On account of the widespread development and propagation of antimicrobial-resistant (AMR) bacteria, essential oils (EOs) have emerged as potential alternatives to antibiotics. However, as already observed for antibiotics, recent studies have raised concerns regarding the potential emergence of resistant variants (RVs) to EOs. In this study, we assessed the emergence of RVs in Escherichia coli and Salmonella enterica Typhimurium after evolution assays under extended exposure to subinhibitory doses of two commercial EOs (AEN and COLIFIT) as well as to two antibiotics (amoxicillin and colistin). Phenotypic characterization of RVs from evolution assays with commercial EOs yielded no relevant increases in the minimum inhibitory concentration (MIC) of E. coli and did not even modify MIC values in S. Typhimurium. Conversely, RVs of E. coli and S. Typhimurium isolated from evolution assays with antibiotics showed increased resistance. Genotypic analysis demonstrated that resistance to commercial EOs was associated with enhanced protection against oxidative stress and redirection of cell energy toward efflux activity, while resistance to antibiotics was primarily linked to modifications in the cell binding sites of antibiotics. These findings suggest that AEN and COLIFIT could serve as safe alternatives to antibiotics in combating the emergence and dissemination of antimicrobial resistance within the agrifood system.

Introduction: Microbiota plays a pivotal role in promoting the health and wellbeing of poultry. Essential oils (EOs) serve as an alternative solution for modulating poultry microbiota. This study aimed to investigate, using amplicon sequencing, the effect of a complex and well-defined combination of EOs feed supplement on both ileal and caecal broiler microbiota, within the context of Salmonella and Campylobacter intestinal colonization.Material and methods: For this experiment, 150-day-old Ross chicks were randomly allocated to two groups: T+ (feed supplementation with EO mix 500 g/t) and T– (non-supplemented). At day 7, 30 birds from each group were orally inoculated with 10⁶ CFU/bird of a Salmonella enteritidis and transferred to the second room, forming the following groups: TS+ (30 challenged birds receiving infeed EO mix at 500g/t) and TS– (30 challenged birds receiving a non-supplemented control feed). At day 14, the remaining birds in the first room were orally inoculated with 10³ CFU/bird of two strains of Campylobacter jejuni, resulting in the formation of groups T+C+ and T–C+. Birds were sacrificed at day 7, D10, D14, D17, and D21. Ileal and caecal microbiota samples were analyzed using Illumina MiSeq sequencing. At D7 and D14, ileal alpha diversity was higher for treated birds (p 0.05). The ileal beta diversity exhibited differences between groups at D7 (p < 0.008), D10 (p = 0.029), D14 (p= 0.001) and D17 (p= 0.018), but not at D21 (p = 0.54). For all time points, the analysis indicated that 6 biomarkers were negatively impacted, while 10 biomarkers were positively impacted. Sellimonas and Weissella returned the lowest (negative) and highest (positive) coefficient, respectively. At each time point, treatments influenced caecal microbiota beta diversity (p < 0.001); 31 genera were associated with T+: 10 Ruminoccocaceae genera were alternatively more abundant and less abundant from D7, 7 Lachnospiraceae genera were alternatively more and less abundant from D10, 6 Oscillospiraceae genera were variable depending on the date and 4 Enterobacteriaceae differed from D7. During all the experiment, Campylobacter decreased in treated birds (p < 0.05). This study showed that EO mix modulates ileal and caecal microbiota composition both before and during challenge conditions, increasing alpha diversity, especially in ileum during the early stages of chick life.

With the increasing occurrence of bacterial resistance, it is now essential to look for new alternatives to protect the curative utilization of antibiotics within the One Health concept. Here, we adapt and optimize a broth microdilution method and compare it against the broth macrodilution method for evaluating the antibacterial activity of a complex essential oils mix (EO mix) against four livestock pathogens: Escherichia coli, Bacillus cereus, Pseudomonas aeruginosa, and Staphylococcus aureus. Microdilution method performance (final volume well: 300 µL; inoculum: 1.0 × 10⁶ CFU/mL) was evaluated following CLSI recommendations, by comparing the MIC of each of the four strains with the MICs obtained with the macrodilution method (final volume tube: 2 mL; inoculum 1.0 × 10⁶ CFU/mL). Microdilution analysis was performed with an automated plate reader (Bioscreen C), and three bacterial growth parameters (OD max, lag phase, and growth rate) were calculated (DMFit curve-fitting software (v2.1; courtesy of the Institute of Food Research, Norwich, UK)). EO mix MICs were determined for E. coli, S. aureus, and B. cereus. Our results emphasize the importance of ensuring the accuracy of MIC results by performing three technical and three biological replicates, and combining OD max, lag phase, and growth rate to assess the impact of an EO mix at sub-MIC levels.

With the increasing occurrence of bacterial resistance, it is now essential to look for new alternatives to protect antibiotics curative utilization in One Health concept. Here we adapt and optimize a broth microdilution method and compare it against the broth macrodilution method for evaluating the antibacterial activity of a complex essential oils mix (EO mix) against 4 livestock pathogens Escherichia coli (E. coli), Bacillus cereus (B. cereus), Pseudomonas aeruginosa (P. aeruginosa) and Staphylococcus aureus (S. aureus. Microdilution method performance (final volume well: 300 µL; inoculum: 106 CFU/mL), was evaluated following CLSI recommendations, by comparing each MIC of each 4 strains, with each MIC’s obtained with macrodilution method (final volume tube: 2 mL; inoculum 106 CFU/mL). Microdilution was performed with automated plate reeder (Bioscreen C) and 3 bacterial growth parameters (OD max, lag phase, and growth rate) were calculated (Dmfit software). EO Mix MICs were determined for E. coli, S. aureus and B. cereus. Our results emphasize the importance of ensuring the accuracy of MIC results by performing 3 technical and 3 biological replicates, and combining OD max, lag phase, and growth rate to assess the impact of an EO mix at sub-MIC levels.