Summary of Study ST002428

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 PR001562. The data can be accessed directly via it's Project DOI: 10.21228/M8SM54 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 IDST002428
Study TitleMass Spectrometry-based Proteomic and Metabolomic profiling of serum samples for discovery and validation of Tuberculosis diagnostic biomarker signature
Study SummaryTuberculosis (TB) is a transmissible disease listed as one of the 10 leading causes of death worldwide (10 million infected in 2019). A swift and precise diagnosis is essential to forestall its transmission, for which is crucial the discovery of effective diagnostic biomarkers. In this study, we aimed to discover molecular biomarkers for the early diagnosis of tuberculosis. Two independent cohorts comprising 29 and 34 subjects were assayed by proteomics, and 49 were included for metabolomic analysis. All subjects were arranged into 3 experimental groups – healthy controls (Controls), Latent TB infection (LTBI) and TB patients. LC-MS/MS blood serum protein and metabolite levels were submitted to univariate, multivariate and ROC analysis. From the 149 proteins quantified in the discovery set, 25 were found to be differentially abundant between Controls and TB patients. The AUC, specificity and sensitivity, determined by ROC statistical analysis of the model composed by four of these proteins considering both proteomic sets, were 0.96; 93% and 91%, respectively. The five metabolites (9-methyluric acid, indole-3-lactic acid, trans-3-indoleacrylic acid, hexanoylglycine and N-acetyl-L-leucine) that better discriminate the control and TB patient groups (VIP > 1.75) from a total of 92 metabolites quantified in both ionization modes, were submitted to ROC analysis. An AUC=1 was determined with all samples being correctly assigned to the respective experimental group. An integrated ROC analysis enrolling 1 protein and 4 metabolites was also performed for the common control and TB patients in the proteomic and metabolomic groups. This combined signature has correctly assigned the 12 controls and 12 patients used only for prediction (AUC=1, specificity=100% and sensitivity=100%). This multi-omics approach has revealed a biomarker signature for tuberculosis diagnosis that could be potentially used for developing a point-of-care diagnosis clinical test.
Institute
ITQB NOVA
LaboratoryProteomics of Non-Model Organisms
Last NameGonçalves
First NameLuís
AddressAvenida Republica
Emaillgafeira@itqb.unl.pt
Phone214469464
Submit Date2022-10-04
Num Groups3
Raw Data AvailableYes
Raw Data File Type(s)mzML
Analysis Type DetailLC-MS
Release Date2023-01-20
Release Version1
Luís Gonçalves Luís Gonçalves
https://dx.doi.org/10.21228/M8SM54
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Project:

Project ID:PR001562
Project DOI:doi: 10.21228/M8SM54
Project Title:Mass Spectrometry-based Proteomic and Metabolomic profiling of serum samples for discovery and validation of Tuberculosis diagnostic biomarker signature
Project Type:Proteomic and metabolomic study
Project Summary:Tuberculosis (TB) is a transmissible disease listed as one of the 10 leading causes of death worldwide (10 million infected in 2019). A swift and precise diagnosis is essential to forestall its transmission, for which is crucial the discovery of effective diagnostic biomarkers. In this study, we aimed to discover molecular biomarkers for the early diagnosis of tuberculosis. Two independent cohorts comprising 29 and 34 subjects were assayed by proteomics, and 49 were included for metabolomic analysis. All subjects were arranged into 3 experimental groups – healthy controls (Controls), Latent TB infection (LTBI) and TB patients. LC-MS/MS blood serum protein and metabolite levels were submitted to univariate, multivariate and ROC analysis. From the 149 proteins quantified in the discovery set, 25 were found to be differentially abundant between Controls and TB patients. The AUC, specificity and sensitivity, determined by ROC statistical analysis of the model composed by four of these proteins considering both proteomic sets, were 0.96; 93% and 91%, respectively. The five metabolites (9-methyluric acid, indole-3-lactic acid, trans-3-indoleacrylic acid, hexanoylglycine and N-acetyl-L-leucine) that better discriminate the control and TB patient groups (VIP > 1.75) from a total of 92 metabolites quantified in both ionization modes, were submitted to ROC analysis. An AUC=1 was determined with all samples being correctly assigned to the respective experimental group. An integrated ROC analysis enrolling 1 protein and 4 metabolites was also performed for the common control and TB patients in the proteomic and metabolomic groups. This combined signature has correctly assigned the 12 controls and 12 patients used only for prediction (AUC=1, specificity=100% and sensitivity=100%). This multi-omics approach has revealed a biomarker signature for tuberculosis diagnosis that could be potentially used for developing a point-of-care diagnosis clinical test.
Institute:ITQB NOVA
Laboratory:Proteomics of Non-Model Organisms
Last Name:Gonçalves
First Name:Luís
Address:Avenida Republica
Email:lgafeira@itqb.unl.pt
Phone:214469464
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