Urinary proteomic analysis to identify a potential protein biomarker panel for the diagnosis of tuberculosis.

Tuberculosis (TB) is caused by Mycobacterium tuberculosis and is one of the primary causes of death worldwide. Rapid and accurate diagnosis of TB is one of the most direct means to reduce the incidence of TB. In this study, urinary proteomic profiling of TB patients and non-TB individual controls (HCs) was performed, and differentially expressed urinary proteins between TB and HCs were compared and exclusively expressed proteins in TB patients were selected to establish a clinically useful disease marker panel. In total, these top 11 targeted proteins with 265 peptides were scheduled for multiple reaction monitoring validation analysis by using urine samples from 52 TB patients and 52 HCs. The result demonstrated that a three-protein combination out of the five-protein panel (namely P22352, Q9P121, P15151, Q13291, and Q8NDA2) exhibited sensitivity rate of 82.7% in the diagnosis of TB. Furthermore, the three-protein combination could differentiate TB from the latent tuberculosis (LTB) effectively, which exhibited specificity rate of 92.3% for the diagnosis of TB from the LTB category. Although more numbers of clinical samples are required for further verification, the results provided preliminary evidence that this "three-protein combination" out of the five-protein panel could probably be a novel TB diagnostic biomarker in clinical application.

Urinary metabolomic analysis to identify potential markers for the diagnosis of tuberculosis and latent tuberculosis.

Tuberculosis (TB) is a serious infectious disease with high infection and mortality rates. 5%-10% of the latent tuberculosis infections (LTBI) are likely to develop into active TB, and there are currently no clinical biomarkers that can distinguish between LTBI, active TB and other non-tuberculosis populations. Therefore, it is necessary to develop rapid diagnostic methods for active TB and LTBI. In this study, urinary metabolome of 30 active TB samples and the same number of LTBI and non-TB control samples were identified and analyzed by UPLC-Q Exactive MS. In total, 3744 metabolite components were obtained in ESI- mode and 4086 in ESI + mode. Orthogonal partial least square discriminant analysis (OPLS-DA) and hierarchical cluster analysis (HCA) showed that there were significant differences among LTBI, active TB and non-TB. Six differential metabolites were screened in positive and negative mode, 3-hexenoic acid, glutathione (GSH), glycochenodeoxycholate-3-sulfate, N-[4'-hydroxy-(E)-cinnamoyl]-l-aspartic acid, deoxyribose 5-phosphate and histamine. The overlapping pathways differential metabolites involved were mainly related to immune regulation and urea cycle. The results showed that the urine metabolism of TB patients was disordered and many metabolic pathways changed. Multivariate statistical analysis revealed that GSH and histamine were selected as potential molecular markers, with area under curve of receiver operating characteristic curve over 0.75. Among the multiple differential metabolites, GSH and histamine changed to varying degrees in active TB, LTBI and the non-TB control group. The levels of GSH and histamine in 48 urinary samples were measured by ELISA in validation phase, and the result in our study provided the potential for non-invasive biomarkers of TB.

The identification of tuberculosis biomarkers in human urine samples.

We aimed to determine whether shotgun proteomic approaches could be used to identify tuberculosis (TB)-specific biomarkers in the urine of well-characterised patients with active TB versus no TB. Patients with suspected TB (n=63) were classified as: definite TB (Mycobacterium tuberculosis positive culture, n=21); presumed latent-TB infection (LTBI) (M. tuberculosis negative culture, no radiological features of active TB, a positive QuantiFERON-TB Gold In-Tube (QFT-IT) test and a positive T-SPOT.TB test, n=24); and presumed non-TB/non-LTBI (M. tuberculosis negative culture, no radiological features of active TB, a negative QFT-IT test and a negative T-SPOT.TB test, n=18). Urine proteins, in the range of 3-50 kDa, were collected, separated by a one-dimensional SDS-PAGE gel and digested using trypsin, after which high-performance liquid chromatography-tandem mass spectrometry was used to identify the urinary proteome. 10 mycobacterial proteins were observed exclusively in the urine of definite TB patients, while six mycobacterial proteins were found exclusively in the urine of presumed LTBI patients. In addition, a gene ontology enrichment analysis identified a panel of 20 human proteins that were significant discriminators (p<0.05) for TB disease compared to no TB disease. Furthermore, seven common human proteins were differentially over- or under-expressed in the TB versus the non-TB group. These biomarkers hold promise for the development of new point-of-care diagnostics for TB.

Point-of-care urine tests for smoking status and isoniazid treatment monitoring in adult patients.

BACKGROUND: Poor adherence to isoniazid (INH) preventive therapy (IPT) is an impediment to effective control of latent tuberculosis (TB) infection. TB patients who smoke are at higher risk of latent TB infection, active disease, and TB mortality, and may have lower adherence to their TB medications. The objective of our study was to validate IsoScreen and SmokeScreen (GFC Diagnostics, UK), two point-of-care tests for monitoring INH intake and determining smoking status. The tests could be used together in the same individual to help identify patients with a high-risk profile and provide a tailored treatment plan that includes medication management, adherence interventions, and smoking cessation programs. METHODOLOGY/PRINCIPAL FINDINGS: 200 adult outpatients attending the TB and/or the smoking cessation clinic were recruited at the Montreal Chest Institute. Sensitivity and specificity were measured for each test against the corresponding composite reference standard. Test reliability was measured using kappa statistic for intra-rater and inter-rater agreement. Univariate and multivariate logistic regression models were used to explore possible covariates that might be related to false-positive and false-negative test results. IsoScreen had a sensitivity of 93.2% (95% confidence interval [CI] 80.3, 98.2) and specificity of 98.7% (94.8, 99.8). IsoScreen had intra-rater agreement (kappa) of 0.75 (0.48, 0.94) and inter-rater agreement of 0.61 (0.27, 0.90). SmokeScreen had a sensitivity of 69.2% (56.4, 79.8), specificity of 81.6% (73.0, 88.0), intra-rater agreement of 0.77 (0.56, 0.94), and inter-rater agreement of 0.66 (0.42, 0.88). False-positive SmokeScreen tests were strongly associated with INH treatment. CONCLUSIONS: IsoScreen had high validity and reliability, whereas SmokeScreen had modest validity and reliability. SmokeScreen tests did not perform well in a population receiving INH due to the association between INH treatment and false-positive SmokeScreen test results. Development of the next generation SmokeScreen assay should account for this potential interference.

The reliability and practicality of the Arkansas method assay of isoniazid adherence.

The Arkansas method (AM) for isoniazid (INH) metabolite detection is a relatively inexpensive, simple, objective measure of adherence. The purpose of the study was to explore whether variations in urine sample handling and storage will produce accurate assay outcomes. Participants were a convenience sample of 28 adults and adolescents prescribed INH for latent tuberculosis infection. Participants provided one sample to test effects of the following: mixing processes; durations at room temperature, in a refrigerator, or frozen; and effects of freeze/thaw cycles on AM outcomes. No manipulations had a discernible impact on outcomes with concordant positive rates from 85% to 100%. Concordance rates of manipulated samples did not appear to differ from rates of norm samples. Results suggest that urine samples can withstand a variety of manipulations in both handling and storage without affecting the accuracy of AM assay results. These findings have important implications for providers of treatment and researchers and provide the impetus for both to examine the potential of using the AM of INH metabolite testing as a measure of medication adherence.