Evolved bacterial resistance to the chemotherapy gemcitabine modulates its efficacy in co-cultured cells
- University of Massachusetts Medical School, United States
Abstract
Drug metabolism by the microbiome can influence anti-cancer treatment success. We previously suggested that chemotherapies with antimicrobial activity can select for adaptations in bacterial drug metabolism that can inadvertently influence the host's chemoresistance. We demonstrated that evolved resistance against fluoropyrimidine chemotherapy lowered its efficacy in worms feeding on drug-evolved bacteria (Rosener et al., 2020). Here we examine a model system that captures local interactions that can occur in the tumor microenvironment. Gammaproteobacteria colonizing pancreatic tumors can degrade the nucleoside-analog chemotherapy gemcitabine and, in doing so, can increase the tumor's chemoresistance. Using a genetic screen in Escherichia coli, we mapped all loss-of-function mutations conferring gemcitabine resistance. Surprisingly, we infer that one third of top resistance mutations increase or decrease bacterial drug breakdown and therefore can either lower or raise the gemcitabine load in the local environment. Experiments in three E. coli strains revealed that evolved adaptation converged to inactivation of the nucleoside permease NupC, an adaptation that increased the drug burden on co-cultured cancer cells. The two studies provide complementary insights on the potential impact of microbiome adaptation to chemotherapy by showing that bacteria-drug interactions can have local and systemic influence on drug activity.
Data availability
Barcode Sequencing and whole genome sequencing data have been deposited in NCBI SRA under the bioproject IDs PRJNA797841,PRJNA911755 and PRJNA855939.Supplementary Table 1 includes entire numerical data of the original barcoded genetic screen results and validation screen results.Supplementary Table 2 includes the all enriched and depleted pathways found in barcoded genetic screen visualized in Figure 1.Supplementary Table 3 includes the numerical data from Luria Delbruck Fluctuation experiment which is visualized on Supplementary Figure 6.Supplementary Table 4 includes the detailed annotation of genomic mutations found in all evolved clones shown in concentric circa plots at figure 4A.Supplementary Table 5 includes the list of inferred mutations from sanger sequencing files for the data used in Figure 4B.
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Whole genome sequencing of gemcitabine evolved E. coli strainsNCBI BioProject, PRJNA855939.
Article and author information
Author details
Albertha JM Walhout
Funding
National Institutes of Health (R35GM133775)
- Amir Mitchell
National Institutes of Health (R01AI170722)
- Amir Mitchell
National Institutes of Health (DK068429)
- Albertha JM Walhout
National Institutes of Health (R35GM122502)
- Albertha JM Walhout
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2023, Sayin et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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