Detecting abiotic stresses in rice plants using a smart optical biosensor based on gold nanoparticles

Document Type : Research Paper

Author

Assistant Professor, Department of Biosystems Engineering,, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.

Abstract

 
The specific detection of the type and severity of plant abiotic stresses to take timely measures helps prevent yield reduction. This study introduces a new method to detect the type and severity of stress in rice plants under salinity, drought, and heat conditions by investigating microRNAs. The concentration of eight microRNAs in the tissue of plants subjected to salinity, drought, and heat conditions was measured with the help of an optical biosensor based on gold nanoparticles. The biosensor worked based on probe-target hybridization, in which the mixture of probe/citrate-capped gold nanoparticles (compound 1) and microRNA/polyethyleneimine-capped nanoparticles (compound 2) resulted in the aggregation of nanoparticles and changes in their spectroscopic properties. In the following, machine learning methods were used to predict the type and severity of stress using such concentrations. The results showed that the support vector machine optimized by the genetic algorithm was able to detect the severity of salinity, drought, and heat stress applied to rice plants with appropriate performance and with coefficients of determination of 0.94, 0.91, and 0.86, respectively. Then, the results of feature selection based on the cooperative game theory showed that among the microRNAs studied, miRNA-156, miRNA-393, and miRNA-159 had the largest contribution in predicting drought, salinity, and heat stresses in the rice plants, respectively. The findings of the research show that the examination of plant microRNAs with the help of optical biosensors can lead to reliable features for determining plant growth conditions and plant stresses in the early stage.

Keywords

Detecting abiotic stresses in rice plants using a smart optical biosensor based on gold nanoparticles

EXTENDED ABSTRACT

Introduction

MicroRNAs are small non-coding molecules that are found in the cells of living organisms, affecting gene expression and, thus, proteins involved in physiological and biochemical processes. These compounds determine the response of plants toward biotic and abiotic stresses. Therefore, it seems that by measuring the concentration of such compounds, it is possible to predict the severity of the stress applied to the rice plants, which can significantly reduce the yield and increase its losses. The objectives of this study include using a biosensor based on the gold nanoparticle aggregation to collect data on microRNA concentrations in rice plants under stress, using machine learning algorithms for the specific detection of plant stress, and identifying the contribution of each microRNA to the efficient detection of stresses based on feature selection methods.

Material and methods

Rice seedlings were subjected to salinity (at six levels), drought (at six levels), and heat (at four levels) for seven days. The concentrations of miRNA-156, miRNA-159, miRNA-164, miRNA-169, miRNA-393, miRNA-395, miRNA402, and miRNA-528 were measured from the root and stem samples collected from the plants under stress. Due to the high number of samples and measurable compounds in this study, it was not possible to use time-consuming methods such as polymerase chain reaction. For this purpose, an optical biosensor based on probe-target hybridization was used. In this biosensor, the mixture of probe/gold nanoparticles capped with citrate and microRNA/nanoparticles capped with polyethyleneimine led to the aggregation of nanoparticles and a change in the spectroscopic characteristics of the sample. The artificial neural network, support vector machine, and decision tree were used as machine learning models to train the data. The genetic algorithm was used to optimize the hyperparameters of the methods.

Results and discussion

The most suitable results for predicting the level of stresses applied to rice seedlings were obtained by the support vector machine optimized by genetic algorithm, which was able to predict the level of salinity, drought, and heat with coefficients of determination equal to 0.94, 0.91, and 0.86, respectively. These results show that, as expected, the concentration of microRNAs changed toward various levels of stress. The genetic algorithm significantly increased the performance of the machines for efficient prediction of all three types of stresses. In general, the machines performed best and worst in predicting salinity and heat stresses, respectively. Based on the cooperative game theory, the Banzhaf power index was considered to determine the effects of the concentration of each microRNA in predicting stresses. Based on the values ​​obtained for the Banzhaf power index, miRNA-156, miRNA-393, and miRNA-159 exerted the greatest contribution in predicting drought, salinity, and heat stress in the studied rice plants, respectively.

Conclusion

The results of this research showed that for various levels of drought, salinity, and heat stresses, a microRNA optical biosensor equipped with the support vector machine optimized with meta-heuristic methods can find a relationship between the stress levels and the concentration of microRNAs. In this situation, it seems that by extracting microRNA data with the help of a portable spectroscopic device, it is no longer necessary to extract the morphological and physiological characteristics of the plant, which is usually time-consuming and requires the transfer of samples to well-equipped laboratories, to provide a reliable understanding of plant stress status. Furthermore, the proposed method has the ability to be implemented in Internet of Things (IoT) systems to move toward digital agriculture.

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Iranian Journal of Biosystems Engineering
Volume 55, Issue 1
April 2024
Pages 51-69

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History

  • Receive Date: 30 July 2024
  • Revise Date: 05 September 2024
  • Accept Date: 22 September 2024

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