Back

Non-antibiotic pharmaceuticals can enhance the spread of antibiotic resistance via conjugation

Wang, Y.; Lu, J.; Zhang, S.; Li, J.; Mao, L.; Yuan, Z.; Bond, P.; Guo, J.

2019-08-05 microbiology
10.1101/724500 bioRxiv
Show abstract

Antibiotic resistance is a global threat for public health. It is widely acknowledged that antibiotics at sub-inhibitory concentrations are important in disseminating antibiotic resistance via horizontal gene transfer. While there is high use of non-antibiotic human-targeted pharmaceuticals in our societies, the potential contribution of these on the spread of antibiotic resistance has been overlooked so far. Here, we report that commonly consumed non-antibiotic pharmaceuticals, including nonsteroidal anti-inflammatories (ibuprofen, naproxen, diclofenac), a lipid-lowering drug (gemfibrozil), and a {beta}-blocker (propanolol), at clinically and environmentally relevant concentrations, significantly accelerated the conjugation of plasmid-borne antibiotic resistance genes. We looked at the response to these drugs by the bacteria involved in the gene transfer through various analyses that included monitoring reactive oxygen species (ROS) and cell membrane permeability by flow cytometry, cell arrangement, and whole-genome RNA and protein sequencing. We found the enhanced conjugation correlated well with increased production of ROS and cell membrane permeability. We also detected closer cell-to-cell contact and upregulated conjugal genes. Additionally, these non-antibiotic pharmaceuticals caused the bacteria to have responses similar to those detected when exposed to antibiotics, such as inducing the SOS response, and enhancing efflux pumps. The findings advance our understanding of the bacterial transfer of antibiotic resistance genes, and importantly emphasize concerns of non-antibiotic human-targeted pharmaceuticals for enhancing the spread of antibiotic resistance.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.