Bioinformatic tools support decision-making in plant disease management

Fungal Disease Tolerance with a Focus on Wheat: A Review

In this paper, an extensive review of the literature is provided examining the significance of tolerance to fungal diseases in wheat amidst the escalating global demand for wheat and threats from environmental shifts and pathogen movements. The current comprehensive reliance on agrochemicals for disease management poses risks to food safety and the environment, exacerbated by the emergence of fungicide resistance. While resistance traits in wheat can offer some protection, these traits do not guarantee the complete absence of losses during periods of vigorous or moderate disease development. Furthermore, the introduction of individual resistance genes into wheat monoculture exerts selection pressure on pathogen populations. These disadvantages can be addressed or at least mitigated with the cultivation of tolerant varieties of wheat. Research in this area has shown that certain wheat varieties, susceptible to severe infectious diseases, are still capable of achieving high yields. Through the analysis of the existing literature, this paper explores the manifestations and quantification of tolerance in wheat, discussing its implications for integrated disease management and breeding strategies. Additionally, this paper addresses the ecological and evolutionary aspects of tolerance in the pathogen-plant host system, emphasizing its potential to enhance wheat productivity and sustainability.

POOE: predicting oomycete effectors based on a pre-trained large protein language model

Oomycetes are fungus-like eukaryotic microorganisms which can cause catastrophic diseases in many plants. Successful infection of oomycetes depends highly on their effector proteins that are secreted into plant cells to subvert plant immunity. Thus, systematic identification of effectors from the oomycete proteomes remains an initial but crucial step in understanding plant-pathogen relationships. However, the number of experimentally identified oomycete effectors is still limited. Currently, only a few bioinformatics predictors exist to detect potential effectors, and their prediction performance needs to be improved. Here, we used the sequence embeddings from a pre-trained large protein language model (ProtTrans) as input and developed a support vector machine-based method called POOE for predicting oomycete effectors. POOE could achieve a highly accurate performance with an area under the precision-recall curve of 0.804 (area under the receiver operating characteristic curve = 0.893, accuracy = 0.874, precision = 0.777, recall = 0.684, and specificity = 0.936) in the fivefold cross-validation, considerably outperforming various combinations of popular machine learning algorithms and other commonly used sequence encoding schemes. A similar prediction performance was also observed in the independent test. Compared with the existing oomycete effector prediction methods, POOE provided very competitive and promising performance, suggesting that ProtTrans effectively captures rich protein semantic information and dramatically improves the prediction task. We anticipate that POOE can accelerate the identification of oomycete effectors and provide new hints to systematically understand the functional roles of effectors in plant-pathogen interactions. The web server of POOE is freely accessible at http://zzdlab.com/pooe/index.php. The corresponding source codes and data sets are also available at https://github.com/zzdlabzm/POOE.IMPORTANCEIn this work, we use the sequence representations from a pre-trained large protein language model (ProtTrans) as input and develop a Support Vector Machine-based method called POOE for predicting oomycete effectors. POOE could achieve a highly accurate performance in the independent test set, considerably outperforming existing oomycete effector prediction methods. We expect that this new bioinformatics tool will accelerate the identification of oomycete effectors and further guide the experimental efforts to interrogate the functional roles of effectors in plant-pathogen interaction.

AraPathogen2.0: An Improved Prediction of Plant-Pathogen Protein-Protein Interactions Empowered by the Natural Language Processing Technique

Plant-pathogen protein-protein interactions (PPIs) play crucial roles in the arm race between plants and pathogens. Therefore, the identification of these interspecies PPIs is very important for the mechanistic understanding of pathogen infection and plant immunity. Computational prediction methods can complement experimental efforts, but their predictive performance still needs to be improved. Motivated by the rapid development of natural language processing and its successful applications in the field of protein bioinformatics, here we present an improved XGBoost-based plant-pathogen PPI predictor (i.e., AraPathogen2.0), in which sequence encodings from the pretrained protein language model ESM2 and Arabidopsis PPI network-related node representations from the graph embedding technique struc2vec are used as input. Stringent benchmark experiments showed that AraPathogen2.0 could achieve a better performance than its precedent version, especially for processing the test data set with novel proteins unseen in the training data.

Integrated Molecular and Bioinformatics Approaches for Disease-Related Genes in Plants

Modern plant pathology relies on bioinformatics approaches to create novel plant disease diagnostic tools. In recent years, a significant amount of biological data has been generated due to rapid developments in genomics and molecular biology techniques. The progress in the sequencing of agriculturally important crops has made it possible to develop a better understanding of plant-pathogen interactions and plant resistance. The availability of host-pathogen genome data offers effective assistance in retrieving, annotating, analyzing, and identifying the functional aspects for characterization at the gene and genome levels. Physical mapping facilitates the identification and isolation of several candidate resistance (R) genes from diverse plant species. A large number of genetic variations, such as disease-causing mutations in the genome, have been identified and characterized using bioinformatics tools, and these desirable mutations were exploited to develop disease resistance. Moreover, crop genome editing tools, namely the CRISPR (clustered regulatory interspaced short palindromic repeats)/Cas9 (CRISPR-associated) system, offer novel and efficient strategies for developing durable resistance. This review paper describes some aspects concerning the databases, tools, and techniques used to characterize resistance (R) genes for plant disease management.

Discovery of Barakacin and Its Derivatives as Novel Antiviral and Fungicidal Agents

Pesticides are essential for the development of agriculture. It is urgent to develop green, safe and efficient pesticides. Bisindole alkaloids have unique and concise structures and broad biological activities, which make them an important leading skeleton in the creation of new pesticides. In this work, we synthesized bisindole alkaloid barakacin in a simple seven-step process, and simultaneously designed and synthesized a series of its derivatives. Biological activity research indicated that most of these compounds displayed good antiviral activities against tobacco mosaic virus (TMV). Among them, compound 14b exerted a superior inhibitory effect in comparison to commercially available antiviral agent ribavirin, and could be expected to become a novel antiviral candidate. Molecular biology experiments and molecular docking research found that the potential target of compound 14b was TMV coat protein (CP). These compounds also showed broad-spectrum anti-fungal activities against seven kinds of plant fungi.

Root-Associated Antagonistic Pseudomonas spp. Contribute to Soil Suppressiveness against Banana Fusarium Wilt Disease of Banana

Members of the microbiotas colonizing the plant endophytic compartments and the surrounding bulk and rhizosphere soil play an important role in determining plant health. However, the relative contributions of the soil and endophytic microbiomes and their mechanistic roles in achieving disease suppression remain elusive. To disentangle the relative importance of the different microbiomes in the various plant compartments in inhibiting pathogen infection, we conducted a field experiment to track changes in the composition of microbial communities in bulk and rhizosphere soil and of root endophytes and leaf endosphere collected from bananas planted on Fusarium-infested orchards in disease-suppressive and disease-conducive soils. We found that the rhizosphere and roots were the two dominant plant parts whose bacterial communities contributed to pathogen suppression. We further observed that Pseudomonas was potentially a key organism acting as a pathogen antagonist, as illustrated by microbial community composition and network analysis. Subsequently, culturable pathogen-antagonistic Pseudomonas strains were isolated, and their potential suppressive functions or possible antibiosis in terms of auxin or siderophore synthesis and phosphate solubilization were screened to analyze the mode of action of candidate disease-suppressive Pseudomonas strains. In a follow-up in vivo and greenhouse experiment, we revealed that microbial consortia of culturable Pseudomonas strains P8 and S25 (or S36), isolated from banana plantlet rhizosphere and roots, respectively, significantly suppressed the survival of pathogens in the soil, manipulated the soil microbiome, and stimulated indigenous beneficial microbes. Overall, our study demonstrated that root-associated microbiomes, especially the antagonistic Pseudomonas sp. components, contribute markedly to soil suppression of banana Fusarium wilt. IMPORTANCE Soil suppression of Fusarium wilt disease has been proven to be linked with the local microbial community. However, the contribution of endophytic microbes to disease suppression in wilt-suppressive soils remains unclear. Moreover, the key microbes involving in Fusarium wilt-suppressive soils and in the endophytic populations have not been fully characterized. In this study, we demonstrate that root-associated microbes play vitally important roles in disease suppression. Root-associated Pseudomonas consortia were recognized as a key component in inhibiting pathogen abundance associated with the host banana plants. This finding is crucial to developing alternate strategies for soilborne disease management by harnessing the plant microbiome.

Protein Kinases as Potential Targets Contribute to the Development of Agrochemicals

Using agrochemicals against pest insects, fungi, and weeds plays a major part in maintaining and improving crop yields, which helps to solve the issue of food security. Due to the limited targets and resistance of agrochemicals, protein kinases are regarded as attractive potential targets to develop new agrochemicals. Recently, a lot of investigations have shown the extension of agrochemicals by targeting protein kinases, implying an increasing concern for this kind of method. However, few people have summarized and discussed the targetability of protein kinases contributing to the development of agrochemicals. In this work, we introduce the research on protein kinases as potential targets used in crop protection and discuss the prospects of protein kinases in the field of agrochemical development. This study may not only provide guidance for the contribution of protein kinases to the development of agrochemicals but also help nonprofessionals such as students learn and understand the role of protein kinases quickly.

Bioinformatics in Plant Breeding and Research on Disease Resistance

In the context of plant breeding, bioinformatics can empower genetic and genomic selection to determine the optimal combination of genotypes that will produce a desired phenotype and help expedite the isolation of these new varieties. Bioinformatics is also instrumental in collecting and processing plant phenotypes, which facilitates plant breeding. Robots that use automated and digital technologies to collect and analyze different types of information to monitor the environment in which plants grow, analyze the environmental stresses they face, and promptly optimize suboptimal and adverse growth conditions accordingly, have helped plant research and saved human resources. In this paper, we describe the use of various bioinformatics databases and algorithms and explore their potential applications in plant breeding and for research on plant disease resistance.

Identification of plant leaf diseases by deep learning based on channel attention and channel pruning

Plant diseases cause significant economic losses and food security in agriculture each year, with the critical path to reducing losses being accurate identification and timely diagnosis of plant diseases. Currently, deep neural networks have been extensively applied in plant disease identification, but such approaches still suffer from low identification accuracy and numerous parameters. Hence, this paper proposes a model combining channel attention and channel pruning called CACPNET, suitable for disease identification of common species. The channel attention mechanism adopts a local cross-channel strategy without dimensionality reduction, which is inserted into a ResNet-18-based model that combines global average pooling with global max pooling to effectively improve the features' extracting ability of plant leaf diseases. Based on the model's optimum feature extraction condition, unimportant channels are removed to reduce the model's parameters and complexity via the L1-norm channel weight and local compression ratio. The accuracy of CACPNET on the public dataset PlantVillage reaches 99.7% and achieves 97.7% on the local peanut leaf disease dataset. Compared with the base ResNet-18 model, the floating point operations (FLOPs) decreased by 30.35%, the parameters by 57.97%, the model size by 57.85%, and the GPU RAM requirements by 8.3%. Additionally, CACPNET outperforms current models considering inference time and throughput, reaching 22.8 ms/frame and 75.5 frames/s, respectively. The results outline that CACPNET is appealing for deployment on edge devices to improve the efficiency of precision agriculture in plant disease detection.

Marine Sesquiterpenes for Plant Protection: Discovery of Laurene Sesquiterpenes and Their Derivatives as Novel Antiviral and Antiphytopathogenic Fungal Agents

The unreasonable use or long-term use of a single variety of pesticide has led to drug resistance and made the pesticides ineffective. Therefore, the creation of new, efficient, and low-risk pesticides is imminent. Marine natural products play a vital role in serving as new lead compounds. In this work, we realized the efficient preparation of nine marine sesquiterpenes with the Stork-Danheiser reaction as the key step and designed and synthesized a series of their derivatives. The antiviral activity and antifungal activity research showed that these compounds exhibited good to excellent biological activities. Compounds 7b and 8e displayed significantly higher antiviral activities against tobacco mosaic virus (TMV) than ribavirin and could be used as new antiviral candidates. The antiviral mode of action research indicated that compound 8e inhibited the formation of the 20S protein disk by acting on the TMV coat protein and therefore inhibited the assembly of TMV particles. This work provides a new idea for the development of new pesticides based on marine sesquiterpenes.

Shotgun Proteomics as a Powerful Tool for the Study of the Proteomes of Plants, Their Pathogens, and Plant-Pathogen Interactions

The interaction between plants and pathogenic microorganisms is a multifaceted process mediated by both plant- and pathogen-derived molecules, including proteins, metabolites, and lipids. Large-scale proteome analysis can quantify the dynamics of proteins, biological pathways, and posttranslational modifications (PTMs) involved in the plant-pathogen interaction. Mass spectrometry (MS)-based proteomics has become the preferred method for characterizing proteins at the proteome and sub-proteome (e.g., the phosphoproteome) levels. MS-based proteomics can reveal changes in the quantitative state of a proteome and provide a foundation for understanding the mechanisms involved in plant-pathogen interactions. This review is intended as a primer for biologists that may be unfamiliar with the diverse range of methodology for MS-based shotgun proteomics, with a focus on techniques that have been used to investigate plant-pathogen interactions. We provide a summary of the essential steps required for shotgun proteomic studies of plants, pathogens and plant-pathogen interactions, including methods for protein digestion, identification, separation, and quantification. Finally, we discuss how protein PTMs may directly participate in the interaction between a pathogen and its host plant.

Technological Breakthrough for the Afforestation of Populus euphratica in the Mu Us Desert in China

The Mu Us Desert (MUD) is one of the four largest sandy lands in China. On 22 April 2020, the Shaanxi Forestry Bureau announced that the desertification land control rate in Yulin reached 93.24%, which means that the Mu Us Desert was about to “disappear” from the territory of Shaanxi. However, the problem of biological diversity, mostly for Pinus sylvestris and shrubs in the Mu Us Desert, remains serious. In order to consolidate the current forest conservation efforts, Populus euphratica has been considered an ideal candidate since the 1950s. However, the low survival rate and conservation rate of Populus euphratica in the MUD led us to perform further large-scale introduction for over 70 years. In this study, by using root control seedling technology, the survival and the conservation rate of Populus euphratica were increased to more than 90%. This study makes possible the introduction of Populus euphratica in the MUD, and the successful introduction of Populus euphratica will provide a new barrier for forest ecosystem stability in the desertification control project in the Yulin area.