| dc.description.abstract | Tuberculosis (TB), a highly contagious bacterial disease caused by Mycobacterium
tuberculosis, is considered the leading cause of death globally from a single bacterial
pathogen. The latest reports indicate 10.4 million new TB cases are diagnosed globally per
annum, of which approximately only 5.7 million actually receive treatment, resulting in an
estimated 1.8 million deaths. This high TB prevalence may be ascribed to a number of
factors, including, amongst others, untimely and inaccurate diagnostics, inadequate
treatment regimens, drug-resistant M. tuberculosis strains, human immunodeficiency virus
(HIV) co-infection, and inadequate knowledge of the TB disease in general.
This study is novel in the sense that it used a metabolomics research approach to identify
new metabolite markers from patient-collected urine, for better characterising/understanding
the TB disease state and treatment failure thereof.
Using a validated urinary organic acid extraction and a two-dimensional gas chromatography
time-of-flight mass spectrometry (GCxGC-TOFMS) metabolomics approach, we were able to
differentiate a culture-confirmed active TB-positive group and TB-negative healthy control
group, based on their detected metabolite differences, utilising a variety of multi- and
univariate statistical methods. We identified the most significant urinary TB metabolite
markers contributing to these differences, which shed light on previously unknown
mechanisms/adaptations of the host in response to M. tuberculosis and other host–pathogen
interactions. The most significant of these were the TB-induced changes resulting in an
abnormal host fatty acid and amino acid metabolism, mediated through changes in interferon
gamma and possibly insulin. This also explains some of the symptoms associated with TB
and provides clues to better treatment approaches.
Thereafter, the same approach was used to compare TB-positive patients with an
unsuccessful treatment outcome to those successfully treated. Differentiation of the groups
was achieved using the urine samples collected from these patients at time of diagnosis, i.e.
before any treatment was administered. The identified urinary biomarkers were then used to
better understand the underlying biology related to TB treatment failure. The most significant
observations were the elevated levels of those metabolites associated with a gut microbiome
imbalance, which has been shown to alter an individual’s response to anti-TB drugs and also
negatively influence their immune function, contributing to an unsuccessful treatment
outcome. Another interesting observation was those metabolites traditionally used for
diagnosing inborn abnormalities in any of the three enzymes of the mitochondrial trifunctional
protein complex in the treatment failure group. Since L-carnitine and various short-chain
fatty acids are also reduced in these individuals, and are well-known for their antimycobacterial
properties, this metabolic profile may explain an additional mechanism
responsible for these individuals having an increased disease severity and/or a poor
response to TB treatment.
Considering the possible significance of these findings from a diagnostic perspective, the
GCxGC-TOFMS data generated from the aforementioned treatment outcome experiment
was reanalysed using a univariate statistical approach, in order to find possible diagnostic
markers for predicting treatment outcome, utilising urine collected at time of diagnosis. Using
a logistic regression model, two predictors, i.e. 3,5-dihydroxybenzoic acid and 3-(4-hydroxy-
3-methoxyphenyl)propionic acid, displayed the capacity to predict an unsuccessful treatment
with an area under the receiver operating characteristic curve value of 0.94, and a leave-oneout
cross-validation value of 0.89, indicating high sensitivity and specificity. Furthermore,
these two identified predictors are also associated with an imbalance in gut microbiota,
confirming the previously proposed mechanisms related to treatment failure in these
individuals.
Considering the results, this study not only proved the capability of a metabolomics research
approach to identify new metabolite markers which could be used towards better
understanding TB, and treatment failure thereof, but also possibly diagnostically for
predicting treatment outcome of first-line anti-TB drugs at time of diagnosis. Furthermore,
the fact that these markers can be detected from patient-collected urine, as opposed to
sputum, has additional benefits for both research and diagnostic applications, considering
the ease by which such samples can be obtained, with very little discomfort to the individual. | en_US |
