Summary of Study ST003303

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 PR002053. The data can be accessed directly via it's Project DOI: 10.21228/M87N78 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.

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Study IDST003303
Study TitleMetabolic Alterations in Aneurysmal Subarachnoid Hemorrhage
Study SummaryAneurysmal subarachnoid hemorrhage (aSAH) is a severe type of stroke that is associated with poor outcome. A subset of patients with aSAH will develop secondary complications, most notably delayed cerebral ischemia (DCI), which potentiates neurological injury. In this study, we investigate the relationship between cerebrospinal fluid (CSF) iron accumulation, brain metabolism, and neuronal injury in aSAH patients with or without DCI. We collected longitudinal CSF samples of patients immediately after hospitalization and 5-8 days after onset of ictus. CSF was analyzed with metabolomics to determine metabolic alterations associated with aSAH and DCI. Metabolomic profiling of the CSF samples uncovered significant dysregulation of metabolic pathways associated with energy generation and amino acid utilization, consistent with mitochondrial dysfunction. Using machine learning, we identified a set of metabolites that predicted ICU length of stay (LOS). aSAH alters the CSF metabolome involved in mitochondrial function and a subset of these metabolites are predictive of ICU stay. These results identify potential biomarkers for mitochondrial pathology and provide insight into alterations in brain iron metabolism triggered by aSAH.
Institute
University of Akron
DepartmentChemistry
Last NamePacheco
First NameGardenia
Address190 E. Buchtel Common, Akron, OH 44325
Emailgardenia.pacheco2@gmail.com
Phone815-299-2731
Submit Date2024-07-02
Raw Data AvailableYes
Raw Data File Type(s)mzML
Analysis Type DetailLC-MS
Release Date2024-07-29
Release Version1
Gardenia Pacheco Gardenia Pacheco
https://dx.doi.org/10.21228/M87N78
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Project:

Project ID:PR002053
Project DOI:doi: 10.21228/M87N78
Project Title:Metabolic Alterations in Aneurysmal Subarachnoid Hemorrhage
Project Summary:Aneurysmal subarachnoid hemorrhage (aSAH) is a severe type of stroke that is associated with poor outcome. A subset of patients with aSAH will develop secondary complications, most notably delayed cerebral ischemia (DCI), which potentiates neurological injury. In this study, we investigate the relationship between cerebrospinal fluid (CSF) iron accumulation, brain metabolism, and neuronal injury in aSAH patients with or without DCI. We collected longitudinal CSF samples of patients immediately after hospitalization and 5-8 days after onset of ictus. CSF was analyzed with metabolomics to determine metabolic alterations associated with aSAH and DCI. Metabolomic profiling of the CSF samples uncovered significant dysregulation of metabolic pathways associated with energy generation and amino acid utilization, consistent with mitochondrial dysfunction. Using machine learning, we identified a set of metabolites that predicted ICU length of stay (LOS). aSAH alters the CSF metabolome involved in mitochondrial function and a subset of these metabolites are predictive of ICU stay. These results identify potential biomarkers for mitochondrial pathology and provide insight into alterations in brain iron metabolism triggered by aSAH.
Institute:University of Akron
Department:Chemistry
Last Name:Pacheco
First Name:Gardenia
Address:190 E. Buchtel Common, Akron, OH 44325
Email:gardenia.pacheco2@gmail.com
Phone:815-299-2731

Subject:

Subject ID:SU003424
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 Sample_Group Sample source
SA35854610CControl CSF
SA3585473CControl CSF
SA35854812CControl CSF
SA35854911CControl CSF
SA3585501CControl CSF
SA3585519CControl CSF
SA3585526CControl CSF
SA3585535CControl CSF
SA3585547CControl CSF
SA3585554CControl CSF
SA35855614P1SAH_Early CSF
SA35855720P1SAH_Early CSF
SA35855819P1SAH_Early CSF
SA35855915P1SAH_Early CSF
SA35856017P1SAH_Early CSF
SA35856113P1SAH_Early CSF
SA35856212P1SAH_Early CSF
SA35856311P1SAH_Early CSF
SA3585647P1SAH_Early CSF
SA3585656P1SAH_Early CSF
SA3585665P1SAH_Early CSF
SA35856712P2SAH_Late CSF
SA35856820P2SAH_Late CSF
SA35856919P2SAH_Late CSF
SA35857018P2SAH_Late CSF
SA35857117P2SAH_Late CSF
SA35857216P2SAH_Late CSF
SA35857315P2SAH_Late CSF
SA35857414P2SAH_Late CSF
SA35857513P2SAH_Late CSF
SA3585764P2SAH_Late CSF
SA35857711P2SAH_Late CSF
SA35857810P2SAH_Late CSF
SA3585799P2SAH_Late CSF
SA3585808P2SAH_Late CSF
SA3585817P2SAH_Late CSF
SA3585826P2SAH_Late CSF
SA3585835P2SAH_Late CSF
SA3585843P2SAH_Late CSF
SA3585852P2SAH_Late CSF
SA3585861P2SAH_Late CSF
Showing results 1 to 41 of 41

Collection:

Collection ID:CO003417
Collection Summary:Samples of cerebrospinal fluid (CSF) were obtained from the EVD within the first 24 hours (early sample), and once between days 5 and 8 (late sample) following the onset of ictus. Fluid was obtained from the burette attached to the EVD system, allowing only sampling of fresh CSF. A total of 6 mL were collected at each time point and immediately centrifuged at 2,000 g for 10 minutes in the Cleveland Clinic Genetics core laboratory. The (non-cellular) supernatant was aliquoted in small polypropylene cryovials and stored in liquid nitrogen to avoid auto-oxidation of samples (9). The time elapsed from sample collection to storage in liquid nitrogen was kept under 30 minutes. Control CSF was obtained from patients with suspected neurological disease and seen at the “lumbar puncture clinic” that resulted normal after testing analysis and imaging studies. This study was conducted in accordance with all local IRB guidelines, and informed consent was obtained from all individual participants or their next of kin/legally authorized representatives. Additional CSF samples from aSAH patients and controls were obtained as diagnostic remnants from Accio Biobank Online.
Sample Type:Cerebrospinal fluid
Volumeoramount Collected:6 mL
Storage Conditions:Described in summary
Storage Vials:Polypropylene cryovials

Treatment:

Treatment ID:TR003433
Treatment Summary:Samples of cerebrospinal fluid (CSF) were obtained from the EVD within the first 24 hours (early sample), and once between days 5 and 8 (late sample) following the onset of ictus.

Sample Preparation:

Sampleprep ID:SP003431
Sampleprep Summary:Small molecules were extracted from patient CSF samples using a modified method prior to metabolomic analysis (11). A 100 μL CSF aliquot was selected and thawed on ice for all patients analyzed. Each sample was transferred to a 1.5 mL microcentrifuge tube. For protein precipitation, 400 μL of cold methanol (4X sample volume) was added to each sample, vortexed, and incubated at -20°C for 2 h. Following this incubation period, samples were centrifuged at 13,200 rpm for 20 min at 4°C. The supernatant was transferred to a new 1.5 mL microcentrifuge tube and then dried down in a CentriVap Concentrator (LABCONCO, Kansas, MO, USA). The dry samples were maintained at -80°C until analysis was performed.
Extract Storage:-80℃

Combined analysis:

Analysis ID AN005413
Analysis type MS
Chromatography type HILIC
Chromatography system Agilent 1290 Infinity II
Column HILICON iHILIC-(P) Classic HILIC column (100 x 2.1 mm, 5 µm)
MS Type ESI
MS instrument type QTOF
MS instrument name Agilent 6545 QTOF
Ion Mode NEGATIVE
Units Peak area

Chromatography:

Chromatography ID:CH004104
Chromatography Summary:Ultra-high performance LC (UHPLC)/MS was performed with an Agilent 1290 Infinity II LC System interfaced with an Agilent QTOF 6545 Mass Spectrometer. Hydrophilic interaction liquid chromatography (HILIC) was conducted with a HILICON iHILIC-(P) Classic HILIC column (100 mm x 2.1 mm, 5 µm). Mobile-phase solvents were composed of A = 20 mM ammonium bicarbonate, 0.1 % ammonium hydroxide and 2.5 µM medronic acid in water:acetonitrile (95:5) and B = 2.5 µM medronic acid in acetonitrile:water (95:5). The column compartment was maintained at 45 ºC for all experiments. The following linear gradient was applied at a flow rate of 250 µL min-1: 0-1 min: 90 % B, 1-12 min: 90-35 % B, 12-12.5 min: 35-25 % B, 12.5-14.5 min: 25 % B. The column was re-equilibrated with 20 column volumes of 90% B. The injection volume was 4 µL for all samples.
Instrument Name:Agilent 1290 Infinity II
Column Name:HILICON iHILIC-(P) Classic HILIC column (100 x 2.1 mm, 5 µm)
Column Temperature:45
Flow Gradient:0-1 min: 90 % B, 1-12 min: 90-35 % B, 12-12.5 min: 35-25 % B, 12.5-14.5 min: 25 % B
Flow Rate:250 µL min
Sample Injection:4 µL
Solvent A:95% Water, 5% Acetonitrile; 20 mM ammonium bicarbonate; 0.1% ammonium hydroxide; 2.5 µM medronic acid
Solvent B:95% Acetonitrile, 5% Water; 2.5 µM medronic acid
Capillary Voltage:-3 kV
Chromatography Type:HILIC

MS:

MS ID:MS005140
Analysis ID:AN005413
Instrument Name:Agilent 6545 QTOF
Instrument Type:QTOF
MS Type:ESI
MS Comments:Data were collected with the following settings: capillary voltage, -3 kV; gas, 200ºC at 10 L/min; nebulizer, 44 psi; sheath gas, 300ºC at 12 L/min; fragmentor voltage, 100 V; scan rate, one scan per second; mass range, 67-1500 Da; polarity, negative. LC/MS data were processed and analyzed with the open-source Skyline software (12) and XCMS (13). Metabolomics data analysis and visualization was completed in MetaboAnalyst 5.0 (14). Features were putatively identified via DecoID by matching MS/MS fragmentation to library standards15 and identifications confirmed with level 1 or 2 confidence according to the Metabolomics Standards Initiative (16). Heatmaps of row Z-score values for the 49 metabolites identified were generated with the open-source Morpheus (Broad Institute, https://software.broadinstitute.org/morpheus) software. Hierarchical clustering of the patients (columns) was completed using the one minus Pearson correlation metric with an average linkage. Graphs were made using Prism 9 software (GraphPad Software, San Diego, CA). Machine learning was performed as previously described (17). Five different machine learning models: logistic regression, ElasticNet linear regression, partial least squares discriminant analysis (PLSDA), support vector machine (SVM), and random forest were tested on CSF samples collected at 24 hours and 5-8 days after aSAH ictus using a leave-one-out cross validation. The significance of the model fit was evaluated with a permutation test.
Ion Mode:NEGATIVE
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