intracellular space, instead delivering effectors using the secretion system. The effectors can diffuse through the interphase between the host cell and bacteria (apoplastic effectors) or translocate through the host cell through the PD (symplastic effectors). In both cases, diffusing and translocating effectors can act on several cells.
The predicted translocating signals are type III secretions in bacteria. However, in several cases, the translocation is a pathogen-derived trigger. After translocation, effectors can then localize to different intracellular compartments such as nucleocytoplasm (HopU1), chloroplasts (HopN1), mitochondria (HopG1), and the trans-Golgi network or early endosome (HopM1) (Fu et al. 2007; Nomura et al. 2011). Effectors could manipulate the PD function for further susceptibility of plants. For example, HopO1-1, the effector from P. syringae DC3000, interacts with PDLP5 and PDLP7 (Aung et al. 2020). Given that PDLP5 is crucial for bacterial immunity, HopO1-1 degrades PDLP7, presumably after ribosylation. Moreover, HopO1-1 alters PD trafficking contributing to bacterial virulence.
CELL-TO-CELL MOVEMENT OF IMMUNITY-RELATED PROTEINS AND RNAS
Several proteins involved in growth and development move symplastically from cell to cell (Gundu et al. 2020). However, the intercellular movement of proteins due to biotic or abiotic stress is not yet clearly illustrated. Signaling molecules such as ROS participate in various processes, including development and defense.
A novel transcription factor, UPBEAT1 (UPB1), modulates ROS by directly regulating peroxidase genes and is also believed to move from cell to cell to specify the position of cellular differentiation (Tsukagoshi et al. 2010). In addition, UPB1 modulates the expression of peroxidases and balances ROS between zone proliferation and the elongation zone in the roots. The stabilization and transcriptional activity of UPB1 are enhanced upon phosphorylation by the BIN2 kinase, a negative regulator of brassinosteroid signaling (Li et al. 2020). Although brassinosteroids and PTI signaling are antagonistically coupled (Belkhadir et al. 2012), the direct involvement of a mobile protein such as UBP1 in the PAMP response has not yet been demonstrated.
Another membrane-associated protein, thioredoxin h9 (TRX h9), acts as an antioxidant through the response to the ROS species and undergoes intercellular movement (Meng et al. 2010). However, TRX h9 has no transmembrane domain. It associates with the membrane through palmitoylation of the Gly and Cys residue in the N-terminal domain. Mutation in these residues also restricts the cell-to-cell movement, indicating that this posttranslational modification not only regulates solubility but also is critical for the cell-to-cell movement of TRX h9. Although it has not yet been demonstrated that redox-based signaling is affected by the movement of TRX h9 or UPB1, it is tempting to suggest that regulating the cell-to-cell movement of antioxidants could play a role in preventing oxidative stress in the development and stress responses.
Noncell-autonomous functions in plants are also executed by the cell-to-cell or long-distance movement of mRNAs. However, long-distance trafficking of mRNA through the phloem relies on multiple translocation steps from the source to sink through the phloem (Kehr and Buhtz 2008). The detection of mobile mRNA has been demonstrated through grafting, although it has been challenging to identify the population of RNA involved in stress signaling against a background of general systemic signaling.
Mobile mRNA is believed to play a critical role in growth and defense or stress regulation (Ham and Lucas 2017). In pumpkin (Cucurbita maxima), CmWRKYP transcripts, which have a role in the defense response, were detected in the phloem (Ruiz-Medrano et al. 1999). In 2016, Zhang and colleagues found that many mRNAs are translocated through the phloem under phosphate stress (Zhang et al. 2016). The expression and translocation of phloem-mobile mRNA and micro-RNA (miRNA) occur in a tissue-specific manner. For instance, the miRNA miR399, which plays a role in phosphate homeostasis, is predominantly expressed in vasculature. Under phosphate deficiency, its expression increases and it accumulates in roots, where it targets PHO2, a negative regulator of phosphate transport (Ham and Lucas 2017).
Although the specific subset of miRNA and all small-interfering RNAs are present in phloem extracts due to their smaller sizes, their role in plant defense is well known (Kehr and Buhtz 2008; Liu et al. 2017; Muhammad et al. 2019). For instance, the tomato miR482 and 2118 miRNAs target numerous NBS-LRR mRNAs that encode plants’ innate immunity receptors (Shivaprasad et al. 2012). This regulation is lost in virus- and bacteria-infected plants, indicating that miRNAs can be a crucial regulator in some plant diseases. Epitranscriptomic modifications such as the methylation of cytosine (m5C) and N6-methyladenosine (m6A) regulate gene expression at the posttranscriptional level and the mobility of RNA during stress (Hu et al. 2019; Yang et al. 2019). Overall, these suggest that plants may tightly regulate their defense responses through mobile RNAs.
CONCLUSION AND PERSPECTIVE
Intercellular communication is an essential and highly regulated process, especially during plant defense. This includes the apoplastic and symplastic movement of micromolecules (ROS, hormones, and peptides) and macromolecules (mobile transcription factors). These mobile signals may interact synergistically or antagonistically during PTI, ETI, and SAR and differentially regulate the apoplastic composition or PD permeability. However, how the exact regulatory mechanism works at the transcriptional, epitranscriptional, posttranscriptional, and posttranslational levels is still unclear.
Furthermore, how the overall information is integrated at the PD or apoplast and executed at local and systemic levels remains unanswered. A time-efficient realistic approach to exploring a particular plant-pathogen interaction is to use the machine-learning technique for multiomics data integration obtained from organs, tissues, and single cells. Network biology, deep learning, and other machine-learning approaches can be applied to predict the strategy through which a pathogen exploits cell-to-cell communication for pathogenesis. Real-time monitoring and advanced imaging of intercellularly transported signaling molecules would help explain how plants (and plant organs) respond, tolerate, and adapt to a given biotic or abiotic stress. In the long run, these would improve crop resistance, yield, and nutritional quality in efficient and sustainable agriculture, which is the ultimate aspiration of this research.
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