Evolution of AMR in the marine environment: influence of secondary metabolites produced by British seaweed microbiomes

The rapid increase in water pollution has occurred worldwide as a result of human wastes discharged into the seas and low-level water sanitation practices. These increased the chance of the spread of antimicrobial resistant (AMR) genes along with AMR bacteria also within marine habitats. Water pollution accelerates the spread of AMR because polluted water creates an environment for antibiotics, heavy metals, chemicals and pathogenic bacteria. In particular, sources such as industrial discharges cause bacteria to be constantly exposed to these substances. Bacteria develop resistance and carry these genes to survive. Known as a significant producer of diverse antimicrobial compounds, seaweed has affected the ecology of marine microbiota. While the effect of live seaweed on antimicrobial resistance has been examined, little is known about its potential role in influencing AMR levels. In the previous study, (samples used in this study) researchers examined the antimicrobial effects of seaweeds on E. coli and horizontal gene transfer. U. lactuca demonstrated the strongest antibacterial effect, while F. serratus showed the weakest effect. Therefore, using the 16S rRNA gene to standardise intI1 to community density, the intI1 gene was employed to quantify the spread of antimicrobial resistance genes. This research aims to determine whether the generation of secondary metabolites by seaweeds could affect AMR levels. A total of 124 samples and DNA extraction was performed for microbiome analysis using qPCR targeting the 16S rRNA gene. The experiment involved exposing three E. coli strains to the presence of seaweed microbiomes in both live and senescent conditions and on 2 different time schedules. Timepoint 0 refers to day 0 at the beginning of the experiment and time point 8 at the end of the experiment of day 8. Survival rates of E. coli were monitored over time, while the production of secondary metabolites by the seaweed microbiomes was assessed. The survival rates of E. coli were measured and the production of secondary metabolites by conducting DNA extractions followed by qPCR analysis targeting the 16S rRNA gene and the intl1 gene. The 16S gene allowed to quantify bacterial populations, while the intl1 gene helped determine the prevalence of antibiotic resistance-related integrons. Based on the qPCR data, the intl1 prevalence is used to assess its correlation with bacterial survival and secondary metabolite production. These results indicated significant differences in the live condition and between three different E.coli strains AL120 strain. The results indicated no significant differences between seaweed samples and the time schedule. These results indicate that live conditions and AL120 E.coli strain is significantly important and affects secondary metabolites production. There was more antimicrobial resistance in the senescent condition, and antimicrobial resistance increased in the live conditions. This finding suggests that metabolites produced by microbiomes affect antimicrobial resistance under light and dark conditions and the presence of the AL120 E. coli strain.