Uncovering key metabolic determinants of the drug interactions between trimethoprim and erythromycin in Escherichia coli
Qi, Qin; Angermayr, S Andreas; Bollenbach, Tobias (2021), Uncovering key metabolic determinants of the drug interactions between trimethoprim and erythromycin in Escherichia coli, Dryad, Dataset, https://doi.org/10.5061/dryad.bk3j9kdcn
Understanding interactions between antibiotics used in combination is an important theme in microbiology. Using the interactions between the antifolate drug trimethoprim and the ribosome-targeting antibiotic erythromycin in Escherichia coli as a model, we applied a transcriptomic approach for dissecting interactions between two antibiotics with different modes of action. When trimethoprim and erythromycin were combined, the transcriptional response of genes from the sulfate reduction pathway deviated from the dominant effect of trimethoprim on the transcriptome. We successfully altered the drug interaction from additivity to suppression by increasing the sulfate level in the growth environment and identified sulfate reduction as an important metabolic determinant that shapes the interaction between the two drugs. Our work highlights the potential of using prioritization of gene expression patterns as a tool for identifying key metabolic determinants that shape drug-drug interactions. We further demonstrated that the sigma factor-binding protein gene crl shapes the interactions between the two antibiotics, which provides a rare example of how naturally occurring variations between strains of the same bacterial species can sometimes generate different drug interactions.
FASTQ files from HiSeq 2500 V4 paired-end 125 bp sequencing were trimmed to remove Illumina adaptors. The reads were then quality filtered and mapped to the E. coli MG1655 reference genome with gene annotations retrieved from Ensembl Bacteria (genome assembly: ASM584v2):
MG1655.fa: Reference genome FASTA file
MG1655.genome: Gene annotation file
BAM files were generated using Samtools commands for viewing, indexing and sorting:
1-1_s.bam: E. coli MG1655 treated with trimethoprim (IC50); biological replicate 1
2-1_s.bam: E. coli MG1655 treated with trimethoprim (IC50); biological replicate 2
1-6_s.bam: E. coli MG1655 treated with erythromycin (IC50); biological replicate 1
2-6_s.bam: E. coli MG1655 treated with erythromycin (IC50); biological replicate 2
1-7_s.bam: E. coli MG1655 treated with trimethoprim (IC50) + erythromycin (IC50); biological replicate 1
2-7_s.bam: E. coli MG1655 treated with trimethoprim (IC50) + erythromycin (IC50); biological replicate 2
1-8_s.bam: E. coli MG1655 no-drug control; biological replicate 1
2-8_s.bam: E. coli MG1655 no-drug control; biological replicate 2
*.bam.bai files are accompanying BAM index files.
Gene counts were extracted from the sorted and indexed BAM files using the Python script HTSeq. Subsequent differential gene expression analysis was performed using the DESeq2 Bioconductor package as described in the Materials and Methods section of the manuscript.