Leyla Nazari1, Zahra Zinati *2
1- Crop and Horticultural Science Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, Iran
2- Department of Agroecology, College of Agriculture and Natural Resources of Darab, Shiraz University, Shiraz, Iran.
2- Department of Agroecology, College of Agriculture and Natural Resources of Darab, Shiraz University, Shiraz, Iran.
Abstract: (18 Views)
Introduction and Objective: Sorghum, a C4 plant, is relatively tolerant to various abiotic stresses. However, its performance is significantly affected by temperatures above 32 °C and below 15 °C. Identifying key genes through gene expression data and feature selection methods is a valuable approach to understanding stress tolerance. Feature selection filters out the most relevant genes using statistical and computational algorithms. Filter-based methods, which are independent of machine learning algorithms, offer a fast and efficient way to identify relevant features. Combining multiple filter methods allows for a more precise and robust selection of key genes involved in sorghum’s response to temperature stress. Therefore, this study aimed to identify key genes involved in cold and heat stress response in sorghum using transcriptomic data and filter-based methods including Information Gain, Gain Ratio, and Relief.
Materials and Methods: Gene expression count data were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) with the accession number GSE225632. Analysis was conducted on sorghum leaf samples collected under control and temperature stress conditions at four different times of day. The samples were categorized into control and stress (cold and heat) groups. Differential expression analysis was performed using the DESeq2 package in R to identify significantly differentially expressed genes. The top genes were selected using three feature selection algorithms (Information Gain, Gain Ratio, and Relief), and the overlapping genes were identified using a Venn diagram. To validate the results, two machine learning algorithms, Bayes Net and Random Forest, were applied in Weka 3.7 to assess classification accuracy based on the selected features.
Results: Out of 34,129 genes analyzed, 2,136 genes showed statistically significant differential expression (adjusted p-value < 0.05 and log2FoldChange > 1). From these, nine genes were commonly identified by all three feature selection methods. The performance of machine learning algorithms showed that Bayes Net achieved high classification accuracy, with a sensitivity of 1.0 and prediction accuracy of 0.98 in the control group. Random Forest also yielded similar results, demonstrating its effectiveness in correctly classifying samples.
Conclusion: This study demonstrates that identifying and analyzing key genes involved in sorghum’s temperature stress response can provide insights into the biological pathways and regulatory networks activated under such conditions. Among the 2,136 differentially expressed genes, nine were consistently identified by three different selection algorithms. These genes can serve as potential molecular markers, but further biological validation across different sorghum varieties is necessary. The high accuracy of Bayes Net and Random Forest confirms the strength of these models in distinguishing gene expression patterns between stressed and control conditions.
Homology analysis of genes such as Sobic.004G283300, Sobic.010G100600, and Sobic.006G093500 in Arabidopsis and maize supports their role in heat stress response. However, six genes (Sobic.010G128900, Sobic.001G093100, Sobic.007G168100, Sobic.002G269100, Sobic.006G183701, and Sobic.002G047800) remain uncharacterized, with no documented molecular function. Further research is required to explore the roles of these genes in physiological and stress-related processes. Understanding their functions could contribute to breeding sorghum varieties that are more resilient to environmental stresses, ultimately supporting sustainable agriculture. Field-based and experimental validation of these molecular markers is also recommended to confirm their applicability under real-world farming conditions.
Materials and Methods: Gene expression count data were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) with the accession number GSE225632. Analysis was conducted on sorghum leaf samples collected under control and temperature stress conditions at four different times of day. The samples were categorized into control and stress (cold and heat) groups. Differential expression analysis was performed using the DESeq2 package in R to identify significantly differentially expressed genes. The top genes were selected using three feature selection algorithms (Information Gain, Gain Ratio, and Relief), and the overlapping genes were identified using a Venn diagram. To validate the results, two machine learning algorithms, Bayes Net and Random Forest, were applied in Weka 3.7 to assess classification accuracy based on the selected features.
Results: Out of 34,129 genes analyzed, 2,136 genes showed statistically significant differential expression (adjusted p-value < 0.05 and log2FoldChange > 1). From these, nine genes were commonly identified by all three feature selection methods. The performance of machine learning algorithms showed that Bayes Net achieved high classification accuracy, with a sensitivity of 1.0 and prediction accuracy of 0.98 in the control group. Random Forest also yielded similar results, demonstrating its effectiveness in correctly classifying samples.
Conclusion: This study demonstrates that identifying and analyzing key genes involved in sorghum’s temperature stress response can provide insights into the biological pathways and regulatory networks activated under such conditions. Among the 2,136 differentially expressed genes, nine were consistently identified by three different selection algorithms. These genes can serve as potential molecular markers, but further biological validation across different sorghum varieties is necessary. The high accuracy of Bayes Net and Random Forest confirms the strength of these models in distinguishing gene expression patterns between stressed and control conditions.
Homology analysis of genes such as Sobic.004G283300, Sobic.010G100600, and Sobic.006G093500 in Arabidopsis and maize supports their role in heat stress response. However, six genes (Sobic.010G128900, Sobic.001G093100, Sobic.007G168100, Sobic.002G269100, Sobic.006G183701, and Sobic.002G047800) remain uncharacterized, with no documented molecular function. Further research is required to explore the roles of these genes in physiological and stress-related processes. Understanding their functions could contribute to breeding sorghum varieties that are more resilient to environmental stresses, ultimately supporting sustainable agriculture. Field-based and experimental validation of these molecular markers is also recommended to confirm their applicability under real-world farming conditions.
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