Processing by proteases irreversibly regulates the fate of plant proteins and hampers the production of recombinant protein in plants, yet only few processing events have been described in agroinfiltrated Nicotiana benthamiana, which has emerged as a favorite transient protein expression platform in plant science and molecular pharming. Here, we used in-gel digests and mass spectrometry to monitor the migration and topography of 5,040 plant proteins of agroinfiltrated N. benthamiana within a protein gel. By plotting the peptides over the gel slices, we generated peptographs that reveal where which part of each protein was detected within the protein gel. These data uncovered that 60% of the detected proteins have proteoforms that migrate at lower than predicted molecular weights, implicating extensive proteolytic processing. For instance, this analysis confirms the proteolytic removal and degradation of autoinhibitory prodomains of most but not all proteases, and revealed differential processing within pectinemethylesterase and lipase families. This analysis also uncovered intricate processing of glycosidases and uncovered that ectodomain shedding might be common for a diverse range of receptor-like kinases. Transient expression of double-tagged candidate proteins confirmed various processing events in vivo. This extensive proteomic dataset can be investigated further and demonstrates that most plant proteins are proteolytically processed and implicates an extensive proteolytic machinery shaping the proteome of agroinfiltrated N. benthamiana.

In addition to jasmonoyl-isoleucine (JA-Ile), a well-established signaling molecule for plant growth and defense, its precursor, cis-12-oxo-phytodienoic acid (OPDA), is thought to possess independent signaling functions. Its perception in vascular plants is still uncharacterized. Several OPDA functions in Arabidopsis were inferred from a mutant that is affected in the function of the OPDA REDUCTASE3 (OPR3), catalyzing the conversion of OPDA within peroxisomes. Recently, opr3 plants were found to accumulate JA-Ile via a cytosolic OPR2-mediated bypass. Given the uncoupling of OPDA and JA biosynthesis in the JA-deficient mutant opr2opr3, potential OPDA signaling was investigated by a transcriptome approach comparing wild type, opr2opr3 and the JA- and OPDA-deficient mutantallene oxide synthase. Dissecting the wound response of seedlings revealed that OPDA lacked a transcriptional signature, and that previously characterized OPDA-response genes were wound-induced independently of OPDA. Exogenous application of OPDA to opr2opr3 seedlings led to JA-Ile formation and signaling even in absence of OPR2 and OPR3 and resulted in activation of sulfur assimilation. These divergent responses to endogenously synthesized and applied OPDA suggest a compartmentalization of endogenous OPDA which was investigated by a trans-organellar complementation approach. OPR3 complemented the opr2opr3 mutant in terms of fertility and wound-induced JA-Ile production irrespective of its subcellular localization. In vitro enzymatic activity of OPR3, however, showed conversion of OPDA and 4,5-didehydro-JA (4,5-ddh-JA), therefore not allowing to conclude which compound is translocated. Dissecting the conversion of either OPDA or 4,5-ddh-JA by OPR2 and OPR1 organelle variants pointed to a strong OPDA compartmentalization supporting its lacking signaling capacity.

Protein engineering using directed evolution and (semi)rational design has emerged as a powerful strategy for optimizing and enhancing enzymes or proteins with desired properties. Integrating artificial intelligence methods has further enhanced and accelerated protein engineering through predictive models developed in data-driven strategies. However, the lack of explainability and interpretability in these models poses challenges. Explainable Artificial Intelligence addresses the interpretability and explainability of machine learning models, providing transparency and insights into predictive processes. Nonetheless, there is a growing need to incorporate explainable techniques in predicting protein properties in machine learning-assisted protein engineering. This work explores incorporating explainable artificial intelligence in predicting protein properties, emphasizing its role in trustworthiness and interpretability. It assesses different machine learning approaches, introduces diverse explainable methodologies, and proposes strategies for seamless integration, improving trust-worthiness. Practical cases demonstrate the explainable model’s effectiveness in identifying DNA binding proteins and optimizing Green Fluorescent Protein brightness. The study highlights the utility of explainable artificial intelligence in advancing computationally assisted protein design, fostering confidence in model reliability.

Aphids are small insects that have developed specialized mouthparts and effector proteins to establish long-term relationships with plants. The peach-potato aphid, Myzus persicae, is a generalist, feeding on many plant species and capable of transmitting numerous pathogens. This study reveals how host-responsive cathepsins B (CathB) in the oral secretions of M. persicae facilitate aphid survival by modulating plant immune responses. Aphid CathB localize to processing bodies (p-bodies) and recruit key immune regulators EDS1, PAD4, and ADR1 to these bodies, suppressing plant defenses. A plant protein, Acd28.9 (Hsp20 family), counteracts this CathB activity and contributes to plant resistance to aphids. These findings highlight a novel role for p-bodies in plant immunity and uncover a plant resistance mechanism to aphid infestation.

Secreted immune proteases Rcr3 and Pip1 of tomato are both inhibited by Avr2 from the fungal plant pathogen Cladosporium fulvumbut only Rcr3 act as a decoy co-receptor that detects Avr2 in the presence of the Cf-2 immune receptor. Here, we identified crucial residues from tomato Rcr3 required for Cf-2-mediated signalling and bioengineered various proteases to trigger Avr2/Cf-2 dependent immunity. Despite substantial divergences in Rcr3 orthologs from eggplant and tobacco, only minimal alterations were sufficient to trigger Avr2/Cf-2-triggered immune signalling. Tomato Pip1, by contrast, was bioengineered with 16 Rcr3-specific residues to initiate Avr2/Cf-2-triggered immune signalling. These residues cluster on one side next to the substrate binding groove, indicating a potential Cf-2 interaction site. Our findings also revealed that Rcr3 and Pip1 have distinct substrate preferences determined by two variant residues and that both are suboptimal for binding Avr2. This study advances our understanding of Avr2 perception and opens avenues to bioengineer proteases to broaden pathogen recognition in other crops.

Plant cells experience a variety of mechanical stresses from both internal and external sources, including turgor pressure, mechanical strains arising from heterogeneous growth between neighboring cells, and environmental factors like touch from soil, rain, or wind [1,2]. These stresses serve as signals at the cell-, tissue- and organismal level to coordinate plant growth during development and stress responses [3]. In plants, the physical cell wall-plasma membrane-microtubule continuum is proposed to be integral in transducing mechanical signals from the exterior to intracellular components [4–6]. Cortical microtubules (CMTs) rapidly reorient in response to mechanical stress to align with the maximal tensile stress direction [7,8]. Several studies proposed that CMTs themselves may act as stress sensors; the precise mechanisms involved in the regulation of CMTs and the modes of sensing, however, are still not clearly understood. Here, we show that IQD2 and KLCR1 are enriched at CMTs in proximity to the plasma membrane. IQD2, which is a bona fide microtubule-associated protein, promotes microtubule localization of KLCR1. By combining cross-linking mass spectrometry (XL-MS) and computational modeling with structure-function studies, we present first experimental insights into the composition and structure of IQD2-KLCR1 complexes. Further, we demonstrate that the IQD2-KLCR1 module is a positive regulator of microtubule mechano-responses in pavement cells. Collectively, our work identifies the IQD2-KLCR1 module as novel regulator of mechanostress-mediated CMT reorientation and provides a framework for future mechanistic studies aimed at a functional dissection of mechanotransduction at the plasma membrane-CMT interface during growth and plant morphogenesis.HighlightsIQD2 and KLCR1 localize to the plasma membrane-microtubule nexusIQD2 is required for efficient microtubule targeting of KLCR1in plantaIQD2 physically interacts with KLCR1 and microtubulesThe IQD2-KLCR1 module promotes mechano-stress induced microtubule reorganization

Crop protection strategies relying on the improvement of the natural plant immune system via genetic engineering are sustainable solutions against the pathogen thread on food security. Here we describe a novel way to improve the plant immune system by immune protease engineering. As proof of concept, we increased resistance against the late blight pathogen Phytopththora infestans by rendering the tomato secreted immune protease Pip1 insensitive to the P. infestans-secreted inhibitor Epic2B. This concept can be applied to secreted immune proteases in crops by precision breeding.

One class of enzymes that plant pathogens employ to manipulate innate immunity and physiology of the infected cells are host-targeted ADP-ribosyltransferases. The bacterial pathogen Pseudomonas syringae uses its type III secretion system to inject several effector proteins with ADP-ribosyltransferase activity into plant cells. One of them, AvrRpm1, ADP-ribosylates the plasma membrane-associated RPM1-INTERACTING PROTEIN 4 (RIN4) in Glycine max and Arabidopsis thaliana to attenuate targeted secretion of defense-promoting compounds. Substrate identification of host-targeted ADP-ribosyltransferases is complicated by the biochemical lability of the protein modification during plant protein extraction and in several cases required prior knowledge on plant immune signaling pathways that are impaired by the ADP-ribosylating type III effector. Using the AvrRpm1-RIN4 pair as a proof-of-concept, we present an untargeted proteomics workflow for enrichment and detection of ADP-ribosylated proteins and peptides from plant cell extracts that in several cases provides site-resolution for the modification.

Jasmonates (JAs) are a family of oxylipin phytohormones regulating plant development and growth and mediating ‘defense versus growth’ responses. The upstream JA biosynthetic precursor cis-(+)-12-oxo-phytodienoic acid (cis-OPDA) has been reported to act independently of the COI1-mediated JA signaling in several stress-induced and developmental processes. However, its means of perception and metabolism are only partially understood. Furthermore, cis-OPDA, but not JA, occurs in non-vascular plant species, such as bryophytes, exhibiting specific functions in defense and development. A few years ago, a low abundant isoleucine analog of the biologically active JA-Ile, OPDA-Ile, was detected in wounded leaves of flowering plants, opening up to the possibility that conjugation of cis-OPDA to amino acids might be a relevant mechanism for cis-OPDA regulation. Here, we extended the analysis of amino acid conjugates of cis-OPDA and identified naturally occurring OPDA-Val, OPDA-Phe, OPDA-Ala, OPDA-Glu, and OPDA-Asp in response to biotic and abiotic stress in Arabidopsis. The newly identified OPDA-amino acid conjugates show cis-OPDA-related plant responses in a JAR1-dependent manner. We also discovered that the synthesis and hydrolysis of cis-OPDA amino acid conjugates are regulated by members of the amidosynthetase GH3 and the amidohydrolase ILR1/ILL families. Finally, we found that the cis-OPDA conjugative pathway already functions in non-vascular plants and gymnosperms. Thus, one level of regulation by which plants modulate cis-OPDA homeostasis is the synthesis and hydrolysis of OPDA-amino acid conjugates, which temporarily store cis-OPDA in stress responses.

Mapping the chemical space of compounds to chemical structures remains a challenge in metabolomics. Despite the advancements in untargeted liquid chromatography-mass spectrometry (LC-MS) to achieve a high-throughput profile of metabolites from complex biological resources, only a small fraction of these metabolites can be annotated with confidence. Many novel computational methods and tools have been developed to enable chemical structure annotation to known and unknown compounds such as in silico generated spectra and molecular networking. Here, we present an automated and reproducible Metabolome Annotation Workflow (MAW) for untargeted metabolomics data to further facilitate and automate the complex annotation by combining tandem mass spectrometry (MS2) input data pre-processing, spectral and compound database matching with computational classification, and in silico annotation. MAW takes the LC-MS2 spectra as input and generates a list of putative candidates from spectral and compound databases. The databases are integrated via the R package Spectra and the metabolite annotation tool SIRIUS as part of the R segment of the workflow (MAW-R). The final candidate selection is performed using the cheminformatics tool RDKit in the Python segment (MAW-Py). Furthermore, each feature is assigned a chemical structure and can be imported to a chemical structure similarity network. MAW is following the FAIR (Findable, Accessible, Interoperable, Reusable) principles and has been made available as the docker images, maw-r and maw-py. The source code and documentation are available on GitHub. The performance of MAW is evaluated on two case studies. We found that MAW can improve candidate ranking by integrating spectral databases with annotation tools like SIRIUS which contributes to an efficient candidate selection procedure. The results from MAW are also reproducible and traceable, compliant with the FAIR guidelines. Taken together, MAW could greatly facilitate automated metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery.