Summary of Study ST002921

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 PR001815. The data can be accessed directly via it's Project DOI: 10.21228/M83Q7Q 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 IDST002921
Study TitleMetabolomic Characteristics of Nontuberculous Mycobacterial Pulmonary Disease
Study SummaryWhile the burden of nontuberculous mycobacterial pulmonary disease (NTM-PD) continues to increase, knowledge of biomarkers for NTM-PD remains insufficient. Furthermore, metabolic changes in NTM-PD have not yet been investigated. The identification of specific metabolites and associated metabolic pathways unique to NTM-PD will provide a deeper understanding of its pathogenesis. In this study, we aimed to discover specific metabolic biomarkers for NTM-PD using a metabolomics approach. In this study, we underwent untargeted metabolomic profiling using a liquid chromatography system coupled with the quadrupole-orbitrap mass spectrometer to analyze serum samples from patients with NTM-PD (n = 50), patients with non-NTM bronchiectasis (n = 50), and HC subjects (n = 60). To validate the results, an additional 86 serum samples for each group were analyzed using the same analytical methods. We identified several NTM-PD significant metabolites that differentiate patients with NTM-PD from healthy individuals. The machine learning-based classification model demonstrated the proficiency of the selected metabolic features in distinguishing between patients with NTM-PD and healthy individuals. These findings may contribute to a better understanding of the pathogenesis of NTM-PD and provide insights for the development of novel treatment approaches.
Institute
Seoul National University College of Medicine and Hospital
Last NameJungeun
First NameKim
Address101 Daehak-ro, Jongno-gu, Seoul, Korea
Emailjeunk@snu.ac.kr
Phone+821026965910
Submit Date2023-10-05
Num Groups3
Total Subjects418
Raw Data AvailableYes
Raw Data File Type(s)raw(Thermo)
Analysis Type DetailLC-MS
Release Date2024-01-31
Release Version1
Kim Jungeun Kim Jungeun
https://dx.doi.org/10.21228/M83Q7Q
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

Select appropriate tab below to view additional metadata details:

Project:

Project ID:PR001815
Project DOI:doi: 10.21228/M83Q7Q
Project Title:Metabolomic Characteristics of Nontuberculous Mycobacterial Pulmonary Disease
Project Summary:While the burden of nontuberculous mycobacterial pulmonary disease (NTM-PD) continues to increase, knowledge of biomarkers for NTM-PD remains insufficient. Furthermore, metabolic changes in NTM-PD have not yet been investigated. The identification of specific metabolites and associated metabolic pathways unique to NTM-PD will provide a deeper understanding of its pathogenesis. In this study, we aimed to discover specific metabolic biomarkers for NTM-PD using a metabolomics approach. In this study, we underwent untargeted metabolomic profiling using a liquid chromatography system coupled with the quadrupole-orbitrap mass spectrometer to analyze serum samples from patients with NTM-PD (n = 50), patients with non-NTM bronchiectasis (n = 50), and HC subjects (n = 60). To validate the results, an additional 86 serum samples for each group were analyzed using the same analytical methods. We identified several NTM-PD significant metabolites that differentiate patients with NTM-PD from healthy individuals. The machine learning-based classification model demonstrated the proficiency of the selected metabolic features in distinguishing between patients with NTM-PD and healthy individuals. These findings may contribute to a better understanding of the pathogenesis of NTM-PD and provide insights for the development of novel treatment approaches.
Institute:Seoul National University College of Medicine and Hospital
Department:Department of Clinical Pharmacology and Therapeutics
Last Name:Jungeun
First Name:Kim
Address:101 Daehak-ro, Jongno-gu, Seoul, Korea
Email:jeunk@snu.ac.kr
Phone:+821026965910

Subject:

Subject ID:SU003034
Subject Type:Human
Subject Species:Homo sapiens
Taxonomy ID:9606
Gender:Male and female

Factors:

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

mb_sample_id local_sample_id Group Cohort
SA316890P-Sr-B-M-067bronchiectasis Discovery cohort
SA316891P-Sr-B-F-068bronchiectasis Discovery cohort
SA316892P-Sr-B-F-066bronchiectasis Discovery cohort
SA316893P-Sr-B-M-065bronchiectasis Discovery cohort
SA316894P-Sr-B-M-064bronchiectasis Discovery cohort
SA316895P-Sr-B-M-069bronchiectasis Discovery cohort
SA316896P-Sr-B-F-070bronchiectasis Discovery cohort
SA316897P-Sr-B-F-075bronchiectasis Discovery cohort
SA316898P-Sr-B-F-073bronchiectasis Discovery cohort
SA316899P-Sr-B-M-072bronchiectasis Discovery cohort
SA316900P-Sr-B-M-071bronchiectasis Discovery cohort
SA316901P-Sr-B-F-063bronchiectasis Discovery cohort
SA316902P-Sr-B-F-062bronchiectasis Discovery cohort
SA316903P-Sr-B-F-055bronchiectasis Discovery cohort
SA316904P-Sr-B-M-054bronchiectasis Discovery cohort
SA316905P-Sr-B-F-053bronchiectasis Discovery cohort
SA316906P-Sr-B-F-052bronchiectasis Discovery cohort
SA316907P-Sr-B-F-056bronchiectasis Discovery cohort
SA316908P-Sr-B-F-057bronchiectasis Discovery cohort
SA316909P-Sr-B-M-061bronchiectasis Discovery cohort
SA316910P-Sr-B-F-060bronchiectasis Discovery cohort
SA316911P-Sr-B-F-059bronchiectasis Discovery cohort
SA316912P-Sr-B-F-058bronchiectasis Discovery cohort
SA316913P-Sr-B-F-076bronchiectasis Discovery cohort
SA316914P-Sr-B-M-078bronchiectasis Discovery cohort
SA316915P-Sr-B-F-093bronchiectasis Discovery cohort
SA316916P-Sr-B-F-094bronchiectasis Discovery cohort
SA316917P-Sr-B-M-092bronchiectasis Discovery cohort
SA316918P-Sr-B-F-091bronchiectasis Discovery cohort
SA316919P-Sr-B-M-090bronchiectasis Discovery cohort
SA316920P-Sr-B-F-095bronchiectasis Discovery cohort
SA316921P-Sr-B-M-096bronchiectasis Discovery cohort
SA316922P-Sr-B-M-100bronchiectasis Discovery cohort
SA316923P-Sr-B-F-099bronchiectasis Discovery cohort
SA316924P-Sr-B-F-098bronchiectasis Discovery cohort
SA316925P-Sr-B-F-097bronchiectasis Discovery cohort
SA316926P-Sr-B-F-089bronchiectasis Discovery cohort
SA316927P-Sr-B-F-088bronchiectasis Discovery cohort
SA316928P-Sr-B-F-081bronchiectasis Discovery cohort
SA316929P-Sr-B-F-080bronchiectasis Discovery cohort
SA316930P-Sr-B-F-079bronchiectasis Discovery cohort
SA316931P-Sr-B-M-051bronchiectasis Discovery cohort
SA316932P-Sr-B-M-082bronchiectasis Discovery cohort
SA316933P-Sr-B-F-083bronchiectasis Discovery cohort
SA316934P-Sr-B-F-087bronchiectasis Discovery cohort
SA316935P-Sr-B-M-086bronchiectasis Discovery cohort
SA316936P-Sr-B-F-085bronchiectasis Discovery cohort
SA316937P-Sr-B-M-084bronchiectasis Discovery cohort
SA316938P-Sr-B-M-077bronchiectasis Discovery cohort
SA316939P-Sr-B-M-074bronchiectasis Discovery cohort
SA316940P-Sr-V1-B-F-097bronchiectasis Validation cohort
SA316941P-Sr-V1-B-M-096bronchiectasis Validation cohort
SA316942P-Sr-V1-B-F-095bronchiectasis Validation cohort
SA316943P-Sr-V1-B-F-094bronchiectasis Validation cohort
SA316944P-Sr-V1-B-F-098bronchiectasis Validation cohort
SA316945P-Sr-V1-B-F-099bronchiectasis Validation cohort
SA316946P-Sr-V1-B-F-102bronchiectasis Validation cohort
SA316947P-Sr-V1-B-F-101bronchiectasis Validation cohort
SA316948P-Sr-V1-B-M-100bronchiectasis Validation cohort
SA316949P-Sr-V1-B-F-093bronchiectasis Validation cohort
SA316950P-Sr-V1-B-F-092bronchiectasis Validation cohort
SA316951P-Sr-V1-B-M-125bronchiectasis Validation cohort
SA316952P-Sr-V2-B-F-257bronchiectasis Validation cohort
SA316953P-Sr-V2-B-F-256bronchiectasis Validation cohort
SA316954P-Sr-V2-B-F-255bronchiectasis Validation cohort
SA316955P-Sr-V1-B-M-087bronchiectasis Validation cohort
SA316956P-Sr-V1-B-M-088bronchiectasis Validation cohort
SA316957P-Sr-V1-B-M-091bronchiectasis Validation cohort
SA316958P-Sr-V1-B-M-090bronchiectasis Validation cohort
SA316959P-Sr-V1-B-F-089bronchiectasis Validation cohort
SA316960P-Sr-V1-B-F-103bronchiectasis Validation cohort
SA316961P-Sr-V1-B-F-104bronchiectasis Validation cohort
SA316962P-Sr-V1-B-F-119bronchiectasis Validation cohort
SA316963P-Sr-V1-B-F-118bronchiectasis Validation cohort
SA316964P-Sr-V1-B-F-117bronchiectasis Validation cohort
SA316965P-Sr-V1-B-F-116bronchiectasis Validation cohort
SA316966P-Sr-V1-B-F-120bronchiectasis Validation cohort
SA316967P-Sr-V1-B-F-121bronchiectasis Validation cohort
SA316968P-Sr-V1-B-F-124bronchiectasis Validation cohort
SA316969P-Sr-V1-B-F-123bronchiectasis Validation cohort
SA316970P-Sr-V1-B-M-122bronchiectasis Validation cohort
SA316971P-Sr-V1-B-M-115bronchiectasis Validation cohort
SA316972P-Sr-V1-B-M-114bronchiectasis Validation cohort
SA316973P-Sr-V1-B-F-108bronchiectasis Validation cohort
SA316974P-Sr-V1-B-M-107bronchiectasis Validation cohort
SA316975P-Sr-V1-B-M-106bronchiectasis Validation cohort
SA316976P-Sr-V1-B-M-105bronchiectasis Validation cohort
SA316977P-Sr-V1-B-M-109bronchiectasis Validation cohort
SA316978P-Sr-V1-B-F-110bronchiectasis Validation cohort
SA316979P-Sr-V1-B-F-113bronchiectasis Validation cohort
SA316980P-Sr-V1-B-F-112bronchiectasis Validation cohort
SA316981P-Sr-V1-B-F-111bronchiectasis Validation cohort
SA316982P-Sr-V2-B-M-254bronchiectasis Validation cohort
SA316983P-Sr-V2-B-F-258bronchiectasis Validation cohort
SA316984P-Sr-V2-B-M-226bronchiectasis Validation cohort
SA316985P-Sr-V2-B-F-225bronchiectasis Validation cohort
SA316986P-Sr-V2-B-F-224bronchiectasis Validation cohort
SA316987P-Sr-V2-B-F-223bronchiectasis Validation cohort
SA316988P-Sr-V2-B-F-227bronchiectasis Validation cohort
SA316989P-Sr-V2-B-F-253bronchiectasis Validation cohort
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Collection:

Collection ID:CO003027
Collection Summary:Blood samples were obtained when the patients were diagnosed with NTM-PD or BE during their routine health check-up. The collected samples were incubated at 25°C for 15–30 min and centrifuged at 13,756 × g for 20 min to isolate serum.
Sample Type:Blood (serum)

Treatment:

Treatment ID:TR003043
Treatment Summary:NA

Sample Preparation:

Sampleprep ID:SP003040
Sampleprep Summary:The serum samples (500 μL) were stored at −80°C until further analysis. Before metabolomic analysis, the frozen samples were thawed on ice and vortexed. Then, each serum (80 μL) was deproteinized by adding 350 μL prechilled acetonitrile/methanol (1:1, v/v), followed by shaking for 5 min and centrifugation at 18,341 × g for 10 min. Supernatant (400 μL) was transferred to a new tube and then further centrifuged. The resulting supernatant (350 μL) was diluted with equal volume of distilled water, and 150 μL of the final solution was transferred to an autosampler vial for analysis. A pooled quality control (PQC) sample was created by combining 50 μL aliquots from all serum samples. The PQC sample was randomly placed within the sequence after preparation, alongside the analysis samples.
Processing Storage Conditions:-80℃
Extract Storage:On ice

Combined analysis:

Analysis ID AN004791
Analysis type MS
Chromatography type Reversed phase
Chromatography system Thermo Dionex Ultimate 3000
Column Waters ACQUITY UPLC HSS T3 (100 x 2.1mm,1.8um)
MS Type ESI
MS instrument type Orbitrap
MS instrument name Thermo Q Exactive Plus Orbitrap
Ion Mode POSITIVE
Units Peak area

Chromatography:

Chromatography ID:CH003622
Instrument Name:Thermo Dionex Ultimate 3000
Column Name:Waters ACQUITY UPLC HSS T3 (100 x 2.1mm,1.8um)
Column Temperature:50
Flow Gradient:% B for 1 min, 5%–95% B for 1–6 min, 95%–98% for 6–12 min, stayed at 98% B for 12–22 min, and equilibrated to the initial condition for 22–25 min.
Flow Rate:0.4ml/min
Solvent A:100% water; 0.1% formic acid
Solvent B:100% methanol; 0.1% formic acid
Chromatography Type:Reversed phase

MS:

MS ID:MS004537
Analysis ID:AN004791
Instrument Name:Thermo Q Exactive Plus Orbitrap
Instrument Type:Orbitrap
MS Type:ESI
MS Comments:Raw data processing, including peak picking, alignment, data cleaning, and metabolite annotation, were carried out using the R package “TidyMass” (version 1.0.6). Missing values were imputed with the values calculated using the k-nearest neighbor (KNN) algorithm.
Ion Mode:POSITIVE
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