Summary of Study ST003216

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 PR002006. The data can be accessed directly via it's Project DOI: 10.21228/M89R6J This work is supported by NIH grant, U2C- DK119886. See: https://www.metabolomicsworkbench.org/about/howtocite.php

Study ID	ST003216
Study Title	Balancing brain metabolic states during sickness and recovery sleep
Study Summary	Sickness sleep and rebound following sleep deprivation share humoral signals including the rise of cytokines, in particular interleukins. Nevertheless, they represent unique physiological states with unique brain firing patterns and involvement of specific circuitry. Here we performed untargeted metabolomics of mouse cortex and hippocampus to uncover acute changes with sickness and rebound sleep as compared to normal daily sleep. We found that the three states are biochemically unique with larger differences in the cortex than in the hippocampus. Both sickness and rebound sleep shared an increase in tryptophan, with the highest levels during sickness. Surprisingly these two sleep states showed stark differences in terms of the energetic signature, with sickness impinging on glycolysis intermediates whilst rebound increased the triphosphorylated form of nucleotides. These findings indicate that rebound following sleep deprivation stimulates an energy rich state in the brain that is devoid during sickness sleep in line with the energy conservation hypothesis of sickness behavior.
Institute	University of Pennsylvania
Last Name	Sehgal
First Name	Amita
Address	Perelman School of Medicine, University of Pennsylvania, 10-136 Smilow Research Center, 3400 Civic Center Blvd, Bldg 421, Philadelphia, PA 19104, USA
Email	amita@pennmedicine.upenn.edu
Phone	215-898-2799
Submit Date	2024-05-20
Raw Data Available	Yes
Raw Data File Type(s)	mzML, raw(Thermo)
Analysis Type Detail	LC-MS
Release Date	2025-05-21
Release Version	1

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Project:

Project ID:	PR002006
Project DOI:	doi: 10.21228/M89R6J
Project Title:	Balancing brain metabolic states during sickness and recovery sleep
Project Summary:	Sickness sleep and rebound following sleep deprivation share humoral signals including the rise of cytokines, in particular interleukins. Nevertheless, they represent unique physiological states with unique brain firing patterns and involvement of specific circuitry. Here we performed untargeted metabolomics of mouse cortex and hippocampus to uncover acute changes with sickness and rebound sleep as compared to normal daily sleep. We found that the three states are biochemically unique with larger differences in the cortex than in the hippocampus. Both sickness and rebound sleep shared an increase in tryptophan, with the highest levels during sickness. Surprisingly these two sleep states showed stark differences in terms of the energetic signature, with sickness impinging on glycolysis intermediates whilst rebound increased the triphosphorylated form of nucleotides. These findings indicate that rebound following sleep deprivation stimulates an energy rich state in the brain that is devoid during sickness sleep in line with the energy conservation hypothesis of sickness behavior.
Institute:	University of Pennsylvania
Last Name:	Sehgal
First Name:	Amita
Address:	Perelman School of Medicine, University of Pennsylvania, 10-136 Smilow Research Center, 3400 Civic Center Blvd, Bldg 421, Philadelphia, PA 19104, USA
Email:	amita@pennmedicine.upenn.edu
Phone:	215-898-2799

Subject:

Subject ID:	SU003335
Subject Type:	Mammal
Subject Species:	Mus musculus
Taxonomy ID:	10090
Species Group:	Mammals

Factors:

Subject type: Mammal; Subject species: Mus musculus (Factor headings shown in green)

mb_sample_id	local_sample_id	Sample source	Treatment
SA351778	A1	Cortex	Base Line
SA351779	A3	Cortex	Base Line
SA351780	A4	Cortex	Base Line
SA351781	A2	Cortex	Base Line
SA351782	C2	Cortex	LPS
SA351783	C4	Cortex	LPS
SA351784	C1	Cortex	LPS
SA351785	C3	Cortex	LPS
SA351786	B2	Cortex	Sleep Deprivation
SA351787	B4	Cortex	Sleep Deprivation
SA351788	B1	Cortex	Sleep Deprivation
SA351789	B3	Cortex	Sleep Deprivation
SA351790	AA2	Hippocampus	Base Line
SA351791	AA4	Hippocampus	Base Line
SA351792	AA1	Hippocampus	Base Line
SA351793	AA3	Hippocampus	Base Line
SA351794	CC1	Hippocampus	LPS
SA351795	CC2	Hippocampus	LPS
SA351796	CC3	Hippocampus	LPS
SA351797	CC4	Hippocampus	LPS
SA351798	BB4	Hippocampus	Sleep Deprivation
SA351799	BB3	Hippocampus	Sleep Deprivation
SA351800	BB2	Hippocampus	Sleep Deprivation
SA351801	BB1	Hippocampus	Sleep Deprivation
SA351802	QC-5	n/a	n/a
SA351803	QC-4	n/a	n/a
SA351804	QC-7	n/a	n/a
SA351805	QC-6	n/a	n/a

Showing results 1 to 28 of 28

Showing results 1 to 28 of 28

Collection:

Collection ID:	CO003328
Collection Summary:	6 hours after lights on, the middle of the rest phase in mice, animals were euthanized using isoflurane anesthesia followed by cervical dislocation. Cortex and hippocampus were collected, cleaned in ice cold PBS, gently dried and flash frozen.
Sample Type:	Brain

Treatment:

Treatment ID:	TR003344
Treatment Summary:	Base Line: mice were undisturbed before ZT6 collection, meaning 6h after Light on. SD: 4 hours of gentle handling sleep deprivation (SD) from ZT0 to ZT4 followed by 2 h of recovery sleep leading to ZT6 collection. LPS: intraperitoneal injection of 0.5 mgKg-1 lipopolysaccharide (LPS) at ZT4 followed by 2h of recovery sleep leading to ZT6 collection.

Sample Preparation:

Sampleprep ID:	SP003342
Sampleprep Summary:	Frozen samples were weighed (10 mg) and homogenized using a mortar and pestle with 0.45 ml cold extraction solution (80% methanol, 20% water with heavy-labeled internal standard mix). Samples were placed on dry ice for 30 min then centrifuged at 10,000 x g for 15 min at 4 °C. Supernatants were collected, and the pellet was re-extracted with an additional 200 μl of cold extraction solution. The supernatants from both extractions were combined, clarified again by centrifugation, and stored at -80 °C in cryovials prior to analysis. A quality control (QC) sample was generated by pooling equal volumes of all samples immediately before LC-MS analysis.

Sampleprep ID:

SP003342

Sampleprep Summary:

Frozen samples were weighed (10 mg) and homogenized using a mortar and pestle with 0.45 ml cold extraction solution (80% methanol, 20% water with heavy-labeled internal standard mix). Samples were placed on dry ice for 30 min then centrifuged at 10,000 x g for 15 min at 4 °C. Supernatants were collected, and the pellet was re-extracted with an additional 200 μl of cold extraction solution. The supernatants from both extractions were combined, clarified again by centrifugation, and stored at -80 °C in cryovials prior to analysis. A quality control (QC) sample was generated by pooling equal volumes of all samples immediately before LC-MS analysis.

Combined analysis:

Analysis ID	AN005273	AN005274
Chromatography ID	CH003990	CH003990
MS ID	MS005004	MS005005
Analysis type	MS	MS
Chromatography type	HILIC	HILIC
Chromatography system	Thermo Vanquish	Thermo Vanquish
Column	SeQuant ZIC- pHILIC (150 x 2.1mm,5um)	SeQuant ZIC- pHILIC (150 x 2.1mm,5um)
MS Type	ESI	ESI
MS instrument type	Orbitrap	Orbitrap
MS instrument name	Thermo Q Exactive HF-X Orbitrap	Thermo Q Exactive HF-X Orbitrap
Ion Mode	POSITIVE	NEGATIVE
Units	Peak Area	Peak Area

Chromatography:

Chromatography ID:	CH003990
Chromatography Summary:	Hydrophilic interaction liquid chromatography (HILIC) was performed at 0.2 ml/min on a ZIC-pHILIC column (2.1 mm × 150 mm, 5 μm particle size, EMD Millipore) at 45 °C. Solvent A was 20 mM ammonium carbonate, 0.1% ammonium hydroxide, pH 9.2, and solvent B was acetonitrile. The gradient was 85% B for 2 min, 85% B to 20% B over 15 min, 20% B to 85% B over 0.1 min, and 85% B for 8.9 min. The autosampler was held at 4 °C. For each analysis, 4 µl of sample was injected.
Instrument Name:	Thermo Vanquish
Column Name:	SeQuant ZIC- pHILIC (150 x 2.1mm,5um)
Column Temperature:	45
Flow Gradient:	85% B for 2 min, 85% B to 20% B over 15 min, 20% B to 85% B over 0.1 min, and 85% B for 8.9 min
Flow Rate:	0.2 mL/min
Solvent A:	100% Water; 20 mM ammonium carbonate, 0.1% ammonium hydroxide, pH 9.2
Solvent B:	100% Acetonitrile
Chromatography Type:	HILIC

MS:

MS ID:	MS005004
Analysis ID:	AN005273
Instrument Name:	Thermo Q Exactive HF-X Orbitrap
Instrument Type:	Orbitrap
MS Type:	ESI
MS Comments:	The following parameters were used for the MS analysis: sheath gas flow rate, 40; auxiliary gas flow rate, 10; sweep gas flow rate, 2; auxiliary gas heater temperature, 350 °C; spray voltage, 3.5 kV for positive mode and 3.2 kV for negative mode; capillary temperature, 325 °C; and funnel RF level, 40. All samples were analyzed by full MS with polarity switching. The QC sample was analyzed at the start of the sample sequence and after every 3-4 samples. The QC sample was also analyzed by data-dependent MS/MS with separate runs for positive and negative ion modes. Full MS scans were acquired at 120,000 resolution with a scan range of 65-975 m/z. Data-dependent MS/MS scans were acquired for the top 10 highest intensity ions at 15,000 resolution with an isolation width of 1.0 m/z and stepped normalized collision energy of 20-40-60. Data analysis was performed using Compound Discoverer 3.3SP1 (ThermoFisher Scientific) with separate analyses for positive and negative polarities. Peak detection required less than 5 ppm mass error for extracted ion chromatograms with a 100,000 minimum peak intensity. Peaks were required to have a width at half height less than 1.0 min and a minimum of 5 scans. Components that had only a monoisotopic peak and no further isotopes were discarded. The maximum element count for isotope pattern modeling was C90H190N10O20P3S5. Compounds were grouped across samples with 5 ppm mass error and 0.2 min retention time shift. Only [M+H]+1 and [M-H]-1 adducts were considered. Compounds not detected in a given sample were quantified using the fill gaps algorithm with 5 ppm mass error and 1.5 S/N threshold with real peak detection. Each compound was required to be detected in all QC runs with an RSD less than 40%. Metabolites were identified by accurate mass (5 ppm mass error) and retention time (0.5 min shift) using a database generated from pure standards or by accurate mass and MS2 spectra using the mzCloud spectral database (mzCloud.org), specifically the ‘Endogenous Metabolites’ and ‘Steroids/Vitamins/Hormones’ compound classes, and selecting the best matches with HighChem HighRes identity search match factors of 50 or greater. Results were manually processed to remove entries with apparent peak mis-integrations and correct commonly misannotated metabolites. Positive and negative data sets of identified compounds were merged, and the preferred polarity was selected for compounds identified in both polarities. For compounds identified multiple times at different retention times, a single entry was selected with priority given to standards database matches followed by greater mzCloud match factors and peak areas. Metabolites present near background levels based on extraction blanks were considered as background (sample peak area/Control extraction peak area < 5). Quantitation was based on the equivalent percentage of material analyzed for each sample. Values were then normalized to QC pool sample runs and subsequently by total signal of annotated and filtered metabolites.
Ion Mode:	POSITIVE

MS ID:	MS005005
Analysis ID:	AN005274
Instrument Name:	Thermo Q Exactive HF-X Orbitrap
Instrument Type:	Orbitrap
MS Type:	ESI
MS Comments:	The following parameters were used for the MS analysis: sheath gas flow rate, 40; auxiliary gas flow rate, 10; sweep gas flow rate, 2; auxiliary gas heater temperature, 350 °C; spray voltage, 3.5 kV for positive mode and 3.2 kV for negative mode; capillary temperature, 325 °C; and funnel RF level, 40. All samples were analyzed by full MS with polarity switching. The QC sample was analyzed at the start of the sample sequence and after every 3-4 samples. The QC sample was also analyzed by data-dependent MS/MS with separate runs for positive and negative ion modes. Full MS scans were acquired at 120,000 resolution with a scan range of 65-975 m/z. Data-dependent MS/MS scans were acquired for the top 10 highest intensity ions at 15,000 resolution with an isolation width of 1.0 m/z and stepped normalized collision energy of 20-40-60. Data analysis was performed using Compound Discoverer 3.3SP1 (ThermoFisher Scientific) with separate analyses for positive and negative polarities. Peak detection required less than 5 ppm mass error for extracted ion chromatograms with a 100,000 minimum peak intensity. Peaks were required to have a width at half height less than 1.0 min and a minimum of 5 scans. Components that had only a monoisotopic peak and no further isotopes were discarded. The maximum element count for isotope pattern modeling was C90H190N10O20P3S5. Compounds were grouped across samples with 5 ppm mass error and 0.2 min retention time shift. Only [M+H]+1 and [M-H]-1 adducts were considered. Compounds not detected in a given sample were quantified using the fill gaps algorithm with 5 ppm mass error and 1.5 S/N threshold with real peak detection. Each compound was required to be detected in all QC runs with an RSD less than 40%. Metabolites were identified by accurate mass (5 ppm mass error) and retention time (0.5 min shift) using a database generated from pure standards or by accurate mass and MS2 spectra using the mzCloud spectral database (mzCloud.org), specifically the ‘Endogenous Metabolites’ and ‘Steroids/Vitamins/Hormones’ compound classes, and selecting the best matches with HighChem HighRes identity search match factors of 50 or greater. Results were manually processed to remove entries with apparent peak mis-integrations and correct commonly misannotated metabolites. Positive and negative data sets of identified compounds were merged, and the preferred polarity was selected for compounds identified in both polarities. For compounds identified multiple times at different retention times, a single entry was selected with priority given to standards database matches followed by greater mzCloud match factors and peak areas. Metabolites present near background levels based on extraction blanks were considered as background (sample peak area/Control extraction peak area < 5). Quantitation was based on the equivalent percentage of material analyzed for each sample. Values were then normalized to QC pool sample runs and subsequently by total signal of annotated and filtered metabolites.
Ion Mode:	NEGATIVE