4.7 Article

Mass Spectrometry-Based Proteomic and Metabolomic Profiling of Serum Samples for Discovery and Validation of Tuberculosis Diagnostic Biomarker Signature

Journal

Publisher

MDPI
DOI: 10.3390/ijms232213733

Keywords

tuberculosis; diagnosis; biomarkers; multiomics; mass spectrometry; blood serum

Funding

  1. FCT [INDIGO-DBT2-062, DL57/2016, SFRH/BPD/111100/2015]
  2. Fundacao para a Ciencia a Tecnologia [PTDC/CVT-CVT/29510/2017]
  3. FEDER funds through COMPETE2020-Programa Operacional Competitividade e Internacionalizacao (POCI) [LISBOA-01-0145-FEDER-007660, UID/Multi/04378/2019]
  4. ONEIDA project - FEEI-Fundos Europeus Estruturais e de Investimento from Programa Operacional Regional Lisboa 2020 [LISBOA-01-0145-FEDER-016417]
  5. Fundacao para a Ciencia e a Tecnologia
  6. National Mass Spectrometry Network (RNEM) [POCI-01-0145-FEDER-402-022125, ROTEIRO/0028/2013]
  7. European Regional Development Fund (ERDF), through the COMPETE 2020-Operational Programme for Competitiveness and Internationalisation
  8. Portuguese national funds via FCT-Fundacao para a Ciencia e a Tecnologia, I.P. [POCI-01-0145-FEDER-007440, UIDB/04539/2020, UIDP/04539/2020]

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This study aimed to discover molecular biomarkers for the early diagnosis of tuberculosis. Proteomics and metabolomics analyses were performed, and a combination of protein and metabolite markers was found to accurately identify tuberculosis patients.
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 the discovery of effective diagnostic biomarkers is crucial. 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 three 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 of 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 one protein and four metabolites was also performed for the common control and TB patients in the proteomic and metabolomic groups. This combined signature correctly assigned the 12 controls and 12 patients used only for prediction (AUC = 1, specificity = 100%, and sensitivity = 100%). This multiomics approach revealed a biomarker signature for tuberculosis diagnosis that could be potentially used for developing a point-of-care diagnosis clinical test.

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