Although bacteria with 1-aminocyclopropane-1-carboxylate (ACC) deaminase activity have been used to mitigate biotic and abiotic stresses in crops, it is not well known whether the ACC deaminase gene (acdS) in Pseudomonas azotoformans is related to the alleviation of salt stress by the bacterium. This study aimed to evaluate the effects of acdS in P. azotoformans strain CHB 1107 on the nutrient uptake and growth of tomato plants under salt stress. The acdS mutant (CHB 1107 M) of P. azotoformans CHB 1107 was obtained through bacterial conjugation. Wild-type (CHB 1107 WT) and CHB 1107 M were used to inoculate tomato plants grown in a soil or solution with an electrical conductivity of 6 dS/m adjusted by NaCl. CHB 1107 M completely lost the ability to produce ACC deaminase, whereas the complementation of acdS in CHB 1107 M preserved its ACC deaminase activity. CHB 1107 WT significantly reduced the production of ethylene and proline by tomato plants under salt stress, increasing the shoot and root dry weights of tomato plants compared with the noninoculated control and CHB 1107 M. In addition, tomato plants inoculated with CHB 1107 M showed a significant reduction in K (27.5%), Ca (23.0%), and Mn uptake (17.5%) compared with those inoculated with CHB 1107 WT. In contrast, CHB 1107 WT significantly reduced Na uptake by tomato plants in comparison to CHB 1107 M in saline soil conditions. In addition, the inoculation of tomato plants with CHB 1107 WT resulted in a higher K/Na ratio than in those inoculated with CHB 1107 M and the noninoculated control. These findings suggest that acdS in P. azotoformans is associated with the amelioration of salinity stress in tomato. Plant transformation with acdS and the field application of P. azotoformans may be used as potential management tools for crops under salt stress.

Antimicrobial resistance (AMR) is a global health issue and surveillance of AMR can be useful for understanding AMR trends and planning intervention strategies. Salmonella, widely distributed in food-producing animals, has been considered the first priority for inclusion in the AMR surveillance program by the World Health Organization (WHO). Recent advances in rapid and affordable whole-genome sequencing (WGS) techniques lead to the emergence of WGS as a one-stop test to predict the antimicrobial susceptibility. Since the variation of sequencing and minimum inhibitory concentration (MIC) measurement methods could result in different results, this study aimed to develop WGS-based random forest models for predicting MIC values of 24 drugs using data generated from the same laboratories in Taiwan. The WGS data have been transformed as a feature vector of 10-mers for machine learning. Based on rigorous validation and independent tests, a good performance was obtained with an average mean absolute error (MAE) less than 1 for both validation and independent test. Feature selection was then applied to identify top-ranked 10-mers that can further improve the prediction performance. For surveillance purposes, the genome sequence-based machine learning methods could be utilized to monitor the difference between predicted and experimental MIC, where a large difference might be worthy of investigation on the emerging genomic determinants.

Actinobacillus pleuropneumoniae is a causative agent of pleuropneumonia in pigs of all ages. A . pleuropneumoniae is divided into 19 serovars based on capsular polysaccharides (CPSs) and lipopolysaccharides. The serovars of isolates are commonly determined by serological tests and multiplex PCR. This study aimed to develop a genomic approach for in silico A. pleuropneumoniae typing by screening for the presence of the species-specific apxIV gene in whole-genome sequencing (WGS) reads and identifying capsule locus (KL) types in genome assemblies. A database of the A . pleuropneumoniae KL, including CPS synthesis and CPS export genes, was established and optimized for Kaptive. To test the developed genomic approach, WGS reads of 189 A . pleuropneumoniae isolates and those of 66 samples from 14 other bacterial species were analysed. ariba analysis showed that apxIV was detected in all 189 A . pleuropneumoniae samples. These apxIV-positive WGS reads were de novo assembled into genome assemblies and assessed. A total of 105 A . pleuropneumoniae genome assemblies that passed the quality assessment were analysed by Kaptive analysis against the A . pleuropneumoniae KL database. The results showed that 97 assemblies were classified and predicted as 13 serovars, which matched the serovar information obtained from the literature. The six genome assemblies from previously nontypable isolates were typed and predicted as serovars 17 and 18. Notably, one of the two “Actinobacillus porcitonsillarum” samples was apxIV positive, and its genome assembly was typed as KL03 with high identity and predicted as A . pleuropneumoniae serovar 3. Collectively, a genomic approach was established and could accurately determine the KL type of A . pleuropneumoniae isolates using WGS reads. This approach can be used with high-quality genome assemblies for predicting A . pleuropneumoniae serovars and for retrospective analysis.