Comprehensive plasma proteomic profiling reveals biomarkers for active tuberculosis

Garay‐Baquero, Diana J.; White, Cory H.; Walker, Naomi F.; Tebruegge, Marc; Schiff, Hannah F.; Ugarte-Gil, César; Morris-Jones, Stephen; Marshall, Ben G.; Manousopoulou, Antigoni; Adamson, John; Vallejo, Andrés F.; Bielecka, Magdalena K; Wilkinson, Robert J.; Tezera, Liku B.; Woelk, Christopher H.; Garbis, Spiros D.; Elkington, Paul

doi:10.1172/jci.insight.137427

Cited by 45 publications

(35 citation statements)

References 69 publications

(117 reference statements)

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“…Interestingly, this has also been observed in another proteomics study. 7 Overall our analysis recapitulates previous findings and showcases the applicability of DIA and multi-protease digestion for robust analysis of clinical samples.…”

Section: Identification and Quantification Of Mtb Proteinssupporting

confidence: 85%

“…To mitigate issues in protein detection, numerous studies have successfully employed extensive off-line chromatographic fractionation, allowing for the injection of individual fractions of reduced complexity into the mass spectrometer. This approach has been highly successful to increase the number of proteins detectable in plasma, 6,7 albeit at the cost of a correspondingly dramatic increase in MS acquisition time to analyze dozens of fractions. Furthermore, the reliance on off-line fractionation introduces a low-throughput and cumbersome additional step in sample preparation that is not accessible to many labs.…”

Section: Introductionmentioning

confidence: 99%

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Towards comprehensive plasma proteomics by orthogonal protease digestion

Fossati

Richards

Chen

et al. 2021

Preprint

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Rapid and consistent protein identification across large clinical cohorts is an important goal for clinical proteomics. With the development of data-independent technologies (DIA/SWATH-MS), it is now possible to analyze hundreds of samples with great reproducibility and quantitative accuracy. However, this technology benefits from empirically derived spectral libraries that define the detectable set of peptides and proteins. Here we apply a simple and accessible tip-based workflow for the generation of spectral libraries to provide a comprehensive overview on the plasma proteome in individuals with and without active tuberculosis (TB). To boost protein coverage, we utilized non-conventional proteases such as GluC and AspN together with the gold standard trypsin, identifying more than 30,000 peptides mapping to 3,309 proteins. Application of this library to quantify plasma proteome differences in TB infection recovered more than 400 proteins in 50 minutes of MS-acquisition, including diagnostic Mycobacterium tuberculosis (Mtb) proteins that have previously been detectable primarily by antibody-based assays and intracellular proteins not previously described to be in plasma.

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Section: Identification and Quantification Of Mtb Proteinssupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Towards comprehensive plasma proteomics by orthogonal protease digestion

Fossati

Richards

Chen

et al. 2021

Preprint

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“…However, the current use of blood-based available immunological tests including T-SPOT.TB (T-SPOT) and QuantiFERON-TB Gold In-Tube (QFT-GIT) was limited by their poor ability to reliably stratify ATB from LTBI especially in TB endemic areas (8,9). Recent advances have been developed in blood signatures including transcriptome (10), proteome (11), genome (12), metabolome (13), cytokines (14,15), and markers on immune cells (16,17) for identifying ATB, raising hopes for translation into available assays. However, due to the fact that the application of these emerging methods has not been verified with sufficient repetition and large sample size, the real diagnostic utility under actual clinical conditions remains unclear (18).…”

Section: Introductionmentioning

confidence: 99%

Lymphocyte Non-Specific Function Detection Facilitating the Stratification of Mycobacterium tuberculosis Infection

et al. 2021

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BackgroundInadequate tuberculosis (TB) diagnostics, especially for discrimination between active TB (ATB) and latent TB infection (LTBI), are major hurdle in the reduction of the disease burden. The present study aims to investigate the role of lymphocyte non-specific function detection for TB diagnosis in clinical practice.MethodsA total of 208 participants including 49 ATB patients, 64 LTBI individuals, and 95 healthy controls were recruited at Tongji hospital from January 2019 to October 2020. All subjects were tested with lymphocyte non-specific function detection and T-SPOT assay.ResultsSignificantly positive correlation existed between lymphocyte non-specific function and phytohemagglutinin (PHA) spot number. CD4+ T cell non-specific function showed the potential for differentiating patients with negative T-SPOT results from those with positive T-SPOT results with an area under the curve (AUC) of 0.732 (95% CI, 0.572-0.893). The non-specific function of CD4+ T cells, CD8+ T cells, and NK cells was found significantly lower in ATB patients than in LTBI individuals. The AUCs presented by CD4+ T cell non-specific function, CD8+ T cell non-specific function, and NK cell non-specific function for discriminating ATB patients from LTBI individuals were 0.845 (95% CI, 0.767-0.925), 0.770 (95% CI, 0.683-0.857), and 0.691 (95% CI, 0.593-0.789), respectively. Application of multivariable logistic regression resulted in the combination of CD4+ T cell non-specific function, NK cell non-specific function, and culture filtrate protein-10 (CFP-10) spot number as the optimally diagnostic model for differentiating ATB from LTBI. The AUC of the model in distinguishing between ATB and LTBI was 0.939 (95% CI, 0.898-0.981). The sensitivity and specificity were 83.67% (95% CI, 70.96%-91.49%) and 90.63% (95% CI, 81.02%-95.63%) with the threshold as 0.57. Our established model showed superior performance to TB-specific antigen (TBAg)/PHA ratio in stratifying TB infection status.ConclusionsLymphocyte non-specific function detection offers an attractive alternative to facilitate TB diagnosis. The three-index diagnostic model was proved to be a potent tool for the identification of different events involved in TB infection, which is helpful for the treatment and management of patients.

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“…Multiple limitations registered by conventional tests of etiology hurdles to the timely diagnosis of disease and contribute to promote clinical progression as well as continued transmission. Recent advances in genomics (16,17), transcriptomics (18)(19)(20), proteomics (21)(22)(23), and metabolomics (24)(25)(26) have effectively facilitated the diagnosis of TB. But these emerging methods often require prohibitively complex equipment and operations, which hinder their promotion of clinical applications.…”

Section: Introductionmentioning

confidence: 99%

Lymphocyte-Related Immunological Indicators for Stratifying Mycobacterium tuberculosis Infection

et al. 2021

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BackgroundEasily accessible tools that reliably stratify Mycobacterium tuberculosis (MTB) infection are needed to facilitate the improvement of clinical management. The current study attempts to reveal lymphocyte-related immune characteristics of active tuberculosis (ATB) patients and establish immunodiagnostic model for discriminating ATB from latent tuberculosis infection (LTBI) and healthy controls (HC).MethodsA total of 171 subjects consisted of 54 ATB, 57 LTBI, and 60 HC were consecutively recruited at Tongji hospital from January 2019 to January 2021. All participants were tested for lymphocyte subsets, phenotype, and function. Other examination including T-SPOT and microbiological detection for MTB were performed simultaneously.ResultsCompared with LTBI and HC, ATB patients exhibited significantly lower number and function of lymphocytes including CD4+ T cells, CD8+ T cells and NK cells, and significantly higher T cell activation represented by HLA-DR and proportion of immunosuppressive cells represented by Treg. An immunodiagnostic model based on the combination of NK cell number, HLA-DR+CD3+ T cells, Treg, CD4+ T cell function, and NK cell function was built using logistic regression. Based on receiver operating characteristic curve analysis, the area under the curve (AUC) of the diagnostic model was 0.920 (95% CI, 0.867-0.973) in distinguishing ATB from LTBI, while the cut-off value of 0.676 produced a sensitivity of 81.48% (95% CI, 69.16%-89.62%) and specificity of 91.23% (95% CI, 81.06%-96.20%). Meanwhile, AUC analysis between ATB and HC according to the diagnostic model was 0.911 (95% CI, 0.855-0.967), with a sensitivity of 81.48% (95% CI, 69.16%-89.62%) and a specificity of 90.00% (95% CI, 79.85%-95.34%).ConclusionsOur study demonstrated that the immunodiagnostic model established by the combination of lymphocyte-related indicators could facilitate the status differentiation of MTB infection.

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Comprehensive plasma proteomic profiling reveals biomarkers for active tuberculosis

Cited by 45 publications

References 69 publications

Towards comprehensive plasma proteomics by orthogonal protease digestion

Towards comprehensive plasma proteomics by orthogonal protease digestion

Lymphocyte Non-Specific Function Detection Facilitating the Stratification of Mycobacterium tuberculosis Infection

Lymphocyte-Related Immunological Indicators for Stratifying Mycobacterium tuberculosis Infection

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