Despite the considerable loss inflicted on important crops yearly by Pectobacterium and Dickeya diseases, investigations on key virulence and interbacterial competition assets relying on extensive comparative genomics are still surprisingly lacking for these genera. Such approaches become more powerful over time, underpinned by the growing amount of genomic information in public databases. In particular, our findings point to new functional associations among well-known genomic themes enabling alternative means of neutralizing SRE diseases through disruption of pivotal virulence programs. By elucidating novel transcriptional and genomic associations, this study adds valuable information on virulence candidates that could be decisive in molecular applications in the near future. The utilization of 100 genomes of Pectobacterium and Dickeya strains in this study is unprecedented for comparative analyses in these taxa, and it provides novel insights on the biology of economically important plant pathogens.

Despite its low morbidity, listeriosis has a high mortality rate due to the severity of its clinical manifestations. The source of human listeriosis is often unclear. In this study, we investigate the ability of machine learning to predict the food source from which clinical Listeria monocytogenes isolates originated. Four machine learning classification algorithms were trained on core genome multilocus sequence typing data of 1212 L. monocytogenes isolates from various food sources. The average accuracies of random forest, support vector machine radial kernel, stochastic gradient boosting, and logit boost were found to be 0.72, 0.61, 0.7, and 0.73, respectively. Logit boost showed the best performance and was used in model testing on 154 L. monocytogenes clinical isolates. The model attributed 17.5 % of human clinical cases to dairy, 32.5% to fruits, 14.3% to leafy greens, 9.7% to meat, 4.6% to poultry, and 18.8% to vegetables. The final model also provided us with genetic features that were predictive of specific sources. Thus, this combination of genomic data and machine learning-based models can greatly enhance our ability to track L. monocytogenes from different food sources.

Iron is an important nutrient for the survival and growth of many organisms. In order to survive, iron uptake from the environment must be strictly regulated and maintained to avoid iron toxicity. The ferric uptake regulator protein (Fur) regulates genes involved in iron homeostasis in many bacteria, including phytopathogens. However, to date, the role played by Fur in the biology of Pectobacterium carotovorum subsp. brasiliense (Pcb1692), an important pathogen of potatoes, has not yet been studied. To this end, we used the lambda recombineering method to generate a fur mutant strain of Pcb1692 and assessed the virulence and fitness of the mutant strain. The results showed that production of siderophores in Pcb1692Δfur increased compared to the Pcb1692 wild-type and the complemented strain Pcb1692Δfur-pfur. However, production of N-acyl homoserine lactone (AHLs), biofilm formation, exopolysaccharide (EPS) production, virulence on potato tubers and swimming motility, were all significantly decreased in Pcb1692Δfur compared to the wild-type and complemented Pcb1692Δfur-pfur strains. The Pcb1692Δfur mutant also demonstrated significant sensitivity to oxidative stress when exposed to H₂O₂. Consistent with phenotypic results, qRT-PCR results demonstrated that Fur down-regulates genes which encode proteins associated with: iron uptake (HasA-extracellular heme-binding protein and Ferrodoxin-AED-0004132), stress response (SodC-superoxide dismutase), plant cell wall degrading enzymes (PrtA and CelV) and motility (FlhC and MotA). We conclude that the ferric uptake regulator protein (Fur) of Pcb1692 regulates traits that are important to host-pathogens interactions.

Pantoea ananatis LMG 2665^T synthesizes and utilizes acyl homoserine lactones (AHLs) for signalling. The complete set of genes regulated by the EanI/R quorum sensing (QS) system in this strain is still not fully known. In this study, RNA-sequencing (RNA-seq) was used to identify the EanI/R regulon in LMG 2665^T. Pairwise comparisons of LMG 2665^T in the absence of AHLs (Optical density (OD)₆₀₀ = 0.2) and in the presence of AHLs (OD₆₀₀ = 0.5) were performed. Additionally, pairwise comparisons of LMG 2665^T and its QS mutant at OD₆₀₀ = 0.5 were undertaken. In total, 608 genes were differentially expressed between LMG 2665^T at OD₆₀₀ = 0.5 versus the same strain at OD₆₀₀ = 0.2 and 701 genes were differentially expressed between LMG 2665^T versus its QS mutant at OD₆₀₀ = 0.5. A total of 196 genes were commonly differentially expressed between the two approaches. These constituted approximately 4.5% of the whole transcriptome under the experimental conditions used in this study. The RNA-seq data was validated by reverse transcriptase quantitative polymerase chain reaction (RT-qPCR). Genes found to be regulated by EanI/R QS were those coding for redox sensing, metabolism, flagella formation, flagella dependent motility, cell adhesion, biofilm formation, regulators, transport, chemotaxis, methyl accepting proteins, membrane proteins, cell wall synthesis, stress response and a large number of hypothetical proteins. The results of this study give insight into the genes that are regulated by the EanI/R system in LMG 2665^T. Functional characterization of the QS regulated genes in LMG 2665^T could assist in the formulation of control strategies for this plant pathogen.