{
"METABOLOMICS WORKBENCH":{"STUDY_ID":"ST003036","ANALYSIS_ID":"AN004978","VERSION":"1","CREATED_ON":"January 8, 2024, 9:44 am"},

"PROJECT":{"PROJECT_TITLE":"Identifying and mathematically modeling the time-course of extracellular metabolic markers associated with resistance to ceftolozane/tazobactam in Pseudomonas aeruginosa","PROJECT_TYPE":"Biomedical research","PROJECT_SUMMARY":"Extracellular bacterial metabolites have potential as markers of bacterial growth and resistance emergence, but have not been evaluated in dynamic in vitro studies. We investigated the dynamic metabolomic footprint of a multidrug-resistant hypermutable Pseudomonas aeruginosa isolate exposed to ceftolozane/tazobactam as continuous infusion (4.5g/day, 9g/day) in a hollow-fiber infection model over 7-9 days in biological replicates (n=5). Bacterial samples were collected at 0, 7, 23, 47, 71, 95, 143, 167, 191 and 215h, the supernatant quenched and extracellular metabolites extracted. Metabolites were analyzed via untargeted metabolomics, including hierarchical clustering and correlation with quantified total and resistant bacterial populations. The time-courses of five metabolites were mathematically modeled. These five (of 1921 detected) metabolites were from enriched pathways (arginine and central carbon metabolism). Absorbed L-arginine and secreted L-ornithine were highly correlated with the total bacterial population (r -0.79 and 0.82 respectively, p<0.0001). Ribose-5-phosphate, sedoheptulose-7-phosphate and trehalose-6-phosphate correlated with the resistant subpopulation (0.64, 0.64 and 0.67, respectively, p<0.0001), and were likely secreted due to resistant growth overcoming oxidative and osmotic stress induced by ceftolozane/tazobactam. Using PK/PD-based transduction models, these metabolites were successfully modeled based on the total or resistant bacterial populations. The models well described the abundance of each metabolite across the differing time-course profiles of biological replicates, based on bacterial killing and, importantly, resistant regrowth. These proof-of-concept studies suggest further exploration is warranted to determine the generalizability of these findings. The metabolites modeled in this work are not exclusive to bacterial cells. Future studies may use this approach to identify bacteria-specific metabolites correlating with resistance, which would ultimately be extremely useful for clinical translation.","INSTITUTE":"Monash Institute of Pharmaceutical Sciences","DEPARTMENT":"Drug Delivery, Disposition and Dynamics","LABORATORY":"Cornelia Landersdorfer","LAST_NAME":"Landersdorfer","FIRST_NAME":"Cornelia","ADDRESS":"399 Royal Pd","EMAIL":"cornelia.landersdorfer@monash.edu","PHONE":"+61 3 9903 9061"},

"STUDY":{"STUDY_TITLE":"Identifying and mathematically modeling the time-course of extracellular metabolic markers associated with resistance to ceftolozane/tazobactam in Pseudomonas aeruginosa - Part 2","STUDY_SUMMARY":"Extracellular bacterial metabolites have potential as markers of bacterial growth and resistance emergence, but have not been evaluated in dynamic in vitro studies. We investigated the dynamic metabolomic footprint of a multidrug-resistant hypermutable Pseudomonas aeruginosa isolate exposed to ceftolozane/tazobactam as continuous infusion (4.5g/day, 9g/day) in a hollow-fiber infection model over 7-9 days in biological replicates (n=5). Bacterial samples were collected at 0, 7, 23, 47, 71, 95, 143, 167, 191 and 215h, the supernatant quenched and extracellular metabolites extracted. Metabolites were analyzed via untargeted metabolomics, including hierarchical clustering and correlation with quantified total and resistant bacterial populations. The time-courses of five metabolites were mathematically modeled. These five (of 1921 detected) metabolites were from enriched pathways (arginine and central carbon metabolism). Absorbed L-arginine and secreted L-ornithine were highly correlated with the total bacterial population (r -0.79 and 0.82 respectively, p<0.0001). Ribose-5-phosphate, sedoheptulose-7-phosphate and trehalose-6-phosphate correlated with the resistant subpopulation (0.64, 0.64 and 0.67, respectively, p<0.0001), and were likely secreted due to resistant growth overcoming oxidative and osmotic stress induced by ceftolozane/tazobactam. Using PK/PD-based transduction models, these metabolites were successfully modeled based on the total or resistant bacterial populations. The models well described the abundance of each metabolite across the differing time-course profiles of biological replicates, based on bacterial killing and, importantly, resistant regrowth. These proof-of-concept studies suggest further exploration is warranted to determine the generalizability of these findings. The metabolites modeled in this work are not exclusive to bacterial cells. Future studies may use this approach to identify bacteria-specific metabolites correlating with resistance, which would ultimately be extremely useful for clinical translation.","INSTITUTE":"Monash Institute of Pharmaceutical Sciences","DEPARTMENT":"Drug Delivery, Disposition and Dynamics","LABORATORY":"Cornelia Landersdorfer","LAST_NAME":"Landersdorfer","FIRST_NAME":"Cornelia","ADDRESS":"399 Royal Pd","EMAIL":"cornelia.landersdorfer@monash.edu","NUM_GROUPS":"6 groups with time points","TOTAL_SUBJECTS":"NA","NUM_MALES":"NA","NUM_FEMALES":"NA","PUBLICATIONS":"Identifying and mathematically modeling the time-course of extracellular metabolic markers associated with resistance to ceftolozane/tazobactam in Pseudomonas aeruginosa","STUDY_TYPE":"Biomedical research","PHONE":"+61 3 9903 9061"},

"SUBJECT":{"SUBJECT_TYPE":"Bacteria","SUBJECT_SPECIES":"Pseudomonas aeruginosa","TAXONOMY_ID":"287","GENOTYPE_STRAIN":"CW41","AGE_OR_AGE_RANGE":"NA","WEIGHT_OR_WEIGHT_RANGE":"NA","GENDER":"Not applicable"},
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{
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{
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{
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{
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"Additional sample data":{"RAW_FILE_NAME":"QC_5"}
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{
"Subject ID":"-",
"Sample ID":"QC_6",
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],
"COLLECTION":{"COLLECTION_SUMMARY":"A hypermutable P. aeruginosa clinical isolate, CW41, was challenged with ceftolozane-tazobactam (Zerbaxa®, MSD, Australia) in the HFIM (C3008-1 cartridges; FiberCell Systems Inc., Frederick, MD, USA), in five biological replicates performed across two studies. The first study, with replicates 1 and 2, was conducted over 167 h and the second study, with replicates 3, 4 and 5, over 215 h. Briefly, the studied isolate was characterized as susceptible to ceftolozane-tazobactam (MIC 4 mg/L), and MDR (i.e. resistant to at least 1 antibiotic from each of ≥3 antibiotic classes) (17-20). The HFIM studies used cation-adjusted Mueller Hinton broth (CAMHB) and agar (CAMHA) [Becton Dickinson & Co., Sparks, MD, USA, with 25.0 mg/L Ca2+ and 12.5 mg/L Mg2+]. Ceftolozane-tazobactam was administered to simulate steady-state concentrations of ceftolozane predicted to occur in the epithelial lining fluid of the lung in patients with CF, following daily doses of 3 g/1.5 g and 6 g/3 g via continuous infusion (10.6 and 21.3 mg/L, respectively) (21-23). Total bacterial populations were quantified on antibiotic-free CAMHA, and resistant subpopulations on CAMHA containing ceftolozane-tazobactam (12 and 20 mg/L).","SAMPLE_TYPE":"Bacterial cells"},

"TREATMENT":{"TREATMENT_SUMMARY":"Ceftolozane-tazobactam was administered to simulate steady-state concentrations of ceftolozane predicted to occur in the epithelial lining fluid of the lung in patients with CF, following daily doses of 3 g/1.5 g and 6 g/3 g via continuous infusion (10.6 and 21.3 mg/L, respectively) (21-23). Total bacterial populations were quantified on antibiotic-free CAMHA, and resistant subpopulations on CAMHA containing ceftolozane-tazobactam (12 and 20 mg/L).","TREATMENT_COMPOUND":"Ceftolozane-tazobactam","TREATMENT_DOSE":"3 g and 6 g","TREATMENT_DOSEVOLUME":"10.6 and 21.3 mg/L"},

"SAMPLEPREP":{"SAMPLEPREP_SUMMARY":"Each sample (25 µL) was added to 100 µL of pre-chilled methanol containing the internal standards (CHAPS, CAPS, TRIS and PIPES) at 1 µM. This mixture was vortexed, and subsequently centrifuged at 14800 x g and 4°C for 10 min .The final supernatant samples containing the extracted extracellular metabolites were stored at -80°C until LC-MS analysis was performed (Figure S2B).","PROCESSING_METHOD":"IDEOM","PROCESSING_STORAGE_CONDITIONS":"-80℃","EXTRACT_STORAGE":"-80℃"},

"CHROMATOGRAPHY":{"CHROMATOGRAPHY_SUMMARY":"ZIC-pHILIC chromatography at pH 9 using pos neg switching","CHROMATOGRAPHY_TYPE":"HILIC","INSTRUMENT_NAME":"Thermo Dionex Ultimate 3000","COLUMN_NAME":"Merck SeQuant ZIC-pHILIC (150 x 4.6mm,5um)","SOLVENT_A":"20 mM ammonium carbonate","SOLVENT_B":"acetonitrile","FLOW_GRADIENT":"0 min - 80%B, 15 min - 50%B, 18 min - 5%B, 21 min - 5%B, 24 min - 80%B, 32 min - 80%B","FLOW_RATE":"0.3 ml/min","COLUMN_TEMPERATURE":"25 C","METHODS_FILENAME":"Metabolomics_pHILIC_Parkville_v1.pdf","WASHING_BUFFER":"syringe wash 50% IPA"},

"ANALYSIS":{"ANALYSIS_TYPE":"MS","LABORATORY_NAME":"Monash Proteomics and Metabolomics Platform","OPERATOR_NAME":"Dovile Anderson","DETECTOR_TYPE":"Full scan","SOFTWARE_VERSION":"XCalibur 4.6","ACQUISITION_DATE":"24/04/2020","ANALYSIS_PROTOCOL_FILE":"PQMS3-MPMF-WIN-0501_LCMS_data_acquisition_for_untargeted_metabolomics_analysis.pdf","ACQUISITION_PARAMETERS_FILE":"Metabolomics_pHILIC_Parkville_v1.pdf","DATA_FORMAT":".RAW"},

"MS":{"INSTRUMENT_NAME":"Thermo Q Exactive Orbitrap","INSTRUMENT_TYPE":"Orbitrap","MS_TYPE":"ESI","ION_MODE":"NEGATIVE","MS_COMMENTS":"polarity switching used, resolution 35 k, full scan","CAPILLARY_TEMPERATURE":"300 C","CAPILLARY_VOLTAGE":"4 kV","COLLISION_ENERGY":"NA","DRY_GAS_FLOW":"50","DRY_GAS_TEMP":"120","ION_SOURCE_TEMPERATURE":"120 C","MASS_ACCURACY":"3 ppm","PRECURSOR_TYPE":"[M-H]-"},

"MS_METABOLITE_DATA":{
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}