Tuberculosis (TB) is the deadliest infectious disease, and yet accurate diagnostics for the disease are unavailable for many subpopulations. In this study, we investigate the possibility of using human breath for the diagnosis of active TB among TB suspect patients, considering also several risk factors for TB for smokers and those with human immunodeficiency virus (HIV). The analysis of exhaled breath, as an alternative to sputum-dependent tests, has the potential to provide a simple, fast, non-invasive, and readily available diagnostic service that could positively change TB detection. A total of 50 individuals from a clinic in South Africa were included in this pilot study. Human breath has been investigated in the setting of active TB using the thermal desorption-comprehensive two-dimensional gas chromatography-time of flight mass spectrometry methodology and chemometric techniques. From the entire spectrum of volatile metabolites in breath, three machine learning algorithms (support vector machines, partial least squares discriminant analysis, and random forest) to select discriminatory volatile molecules that could potentially be useful for active TB diagnosis were employed. Random forest showed the best overall performance, with sensitivities of 0.82 and 1.00 and specificities of 0.92 and 0.60 in the training and test data respectively. Unsupervised analysis of the compounds implicated by these algorithms suggests that they provide important information to cluster active TB from other patients. These results suggest that developing a non-invasive diagnostic for active TB using patient breath is a potentially rich avenue of research, including among patients with HIV comorbidities.
Breath is hypothesized to contain clinically relevant information, useful for the diagnosis and monitoring of disease, as well as understanding underlying pathogenesis. Nonhuman primates, such as the cynomolgus macaque, serve as an important model for the study of human disease, including over 70 different human infections. In this feasibility study, exhaled breath was successfully collected in less than 5 min under Biosafety Level 3 conditions from five anesthetized, intubated cynomolgus and rhesus macaques, before and after lung infection with The breath was subsequently analyzed using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry. A total of 384 macaque breath features were detected, with hydrocarbons being the most abundant. We provide putative identification for 19 breath molecules and report on overlap between the identified macaque breath compounds and those identified in previous human studies. To the best of our knowledge, this is the first time the volatile molecule content of macaque breath has been comprehensively sampled and analyzed. We do so here in a Biosafety Level 3 setting in the context of lung infection. The breath of nonhuman primates represents a novel fluid that could provide insight into disease pathogenesis.
In the present research, the potential of breath analysis by comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-MS) was investigated for the discrimination between healthy and infected mice. A pilot study employing a total of 16 animals was used to develop a method for breath analysis in a murine model for studying Mycobacterium tuberculosis complex (MTBC) using the M. bovis bacillus Calmette-Guérin. Breath was collected in Tedlar bags and concentrated onto thermal desorption tubes for subsequent analysis by GC×GC-MS. Immunological test and bacterial cell count in bronchoalveolar lavage fluid and mice lung homogenate confirmed the presence of bacteria in the infected group. From the GC×GC-MS analysis, 23 molecules were found to mainly drive the separation between control and infected mice and their tentative identification is provided.This study shows that the overall used methodology is able to differentiate breath between healthy and infected animals, and the information herein can be used to further develop the mouse breath model to study MTBC pathogenesis, evaluate pre-clinical drug regimen efficacy, and to further develop the concept of breath-based diagnostics.
In this pilot study, volatile molecules produced by cultures of Mycobacterium tuberculosis were evaluated to determine whether they could be used to discriminate between uninfected and M. tuberculosis-infected macaques. Thirty seven of the culture biomarkers were detectable in macaque breath and were shown to discriminate between uninfected and infected animals with an area under the curve (AUC) of 87%. An AUC of 98% was achieved when using the top 38 discriminatory molecules detectable in breath. We report two newly discovered volatile biomarkers, not previously associated with M. tuberculosis, that were selected in both our in vitro and in vivo discriminatory biomarker suites: 4-(1,1-dimethylpropyl)phenol and 4-ethyl-2,2,6,6-tetramethylheptane. Additionally, we report the detection of heptanal, a previously identified M. tuberculosis breath biomarker in humans, as an in vitro culture biomarker that was detected in every macaque breath sample analyzed, though not part of the in vivo discriminatory suite. This pilot study suggests that molecules from the headspace of M. tuberculosis culture show potential to translate as breath biomarkers for macaques infected with the same strain.
Objectives: For rheumatoid arthritis (RA) patients failing to achieve treatment targets with conventional synthetic disease-modifying antirheumatic drugs, tumor necrosis factor (TNF)-a inhibitors (anti-TNF therapies) are the primary first-line biologic therapy. In a cross-cohort, cross-platform study, we developed a molecular test that predicts inadequate response to anti-TNF therapies in biologic-naive RA patients. Materials and Methods: To identify predictive biomarkers, we developed a comprehensive human interactome-a map of pairwise protein/protein interactions-and overlaid RA genomic information to generate a model of disease biology. Using this map of RA and machine learning, a predictive classification algorithm was developed that integrates clinical disease measures, whole-blood gene expression data, and diseaseassociated transcribed single-nucleotide polymorphisms to identify those individuals who will not achieve an ACR50 improvement in disease activity in response to anti-TNF therapy. Results: Data from two patient cohorts (n = 58 and n = 143) were used to build a drug response biomarker panel that predicts nonresponse to anti-TNF therapies in RA patients, before the start of treatment. In a validation cohort (n = 175), the drug response biomarker panel identified nonresponders with a positive predictive value of 89.7 and specificity of 86.8. Conclusions: Across gene expression platforms and patient cohorts, this drug response biomarker panel stratifies biologic-naive RA patients into subgroups based on their probability to respond or not respond to anti-TNF therapies. Clinical implementation of this predictive classification algorithm could direct nonresponder patients to alternative targeted therapies, thus reducing treatment regimens involving multiple trial and error attempts of anti-TNF drugs.
Mycobacteria are the leading cause of death from infectious disease worldwide and limitations in current diagnostics are hampering control efforts. In recent years, the use of small volatile molecules as diagnostic biomarkers for mycobacteria has shown promise for use in the rapid analysis of in vitro cultures as well as ex vivo diagnosis using breath or sputum. In this study, 18 strains from four mycobacteria species (Mycobacterium avium, M. bovis BCG, M. intracellulare and M. xenopi) were analyzed for the first time using two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS). This study represents the first time volatile molecules associated with M. intracellulare and M. xenopi have ever been reported. A total of 217 chromatographic features were identified and 58 features were selected that discriminate between these four species. Putative identifications are provided for 17 of the 58 discriminatory features, three of which have been reported previously in mycobacteria. The identification of mycobacteria-associated volatile biomarker suites could reduce the time-to-diagnosis for mycobacterial infections, either from in vitro cultures prior to the visualization of colonies or directly from ex vivo specimens, thereby shortening the empiric treatment window and potentially improving outcomes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.