Summary of Study ST001548
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 PR001029. The data can be accessed directly via it's Project DOI: 10.21228/M8PM56 This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
| Study ID | ST001548 |
| Study Title | β-Adrenergic regulation of metabolism in macrophages (part-II) |
| Study Summary | Macrophages have important roles in the immune system including clearing pathogens and wound healing. Metabolic phenotypes have been associated with functional phenotypes, where pro-inflammatory macrophages have an increased rate of glycolysis and anti-inflammatory macrophages primarily use oxidative phosphorylation. β-adrenoceptor (βAR) signalling in macrophages has been implicated in disease states such as cancer, atherosclerosis and rheumatoid arthritis. The impact of β-adrenoceptor signalling on macrophage metabolism has not been defined. Here we expand on defining the phenotype of macrophages treated with isoprenaline and describe the impact that βAR signalling has on the metabolome and proteome. We found that βAR signalling alters proteins involved in cytoskeletal rearrangement and redox control of the cell. We showed that βAR signalling in macrophages shifts glucose metabolism from glycolysis towards the tricarboxylic acid cycle and pentose phosphate pathways. We also show that βAR signalling perturbs purine metabolism by accumulating adenylate pools. Taken together these results indicate that βAR signalling shifts metabolism to support redox perturbations and upregulate proteins involved in cytoskeletal changes that may impact migration and phagocytosis processes. |
| Institute | Monash University |
| Last Name | Peterson |
| First Name | Amanda |
| Address | Drug delivery, disposition and dynamics, Pharmacy and Pharmaceutical Sciences, 381 Royal Parade, Parkville, Victoria, 3052, Australia |
| amanda.peterson@monash.edu | |
| Phone | 99039282 |
| Submit Date | 2020-11-17 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | raw(Thermo) |
| Analysis Type Detail | LC-MS |
| Release Date | 2020-12-01 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Project:
| Project ID: | PR001029 |
| Project DOI: | doi: 10.21228/M8PM56 |
| Project Title: | β-Adrenergic regulation of metabolism in macrophages |
| Project Summary: | Macrophages have important roles in the immune system including clearing pathogens and wound healing. Metabolic phenotypes have been associated with functional phenotypes, where pro-inflammatory macrophages have an increased rate of glycolysis and anti-inflammatory macrophages primarily use oxidative phosphorylation. β-adrenoceptor (βAR) signalling in macrophages has been implicated in disease states such as cancer, atherosclerosis and rheumatoid arthritis. The impact of β-adrenoceptor signalling on macrophage metabolism has not been defined. Here we expand on defining the phenotype of macrophages treated with isoprenaline and describe the impact that βAR signalling has on the metabolome and proteome. We found that βAR signalling alters proteins involved in cytoskeletal rearrangement and redox control of the cell. We showed that βAR signalling in macrophages shifts glucose metabolism from glycolysis towards the tricarboxylic acid cycle and pentose phosphate pathways. We also show that βAR signalling perturbs purine metabolism by accumulating adenylate pools. Taken together these results indicate that βAR signalling shifts metabolism to support redox perturbations and upregulate proteins involved in cytoskeletal changes that may impact migration and phagocytosis processes. |
| Institute: | Monash University |
| Last Name: | Peterson |
| First Name: | Amanda |
| Address: | Drug delivery, disposition and dynamics, Pharmacy and Pharmaceutical Sciences, 381 Royal Parade, Parkville, Victoria, 3052, Australia |
| Email: | amanda.peterson@monash.edu |
| Phone: | 99039282 |
Subject:
| Subject ID: | SU001624 |
| Subject Type: | Cultured cells |
| Subject Species: | Homo sapiens |
| Taxonomy ID: | 9606 |
Factors:
Subject type: Cultured cells; Subject species: Homo sapiens (Factor headings shown in green)
| mb_sample_id | local_sample_id | Treatment |
|---|---|---|
| SA130686 | M_control_1 | Control_13C-Glucose |
| SA130687 | M_control_4 | Control_13C-Glucose |
| SA130688 | M_control_3 | Control_13C-Glucose |
| SA130689 | M_control_2 | Control_13C-Glucose |
| SA130690 | M_isoprenaline_4 | Isoprenaline_13C-Glucose |
| SA130691 | M_isoprenaline_1 | Isoprenaline_13C-Glucose |
| SA130692 | M_isoprenaline_2 | Isoprenaline_13C-Glucose |
| SA130693 | M_isoprenaline_3 | Isoprenaline_13C-Glucose |
| Showing results 1 to 8 of 8 |
Collection:
| Collection ID: | CO001617 |
| Collection Summary: | 500 μL of culture medium was collected and centrifuged for 5 minutes at 200 x g at 4°C. 20 μL of the supernatant is transferred to a new Eppendorf tube where 180 μL of 50:50, methanol: acetonitrile (ACN) containing 1 μM internal standards (CHAPS, CAPS, PIPES and TRIS) is added. The samples are placed on a shaker for 30 minutes. Samples were then vortexed for 30 seconds before centrifuging at 20,000 x g for 10 minutes at 4°C. 160 μL of supernatant was transferred to a plastic LC-MS vial and stored at -80°C until LC-MS analysis. |
| Sample Type: | Macrophages |
Treatment:
| Treatment ID: | TR001637 |
| Treatment Summary: | Differentiated macrophage-like cells (9.9x106) were seeded onto fibronectin-coated glass dishes. After 24 hours in culture, cells were treated with 1 µM isoprenaline (or vehicle) and 11 mM U-13C6-D-Glucose labelled medium (to give a final ratio of 50:50 of U-13C and U-12C D-glucose). |
Sample Preparation:
| Sampleprep ID: | SP001630 |
| Sampleprep Summary: | 500 μL of culture medium was collected and centrifuged for 5 minutes at 200 x g at 4°C. 20 μL of the supernatant is transferred to a new Eppendorf tube where 180 μL of 50:50, methanol: acetonitrile (ACN) containing 1 μM internal standards (CHAPS, CAPS, PIPES and TRIS) is added. The samples are placed on a shaker for 30 minutes. Samples were then vortexed for 30 seconds before centrifuging at 20,000 x g for 10 minutes at 4°C. 160 μL of supernatant was transferred to a plastic LC-MS vial and stored at -80°C until LC-MS analysis. |
Combined analysis:
| Analysis ID | AN002578 | AN002579 |
|---|---|---|
| Analysis type | MS | MS |
| Chromatography type | HILIC | HILIC |
| Chromatography system | Thermo Dionex Ultimate 3000 | Thermo Dionex Ultimate 3000 |
| Column | ZIC-pHILIC (150 x 4.6mm,5um) | ZIC-pHILIC (150 x 4.6mm,5um) |
| MS Type | ESI | ESI |
| MS instrument type | Orbitrap | Orbitrap |
| MS instrument name | Thermo Q Exactive Orbitrap | Thermo Exactive Plus Orbitrap |
| Ion Mode | POSITIVE | NEGATIVE |
| Units | Intensity | Intensity |
Chromatography:
| Chromatography ID: | CH001888 |
| Instrument Name: | Thermo Dionex Ultimate 3000 |
| Column Name: | ZIC-pHILIC (150 x 4.6mm,5um) |
| Chromatography Type: | HILIC |
| Chromatography ID: | CH001889 |
| Instrument Name: | Thermo Dionex Ultimate 3000 |
| Column Name: | ZIC-pHILIC (150 x 4.6mm,5um) |
| Chromatography Type: | HILIC |
MS:
| MS ID: | MS002390 |
| Analysis ID: | AN002578 |
| Instrument Name: | Thermo Q Exactive Orbitrap |
| Instrument Type: | Orbitrap |
| MS Type: | ESI |
| MS Comments: | Mass spectrometry was performed in polarity switching mode, with the following settings: resolution 35 000, AGC 1x106, m/z range 85-1275, sheath gas 50, auxiliary gas 20, sweep gas 2, probe temperature 150 °C, and capillary temperature 300 °C. For positive ionisation mode the source voltage was set at +4 kV and the S-lens voltage at +50 V. For negative ionisation mode the source voltage was set at -3.5 kV and the S-lens voltage at -50 V. Mass calibration was performed for each polarity before running a metabolomics batch to ensure mass accuracy of < 2 ppm. Approximately 300 authentic metabolite standards were analysed at the start of each batch to provide accurate retention times to facilitate metabolite identification. Metabolomics samples were analysed in random order with periodic injections of the pooled QC, and blank samples, to assess analytical quality and aid downstream metabolite identification procedures. Metabolomics LC-MS Data Processing Raw metabolite data was processed using XCMS (Centwave) software for peak picking and mzMatch.R software for alignment and annotation of related metabolite peaks. Metabolites were then identified using the Excel-based IDEOM software by matching the mass of each peak and its retention time with a database, using a mass accuracy window of 2 ppm and a retention time window of 5% for metabolites matching authentic standards, and 35% for other putative metabolites based on a retention time prediction model (15). Noise and mass spectrometry artefacts were filtered using previously described algorithms (15, 16) to minimise false identifications. Detection of stable isotope labelled metabolite peaks were performed using mzMatch-ISO (17). Initial statistical analysis was performed with IDEOM using peak intensities (height) for all detected putative metabolites. Untargeted multivariate analysis was performed using MetaboAnalyst 4.0 (18), where the complete data sets were also analysed using enrichment analysis and pathway analysis functions (19, 20). Further analysis was undertaken using TraceFinderTM (Thermo) to obtain manually curated accurate peak areas for targeted univariate analyses for metabolites in key pathways. Metabolomics data are presented as fold-change values from 2 individual experiments, each with 4 replicates. Labelled metabolomics data is from a single metabolomics experiment and presented as a mean ± SD. Differences were determined using Student’s t-test where significant interactions were observed. Significance was determined at p values < 0.05. |
| Ion Mode: | POSITIVE |
| MS ID: | MS002391 |
| Analysis ID: | AN002579 |
| Instrument Name: | Thermo Exactive Plus Orbitrap |
| Instrument Type: | Orbitrap |
| MS Type: | ESI |
| MS Comments: | Mass spectrometry was performed in polarity switching mode, with the following settings: resolution 35 000, AGC 1x106, m/z range 85-1275, sheath gas 50, auxiliary gas 20, sweep gas 2, probe temperature 150 °C, and capillary temperature 300 °C. For positive ionisation mode the source voltage was set at +4 kV and the S-lens voltage at +50 V. For negative ionisation mode the source voltage was set at -3.5 kV and the S-lens voltage at -50 V. Mass calibration was performed for each polarity before running a metabolomics batch to ensure mass accuracy of < 2 ppm. Approximately 300 authentic metabolite standards were analysed at the start of each batch to provide accurate retention times to facilitate metabolite identification. Metabolomics samples were analysed in random order with periodic injections of the pooled QC, and blank samples, to assess analytical quality and aid downstream metabolite identification procedures. Metabolomics LC-MS Data Processing Raw metabolite data was processed using XCMS (Centwave) software for peak picking and mzMatch.R software for alignment and annotation of related metabolite peaks. Metabolites were then identified using the Excel-based IDEOM software by matching the mass of each peak and its retention time with a database, using a mass accuracy window of 2 ppm and a retention time window of 5% for metabolites matching authentic standards, and 35% for other putative metabolites based on a retention time prediction model (15). Noise and mass spectrometry artefacts were filtered using previously described algorithms (15, 16) to minimise false identifications. Detection of stable isotope labelled metabolite peaks were performed using mzMatch-ISO (17). Initial statistical analysis was performed with IDEOM using peak intensities (height) for all detected putative metabolites. Untargeted multivariate analysis was performed using MetaboAnalyst 4.0 (18), where the complete data sets were also analysed using enrichment analysis and pathway analysis functions (19, 20). Further analysis was undertaken using TraceFinderTM (Thermo) to obtain manually curated accurate peak areas for targeted univariate analyses for metabolites in key pathways. Metabolomics data are presented as fold-change values from 2 individual experiments, each with 4 replicates. Labelled metabolomics data is from a single metabolomics experiment and presented as a mean ± SD. Differences were determined using Student’s t-test where significant interactions were observed. Significance was determined at p values < 0.05. |
| Ion Mode: | NEGATIVE |
