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      Mass Spectrometric Identification of Urinary Biomarkers of Pulmonary Tuberculosis

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          Abstract

          Background

          Tuberculosis (TB) is the leading infectious cause of death worldwide. A major barrier to control of the pandemic is a lack of clinical biomarkers with the ability to distinguish active TB from healthy and sick controls and potential for development into point-of-care diagnostics.

          Methods

          We conducted a prospective case control study to identify candidate urine-based diagnostic biomarkers of active pulmonary TB (discovery cohort) and obtained a separate blinded “validation” cohort of confirmed cases of active pulmonary TB and controls with non-tuberculous pulmonary disease for validation. Clean-catch urine samples were collected and analyzed using high performance liquid chromatography-coupled time-of-flight mass spectrometry.

          Results

          We discovered ten molecules from the discovery cohort with receiver-operator characteristic (ROC) area-under-the-curve (AUC) values >85%. These 10 molecules also significantly decreased after 60 days of treatment in a subset of 20 participants followed over time. Of these, a specific combination of diacetylspermine, neopterin, sialic acid, and N-acetylhexosamine exhibited ROC AUCs >80% in a blinded validation cohort of participants with active TB and non-tuberculous pulmonary disease.

          Conclusion

          Urinary levels of diacetylspermine, neopterin, sialic acid, and N-acetylhexosamine distinguished patients with tuberculosis from healthy controls and patients with non-tuberculous pulmonary diseases, providing a potential noninvasive biosignature of active TB.

          Funding

          This study was funded by Weill Cornell Medicine, the National Institute of Allergy and Infectious Diseases, the Clinical and Translational Science Center at Weill Cornell, the NIH Fogarty International Center grants, and the NIH Tuberculosis Research Unit (Tri-I TBRU).

          Highlights

          • Urinary levels of small metabolites appear capable of distinguishing cases of active pulmonary tuberculosis from sick and healthy controls.

          • Levels of these biomarkers decrease after 60 days of treatment in a longitudinal cohort of 20 participants.

          • - Many of the identified biomarkers are known inflammatory intermediates that may reflect a specific immune response to tuberculosis.

          Urine tests are commonly used to enable non-invasive, rapid and point-of-care diagnosis of various infectious diseases. We identified diacetylspermine, neopterin, sialic acid and N-acetylhexosamine as potential urine-based biomarkers for tuberculosis from two independent patient cohorts. These metabolites are known inflammatory intermediates and appear to decrease with anti-tuberculosis therapy in a subset of participants followed over 2 months. If validated, these metabolites have potential to both improve our understanding of the immune reaction to active tuberculosis and facilitate the development of a much-needed clinical biomarker for tuberculosis.

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          Most cited references41

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          Innovation: Metabolomics: the apogee of the omics trilogy.

          Metabolites, the chemical entities that are transformed during metabolism, provide a functional readout of cellular biochemistry. With emerging technologies in mass spectrometry, thousands of metabolites can now be quantitatively measured from minimal amounts of biological material, which has thereby enabled systems-level analyses. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanism are being revealed and are shaping our understanding of cell biology, physiology and medicine.
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            Xpert MTB/RIF assay for the diagnosis of extrapulmonary tuberculosis: a systematic review and meta-analysis.

            Xpert MTB/RIF (Cepheid, Sunnyvale, CA, USA) is endorsed for the detection of pulmonary tuberculosis (TB). We performed a systematic review and meta-analysis to assess the accuracy of Xpert for the detection of extrapulmonary TB. We searched multiple databases to October 15, 2013. We determined the accuracy of Xpert compared with culture and a composite reference standard (CRS). We grouped data by sample type and performed meta-analyses using a bivariate random-effects model. We assessed sources of heterogeneity using meta-regression for predefined covariates. We identified 18 studies involving 4461 samples. Sample processing varied greatly among the studies. Xpert sensitivity differed substantially between sample types. In lymph node tissues or aspirates, Xpert pooled sensitivity was 83.1% (95% CI 71.4-90.7%) versus culture and 81.2% (95% CI 72.4-87.7%) versus CRS. In cerebrospinal fluid, Xpert pooled sensitivity was 80.5% (95% CI 59.0-92.2%) against culture and 62.8% (95% CI 47.7-75.8%) against CRS. In pleural fluid, pooled sensitivity was 46.4% (95% CI 26.3-67.8%) against culture and 21.4% (95% CI 8.8-33.9%) against CRS. Xpert pooled specificity was consistently >98.7% against CRS across different sample types. Based on this systematic review, the World Health Organization now recommends Xpert over conventional tests for diagnosis of TB in lymph nodes and other tissues, and as the preferred initial test for diagnosis of TB meningitis. ©ERS 2014.
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              Diagnosis of childhood tuberculosis and host RNA expression in Africa.

              Improved diagnostic tests for tuberculosis in children are needed. We hypothesized that transcriptional signatures of host blood could be used to distinguish tuberculosis from other diseases in African children who either were or were not infected with the human immunodeficiency virus (HIV). The study population comprised prospective cohorts of children who were undergoing evaluation for suspected tuberculosis in South Africa (655 children), Malawi (701 children), and Kenya (1599 children). Patients were assigned to groups according to whether the diagnosis was culture-confirmed tuberculosis, culture-negative tuberculosis, diseases other than tuberculosis, or latent tuberculosis infection. Diagnostic signatures distinguishing tuberculosis from other diseases and from latent tuberculosis infection were identified from genomewide analysis of RNA expression in host blood. We identified a 51-transcript signature distinguishing tuberculosis from other diseases in the South African and Malawian children (the discovery cohort). In the Kenyan children (the validation cohort), a risk score based on the signature for tuberculosis and for diseases other than tuberculosis showed a sensitivity of 82.9% (95% confidence interval [CI], 68.6 to 94.3) and a specificity of 83.6% (95% CI, 74.6 to 92.7) for the diagnosis of culture-confirmed tuberculosis. Among patients with cultures negative for Mycobacterium tuberculosis who were treated for tuberculosis (those with highly probable, probable, or possible cases of tuberculosis), the estimated sensitivity was 62.5 to 82.3%, 42.1 to 80.8%, and 35.3 to 79.6%, respectively, for different estimates of actual tuberculosis in the groups. In comparison, the sensitivity of the Xpert MTB/RIF assay for molecular detection of M. tuberculosis DNA in cases of culture-confirmed tuberculosis was 54.3% (95% CI, 37.1 to 68.6), and the sensitivity in highly probable, probable, or possible cases was an estimated 25.0 to 35.7%, 5.3 to 13.3%, and 0%, respectively; the specificity of the assay was 100%. RNA expression signatures provided data that helped distinguish tuberculosis from other diseases in African children with and those without HIV infection. (Funded by the European Union Action for Diseases of Poverty Program and others).
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                Author and article information

                Contributors
                Journal
                EBioMedicine
                EBioMedicine
                EBioMedicine
                Elsevier
                2352-3964
                22 April 2018
                May 2018
                22 April 2018
                : 31
                : 157-165
                Affiliations
                [a ]Department of Medicine, Weill Cornell Medicine, New York, NY, United States
                [b ]Department of Medicine, Stanford Medicine, Stanford, CA, United States
                [c ]Center for Global Health, Weill Cornell Medicine, New York, NY, United States
                [d ]Groupe Haitien d'Etude du Sarcome de Kaposi et des Infections Opportunistes (GHESKIO), Port au Prince, Haiti
                [e ]Mayo Clinic, Rochester, MN, United States
                [f ]Department of Statistical Science, Cornell University, Ithaca, NY, United States
                [g ]Agilent Technologies, Santa Clara, CA, United States
                [h ]Memorial Sloan Kettering Cancer Center, New York, NY, United States
                Author notes
                [* ]Corresponding authors at: Department of Medicine, Weill Cornell Medicine, New York, NY, United States. kyr9001@ 123456med.cornell.edu
                Article
                S2352-3964(18)30142-7
                10.1016/j.ebiom.2018.04.014
                6013777
                29752217
                a9d7c948-945d-4001-97b8-27cd47c18241
                © 2018 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 31 January 2018
                : 4 April 2018
                : 17 April 2018
                Categories
                Research Paper

                tuberculosis,biomarker,metabolomics,urine
                tuberculosis, biomarker, metabolomics, urine

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