Metabolomics biomarkers for tuberculosis diagnostics: current status and future objectives

Infants with biliary atresia exhibit an altered amino acid profile in their newborn screening

INTRODUCTION

Biliary atresia (BA) is a rare progressive neonatal cholangiopathy with unknown pathophysiology and time of onset. Newborn Screening (NBS) in Germany is routinely performed in the first days of life to identify rare congenital diseases utilizing dried blood spot (DBS) card analyses. Infants with biliary atresia (BA) are known to have altered amino acid profiles (AAP) at the time point of diagnosis, but it is unclear whether these alterations are present at the time point of NBS.

OBJECTIVES

We aimed to analyze amino acid profiles in NBS-DBS of infants with Biliary Atresia.

METHODS

Original NBS-DBS cards of 41 infants who were later on diagnosed with BA were retrospectively obtained. NBS-DBS cards from healthy newborns (n = 40) served as controls. In some BA infants (n = 14) a second DBS card was obtained at time of Kasai surgery. AAP in DBS cards were analyzed by targeted metabolomics.

RESULTS

DBS metabolomics in the NBS of at that time point seemingly healthy infants later diagnosed with BA revealed significantly higher levels of Methionine (14.6 ± 8.6 μmol/l), Histidine (23.5 ± 50.3 μmol/l), Threonine (123.9 ± 72.8 μmol/l) and Arginine (14.1 ± 11.8 μmol/l) compared to healthy controls (Met: 8.1 ± 2.6 μmol/l, His: 18.6 ± 10.1 μmol/l, Thr: 98.1 ± 34.3 μmol/l, Arg: 9.3 ± 6.6 μmol/l). Methionine, Arginine and Histidine showed a further increase at time point of Kasai procedure. No correlation between amino acid levels and clinical course was observed.

CONCLUSION

Our data demonstrate that BA patients exhibit an altered AAP within 72 h after birth, long before the infants become symptomatic. This supports the theory of a prenatal onset of the disease and, thus, the possibility of developing a sensitive and specific NBS. Methionine might be particularly relevant due to its involvement in glutathione metabolism. Further investigation of AAP in BA may help in understanding the underlying pathophysiology.

Betaine and I-LG may have a predictive value for ATB: A causal study in a large European population

PURPOSE

To analyze the causal relationship between 486 human serum metabolites and the active tuberculosis (ATB) in European population.

METHODS

In this study, the causal relationship between human serum metabolites and the ATB was analyzed by integrating the genome-wide association study (GWAS). The 486 human serum metabolites were used as the exposure variable, three different ATB GWAS databases in the European population were set as outcome variables, and single nucleotide polymorphisms were used as instrumental variables for Mendelian Randomization. The inverse variance weighting was estimated causality, the MR-Egger intercept to estimate horizontal pleiotropy, and the combined effects of metabolites were also considered in the meta-analysis. Furthermore, the web-based MetaboAnalyst 6.0 was engaged for enrichment pathway analysis, while R (version 4.3.2) software and Review Manager 5.3 were employed for statistical analysis.

RESULTS

A total of 21, 17, and 19 metabolites strongly associated with ATB were found in the three databases after preliminary screening (P < 0.05). The intersecting metabolites across these databases included tryptophan, betaine, 1-linoleoylglycerol (1-monolinolein) (1-LG), 1-eicosatrienoylglycerophosphocholine, and oleoylcarnitine. Among them, betaine (I2 = 24%, P = 0.27) and 1-LG (I2 = 0%, P = 0.62) showed the lowest heterogeneity among the different ATB databases. In addition, the metabolic pathways of phosphatidylethanolamine biosynthesis (P = 0.0068), methionine metabolism (P = 0.0089), betaine metabolism (P = 0.0205) and oxidation of branched-chain fatty acids (P = 0.0309) were also associated with ATB.

CONCLUSION

Betaine and 1-LG may be biomarkers or auxiliary diagnostic tools for ATB. They may provide new guidance for medical practice in the early diagnosis and surveillance of ATB. In addition, by interfering with phosphatidylethanolamine biosynthesis, methionine metabolism, betaine metabolism, oxidation of branched-chain fatty acids, and other pathways, it is helpful to develop new anti-tuberculosis drugs and explore the virulence or pathogenesis of ATB at a deeper level, providing an effective reference for future studies.

Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning

Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to inappropriate treatment. Herein, we employed noninvasive metabolic phenotyping coupled with comprehensive statistical modeling to discover potential biomarkers for the differential diagnosis of NTM infection versus TB. Urine samples from 19 NTM and 35 TB patients were collected, and untargeted metabolomics was performed using rapid liquid chromatography-mass spectrometry. The urine metabolome was analyzed using a combination of univariate and multivariate statistical approaches, incorporating machine learning. Univariate analysis revealed significant alterations in amino acids, especially tryptophan metabolism, in NTM infection compared to TB. Specifically, NTM infection was associated with upregulated levels of methionine but downregulated levels of glutarate, valine, 3-hydroxyanthranilate, and tryptophan. Five machine learning models were used to classify NTM and TB. Notably, the random forest model demonstrated excellent performance [area under the receiver operating characteristic (ROC) curve greater than 0.8] in distinguishing NTM from TB. Six potential biomarkers for NTM infection diagnosis, including methionine, valine, glutarate, 3-hydroxyanthranilate, corticosterone, and indole-3-carboxyaldehyde, were revealed from univariate ROC analysis and machine learning models. Altogether, our study suggested new noninvasive biomarkers and laid a foundation for applying machine learning to NTM differential diagnosis.

Urinary metabolic characterization of advanced tuberculous meningitis cases in a South African paediatric population

Tuberculous meningitis (TBM) is a severe form of tuberculosis with high neuro-morbidity and mortality, especially among the paediatric population (aged ≤12 years). Little is known of the associated metabolic changes. This study aimed to identify characteristic metabolic markers that differentiate severe cases of paediatric TBM from controls, through non-invasive urine collection. Urine samples selected for this study were from two paediatric groups. Group 1: controls (n = 44): children without meningitis, no neurological symptoms and from the same geographical region as group 2. Group 2: TBM cases (n = 13): collected from paediatric patients that were admitted to Tygerberg Hospital in South Africa on the suspicion of TBM, mostly severely ill; with a later confirmation of TBM. Untargeted 1H NMR-based metabolomics data of urine were generated, followed by statistical analyses via MetaboAnalyst (v5.0), and the identification of important metabolites. Twenty nine urinary metabolites were identified as characteristic of advanced TBM and categorized in terms of six dysregulated metabolic pathways: 1) upregulated tryptophan catabolism linked to an altered vitamin B metabolism; 2) perturbation of amino acid metabolism; 3) increased energy production-metabolic burst; 4) disrupted gut microbiota metabolism; 5) ketoacidosis; 6) increased nitrogen excretion. We also provide original biological insights into this biosignature of urinary metabolites that can be used to characterize paediatric TBM patients in a South African cohort.

Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review

Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.

Metabolomics: Challenges and Opportunities in Systems Biology Studies

Metabolomics can provide diagnostic, prognostic, and therapeutic biomarker profiles of individual patients because a large number of metabolites can be simultaneously measured in biological samples in an unbiased manner. Minor stimuli can result in substantial alterations, making it a valuable target for analysis. Due to the complexity and sensitivity of the metabolome, studies must be devised to maintain consistency, minimize subject-to-subject variation, and maximize information recovery. This effort has been aided by technological advances in experimental design, rodent models, and instrumentation. Proton Nuclear Magnetic Resonance (1H-NMR) spectroscopy of biofluids, such as plasma, urine, and faeces provide the opportunity to identify biomarker change patterns that reflect the physiological or pathological status of an individual patient. Metabolomics has the ultimate potential to be useful in a clinical context, where it could be used to predict treatment response and survival and for early disease diagnosis. During drug treatment, an individual's metabolic status could be monitored and used to predict deleterious effects. Therefore, metabolomics has the potential to improve disease diagnosis, treatment, and follow-up care. In this chapter, we demonstrate how a metabolomics study can be used to diagnose a disease by classifying patients as either healthy or pathological, while accounting for individual variation.

Clinical metabolomics by NMR revealed serum metabolic signatures for differentiating sarcoidosis from tuberculosis

BACKGROUND

Pulmonary sarcoidosis (SAR) and tuberculosis (TB) are two granulomatous lung-diseases and often pose a diagnostic challenge to a treating physicians.

OBJECTIVE

The present study aims to explore the diagnostic potential of NMR based serum metabolomics approach to differentiate SAR from TB.

MATERIALS AND METHOD

The blood samples were obtained from three study groups: SAR (N = 35), TB (N = 28) and healthy normal subjects (NC, N = 56) and their serum metabolic profiles were measured using 1D 1H CPMG (Carr-Purcell-Meiboom-Gill) NMR spectra recorded at 800 MHz NMR spectrometer. The quantitative metabolic profiles were compared employing a combination of univariate and multivariate statistical analysis methods and evaluated for their diagnostic potential using receiver operating characteristic (ROC) curve analysis.

RESULTS

Compared to SAR, the sera of TB patients were characterized by (a) elevated levels of lactate, acetate, 3-hydroxybutyrate (3HB), glutamate and succinate (b) decreased levels of glucose, citrate, pyruvate, glutamine, and several lipid and membrane metabolites (such as very-low/low density lipoproteins (VLDL/LDL), polyunsaturated fatty acids, etc.).

CONCLUSION

The metabolic disturbances not only found to be well in concordance with various previous reports, these further demonstrated very high sensitivity and specificity to distinguish SAR from TB patients suggesting serum metabolomics analysis can serve as surrogate method in the diagnosis and clinical management of SAR.

Advances in Diagnostics and Drug Discovery against Resistant and Latent Tuberculosis Infection

Latent tuberculosis infection (LTBI) represents a subclinical, asymptomatic mycobacterial state affecting approximately 25% of the global population. The substantial prevalence of LTBI, combined with the risk of progressing to active tuberculosis, underscores its central role in the increasing incidence of tuberculosis (TB). Accurate identification and timely treatment are vital to contain and reduce the spread of the disease, forming a critical component of the global strategy known as "End TB." This review aims to examine and highlight the most recent scientific evidence related to new diagnostic approaches and emerging therapeutic treatments for LTBI. While prevalent diagnostic methods include the tuberculin skin test (TST) and interferon gamma release assay (IGRA), WHO's approval of two specific IGRAs for Mycobacterium tuberculosis (MTB) marked a significant advancement. However, the need for a specific test with global application viability has propelled research into diagnostic tests based on molecular diagnostics, pulmonary immunity, epigenetics, metabolomics, and a current focus on next-generation MTB antigen-based skin test (TBST). It is within these emerging methods that the potential for accurate distinction between LTBI and active TB has been demonstrated. Therapeutically, in addition to traditional first-line therapies, anti-LTBI drugs, anti-resistant TB drugs, and innovative candidates in preclinical and clinical stages are being explored. Although the advancements are promising, it is crucial to recognize that further research and clinical evidence are needed to solidify the effectiveness and safety of these new approaches, in addition to ensuring access to new drugs and diagnostic methods across all health centers. The fight against TB is evolving with the development of more precise diagnostic tools that differentiate the various stages of the infection and with more effective and targeted treatments. Once consolidated, current advancements have the potential to transform the prevention and treatment landscape of TB, reinforcing the global mission to eradicate this disease.

Metabolomic analysis of Mycobacterium tuberculosis reveals metabolic profiles for identification of drug-resistant tuberculosis

The detection of pre-extensively (pre-XDR) and extensively drug-resistant tuberculosis (XDR-TB) is challenging. Drug-susceptibility tests for some anti-TB drugs, especially ethambutol (ETH) and ethionamide (ETO), are problematic due to overlapping thresholds to differentiate between susceptible and resistant phenotypes. We aimed to identify possible metabolomic markers to detect Mycobacterium tuberculosis (Mtb) strains causing pre-XDR and XDR-TB. The metabolic patterns of ETH- and ETO-resistant Mtb isolates were also investigated. Metabolomics of 150 Mtb isolates (54 pre-XDR, 63 XDR-TB and 33 pan-susceptible; pan-S) were investigated. Metabolomics of ETH and ETO phenotypically resistant subgroups were analyzed using UHPLC-ESI-QTOF-MS/MS. Orthogonal partial least-squares discriminant analysis revealed distinct separation in all pairwise comparisons among groups. Two metabolites (meso-hydroxyheme and itaconic anhydride) were able to differentiate the pre-XDR and XDR-TB groups from the pan-S group with 100% sensitivity and 100% specificity. In comparisons of the ETH and ETO phenotypically resistant subsets, sets of increased (ETH = 15, ETO = 7) and decreased (ETH = 1, ETO = 6) metabolites specific for the resistance phenotype of each drug were found. We demonstrated the potential for metabolomics of Mtb to differentiate among types of DR-TB as well as between isolates that were phenotypically resistant to ETO and ETH. Thus, metabolomics might be further applied for DR-TB diagnosis and patient management.

Metabolomic profiling of early inactive hepatic alveolar and cystic echinococcosis

Hepatic alveolar echinococcosis (AE) and cystic echinococcosis (CE) are severe helminthic zoonoses and leading causes of parasitic liver damage. They pose a high mortality risk due to invisible clinical signs, especially at the early inactive stage. However, the specific metabolic profiles induced by inactive AE and CE lesions remain largely unclear. Therefore, we used gas chromatography-mass spectrometry-based metabolomic profiling to identify the global metabolic variations in AE and CE patient sera to differentiate between the two diseases and reveal the mechanisms underlying their pathogenesis. In addition, specific serum biomarkers of inactive hepatic AE and CE were screened using receiver operating curves, which can contribute to the clinical diagnosis of both diseases, especially in the earlier phase. These differential metabolites are involved in glycine, serine, tyrosine, and phenylalanine metabolism. Further analysis of key metabolic pathways showed that inactive AE lesions strongly alter amino acid metabolism in the host. CE lesions have an altered metabolism of oxidative stress response. These changes suggest these metabolite-associated pathways can serve as biomarkers to distinguish individuals with inactive AE and CE from healthy populations. This study also investigated the differences in serum metabolic profiles in patients with CE and AE. The biomarkers identified belonged to different metabolic pathways, including lipid, carnitine, androgen, and bile acid metabolism. Taken together, by investigating the different phenotypes of CE and AE with metabolomic profiling, serum biomarkers facilitating early diagnosis were identified.

Biomarker discovery for tuberculosis using metabolomics

Tuberculosis (TB) is the leading cause of death among infectious diseases, and the ratio of cases in which its pathogen Mycobacterium tuberculosis (Mtb) is drug resistant has been increasing worldwide, whereas latent tuberculosis infection (LTBI) may develop into active TB. Thus it is important to understand the mechanism of drug resistance, find new drugs, and find biomarkers for TB diagnosis. The rapid progress of metabolomics has enabled quantitative metabolite profiling of both the host and the pathogen. In this context, we provide recent progress in the application of metabolomics toward biomarker discovery for tuberculosis. In particular, we first focus on biomarkers based on blood or other body fluids for diagnosing active TB, identifying LTBI and predicting the risk of developing active TB, as well as monitoring the effectiveness of anti-TB drugs. Then we discuss the pathogen-based biomarker research for identifying drug resistant TB. While there have been many reports of potential candidate biomarkers, validations and clinical testing as well as improved bioinformatics analysis are needed to further substantiate and select key biomarkers before they can be made clinically applicable.

Mass Spectrometry-Based Proteomic and Metabolomic Profiling of Serum Samples for Discovery and Validation of Tuberculosis Diagnostic Biomarker Signature

Tuberculosis (TB) is a transmissible disease listed as one of the 10 leading causes of death worldwide (10 million infected in 2019). A swift and precise diagnosis is essential to forestall its transmission, for which the discovery of effective diagnostic biomarkers is crucial. In this study, we aimed to discover molecular biomarkers for the early diagnosis of tuberculosis. Two independent cohorts comprising 29 and 34 subjects were assayed by proteomics, and 49 were included for metabolomic analysis. All subjects were arranged into three experimental groups—healthy controls (controls), latent TB infection (LTBI), and TB patients. LC-MS/MS blood serum protein and metabolite levels were submitted to univariate, multivariate, and ROC analysis. From the 149 proteins quantified in the discovery set, 25 were found to be differentially abundant between controls and TB patients. The AUC, specificity, and sensitivity, determined by ROC statistical analysis of the model composed of four of these proteins considering both proteomic sets, were 0.96, 93%, and 91%, respectively. The five metabolites (9-methyluric acid, indole-3-lactic acid, trans-3-indoleacrylic acid, hexanoylglycine, and N-acetyl-L-leucine) that better discriminate the control and TB patient groups (VIP > 1.75) from a total of 92 metabolites quantified in both ionization modes were submitted to ROC analysis. An AUC = 1 was determined, with all samples being correctly assigned to the respective experimental group. An integrated ROC analysis enrolling one protein and four metabolites was also performed for the common control and TB patients in the proteomic and metabolomic groups. This combined signature correctly assigned the 12 controls and 12 patients used only for prediction (AUC = 1, specificity = 100%, and sensitivity = 100%). This multiomics approach revealed a biomarker signature for tuberculosis diagnosis that could be potentially used for developing a point-of-care diagnosis clinical test.

Novel Potential Diagnostic Serum Biomarkers of Metabolomics in Osteoarticular Tuberculosis Patients: A Preliminary Study

Osteoarticular tuberculosis is one of the extrapulmonary tuberculosis, which is mainly caused by direct infection of Mycobacterium tuberculosis or secondary infection of tuberculosis in other parts. Due to the low specificity of the current detection method, it is leading to a high misdiagnosis rate and subsequently affecting the follow-up treatment and prognosis. Metabolomics is mainly used to study the changes of the body’s metabolites in different states, so it can serve as an important means in the discovery of disease-related metabolic biomarkers and the corresponding mechanism research. Liquid chromatography tandem mass spectrometry (LC-MS/MS) was used to detect and analyze metabolites in the serum with osteoarticular tuberculosis patients, disease controls, and healthy controls to find novel metabolic biomarkers that could be used in the diagnosis of osteoarticular tuberculosis. Our results showed that 68 differential metabolites (p<0.05, fold change>1.0) were obtained in osteoarticular tuberculosis serum after statistical analysis. Then, through the evaluation of diagnostic efficacy, PC[o-16:1(9Z)/18:0], PC[20:4(8Z,11Z,14Z,17Z)/18:0], PC[18:0/22:5(4Z,7Z,10Z,13Z,16Z)], SM(d18:1/20:0), and SM[d18:1/18:1(11Z)] were found as potential biomarkers with high diagnostic efficacy. Using bioinformatics analysis, we further found that these metabolites share many lipid metabolic signaling pathways, such as choline metabolism, sphingolipid signaling, retrograde endocannabinoid signaling, and sphingolipid and glycerophospholipid metabolism; these results suggest that lipid metabolism plays an important role in the pathological process of tuberculosis. This study can provide certain reference value for the study of metabolic biomarkers of osteoarticular tuberculosis and the mechanism of lipid metabolism in osteoarticular tuberculosis and even other tuberculosis diseases.

Differential Diagnosis of Latent Tuberculosis Infection and Active Tuberculosis: A Key to a Successful Tuberculosis Control Strategy

As an ancient infectious disease, tuberculosis (TB) is still the leading cause of death from a single infectious agent worldwide. Latent TB infection (LTBI) has been recognized as the largest source of new TB cases and is one of the biggest obstacles to achieving the aim of the End TB Strategy. The latest data indicate that a considerable percentage of the population with LTBI and the lack of differential diagnosis between LTBI and active TB (aTB) may be potential reasons for the high TB morbidity and mortality in countries with high TB burdens. The tuberculin skin test (TST) has been used to diagnose TB for > 100 years, but it fails to distinguish patients with LTBI from those with aTB and people who have received Bacillus Calmette–Guérin vaccination. To overcome the limitations of TST, several new skin tests and interferon-gamma release assays have been developed, such as the Diaskintest, C-Tb skin test, EC-Test, and T-cell spot of the TB assay, QuantiFERON-TB Gold In-Tube, QuantiFERON-TB Gold-Plus, LIAISON QuantiFERON-TB Gold Plus test, and LIOFeron TB/LTBI. However, these methods cannot distinguish LTBI from aTB. To investigate the reasons why all these methods cannot distinguish LTBI from aTB, we have explained the concept and definition of LTBI and expounded on the immunological mechanism of LTBI in this review. In addition, we have outlined the research status, future directions, and challenges of LTBI differential diagnosis, including novel biomarkers derived from Mycobacterium tuberculosis and hosts, new models and algorithms, omics technologies, and microbiota.

Benchtop nuclear magnetic resonance-based metabolomic approach for the diagnosis of bovine tuberculosis

Even though enormous efforts and control strategies have been implemented, bovine tuberculosis (TB) remains a significant source of health and socioeconomic concern. The standard method used in TB eradication programs for in vivo detection is the tuberculin skin test. However, the specificity of the tuberculin skin test is affected by infection with non-tuberculous mycobacteria or by vaccination. Thus, some animals are not correctly diagnosed. This study aimed first to identify a plasma metabolic TB profile by high-field (HF) nuclear magnetic resonance (NMR) spectroscopy and second measure this characteristic TB metabolic profile using low-field benchtop (LF) NMR as an affordable molecular technology for TB diagnosis. Plasma samples from cattle diagnosed with TB (derivation set, n = 11), diagnosed with paratuberculosis (PTB, n = 10), PTB-vaccinated healthy control (n = 10) and healthy PTB-unvaccinated control (n = 10) were analyzed by NMR. Unsupervised Principal Component Analysis (PCA) was used to identify metabolic differences between groups. We identified 14 metabolites significantly different between TB and control animals. The second group of TB animals was used to validate the results (validation set, n = 14). Predictive models based on metabolic fingerprint acquired by both HF and LF NMR spectroscopy successfully identified TB versus control subjects (Area under the curve of Receiver Operating Characteristic over 0.92, in both models; Confidence Interval 0.77-1). In summary, plasma fingerprinting using HF and LF-NMR differentiated TB subjects from uninfected animals, and PTB and PTB-vaccinated subjects who may provide a TB-false positive, highlighting the use of LF-NMR-based metabolomics as a complementary or alternative diagnostic tool to the current diagnostic methods.

Discovery of serum biomarkers for diagnosis of tuberculosis by NMR metabolomics including cross-validation with a second cohort

Background

Tuberculosis (TB) is a disease with worldwide presence and a major cause of death in several developing countries. Current diagnostic methodologies often lack specificity and sensitivity, whereas a long time is needed to obtain a conclusive result.

Methods

In an effort to develop better diagnostic methods, this study aimed at the discovery of a biomarker signature for TB diagnosis using a Nuclear Magnetic Resonance based metabolomics approach. In this study, we acquired ¹H NMR spectra of blood serum samples of groups of healthy subjects, individuals with latent TB and of patients with pulmonary and extra-pulmonary TB. The resulting data were treated with uni- and multivariate statistical analysis.

Results

Six metabolites (inosine, hypoxanthine, mannose, asparagine, aspartate and glutamate) were validated by an independent cohort, all of them related with metabolic processes described as associated with TB infection.

Conclusion

The findings of the study are according with the WHO Target Product Profile recommendations for a triage test to rule-out active TB.

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.

M. tuberculosis curli pili (MTP) is associated with alterations in carbon, fatty acid and amino acid metabolism in a THP-1 macrophage infection model

The initial host-pathogen interaction is crucial for the establishment of infection. An improved understanding of the pathophysiology of Mycobacterium tuberculosis (M. tuberculosis) during macrophage infection can aid the development of intervention therapeutics against tuberculosis. M. tuberculosis curli pili (MTP) is a surface located adhesin, involved in the first point-of-contact between pathogen and host. This study aimed to better understand the role of MTP in modulating the intertwined metabolic pathways of M. tuberculosis and its THP-1 macrophage host. Metabolites were extracted from pelleted wet cell mass of THP-1 macrophages infected with M. tuberculosis wild-type V9124 (WT), Δmtp-deletion mutant and the mtp-complemented strains, respectively, via a whole metabolome extraction method using a 1:3:1 ratio of chloroform:methanol:water. Metabolites were detected by two-dimensional gas chromatography time-of-flight mass spectrometry. Significant metabolites were determined through univariate and multivariate statistical tests and online pathway databases. Relative to the WT, a total of nine and ten metabolites were significantly different in the Δmtp and complement strains, respectively. All nine significant metabolites were found in elevated levels in the Δmtp relative to the WT. Additionally, of the ten significant metabolites, eight were detected in lower levels and two were detected in higher levels in the complement relative to the WT. The absence of the MTP adhesin resulted in reduced virulence of M. tuberculosis leading to alterations in metabolites involved in carbon, fatty acid and amino acid metabolism during macrophage infection, suggesting that MTP plays an important role in the modulation of host metabolic activity. These findings support the prominent role of the MTP adhesin as a virulence factor as well as a promising biomarker for possible diagnostic and therapeutic intervention.

Metabolic characterization of tuberculous meningitis in a South African paediatric population using 1H NMR metabolomics

OBJECTIVE

To better characterize the cerebrospinal fluid (CSF) metabolic profile of tuberculous meningitis (TBM) cases using a South African paediatric cohort.

METHODS

¹H NMR metabolomics was used to analyse the CSF of a South African paediatric cohort. Univariate and multivariate statistical analyses were performed to compare a homogeneous control group with a well-defined TBM group.

RESULTS

Twenty metabolites were identified to discriminate TBM cases from controls. As expected, reduced glucose and elevated lactate were the dominating discriminators. A closer investigation of the CSF metabolic profile yielded 18 metabolites of statistical significance. Ten metabolites (acetate, alanine, choline, citrate, creatinine, isoleucine, lysine, myo-inositol, pyruvate and valine) overlapped with two other prior investigations. Eight metabolites (2-hydroxybutyrate, carnitine, creatine, creatine phosphate, glutamate, glutamine, guanidinoacetate and proline) were unique to our paediatric TBM cohort.

CONCLUSIONS

Through strict exclusion criteria, quality control checks and data filtering, eight unique CSF metabolites associated with TBM were identified for the first time and linked to: uncontrolled glucose metabolism, upregulated proline and creatine metabolism, detoxification and disrupted glutamate-glutamine cycle in the TBM samples. Associated with oxidative stress and chronic neuroinflammation, our findings collectively imply destabilization, and hence increased permeability, of the blood-brain barrier in the TBM cases.

Overview of Brain-to-Gut Axis Exposed to Chronic CNS Bacterial Infection(s) and a Predictive Urinary Metabolic Profile of a Brain Infected by Mycobacterium tuberculosis

A new paradigm in neuroscience has recently emerged - the brain-gut axis (BGA). The contemporary focus in this paradigm has been gut → brain ("bottom-up"), in which the gut-microbiome, and its perturbations, affects one's psychological state-of-mind and behavior, and is pivotal in neurodegenerative disorders. The emerging brain → gut ("top-down") concept, the subject of this review, proposes that dysfunctional brain health can alter the gut-microbiome. Feedback of this alternative bidirectional highway subsequently aggravates the neurological pathology. This paradigm shift, however, focuses upon non-communicable neurological diseases (progressive neuroinflammation). What of infectious diseases, in which pathogenic bacteria penetrate the blood-brain barrier and interact with the brain, and what is this effect on the BGA in bacterial infection(s) that cause chronic neuroinflammation? Persistent immune activity in the CNS due to chronic neuroinflammation can lead to irreversible neurodegeneration and neuronal death. The properties of cerebrospinal fluid (CSF), such as immunological markers, are used to diagnose brain disorders. But what of metabolic markers for such purposes? If a BGA exists, then chronic CNS bacterial infection(s) should theoretically be reflected in the urine. The premise here is that chronic CNS bacterial infection(s) will affect the gut-microbiome and that perturbed metabolism in both the CNS and gut will release metabolites into the blood that are filtered (kidneys) and excreted in the urine. Here we assess the literature on the effects of chronic neuroinflammatory diseases on the gut-microbiome caused by bacterial infection(s) of the CNS, in the context of information attained via metabolomics-based studies of urine. Furthermore, we take a severe chronic neuroinflammatory infectious disease - tuberculous meningitis (TBM), caused by Mycobacterium tuberculosis, and examine three previously validated CSF immunological biomarkers - vascular endothelial growth factor, interferon-gamma and myeloperoxidase - in terms of the expected changes in normal brain metabolism. We then model the downstream metabolic effects expected, predicting pivotal altered metabolic pathways that would be reflected in the urinary profiles of TBM subjects. Our cascading metabolic model should be adjustable to account for other types of CNS bacterial infection(s) associated with chronic neuroinflammation, typically prevalent, and difficult to distinguish from TBM, in the resource-constrained settings of poor communities.

Combining bioinformatics and biological detection to identify novel biomarkers for diagnosis and prognosis of pulmonary tuberculosis

OBJECTIVES

To identify the novel and promising indicators for pulmonary tuberculosis (PTB) patients.

METHODS

The study was carried out between June 2016 and June 2019. Three RNA sequencing or microarray datasets of TB infection were used to identify the potential genes showing a common expression trend. The expression level of screened targets was determined by reverse transcription polymerase chain reaction and ELISA using samples of whole blood and peripheral blood mononuclear cells (PBMCs) isolated from 69 PTB patients and 69 healthy volunteers. The potential of the identified targets to predict the treatment outcomes was further studied.

RESULTS

Bioinformatics analysis demonstrated that a total of 91 genes were up-regulated in all the 3 datasets; among them, the expression of SLAMF8, LILRB4, and IL-10Ra was significantly increased at both the mRNA and protein levels in whole blood and PBMC samples of PTB patients compared with the healthy controls. The mortality rate increased significantly in SLAMF8 or LILRB4 high expression group compared with SLAMF8 or LILRB4 low expression group. Further, the decrease rate of bacteria in patients with SLAMF8 or LILRB4 high expression was slower than that in patients with SLAMF8 or LILRB4 low expression.

CONCLUSION

This study provides a promising way to identify novel indicators for PTB. Moreover, the LILRB4 expression may play a role in predicting the outcome of treatments on PTB patients.

Metabolomics describes previously unknown toxicity mechanisms of isoniazid and rifampicin

Isoniazid and rifampicin are well-known anti-mycobacterial agents and are widely used to treat pulmonary tuberculosis (TB) as part of the combined therapy approach, recommended by the World Health Organization. The ingestion of these first-line TB drugs are, however, not free of side effects, and are toxic to the liver, kidney, and central nervous system. These side effects are associated with poor treatment compliance, resulting in TB treatment failure, relapse and drug resistant TB. This occurrence has subsequently led to the recent application of novel research technologies, towards a better understanding of the underlying toxicity mechanisms of TB drugs in humans, mostly focussing on the 2 most important TB drugs: isoniazid and rifampicin. In this review, we discuss the contribution that one such an approach, termed metabolomics has made toward this field, and also highlight the impact that this might have towards the development of improved TB treatment regimens.

The application of metabolomics toward pulmonary tuberculosis research

In the quest to identify novel biomarkers for pulmonary tuberculosis (TB), high-throughput systems biology approaches such as metabolomics has become increasingly widespread. Such biomarkers have not only successfully been used for better disease characterization, but have also provided new insights toward the future development of improved diagnostic and therapeutic approaches. In this review, we give a summary of the metabolomics studies done to date, with a specific focus on those investigating various aspects of pulmonary TB, and the infectious agent responsible, Mycobacterium tuberculosis. These studies, done on a variety of sample matrices, including bacteriological culture, sputum, blood, urine, tissue, and breath, are discussed in terms of their intended research outcomes or future clinical applications. Additionally, a summary of the research model, sample cohort, analytical apparatus and statistical methods used for biomarker identification in each of these studies, is provided.

High-resolution quantitative proteomics applied to the study of the specific protein signature in the sputum and saliva of active tuberculosis patients and their infected and uninfected contacts

Our goal was to establish panels of protein biomarkers that are characteristic of patients with microbiologically confirmed pulmonary tuberculosis (TB) and their contacts, including latent TB-infected (LTBI) and uninfected patients. Since the first pathogen-host contact occurs in the oral and nasal passages the saliva and sputum were chosen as the biological fluids to be studied. Quantitative shotgun proteomics was performed using a LTQ-Orbitrap-Elite platform. For active TB patients, both fluids exhibited a specific accumulation of proteins that were related to complement activation, inflammation and modulation of immune response. In the saliva of TB patients, a decrease of in proteins related to glucose and lipid metabolism was detected. In contrast, the sputum of uninfected contacts presented a specific proteomic signature that was composed of proteins involved in the perception of bitter taste, defense against pathogens and innate immune response, suggesting that those are key events during the initial entry of the pathogen in the host. SIGNIFICANCE: This is the first study to compare the saliva and sputum from active TB patients and their contacts. Our findings strongly suggest that TB patients show not only an activation of processes that are related to complement activation and modulation of inflammation but also an imbalance in carbohydrate and lipid metabolism. In addition, those individuals who do not get infected after direct exposure to the pathogen display a typical proteomic signature in the sputum, which is a reflection of the secretion from the nasal and oral mucosa, the first immunological barriers that M. tuberculosis encounters in the host. Thus, this result indicates the importance of the processes related to the innate immune response in fighting the initial events of the infection.

Use of Liquid Chromatography-Mass Spectrometry-Based Metabolomics to Identify Biomarkers of Tuberculosis

Liquid chromatography-mass spectrometry (LC-MS) based metabolomics has proven to be a powerful analytical tool for biomarker screening. Here we describe two workflows which employ untargeted metabolomics to study serum biomarkers in tuberculosis patients. Expression profiles for samples of hydrophilic metabolites and hydrophobic metabolites (lipids) may be obtained by this method.

The host response as a tool for infectious disease diagnosis and management

INTRODUCTION

A century of advances in infectious disease diagnosis and treatment changed the face of medicine. However, challenges continue to develop including multi-drug resistance, globalization that increases pandemic risks, and high mortality from severe infections. These challenges can be mitigated through improved diagnostics, and over the past decade, there has been a particular focus on the host response. Since this article was originally published in 2015, there have been significant developments in the field of host response diagnostics, warranting this updated review. Areas Covered: This review begins by discussing developments in single biomarkers and pauci-analyte biomarker panels. It then delves into 'omics, an area where there has been truly exciting progress. Specifically, progress has been made in sepsis diagnosis and prognosis; differentiating viral, bacterial, and fungal pathogen classes; pre-symptomatic diagnosis; and understanding disease-specific diagnostic challenges in tuberculosis, Lyme disease, and Ebola. Expert Commentary: As 'omics have become faster, more precise, and less expensive, the door has been opened for academic, industry, and government efforts to develop host-based infectious disease classifiers. While there are still obstacles to overcome, the chasm separating these scientific advances from the patient's bedside is shrinking.

Mass spectrometry-based metabolomics for tuberculosis meningitis

Tuberculosis meningitis (TBM) is a prevalent form of extra-pulmonary tuberculosis that causes substantial morbidity and mortality. Diagnosis of TBM is difficult because of the limited sensitivity of existing laboratory techniques. A metabolomics approach can be used to investigate the sets of metabolites of both bacteria and host, and has been used to clarify the mechanisms underlying disease development, and identify metabolic changes, leadings to improved methods for diagnosis, treatment, and prognostication. Mass spectrometry (MS) is a major analysis platform used in metabolomics, and MS-based metabolomics provides wide metabolite coverage, because of its high sensitivity, and is useful for the investigation of Mycobacterium tuberculosis (Mtb) and related diseases. It has been used to investigate TBM diagnosis; however, the processes involved in the MS-based metabolomics approach are complex and flexible, and often consist of several steps, and small changes in the methods used can have a huge impact on the final results. Here, the process of MS-based metabolomics is summarized and its applications in Mtb and Mtb-related diseases discussed. Moreover, the current status of TBM metabolomics is described.

Discovery and Validation of a Six-Marker Serum Protein Signature for the Diagnosis of Active Pulmonary Tuberculosis

New non-sputum biomarker tests for active tuberculosis (TB) diagnostics are of the highest priority for global TB control. We performed in-depth proteomic analysis using the 4,000-plex SOMAscan assay on 1,470 serum samples from seven countries where TB is endemic. All samples were from patients with symptoms and signs suggestive of active pulmonary TB that were systematically confirmed or ruled out for TB by culture and clinical follow-up. HIV coinfection was present in 34% of samples, and 25% were sputum smear negative. Serum protein biomarkers were identified by stability selection using L1-regularized logistic regression and by Kolmogorov-Smirnov (KS) statistics. A naive Bayes classifier using six host response markers (HR6 model), including SYWC, kallistatin, complement C9, gelsolin, testican-2, and aldolase C, performed well in a training set (area under the sensitivity-specificity curve [AUC] of 0.94) and in a blinded verification set (AUC of 0.92) to distinguish TB and non-TB samples. Differential expression was also highly significant (P < 10⁻²⁰) for previously described TB markers, such as IP-10, LBP, FCG3B, and TSP4, and for many novel proteins not previously associated with TB. Proteins with the largest median fold changes were SAA (serum amyloid protein A), NPS-PLA2 (secreted phospholipase A2), and CA6 (carbonic anhydrase 6). Target product profiles (TPPs) for a non-sputum biomarker test to diagnose active TB for treatment initiation (TPP#1) and for a community-based triage or referral test (TPP#2) have been published by the WHO. With 90% sensitivity and 80% specificity, the HR6 model fell short of TPP#1 but reached TPP#2 performance criteria. In conclusion, we identified and validated a six-marker signature for active TB that warrants diagnostic development on a patient-near platform.