Summary of Study ST001547

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	ST001547
Study Title	β-Adrenergic regulation of metabolism in macrophages (part-I)
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
Email	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:	SU001623
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
SA130678	CP_control_1	Control_13C-Glucose
SA130679	CP_control_4	Control_13C-Glucose
SA130680	CP_control_3	Control_13C-Glucose
SA130681	CP_control_2	Control_13C-Glucose
SA130682	CP_isoprenaline_4	Isoprenaline_13C-Glucose
SA130683	CP_isoprenaline_1	Isoprenaline_13C-Glucose
SA130684	CP_isoprenaline_2	Isoprenaline_13C-Glucose
SA130685	CP_isoprenaline_3	Isoprenaline_13C-Glucose

Showing results 1 to 8 of 8

Showing results 1 to 8 of 8

Collection:

Collection ID:	CO001616
Collection Summary:	Cells (9.9x106) were differentiated in 10 cm glass dishes (Corning) coated with 10 mM fibronectin. After 24 hours in culture, cells were treated with 1 µM isoprenaline (or vehicle) for a further 24 hours. Cells were extracted as previously described (14). Briefly, cells were quenched and washed three times with 4 °C Dulbecco’s phosphate buffered saline (DPBS; Invitrogen), then scraped in 750 µL of ice-cold extraction solvent (chloroform: methanol: water = 1:3:1). After scraping, the samples were mixed thoroughly on a vortex mixer for 30 min at 1200 rpm (4 °C) and centrifuged at 20,000 g for 10 min (4 °C). Samples were then evaporated to dryness under a nitrogen stream and frozen at -80 °C until LC-MS analysis.
Sample Type:	Macrophages

Treatment:

Treatment ID:	TR001636
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:	SP001629
Sampleprep Summary:	Cells were extracted as previously described (14). Briefly, cells were quenched and washed three times with 4 °C Dulbecco’s phosphate buffered saline (DPBS; Invitrogen), then scraped in 750 µL of ice-cold extraction solvent (chloroform: methanol: water = 1:3:1). After scraping, the samples were mixed thoroughly on a vortex mixer for 30 min at 1200 rpm (4 °C) and centrifuged at 20,000 g for 10 min (4 °C). Samples were then evaporated to dryness under a nitrogen stream and frozen at -80 °C until LC-MS analysis.

Sampleprep ID:

SP001629

Sampleprep Summary:

Cells were extracted as previously described (14). Briefly, cells were quenched and washed three times with 4 °C Dulbecco’s phosphate buffered saline (DPBS; Invitrogen), then scraped in 750 µL of ice-cold extraction solvent (chloroform: methanol: water = 1:3:1). After scraping, the samples were mixed thoroughly on a vortex mixer for 30 min at 1200 rpm (4 °C) and centrifuged at 20,000 g for 10 min (4 °C). Samples were then evaporated to dryness under a nitrogen stream and frozen at -80 °C until LC-MS analysis.

Combined analysis:

Analysis ID	AN002576	AN002577
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:	CH001886
Instrument Name:	Thermo Dionex Ultimate 3000
Column Name:	ZIC-pHILIC (150 x 4.6mm,5um)
Chromatography Type:	HILIC

Chromatography ID:	CH001887
Instrument Name:	Thermo Dionex Ultimate 3000
Column Name:	ZIC-pHILIC (150 x 4.6mm,5um)
Chromatography Type:	HILIC

MS:

MS ID:	MS002388
Analysis ID:	AN002576
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:	MS002389
Analysis ID:	AN002577
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