Summary of Study ST002715
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001683. The data can be accessed directly via it's Project DOI: 10.21228/M8599Z This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST002715 |
| Study Title | Metabolic alteration of MCF-7 cells upon indirect exposure to E. coli secretome: A model of studying the microbiota effect on human breast tissue |
| Study Summary | Cancer is a challenging disease that requires a comprehensive approach for effective treatment. Various bacterial species, including clostridia, bifidobacteria, and salmonellae, have been investigated in numerous animal tumor models, cell lines, and clinical trials as gene carriers for anti-cancerous genes, including tumor suppressor genes, suicide genes, or tumor-associated antigens. Therefore, they render cell cancer more sensitive to treatment, and they can be used as drug/gene delivery vehicles. E. coli, as one of the breast tissue microbiomes, secretes metabolites that could influence the metabolism of MCF-7 cells to ensure their survival. This in vitro investigation concentrated primarily on the role of E. coli secretome modulation on the MCF-7 cells metabolism. The intra- and extracellular metabolomes of the E. coli secretome and secretome exposed MCF-7 cells were profiled using the liquid chromatography-mass spectrometry (LC-MS) metabolomics approach. Secretome-exposed MCF-7 cells were compared to unexposed controls; a total of 31 and 56 metabolites were significantly altered intra- and extracellularly, respectively. The most common metabolic pathways dysregulated after exposure were aminoacyl-tRNA biosynthesis, purine metabolism, and energy metabolism. The decrease in some purine metabolites would suggest that altering nucleotide metabolism is one of the ways the bacterial secretome kills cancer cells. The maximum discrimination between the two groups was found in lactate levels, which plays a crucial role in cancer progression. The Warburg effect causes cancer tissue to have an acidic microenvironment, which impacts cancer cell metastasis and proliferation, inflammation, immune cell function, and blood vessel development; the decrease in lactate content may also be a method by which the secretome affects cancer. Finally, some microbial metabolites from bacterial secretome have shown promising anticancer effects and can be employed as innovative ways for cancer treatment, either alone or in combination with other medicines. |
| Institute | King Saud University |
| Last Name | AlMalki |
| First Name | Reem |
| Address | King Fahad road |
| 439203044@student.ksu.edu.sa | |
| Phone | 0534045397 |
| Submit Date | 2023-05-21 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | raw(Waters) |
| Analysis Type Detail | LC-MS |
| Release Date | 2023-06-22 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Project:
| Project ID: | PR001683 |
| Project DOI: | doi: 10.21228/M8599Z |
| Project Title: | Metabolic alteration of MCF-7 cells upon indirect exposure to E. coli secretome: A model of studying the microbiota effect on human breast tissue |
| Project Type: | Microbiome-breast cancer microenvironment metabolomics |
| Project Summary: | Cancer is a challenging disease that requires a comprehensive approach for effective treatment. Various bacterial species, including clostridia, bifidobacteria, and salmonellae, have been investigated in numerous animal tumor models, cell lines, and clinical trials as gene carriers for anti-cancerous genes, including tumor suppressor genes, suicide genes, or tumor-associated antigens. Therefore, they render cell cancer more sensitive to treatment, and they can be used as drug/gene delivery vehicles. E. coli, as one of the breast tissue microbiomes, secretes metabolites that could influence the metabolism of MCF-7 cells to ensure their survival. This in vitro investigation concentrated primarily on the role of E. coli secretome modulation on the MCF-7 cells metabolism. The intra- and extracellular metabolomes of the E. coli secretome and secretome exposed MCF-7 cells were profiled using the liquid chromatography-mass spectrometry (LC-MS) metabolomics approach. Secretome-exposed MCF-7 cells were compared to unexposed controls; a total of 31 and 56 metabolites were significantly altered intra- and extracellularly, respectively. The most common metabolic pathways dysregulated after exposure were aminoacyl-tRNA biosynthesis, purine metabolism, and energy metabolism. The decrease in some purine metabolites would suggest that altering nucleotide metabolism is one of the ways the bacterial secretome kills cancer cells. The maximum discrimination between the two groups was found in lactate levels, which plays a crucial role in cancer progression. The Warburg effect causes cancer tissue to have an acidic microenvironment, which impacts cancer cell metastasis and proliferation, inflammation, immune cell function, and blood vessel development; the decrease in lactate content may also be a method by which the secretome affects cancer. Finally, some microbial metabolites from bacterial secretome have shown promising anticancer effects and can be employed as innovative ways for cancer treatment, either alone or in combination with other medicines. |
| Institute: | King Saud University |
| Last Name: | AlMalki |
| First Name: | Reem |
| Address: | King Fahad road |
| Email: | 439203044@student.ksu.edu.sa |
| Phone: | 0534045397 |
Subject:
| Subject ID: | SU002820 |
| Subject Type: | Human |
| Subject Species: | Homo sapiens |
| Taxonomy ID: | 9606 |
Factors:
Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)
| mb_sample_id | local_sample_id | Treatment |
|---|---|---|
| SA273562 | nMCF7_36_EC | No |
| SA273563 | nMCF7_26_EC | No |
| SA273564 | nMCF7_32_EC | No |
| SA273565 | nMCF7_18_EC | No |
| SA273566 | nMCF7_10_EC | No |
| SA273567 | nMCF7_38_EC | No |
| SA273568 | nMCF7_324_EC | No |
| SA273569 | nMCF7_224_EC | No |
| SA273570 | nMCF7_124_EC | No |
| SA273571 | nMCF7_22_EC | No |
| SA273572 | nMCF7_28_EC | No |
| SA273573 | nMCF7_16_EC | No |
| SA273574 | nMCF7_12_EC | No |
| SA273575 | nMCF7_11_EC | No |
| SA273576 | nMCF7_30_EC | No |
| SA273577 | nMCF7_20_EC | No |
| SA273578 | nMCF7_31_EC | No |
| SA273579 | nMCF7_21_EC | No |
| SA273580 | tMCF7_10_EC | Yes |
| SA273581 | tMCF7_20_EC | Yes |
| SA273582 | tMCF7_38_EC | Yes |
| SA273583 | tMCF7_12_EC | Yes |
| SA273584 | tMCF7_124_EC | Yes |
| SA273585 | tMCF7_31_EC | Yes |
| SA273586 | tMCF7_324_EC | Yes |
| SA273587 | tMCF7_224_EC | Yes |
| SA273588 | tMCF7_28_EC | Yes |
| SA273589 | tMCF7_18_EC | Yes |
| SA273590 | tMCF7_26_EC | Yes |
| SA273591 | tMCF7_16_EC | Yes |
| SA273592 | tMCF7_21_EC | Yes |
| SA273593 | tMCF7_36_EC | Yes |
| SA273594 | tMCF7_32_EC | Yes |
| SA273595 | tMCF7_30_EC | Yes |
| SA273596 | tMCF7_22_EC | Yes |
| SA273597 | tMCF7_11_EC | Yes |
| Showing results 1 to 36 of 36 |
Collection:
| Collection ID: | CO002813 |
| Collection Summary: | See MCF-7_biological_samples.docx |
| Collection Protocol Filename: | MCF-7_biological_samples.docx |
| Sample Type: | Cultured cells |
Treatment:
| Treatment ID: | TR002829 |
| Treatment Summary: | See MCF-7_biological_samples.docx |
| Treatment Protocol Filename: | MCF-7_biological_samples.docx |
Sample Preparation:
| Sampleprep ID: | SP002826 |
| Sampleprep Summary: | See Metabolites_Extraction.docx |
| Sampleprep Protocol Filename: | Metabolites_Extraction.docx |
Combined analysis:
| Analysis ID | AN004401 | AN004402 |
|---|---|---|
| Analysis type | MS | MS |
| Chromatography type | Reversed phase | Reversed phase |
| Chromatography system | Waters Acquity UPLC | Waters Acquity UPLC |
| Column | Waters XSelect HSS C18 (100 × 2.1mm, 2.5um) | Waters XSelect HSS C18 (100 × 2.1mm, 2.5um) |
| MS Type | ESI | ESI |
| MS instrument type | QTOF | QTOF |
| MS instrument name | Waters Xevo G2-S | Waters Xevo G2-S |
| Ion Mode | POSITIVE | NEGATIVE |
| Units | peak area | peak area |
Chromatography:
| Chromatography ID: | CH003303 |
| Chromatography Summary: | See LC-MS_Metabolomics_MCF-7.docx |
| Methods Filename: | LC-MS_Metabolomics_MCF-7.docx |
| Instrument Name: | Waters Acquity UPLC |
| Column Name: | Waters XSelect HSS C18 (100 × 2.1mm, 2.5um) |
| Column Temperature: | 55 |
| Flow Gradient: | 0–16 min 95%–5% A, 16–19 min 5% A, 19–20 min 5%–95% A, and 20–22 min, 95%– 95% A |
| Flow Rate: | 300 μl/min. |
| Solvent A: | 0.1% formic acid in dH2O |
| Solvent B: | 0.1% formic acid in 50% ACN:MeOH |
| Chromatography Type: | Reversed phase |
MS:
| MS ID: | MS004150 |
| Analysis ID: | AN004401 |
| Instrument Name: | Waters Xevo G2-S |
| Instrument Type: | QTOF |
| MS Type: | ESI |
| MS Comments: | Data Independent Acquisition (DIA) was collected in continuum mode with Masslynx™ V4.1 workstation (Waters Inc., Milford, Massachusetts, USA). The MS raw data were processed following a standard pipeline starting from alignment based on the m/z value and the ion signals' retention time, peak picking, and signal filtering based on the peak quality using the Progenesis QI v.3.0 software from Waters |
| Ion Mode: | POSITIVE |
| Analysis Protocol File: | LC-MS_Metabolomics_MCF-7.docx |
| MS ID: | MS004151 |
| Analysis ID: | AN004402 |
| Instrument Name: | Waters Xevo G2-S |
| Instrument Type: | QTOF |
| MS Type: | ESI |
| MS Comments: | Data Independent Acquisition (DIA) was collected in continuum mode with Masslynx™ V4.1 workstation (Waters Inc., Milford, Massachusetts, USA). The MS raw data were processed following a standard pipeline starting from alignment based on the m/z value and the ion signals' retention time, peak picking, and signal filtering based on the peak quality using the Progenesis QI v.3.0 software from Waters |
| Ion Mode: | NEGATIVE |
| Analysis Protocol File: | LC-MS_Metabolomics_MCF-7.docx |
