Genomics and Applied Biology 2024, Vol.15, No.3, 120-131 http://bioscipublisher.com/index.php/gab 123 4.3 Temporal and spatial expression patterns The temporal and spatial expression patterns of cold-responsive genes and miRNAs are crucial for understanding the regulatory mechanisms underlying cold stress tolerance. For instance, the expression of miR1320 and its target OsERF096 exhibits opposite patterns in different tissues and under cold stress conditions, indicating a tightly regulated response to cold stress (Sun et al., 2022). Similarly, the expression of miRNAs and their target genes in wild rice varies between different genotypes, suggesting that the regulation of miRNAs plays a significant role in the phenotypic variation of chilling tolerance (Zhao et al., 2022). Additionally, the cold-responsive transcription factors identified in both Arabidopsis and rice show distinct expression patterns, with some being more responsive in rice due to its larger genome size and different adaptation strategies (Maryan et al., 2023). The transcriptome dynamics under cold stress in rice involve complex regulatory networks of miRNAs and TFs that modulate the expression of a wide range of genes and pathways. These findings provide valuable insights into the molecular mechanisms of cold stress tolerance and highlight potential targets for developing cold-tolerant rice varieties. 5 Integration of MiRNAs and Transcriptome Data 5.1 Computational approaches for data integration The integration of miRNA and transcriptome data is crucial for understanding the complex regulatory networks in rice under cold stress. Computational approaches play a pivotal role in this integration by leveraging high-throughput sequencing data and advanced bioinformatics tools. One such approach involves the use of deep sequencing of small RNA libraries combined with degradome analyses to identify miRNA/target gene pairs and their regulatory networks (Baldrich et al., 2015). Additionally, network frameworks that systematically integrate various high-throughput datasets, such as ChIP-Seq and RNA-Seq, have been developed to construct integrated regulatory networks. These networks include TF to gene, TF to miRNA, and miRNA to gene interactions, providing a comprehensive view of multi-level regulation (Cheng et al., 2011). Mathematical modeling further aids in dissecting the role of miRNAs in gene regulatory networks, elucidating key features such as miRNA-mediated feedback and feedforward loops, and the competition for shared miRNAs (Lai et al., 2016). 5.2 Construction of MiRNAs-gene regulatory networks Constructing miRNA-gene regulatory networks involves identifying miRNAs and their target genes, followed by the integration of these interactions into a cohesive network. High-throughput sequencing technologies, such as sRNA-seq and RNA-seq, enable the detection of miRNAs and their corresponding mRNA targets. For instance, integrative analyses of sRNA and mRNA sequencing data have identified miRNA/mRNA target pairs with discordant expression patterns, revealing regulatory networks involved in cold response (Mazurier et al., 2022). Additionally, the construction of integrated regulatory networks can be enhanced by incorporating other types of data, such as protein-protein interactions and TF binding profiles, to provide a more detailed and accurate representation of the regulatory landscape (Cheng et al., 2011). Tools like IReNA (Integrated Regulatory Network Analysis) further facilitate the construction of modular regulatory networks, identifying key regulatory factors and significant regulatory relationships among modules (Jiang et al., 2021). 5.3 Validation and functional analysis of integrated networks Validation and functional analysis of integrated miRNA-gene regulatory networks are essential to confirm the predicted interactions and understand their biological significance. Experimental validation techniques, such as qRT-PCR and reporter assays, are commonly used to verify miRNA-target interactions. Functional analysis involves examining the roles of these interactions in specific biological processes, such as cold stress response. For example, miRNA-mediated regulation of genes involved in hormone signaling and cross-talk among hormone pathways has been shown to play a significant role in rice immunity (Baldrich et al., 2015). Additionally, the integration of miRNA and conserved peptide upstream open reading frame (CPuORF) functions suggests novel regulatory networks that could be crucial for plant stress responses (Baldrich et al., 2015). Computational tools and mathematical models also contribute to the functional analysis by predicting the behavior of regulatory networks and identifying key regulatory motifs, such as feedforward loops, that are over-represented in the network (Cheng et al., 2011; Lai et al., 2016).
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