Computational Molecular Biology 2025, Vol.15, No.5, 227-234 http://bioscipublisher.com/index.php/cmb 232 Figure 2 Co-expression network analysis in well water and water stress treatments. (A) Network visualization in Cytoscape. The nodes were colored by module membership. (B) Correlations between module eigengenes and root phenotypic traits. The numbers within the heatmap represent correlations and p-value (red, positively correlated; green, negatively correlated) for the module-trait associations (SDW, shoot dry weight; SFW, shoot fresh weight; RL, root length; TRL, total root length; Tips, root branches; Forks, root forks; RSA, root surface area; RV, root volume). (C) The connection between zma-miR394b precursor expression and total dry weight. On the left is the root phenotype of some lines from a natural group containing 368 lines. Red means that the expression of zma-miR394b precursor is higher (right) (Adopted from Tang et al., 2022) 7 Conclusions and Future Perspectives The regulation of drought response by miRNA is neither simple nor straightforward. These small molecules in corn form a rather complex network, among which classic "partners" like miR164-MYB/NAC, miR159-MYB, miR156-SPL, and miR160-ARF often appear in pairs, with their expression levels fluctuating. The expression patterns of drought-tolerant and drought-intolerant varieties in this regard also differ significantly. Moreover, these regulatory relationships are not linear. For instance, some modules are closely bound to the ABA signaling pathway. Especially in some drought-resistant materials, the way miR164 regulates NAC and MYB is somewhat dependent on the rhythm of ABA. As for those newly identified mirnas, although they seem to have potential, it
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