Decoding Plant–Pathogen Interactions: A Comprehensive Exploration of Effector–Plant Transcription Factor Dynamics

ABSTRACT

In the coevolutionary process between plant pathogens and hosts, pathogen effectors, primarily proteinaceous, engage in interactions with host proteins, such as plant transcription factors (TFs), during the infection process. This review delves into the intricate interplay between TFs and effectors, a key aspect in the prolonged and complex battle between plants and pathogens. Effectors strategically manipulate TFs using diverse tactics. These include modulating activity of TFs, influencing their incorporation into multimeric complexes, directly changing TF expression levels, promoting their degradation via the ubiquitin‐proteasome system, and inducing their subcellular relocalization. The review systematically presents documented interactions, elucidating key mechanisms and their profound impact on host–pathogen dynamics. It emphasises the central role of TFs in plant defence and investigates the convergent evolution of effectors targeting TFs. By providing this overview, we offer valuable insights into this dynamic interaction landscape and suggest potential directions for future research.

Pathogen effectors from fungi, oomycetes, bacteria and nematodes manipulate plant transcription factors through diverse strategies, including degradation, activity modulation, and subcellular relocalization.

Abbreviations

ER

endoplasmic reticulum

ERF

ethylene responsive factor

ET

ethylene

ETI

effector‐triggered immunity

JA

jasmonic acid

NAC

NAM, ATAF1/2, and CUC2

PT

pattern‐triggered immunity

ROS

reactive oxygen species

SA

salicylic acid

SUM

small ubiquitin like modifier

TCP

teosinte branched1/cycloidea/proliferating cell factor

TF

transcription factor

TGA

TGACG sequence binding protein

UPS

ubiquitin‐proteasome system

1. Introduction

Plant immunity comprises two overlapping components known as pattern‐triggered immunity (PTI) and effector‐triggered immunity (ETI). PTI serves as the initial line of defence activated upon the recognition of pathogen‐associated molecular patterns (PAMPs), which are conserved molecules commonly found in pathogens, such as chitin in fungi, flagellin in bacteria, and ascarosides in nematodes (Nicaise, Roux, and Zipfel 2009; Zhang and Zhou 2010; Remick, Gaidt, and Vance 2023). On the other hand, ETI is a more specific and robust immune response that occurs when the plant recognises effectors produced by the pathogen. Diverse plant pathogens use various secretion systems to release effectors to the host plant. These effectors are essential for the pathogen during infection, leading to the development of a comprehensive defence system in plants (Jones and Dangl 2006; Tsuda and Katagiri 2010). The exploration of effectors from plant‐pathogenic organisms has led to the discovery and validation of numerous interacting plant proteins (Gheysen and Mitchum 2011; Galán et al. 2014; Mejias et al. 2019; Rocafort, Fudal, and Mesarich 2020). Among these target host proteins, transcription factors (TFs) have emerged as a focal point. TFs bind to regulatory sequences in DNA and directly modulate the expression of associated genes, thus playing a central role in plant growth, development, and resistance to biotic and abiotic stress (Moore, Loake, and Spoel 2011; Tsuda and Somssich 2015). Studying the interaction between TFs and effector proteins is of significant importance for a comprehensive analysis of the fine‐tuning mechanisms involved in pathogen modulation of host immunity and many other aspects. Notably, effectors from various plant pathogens recognise and target proteins belonging to the same TF families, including TCP, WRKY, NAC, and others (Li 2015; Ma et al. 2022; Goyal et al. 2023). These TF families, characterised by a substantial number of members, can regulate the balance between plant growth and immunity, and different members from the same family can have opposing roles; for example, Class I TCP TFs inhibit the jasmonic acid (JA) biosynthesis gene LOX2, while TCP4 (Class II) activates its expression (Danisman et al. 2012).

In addition to their interactions with TFs, effectors can directly modulate gene expression by binding to specific DNA sequences in the host genome (Barnes et al. 2018). This interaction allows effectors to directly regulate the expression of genes involved in various cellular processes, including those encoding TFs, and this regulatory mechanism enables effectors to have a wide and far‐reaching influence because they can bypass traditional signal transduction pathways and intervene in the transcriptional regulation process (Teper and Wang 2021).

This review aims to systematically consolidate and summarise the documented interactions between plant pathogen effectors and TFs. Simultaneously, it provides an overview of the mechanisms through which effectors modulate the activities of TFs. Furthermore, the review delves into why TFs serve as hubs for effectors, discusses strategies for additional screening and studying of the interactions between TFs and effectors, and addresses lingering questions in the field.

2. Main Mechanisms for Effector Impact on Their Target Transcription Factors

2.1. Modulation of DNA Binding or Transcriptional Activation

Plant TFs are grouped into different families based on their distinctive DNA‐binding domain (DBD) (Yamasaki et al. 2013). These DBDs selectively bind to specific DNA sequences, typically located in gene promoter regions, thus regulating the expression of the downstream gene. TFs usually also have an activation domain that attracts RNA polymerase or stimulates its activity, but in other cases, the TF acts as a repressor (McGrath et al. 2005). Upon invasion by pathogens, the plant innate defence mechanisms activate the expression of TFs controlling defence‐related genes. These genes may be associated with signalling pathways involving the defence hormones salicylic acid (SA), JA and ethylene (ET) as well as reactive oxygen species (ROS) (Ishihama and Yoshioka 2012; Noman et al. 2019; Contreras et al. 2020). Pathogens have evolved effectors that target these TFs to suppress plant defence (Figure 1), usually by preventing their DNA‐binding and/or transcriptional activation role, but alternatively by enhancing their DNA‐binding and repressor function (Yang et al. 2024).

FIGURE 1.

Effectors modulate DNA‐binding or transcription activity of target transcription factors (TFs). The interaction between effectors and TFs can modulate TF activity through different mechanisms: inhibiting the DNA‐binding domain (blue box), altering the activity of TFs or unknown (red box), or some complicated mechanisms (yellow box). These interactions subsequently change the expression of downstream genes, facilitating pathogen infections. All boxes have effectors on the left and target TFs on the right. AD, active domain; DBD, DNA‐binding domain.

For instance, the effector CRN12_997, a member of the crinkling and necrosis protein family from the oomycete Phytophthora capsici, inhibits the DNA‐binding ability of tomato SlTCP14‐2, resulting in a decrease of SlTCP14‐mediated immunity (Stam et al. 2013, 2021). A recent report indicates that a Verticillium dahliae effector, Vd6317, targets AtNAC53, and represses its DNA‐binding activity (Liu et al. 2024); AtNAC53 directly targets AtUGT74E2, which contributes to host resistance to V. dahliae. The interaction between Vd6317 and AtNAC53 decreases the expression level of AtUGT74E2, thereby enhancing host susceptibility. Another instance involves the effector ArPEC25 from the necrotrophic fungus Ascochyta rabiei, which interacts with the TF CaβLIM1a (Singh et al. 2023). ArPEC25 inhibits the binding of CaβLIM1a to the CaPAL1 promoter, leading to reduced biosynthesis of phenylpropanoids and lignin in the chickpea host. HaRXLL470 is a conserved RxLR effector from the oomycete Hyaloperonospora arabidopsidis (Anderson et al. 2012) that suppresses plant immunity by reducing DNA binding of the Arabidopsis thaliana TF HY5 (Elongated hypocotyl 5) (Chen et al. 2021). Also, the effector UvSec117 from Ustilaginoidea virens hijacks OsWRKY31, inhibiting its DNA‐binding activity with OsAOC, thereby suppressing JA‐dependent defence responses (Duan et al. 2024).

Instead of interfering with the DNA binding of TFs, effectors can also block their transcriptional activation domain. The type III effector RipAB from the bacterium Ralstonia solanacearum targets several TGA TFs in Arabidopsis, to inhibit their ability to recruit RNA polymerase II (Qi et al. 2022). RipAB modulates the transcriptional activity of TGA1 and TGA4, affecting downstream genes like the SA signalling TFs systemic acquired resistance deficient 1 (SARD1) and calmodulin binding protein 60 (CBP60g), thus suppressing SA biosynthesis and PTI. Additionally, RipAB inhibits TGA2/5/6 directly regulating the genes RBOHD and RBOHF involved in reactive oxygen species (ROS) production. Another example is the VdSCP41 effector from the fungus V. dahliae, which enhances V. dahliae infection by targeting SARD1 and CBP60g in Arabidopsis and GhCBP60b in cotton, thereby inhibiting their TF activity (Qin et al. 2018). Magnaporthe oryzae, a hemibiotrophic fungal pathogen, secretes the effector AvrPit‐z during the necrotrophic phase to interact with the bZIP TF APIP5, suppressing its transcriptional activity and protein accumulation (Wang et al. 2016). Silencing APIP5 in rice results in cell death, indicating that the effector promotes this in the necrotrophic phase of the pathogen.

Certainly, the mechanisms by which some effectors act on target TFs are not well understood, but they influence the activity of TFs or their regulation of downstream genes has been validated. For instance, the AopP effector from the bacteriu Acidovorax citrulli interacts with ClWRKY6 to reduce levels of ROS and SA content in watermelon (Zhang et al. 2020). A recent report expounds the interaction between the Phytophthora sojae effector PsCRN108 and the plant TF NbCAMTA2, which is a negative regulator of plant immunity. The interaction leads to stronger inhibition of HSP40 expression than by NbCAMTA2 alone (Yang et al. 2024).

In some cases, the action of an effector is more complex such as interference with protein–protein interactions including TFs. The Pseudomonas syringae type III effector HopBB1 disturbs the JAZ3–MYC2 association, liberating MYC2 for activation of the JA response, which enhances plant susceptibility to Pseudomonas (Yang et al. 2017). An interesting case is the cyst nematode effector protein 10A07 that can interact with an auxin‐responsive TF, IAA16 (INDOLE‐3‐ACETIC ACID INDUCIBLE 16), and a protein kinase (IPK) (Hewezi et al. 2015). After phosphorylation by IPK, 10A07 translocates to the nucleus and there, it can physically interact with IAA16. These processes enable 10A07 to enhance the expression of auxin response‐related genes during nematode infection.

These examples illustrate how plant pathogens modulate the DNA binding or transcriptional activity of plant TFs to influence downstream gene expression.

2.2. Effector‐Mediated Modulation of Transcription Factor Multimerization

The ability of proteins to form homodimers and heterodimers is crucial for various biological processes in plants, ranging from transport and sorting to regulatory functions. Many plant TFs can form hetero‐ and/or homo‐oligomers, which is crucial for DNA‐binding specificity (Iven et al. 2010; Feller et al. 2011; Nakashima et al. 2012; Sornaraj et al. 2016). For example, homo‐ and heterodimer interactions were observed in WRKY18, WRKY40, and WRKY60, displaying a complex pattern of overlapping, antagonistic, and distinct roles in plant responses to various types of microbial pathogens (Xu et al. 2006). Interestingly, effectors can achieve their goals by interfering with the multimeric structures of TFs (Figure 2).

FIGURE 2.

Effectors influence formation of transcription factor protein complexes. (A, B) Effectors drive dissociation of homodimers and polymer complexes of transcription factors. (C) Effectors prompt transcription factors to form homodimers.

For the R. solanacearum effector RipAK, a notable interaction with the pepper TF ERF098 has been revealed (Liu et al. 2023). This interaction inhibits the homodimerization and DNA‐binding activity of ERF098, leading to the suppression of downstream immunity and dehydration response genes. As a result, the host plant's susceptibility to infection is increased.

The ASR (ABA, stress, ripening) TFs play a positive role in plant responses to biotic/abiotic stress, especially connected to ROS accumulation (Dominguez and Carrari 2015; Dominguez et al. 2021). TaASR3 forms a multimer and plays a pivotal role in wheat resistance to stripe rust Puccinia striiformis f. sp. tritici. The effector Pst21674 inhibits the polymerisation of TaASR3, thereby suppressing the expression of defence genes (Zheng et al. 2023).

The StKNOX3 TF plays an important role in Phytophthora infestans infection of potato. StKNOX3 interacts with itself and StKNOX7 to form homodimers or heterodimers. Interestingly, the P. infestans RXLR effector Pi22798 stimulates the formation of StKNOX3 homodimers but does not affect the heterodimers. The formation of the StKNOX3 homodimers was proven to be important for enhancing potato susceptibility to P. infestans (Zhou et al. 2022).

In summary, the effectors of plant pathogens can influence the multimerization of target TFs, affecting their function and suppressing plant immunity, ultimately facilitating pathogen invasion.

2.3. Effectors as Transcription Factor Mimics

Interestingly, some effectors can also act as a TF. By directly binding the regulatory sequences of plant target genes, effectors can modulate their expression. For example, in addition to interacting with TFs (see section 2.1), PsCRN108 also binds the promoter of and suppresses the expression of heat shock protein (HSP) family genes, including HSP90 and HSP40, resulting in impaired plant immunity (Song et al. 2015).

Transcription activator‐like effectors (TALEs) have been discovered in the plant‐pathogenic bacterium Xanthomonas (Bogdanove, Schornack, and Lahaye 2010; Boch, Bonas, and Lahaye 2014; Jankele and Svoboda 2014). TALEs have a signal secretion peptide at their N‐terminus but further consist of typical plant TF domains, a DNA‐binding domain with amino acid repeat‐variable diresidues and an acidic activation domain (Schornack et al. 2013). In the realm of TALEs from Xanthomonas bacteria, the ERF121 TF from Brassica oleracea is identified as a direct gene target, and the TALE interaction promotes susceptibility to Xanthomonas (Zlobin et al. 2021).

Apart from TALEs, several other types of pathogen effectors have been found to possess TF functionality (Ramirez‐Garcés et al. 2016; Barnes et al. 2018), but only a few of them are known to target plant TF genes. For instance, MIp124478 is a DNA‐binding effector from the fungus Melampsora larici‐populina (Ahmed et al. 2018), and its binding suppresses expression of plant defence genes including several TFs. Although chromatin immunoprecipitation‐quantitative PCR (ChIP‐qPCR) shows the evidence that Mip122478 interacts with the TGA1a binding DNA sequence, electrophoretic mobility shift assay (EMSA) results could not further support this conclusion.

These types of interactions differ from protein–protein interactions as they involve interactions between effector proteins and DNA. TALEs are a typical example of this; although there are a few reports from other pathogen effectors, more exploration is needed. The discovery and functional analysis of such special effectors will further enhance our understanding of the interaction mechanisms between plants and pathogens.

2.4. Influencing Degradation by the Ubiquitin‐Proteasome System or Other Processes

Research on plant protein degradation has highlighted its major role in plant immunity and plant–pathogen interactions (Vierstra 1993; Hellmann and Estelle 2002; Xu and Xue 2019). Numerous studies have unveiled intricate and finely tuned regulatory mechanisms (Zeng et al. 2006; Araújo et al. 2011; Zhang et al. 2015). Among them, the ubiquitin‐proteasome system (UPS)‐mediated degradation is the most common one. The UPS consists of three specific enzymes, E1 (ubiquitin‐activating enzyme), E2 (ubiquitin‐conjugating enzyme), and E3 (ubiquitin ligase) to conjugate ubiquitin to the substrate, which is subsequently degraded by the 26S proteasome (Vierstra 2009; Langin, González‐Fuente, and Üstün 2023). In the regulation of plant immunity, the UPS plays an indispensable role in the degradation or stabilisation of many dominant TF families, such as WRKY, TCP, and NAC (Miao and Zentgraf 2010; Peng et al. 2015; Miao et al. 2016; Ye et al. 2018).

To date, many effectors have been reported to use the host UPS (Janjusevic et al. 2006; Abramovitch et al. 2006; Singer et al. 2013; Nakano, Oda, and Mukaihara 2017; Kud et al. 2019), and some of them are targeting TFs in plants. The mechanism (Figure 3) can be to either stabilise or destabilise the protein by changing its structure or by affecting the UPS machinery or by relocalizing the protein to promote degradation (see section 2.5).

FIGURE 3.

Effectors exploit the ubiquitin proteasome system to influence plant transcription factors (TFs). Plant TFs are degraded through the 26S proteasome system and effectors can achieve their mechanisms by either degrading or stabilising target TFs. On the one hand, effectors can promote the degradation through multiple ways, like forming complex, promoting ubiquitination (Ub), or some unclear mechanism (blue boxes). On the other hand, effectors can also inhibit ubiquitination or utilise SUMOylation to stabilise TFs, preventing their degradation, or some unclear mechanisms (red boxes).

Ubiquitin E3 ligases play a pivotal role in degrading numerous TFs (Miao and Zentgraf 2010; Ye et al. 2018; Li et al. 2020; Liu et al. 2021; Sun et al. 2023). Notably, plant pathogens employ various strategies to modulate E3 ligase activity within the host by secreting specific effectors (Langin, González‐Fuente, and Üstün 2023; Sharma, Prasad, and Prasad 2023). Briefly, some pathogenic effectors mimic or act as E3 ligase, and others target the host E3 ligase. In the case of the V. dahliae effector PevD1, it serves as an inducer to modulate leaf senescence. Zhang et al. identified its physical targets in both Arabidopsis and cotton. PevD1 stabilises the ORE1 (ANAC092) protein by disrupting its interaction with the RING‐type ubiquitin E3 ligase NLA (Zhang et al. 2021). ORE1 is a member of the senescence‐associated NAC TF family, activating a key ET biosynthetic gene, ACS6 (1‐aminocyclopropane‐1‐carboxylate synthase). ChIP‐qPCR results indicate that PevD1 modulates the expression of ACS6 via ORE1, promoting ET synthesis for nutrient release from the host. Another effector XopS can stabilise CaWRKY40a to repress defence genes, promoting JA signalling and suppressing stomatal closure, aiding Xanthomonas campestris pv. vesicatoria infection in pepper (Raffeiner et al. 2019, 2022).

Phytoplasma, a class of bacteria that notably lack cell walls, primarily infects plant phloem tissues and is often transmitted by insect vectors (Weintraub and Beanland 2006). The aster yellows phytoplasma strain Witches' Broom (AY‐WB; “Candidatus Phytoplasma asteris”) releases certain proteins called SAP (Secreted AY‐WB Proteins) that are instrumental in causing the shoot and leaf phenotypes of witches' broom (Bai et al. 2009). SAP11_AYWB specifically targets and destabilises TCP TFs, including TCP2, TCP4, TCP13, and TCP18 in class II and TCP7 in class I (Sugio et al. 2011; Sugio, Maclean, and Hogenhout 2014; Chang et al. 2018). SAP11 is conserved in several other phytoplasmas such as SAP11_MBSP from maize bushy stunt phytoplasma. Both SAP11_AYWB and SAP11_MBSP interact with TCP2 and TCP18 in Arabidopsis and maize and degrade them to promote infection (Pecher et al. 2019). Additionally, there are SAP11 effectors from other phytoplasmas that target TCP TFs, and in‐depth research is still ongoing (Boonrod et al. 2022; Mittelberger, Hause, and Janik 2022; Marrero et al. 2024).

Similarly, SAP11 like effector (SWP1) from wheat blue dwarf (WBD) phytoplasma destabilises AtTCP18 through the UPS (Wang et al. 2018). Additionally, two other SAP11‐like effectors from “Candidatus Phytoplasma ziziphin” known as SJP1 and SJP2 target jujube ZjBRC1 and ZjTCP7 (Zhou et al. 2021). SJP1 and SJP2 interact with and destabilise ZjBRC1, removing its inhibition of ZjPIN1c and ZjPIN3, thus promoting lateral bud outgrowth (Zhou et al. 2021; Ma et al. 2024). Furthermore, ZjTCP7 has also been confirmed to be destabilised by SJP1 and SJP2, which delays flowering time and mediates shoot branching. Another phytoplasma effector SWP12 has been confirmed to interact with the wheat TaWRKY74, stimulating its ubiquitin‐dependent degradation (Bai et al. 2022, 2023). TaWRKY74 is a positive regulator of plant defence, including ROS production.

Except the direct destabilisation or via UPS, several effectors have been reported to destabilise target TFs by a more complex mechanism. The P. syringae type III effector HopBB1 plays a dual role to activate the host JA response. Besides its action on JAZ3–MYC2 (see section 2.1), it can interact with TCP14 and connect it to the JAZ3 repressor, leading TCP14 to degradation via the ubiquitin‐ligase SCF^COI1 complex, and this process is accompanied with subcellular relocating (see section 2.5) (Wessling et al. 2014; Yang et al. 2017). Another SAP effector, SAP54, executes specific UPS‐mediated degradation of type II MADS‐domain TFs in Arabidopsis (MacLean et al. 2014). It has been found that the ubiquitin‐binding proteins and proteasome‐shuttle factors RAD23C and RAD23D interact with SAP54, highlighting how SAP54 exploits RAD23 to destabilise MADS proteins. SAP05's mechanism is different from other known SAP effectors; it interacts with TFs GATAs, SPLs, and the 26S proteasome ubiquitin receptor RPN10 (Huang et al. 2021). The mechanism behind SAP05's action lies in its ability to drive the binding of those TFs and AtRPN10, subsequently facilitating their degradation via the proteasome but independent of ubiquitination.

Some other effectors have been shown to affect the stability of target TFs but not much is known about the precise mechanism. For instance, association of the P. sojae RxLR effector PsAvh113 with the soybean TF GmDPB targets this TF for degradation via the 26S proteasome (Zhu et al. 2023). This degradation affects the expression of GmCAT1, a catalase that assists immunity during Phytophthora spp. infection. One recent report highlights the effector Mi2G02 from root‐knot nematodes that stabilises the Arabidopsis TF GT‐3a by inhibiting its UPS degradation. A. thaliana GT‐3a knockouts are less susceptible while overexpression lines are more, which underscores the critical role of GT‐3a in root‐knot nematode infection (Zhao et al. 2023). Also, Plasmopara viticola secretes the RxLR effector PvRxLR111, which stabilises VvWRKY40, facilitating colonisation (Ma et al. 2021).

Small ubiquitin‐like modifier (SUMO) regulates protein attachment and cleavage via SUMO proteases. While not directly targeting degradation, SUMO modification can serve as a secondary signal mediating UPS‐dependent degradation. SUMOylation and SUMO proteases are involved in various pathways related to plant development and defence, making their role in protein regulation complex alongside the ubiquitin‐proteasome system. Genetic studies have shown that altering SUMOylation levels in plants affects their susceptibility to fungal, bacterial, and viral pathogens (Novatchkova et al. 2004). Recent research indicates that pathogens exploit the SUMO modification system to modulate plant disease resistance, promoting infection. Pathogen effectors can disrupt the host's SUMOylation machinery by inhibiting SUMO conjugation, promoting SUMO deconjugation, or altering SUMO conjugate homeostasis (Gupta et al. 2020). Furthermore, some pathogen effectors have been found to either act as SUMO proteases or mimic their activity within eukaryotic cells (Sharma et al. 2021). Investigation of the bacterial effector XopD during Xanthomonas euvesicatoria infection of tomato has revealed that XopD acts as a SUMO protease (Kim, Stork, and Mudgett 2013). XopD possesses ERF‐associated amphiphilic repression motifs in its N‐terminal region and a SUMO protease domain in its C‐terminal region. It functions by de‐SUMOylating the ET‐responsive TF SlERF4 during parasitism to suppress ET production. A homologous effector XopD_xcc8004 (Kim, Taylor, and Mudgett 2011) targets an Arabidopsis bHLH type TF HFR1 (long hypocotyl in far‐red) and deSUMOylates it, while the precise influence of XopD_Xcc8004's SUMO protease activity on HFR1 function remains uncertain (Tan et al. 2015).

Altogether, these reports demonstrate how effectors use UPS or other mechanisms to degrade or stabilise crucial target TFs to modulate host immunity or induce developmental changes that enhance parasitism (such as witches' broom).

2.5. Interactions Between Effectors and Transcription Factors Can Influence Their Subcellular Localization

Plant cells encompass a variety of organelles, each defined by distinct spatial arrangements and functions, including the nucleus, Golgi apparatus, endoplasmic reticulum (ER), mitochondria, and chloroplasts. The synthesis of proteins by ribosomes is followed by their precise targeting to specific organelles through signal sorting mechanisms (Neumann, Brandizzi, and Hawes 2003; Sparkes et al. 2009; Moller, Rasmusson, and Van Aken 2021). Ongoing studies suggest that subcellular localization of many proteins, including TFs, can change in response to different environmental conditions (Cornell and Northwood 2000; Byun‐McKay and Geeta 2007; Foyer et al. 2020; Schenck and Last 2020). When plants encounter biotic stress, some TFs relocate to alternative subcellular locations as part of their defence mechanisms (Froidure et al. 2010; Ng, Abeysinghe, and Kamali 2018). This can also be a different location within the same compartment, for instance, from an even distribution in the nucleus to specific subnuclear foci. In this regard, some effectors of plant pathogens promote infection by influencing the localization of target proteins (Figure 4).

FIGURE 4.

Effectors and target transcription factor interactions affecting subcellular localization. Effectors interact with transcription factors to prevent the translocation of transcription factors from the endoplasmic reticulum (ER), cytoplasm, or plasma membrane (PM) to the nucleus (N). In nuclei, some effectors (E) can dissociate transcription factors from chromatin to nucleosome and relocalize them to subnuclear foci, for example, CRN12; HopBB1.

Arabidopsis TCP14 stands out as a pivotal core in the manipulation of host defence mechanisms by effectors (Wessling et al. 2014; Yang et al. 2017). Effectors from three distinct pathogens (ascomycete, oomycete, and eubacteria) have been verified to interact with AtTCP14, and a root‐knot nematode effector has been shown to interact with its soybean homologue (Mendes et al. 2021). The expression of these effectors together with the AtTCP14 led to varying degrees of subnuclear relocalization, indicating that TCP14 can relocalize effectors into subnuclear foci, providing insights into the convergent effects during effector–host interactions (Wessling et al. 2014). As mentioned above (see section 2.4), Yang et al. focused on HopBB1, one of the interacting effectors, revealing the mechanism through which this effector enhances bacterial parasitism by modulating AtTCP14 (Yang et al. 2017). AtTCP14 represses genes involved in JA synthesis, while HopBB1 connects TCP14 and JAZ3 relocating them to the specific subnuclear foci for degradation of TCP14 by the ubiquitin‐ligase SCF^COI1 complex. Additionally, the CRN12_997 effector mentioned in section 2.1 achieves its effect by dissociating SlTCP14‐2 from chromatin, thereby causing the repositioning of SlTCP14‐2 (Stam et al. 2021).

In another case, the RxLR effector Pi03192 from P. infestans interacts with StNTP1 and StNTP2, two NAC TFs in potato (Oh et al. 2009; McLellan et al. 2013). These NAC TFs are ER‐associated TFs and undergo PAMP‐triggered relocalization from the ER to the nucleus to activate plant defence (Seo, Kim, and Park 2008; Seo 2014). However, co‐expression of Pi03192 with either StNTP1 or StNTP2 inhibits their release from the ER, disrupting the TF‐regulated immune response and facilitating P. infestans colonisation (McLellan et al. 2013). The A. thaliana NAC TF AtNTL9 has been found to interact with several effectors (Gonzalez‐Fuente et al. 2020). The P. syringae virulence effector HopD1 specifically prevents NTL9 from relocating from the ER to the nucleus, suppressing NTL9 action during ETI (Block et al. 2014). A similar mechanism is observed for the Bremia lactucae fungal effectors BLR05 and BLR09, which interact with the C‐terminal domain of the lettuce NAC TF LsNAC069 involved in stress responses (Meisrimler et al. 2019). LsNAC069 normally undergoes proteolytic cleavage facilitating its relocalization from ER to nucleus, but co‐expression with the effectors reduces its nuclear accumulation.

SJP1 plays a different role from SJP2 when interacting with ZjBRC1 (see section 2.4), as it can induce ZjBRC1 to translocate from cytoplasm to nucleus, but SJP2 does not perform in the same way (Zhou et al. 2021). Another effector (PstGSRE1) from the fungus P. striiformis f. sp. tritici targets the wheat TaLOL2 TF, preventing its passage from the cytoplasm to the nucleus and thereby suppressing the hypersensitive response (HR) (Qi et al. 2019).

In a recent study, Yang et al. identified the effector CgNLP1 in the fungus Colletotrichum gloeosporioides, which plays a significant role in the invasion and germination processes of the rubber tree fungus. Through yeast two‐hybrid (Y2H) experiments, it was found that CgNLP1 interacts with a MYB TF known as HbMYB8‐like. This interaction was subsequently confirmed in planta (Yang et al. 2022). Normally, HbMYB8‐like is localised within the nucleus, but when co‐expressed with CgNLP1, it undergoes partial relocalization to the plasma membrane. This finding suggests a potential regulatory mechanism involving CgNLP1 in modulating the subcellular localization of HbMYB8‐like during fungal invasion and germination.

Previous studies have demonstrated that the effectors PopP and AvrRps4 are recognised via the WRKY domain of the NB‐LRR proteins RPS4/RRS1, activating host resistance (Mukhi et al. 2021). Recent findings reveal that AvrRps4^C interacts with multiple host WRKY TFs. Notably, AvrRps4^C suppresses the transcriptional and DNA‐binding activities of WRKY54, reducing expression of the target gene SARD1. Additionally, AvrRps4^C enhances the formation of homo‐/heterotypic complexes of four WRKYs promoting their cytoplasmic localization, thus inhibiting their nuclear function (Nguyen et al. 2024). In conclusion, the subcellular relocalization of target proteins by effectors is also an important mechanism for enhancing infection (Figure 4). Most reports are dealing with relocalization between the ER and the nucleus or between subnuclear compartments.

2.6. Uncharted Interactions

More TFs have been identified as targets of effectors from plant pathogens, but their mechanisms for enhancing pathogenicity remain elusive. For example, a peptide secreted from root‐knot nematodes can stimulate root growth by targeting two SCARECROW‐LIKE TFs (Huang et al. 2006). Another effector from Meloidogyne incognita Mi‐EFF1/Minc17998 has been found to interact with GmHub6, a TF homologous to TCP14 in Arabidopsis (Mendes et al. 2021). Fusarium oxysporum f. sp. cubense effector FSE1 targets a MYB TF from banana ( Musa acuminata ) (Yang et al. 2023). R. solanacearum effector Ripl targets Nb‐bHLH TF to suppress the HR (Zhuo et al. 2020). While these examples have been confirmed at the interaction level, their specific mechanisms still require further research.

3. Conclusion and Perspectives

In recent years, there has been a heightened focus on confirming and investigating interactions between effectors and TFs in plant–pathogen interactions. This review has made an attempt to summarise these interactions, shedding light on the intricate relationships between plant pathogen effectors and plant TFs (Table 1). Interestingly, similar interactions have also been observed for pest effectors capable of targeting plant TFs to modulate host responses (Hogenhout and Bos 2011; Wang et al. 2019; Xu et al. 2019; Naalden et al. 2021, see Table 1 for some examples). The review delves into the reported key mechanisms, contributing to a deeper understanding of the molecular processes that underlie host–pathogen dynamics. In addition to direct interactions, some effectors can modulate TFs indirectly through interactions with intermediate proteins or signalling pathways (Toruno, Stergiopoulos, and Coaker 2016; Zhang et al. 2017, 2022; Huang et al. 2024). Due to their unique status and mechanisms, the exploration and investigation of TFs are anticipated to remain a prominent trend. Numerous studies have affirmed the significance of TFs in plant defence. Notably, transcriptional reprogramming rapidly unfolds during plant pathogen infections, driven by the interplay of plant TF's activating defence and their modulation by pathogen effectors.

TABLE 1.

The interactions between plant transcription factors (TFs) and effectors with brief description of mechanisms in the text.

Domain name	Host plant	Target protein	Plant pathogen	Effector name	Mechanism	Reference
TCP	Arabidopsis, maize	AtTCP2, TCP4, TCP7, TCP13, TCP18, ZmTCP2, TCP18	Aster yellows phytoplasma strain Witches' Broom (AY‐WB)	SAP11AYWB	Stimulation of TF degradation	Sugio et al. (2011), Sugio, Maclean, and Hogenhout (2014)
	Arabidopsis	TCP14	Pseudomonas syringae	HopBB1	Relocalization/stimulation of TF degradation	Yang et al. (2017)
	Arabidopsis	TCP2, TCP18	Wheat blue dwarf phytoplasma	SWP1	Stimulation of TF degradation	Wang et al. (2018)
	Arabidopsis, maize	TCP2, TCP18	Maize bushy stunt phytoplasma	SAP11MBSP	Stimulation of TF degradation	Pecher et al. (2019)
	Arabidopsis, tomato	SlTCP14	Phytophthora capsici	CRN12_997	Relocalization/inhibition DNA binding	Stam et al. (2021)
	Jujube	BRC1, BRC2, TCP7	‘Candidatus Phytoplasma ziziphi’	SJP1 & SJP2	Relocalization/stimulation of TF degradation	Zhou et al. (2021), Ma et al. (2024)
	Jujube	TCP1	‘Candidatus Phytoplasma ziziphi’	SJP1	Relocalization	Zhou et al. (2021)
	Soybean, tomato	GmHub6, SlTCP14	Meloidogyne incognita	Mi‐EFF1	Not known	Mendes et al. (2021)
	Arabidopsis	TCP3, TCP6, TCP19	‘Candidatus Phytoplasma mali’ strain PM19	SAP11PM19	Stimulation of TF degradation	Boonrod et al. (2022)
	Apple	TCP16	Candidatus Phytoplasma asteris'	SAP11CaPm	Stimulation of TF degradation	Mittelberger, Hause, and Janik (2022)
ERF	Medicago	ERF19	Glomus intraradices, new name Rhizophagus intraradices	SP7	Not known	Kloppholz, Kuhn, and Requena (2011), Schϋßler and Walker (2010)
	Tomato	ERF4	Xanthomonas euvesicatoria	XopD	De‐SUMOylation of target TF	Kim, Stork, and Mudgett (2013)
	Brassica oleracea	ERF121	Xanthomonas campestris pv. campestris	TALEs	Direct binding to target gene to modulate TF expression	Zlobin et al. (2021)
	Pepper	ERF098	Ralstonia solanacearum	RipAK	Abolishing TF homodimerization	Liu et al. (2023)
WRKY	Arabidopsis	WRKY33	Trialeurodes vaporariorum	BSP9	Inhibition of DNA‐binding	Wang et al. (2019)
	Watermelon	WRKY6	Acidovorax citrulli	AopP	Not known	Zhang et al. (2020)
	Grape	WRKY40	Plasmopara viticola	PvRxLR111	Stabilisation of TF	Ma et al. (2021)
	Wheat	WRKY74	‘Candidatus Phytoplasma tritici’	SWP12	Stimulation of TF degradation	Bai et al. (2022), Bai et al. (2023)
	Pepper	WRKY40a	X. campestris pv. vesicatoria	XopS	Stabilisation of TF	Raffeiner et al. (2022)
	Rice	OsWRKY31	Ustilaginoidea virens	UvSEC117	Inhibition of DNA binding	Duan et al. (2024)
	Arabidopsis	WRKYs	P. syringae pv. pisi	AvrRPS4c	Multiple mechanisms (see section 2.5)	Nguyen et al. (2024)
MYB	Rubber tree	MYB8‐like	Colletotrichum gloeosporioides	CgNLP1	Prevention of TF relocalization from cytoplasm to nucleus	Yang et al. (2022)
	Banana	EFM‐like	Fusarium oxysporum f. sp. cubense	FSE1	Not known	Yang et al. (2023)
NAC	Potato	NTP1, NTP2	Phytophthora infestans	PiTG_03192	Prevention of TF relocalization from endoplasmic reticulum (ER) to nucleus	McLellan et al. (2013)
	Arabidopsis	NTL9	P. syringae	HopD1	Prevention of TF relocalization from ER to nucleus	Block et al. (2014)
	Lettuce	NAC069	Bremia lactucae	BLR05/BLR09	Prevention of TF relocalization from ER to nucleus	Meisrimler et al. (2019)
	Arabidopsis	ORE‐1	Verticillium dahliae	PevD1	Stabilisation of TF via E3 ligase disruption	Zhang et al. (2021)
	Soybean	NAC83	Phakopsora pachyrhizi	PpEC15	Not known	Chicowski et al. (2023)
	Arabidopsis	NAC53	V. dahliae	Vd6317	Inhibition of DNA‐binding	Liu et al. (2024)
bHLH	Arabidopsis	HFR1	X. campestris pv. campestris 8004	XopDxcc8004	De‐SUMOylation of target TFs	Tan et al. (2015)
	Tobacco	bHLH93	R. solanacearum	Ripl	Not known	Zhuo et al. (2020)
bZIP	Rice	APIP5	Magnaporthe oryzae	AvrPiz‐t	Influencing transcription activity	Wang et al. (2016)
	Arabidopsis	HY5	Hyaloperonospora arabidopsidis	HaRxLL470	Inhibition of DNA binding	Chen et al. (2021)
	Arabidopsis	TGA1, TGA4	R. solanacearum	RipAB	Modulation of TF activity	Qi et al. (2022)
Other	Arabidopsis	SCL6, SCL21	M. incognita	16D10	Not known	Huang et al. (2006)
	Arabidopsis	Type II MADS	AY‐WB	SAP54	Stimulation of TF degradation	MacLean et al. (2014)
	Arabidopsis	IAA16	Heterodera schachtii	10A07	Modulation of target genes expression	Hewezi et al. (2015)
	Tobacco	CAMTA2	Phytophthora sojae	PsCRN108	Stronger inhibition of downstream gene expression	Song et al. (2015), Yang et al. (2024)
	Arabidopsis	CBP60g, SARD1	V. dahliae	VdSCP41	Modulating of DNA binding	Qin et al. (2018)
	Wheat	LOL2	Puccinia striiformis f. sp. tritici	PstGSRE1	Prevention of TF relocalization to nucleus	Qi et al. (2019)
	Arabidopsis	GATAs, SPLs	AY‐WB	SAP05	Destabilisation of TF independent of Ubi	Huang et al. (2021)
	Potato	KNOX3	P. infestans	Pi22798	Promotion of TF homodimerization	Zhou et al. (2022)
	Chickpea	βLIM1a	Ascochyta rabiei	ArPEC25	Inhibition of DNA binding	Singh et al. (2023)
	Wheat	ASR3	P. striiformis f. sp. tritici	Pst21674	Abolishing TF polymerisation	Zheng et al. (2023)
	Soybean	DPB	P. sojae	PsAvh113	Stimulation of TF degradation	Zhu et al. (2023)
	Arabidopsis	GT‐3a	M. incognita	Mi2G02	Stabilisation of TF	Zhao et al. (2023)

The analysis of protein–protein interactions has revealed that different effector proteins went through convergent evolution, targeting the same host proteins. Over the past decade, research teams have made significant strides in studying common targets of effectors. As mentioned earlier, Wessling et al. discovered a clustering effect of effectors from different evolutionary branches on host target proteins, referred to as hubs (Wessling et al. 2014). TFs are prominent among these plant effector hubs, especially those of the WRKY, TCP, NAC, and ERF families, known to play central roles in plant development and defence. For example, TCPs are recurrent targets for effectors from various pathogens, including bacteria, oomycetes, and nematodes. Interestingly, recent findings on the interaction between symbiotic fungi and hosts show this pattern also to occur in nonpathogenic interactions. Arbuscular mycorrhiza fungi are soil inhabitants that form symbiosis with plant roots. Rhizophagus intraradices, formerly Glomus intraradices (Walker et al. 2021), secretes effector SP7 to interact with ERF19, which enhances mycorrhization while dampening defence responses, implying its role as an effector that facilitates the biotrophic status of arbuscular mycorrhizal fungi in root systems by modulating plant immunity (Kloppholz, Kuhn, and Requena 2011). Effectors from the endophyte Serendipita indica share common targets with plant pathogens, exemplified by TCP9, which interacts with effectors from Ralstonia pseudosolanacearum, Xanthomonas campestris, Golovinomyces orontii, and S. indica (Osborne et al. 2023). In this intricate network, five effectors from S. indica modulate several hormone pathways by interacting with TCP9. These studies collectively illustrate the convergent effects observed in the interaction between microorganisms and host plants. Moreover, in the evolutionary process, plant hubs are more likely to become effector hubs, and TFs with multifaceted functions are more prone to becoming effector hubs.

3.1. How to Find More Interactions Between Transcription Factors and Effectors?

To discover interactions between TFs and effectors, Y2H and immunoprecipitation coupled with mass spectrometry (IP‐MS) remain the most widely employed approach for screening protein–protein interactions. While Y2H is a powerful tool for detecting direct binary protein–protein interactions in yeast, it has limitations, including false positives caused by auto‐activity and false negatives due to low protein expression. Moreover, as Y2H is not an in‐planta method, it does not account for post‐translational modifications, protein folding, or other interacting plant proteins, which can affect interaction accuracy. In contrast, IP‐MS is conducted in a cellular environment, allowing for the detection of protein complexes and interactions under more physiologically relevant conditions (Huang et al. 2008). However, IP‐MS also has its challenges, such as requiring high‐affinity antibodies and the potential for nonspecific binding or loss of weakly interacting proteins during sample preparation. Additionally, both techniques face throughput limitations: Y2H screens a limited number of interactions per experiment, while IP‐MS can be resource‐intensive for large‐scale interaction studies (Koegl and Uetz 2007; Shin et al. 2020).

With the evolution of next‐generation sequencing (NGS) technology, large screenings have become more feasible, not only for screening interactions of a single protein but also for “group‐to‐group” interactions. For example, an A. thaliana TF library is available and can be used for such screenings with one or many plant pathogen effectors (Mitsuda et al. 2010; Pruneda‐Paz et al. 2014). In recent years, technologies for conducting “group‐to‐group” interaction screening have gradually been developed, such as barcode fusion genetic Y2H (BGF‐Y2H) and Cre‐LoxP yeast two‐hybrid sequencing (CrY2H‐Seq) (Yachie et al. 2016; Trigg et al. 2017; Marrero et al. 2024). These technologies are also constantly being optimised and improved (Shivhare et al. 2021; Evans‐Yamamoto et al. 2022; Yin et al. 2024). Constructing effector bait libraries of plant pathogens and employing these “group‐to‐group” Y2H methods with TF libraries will identify more interactions and efficiently find potential hubs in a short time.

Proximity labelling is a newly developed method for protein–protein interactions that has already been used in plant–pathogen interactions (Branon et al. 2018; Huang et al. 2020; Mair and Bergmann 2022; Xu et al. 2023). TurboID is a modified biotin ligase widely used in proximity labelling experiments, with high catalytic activity and substrate promiscuity, capable of efficiently biotinylating lysine residues on proteins in its proximity. It is commonly used as a tool in research areas such as protein–protein interactions, organelle localization, and protein translocation, enabling the labelling and tracking of specific proteins. For example, a recent report indicates an interaction between the Phakopsora pachyrhizi fungal effector PpEC15 and soybean NAC83 via Y2H and proximity labelling (Chicowski et al. 2023). It can be anticipated that TurboID will find more applications in the study of interactions between plant pathogens and hosts.

An aspect that cannot be overlooked is the profound impact of deep learning facilitated by artificial intelligence (AI) on research and innovation across various fields, including the domain of protein interactions. Currently, AI‐based deep‐learning tools for protein structure prediction are also being applied to predict protein–protein interactions, for example, Alphafold (Li et al. 2022; Durham et al. 2023; Homma, Huang, and Van Der Hoorn 2023). Recently, Alphafold has upgraded to Alphafold 3, which provides a higher accuracy prediction of complex protein interactions (Abramson et al. 2024). Excitingly, a recent paper has been published on using AI to predict the interaction between effectors and host proteins (Waksman et al. 2024). Although AI predictions still face limitations, such as dealing with disordered proteins and some inaccuracies, the continuous evolution of AI and the further exploration and refinement of protein structures suggest that deep learning‐based exploration of interacting proteins and construction of interaction networks may become integral components of future research.

3.2. Deeper Insights Into the Mechanisms of Effectors and TF Interactions

In the exploration of effectors and TF mechanisms, identifying functional mutations is a crucial step. Mutations at interaction sites can result in the loss of effector impact, including TF degradation via the UPS and subcellular relocalization (Kim, Stork, and Mudgett 2013; Yang et al. 2017). Western blot and co‐immunoprecipitation (Co‐IP) are relatively intuitive methods for observing protein degradation, offering insights into how effector‐mediated TF degradation influences cellular processes (Shi et al. 2023). Bimolecular fluorescence complementation (BiFC) reveals the influence of an effector on TF subcellular localization through fluorescent protein visualisation (Schutze, Harter, and Chaban 2009). Alternatively, analysing TF–DNA interactions in plants using split luciferase provides insights into interactions with effectors and involvement in biological processes (Wang et al. 2020; Cai, Witham, and Patron 2023). TransitID is a novel technique that uses two promiscuous ligases, LOV‐TurBo and APEX2 (Qin et al. 2023). In essence, this method involves labelling proteins in their original compartments using TurboID. Once translocated to their destination, the target proteins are labelled with APEX2. Subsequently, the labelled proteins are treated with azide‐fluorescein, followed by anti‐fluorescein immunoprecipitation and bead enrichment. This results in the dual‐labelling and enrichment of proteins undergoing translocation. TransitID can thus be useful to study effector‐targeted TF relocalization (Cao and Dong 2023; Typas 2023).

In a functional analysis of plant TFs and their interactions, the phenotype of mutants in those TFs is crucial. However, redundancy among different members of a plant TF family obscures the role of an individual member (Wu and Lai 2015). Consequently, mutations in individual TFs may not always produce significant phenotypical differences during pathogen infection (Riechmann and Ratcliffe 2000). Addressing this, fusions with the repressive SRDX domain are used in genetic engineering approaches to modify the activity of TFs (Hiratsu et al. 2003). Alternatively, the CRISPR‐based “Multi‐Knock” toolbox targets gene‐family members with optimal single‐guide RNAs, holding promise for co‐editing functionally redundant TF genes (Hu et al. 2023).

In addition, in the process of studying the interactions between pathogens effectors and host plant, the combination of multi‐omics research (such as transcriptomics, proteomics, metabolomics, and epigenomics) can provide a systematic perspective and in‐depth analysis capabilities for revealing the functions of effectors and their regulatory mechanism, as TFs can modulate extensive gene networks, triggering complex biological responses (Shaw et al. 2021; Zhang et al. 2022; Diwan, Rashid, and Vaishnav 2022). By integrating multi‐omics datasets, researchers can not only identify the direct impact of effectors on TF activity, stability, and localization but also elucidate downstream transcriptional reprogramming, metabolic shifts, and epigenetic modifications. This comprehensive analysis can offer new insights into how effectors manipulate host cellular processes.

3.3. How Did Plant Hubs Become Key Targets for Pathogen Effectors, and What Can Comparative Genomics Reveal About Their Evolution?

Hubs in plant biology have gained their central position by playing crucial roles in plant signalling networks. Many hubs have been discovered, encompassing plant hubs, effector hubs, and microbe‐associated molecular pattern (MAMP) hubs. Although some effector hubs have been identified, there are still unresolved questions regarding their function (Shan et al. 2008; Chinchilla et al. 2009; Mukhtar et al. 2011; Wessling et al. 2014; Liebrand, Van Den Burg, and Joosten 2014; Üstün et al. 2016; Desaki et al. 2018; Ceulemans et al. 2021; Mishra, Kumar, and Mukhtar 2022; Iakovidis et al. 2023). Plant hubs are not limited to TFs but also include transcription corepressors, receptor kinases, and other potential proteins (Vandereyken et al. 2018; Khan and Djamei 2024; Fontes 2024). Although it is known that many effectors tend to converge on certain plant hub proteins, the shared characteristics of these hubs and how these features make them targets for pathogen effectors remain areas of ongoing research.

One hypothesis suggests that plant pathogens evolve effectors specifically targeting these multifunctional hubs as a strategy to manipulate plant signalling networks regulated by these hubs, thus achieving better efficiency for promoting disease (Carella, Evangelisti, and Schornack 2018). Regarding this question, is it possible to prove it through functional analysis of these hub genes in plant lineages? From the perspective of coevolution, the study of plant genomics at different evolutionary stages, such as mosses, algae, to mature gymnosperms and angiosperms is required to unravel these aspects (Delaux and Schornack 2021). Comparative analyses of TFs and effectors of their pathogens across diverse plant taxa, coupled with an understanding of their evolutionary dynamics, can offer valuable insights into their central roles in plant biology and the evolution of convergent features in effectors.

Comparative analyses of TFs and pathogen effectors across diverse plant taxa, coupled with an understanding of their evolutionary dynamics, are essential to uncover the shared features of these hubs that make them targets for pathogen effectors. Identifying plant hubs for effectors is a multifaceted and time‐intensive endeavour, typically entailing the amalgamation and synthesis of findings from various interaction group studies. Addressing this challenge, Gonzalez‐Fuente et al. (2020) introduced EffectorK, a comprehensive database housing interactions between host and/or effector proteins. This resource has the potential to significantly enhance our capacity to extract valuable insights from extant data. Moreover, the platform actively encourages users to contribute interactome data, thereby ensuring ongoing updates and refinement of the database. By integrating data from various studies, researchers can better understand the evolutionary processes that have positioned these hubs at the centre of plant biology.

3.4. How to Apply Knowledge on Hub Genes Into Agriculture?

How to effectively utilise effector‐plant hubs for improved agricultural production? Identifying and understanding hubs can assist agricultural scientists in better comprehending the interaction between plants and pathogens.

It is known that plants recognise pathogen effectors via resistance (R) proteins, activating defence pathways. Transcriptional reprogramming further strengthens immunity by upregulating defence‐related genes. To enhance plant immunity, genome editing techniques like CRISPR can modify resistance genes or disrupt susceptibility genes, leading to stronger, nonhost‐like resistance (Borrelli et al. 2018). In potato, knocking down the susceptibility genes StDND1, StDMR1, and StDMR6 resulted in reduced susceptibility to P. infestans at various development stages (Kieu et al. 2021; Sun et al. 2022). On the other hand, overexpression of TF MtERF1 in Medicago root could enhance the host resistance to Rhizoctonia solani and Phytophthora medicaginis, without negatively affecting host development, although it had no effect against root‐knot nematodes (Anderson et al. 2010). Targeting common effector interaction points across multiple pathogens could provide broad‐spectrum resistance.

Discovering and conserving naturally occurring hub gene mutants in crop germplasm offers substantial potential for hybridization, leading to the development of highly resistant lines. Some pathogens are difficult to detect and manage in the early stages of infection due to the absence of obvious symptoms (e.g., some soilborne fungal and plant nematodes). However, by understanding the gene networks regulated by hub genes, it becomes possible to monitor changes in the expression levels of target genes. This, in turn, can serve as an early indicator of infection by these hard‐to‐detect pathogens, facilitating timely disease monitoring and prediction.

Additionally, certain symbiotic microbes can interact with plant hub proteins, influencing both growth and immunity. As mentioned above, the interactome of S. indica revealed that several effector convergent hubs are not targeted by other pathogen effectors, suggesting that these exclusive hubs could offer insights into novel strategies for enhancing host resistance through interaction manipulation (Osborne et al. 2023).

These approaches hold promise for promoting sustainable agricultural practices by improving both crop yield and quality. However, several critical questions remain unresolved. For instance, it is still unclear how plants can systematically distinguish between beneficial microorganisms and pathogens, as both can trigger similar immune responses in plants.

Considering these observations and questions, it becomes apparent that we have only begun to scratch the surface of this intricate interplay. Deciphering the fine‐tuning of effectors and TFs at every level is crucial. Therefore, we need to continue delving deeper into this complex network of interactions centred around TF hubs.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

Data sharing is not applicable to this article as no new data were generated.