Introduction
Tuberculosis is a contagious, chronic granulomatous disease, which is caused by any of several microorganisms. It can touch all body organs, but it mostly touches lungs (1)(2).This infection can be arrested permanently in many people. In some of infected people, the disease breaks out again and becomes active years later, usually when the immunity is lowered(2). There are three closely related microorganisms that cause the disease: Mycobacterium tuberculosis, Mycobacterium africanum and Mycobacterium bovis(3). Mycobacterium tuberculosis, first identified in 1882 by Robert Koch, is the most usual one, it is a gram-positive acid fast bacillus (i.e., they have a high content of complex lipids that readily bind the Ziehl-Neelsen [carbol fuchsin] stain and subsequently stubbornly resist decolorization), with unique virulent factors for destroying host immunity and creating disease(1)(2)(3). The reservoir of infection typically is found in individuals with active pulmonary disease. Transmission usually is direct, by inhalation of airborne organisms in aerosols generated by exposure to contaminated secretions of infected individuals. Tuberculosis could be contracted by drinking milk contaminated with Mycobacterium bovis infection in those countries with tuberculous dairy cows and sales of unpasteurized milk. Mycobacterium avium complex is much less virulent than M. tuberculosis and rarely cause disease in immunocompetent individuals. However, they cause disease in 10% to 30% of patients with AIDS(1).
Fig.1 shows M.tuberculosis in microscopic view. Image adapted from(4)
Fig. 2 shows a portrait of Robert Koch, who was the first to describe M. tuberculosis, then known as the “tubercle bacillus”, on 24 March 1882.He subsequently received the Nobel Prize in Physiology or Medicine for this discovery in 1905; the bacterium is also known as “Koch’s bacillus”. Image adapted from (5)
Statistics
It is estimated that about 1.7 billion people worldwide have a latent tuberculous(TB) infection, therefore in risk for developing active TB disease during their lifetime. Tuberculosis is classified as one of the top 10 causes of death worldwide, and the number one cause of infectious disease caused by one single agent(above HIV/AIDS). In year 2017, 10 million people developed Tuberculosis, mostly adults(90% >15 years), 9% were people living with HIV (72% in Africa) and two thirds were in eight countries: India (27%), China (9%), Indonesia (8%), the Philippines (6%), Pakistan (5%), Nigeria (4%), Bangladesh (4%) and South Africa (3%). These and 22 other countries in WHO’s list of 30 high TB burden countries accounted for 87% of the world’s cases. Only 6% of global cases were in the WHO European Region (3%) and WHO Region of the Americas (3%). 1.3 million deaths were estimated among HIV-negative people, and 300.000 among HIV-positive people. However there is a progression of 29% in reducing deaths caused by TB in HIV-negative people now in comparison with the year 2000(1.8 million deaths)(6).
Fig. 3 shows the TB incidence rates in year 2017. Image adapted from (7)
Diagnosis
Tuberculosis has to stages of development. The first stage, called latent TB, a person is infected with bacteria. The second stage, known as active TB or TB disease, the bacteria is reproduced sufficiently to cause the disease. Therefore tests are classified for the first or the second stage, or for both. Only the definite evidence of bacteria can ensure the diagnosis of active Tuberculosis(8). Mantoux test looks directly to find the bacteria by injecting a mass of fluid(called tuberculin) into the skin in the lower part of the arm, and waiting for 48-72 hours to see if a raised hard area or swelling is formed and then measure the size of that area. The test result depends on the size of the raised hard area or swelling. However this test is not very much reliable, because it can give false positive if the person has been infected with a different type of bacteria, rather than the one that causes TB, or it can happen due to vaccination with BCG vaccine, which is usually used in countries with high rates of TB infection(3)(8). A new type of more accurate TB test are The Interferon Gamma Release Assays (IGRAs). The work of these tests is based in the detection of a cytokine called Interferon gamma cytokine. The test is very practical, but it is only used for latent TB. Sputum smear microscopy, fluorescent microscopy, chest X-ray, serological tests, molecular tests are used for detecting TB. Yet, there is not a single test used in all circumstances for TB, each of them has its disadvantages(8).
Medications
The therapy between the latent and active TB differs. If you have latent tuberculosis you may take only one drug, otherwise you have to take several drugs at once, especially if it’s a drug-resistant strain.
The most common medications used to treat tuberculosis include(8):
- Isoniazid
- Rifampin (Rifadin, Rimactane)
- Ethambutol (Myambutol)
- Pyrazinamid
Latest research
Positive TST or IGRA test results were the only measurable biomarkers associated with increased risk of developing TB until a short time ago. The specificity for identifying new cases of TB is poor as over 95% of HIV-negative and ~70% of HIV-positive individuals with TST/IGRA positivity never progress to active TB disease.The treatment based on these tests screening in TB endemic countries would not be cost-effective nor practicable and would not preclude re-infection in high incidence situations, because it would require treatment of 50-80% of the population. This means that 85 people with latent TB should be treated in order to prevent only a single case of active TB. Such a strategy would put many healthy individuals at unnecessary risk of adverse events.The identification of new blood trancriptomic biomarkers of TB that distinguish patients from healthy individuals have been made in several studies.
A 16-gene transcriptomic signature was identified in the Adolescent Cohort Study (ACS) with the ability to predict progression to active TB, in a recent prospective study. The signature was validated with samples from two African sites from the GC6 project(The Grand Challenges in Global Health GC6-74 project (GC6 project) was initiated in 2003 with the goal of identifying TB biomarkers with prognostic potential), which shows a sensitivity of 66% and a specificity of 80% in the 12 months preceding the diagnosis of TB. a sensitivity of 66% and a specificity of 80% in the 12 months preceding the diagnosis of TB. In the further quest of a transcriptomic risk signature, a mixture of 2 gene pairs was noticed to predict risk of TB at62% sensitivity and 63% specificity in the tested population.Two hypotheses were tested: (i) are there metabolites that can predict progression from infection to TB; and, if yes, (ii) does prediction rely on innate metabolic risk factors, or on metabolic processes occurring during disease progression? Accordingly, predictive metabolites fell into the following classes: (a) metabolites that reflect baseline (BL) risk factors and show a consistently significant difference between progressors and controls. We term these risk-associated metabolites, as these indicate a higher likelihood of progression to TB; (b) metabolites predictive of active TB, which show time-dependent differences between study groups, indicating progression to disease. We term these disease-associated metabolites, as the absolute difference in abundances between progressors and controls increases towards clinical manifestation of TB implying that these metabolites are indicators of the host response to subclinical TB.
The detection of TB at an early stage after exposure of Mtb, but before clinical signs arise, gives us the advantage of an early intervention needed for control of the continuing pandemic. Biosignatures that indicate risk factors give us the advantage of taking steps to prevent the developing of the clinical disease of TB. It is demonstrated that in serum or plasma abundances of small metabolic compounds identify individuals who progressed to clinical TB. An increase in the magnitudes of these changes was detected as the disease onset approached. Some metabolites associated with TB progression in this study had been found previously to differ between TB patients and healthy people in a recent study. These metabolite differences are specific to TB. Correspondingly, the identified metabolomic signatures show specific and robust performance in predicting subclinical TB and progression to active TB.They moreover showed the benefit of metabolic profiling for predicting progression to TB by successful application of a TB diagnostic signature derived from an independent study cohort. Both the comparison of the machine earning models and the descriptive analysis of changes of serum abundances demonstrate that variations in concentrations of metabolites in progressors were well aligned to differences in these metabolites between TB patients and healthy individuals. This strongly supports the hypothesis that metabolic profiling identifies subclinical TB. In fact, for proximate samples, at 75% specificity, the sensitivity of these models approached (TB-HEALTHY, 73%) or exceeded (TB-ORD, 76%) the proposed requirements for a target product profile for the development of a test for predicting progression to active TB disease.
Apart from alterations in sample type, life style, genotype, diet, etc. between the different cohorts, a single predictive metabolic signature predicted progression across these cohorts and populations. Consistent with this, the TB-HEALTHY signature derived from a cohort in South Africa showed a strong performance when applied to proximate samples from The Gambia (AUC: 0.86; 95% CI: 0.75–0.96) and Ethiopia (AUC: 0.89; 95% CI: 0.75–1.00).In this study, they have not included HIV-positive individuals by design, even though the interplay between these two diseases plays a pivotal role in TB epidemiology. It is unknown to what extent the presence of opportunistic infections and the perturbed immune responses associated with HIV infection will alter the predictive signatures.Nevertheless, recent studies show hardly any overlap between the TB and HIV metabolic profiles, with plasma glutamate being the only biomarker common for both TB and HIV.The performance of models based on external data sets was better than that of the models derived from the progressors vs. controls of the GC6 cohort. This can be explained as follows: the signature derived from asymptomatic individuals is based on a less pronounced phenotype of a slowly emerging TB, which might lead to a noisy signature. In contrast, a clearly defined signature based on TB patients who all show the molecular markers of symptomatic, clinical TB performs well when applied to noisy data.
A similar phenomenon has been observed for transcriptomic data, where the more pronounced TB signatures from HIV-positive patients performed better even when applied to the noisier data from HIV-negative patients There was a concordance among temporal changes in metabolite levels between progressors and healthy controls and the hypothesis that metabolic profiling detects subclinical disease in progressors, rather than capturing a set of stable risk-associated markers. For example, the abundances of amino acids that were previously shown to be decreased in TB showed a gradual decline in progressor samples approaching clinical diagnosis. This has been further corroborated by comparing the signatures of progressors with the signatures of TB patients, suggesting a quantitative rather than qualitative change from the subclinical stage of TB development preceding clinical diagnosis to clinical TB, at least at the molecular level.Mannose, another metabolite showing differences between progressors and controls irrespective of time to diagnosis, plays a central role in mammalian energy generation and regulation, and can have both beneficial and detrimental effects. The consistently elevated levels of mannose in progressors may hint to an impaired glucose tolerance or insulin resistance and could even be associated with an inherent risk of developing type 2 diabetes in these individuals. The observation of differences in mannose and cotinine levels at basal time points emphasizes that metabolic markers of risk could be detected by metabolic profiling.
Decreasing glutamine levels are observed under inflammatory conditions and this nonessential amino acid may become essential during infection and disease, such that dietary supplementation of glutamine can be beneficial in some patient populations. Glutamine is required for the proper functioning of the immune system and during mycobacterial infection lymphocytes, neutrophils, and macrophages rapidly consume glutamine. In this respect, the gradual drop in glutamine levels observed in progressors likely reflects increasingly exacerbated lung pathology in these individuals.Changes in amino acid and cortisol levels can be detected as early as 12 months before disease onset, becoming even more prominent toward clinical diagnosis of TB. We conclude that manifestation of active TB is the apex of a prolonged process which remains subclinical for many months. Since these metabolomic changes can already be detected during the asymptomatic phase, metabolic profiling allows stratification of TB risk in individuals with latent TB into high- and low-risk individuals, as was recently shown for blood transcriptomic signatures of TB risk.
The metabolomic signatures identified here can potentially be combined with transcriptomic signatures to further improve sensitivity and specificity of TB risk prediction. This signature can identify high-risk individuals in the absence of available sputum for microbiological diagnosis, facilitating treatment prior to development of disease pathology when the bacterial load and the likelihood of disease transmission is low. A proof of concept trial is currently underway stratifying participants based on the 16-gene transcriptomic correlate of risk to test its potential for targeted intervention.While the development of a diagnostic test for a metabolite can be a costly process, such tests are already available for a number of relevant compounds such as cortisol.Furthermore, their study did not include samples from the progressors collected after clinical diagnosis and instead relied on separate data sets which included TB patients. Hence, a detailed time course characterization of patients before clinical diagnosis as well as during and after treatment would be an important step to corroborate our results and to progress toward practical implementation of a metabolic signature.Along with identifying high-risk individuals for prophylactic treatment, these risk signatures have potential value for clinical trials of new intervention measures. Their metabolomic signature will contribute both to TB control and to better understanding of TB pathogenesis(9).
COPYRIGHT: This article is the property of We Speak Science, a non-profit institution co-founded by Dr. Detina Zalli and Dr. Argita Zalli. The article is written by Veton Guraziu (University of Prishtina, Kosovo).
References:
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