sensitive quantitative real-time PCR (qPCR)-based method was developed to identify oleander in mixture systems and simulated forensic specimens (SFS). First, a new pair of oleander-specific primers, JZT-BF/BR, was designed and validated. Then, a qPCR method was developed using the primers, and its detective sensitivity was examined. The results showed that JZT-BF/BR could specifically identify oleander in forage and food mixtures, and qPCR was capable of accurate authentication even at a low DNA concentration of 0.001 ng/μL. This method was further applied to the analysis of SFS containing different ratios of N. oleander. The method was confirmed to be applicable to digested samples, and the detection limit reached 0.1% (w/w) oleander in mixture systems. Thus, this study undoubtedly provides strong support for the detection of highly toxic oleander and the diagnosis of food poisoning in humans and animals.

Data Science and Plant Metabolomics

“…Gene expression analysis can also be used to study the regulation of metabolic pathways and identify the genes responsible for the production of specific metabolites [ 128 , 129 ]. Some plants contain cyanide or other poisons [ 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 ]. Machine learning-based classification algorithms can be used to classify cells of different plant species based on their metabolic profiles and predict the potential production of harmful metabolites, such as allelochemicals and cyanide.…”