Classification of the plant-associated lifestyle of Pseudomonas strains using genome properties and machine learning

Phytobial remediation advances and application of omics and artificial intelligence: a review

Industrialization and urbanization increased the use of chemicals in agriculture, vehicular emissions, etc., and spoiled all environmental sectors. It causes various problems among living beings at multiple levels and concentrations. Phytoremediation and microbial association are emerging as a potential method for removing heavy metals and other contaminants from soil. The treatment uses plant physiology and metabolism to remove or clean up various soil contaminants efficiently. In recent years, omics and artificial intelligence have been seen as powerful techniques for phytobial remediation. Recently, AI and modeling are used to analyze large data generated by omics technologies. Machine learning algorithms can be used to develop predictive models that can help guide the selection of the most appropriate plant and plant growth-promoting rhizobacteria combination that is most effective at remediation. In this review, emphasis is given to the phytoremediation techniques being explored worldwide in soil contamination.

The Growth-Promoting and Colonization of the Pine Endophytic Pseudomonas abietaniphila for Pine Wilt Disease Control

In this study, we focused on evaluating the impact of Pseudomonas abietaniphila BHJ04 on the growth of Pinus massoniana seedlings and its biocontrol efficacy against pine wilt disease (PWD). Additionally, the colonization dynamics of P. abietaniphila BHJ04 on P. massoniana were examined. The growth promotion experiment showed that P. abietaniphila BHJ04 significantly promoted the growth of the branches and roots of P. massoniana. Pot control experiments indicated that strain BHJ04 significantly inhibited the spread of PWD. There were significant changes in the expression of several genes related to pine wood nematode defense in P. massoniana, including chitinase, nicotinamide synthetase, and triangular tetrapeptide-like superfamily protein isoform 9. Furthermore, our results revealed significant upregulation of genes associated with the water stress response (dehydration-responsive proteins), genetic material replication (DNA/RNA polymerase superfamily proteins), cell wall hydrolase, and detoxification (cytochrome P450 and cytochrome P450 monooxygenase superfamily genes) in the self-regulation of P. massoniana. Colonization experiments demonstrated that strain BHJ04 can colonize the roots, shoots, and leaves of P. massoniana, and the colonization amount on the leaves was the greatest, reaching 160,000 on the 15th day. However, colonization of the stems lasted longer, with the highest level of colonization observed after 45 d. This study provides a preliminary exploration of the growth-promoting and disease-preventing mechanisms of P. abietaniphila BHJ04 and its ability to colonize pines, thus providing a new biocontrol microbial resource for the biological control of plant diseases.

bacLIFE: a user-friendly computational workflow for genome analysis and prediction of lifestyle-associated genes in bacteria

Bacteria have an extensive adaptive ability to live in close association with eukaryotic hosts, exhibiting detrimental, neutral or beneficial effects on host growth and health. However, the genes involved in niche adaptation are mostly unknown and their functions poorly characterized. Here, we present bacLIFE ( https://github.com/Carrion-lab/bacLIFE ) a streamlined computational workflow for genome annotation, large-scale comparative genomics, and prediction of lifestyle-associated genes (LAGs). As a proof of concept, we analyzed 16,846 genomes from the Burkholderia/Paraburkholderia and Pseudomonas genera, which led to the identification of hundreds of genes potentially associated with a plant pathogenic lifestyle. Site-directed mutagenesis of 14 of these predicted LAGs of unknown function, followed by plant bioassays, showed that 6 predicted LAGs are indeed involved in the phytopathogenic lifestyle of Burkholderia plantarii and Pseudomonas syringae pv. phaseolicola. These 6 LAGs encompassed a glycosyltransferase, extracellular binding proteins, homoserine dehydrogenases and hypothetical proteins. Collectively, our results highlight bacLIFE as an effective computational tool for prediction of LAGs and the generation of hypotheses for a better understanding of bacteria-host interactions.

Comprehensive genome analysis of Pseudomonas sp. SWRIQ11, a new plant growth-promoting bacterium that alleviates salinity stress in olive

The study presents the genome analysis of a new Pseudomonas sp. (SWRIQ11), which can alleviate salinity stress effects on growth of olive seedlings in greenhouse study. The strain SWRIQ11 can tolerate salinity up to 6%, produce siderophores, indole acetic acid (IAA), aminocyclopropane-1-carboxylate (ACC) deaminase, and has the phosphate-solubilizing capability. The SWRIQ11 genome contained an assembly size of 6,196,390 bp with a GC content of 60.1%. According to derived indices based on whole-genome sequences for species delineation, including tetra nucleotide usage patterns (TETRA), genome-to-genome distance (GGDC), and average nucleotide identity (ANI), Pseudomonas sp. SWRIQ11 can be considered a novel species candidate. The phylogenetic analysis revealed SWRIQ11 clusters with Pseudomonas tehranensis SWRI196 in the same clade. The SWRIQ11 genome was rich in genes related to stress sensing, signaling, and response, chaperones, motility, attachments, colonization, and enzymes for degrading plant-derived carbohydrates. Furthermore, the genes for production of exopolysaccharides, osmoprotectants, phytohormones, and ACC deaminase, ion homeostasis, nutrient acquisition, and antioxidant defenses were identified in the SWRIQ11 genome. The results of genome analysis (identification of more than 825 CDSs related to plant growth-promoting and stress-alleviating traits in the SWRIQ11 genome which is more than 15% of its total CDSs) are in accordance with laboratory and greenhouse experiments assigning the Pseudomonas sp. SWRIQ11 as a halotolerant plant growth-promoting bacterium (PGPB). This research highlights the potential safe application of this new PGPB species in agriculture as a potent biofertilizer.

An Efficient Triplex TaqMan Quantitative PCR to Detect a Blackleg-Causing Lineage of Pectobacterium brasiliense in Potato Based on a Pangenome Analysis

P. brasiliense is an important bacterial pathogen causing blackleg (BL) in potatoes. Nevertheless, P. brasiliense is often detected in seed lots that do not develop any of the typical blackleg symptoms in the potato crop when planted. Field bioassays identified that P. brasiliense strains can be categorized into two distinct classes, some able to cause blackleg symptoms and some unable to do it. A comparative pangenomic approach was performed on 116 P. brasiliense strains, of which 15 were characterized as BL-causing strains and 25 as non-causative. In a genetically homogeneous clade comprising all BL-causing P. brasiliense strains, two genes only present in the BL-causing strains were identified, one encoding a predicted lysozyme inhibitor Lprl (LZI) and one encoding a putative Toll/interleukin-1 receptor (TIR) domain-containing protein. TaqMan assays for the specific detection of BL-causing P. brasiliense were developed and integrated with the previously developed generic P. brasiliense assay into a triplex TaqMan assay. This simultaneous detection makes the scoring more efficient as only a single tube is needed, and it is more robust as BL-causing strains of P. brasiliense should be positive for all three assays. Individual P. brasiliense strains were found to be either positive for all three assays or only for the P. brasiliense assay. In potato samples, the mixed presence of BL-causing and not BL-causing P. brasiliense strains was observed as shown by the difference in Ct value of the TaqMan assays. However, upon extension of the number of strains, it became clear that in recent years additional BL-causing lineages of P. brasiliense were detected for which additional assays must be developed.

Coping with salt stress-interaction of halotolerant bacteria in crop plants: A mini review

Salinity is one of the major environmental abiotic stress factors that limit the growth and yield of crop plants worldwide. It is crucial to understand the importance of several adaptive mechanisms in plants toward salt stress so as to increase agricultural productivity. Plant resilience toward salinity stress is improved by cohabiting with diverse microorganisms, especially bacteria. In the last few decades, increasing attention of researchers has focused on bacterial communities for promoting plant growth and fitness. The biotechnological applications of salt-tolerant plant growth-promoting rhizobacteria (PGPR) gained widespread interest for their numerous metabolites. This review provides novel insights into the importance of halotolerant (HT) bacteria associated with crop plants in enhancing plant tolerance toward salinity stress. Furthermore, the present review highlights several challenges of using HT-PGPR in the agricultural field and possible solutions to overcome those challenges for sustainable agriculture development in the future.

Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies

The plant-associated microbiome is a key component of plant systems, contributing to their health, growth, and productivity. The application of machine learning (ML) in this field promises to help untangle the relationships involved. However, measurements of microbial communities by high-throughput sequencing pose challenges for ML. Noise from low sample sizes, soil heterogeneity, and technical factors can impact the performance of ML. Additionally, the compositional and sparse nature of these datasets can impact the predictive accuracy of ML. We review recent literature from plant studies to illustrate that these properties often go unmentioned. We expand our analysis to other fields to quantify the degree to which mitigation approaches improve the performance of ML and describe the mathematical basis for this. With the advent of accessible analytical packages for microbiome data including learning models, researchers must be familiar with the nature of their datasets.