#METABOLOMICS WORKBENCH ReemAlMalki91_20230521_105808 DATATRACK_ID:4034 STUDY_ID:ST002715 ANALYSIS_ID:AN004401 PROJECT_ID:PR001683 VERSION 1 CREATED_ON May 21, 2023, 12:09 pm #PROJECT PR:PROJECT_TITLE Metabolic alteration of MCF-7 cells upon indirect exposure to E. coli secretome: PR:PROJECT_TITLE A model of studying the microbiota effect on human breast tissue PR:PROJECT_TYPE Microbiome-breast cancer microenvironment metabolomics PR:PROJECT_SUMMARY Cancer is a challenging disease that requires a comprehensive approach for PR:PROJECT_SUMMARY effective treatment. Various bacterial species, including clostridia, PR:PROJECT_SUMMARY bifidobacteria, and salmonellae, have been investigated in numerous animal tumor PR:PROJECT_SUMMARY models, cell lines, and clinical trials as gene carriers for anti-cancerous PR:PROJECT_SUMMARY genes, including tumor suppressor genes, suicide genes, or tumor-associated PR:PROJECT_SUMMARY antigens. Therefore, they render cell cancer more sensitive to treatment, and PR:PROJECT_SUMMARY they can be used as drug/gene delivery vehicles. E. coli, as one of the breast PR:PROJECT_SUMMARY tissue microbiomes, secretes metabolites that could influence the metabolism of PR:PROJECT_SUMMARY MCF-7 cells to ensure their survival. This in vitro investigation concentrated PR:PROJECT_SUMMARY primarily on the role of E. coli secretome modulation on the MCF-7 cells PR:PROJECT_SUMMARY metabolism. The intra- and extracellular metabolomes of the E. coli secretome PR:PROJECT_SUMMARY and secretome exposed MCF-7 cells were profiled using the liquid PR:PROJECT_SUMMARY chromatography-mass spectrometry (LC-MS) metabolomics approach. PR:PROJECT_SUMMARY Secretome-exposed MCF-7 cells were compared to unexposed controls; a total of 31 PR:PROJECT_SUMMARY and 56 metabolites were significantly altered intra- and extracellularly, PR:PROJECT_SUMMARY respectively. The most common metabolic pathways dysregulated after exposure PR:PROJECT_SUMMARY were aminoacyl-tRNA biosynthesis, purine metabolism, and energy metabolism. The PR:PROJECT_SUMMARY decrease in some purine metabolites would suggest that altering nucleotide PR:PROJECT_SUMMARY metabolism is one of the ways the bacterial secretome kills cancer cells. The PR:PROJECT_SUMMARY maximum discrimination between the two groups was found in lactate levels, which PR:PROJECT_SUMMARY plays a crucial role in cancer progression. The Warburg effect causes cancer PR:PROJECT_SUMMARY tissue to have an acidic microenvironment, which impacts cancer cell metastasis PR:PROJECT_SUMMARY and proliferation, inflammation, immune cell function, and blood vessel PR:PROJECT_SUMMARY development; the decrease in lactate content may also be a method by which the PR:PROJECT_SUMMARY secretome affects cancer. Finally, some microbial metabolites from bacterial PR:PROJECT_SUMMARY secretome have shown promising anticancer effects and can be employed as PR:PROJECT_SUMMARY innovative ways for cancer treatment, either alone or in combination with other PR:PROJECT_SUMMARY medicines. PR:INSTITUTE King Saud University PR:LAST_NAME AlMalki PR:FIRST_NAME Reem PR:ADDRESS King Fahad road PR:EMAIL 439203044@student.ksu.edu.sa PR:PHONE 0534045397 #STUDY ST:STUDY_TITLE Metabolic alteration of MCF-7 cells upon indirect exposure to E. coli secretome: ST:STUDY_TITLE A model of studying the microbiota effect on human breast tissue ST:STUDY_SUMMARY Cancer is a challenging disease that requires a comprehensive approach for ST:STUDY_SUMMARY effective treatment. Various bacterial species, including clostridia, ST:STUDY_SUMMARY bifidobacteria, and salmonellae, have been investigated in numerous animal tumor ST:STUDY_SUMMARY models, cell lines, and clinical trials as gene carriers for anti-cancerous ST:STUDY_SUMMARY genes, including tumor suppressor genes, suicide genes, or tumor-associated ST:STUDY_SUMMARY antigens. Therefore, they render cell cancer more sensitive to treatment, and ST:STUDY_SUMMARY they can be used as drug/gene delivery vehicles. E. coli, as one of the breast ST:STUDY_SUMMARY tissue microbiomes, secretes metabolites that could influence the metabolism of ST:STUDY_SUMMARY MCF-7 cells to ensure their survival. This in vitro investigation concentrated ST:STUDY_SUMMARY primarily on the role of E. coli secretome modulation on the MCF-7 cells ST:STUDY_SUMMARY metabolism. The intra- and extracellular metabolomes of the E. coli secretome ST:STUDY_SUMMARY and secretome exposed MCF-7 cells were profiled using the liquid ST:STUDY_SUMMARY chromatography-mass spectrometry (LC-MS) metabolomics approach. ST:STUDY_SUMMARY Secretome-exposed MCF-7 cells were compared to unexposed controls; a total of 31 ST:STUDY_SUMMARY and 56 metabolites were significantly altered intra- and extracellularly, ST:STUDY_SUMMARY respectively. The most common metabolic pathways dysregulated after exposure ST:STUDY_SUMMARY were aminoacyl-tRNA biosynthesis, purine metabolism, and energy metabolism. The ST:STUDY_SUMMARY decrease in some purine metabolites would suggest that altering nucleotide ST:STUDY_SUMMARY metabolism is one of the ways the bacterial secretome kills cancer cells. The ST:STUDY_SUMMARY maximum discrimination between the two groups was found in lactate levels, which ST:STUDY_SUMMARY plays a crucial role in cancer progression. The Warburg effect causes cancer ST:STUDY_SUMMARY tissue to have an acidic microenvironment, which impacts cancer cell metastasis ST:STUDY_SUMMARY and proliferation, inflammation, immune cell function, and blood vessel ST:STUDY_SUMMARY development; the decrease in lactate content may also be a method by which the ST:STUDY_SUMMARY secretome affects cancer. Finally, some microbial metabolites from bacterial ST:STUDY_SUMMARY secretome have shown promising anticancer effects and can be employed as ST:STUDY_SUMMARY innovative ways for cancer treatment, either alone or in combination with other ST:STUDY_SUMMARY medicines. ST:INSTITUTE King Saud University ST:LAST_NAME AlMalki ST:FIRST_NAME Reem ST:ADDRESS King Fahad road ST:EMAIL 439203044@student.ksu.edu.sa ST:PHONE 0534045397 #SUBJECT SU:SUBJECT_TYPE Human SU:SUBJECT_SPECIES Homo sapiens SU:TAXONOMY_ID 9606 #FACTORS #SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Raw file names and additional sample data SUBJECT_SAMPLE_FACTORS non-Treated nMCF7_10_EC Treatment:No RAW_FILE_NAME=nMCF7_10_EC SUBJECT_SAMPLE_FACTORS non-Treated nMCF7_20_EC Treatment:No RAW_FILE_NAME=nMCF7_20_EC SUBJECT_SAMPLE_FACTORS non-Treated nMCF7_30_EC Treatment:No RAW_FILE_NAME=nMCF7_30_EC SUBJECT_SAMPLE_FACTORS Treated tMCF7_10_EC Treatment:Yes RAW_FILE_NAME=tMCF7_10_EC SUBJECT_SAMPLE_FACTORS Treated tMCF7_20_EC Treatment:Yes RAW_FILE_NAME=tMCF7_20_EC SUBJECT_SAMPLE_FACTORS Treated tMCF7_30_EC Treatment:Yes RAW_FILE_NAME=tMCF7_30_EC SUBJECT_SAMPLE_FACTORS non-Treated nMCF7_11_EC Treatment:No RAW_FILE_NAME=nMCF7_11_EC SUBJECT_SAMPLE_FACTORS non-Treated nMCF7_21_EC Treatment:No RAW_FILE_NAME=nMCF7_21_EC SUBJECT_SAMPLE_FACTORS non-Treated nMCF7_31_EC Treatment:No RAW_FILE_NAME=nMCF7_31_EC SUBJECT_SAMPLE_FACTORS Treated tMCF7_11_EC Treatment:Yes RAW_FILE_NAME=tMCF7_11_EC SUBJECT_SAMPLE_FACTORS Treated tMCF7_21_EC Treatment:Yes RAW_FILE_NAME=tMCF7_21_EC SUBJECT_SAMPLE_FACTORS Treated tMCF7_31_EC Treatment:Yes RAW_FILE_NAME=tMCF7_31_EC SUBJECT_SAMPLE_FACTORS non-Treated nMCF7_12_EC Treatment:No RAW_FILE_NAME=nMCF7_12_EC SUBJECT_SAMPLE_FACTORS non-Treated nMCF7_22_EC Treatment:No RAW_FILE_NAME=nMCF7_22_EC SUBJECT_SAMPLE_FACTORS non-Treated nMCF7_32_EC Treatment:No RAW_FILE_NAME=nMCF7_32_EC SUBJECT_SAMPLE_FACTORS Treated tMCF7_12_EC Treatment:Yes RAW_FILE_NAME=tMCF7_12_EC SUBJECT_SAMPLE_FACTORS Treated tMCF7_22_EC Treatment:Yes RAW_FILE_NAME=tMCF7_22_EC SUBJECT_SAMPLE_FACTORS Treated tMCF7_32_EC Treatment:Yes RAW_FILE_NAME=tMCF7_32_EC SUBJECT_SAMPLE_FACTORS non-Treated nMCF7_16_EC Treatment:No RAW_FILE_NAME=nMCF7_16_EC SUBJECT_SAMPLE_FACTORS non-Treated nMCF7_26_EC Treatment:No RAW_FILE_NAME=nMCF7_26_EC SUBJECT_SAMPLE_FACTORS non-Treated nMCF7_36_EC Treatment:No RAW_FILE_NAME=nMCF7_36_EC SUBJECT_SAMPLE_FACTORS Treated tMCF7_16_EC Treatment:Yes RAW_FILE_NAME=tMCF7_16_EC SUBJECT_SAMPLE_FACTORS Treated tMCF7_26_EC Treatment:Yes RAW_FILE_NAME=tMCF7_26_EC SUBJECT_SAMPLE_FACTORS Treated tMCF7_36_EC Treatment:Yes RAW_FILE_NAME=tMCF7_36_EC SUBJECT_SAMPLE_FACTORS non-Treated nMCF7_18_EC Treatment:No RAW_FILE_NAME=nMCF7_18_EC SUBJECT_SAMPLE_FACTORS non-Treated nMCF7_28_EC Treatment:No RAW_FILE_NAME=nMCF7_28_EC SUBJECT_SAMPLE_FACTORS non-Treated nMCF7_38_EC Treatment:No RAW_FILE_NAME=nMCF7_38_EC SUBJECT_SAMPLE_FACTORS Treated tMCF7_18_EC Treatment:Yes RAW_FILE_NAME=tMCF7_18_EC SUBJECT_SAMPLE_FACTORS Treated tMCF7_28_EC Treatment:Yes RAW_FILE_NAME=tMCF7_28_EC SUBJECT_SAMPLE_FACTORS Treated tMCF7_38_EC Treatment:Yes RAW_FILE_NAME=tMCF7_38_EC SUBJECT_SAMPLE_FACTORS non-Treated nMCF7_124_EC Treatment:No RAW_FILE_NAME=nMCF7_124_EC SUBJECT_SAMPLE_FACTORS non-Treated nMCF7_224_EC Treatment:No RAW_FILE_NAME=nMCF7_224_EC SUBJECT_SAMPLE_FACTORS non-Treated nMCF7_324_EC Treatment:No RAW_FILE_NAME=nMCF7_324_EC SUBJECT_SAMPLE_FACTORS Treated tMCF7_124_EC Treatment:Yes RAW_FILE_NAME=tMCF7_124_EC SUBJECT_SAMPLE_FACTORS Treated tMCF7_224_EC Treatment:Yes RAW_FILE_NAME=tMCF7_224_EC SUBJECT_SAMPLE_FACTORS Treated tMCF7_324_EC Treatment:Yes RAW_FILE_NAME=tMCF7_324_EC #COLLECTION CO:COLLECTION_SUMMARY MCF-7_biological samples CO:SAMPLE_TYPE Cultured cells #TREATMENT TR:TREATMENT_SUMMARY yes #SAMPLEPREP SP:SAMPLEPREP_SUMMARY Metabolites extraction #CHROMATOGRAPHY CH:CHROMATOGRAPHY_TYPE Reversed phase CH:INSTRUMENT_NAME Waters Acquity CH:COLUMN_NAME Waters Acquity UPLC XSelect HSS C18 (100 × 2.1mm, 2.5um) CH:SOLVENT_A 0.1% formic acid in dH2O CH:SOLVENT_B 0.1% formic acid in 50% MeOH and ACN CH:FLOW_GRADIENT 0–16 min 95%–5% A, 16–19 min 5% A, 19–20 min 5%–95% A, and 20–22 CH:FLOW_GRADIENT min, 95%– 95% A CH:FLOW_RATE 300 μl/min. CH:COLUMN_TEMPERATURE 55 #ANALYSIS AN:ANALYSIS_TYPE MS #MS MS:INSTRUMENT_NAME Waters Xevo-G2-S MS:INSTRUMENT_TYPE QTOF MS:MS_TYPE ESI MS:ION_MODE POSITIVE MS:MS_COMMENTS Data Independent Acquisition (DIA) was collected in continuum mode with MS:MS_COMMENTS Masslynx™ V4.1 workstation (Waters Inc., Milford, Massachusetts, USA). The MS MS:MS_COMMENTS raw data were processed following a standard pipeline starting from alignment MS:MS_COMMENTS based on the m/z value and the ion signals' retention time, peak picking, and MS:MS_COMMENTS signal filtering based on the peak quality using the Progenesis QI v.3.0 MS:MS_COMMENTS software from Waters MS:MS_RESULTS_FILE ST002715_AN004401_Results.txt UNITS:peak area Has m/z:Yes Has RT:Yes RT units:Minutes #END