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.
-
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.
Metrics
-
- 1,913
- views
-
- 249
- downloads
-
- 14
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Evolved bacterial resistance to the chemotherapy gemcitabine modulates its efficacy in co-cultured cells
eLife 12:e83140.
https://doi.org/10.7554/eLife.83140
Further reading
-
- Computational and Systems Biology
- Genetics and Genomics
Obesity is a major risk factor for type 2 diabetes, dyslipidemia, cardiovascular disease, and hypertension. Intriguingly, there is a subset of metabolically healthy obese (MHO) individuals who are seemingly able to maintain a healthy metabolic profile free of metabolic syndrome. The molecular underpinnings of MHO, however, are not well understood. Here, we report that CTRP10/C1QL2-deficient mice represent a unique female model of MHO. CTRP10 modulates weight gain in a striking and sexually dimorphic manner. Female, but not male, mice lacking CTRP10 develop obesity with age on a low-fat diet while maintaining an otherwise healthy metabolic profile. When fed an obesogenic diet, female Ctrp10 knockout (KO) mice show rapid weight gain. Despite pronounced obesity, Ctrp10 KO female mice do not develop steatosis, dyslipidemia, glucose intolerance, insulin resistance, oxidative stress, or low-grade inflammation. Obesity is largely uncoupled from metabolic dysregulation in female KO mice. Multi-tissue transcriptomic analyses highlighted gene expression changes and pathways associated with insulin-sensitive obesity. Transcriptional correlation of the differentially expressed gene (DEG) orthologs in humans also shows sex differences in gene connectivity within and across metabolic tissues, underscoring the conserved sex-dependent function of CTRP10. Collectively, our findings suggest that CTRP10 negatively regulates body weight in females, and that loss of CTRP10 results in benign obesity with largely preserved insulin sensitivity and metabolic health. This female MHO mouse model is valuable for understanding sex-biased mechanisms that uncouple obesity from metabolic dysfunction.
-
- Computational and Systems Biology
Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community lacks an easy computing and application tool that images any metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs may contribute to the discovery of unanticipated molecules in shared metabolic pathways. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling, markedly enhances spatial image contrast, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent hypothesis generation tool to enhance knowledge obtained from current spatial metabolite profiling technologies.
