Engineering plant immune circuit: walking to the bright future with a novel toolbox

The battle within: Discovering new insights into phytopathogen interactions and effector dynamics

Phytopathogen interactions are complicated and constantly evolving, driven by a never-ending war amongst the host's immune defenses and the pathogen's virulence strategies. This comprehensive review examines the intricate mechanisms of effector-triggered immunity (ETI) and how pathogen effectors use host cellular progressions to promote infection. This review article investigates the modification of Phytopathogen effectors and plant resistance proteins, highlighting the role of meta-population dynamics and rapid adaptation. Additionally, it highlights the influence of environmental impact and climate change on host-pathogen interactions, describing their significant impact on disease dynamics and pathogen evolution. Effector proteins are crucial in sabotaging plant immunity, with bacterial, fungal, oomycete, and nematode effectors targeting common host protein networks and phytohormone pathways. Additionally, the review discusses advanced approaches for classifying effector targets, such as bioinformatics and single-cell transcriptomics, highlighting their importance in developing effective disease management strategies. Further insights are described into how effectors control phytohormone pathways, shedding light on how pathogens exploit host signaling. This review covers structural studies and protein modeling that have advanced effector prediction and our understanding of their functions and evolution, while providing an overview of phytopathogen interactions and future directions for effector research.

Monkeys at Rigged Typewriters: A Population and Network View of Plant Immune System Incompatibility

Immune system incompatibilities between naturally occurring genomic variants underlie many hybrid defects in plants and present a barrier for crop improvement. In this review, we approach immune system incompatibilities from pan-genomic and network perspectives. Pan-genomes offer insights into how natural variation shapes the evolutionary landscape of immune system incompatibilities, and through it, selection, polymorphisms, and recombination resistance emerge as common features that synergistically drive these incompatibilities. By contextualizing incompatibilities within the immune network, immune receptor promiscuity, complex dysregulation, and single-point failure appear to be recurrent themes of immune system defects. As geneticists break genes to investigate their function, so can we investigate broken immune systems to enrich our understanding of plant immune systems and work toward improving them.

Selection of stable reference genes in prunus persica fruit infected with monilinia laxa for normalisation of RT-qPCR gene expression data

Reverse transcription-quantitative PCR (RT-qPCR) is a powerful tool for quantifying gene expression. However, reference genes (RGs) for gene expression analysis in peach (Prunus persica) during interactions with Monilinia laxa, a major fungal pathogen that causes brown rot, have not been established. In this study, we analysed 12 candidate RGs in this pathosystem by analysing samples from 12 to 144 HAI. The stability of the RGs was evaluated using the ΔCq method and BestKeeper, NormFinder, and geNorm algorithms. Our results identified AKT3, RNA pol II (RPII) and SNARE (using geNorm), RPII, AKT3 and TEF2 (using NormFinder), AKT3, SNARE and RPII (using BestKeeper) and RPII, MUB6 and AKT3 (using the ΔCq method) as the most stable RGs for mRNA normalisation in this pathosystem across all tested samples. The geNorm algorithm was used to determine the optimal number of suitable RGs required for proper normalisation under these experimental conditions, indicating that the three RGs were sufficient for normalisation. Analysis of the results obtained using different algorithms showed that AKT3, RPII, and SNARE were the three most stable RGs. Furthermore, to confirm the validity of the reference genes, the expression levels of six genes of interest, involved in different metabolic pathways, were normalized in inoculated and uninoculated peach fruit. These findings provide a set of RGs for accurate RT-qPCR analysis in studies involving peach and M. laxa interactions, facilitating deeper insights into the molecular mechanisms underlying this important plant-pathogen relationship.

Generation of Inheritable A-to-G Transitions Using Adenine Base Editing and NG-PAM Cas9 in Arabidopsis thaliana

Towards precise genome editing, base editors have been developed by fusing catalytically compromised Cas9 with deaminase components, mediating C-to-T (cytosine base editors) or A-to-G (adenine base editors) transition. We developed a set of vectors consisting of a 5'-NG-3' PAM-recognizing variant of SpCas9 with adenosine deaminases TadA7.10 or TadA8e. Using a phenotype-based screen in Arabidopsis thaliana targeting multiple PDS3 intron splice sites, we achieved up to 81% somatic A-to-G editing in primary transformants at a splice acceptor site with NGG PAM, while 35% was achieved for the same target adenine with NGA PAM. Among tested vectors, pECNUS4 (Addgene #184887), carrying TadA8e, showed the highest adenine base editor (ABE) efficiency. With pECNUS4, we recreated a naturally occurring allele of DANGEROUS MIX3 (DM3) in two generations, transgene-free, for NGC PAM. We also simultaneously base-edited four redundant DM1/SSI4 homologs, encoding nucleotide-binding leucine-rich repeat (NLR) proteins, using a single gRNA with NGA PAM targeting the conserved yet functionally crucial P-loop motif of NLR proteins. We found fixation of A-to-G in three NLR genes for all three possible adenine sites within base-editing window 3-9, as the edited genes segregate in T2. Multigene targeting succeeded in rescuing the previously reported autoimmune phenotype in two generations. Mediating desired ABE on seven NLR genes simultaneously was successful as well; above 77% editing was achieved in six of the seven possible targets in a T1 plant, with the remaining having a moderately high (32%) editing. ABE application to specifically inactivate functional motifs is anticipated to expedite the discovery of novel roles for proteins. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Machine learning-based identification of general transcriptional predictors for plant disease

This study investigated the generalizability of Arabidopsis thaliana immune responses across diverse pathogens, including Botrytis cinerea, Sclerotinia sclerotiorum, and Pseudomonas syringae, using a data-driven, machine learning approach. Machine learning models were trained to predict disease development from early transcriptional responses. Feature selection techniques based on network science and topology were used to train models employing only a fraction of the transcriptome. Machine learning models trained on one pathosystem where then validated by predicting disease development in new pathosystems. The identified feature selection gene sets were enriched for pathways related to biotic, abiotic, and stress responses, though the specific genes involved differed between feature sets. This suggests common immune responses to diverse pathogens that operate via different gene sets. The study demonstrates that machine learning can uncover both established and novel components of the plant's immune response, offering insights into disease resistance mechanisms. These predictive models highlight the potential to advance our understanding of multigenic outcomes in plant immunity and can be further refined for applications in disease prediction.

Overexpression of plant chitin receptors in wheat confers broad-spectrum resistance to fungal diseases

Wheat (Triticum aestivum L.) is a globally staple crop vulnerable to various fungal diseases, significantly impacting its yield. Plant cell surface receptors play a crucial role in recognizing pathogen-associated molecular patterns (PAMPs) and activating PAMP-triggered immunity, boosting resistance against a wide range of plant diseases. Although the role of plant chitin receptor CERK1 in immune recognition and defense has been established in Arabidopsis and rice, its function and potential agricultural applications in enhancing resistance to crop diseases remain largely unexplored. Here, we identify and characterize TaCERK1 in Triticeae crop wheat, uncovering its involvement in chitin recognition, immune regulation, and resistance to fungal diseases. By a comparative analysis of CERK1 homologs in Arabidopsis and monocot crops, we demonstrate that AtCERK1 in Arabidopsis elicits the most robust immune response. Moreover, we show that overexpressing TaCERK1 and AtCERK1 in wheat confers resistance to multiple fungal diseases, including Fusarium head blight, stripe rust, and powdery mildew. Notably, transgenic wheat lines with moderately expressed AtCERK1 display superior disease resistance and heightened immune responses without adversely affecting growth and yield, compared to TaCERK1 overexpression transgenics. Our findings highlight the significance of plant chitin receptors across diverse plant species and suggest potential strategies for bolstering crop resistance against broad-spectrum diseases in agricultural production through the utilization of plant immune receptors.

Identifying nucleotide-binding leucine-rich repeat receptor and pathogen effector pairing using transfer-learning and bilinear attention network

MOTIVATION

Nucleotide-binding leucine-rich repeat (NLR) family is a class of immune receptors capable of detecting and defending against pathogen invasion. They have been widely used in crop breeding. Notably, the correspondence between NLRs and effectors (CNE) determines the applicability and effectiveness of NLRs. Unfortunately, CNE data is very scarce. In fact, we've found a substantial 91 291 NLRs confirmed via wet experiments and bioinformatics methods but only 387 CNEs are recognized, which greatly restricts the potential application of NLRs.

RESULTS

We propose a deep learning algorithm called ProNEP to identify NLR-effector pairs in a high-throughput manner. Specifically, we conceptualized the CNE prediction task as a protein-protein interaction (PPI) prediction task. Then, ProNEP predicts the interaction between NLRs and effectors by combining the transfer learning with a bilinear attention network. ProNEP achieves superior performance against state-of-the-art models designed for PPI predictions. Based on ProNEP, we conduct extensive identification of potential CNEs for 91 291 NLRs. With the rapid accumulation of genomic data, we expect that this tool will be widely used to predict CNEs in new species, advancing biology, immunology, and breeding.

AVAILABILITY AND IMPLEMENTATION

The ProNEP is available at http://nerrd.cn/#/prediction. The project code is available at https://github.com/QiaoYJYJ/ProNEP.

Engineering broad-spectrum resistance to rice bacterial blight by editing the OsETR susceptible haplotype using CRISPR/Cas9

KEY MESSAGE

CRISPR/Cas9-mediated knockout of the susceptible haplotype of OsETR, encoding an embryogenesis transmembrane protein, confers broad-spectrum resistance to bacterial leaf blight in a susceptible rice cultivar without yield penalty.

The processed C-terminus of AvrRps4 effector suppresses plant immunity via targeting multiple WRKYs

Pathogens generate and secrete effector proteins to the host plant cells during pathogenesis to promote virulence and colonization. If the plant carries resistance (R) proteins that recognize pathogen effectors, effector-triggered immunity (ETI) is activated, resulting in a robust immune response and hypersensitive response (HR). The bipartite effector AvrRps4 from Pseudomonas syringae pv. pisi has been well studied in terms of avirulence function. In planta, AvrRps4 is processed into two parts. The C-terminal fragment of AvrRps4 (AvrRps4C) induces HR in turnip and is recognized by the paired resistance proteins AtRRS1/AtRPS4 in Arabidopsis. Here, we show that AvrRps4C targets a group of Arabidopsis WRKY, including WRKY46, WRKY53, WRKY54, and WRKY70, to induce its virulence function. Indeed, AvrRps4C suppresses the general binding and transcriptional activities of immune-positive regulator WRKY54 and WRKY54-mediated resistance. AvrRps4C interferes with WRKY54's binding activity to target gene SARD1 in vitro, suggesting WRKY54 is sequestered from the SARD1 promoter by AvrRps4C. Through the interaction of AvrRps4C with four WRKYs, AvrRps4 enhances the formation of homo-/heterotypic complexes of four WRKYs and sequesters them in the cytoplasm, thus inhibiting their function in plant immunity. Together, our results provide a detailed virulence mechanism of AvrRps4 through its C-terminus.

Silica nanoparticles conferring resistance to bacterial wilt in peanut (Arachis hypogaea L.)

Peanut bacterial wilt (PBW) caused by the pathogen Ralstonia solanacearum severely affects the growth and yield potential of peanut crop. In this study, we synthesized silica nanoparticles (SiO2 NPs), a prospective efficient management approach to control PBW, and conducted a hydroponic experiment to investigate the effects of different SiO2 NPs treatments (i.e., 0, 100, and 500 mg L-1 as NP0, NP100, and NP500, respectively) on promoting plant growth and resistance to R. solanacearum. Results indicated that the disease indices of NP100 and NP500 decreased by 51.5 % and 55.4 % as compared with NP0 under R. solanacearum inoculation, respectively, while the fresh and dry weights and shoot length of NP100 and NP500 increased by 7.62-42.05 %, 9.45-32.06 %, and 2.37-17.83 %, respectively. Furthermore, SiO2 NPs induced an improvement in physio-biochemical enzymes (superoxide dismutase, peroxidase, catalase, ascorbate peroxidase, and lipoxygenase) which eliminated the excess production of hydrogen peroxide, superoxide anions, and malondialdehyde to alleviate PBW stress. Notably, the targeted metabolomic analysis indicated that SiO2 NPs enhanced salicylic acid (SA) contents, which involved the induction of systemic acquired resistance (SAR). Moreover, the transcriptomic analysis revealed that SiO2 NPs modulated the expression of multiple transcription factors (TFs) involved in the hormone pathway, such as AHLs, and the identification of hormone pathways related to plant defense responses, such as the SA pathway, which activated SA-dependent defense mechanisms. Meanwhile, the up-regulated expression of the SA-metabolism gene, salicylate carboxymethyltransferase (SAMT), initiated SAR to promote PBW resistance. Overall, our findings revealed that SiO2 NPs, functioning as a plant elicitor, could effectively modulate physiological enzyme activities and enhance SA contents through the regulation of SA-metabolism genes to confer the PBW resistance in peanuts, which highlighted the potential of SiO2 NPs for sustainable agricultural practices.

An ancient cis-element targeted by Ralstonia solanacearum TALE-like effectors facilitates the development of a promoter trap that could confer broad-spectrum wilt resistance

Ralstonia solanacearum, a species complex of bacterial plant pathogens that causes bacterial wilt, comprises four phylotypes that evolved when a founder population was split during the continental drift ~180 million years ago. Each phylotype contains strains with RipTAL proteins structurally related to transcription activator-like (TAL) effectors from the bacterial pathogen Xanthomonas. RipTALs have evolved in geographically separated phylotypes and therefore differ in sequence and potentially functionality. Earlier work has shown that phylotype I RipTAL Brg11 targets a 17-nucleotide effector binding element (EBE) and transcriptionally activates the downstream arginine decarboxylase (ADC) gene. The predicted DNA binding preferences of Brg11 and RipTALs from other phylotypes are similar, suggesting that most, if not all, RipTALs target the Brg11-EBE motif and activate downstream ADC genes. Here we show that not only phylotype I RipTAL Brg11 but also RipTALs from other phylotypes activate host genes when preceded by the Brg11-EBE motif. Furthermore, we show that Brg11 and RipTALs from other phylotypes induce the same quantitative changes of ADC-dependent plant metabolites, suggesting that most, if not all, RipTALs induce functionally equivalent changes in host cells. Finally, we report transgenic tobacco lines in which the RipTAL-binding motif Brg11-EBE mediates RipTAL-dependent transcription of the executor-type resistance (R) gene Bs4C from pepper, thereby conferring resistance to RipTAL-delivering R. solanacearum strains. Our results suggest that cell death-inducing executor-type R genes, preceded by the RipTAL-binding motif Brg11-EBE, could be used to genetically engineer broad-spectrum bacterial wilt resistance in crop plants without any apparent fitness penalty.

CRISPR-Cas-mediated unfolded protein response control for enhancing plant stress resistance

Plants consistently encounter environmental stresses that negatively affect their growth and development. To mitigate these challenges, plants have developed a range of adaptive strategies, including the unfolded protein response (UPR), which enables them to manage endoplasmic reticulum (ER) stress resulting from various adverse conditions. The CRISPR-Cas system has emerged as a powerful tool for plant biotechnology, with the potential to improve plant tolerance and resistance to biotic and abiotic stresses, as well as enhance crop productivity and quality by targeting specific genes, including those related to the UPR. This review highlights recent advancements in UPR signaling pathways and CRISPR-Cas technology, with a particular focus on the use of CRISPR-Cas in studying plant UPR. We also explore prospective applications of CRISPR-Cas in engineering UPR-related genes for crop improvement. The integration of CRISPR-Cas technology into plant biotechnology holds the promise to revolutionize agriculture by producing crops with enhanced resistance to environmental stresses, increased productivity, and improved quality traits.