Summary of Study ST003039

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 PR001891. The data can be accessed directly via it's Project DOI: 10.21228/M88X46 This work is supported by NIH grant, U2C- DK119886.

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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 IDST003039
Study TitleA Non-Targeted Metabolomics Comparative Study on Plasma of Pfizer and Sinopharm COVID- 19 Vaccinated individuals, Assessed by (TIMS-QTOF) Mass Spectrometry.
Study SummaryCOVID-19 is a contagious globally threatening infectious disease that accounted for an ongoing pandemic that manifested in multi-organs diseases and failures. The current study aimed to investigate the effectiveness of the Pfizer and Sinopharm vaccines in relation to metabolomic alterations and their association with immune pathways. The study employed a cross-sectional design and utilized an untargeted metabolomics-based approach. Plasma samples were collected from three groups: non- vaccinated participants, Sinopharm vaccinated participants, and Pfizer vaccinated participants. Comparative metabolomic analysis was performed using TIMS-QTOF, and a one-way ANOVA test was conducted using MetaboAnalyst Software. Out of the 105 detected metabolites, 72 showed statistically significant alterations (p<0.05) among the different groups. Several metabolites, including neopterin, pyridoxal, and syringic acid, were highly altered in individuals vaccinated with Pfizer. On the other hand, sphinganine, neopterin, and sphingosine were impacted in individuals vaccinated with Sinopharm. These metabolites could potentially serve as biomarkers for vaccine efficacy. Furthermore, both Pfizer and Sinopharm vaccinations were found to affect sphingolipid metabolism pathways and histidine metabolism pathways when compared to the control group. The Sinopharm group exhibited altered lysine degradation compared to the control group. When comparing the enriched pathways of the Pfizer and Sinopharm groups, purine metabolism was found to be affected. Additionally, perturbations in tryptophan metabolism and vitamin B6 metabolism were observed when comparing the Pfizer group with both the control and Sinopharm groups. These findings highlight the importance of metabolomics in assessing vaccine effectiveness and identifying potential biomarkers.
Institute
Sharjah Institute for Medical Research
Last NameFacility
First NameCore
AddressM32, SIMR, College of Pharmacy, Health Sciences, University of Sharjah, Sharjah, UAE, Sharjah, 000, United Arab Emirates
Emailtims-tof@sharjah.ac.ae
Phone+971 6 5057656
Submit Date2024-01-02
Raw Data AvailableYes
Raw Data File Type(s)d
Analysis Type DetailLC-MS
Release Date2024-01-31
Release Version1
Core Facility Core Facility
https://dx.doi.org/10.21228/M88X46
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Project:

Project ID:PR001891
Project DOI:doi: 10.21228/M88X46
Project Title:A Non-Targeted Metabolomics Comparative Study on Plasma of Pfizer and Sinopharm COVID- 19 Vaccinated individuals, Assessed by (TIMS-QTOF) Mass Spectrometry.
Project Summary:COVID-19 is a contagious globally threatening infectious disease that accounted for an ongoing pandemic that manifested in multi-organs diseases and failures. The current study aimed to investigate the effectiveness of the Pfizer and Sinopharm vaccines in relation to metabolomic alterations and their association with immune pathways. The study employed a cross-sectional design and utilized an untargeted metabolomics-based approach. Plasma samples were collected from three groups: non- vaccinated participants, Sinopharm vaccinated participants, and Pfizer vaccinated participants. Comparative metabolomic analysis was performed using TIMS-QTOF, and a one-way ANOVA test was conducted using MetaboAnalyst Software. Out of the 105 detected metabolites, 72 showed statistically significant alterations (p<0.05) among the different groups. Several metabolites, including neopterin, pyridoxal, and syringic acid, were highly altered in individuals vaccinated with Pfizer. On the other hand, sphinganine, neopterin, and sphingosine were impacted in individuals vaccinated with Sinopharm. These metabolites could potentially serve as biomarkers for vaccine efficacy. Furthermore, both Pfizer and Sinopharm vaccinations were found to affect sphingolipid metabolism pathways and histidine metabolism pathways when compared to the control group. The Sinopharm group exhibited altered lysine degradation compared to the control group. When comparing the enriched pathways of the Pfizer and Sinopharm groups, purine metabolism was found to be affected. Additionally, perturbations in tryptophan metabolism and vitamin B6 metabolism were observed when comparing the Pfizer group with both the control and Sinopharm groups. These findings highlight the importance of metabolomics in assessing vaccine effectiveness and identifying potential biomarkers.
Institute:Sharjah Institute for Medical Research
Last Name:Facility
First Name:Core
Address:M32, SIMR, College of Pharmacy, Health Sciences, University of Sharjah, Sharjah, UAE, Sharjah, 000, United Arab Emirates
Email:tims-tof@sharjah.ac.ae
Phone:+971 6 5057656

Subject:

Subject ID:SU003153
Subject Type:Human
Subject Species:Homo sapiens
Taxonomy ID:9606
Gender:Male

Factors:

Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)

mb_sample_id local_sample_id Treatment
SA329877Dual 123-8783Dual
SA329878Dual 372-8784Dual
SA329879Dual 573-8787Dual
SA329880Dual 59-8782Dual
SA329881Dual 414-8785Dual
SA329882Dual 517-8786Dual
SA329883Pfizer-50-8429Pfizer
SA329884Pfizer-455-8538Pfizer
SA329885Pfizer-500-8539Pfizer
SA329886Pfizer-452-8537Pfizer
SA329887Pfizer-441-8532Pfizer
SA329888Pfizer-505-8540Pfizer
SA329889Pfizer-445-8533Pfizer
SA329890Pfizer-450-8535Pfizer
SA329891Pfizer-451-8536Pfizer
SA329892Pfizer-515-8544Pfizer
SA329893Pfizer-520-8546Pfizer
SA329894Pfizer-522-8547Pfizer
SA329895Pfizer-524-8548Pfizer
SA329896Pfizer-516-8545Pfizer
SA329897Pfizer-438-8531Pfizer
SA329898Pfizer-51-8430Pfizer
SA329899Pfizer-512-8542Pfizer
SA329900Pfizer-513-8543Pfizer
SA329901Pfizer-509-8541Pfizer
SA329902Pfizer-426-8525Pfizer
SA329903Pfizer-381-8517Pfizer
SA329904Pfizer-393-8518Pfizer
SA329905Pfizer-397-8519Pfizer
SA329906Pfizer-378-8516Pfizer
SA329907Pfizer-377-8515Pfizer
SA329908Pfizer-364-8512Pfizer
SA329909Pfizer-371-8513Pfizer
SA329910Pfizer-374-8514Pfizer
SA329911Pfizer-400-8520Pfizer
SA329912Pfizer-411-8521Pfizer
SA329913Pfizer-429-8526Pfizer
SA329914Pfizer-432-8528Pfizer
SA329915Pfizer-436-8529Pfizer
SA329916Pfizer-525-8549Pfizer
SA329917Pfizer-424-8582Pfizer
SA329918Pfizer-412-8522Pfizer
SA329919Pfizer-415-8523Pfizer
SA329920Pfizer-421-8524Pfizer
SA329921Pfizer-437-8530Pfizer
SA329922Pfizer-531-8552Pfizer
SA329923Pfizer-568-8574Pfizer
SA329924Pfizer-569-8575Pfizer
SA329925Pfizer-571-8576Pfizer
SA329926Pfizer-567-8573Pfizer
SA329927Pfizer-566-8572Pfizer
SA329928Pfizer-563-8569Pfizer
SA329929Pfizer-564-8570Pfizer
SA329930Pfizer-565-8571Pfizer
SA329931Pfizer-572-8577Pfizer
SA329932Pfizer-575-8578Pfizer
SA329933Pfizer-65-8436Pfizer
SA329934Pfizer-67-8437Pfizer
SA329935Pfizer-72-8438Pfizer
SA329936Pfizer-64-8435Pfizer
SA329937Pfizer-58-8434Pfizer
SA329938Pfizer-577-8579Pfizer
SA329939Pfizer-578-8580Pfizer
SA329940Pfizer-579-8581Pfizer
SA329941Pfizer-562-8568Pfizer
SA329942Pfizer-561-8567Pfizer
SA329943Pfizer-542-8555Pfizer
SA329944Pfizer-544-8556Pfizer
SA329945Pfizer-545-8557Pfizer
SA329946Pfizer-536-8554Pfizer
SA329947Pfizer-534-8553Pfizer
SA329948Pfizer-527-8551Pfizer
SA329949Pfizer-53-8431Pfizer
SA329950Pfizer-361-8511Pfizer
SA329951Pfizer-546-8559Pfizer
SA329952Pfizer-547-8560Pfizer
SA329953Pfizer-555-8564Pfizer
SA329954Pfizer-556-8565Pfizer
SA329955Pfizer-560-8566Pfizer
SA329956Pfizer-554-8563Pfizer
SA329957Pfizer-55-8433Pfizer
SA329958Pfizer-548-8561Pfizer
SA329959Pfizer-549-8562Pfizer
SA329960Pfizer-526-8550Pfizer
SA329961Pfizer-446-8534Pfizer
SA329962Pfizer-201-8457Pfizer
SA329963Pfizer-202-8458Pfizer
SA329964Pfizer-203-8459Pfizer
SA329965Pfizer-205-8460Pfizer
SA329966Pfizer-200-8456Pfizer
SA329967Pfizer-20-8424Pfizer
SA329968Pfizer-154-8454Pfizer
SA329969Pfizer-155-8455Pfizer
SA329970Pfizer-18-8423Pfizer
SA329971Pfizer-209-8461Pfizer
SA329972Pfizer-211-8462Pfizer
SA329973Pfizer-220-8468Pfizer
SA329974Pfizer-221-8469Pfizer
SA329975Pfizer-222-8470Pfizer
SA329976Pfizer-22-8425Pfizer
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Collection:

Collection ID:CO003146
Collection Summary:A total of 340 subjects (aged between 10-85 years) were enrolled from Al-Quds Medical Labs (AL-Zarqa, Jordan). Blood samples were collected from 77 healthy individuals (had not received any of the COVID- 19 vaccines), 107 individuals who received the Sinopharm vaccine, and 156 individuals who received the Pfizer vaccine. The samples were collected using heparinized tubes and were then subjected to centrifugation for five minutes to obtain plasma. The study received ethical approval from The Institutional Review Board at The University of Jordan (UHS-HERC-094-21032022), and all participants signed an informed consent before collecting the blood samples. Individuals who tested positive for COVID-19, had received booster doses, had received a third dose of COVID-19 vaccine, or had received a single dose of either Pfizer or Sinopharm vaccines were excluded from the study
Sample Type:Blood (plasma)
Storage Conditions:-80℃

Treatment:

Treatment ID:TR003162
Treatment Summary:Blood samples were collected from 77 healthy individuals (had not received any of the COVID- 19 vaccines), 107 individuals who received the Sinopharm vaccine, and 156 individuals who received the Pfizer vaccine

Sample Preparation:

Sampleprep ID:SP003159
Sampleprep Summary:we used chloroform/methanol extraction protocol to increase the coverage of the extracted metabolites. At first, the samples (cells and buffer) were transferred into Eppendorf tubes then centrifuged at 14000 rpm for 5 min. Afterward, the buffer was discarded, and the cells were preserved. To each sample, 400 µL of the mixture containing one protease inhibitor tablet and 10 mL of lysis buffer was added. Following rest for 10 minutes, samples were transferred to 10 mL tubes, vortexed for 2–4 minutes, and sonicated with a COPLEY probe-sonicator (QSONICA SONICATOR, USA) for 30 seconds while utilizing a 30 % amplifier in an ice bath. The samples were then transferred to Eppendorf tubes and centrifuged for 5 minutes at 14000 rpm. The supernatant was then transferred to another Eppendorf, and 400 µL of methanol and 300 µL of chloroform were added. Following that, the samples were vortexed for 30 seconds and centrifuged for 5 minutes at 14000 rpm. After that, two metabolite-containing layers are obtained, after transferring the upper layer of each sample to glass vials, 400 µl of methanol was added, followed by vertexing and centrifugation. The remaining supernatant was transferred to the same glass vials used before for the drying step, with the remaining protein pellets being air-dried for proteomics. A dried metabolomics sample was resuspended in 200 µL (0.1% formic acid in water) and injected into HPLC to be analysed by Q-TOF MS.In summary, After dividing the samples into 100 µL portions in Eppendorf tubes, 300 µL of methanol from Wunstorfer Strasse, Seelze, Germany, was introduced. The tubes were subsequently vortexed and placed in an incubator at -20°C for two hours. Following incubation, the samples underwent another round of vortexing and were centrifuged for 15 minutes at 14000 rpm. The resulting supernatant was evaporated at 35 to 40 °C through speed vacuum evaporation. To assess the analysis's repeatability, a quality control (QC) sample was created by pooling the same volume of each sample (10µl). The extracted samples were then resuspended in 100 µL of Honeywell's LC-MS CHROMASOLV's 0.1% formic acid in Deionized Water (Wunstorfer Strasse, Seelze, Germany). Following that, 100 µL of the prepared sample was collected in an insert inside LC glass vials after filtration through a 0.45µm hydrophilic nylon syringe filter for LC-MS/MS analysis.

Combined analysis:

Analysis ID AN004986
Analysis type MS
Chromatography type Reversed phase
Chromatography system Bruker Elute
Column Hamilton Intensity Solo 2 C18 (100 x 2.1 mm, 1.8 um)
MS Type ESI
MS instrument type QTOF
MS instrument name Bruker timsTOF
Ion Mode POSITIVE
Units AU

Chromatography:

Chromatography ID:CH003766
Chromatography Summary:Samples were chromatographically separated by inline reversed-phase chromatography using the Elute HPG 1300 pumps and Elute Autosampler (Bruker, Darmstadt, Germany) with solvent A 0.1% FA in HPLC grade water and solvent B 0.1% FA in ACN. A Hamilton Intensity Solo 2 C18 column (100 mm x 2.1 mm, 1.8µm beads) was maintained at 35C. For metabolomics, 10 µL was injected twice for each sample and eluted using a 30-minute gradient as follows: 1% ACN was held for 2 minutes, ramping to 99% ACN over 15 minutes, held at 99% ACN for 3 minutes before re-equilibrating to 1% ACN for 10 minutes. Flow rates were 250 µL/min for elution and 350 µL/min for re-equilibration.
Instrument Name:Bruker Elute
Column Name:Hamilton Intensity Solo 2 C18 (100 x 2.1 mm, 1.8 um)
Column Temperature:35
Flow Gradient:1%B to 99%B in 15 min
Flow Rate:250 uL/min
Solvent A:100% water; 0.1% formic acid
Solvent B:100% acetonitrile; 0.1% formic acid
Chromatography Type:Reversed phase

MS:

MS ID:MS004726
Analysis ID:AN004986
Instrument Name:Bruker timsTOF
Instrument Type:QTOF
MS Type:ESI
MS Comments:The MS analysis was performed using a TimsTOF (Bruker, Darmstadt, Germany) with Apollo II electrospray ionization (ESI) source. The drying gas was set to flow at 10 L/min and the drying temperature to 220C and the nebulizer pressure to 2.2 bar. The capillary voltage was 4500 V and the end plate offset 500V. For metabolomics the scan range was 20-1300 m/z. The collision energy was set to 20 eV, the cycle time to 0.5 seconds with a relative minimum intensity threshold of 400 counts per thousand and target intensity of 20,000. Sodium formate was injected as an external calibrant in the first 0.3 minutes of each LC-MS/MS run. MetaboScape 4.0 software was used for metabolite processing and statistical analysis (Bruker Daltonics). The following parameters for molecular feature identification and "bucketing" were set in the T-ReX 2D/3D workflow: For peak detection, a minimum intensity threshold of 1,000 counts is required, as well as a minimum peak duration of 7 spectra, with feature quantification determine using peak area. The file masses were recalibrated based on the external calibrant injected between 0-0.3 min.
Ion Mode:POSITIVE
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