Identification of host-microbe interaction factors in the genomes of soft rot-associated pathogens Dickeya dadantii 3937 and Pectobacterium carotovorum WPP14 with supervised machine learning

Protein Language Models Uncover Carbohydrate-Active Enzyme Function in Metagenomics

In metagenomics, the pool of uncharacterized microbial enzymes presents a challenge for functional annotation. Among these, carbohydrate-active enzymes (CAZymes) stand out due to their pivotal roles in various biological processes related to host health and nutrition. Here, we present CAZyLingua, the first tool that harnesses protein language model embeddings to build a deep learning framework that facilitates the annotation of CAZymes in metagenomic datasets. Our benchmarking results showed on average a higher F1 score (reflecting an average of precision and recall) on the annotated genomes of Bacteroides thetaiotaomicron, Eggerthella lenta and Ruminococcus gnavus compared to the traditional sequence homology-based method in dbCAN2. We applied our tool to a paired mother/infant longitudinal dataset and revealed unannotated CAZymes linked to microbial development during infancy. When applied to metagenomic datasets derived from patients affected by fibrosis-prone diseases such as Crohn's disease and IgG4-related disease, CAZyLingua uncovered CAZymes associated with disease and healthy states. In each of these metagenomic catalogs, CAZyLingua discovered new annotations that were previously overlooked by traditional sequence homology tools. Overall, the deep learning model CAZyLingua can be applied in combination with existing tools to unravel intricate CAZyme evolutionary profiles and patterns, contributing to a more comprehensive understanding of microbial metabolic dynamics.

Artificial Intelligence: A Promising Tool in Exploring the Phytomicrobiome in Managing Disease and Promoting Plant Health

There is increasing interest in harnessing the microbiome to improve cropping systems. With the availability of high-throughput and low-cost sequencing technologies, gathering microbiome data is becoming more routine. However, the analysis of microbiome data is challenged by the size and complexity of the data, and the incomplete nature of many microbiome databases. Further, to bring microbiome data value, it often needs to be analyzed in conjunction with other complex data that impact on crop health and disease management, such as plant genotype and environmental factors. Artificial intelligence (AI), boosted through deep learning (DL), has achieved significant breakthroughs and is a powerful tool for managing large complex datasets such as the interplay between the microbiome, crop plants, and their environment. In this review, we aim to provide readers with a brief introduction to AI techniques, and we introduce how AI has been applied to areas of microbiome sequencing taxonomy, the functional annotation for microbiome sequences, associating the microbiome community with host traits, designing synthetic communities, genomic selection, field phenotyping, and disease forecasting. At the end of this review, we proposed further efforts that are required to fully exploit the power of AI in studying phytomicrobiomes.

Bacterial secretion system functions: evidence of interactions and downstream implications

Unprecedented insights into the biology and functions of bacteria have been and continue to be gained through studying bacterial secretion systems in isolation. This method, however, results in our understanding of the systems being primarily based on the idea that they operate independently, ignoring the subtleties of downstream interconnections. Gram-negative bacteria are naturally able to adapt to and navigate their frequently varied and dynamic surroundings, mostly because of the covert connections between secretion systems. Therefore, to comprehend some of the linked downstream repercussions for organisms that follow this discourse, it is vital to have mechanistic insights into how the intersecretion system functions in bacterial rivalry, virulence, and survival, among other things. To that purpose, this paper discusses a few key instances of molecular antagonistic and interdependent relationships between bacterial secretion systems and their produced functional products.

Prevalence and Specificity of Chemoreceptor Profiles in Plant-Associated Bacteria

Chemosensory pathways are among the most abundant prokaryotic signal transduction systems, allowing bacteria to sense and respond to environmental stimuli. Signaling is typically initiated by the binding of specific molecules to the ligand binding domain (LBD) of chemoreceptor proteins (CRs). Although CRs play a central role in plant-microbiome interactions such as colonization and infection, little is known about their phylogenetic and ecological specificity. Here, we analyzed 82,277 CR sequences from 11,806 representative microbial species covering the whole prokaryotic phylogeny, and we classified them according to their LBD type using a de novo homology clustering method. Through phylogenomic analysis, we identified hundreds of LBDs that are found predominantly in plant-associated bacteria, including several LBDs specific to phytopathogens and plant symbionts. Functional annotation of our catalogue showed that many of the LBD clusters identified might constitute unknown types of LBDs. Moreover, we found that the taxonomic distribution of most LBD types that are specific to plant-associated bacteria is only partially explained by phylogeny, suggesting that lifestyle and niche adaptation are important factors in their selection. Finally, our results show that the profile of LBD types in a given genome is related to the lifestyle specialization, with plant symbionts and phytopathogens showing the highest number of niche-specific LBDs. The LBD catalogue and information on how to profile novel genomes are available at https://github.com/compgenomicslab/CRs.

IMPORTANCE Considering the enormous variety of LBDs at sensor proteins, an important question resides in establishing the forces that have driven their evolution and selection. We present here the first clear demonstration that environmental factors play an important role in the selection and evolution of LBDs. We were able to demonstrate the existence of LBD families that are highly enriched in plant-associated bacteria but show a wide phylogenetic spread. These findings offer a number of research opportunities in the field of single transduction, such as the exploration of similar relationships in chemoreceptors of bacteria with a different lifestyle, like those inhabiting or infecting the human intestine. Similarly, our results raise the question whether similar LBD types might be shared by members of different sensor protein families. Lastly, we provide a comprehensive catalogue of CRs classified by their LBD region that includes a large number of putative new LBD types.

The Role of ATP-Binding Cassette Transporters in Bacterial Phytopathogenesis

Bacteria use selective membrane transporting strategies to support cell survival in different environments. Of the membrane transport systems, ATP-binding cassette (ABC) transporters, which utilize the energy of ATP hydrolysis to deliver substrate across the cytoplasmic membrane, are the largest and most diverse superfamily. These transporters import nutrients, export molecules, and are required for diverse cell functions, including cell division and morphology, gene regulation, surface motility, chemotaxis, and interspecies competition. Phytobacterial pathogens encode numerous ABC transporter homologs compared with related nonphytopathogens, with up to 160 transporters per genome, suggesting that plant pathogens must be able to import or respond to a greater number of molecules compared with saprophytes or animal pathogens. Despite their importance, ABC transporters have been little examined in plant pathogens. To understand bacterial phytopathogenesis and evolution, we need to understand the roles that ABC transporters play in plant-microbe interactions. In this review, we outline a multitude of roles that bacterial ABC transporters play, using both plant and animal pathogens as examples, to emphasize the importance of exploring these transporters in phytobacteriology.

Systems Biology and Machine Learning in Plant-Pathogen Interactions

Systems biology is an inclusive approach to study the static and dynamic emergent properties on a global scale by integrating multiomics datasets to establish qualitative and quantitative associations among multiple biological components. With an abundance of improved high throughput -omics datasets, network-based analyses and machine learning technologies are playing a pivotal role in comprehensive understanding of biological systems. Network topological features reveal most important nodes within a network as well as prioritize significant molecular components for diverse biological networks, including coexpression, protein-protein interaction, and gene regulatory networks. Machine learning techniques provide enormous predictive power through specific feature extraction from biological data. Deep learning, a subtype of machine learning, has plausible future applications because a domain expert for feature extraction is not needed in this algorithm. Inspired by diverse domains of biology, we here review classic systems biology techniques applied in plant immunity thus far. We also discuss additional advanced approaches in both graph theory and machine learning, which may provide new insights for understanding plant-microbe interactions. Finally, we propose a hybrid approach in plant immune systems that harnesses the power of both network biology and machine learning, with a potential to be applicable to both model systems and agronomically important crop plants.

Comparative genomics of 84 Pectobacterium genomes reveals the variations related to a pathogenic lifestyle

BACKGROUND

Pectobacterium spp. are necrotrophic bacterial plant pathogens of the family Pectobacteriaceae, responsible for a wide spectrum of diseases of important crops and ornamental plants including soft rot, blackleg, and stem wilt. P. carotovorum is a genetically heterogeneous species consisting of three valid subspecies, P. carotovorum subsp. brasiliense (Pcb), P. carotovorum subsp. carotovorum (Pcc), and P. carotovorum subsp. odoriferum (Pco).

RESULTS

Thirty-two P. carotovorum strains had their whole genomes sequenced, including the first complete genome of Pco and another circular genome of Pcb, as well as the high-coverage genome sequences for 30 additional strains covering Pcc, Pcb, and Pco. In combination with 52 other publicly available genome sequences, the comparative genomics study of P. carotovorum and other four closely related species P. polaris, P. parmentieri, P. atrosepticum, and Candidatus P. maceratum was conducted focusing on CRISPR-Cas defense systems and pathogenicity determinants. Our analysis identified two CRISPR-Cas types (I-F and I-E) in Pectobacterium, as well as another I-C type in Dickeya that is not found in Pectobacterium. The core pathogenicity factors (e.g., plant cell wall-degrading enzymes) were highly conserved, whereas some factors (e.g., flagellin, siderophores, polysaccharides, protein secretion systems, and regulatory factors) were varied among these species and/or subspecies. Notably, a novel type of T6SS as well as the sorbitol metabolizing srl operon was identified to be specific to Pco in Pectobacterium.

CONCLUSIONS

This study not only advances the available knowledge about the genetic differentiation of individual subspecies of P. carotovorum, but also delineates the general genetic features of P. carotovorum by comparison with its four closely related species, thereby substantially enriching the extent of information now available for functional genomic investigations about Pectobacterium.

Characterization of Pectobacterium carotovorum proteins differentially expressed during infection of Zantedeschia elliotiana in vivo and in vitro which are essential for virulence

The identification of phytopathogen proteins that are differentially expressed during the course of the establishment of an infection is important to better understand the infection process. In vitro approaches, using plant extracts added to culture medium, have been used to identify such proteins, but the biological relevance of these findings for in planta infection are often uncertain until confirmed by in vivo studies. Here, we compared the proteins of Pectobacterium carotovorum ssp. carotovorum strain PccS1 differentially expressed in Luria-Bertani medium supplemented with extracts of the ornamental plant Zantedeschia elliotiana cultivar 'Black Magic' (in vitro) and in plant tissues (in vivo) by two-dimensional electrophoresis coupled with mass spectrometry. A total of 53 differentially expressed proteins (>1.5-fold) were identified (up-regulated or down-regulated in vitro, in vivo or both). Proteins that exhibited increased expression in vivo but not in vitro, or in both conditions, were identified, and deletions were made in a number of genes encoding these proteins, four of which (clpP, mreB, flgK and eda) led to a loss of virulence on Z. elliotiana, although clpP and mreB were later also shown to be reduced in growth in rich and minimal media. Although clpP, flgK and mreB have previously been reported as playing a role in virulence in plants, this is the first report of such a role for eda, which encodes 2-keto-3-deoxy-6-phosphogluconate (KDPG) aldolase, a key enzyme in Entner-Doudoroff metabolism. The results highlight the value of undertaking in vivo as well as in vitro approaches for the identification of new bacterial virulence factors.

Perspectives on the Transition From Bacterial Phytopathogen Genomics Studies to Applications Enhancing Disease Management: From Promise to Practice

The advent of genomics has advanced science into a new era, providing a plethora of "toys" for researchers in many related and disparate fields. Genomics has also spawned many new fields, including proteomics and metabolomics, furthering our ability to gain a more comprehensive view of individual organisms and of interacting organisms. Genomic information of both bacterial pathogens and their hosts has provided the critical starting point in understanding the molecular bases of how pathogens disrupt host cells to cause disease. In addition, knowledge of the complete genome sequence of the pathogen provides a potentially broad slate of targets for the development of novel virulence inhibitors that are desperately needed for disease management. Regarding plant bacterial pathogens and disease management, the potential for utilizing genomics resources in the development of durable resistance is enhanced because of developing technologies that enable targeted modification of the host. Here, we summarize the role of genomics studies in furthering efforts to manage bacterial plant diseases and highlight novel genomics-enabled strategies heading down this path.

Prediction of bacterial associations with plants using a supervised machine-learning approach

Recent scenarios of fresh produce contamination by human enteric pathogens have resulted in severe food-borne outbreaks, and a new paradigm has emerged stating that some human-associated bacteria can use plants as secondary hosts. As a consequence, there has been growing concern in the scientific community about these interactions that have not yet been elucidated. Since this is a relatively new area, there is a lack of strategies to address the problem of food-borne illnesses due to the ingestion of fruits and vegetables. In the present study, we performed specific genome annotations to train a supervised machine-learning model that allows for the identification of plant-associated bacteria with a precision of ∼93%. The application of our method to approximately 9500 genomes predicted several unknown interactions between well-known human pathogens and plants, and it also confirmed several cases for which evidence has been reported. We observed that factors involved in adhesion, the deconstruction of the plant cell wall and detoxifying activities were highlighted as the most predictive features. The application of our strategy to sequenced strains that are involved in food poisoning can be used as a primary screening tool to determine the possible causes of contaminations.

Role of Dickeya dadantii 3937 chemoreceptors in the entry to Arabidopsis leaves through wounds

Chemotaxis enables bacteria to move towards an optimal environment in response to chemical signals. In the case of plant-pathogenic bacteria, chemotaxis allows pathogens to explore the plant surface for potential entry sites with the ultimate aim to prosper inside plant tissues and to cause disease. Chemoreceptors, which constitute the sensory core of the chemotaxis system, are usually transmembrane proteins which change their conformation when sensing chemicals in the periplasm and transduce the signal through a kinase pathway to the flagellar motor. In the particular case of the soft-rot pathogen Dickeya dadantii 3937, jasmonic acid released in a plant wound has been found to be a strong chemoattractant which drives pathogen entry into the plant apoplast. In order to identify candidate chemoreceptors sensing wound-derived plant compounds, we carried out a bioinformatics search of candidate chemoreceptors in the genome of Dickeya dadantii 3937. The study of the chemotactic response to several compounds and the analysis of the entry process to Arabidopsis leaves of 10 selected mutants in chemoreceptors allowed us to determine the implications of at least two of them (ABF-0020167 and ABF-0046680) in the chemotaxis-driven entry process through plant wounds. Our data suggest that ABF-0020167 and ABF-0046680 may be candidate receptors of jasmonic acid and xylose, respectively.