Data for: Purine and pyrimidine synthesis differently affect the strength of the inoculum effect for aminoglycoside and β-lactam antibiotics
Data files
Oct 30, 2024 version files 1.19 MB
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dryad_data_summary_Figure_1.xlsx
70.98 KB
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dryad_data_summary_Figure_2.xlsx
33.70 KB
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dryad_data_summary_Figure_3.xlsx
152.08 KB
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dryad_data_summary_Figure_4.xlsx
58.37 KB
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dryad_data_summary_Figure_5.xlsx
48.92 KB
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dryad_data_summary_Figure_6.xlsx
32.70 KB
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dryad_data_summary_Figure_7.xlsx
29.45 KB
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dryad_data_summary_Figures_S8_and_S10.xlsx
38.48 KB
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gene_knockout_dryad.m
1.83 KB
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iML_Hernandez.xlsx
353.48 KB
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iML1515_NN.xlsx
353.33 KB
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pathway_activity_dryad.m
1.37 KB
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README.md
7.97 KB
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rxns_to_export.xlsx
9.28 KB
Abstract
The inoculum effect has been observed for nearly all antibiotics and bacterial species. However, explanations accounting for its occurrence and strength are lacking. Previous work found that the relationship between [ATP] and growth rate can account for the strength and occurrence of the inoculum effect for bactericidal antibiotics. However, the molecular pathway(s) underlying this relationship, and therefore determining the inoculum effect, remain undiscovered. Using a combination of flux balance analysis and experimentation, we show that nucleotide synthesis can determine the relationship between [ATP] and growth, and thus the strength of inoculum effect in an antibiotic class-dependent manner. If the [ATP]/growth rate is sufficiently high as determined by exogenously supplied nitrogenous bases, the inoculum effect does not occur. This is consistent for both Escherichia coli and Pseudomonas aeruginosa. Interestingly, and separate from activity through the tricarboxylic acid cycle, we find that transcriptional activity of genes involved in purine and pyrimidine synthesis can predict the strength of the inoculum effect for b-lactam and aminoglycosides antibiotics, respectively. Our work highlights the antibiotic class-specific effect of purine and pyrimidine synthesis on the severity of the inoculum effect, which may pave the way for intervention strategies to reduce the inoculum effect in the clinic.
https://doi.org/10.5061/dryad.931zcrjvt
Description of the data and file structure
All data was collected using the methods associated with the manuscript.
Files
Files: gene_knockout_dryad.m and iML_Hernandez.xlsx
Description: These two files were used for the flux balance analysis and OptKnock. As “gene_knockout_dryad is a .m file, it requires MATLAB or a similar program. The iML_Hernandez.xlsx contains all of the lower and upper bound flux values for the whole genome model. Both files must be used together; the .m file calls the data for the .xlsx file to run. Flux values used are contained in the .xlsx document; modifying these values will produce different results.
The iML_Hernandez.xlsx contains columns that detail reactions in the whole genome model
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The ‘abbreviation’ column indicates the reaction abbreviation.
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The ‘description’ column most often indicates the enzyme responsible for the reaction.
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The ‘reaction’ column indicates the chemical reaction performed in the reaction.
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The ‘gpr’ column indicates the gene associated with the reaction in E. coli.
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The ‘lower bound’ and ‘upper bound’ columns indicate the lower and upper bound flux values to be used in each reaction.
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The ‘subsystem’ column indicates the subsystem assigned to the reaction.
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The ‘EC number’ column indicates the EC number associated with the enzyme performing the reaction.
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The ‘KEGG ID’ indicates the reaction’s KEGG identifier.
File: dryad_data_summary_Figure_1.xlsx
Description: The Excel file contains the ATPsyn and biomass values for flux balance analysis. It also includes the flux balance analysis and Optknock verification data (both simulations (ATPsyn, biomass) and experiments (growth productivity, [ATP] and growth rate); together, this data demonstrates that flux balance analysis can predict changes in [ATP] and growth rate.
File: dryad_data_summary_Figure_2.xlsx
Description: This Excel file contains all experimental growth rates and [ATP] concentrations for E. coli grown with and without nitrogenous bases. Growth rates and [ATP] for each biological replicate used in these figures are included, as are the averages and standard error of the means. [ATP]/growth rate values derived from the averages are also included.
File: dryad_data_summary_Figure_3.xlsx
Description: This Excel file contains all minimum inhibitory concentration (MIC) and deltaMIC data for E. coli treated with streptomycin, carbenicillin, or ciprofloxacin. This includes all average cell density measurements (OD600) at each concentration of streptomycin, carbenicillin, and ciprofloxacin at both high and low initial density. Standard errors of the means are presented, and all t-test P values are used to determine the MIC for each condition.
File: dryad_data_summary_Figure_4.xlsx
Description: This Excel file contains all [ATP], growth rate, MIC, and deltaMIC values determined for Pseudomonas aeruginosa. It includes all [ATP] and growth rate values for each biological replicate, as well as their average and standard error of the mean. All average cell density measurements (OD600) at each concentration of streptomycin and carbenicillin, and at both high and low initial density, are presented. Standard errors of the means are presented, and all t-test P values are used to determine the MIC for each condition. Finally, all delta MIC values are contained in this sheet.
File: dryad_data_summary_Figure_5.xlsx
Description: This Excel sheet contains all the raw simulation and experimental data showing the impact of removing pyrC and purK from E. coli and their effect on ATP, growth rate, MIC, and deltaMIC. It includes all [ATP] and growth rate values for each biological replicate, and their average and standard error of the mean. All average cell density measurements (OD600) at each concentration of streptomycin and carbenicillin, and both high and low initial density, are presented. Standard errors of the means are presented, and all t-test P values are used to determine the MIC for each condition. Finally, all delta MIC values are contained in this sheet.
File: dryad_data_summary_Figure_6.xlsx
Description: This Excel spreadsheet contains data that focuses on the effects of 6-MP and IMP on [ATP], growth rate, MIC, and deltaMIC. It includes all [ATP] and growth rate values for each biological replicate, and their average and standard error of the mean. All average cell density measurements (OD600) at each concentration of streptomycin and carbenicillin, and at both high and low initial density, are presented. Standard errors of the means are presented, and all t-test P values are used to determine the MIC for each condition. Finally, all delta MIC values are contained in this sheet.
File: dryad_data_summary_Figure_7.xlsx
Description: This Excel spreadsheet contains data that focuses on the purine and pyridine synthesis activity and their relationship with deltaMIC. This sheet contains flux values for purine and pyrimidine synthesis, both with and without 6-MP, IMP, adenine, and cytosine. This sheet also includes all GFP values normalized by OD600, their averages, and their standard error of means. It also includes all MIC and deltaMIC data for kanamycin, including average cell densities (OD600) at each kanamycin concentration tested, the SEM, and all t-test P values used to determine the MIC for each condition.
File: dryad_data_summary_Figures_S8_and_S10.
Description: This Excel spreadsheet contains all deltaMIC values for the extra high initial population density. It also includes all average cell densities (OD600) at each streptomycin concentration tested, the SEM, and all t-test P values used to determine the MIC for each condition using the extra high initial population density. It includes all carrying capacities observed in experiments with streptomycin, carbenicillin, and ciprofloxacin. It also consists of all MIC and deltaMIC data for the luxS and mdtA knockouts, including average cell densities (OD600) at each streptomycin concentration tested, the SEM, and all t-test P values used to determine the MIC. It contains all initial and final pH values (averages, individual biological replicates, and standard error of the mean) are presented. Finally, all biological replicates for rRNA normalized by cell density (OD600) along with their averages and their standard of the mean are presented.
File: pathway_activity_dryad.m, iML1515_NN.xlsx, and rxn_to_export.xlsx
Description: These files work together to produce pathway simulations. The ‘pathway_activity_dryad.m’ file requires the use of MATLAB or a similar program. The ‘iML1515_NN.xlsx’ file contains all of the lower and upper bound flux values for the whole genome model and must be used in conjunction with the .m file. The rxn_to_export.xlsx tells the .m file what simulations you would like to export after simulation using the .m file. The flux values in the iML1515_NN.xlsx are those used for simulations that do not contain any nitrogenous bases or inhibitors/activators. Importantly, the reaction names in the rxn_to_export.xlsx must match the spelling of the reaction you would like to examine. All reactions in the whole genome model can be found in the first sheet of iML1515_NN.xlsx file in the ‘abbreviation’ column; further information about each reaction can be found in the ‘description’ and ‘reaction’ columns, which are immediately adjacent to the ‘abbreviation’ column. A detailed description of each column can be found in the description associated with the gene_knockout_dryad.m and iML_Hernandez.xlsx files.