AlphaFold-Multimer predicts cross-kingdom interactions at the plant-pathogen interface

The receptor binding mechanism of mouse sPLA2 group IIE

Secreted phospholipase A2s (sPLA2s) participate in physiological function by their enzyme and receptor binding activity. Muscle-type phospholipase A2 receptor (M-type PLA2R) is the sPLA2 binding protein with the highest affinity so far, and also inhibits the enzyme activity of sPLA2. There is species specificity and pH dependence for the binding of M-type PLA2R to sPLA2. Mouse sPLA2 Group IIE (mGIIE) has been verified to have a high affinity for mouse M-type PLA2R (M-type mPLA2R) at the nanomolar scale. For further exploration of the receptor binding mechanism of GIIE, in this study, we use Alphafold Multimer to generate complex models of mGIIE with the M-type mPLA2R ectodomain, wild-type CTLD5 domain of mPLA2R, and three CTLD5 mutants, respectively. mPLA2R-mGIIE models exhibit heterogeneous extended mPLA2R conformations with uncovered sPLA2-binding surface of CTLD5 domain. Complexed models of mGIIE with wild-type and mutated mCTLD5 further confirm that helix α1 of mCTLD5, especially essential residues F838 and W842, interact with the substrate pocket of mGIIE and thus inhibit its enzyme activity. Peptides from helix α1 of mCTLD5 are verified to inhibit the enzymatic activity of mGIIE. This AI-guided research would substantially accelerate our understanding of the functional study of GIIE, and provide the lead-peptide for the further inhibitor design of sPLA2.

The role of ALDHs in lipid peroxidation-related diseases

Lipid peroxidation presents the oxidative degradation of polyunsaturated fatty acids lincited by reactive species. Excessive accumulation of lipid peroxidation byproducts, including 4-hydroxy-2-nonenal (4-HNE) and malondialdehyde (MDA), causes protein dysfunction and various illnesses. Aldehyde dehydrogenases (ALDHs) catalyze the metabolism of both endogenous and exogenous aldehydes. These enzymes participate in detoxification and intermediary metabolism. Contemporary research has affirmed the involvement of both enzymatic and non-enzymatic pathways of ALDHs in modulating the evolution of diseases associated with lipid peroxidation. This review provides an overview of the biological functions and clinical implications concerning the enzymatic and non-enzymatic pathways of ALDHs in diseases related to lipid peroxidation, such as, non-alcoholic fatty liver disease (NAFLD), atherosclerosis, and type 2 diabetes (T2DM). Furthermore, the activators or inhibitors of ALDHs represent a promising therapeutic strategy for lipid peroxidation-related diseases.

Protein Dynamics in Plant Immunity: Insights into Plant-Pest Interactions

All living organisms regulate biological activities by proteins. When plants encounter pest invasions, the delicate balance between protein synthesis and degradation becomes even more pivotal for mounting an effective defense response. In this review, we summarize the mechanisms by which plants regulate their proteins to effectively coordinate immune responses during plant-pest interactions. Additionally, we discuss the main pathway proteins through which pest effectors manipulate host protein homeostasis in plants to facilitate their infestation. Understanding these processes at the molecular level not only deepens our knowledge of plant immunity but also holds the potential to inform strategies for developing pest-resistant crops, contributing to sustainable and resilient agriculture.

Prediction of protein interactions between pine and pine wood nematode using deep learning and multi-dimensional feature fusion

Pine Wilt Disease (PWD) is a devastating forest disease that has a serious impact on ecological balance ecological. Since the identification of plant-pathogen protein interactions (PPIs) is a critical step in understanding the pathogenic system of the pine wilt disease, this study proposes a Multi-feature Fusion Graph Attention Convolution (MFGAC-PPI) for predicting plant-pathogen PPIs based on deep learning. Compared with methods based on single-feature information, MFGAC-PPI obtains more 3D characterization information by utilizing AlphaFold and combining protein sequence features to extract multi-dimensional features via Transform with improved GCN. The performance of MFGAC-PPI was compared with the current representative methods of sequence-based, structure-based and hybrid characterization, demonstrating its superiority across all metrics. The experiments showed that learning multi-dimensional feature information effectively improved the ability of MFGAC-PPI in plant and pathogen PPI prediction tasks. Meanwhile, a pine wilt disease PPI network consisting of 2,688 interacting protein pairs was constructed based on MFGAC-PPI, which made it possible to systematically discover new disease resistance genes in pine trees and promoted the understanding of plant-pathogen interactions.

Novel structural insights at the extracellular plant-pathogen interface

Plant pathogens represent a critical threat to global agriculture and food security, particularly under the pressures of climate change and reduced agrochemical use. Most plant pathogens initially colonize the extracellular space or apoplast and understanding the host-pathogen interactions that occur here is vital for engineering sustainable disease resistance in crops. Structural biology has played important roles in elucidating molecular mechanisms underpinning plant-pathogen interactions but only few studies have reported structures of extracellular complexes. This article highlights these resolved extracellular complexes by describing the insights gained from the solved structures of complexes consisting of CERK1-chitin, FLS2-flg22-BAK1, RXEG1-XEG1-BAK1 and PGIP2-FpPG. Finally, we discuss the potential of AI-based structure prediction platforms like AlphaFold as an alternative hypothesis generator to rapidly advance our molecular understanding of plant pathology and develop novel strategies to increase crop resilience against disease.

Extracellular proteases from microbial plant pathogens as virulence factors

Plant pathogens deploy specialized proteins to aid disease progression, some of these proteins function in the apoplast and constitute proteases. Extracellular virulence proteases have been described to play roles in plant cell wall manipulation and immune evasion. In this review, we discuss the evidence for the hypothesized virulence functions of bacterial, fungal, and oomycete extracellular proteases. We highlight the contrast between the low number of elucidated functions for these proteins and the size of extracellular protease repertoires among pathogens. Finally, we suggest that the refinement of in planta 'omics' approaches, combined with recent advances in modeling and mechanism-based methods for trapping substrates finally make it possible to address this knowledge gap.

Plant pattern recognition receptors: from evolutionary insight to engineering

The plant immune system relies on germline-encoded pattern recognition receptors (PRRs) that sense foreign and plant-derived molecular patterns, and signal health threats. Genomic and pangenomic data sets provide valuable insights into the evolution of PRRs and their molecular triggers, which is furthering our understanding of plant-pathogen co-evolution and convergent evolution. Moreover, in silico and in vivo methods of PRR identification have accelerated the characterization of receptor-ligand complexes, and advances in protein structure prediction algorithms are revealing novel PRR sensor functions. Harnessing these recent advances to engineer PRRs presents an opportunity to enhance plant disease resistance against a broad spectrum of pathogens, enabling more sustainable agricultural practices. This Review summarizes both established and innovative approaches to leverage genomic data and translate resulting evolutionary insights into engineering PRR recognition specificities.

Extracellular plant subtilases dampen cold-shock peptide elicitor levels

Recognizing pathogen-associated molecular patterns on the cell surface is crucial for plant immunity. The proteinaceous nature of many of these patterns suggests that secreted proteases play important roles in their formation and stability. Here we demonstrate that the apoplastic subtilase SBT5.2a inactivates the immunogenicity of cold-shock proteins (CSPs) of the bacterial plant pathogen Pseudomonas syringae by cleaving within the immunogenic csp22 epitope. Consequently, mutant plants lacking SBT5.2a activity retain higher levels of csp22, leading to enhanced immune responses and reduced pathogen growth. SBT5.2 sensitivity is influenced by sequence variation surrounding the cleavage site and probably extends to CSPs from other bacterial species. These findings suggest that variations in csp22 stability among bacterial pathogens are a crucial factor in plant-bacteria interactions and that pathogens exploit plant proteases to avoid pattern recognition.

MHIPM: Accurate Prediction of Microbe-Host Interactions Using Multiview Features from a Heterogeneous Microbial Network

Current studies have demonstrated that microbe-host interactions (MHIs) play important roles in human public health. Therefore, identifying the interactions between microbes and hosts is beneficial to understanding the role of the microbiome and their underlying mechanisms. However, traditional wet-lab experimental approaches are insufficient for large-scale exploration of candidate microbes, as they are costly, laborious, and time-consuming. Thus, it is critical to prioritize microbe-interacting hosts by computational approaches for further biological experimental validation. In this work, we proposed a novel deep learning-based method called MHIPM, to predict MHIs by utilizing multisource biological information. Specifically, we first constructed a heterogeneous microbial network that consisted of human proteins, viruses, bacteriophages (phages), and pathogenic bacteria. Next, we used one of the largest protein language models, ESM-2, and a document embedding model, doc2vec, combined with a self-attention mechanism to extract the interview features from protein sequences. Then, an inductive learning-based model, GraphSAGE, was used to capture the intraview features from the heterogeneous network. Experimental results on three prediction tasks indicated that the MHIPM model consistently achieved better performance than seven baseline algorithms and its four variants. In addition, case studies and molecular docking experiments for two human proteins further confirmed the effectiveness of our model. In conclusion, MHIPM is an efficient and robust method in predicting MHIs and provides plausible candidate microbes for biological experiments. MHIPM is available at https://github.com/JIENWU/MHIPM.

Tumor-Associated Myeloid Cells Selective Delivery of a Therapeutic Tumor Nano-Vaccine for Overcoming Immune Barriers for Effective and Long-Term Cancer Immunotherapy

Therapeutic cancer vaccines have the potential to induce regression of established tumors, eradicate microscopic residual lesions, and prevent metastasis and recurrence, but their efficacy is limited by the low antigenicity of soluble antigens and the immunosuppressive tumor-associated macrophages (TAMs) that promote tumor growth. In this study, a novel strategy is reported for overcoming these defenses: a dual-targeting nano-vaccine (NV) based on hepatitis B core antigen (HBcAg) derived virus-like particles (VLPs), N-M2T-gp100 HBc NV, equipped with both SIGNR+ dendritic cells (DCs)/TAMs-targeting ability and high-density display of tumor-associated antigen (TAA). N-M2T-gp100 HBc NVs-based immunotherapy has demonstrated an optimal interaction between tumor-associated antigens (TAAs) and the immune composition of the tumor microenvironment. In a melanoma model, N-M2T-gp100 HBc VLPs significantly reducing in situ and abscopal tumor growth, and provide long-term immune protection. This remarkable anti-tumor effect is achieved by efficiently boosting of T cells and repolarizing of M2-like TAMs. This work opens exciting avenues for the development of personalized tumor vaccines targeting not just melanoma but potentially a broad range of cancer types based on functionalized VLPs.

Using AlphaFold Multimer to discover interkingdom protein-protein interactions

Structural prediction by artificial intelligence can be powerful new instruments to discover novel protein-protein interactions, but the community still grapples with the implementation, opportunities and limitations. Here, we discuss and re-analyse our in silico screen for novel pathogen-secreted inhibitors of immune hydrolases to illustrate the power and limitations of structural predictions. We discuss strategies of curating sequences, including controls, and reusing sequence alignments and highlight important limitations caused by different platforms, sequence depth and computing times. We hope these experiences will support similar interactomic screens by the research community.

Trans-crop applications of atypical R genes for multipathogen resistance

Genetic resistance to plant diseases is essential for global food security. Significant progress has been achieved for plant disease-resistance (R) genes comprising nucleotide-binding domain, leucine-rich repeat-containing receptors (NLRs), and membrane-localized receptor-like kinases or proteins (RLKs/RLPs), which we refer to as typical R genes. However, there is a knowledge gap in how non-receptor-type or atypical R genes contribute to plant immunity. Here, we summarize resources and technologies facilitating the study of atypical R genes, examine diverse atypical R proteins for broad-spectrum resistance, and outline potential approaches for trans-crop applications of atypical R genes. Studies of atypical R genes are important for a holistic understanding of plant immunity and the development of novel strategies in disease control and crop improvement.

Unlocking protein-protein interactions in plants: a comprehensive review of established and emerging techniques

Protein-protein interactions orchestrate plant development and serve as crucial elements for cellular and environmental communication. Understanding these interactions offers a gateway to unravel complex protein networks that will allow a better understanding of nature. Methods for the characterization of protein-protein interactions have been around over 30 years, yet the complexity of some of these interactions has fueled the development of new techniques that provide a better understanding of the underlying dynamics. In many cases, the application of these techniques is limited by the nature of the available sample. While some methods require an in vivo set-up, others solely depend on protein sequences to study protein-protein interactions via an in silico set-up. The vast number of techniques available to date calls for a way to select the appropriate tools for the study of specific interactions. Here, we classify widely spread tools and new emerging techniques for the characterization of protein-protein interactions based on sample requirements while providing insights into the information that they can potentially deliver. We provide a comprehensive overview of commonly used techniques and elaborate on the most recent developments, showcasing their implementation in plant research.

The lowdown on breakdown: Open questions in plant proteolysis

Proteolysis, including post-translational proteolytic processing as well as protein degradation and amino acid recycling, is an essential component of the growth and development of living organisms. In this article, experts in plant proteolysis pose and discuss compelling open questions in their areas of research. Topics covered include the role of proteolysis in the cell cycle, DNA damage response, mitochondrial function, the generation of N-terminal signals (degrons) that mark many proteins for degradation (N-terminal acetylation, the Arg/N-degron pathway, and the chloroplast N-degron pathway), developmental and metabolic signaling (photomorphogenesis, abscisic acid and strigolactone signaling, sugar metabolism, and postharvest regulation), plant responses to environmental signals (endoplasmic-reticulum-associated degradation, chloroplast-associated degradation, drought tolerance, and the growth-defense trade-off), and the functional diversification of peptidases. We hope these thought-provoking discussions help to stimulate further research.

Bioengineering secreted proteases converts divergent Rcr3 orthologs and paralogs into extracellular immune co-receptors

Secreted immune proteases "Required for Cladosporium resistance-3" (Rcr3) and "Phytophthora-inhibited protease-1" (Pip1) of tomato (Solanum lycopersicum) are both inhibited by Avirulence-2 (Avr2) from the fungal plant pathogen Cladosporium fulvum. However, only Rcr3 acts as a decoy co-receptor that detects Avr2 in the presence of the Cf-2 immune receptor. Here, we identified crucial residues in tomato Rcr3 that are required for Cf-2-mediated signaling and bioengineered various proteases to trigger Avr2/Cf-2-dependent immunity. Despite substantial divergence in Rcr3 orthologs from eggplant (Solanum melongena) and tobacco (Nicotiana spp.), minimal alterations were sufficient to trigger Avr2/Cf-2-mediated immune signaling. By contrast, tomato Pip1 was bioengineered with 16 Rcr3-specific residues to initiate Avr2/Cf-2-triggered immune signaling. These residues cluster on one side of the protein 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.

Proteolysis in plant immunity

Compared with transcription and translation, protein degradation machineries can act faster and be targeted to different subcellular compartments, enabling immediate regulation of signaling events. It is therefore not surprising that proteolysis has been used extensively to control homeostasis of key regulators in different biological processes and pathways. Over the past decades, numerous studies have shown that proteolysis, where proteins are broken down to peptides or amino acids through ubiquitin-mediated degradation systems and proteases, is a key regulatory mechanism to control plant immunity output. Here, we briefly summarize the roles various proteases play during defence activation, focusing on recent findings. We also update the latest progress of ubiquitin-mediated degradation systems in modulating immunity by targeting plant membrane-localized pattern recognition receptors, intracellular nucleotide-binding domain leucine-rich repeat receptors, and downstream signaling components. Additionally, we highlight recent studies showcasing the importance of proteolysis in maintaining broad-spectrum resistance without obvious yield reduction, opening new directions for engineering elite crops that are resistant to a wide range of pathogens with high yield.

Recruitment of Cdc48 to chloroplasts by a UBX-domain protein in chloroplast-associated protein degradation

The translocon at the outer chloroplast membrane (TOC) is the gateway for chloroplast protein import and so is vital for photosynthetic establishment and plant growth. Chloroplast-associated protein degradation (CHLORAD) is a ubiquitin-dependent proteolytic system that regulates TOC. In CHLORAD, cytosolic Cdc48 provides motive force for the retrotranslocation of ubiquitinated TOC proteins to the cytosol but how Cdc48 is recruited is unknown. Here, we identify plant UBX-domain protein PUX10 as a component of the CHLORAD machinery. We show that PUX10 is an integral chloroplast outer membrane protein that projects UBX and ubiquitin-associated domains into the cytosol. It interacts with Cdc48 via its UBX domain, bringing it to the chloroplast surface, and with ubiquitinated TOC proteins via its ubiquitin-associated domain. Genetic analyses in Arabidopsis revealed a requirement for PUX10 during CHLORAD-mediated regulation of TOC function and plant development. Thus, PUX10 coordinates ubiquitination and retrotranslocation activities of CHLORAD to enable efficient TOC turnover.

Advancing plant biology through deep learning-powered natural language processing

The application of deep learning methods, specifically the utilization of Large Language Models (LLMs), in the field of plant biology holds significant promise for generating novel knowledge on plant cell systems. The LLM framework exhibits exceptional potential, particularly with the development of Protein Language Models (PLMs), allowing for in-depth analyses of nucleic acid and protein sequences. This analytical capacity facilitates the discernment of intricate patterns and relationships within biological data, encompassing multi-scale information within DNA or protein sequences. The contribution of PLMs extends beyond mere sequence patterns and structure--function recognition; it also supports advancements in genetic improvements for agriculture. The integration of deep learning approaches into the domain of plant sciences offers opportunities for major breakthroughs in basic research across multi-scale plant traits. Consequently, the strategic application of deep learning methodologies, particularly leveraging the potential of LLMs, will undoubtedly play a pivotal role in advancing plant sciences, plant production, plant uses and propelling the trajectory toward sustainable agroecological and agro-food transitions.

Dual functionality of pathogenesis-related proteins: defensive role in plants versus immunosuppressive role in pathogens

Plants respond to pathogen exposure by activating the expression of a group of defense-related proteins known as Pathogenesis-Related (PR) proteins, initially discovered in the 1970s. These PR proteins are categorized into 17 distinct families, denoted as PR1-PR17. Predominantly secreted, most of these proteins execute their defensive roles within the apoplastic space. Several PR proteins possess well-defined enzymatic functions, such as β-glucanase (PR2), chitinases (PR3, 4, 8, 11), proteinase (PR7), or RNase (PR10). Enhanced resistance against pathogens is observed upon PR protein overexpression, while their downregulation renders plants more susceptible to pathogen infections. Many of these proteins exhibit antimicrobial activity in vitro, and due to their compact size, some are classified as antimicrobial peptides. Recent research has unveiled that phytopathogens, including nematodes, fungi, and phytophthora, employ analogous proteins to bolster their virulence and suppress plant immunity. This raises a fundamental question: how can these conserved proteins act as antimicrobial agents when produced by the host plant but simultaneously suppress plant immunity when generated by the pathogen? In this hypothesis, we investigate PR proteins produced by pathogens, which we term "PR-like proteins," and explore potential mechanisms by which this class of virulence factors operate. Preliminary data suggests that these proteins may form complexes with the host's own PR proteins, thereby interfering with their defense-related functions. This analysis sheds light on the intriguing interplay between plant and pathogen-derived PR-like proteins, providing fresh insights into the intricate mechanisms governing plant-pathogen interactions.

Integrating Large-Scale Protein Structure Prediction into Human Genetics Research

The last five years have seen impressive progress in deep learning models applied to protein research. Most notably, sequence-based structure predictions have seen transformative gains in the form of AlphaFold2 and related approaches. Millions of missense protein variants in the human population lack annotations, and these computational methods are a valuable means to prioritize variants for further analysis. Here, we review the recent progress in deep learning models applied to the prediction of protein structure and protein variants, with particular emphasis on their implications for human genetics and health. Improved prediction of protein structures facilitates annotations of the impact of variants on protein stability, protein-protein interaction interfaces, and small-molecule binding pockets. Moreover, it contributes to the study of host-pathogen interactions and the characterization of protein function. As genome sequencing in large cohorts becomes increasingly prevalent, we believe that better integration of state-of-the-art protein informatics technologies into human genetics research is of paramount importance.

Computational tools for plant genomics and breeding

Plant genomics and crop breeding are at the intersection of biotechnology and information technology. Driven by a combination of high-throughput sequencing, molecular biology and data science, great advances have been made in omics technologies at every step along the central dogma, especially in genome assembling, genome annotation, epigenomic profiling, and transcriptome profiling. These advances further revolutionized three directions of development. One is genetic dissection of complex traits in crops, along with genomic prediction and selection. The second is comparative genomics and evolution, which open up new opportunities to depict the evolutionary constraints of biological sequences for deleterious variant discovery. The third direction is the development of deep learning approaches for the rational design of biological sequences, especially proteins, for synthetic biology. All three directions of development serve as the foundation for a new era of crop breeding where agronomic traits are enhanced by genome design.

Review of AlphaFold 3: Transformative Advances in Drug Design and Therapeutics

Google DeepMind Technologies Limited (London, United Kingdom) recently released its new version of the biomolecular structure predictor artificial intelligence (AI) model named AlphaFold 3. Superior in accuracy and more powerful than its predecessor AlphaFold 2, this innovation has astonished the world with its capacity and speed. It takes humans years to determine the structure of various proteins and how the shape works with the receptors but AlphaFold 3 predicts the same structure in seconds. The version's utility is unimaginable in the field of drug discoveries, vaccines, enzymatic processes, and determining the rate and effect of different biological processes. AlphaFold 3 uses similar machine learning and deep learning models such as Gemini (Google DeepMind Technologies Limited). AlphaFold 3 has already established itself as a turning point in the field of computational biochemistry and drug development along with receptor modulation and biomolecular development. With the help of AlphaFold 3 and models similar to this, researchers will gain unparalleled insights into the structural dynamics of proteins and their interactions, opening up new avenues for scientists and doctors to exploit for the benefit of the patient. The integration of AI models like AlphaFold 3, bolstered by rigorous validation against high-standard research publications, is set to catalyze further innovations and offer a glimpse into the future of biomedicine.

AvrSr27 is a zinc-bound effector with a modular structure important for immune recognition

Effector proteins are central to the success of plant pathogens, while immunity in host plants is driven by receptor-mediated recognition of these effectors. Understanding the molecular details of effector-receptor interactions is key for the engineering of novel immune receptors. Here, we experimentally determined the crystal structure of the Puccinia graminis f. sp. tritici (Pgt) effector AvrSr27, which was not accurately predicted using AlphaFold2. We characterised the role of the conserved cysteine residues in AvrSr27 using in vitro biochemical assays and examined Sr27-mediated recognition using transient expression in Nicotiana spp. and wheat protoplasts. The AvrSr27 structure contains a novel β-strand rich modular fold consisting of two structurally similar domains that bind to Zn2+ ions. The N-terminal domain of AvrSr27 is sufficient for interaction with Sr27 and triggering cell death. We identified two Pgt proteins structurally related to AvrSr27 but with low sequence identity that can also associate with Sr27, albeit more weakly. Though only the full-length proteins, trigger Sr27-dependent cell death in transient expression systems. Collectively, our findings have important implications for utilising protein prediction platforms for effector proteins, and those embarking on bespoke engineering of immunity receptors as solutions to plant disease.

Zinc-finger (ZiF) fold secreted effectors form a functionally diverse family across lineages of the blast fungus Magnaporthe oryzae

Filamentous plant pathogens deliver effector proteins into host cells to suppress host defence responses and manipulate metabolic processes to support colonization. Understanding the evolution and molecular function of these effectors provides knowledge about pathogenesis and can suggest novel strategies to reduce damage caused by pathogens. However, effector proteins are highly variable, share weak sequence similarity and, although they can be grouped according to their structure, only a few structurally conserved effector families have been functionally characterized to date. Here, we demonstrate that Zinc-finger fold (ZiF) secreted proteins form a functionally diverse effector family in the blast fungus Magnaporthe oryzae. This family relies on the Zinc-finger motif for protein stability and is ubiquitously present in blast fungus lineages infecting 13 different host species, forming different effector tribes. Homologs of the canonical ZiF effector, AVR-Pii, from rice infecting isolates are present in multiple M. oryzae lineages. Wheat infecting strains of the fungus also possess an AVR-Pii like allele that binds host Exo70 proteins and activates the immune receptor Pii. Furthermore, ZiF tribes may vary in the proteins they bind to, indicating functional diversification and an intricate effector/host interactome. Altogether, we uncovered a new effector family with a common protein fold that has functionally diversified in lineages of M. oryzae. This work expands our understanding of the diversity of M. oryzae effectors, the molecular basis of plant pathogenesis and may ultimately facilitate the development of new sources for pathogen resistance.

Decoding the Arsenal: Protist Effectors and Their Impact on Photosynthetic Hosts

Interactions between various microbial pathogens including viruses, bacteria, fungi, oomycetes, and their plant hosts have traditionally been the focus of phytopathology. In recent years, a significant and growing interest in the study of eukaryotic microorganisms not classified among fungi or oomycetes has emerged. Many of these protists establish complex interactions with photosynthetic hosts, and understanding these interactions is crucial in understanding the dynamics of these parasites within traditional and emerging types of farming, including marine aquaculture. Many phytopathogenic protists are biotrophs with complex polyphasic life cycles, which makes them difficult or impossible to culture, a fact reflected in a wide gap in the availability of comprehensive genomic data when compared to fungal and oomycete plant pathogens. Furthermore, our ability to use available genomic resources for these protists is limited by the broad taxonomic distance that these organisms span, which makes comparisons with other genomic datasets difficult. The current rapid progress in genomics and computational tools for the prediction of protein functions and interactions is revolutionizing the landscape in plant pathology. This is also opening novel possibilities, specifically for a deeper understanding of protist effectors. Tools like AlphaFold2 enable structure-based function prediction of effector candidates with divergent protein sequences. In turn, this allows us to ask better biological questions and, coupled with innovative experimental strategies, will lead into a new era of effector research, especially for protists, to expand our knowledge on these elusive pathogens and their interactions with photosynthetic hosts. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Integration of protein sequence and protein-protein interaction data by hypergraph learning to identify novel protein complexes

Protein-protein interactions (PPIs) are the basis of many important biological processes, with protein complexes being the key forms implementing these interactions. Understanding protein complexes and their functions is critical for elucidating mechanisms of life processes, disease diagnosis and treatment and drug development. However, experimental methods for identifying protein complexes have many limitations. Therefore, it is necessary to use computational methods to predict protein complexes. Protein sequences can indicate the structure and biological functions of proteins, while also determining their binding abilities with other proteins, influencing the formation of protein complexes. Integrating these characteristics to predict protein complexes is very promising, but currently there is no effective framework that can utilize both protein sequence and PPI network topology for complex prediction. To address this challenge, we have developed HyperGraphComplex, a method based on hypergraph variational autoencoder that can capture expressive features from protein sequences without feature engineering, while also considering topological properties in PPI networks, to predict protein complexes. Experiment results demonstrated that HyperGraphComplex achieves satisfactory predictive performance when compared with state-of-art methods. Further bioinformatics analysis shows that the predicted protein complexes have similar attributes to known ones. Moreover, case studies corroborated the remarkable predictive capability of our model in identifying protein complexes, including 3 that were not only experimentally validated by recent studies but also exhibited high-confidence structural predictions from AlphaFold-Multimer. We believe that the HyperGraphComplex algorithm and our provided proteome-wide high-confidence protein complex prediction dataset will help elucidate how proteins regulate cellular processes in the form of complexes, and facilitate disease diagnosis and treatment and drug development. Source codes are available at https://github.com/LiDlab/HyperGraphComplex.

Rational management of the plant microbiome for the Second Green Revolution

The Green Revolution of the mid-20th century transformed agriculture worldwide and has resulted in environmental challenges. A new approach, the Second Green Revolution, seeks to enhance agricultural productivity while minimizing negative environmental impacts. Plant microbiomes play critical roles in plant growth and stress responses, and understanding plant-microbiome interactions is essential for developing sustainable agricultural practices that meet food security and safety challenges, which are among the United Nations Sustainable Development Goals. This review provides a comprehensive exploration of key deterministic processes crucial for developing microbiome management strategies, including the host effect, the facilitator effect, and microbe-microbe interactions. A hierarchical framework for plant microbiome modulation is proposed to bridge the gap between basic research and agricultural applications. This framework emphasizes three levels of modulation: single-strain, synthetic community, and in situ microbiome modulation. Overall, rational management of plant microbiomes has wide-ranging applications in agriculture and can potentially be a core technology for the Second Green Revolution.

Symbiont effector-guided mapping of proteins in plant networks to improve crop climate stress resilience: Symbiont effectors inform highly interconnected plant protein networks and provide an untapped resource for crop climate resilience strategies

There is an urgent need for novel protection strategies to sustainably secure crop production under changing climates. Studying microbial effectors, defined as microbe-derived proteins that alter signalling inside plant cells, has advanced our understanding of plant immunity and microbial plant colonisation strategies. Our understanding of effectors in the establishment and beneficial outcome of plant symbioses is less well known. Combining functional and comparative interaction assays uncovered specific symbiont effector targets in highly interconnected plant signalling networks and revealed the potential of effectors in beneficially modulating plant traits. The diverse functionality of symbiont effectors differs from the paradigmatic immuno-suppressive function of pathogen effectors. These effectors provide solutions for improving crop resilience against climate stress by their evolution-driven specification in host protein targeting and modulation. Symbiont effectors represent stringent tools not only to identify genetic targets for crop breeding, but to serve as applicable agents in crop management strategies under changing environments.

Fungal Cell Wall-Associated Effectors: Sensing, Integration, Suppression, and Protection

The cell wall (CW) of plant-interacting fungi, as the direct interface with host plants, plays a crucial role in fungal development. A number of secreted proteins are directly associated with the fungal CW, either through covalent or non-covalent interactions, and serve a range of important functions. In the context of plant-fungal interactions many are important for fungal development in the host environment and may therefore be considered fungal CW-associated effectors (CWAEs). Key CWAE functions include integrating chemical/physical signals to direct hyphal growth, interfering with plant immunity, and providing protection against plant defenses. In recent years, a diverse range of mechanisms have been reported that underpin their roles, with some CWAEs harboring conserved motifs or functional domains, while others are reported to have novel features. As such, the current understanding regarding fungal CWAEs is systematically presented here from the perspective of their biological functions in plant-fungal interactions. An overview of the fungal CW architecture and the mechanisms by which proteins are secreted, modified, and incorporated into the CW is first presented to provide context for their biological roles. Some CWAE functions are reported across a broad range of pathosystems or symbiotic/mutualistic associations. Prominent are the chitin interacting-effectors that facilitate fungal CW modification, protection, or suppression of host immune responses. However, several alternative functions are now reported and are presented and discussed. CWAEs can play diverse roles, some possibly unique to fungal lineages and others conserved across a broad range of plant-interacting fungi. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

The Cryptic Bacterial Microproteome

Microproteins encoded by small open reading frames (smORFs) comprise the "dark matter" of proteomes. Although functional microproteins were identified in diverse organisms from all three domains of life, bacterial smORFs remain poorly characterized. In this comprehensive study of intergenic smORFs (ismORFs, 15-70 codons) in 5,668 bacterial genomes of the family Enterobacteriaceae, we identified 67,297 clusters of ismORFs subject to purifying selection. The ismORFs mainly code for hydrophobic, potentially transmembrane, unstructured, or minimally structured microproteins. Using AlphaFold Multimer, we predicted interactions of some of the predicted microproteins encoded by transcribed ismORFs with proteins encoded by neighboring genes, revealing the potential of microproteins to regulate the activity of various proteins, particularly, under stress. We compiled a catalog of predicted microprotein families with different levels of evidence from synteny analysis, structure prediction, and transcription and translation data. This study offers a resource for investigation of biological functions of microproteins.

A foundational atlas of autism protein interactions reveals molecular convergence

Translating high-confidence (hc) autism spectrum disorder (ASD) genes into viable treatment targets remains elusive. We constructed a foundational protein-protein interaction (PPI) network in HEK293T cells involving 100 hcASD risk genes, revealing over 1,800 PPIs (87% novel). Interactors, expressed in the human brain and enriched for ASD but not schizophrenia genetic risk, converged on protein complexes involved in neurogenesis, tubulin biology, transcriptional regulation, and chromatin modification. A PPI map of 54 patient-derived missense variants identified differential physical interactions, and we leveraged AlphaFold-Multimer predictions to prioritize direct PPIs and specific variants for interrogation in Xenopus tropicalis and human forebrain organoids. A mutation in the transcription factor FOXP1 led to reconfiguration of DNA binding sites and altered development of deep cortical layer neurons in forebrain organoids. This work offers new insights into molecular mechanisms underlying ASD and describes a powerful platform to develop and test therapeutic strategies for many genetically-defined conditions.