Role of the acquisition of a type 3 secretion system in the emergence of novel pathogenic strains of Xanthomonas

Endophyte mediated biocontrol mechanisms of phytopathogens in agriculture

The global human population is growing and demand for food is increasing. Global agriculture faces numerous challenges, including excessive application of synthetic pesticides, emergence of herbicide-and pesticide-resistant pathogenic microbes, and more frequent natural disasters associated with global warming. Searches for valuable endophytes have increased, with the aim of making agriculture more sustainable and environmentally friendly. Endophytic microbes are known to have a variety of beneficial effects on plants. They can effectively transfer nutrients from the soil into plants, promote plant growth and development, increase disease resistance, increase stress tolerance, prevent herbivore feeding, reduce the virulence of pathogens, and inhibit the growth of rival plant species. Endophytic microbes can considerably minimize the need for agrochemicals, such as fertilizers, fungicides, bactericides, insecticides, and herbicides in the cultivation of crop plants. This review summarizes current knowledge on the roles of endophytes focusing on their mechanisms of disease control against phytopathogens through the secretion of antimicrobial substances and volatile organic compounds, and the induction of systemic resistance in plants. Additionally, the beneficial roles of these endophytes and their metabolites in the control of postharvest diseases in plants have been summarized.

Genetic and Functional Diversity Help Explain Pathogenic, Weakly Pathogenic, and Commensal Lifestyles in the Genus Xanthomonas

The genus Xanthomonas has been primarily studied for pathogenic interactions with plants. However, besides host and tissue-specific pathogenic strains, this genus also comprises nonpathogenic strains isolated from a broad range of hosts, sometimes in association with pathogenic strains, and other environments, including rainwater. Based on their incapacity or limited capacity to cause symptoms on the host of isolation, nonpathogenic xanthomonads can be further characterized as commensal and weakly pathogenic. This study aimed to understand the diversity and evolution of nonpathogenic xanthomonads compared to their pathogenic counterparts based on their cooccurrence and phylogenetic relationship and to identify genomic traits that form the basis of a life history framework that groups xanthomonads by ecological strategies. We sequenced genomes of 83 strains spanning the genus phylogeny and identified eight novel species, indicating unexplored diversity. While some nonpathogenic species have experienced a recent loss of a type III secretion system, specifically the hrp2 cluster, we observed an apparent lack of association of the hrp2 cluster with lifestyles of diverse species. We performed association analysis on a large data set of 337 Xanthomonas strains to explain how xanthomonads may have established association with the plants across the continuum of lifestyles from commensals to weak pathogens to pathogens. Presence of distinct transcriptional regulators, distinct nutrient utilization and assimilation genes, transcriptional regulators, and chemotaxis genes may explain lifestyle-specific adaptations of xanthomonads.

Transient expression of anti-HrpE scFv antibody reduces the hypersensitive response in non-host plant against bacterial phytopathogen Xanthomonas citri subsp. citri

Citrus canker is a bacterial disease caused by Xanthomonas citri subsp. citri (Xcc) that affects the citrus industry worldwide. Hrp pili subunits (HrpE), an essential component of Type III secretion system (T3SS) bacteria, play a crucial role in the pathogenesis of Xcc by transporting effector proteins into the host cell and causing canker symptoms. Therefore, development of antibodies that block HrpE can suppress disease progression. In this study, a specific scFv detecting HrpE was developed using phage display technique and characterized using sequencing, ELISA, Western blotting, and molecular docking. In addition, a plant expression vector of pCAMBIA-scFvH6 was constructed and agroinfiltrated into Nicotiana tabacum cv. Samson leaves. The hypersensitive response (HR) in the leaves of transformed and non-transformed plants was evaluated by inoculating leaves with Xcc. After three rounds of biopanning of the phage library, a specific human scFv antibody, named scFvH6, was identified that showed high binding activity against HrpE in ELISA and Western blotting. Molecular docking results showed that five intermolecular hydrogen bonds are involved in HrpE-scFvH6 interaction, confirming the specificity and high binding activity of scFvH6. Successful transient expression of pCAMBIA-scFvH6 in tobacco leaves was verified using immunoassay tests. The binding activity of plant-produced scFvH6 to detect HrpE in Western blotting and ELISA was similar to that of bacterial-produced scFvH6 antibody. Interestingly, tobacco plants expressing scFvH6 showed a remarkable reduction in HR induced by Xcc compared with control plants, so that incidence of necrotic lesions was significantly higher in non-transformed controls (≥ 1.5 lesions/cm2) than in the plants producing scFvH6 (≤ 0.5 lesions/cm2) after infiltration with Xcc inoculum. Our results revealed that the expression of scFvH6 in tobacco leaves can confer resistance to Xcc, indicating that this approach could be considered to provide resistance to citrus bacterial canker disease.

The role of the type VI secretion system in the stress resistance of plant-associated bacteria

The type VI secretion system (T6SS) is a powerful bacterial molecular weapon that can inject effector proteins into prokaryotic or eukaryotic cells, thereby participating in the competition between bacteria and improving bacterial environmental adaptability. Although most current studies of the T6SS have focused on animal bacteria, this system is also significant for the adaptation of plant-associated bacteria. This paper briefly introduces the structure and biological functions of the T6SS. We summarize the role of plant-associated bacterial T6SS in adaptability to host plants and the external environment, including resistance to biotic stresses such as host defenses and competition from other bacteria. We review the role of the T6SS in response to abiotic factors such as acid stress, oxidation stress, and osmotic stress. This review provides an important reference for exploring the functions of the T6SS in plant-associated bacteria. In addition, characterizing these anti-stress functions of the T6SS may provide new pathways toward eliminating plant pathogens and controlling agricultural losses.

Plant disease symptom segmentation in chlorophyll fluorescence imaging with a synthetic dataset

Despite the wide use of computer vision methods in plant health monitoring, little attention is paid to segmenting the diseased leaf area at its early stages. It can be explained by the lack of datasets of plant images with annotated disease lesions. We propose a novel methodology to generate fluorescent images of diseased plants with an automated lesion annotation. We demonstrate that a U-Net model aiming to segment disease lesions on fluorescent images of plant leaves can be efficiently trained purely by a synthetically generated dataset. The trained model showed 0.793% recall and 0.723% average precision against an empirical fluorescent test dataset. Creating and using such synthetic data can be a powerful technique to facilitate the application of deep learning methods in precision crop protection. Moreover, our method of generating synthetic fluorescent images is a way to improve the generalization ability of deep learning models.

Cruciferous Weed Isolates of Xanthomonas campestris Yield Insight into Pathovar Genomic Relationships and Genetic Determinants of Host and Tissue Specificity

Pathovars of Xanthomonas campestris cause distinct diseases on different brassicaceous hosts. The genomic relationships among pathovars as well as the genetic determinants of host range and tissue specificity remain poorly understood despite decades of research. Here, leveraging advances in multiplexed long-read technology, we fully sequenced the genomes of a collection of X. campestris strains isolated from cruciferous crops and weeds in New York and California as well as strains from global collections, to investigate pathovar relationships and candidate genes for host- and tissue-specificity. Pathogenicity assays and genomic comparisons across this collection and publicly available X. campestris genomes revealed a correlation between pathovar and genomic relatedness and provide support for X. campestris pv. barbareae, the validity of which had been questioned. Linking strain host range with type III effector repertoires identified AvrAC (also 'XopAC') as a candidate host-range determinant, preventing infection of Matthiola incana, and this was confirmed experimentally. Furthermore, the presence of a copy of the cellobiosidase gene cbsA with coding sequence for a signal peptide was found to correlate with the ability to infect vascular tissues, in agreement with a previous study of diverse Xanthomonas species; however, heterologous expression in strains lacking the gene gave mixed results, indicating that factors in addition to cbsA influence tissue specificity of X. campestris pathovars. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains

BACKGROUND

The genus Xanthomonas has long been considered to consist predominantly of plant pathogens, but over the last decade there has been an increasing number of reports on non-pathogenic and endophytic members. As Xanthomonas species are prevalent pathogens on a wide variety of important crops around the world, there is a need to distinguish between these plant-associated phenotypes. To date a large number of Xanthomonas genomes have been sequenced, which enables the application of machine learning (ML) approaches on the genome content to predict this phenotype. Until now such approaches to the pathogenomics of Xanthomonas strains have been hampered by the fragmentation of information regarding pathogenicity of individual strains over many studies. Unification of this information into a single resource was therefore considered to be an essential step.

RESULTS

Mining of 39 papers considering both plant-associated phenotypes, allowed for a phenotypic classification of 578 Xanthomonas strains. For 65 plant-pathogenic and 53 non-pathogenic strains the corresponding genomes were available and de novo annotated for the presence of Pfam protein domains used as features to train and compare three ML classification algorithms; CART, Lasso and Random Forest.

CONCLUSION

The literature resource in combination with recursive feature extraction used in the ML classification algorithms provided further insights into the virulence enabling factors, but also highlighted domains linked to traits not present in pathogenic strains.

The role of epiphytic populations in pathogenesis of the genus Xanthomonas bacteria

Global climate warming and involvement of new regions with endemic populations of microorganisms in commercial seed production have led to an increase in the diversity of phytopathogenic bacteria that are affecting major crops, including the fruit trees. As a rule, emergence of new pathogens is associated with importation of infected seeds and planting material, cultivation of new species and varieties of plants, and expansion of agricultural trade with foreign countries. One of the leaders in diversity among phytopathogens is the genus Xanthomonas bacteria, affecting more than 400 plant species. Among the characteristic signs of xanthomonads is the high frequency of horizontal gene transfer both within the genus and between phylogenetically removed bacterial taxa – up to 25% of the genes are of this origin. In this paper, we consider another source of increasing the number of phytopathogenic species – by the epiphytic populations. These bacteria are the likely ancestral form of the phytopathogenic bacteria of the genus Xanthomonas.

A Computation Method Based on the Combination of Chlorophyll Fluorescence Parameters to Improve the Discrimination of Visually Similar Phenotypes Induced by Bacterial Virulence Factors

Phenotyping biotic stresses in plant-pathogen interactions studies is often hindered by phenotypes that can hardly be discriminated by visual assessment. Particularly, single gene mutants in virulence factors could lack visible phenotypes. Chlorophyll fluorescence (CF) imaging is a valuable tool to monitor plant-pathogen interactions. However, while numerous CF parameters can be measured, studies on plant-pathogen interactions often focus on a restricted number of parameters. It could result in limited abilities to discriminate visually similar phenotypes. In this study, we assess the ability of the combination of multiple CF parameters to improve the discrimination of such phenotypes. Such an approach could be of interest for screening and discriminating the impact of bacterial virulence factors without prior knowledge. A computation method was developed, based on the combination of multiple CF parameters, without any parameter selection. It involves histogram Bhattacharyya distance calculations and hierarchical clustering, with a normalization approach to take into account the inter-leaves and intra-phenotypes heterogeneities. To assess the efficiency of the method, two datasets were analyzed the same way. The first dataset featured single gene mutants of a Xanthomonas strain which differed only by their abilities to secrete bacterial virulence proteins. This dataset displayed expected phenotypes at 6 days post-inoculation and was used as ground truth dataset to setup the method. The efficiency of the computation method was demonstrated by the relevant discrimination of phenotypes at 3 days post-inoculation. A second dataset was composed of transient expression (agrotransformation) of Type 3 Effectors. This second dataset displayed phenotypes that cannot be discriminated by visual assessment and no prior knowledge can be made on the respective impact of each Type 3 Effectors on leaf tissues. Using the computation method resulted in clustering the leaf samples according to the Type 3 Effectors, thereby demonstrating an improvement of the discrimination of the visually similar phenotypes. The relevant discrimination of visually similar phenotypes induced by bacterial strains differing only by one virulence factor illustrated the importance of using a combination of CF parameters to monitor plant-pathogen interactions. It opens a perspective for the identification of specific signatures of biotic stresses.