This global survey of vAh brought together scientists that study fish disease to evaluate the evolution, geographical distribution, phylogeny, and hosts of vAh and other Aeromonas sp. isolates. In addition to vAh isolates from China and the United States, four new vAh isolates were isolated from the lower Mekong River basin in Cambodia and Vietnam, indicating the significant threat of vAh to modern aquaculture and the need for improved biosecurity to prevent vAh spread.

Plant growth-promoting rhizobacteria (PGPR) effects on plant yield are highly variable under field conditions due to competition with soil microbiota. Previous research determined that many Bacillus velezensis PGPR strains can use pectin as a sole carbon source and that seed inoculation with PGPR plus pectin-rich orange peel (OP) can enhance PGPR-mediated increases in plant growth. Because the previous studies used a single soybean cultivar, the objective of this research was to test the effect of PGPR plus OP inoculation on plant responses in a wide range of soybean cultivars. Preliminary screening with 20 soybean cultivars in the greenhouse showed that the PGPR plus OP produced a positive increase in all plant growth parameters when all cultivar data was averaged. However, when the inoculation response was examined cultivar by cultivar there was a range of cultivar response from a 60% increase in growth parameters to a 12% decrease, pointing to the presence of a cultivar-PGPR specificity. Further greenhouse and field experiments that studied cultivars with contrast responses to synbiotic inoculation revealed that the environment and/or the molecular interactions between the plant and microorganisms may play an important role in plant responsiveness.

Having the ability to predict the protein-encoding gene content of an incomplete genome or metagenome-assembled genome is important for a variety of bioinformatic tasks. In this study, as a proof of concept, we built machine learning classifiers for predicting variable gene content in Escherichia coli genomes using only the nucleotide k-mers from a set of 100 conserved genes as features. Protein families were used to define orthologs , and a single classifier was built for predicting the presence or absence of each protein family occurring in 10%–90% of all E. coli genomes. The resulting set of 3,259 extreme gradient boosting classifiers had a per-genome average macro F1 score of 0.944 [0.943–0.945, 95% CI]. We show that the F1 scores are stable across multi-locus sequence types and that the trend can be recapitulated by sampling a smaller number of core genes or diverse input genomes. Surprisingly, the presence or absence of poorly annotated proteins, including “hypothetical proteins” was accurately predicted (F1 = 0.902 [0.898–0.906, 95% CI]). Models for proteins with horizontal gene transfer-related functions had slightly lower F1 scores but were still accurate (F1s = 0.895, 0.872, 0.824, and 0.841 for transposon, phage, plasmid, and antimicrobial resistance-related functions, respectively). Finally, using a holdout set of 419 diverse E. coli genomes that were isolated from freshwater environmental sources, we observed an average per-genome F1 score of 0.880 [0.876–0.883, 95% CI], demonstrating the extensibility of the models. Overall, this study provides a framework for predicting variable gene content using a limited amount of input sequence data. IMPORTANCE: Having the ability to predict the protein-encoding gene content of a genome is important for assessing genome quality, binning genomes from shotgun metagenomic assemblies, and assessing risk due to the presence of antimicrobial resistance and other virulence genes. In this study, we built a set of binary classifiers for predicting the presence or absence of variable genes occurring in 10%–90% of all publicly available E. coli genomes. Overall, the results show that a large portion of the E. coli variable gene content can be predicted with high accuracy, including genes with functions relating to horizontal gene transfer. This study offers a strategy for predicting gene content using limited input sequence data.