Analysis of serum metabolic profile by ultra-performance liquid chromatography-mass spectrometry for biomarkers discovery: application in a pilot study to discriminate patients with tuberculosis

A novel humanized mouse model for HIV and tuberculosis co-infection studies

Background

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), continues to be a major public health problem worldwide. The human immunodeficiency virus (HIV) is another equally important life-threatening pathogen. HIV infection decreases CD4+ T cell levels markedly increasing Mtb co-infections. An appropriate animal model for HIV/Mtb co-infection that can recapitulate the diversity of the immune response in humans during co-infection would facilitate basic and translational research in HIV/Mtb infections. Herein, we describe a novel humanized mouse model.

Methods

The irradiated NSG-SGM3 mice were transplanted with human CD34+ hematopoietic stem cells, and the humanization was monitored by staining various immune cell markers for flow cytometry. They were challenged with HIV and/or Mtb, and the CD4+ T cell depletion and HIV viral load were monitored over time. Before necropsy, the live mice were subjected to pulmonary function test and CT scan, and after sacrifice, the lung and spleen homogenates were used to determine Mtb load (CFU) and cytokine/chemokine levels by multiplex assay, and lung sections were analyzed for histopathology. The mouse sera were subjected to metabolomics analysis.

Results

Our humanized NSG-SGM3 mice were able to engraft human CD34+ stem cells, which then differentiated into a full-lineage of human immune cell subsets. After co-infection with HIV and Mtb, these mice showed decrease in CD4+ T cell counts overtime and elevated HIV load in the sera, similar to the infection pattern of humans. Additionally, Mtb caused infections in both lungs and spleen, and induced granulomatous lesions in the lungs. Distinct metabolomic profiles were also observed in the tissues from different mouse groups after co-infections.

Conclusion

The humanized NSG-SGM3 mice are able to recapitulate the pathogenic effects of HIV and Mtb infections and co-infection at the pathological, immunological and metabolism levels and are therefore a reproducible small animal model for studying HIV/Mtb co-infection.

Identifying the Interaction Between Tuberculosis and SARS-CoV-2 Infections via Bioinformatics Analysis and Machine Learning

The number of patients with COVID-19 caused by severe acute respiratory syndrome coronavirus 2 is still increasing. In the case of COVID-19 and tuberculosis (TB), the presence of one disease affects the infectious status of the other. Meanwhile, coinfection may result in complications that make treatment more difficult. However, the molecular mechanisms underpinning the interaction between TB and COVID-19 are unclear. Accordingly, transcriptome analysis was used to detect the shared pathways and molecular biomarkers in TB and COVID-19, allowing us to determine the complex relationship between COVID-19 and TB. Two RNA-seq datasets (GSE114192 and GSE163151) from the Gene Expression Omnibus were used to find concerted differentially expressed genes (DEGs) between TB and COVID-19 to identify the common pathogenic mechanisms. A total of 124 common DEGs were detected and used to find shared pathways and drug targets. Several enterprising bioinformatics tools were applied to perform pathway analysis, enrichment analysis and networks analysis. Protein-protein interaction analysis and machine learning was used to identify hub genes (GAS6, OAS3 and PDCD1LG2) and datasets GSE171110, GSE54992 and GSE79362 were used for verification. The mechanism of protein-drug interactions may have reference value in the treatment of coinfection of COVID-19 and TB.

The metabolic consequences of HIV/TB co-infection

BACKGROUND

The synergy between the human immunodeficiency virus (HIV) and Mycobacterium tuberculosis during co-infection of a host is well known. While this synergy is known to be driven by immunological deterioration, the metabolic mechanisms that contribute to the associated disease burden experienced during HIV/tuberculosis (TB) co-infection remain poorly understood. Furthermore, while anti-HIV treatments suppress viral replication, these therapeutics give rise to host metabolic disruption and adaptations beyond that induced by only infection or disease.

METHODS

In this study, the serum metabolic profiles of healthy controls, untreated HIV-negative TB-positive patients, untreated HIV/TB co-infected patients, and HIV/TB co-infected patients on antiretroviral therapy (ART), were measured using two-dimensional gas chromatography time-of-flight mass spectrometry. Since no global metabolic profile for HIV/TB co-infection and the effect of ART has been published to date, this pilot study aimed to elucidate the general areas of metabolism affected during such conditions.

RESULTS

HIV/TB co-infection induced significant changes to the host's lipid and protein metabolism, with additional microbial product translocation from the gut to the blood. The results suggest that HIV augments TB synergistically, at least in part, contributing to increased inflammation, oxidative stress, ART-induced mitochondrial damage, and its detrimental effects on gut health, which in turn, affects energy availability. ART reverses these trends to some extent in HIV/TB co-infected patients but not to that of healthy controls.

CONCLUSION

This study generated several new hypotheses that could direct future metabolic studies, which could be combined with other research techniques or methodologies to further elucidate the underlying mechanisms of these changes.

Amino acid profiling as a screening and prognostic biomarker in active tuberculosis patients

BACKGROUND

Tuberculosis (TB) is one of the world's most deadly chronic infectious diseases; early diagnosis contributes to reducing disease transmission among populations. However, discovering novel diagnostic and prognostic biomarkers is still an important topic in the field of TB. Amino acid is the basic unit of protein composition, and its structure and physicochemical characteristics are more stable. Therefore, it is a potential target for TB diagnosis and the prediction of TB development.

METHODS

In this study, the blood of healthy people (HC), TB patients (TB), cured TB (RxTB), and other non-TB pneumonia patients (PN) were collected to detect the levels of amino acids in whole blood and plasma using ultra-high performance liquid chromatography coupled with mass spectrometry.

RESULTS

We detected that the amino acid levels correlated with participants status (TB, HC, RxTB, or PN) and the degree of lung damage. The results showed that phenylalanine had a good effect on the screening of TB (AUC = 0.924). We then built a TB prediction model. The model, which was based on the ratio of plasma amino acid content to whole blood amino acid content, showed good performance for the screening of TB, with 84% (95% CI = 60-97) sensitivity and 97% (95% CI = 83-100) specificity. The result of correlation between the HRCT score and amino acid level indicated that the glutamine content of plasma was significantly inversely associated with disease severity. Additionally, ornithine levels in the plasma of RxTB group reduced and four amino acids of which the ratio in plasma to whole blood showed significantly changed.

CONCLUSIONS

Taken together, amino acid profiling can be used for TB screening, and a multiparameter profiling model is better. The profiling can also reflect the severity of lung damage. Moreover, the amino acid profile is useful for reflecting the efficacy of TB treatment.

Tuberculosis is associated with sputum metabolome variations, irrespective of patient sex or HIV status: an untargeted GCxGC-TOFMS study

INTRODUCTION

Various studies have identified TB-induced metabolome variations. However, in most of these studies, a large degree of variation exists between individual patients.

OBJECTIVES

To identify differential metabolites for TB, independent of patients' sex or HIV status.

METHODS

Untargeted GCxGC/TOF-MS analyses were applied to the sputum of 31 TB + and 197 TB- individuals. Univariate statistics were used to identify metabolites which are significantly different between TB + and TB- individuals (a) irrespective of HIV status, and (b) with a HIV + status. Comparisons a and b were repeated for (i) all participants, (ii) males only and (iii) females only.

RESULTS

Twenty-one compounds were significantly different between the TB + and TB- individuals within the female subgroup (11% lipids; 10% carbohydrates; 1% amino acids, 5% other and 73% unannotated), and 6 within the male subgroup (20% lipids; 40% carbohydrates; 6% amino acids, 7% other and 27% unannotated). For the HIV + patients (TB + vs. TB-), a total of 125 compounds were significant within the female subgroup (16% lipids; 8% carbohydrates; 12% amino acids, 6% organic acids, 8% other and 50% unannotated), and 44 within the male subgroup (17% lipids; 2% carbohydrates; 14% amino acids related, 8% organic acids, 9% other and 50% unannotated). Only one annotated compound, 1-oleoyl lysophosphaditic acid, was consistently identified as a differential metabolite for TB, irrespective of sex or HIV status. The potential clinical application of this compound should be evaluated further.

CONCLUSIONS

Our findings highlight the importance of considering confounders in metabolomics studies in order to identify unambiguous disease biomarkers.

Decoding the mechanism of hypertension through multiomics profiling

Hypertension, characterised by a constant high blood pressure, is the primary risk factor for multiple cardiovascular events and a major cause of death in adults. Excitingly, innovations in high-throughput technologies have enabled the global exploration of the whole genome (genomics), revealing dysregulated genes that are linked to hypertension. Moreover, post-genomic biomarkers, from the emerging fields of transcriptomics, proteomics, glycomics and lipidomics, have provided new insights into the molecular underpinnings of hypertension. In this paper, we review the pathophysiology of hypertension, and highlight the multi-omics approaches for hypertension prediction and diagnosis.

Mass spectrometry-based identification of new serum biomarkers in patients with latent infection pulmonary tuberculosis

Noninvasive and simple indicators for diagnosing latent tuberculosis (TB) infection (LTBI) and tracking progression from latent infection to active TB infection are still desperately needed. The aim of this study was to screen and identify possible biomarkers for diagnosing LTBI and monitoring the progression from latent infection to active TB infection, as well as to investigate the underlying processes and functions. To assess changes in metabolite composition associated with active tuberculosis infection in humans, whole blood supernatants were collected from patients with LTBI, drug-susceptible TB patients, drug-resistant TB patients, and healthy controls. The metabolites in all serum samples were extracted by oscillatory, deproteinization, and then detected by liquid chromatography-tandem mass spectrometry/MS analysis. Normalization by Pareto-scaling method, the difference analysis was carried out by Metaboanalyst 4.0 software, and 1-way analysis of variance analysis among groups showed that P-value < 0.05 was regarded as a different metabolite. To clarify the dynamic changes and functions of differential metabolites with disease progression, and explore its significance and mechanism as a marker by further cluster analysis, functional enrichment analysis, and relative content change analysis of differential metabolites. 65 metabolites were substantially different in four groups. Differential metabolites such as Inosine and Prostaglandin E1 may be important blood indicators for diagnosing mycobacterium tuberculosis latent infection, which were all tightly connected to amino acid metabolism, Biosynthesis of various secondary metabolites, Nucleotide metabolism, Endocrine system, Immune system, Lipid metabolism, and Nervous system. This study screened and identified Inosine, 16, 16-dimethyl-6-keto Prostaglandin E1, Theophylline, and Cotinine as potential serum biomarkers for diagnosing latent TB infection, and Cotinine as potential biomarkers for monitoring disease progression from healthy population to LTBI and then to active TB including drug-resistant TB infection and sensitive TB infection. Furthermore, this research provides a preliminary experimental basis to further investigate the development of metabolomics-based diagnosis of LTBI and monitoring the progress from latent infection to active TB infection.

Metabolites enhance innate resistance to human Mycobacterium tuberculosis infection

To determine the mechanisms that mediate resistance to Mycobacterium tuberculosis (M. tuberculosis) infection in household contacts (HHCs) of patients with tuberculosis (TB), we followed 452 latent TB infection-negative (LTBI-) HHCs for 2 years. Those who remained LTBI- throughout the study were identified as nonconverters. At baseline, nonconverters had a higher percentage of CD14+ and CD3-CD56+CD27+CCR7+ memory-like natural killer (NK) cells. Using a whole-transcriptome and metabolomic approach, we identified deoxycorticosterone acetate as a metabolite with elevated concentrations in the plasma of nonconverters, and further studies showed that this metabolite enhanced glycolytic ATP flux in macrophages and restricted M. tuberculosis growth by enhancing antimicrobial peptide production through the expression of the surface receptor sialic acid binding Ig-like lectin-14. Another metabolite, 4-hydroxypyridine, from the plasma of nonconverters significantly enhanced the expansion of memory-like NK cells. Our findings demonstrate that increased levels of specific metabolites can regulate innate resistance against M. tuberculosis infection in HHCs of patients with TB who never develop LTBI or active TB.

Pathological and protective roles of dendritic cells in Mycobacterium tuberculosis infection: Interaction between host immune responses and pathogen evasion

Dendritic cells (DCs) are principal defense components that play multifactorial roles in translating innate immune responses to adaptive immunity in Mycobacterium tuberculosis (Mtb) infections. The heterogeneous nature of DC subsets follows their altered functions by interacting with other immune cells, Mtb, and its products, enhancing host defense mechanisms or facilitating pathogen evasion. Thus, a better understanding of the immune responses initiated, promoted, and amplified or inhibited by DCs in Mtb infection is an essential step in developing anti-tuberculosis (TB) control measures, such as host-directed adjunctive therapy and anti-TB vaccines. This review summarizes the recent advances in salient DC subsets, including their phenotypic classification, cytokine profiles, functional alterations according to disease stages and environments, and consequent TB outcomes. A comprehensive overview of the role of DCs from various perspectives enables a deeper understanding of TB pathogenesis and could be useful in developing DC-based vaccines and immunotherapies.

Multi-block data integration analysis for identifying and validating targeted N-glycans as biomarkers for type II diabetes mellitus

Plasma N-glycan profiles have been shown to be defective in type II diabetes Mellitus (T2DM) and holds a promise to discovering biomarkers. The study comprised 232 T2DM patients and 219 healthy individuals. N-glycans were analysed by high-performance liquid chromatography. The multivariate integrative framework, DIABLO was employed for the statistical analysis. N-glycan groups (GPs 34, 32, 26, 31, 36 and 30) were significantly expressed in T2DM in component 1 and GPs 38 and 20 were related to T2DM in component 2. Four clusters were observed based on the correlation of the expressive signatures of the 39 N-glycans across T2DM and controls. Cluster A, B, C and D had 16, 16, 4 and 3 N-glycans respectively, of which 11, 8, 1 and 1 were found to express differently between controls and T2DM in a univariate analysis \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(p < 0.05)$$\end{document}(p<0.05). Multi-block analysis revealed that trigalactosylated (G3), triantennary (TRIA), high branching (HB) and trisialylated (S3) expressed significantly highly in T2DM than healthy controls. A bipartite relevance network revealed that HB, monogalactosylated (G1) and G3 were central in the network and observed more connections, highlighting their importance in discriminating between T2DM and healthy controls. Investigation of these N-glycans can enhance the understanding of T2DM.

Non-volatile organic compounds in exhaled breath particles correspond to active tuberculosis

Human breath contains trace amounts of non-volatile organic compounds (NOCs) which might provide non-invasive methods for evaluating individual health. In previous work, we demonstrated that lipids detected in exhaled breath aerosol (EBA) could be used as markers of active tuberculosis (TB). Here, we advanced our analytical platform for characterizing small metabolites and lipids in EBA samples collected from participants enrolled in clinical trials designed to identify molecular signatures of active TB. EBA samples from 26 participants with active TB and 73 healthy participants were processed using a dual-phase extraction method, and metabolites and lipids were identified via mass spectrometry database matching. In total, 13 metabolite and 9 lipid markers were identified with statistically different optimized relative standard deviation values between individuals diagnosed with active TB and the healthy controls. Importantly, EBA lipid profiles can be used to separate the two sample types, indicating the diagnostic potential of the identified molecules. A feature ranking algorithm reduced this number to 10 molecules, with the membrane glycerophospholipid, phosphatidylinositol 24:4, emerging as the top driver of segregation between the two groups. These results support the use of this approach to identify consistent NOC signatures from EBA samples in active TB cases. This suggests the potential to apply this method to other human diseases which alter respiratory NOC release.

Targeted metabolomics analysis of serum and Mycobacterium tuberculosis antigen-stimulated blood cultures of pediatric patients with active and latent tuberculosis

Profound tuberculosis (TB)-induced metabolic changes reflected in the blood metabolomic profile may provide an opportunity to identify specific markers of Mycobacterium tuberculosis infection. Using targeted liquid chromatography tandem mass spectrometry, we compared the levels of 30 small metabolites, including amino acids and derivatives, and small organic compounds in serum and M.tb antigen-stimulated whole blood cultures of active TB children, latent TB (LTBI) children, nonmycobacterial pneumonia (NMP) children, and healthy controls (HCs) to assess their potential as biomarkers of childhood TB. We found elevated levels of leucine and kynurenine combined with reduced concentrations of citrulline and glutamine in serum and blood cultures of TB and LTBI groups. LTBI status was additionally associated with a decrease in valine levels in blood cultures. The NMP metabolite profile was characterized by an increase in citrulline and glutamine and a decrease in leucine, kynurenine and valine concentrations. The highest discriminatory potential for identifying M.tb infection was observed for leucine detected in serum and kynurenine in stimulated blood cultures. The use of targeted metabolomics may reveal metabolic changes in M.tb-infected children, and the obtained results are a proof of principle of the usefulness of metabolites in the auxiliary diagnosis of TB in children.

Host lipidome and tuberculosis treatment failure

INTRODUCTION

Host lipids play important roles in tuberculosis (TB) pathogenesis. Whether host lipids at TB treatment initiation (baseline) affect subsequent treatment outcomes has not been well characterised. We used unbiased lipidomics to study the prospective association of host lipids with TB treatment failure.

METHODS

A case-control study (n=192), nested within a prospective cohort study, was used to investigate the association of baseline plasma lipids with TB treatment failure among adults with pulmonary TB. Cases (n=46) were defined as TB treatment failure, while controls (n=146) were those without failure. Complex lipids and inflammatory lipid mediators were measured using liquid chromatography mass spectrometry techniques. Adjusted least-square regression was used to assess differences in groups. In addition, machine learning identified lipids with highest area under the curve (AUC) to classify cases and controls.

RESULTS

Baseline levels of 32 lipids differed between controls and those with treatment failure after false discovery rate adjustment. Treatment failure was associated with lower baseline levels of cholesteryl esters and oxylipin, and higher baseline levels of ceramides and triglycerides compared to controls. Two cholesteryl ester lipids combined in a unique classifier model provided an AUC of 0.79 (95% CI 0.65-0.93) in the test dataset for prediction of TB treatment failure.

CONCLUSIONS

We identified lipids, some with known roles in TB pathogenesis, associated with TB treatment failure. In addition, a lipid signature with prognostic accuracy for TB treatment failure was identified. These lipids could be potential targets for risk-stratification, adjunct therapy and treatment monitoring.

Metabolomics Strategy Assisted by Transcriptomics Analysis to Identify Potential Biomarkers Associated with Tuberculosis

Purpose

To investigate the dysregulated pathways and identify reliable diagnostic biomarkers for tuberculosis using integrated analysis of metabolomics and transcriptomics.

Methods

Three groups of samples, untargeted metabolomics analysis of healthy controls (HC), latent tuberculosis infection patients (LTBI), and active tuberculosis patients (TB), were analyzed using gas chromatography time-of-flight mass spectrometry (GC-TOF MS) and ultra-high performance liquid chromatography-quantitative mass spectrometry (UHPLC-QE-MS). Both univariate and multivariate and statistical analyses were used to select differential metabolites (DMs) among group comparison, and LASSO regression analysis was employed to discover potential diagnostic biomarkers. Metabolite set enrichment analysis was performed to identify the altered metabolic pathways specifically in patients with TB. Meanwhile, a transcriptomic dataset GSEG4992 was downloaded from the GEO database to explore the differentially expressed genes (DEGs) between TB and HC identified in significantly enriched pathways. Finally, an integrative analysis of DMs and DEGs was performed to investigate the possible molecular mechanisms of TB.

Results

Thirty-three specific metabolites were significantly different between TB and HC, of which 7 (5-hydroxyindoleacetic acid, isoleucyl-isoleucine, heptadecanoic acid, indole acetaldehyde, 5-ethyl-2,4-dimethyloxazole, and 2-hydroxycaproic acid, unknown 71) were chosen as combinational potential biomarkers for TB. The area under the curve (AUC) value of these biomarkers was 0.97 (95% CI: 0.92–1.00). Metabolites set enrichment analysis (MSEA) displayed that there were 3 significantly enriched pathways among all. The genes in 3 significantly enriched pathways were further analyzed, of which 9(ALDH3B1, BCAT1, BCAT2, GLYAT, GOT1, IL4I1, MIF, SDS, SDSL) were expressed differentially. The area under the curve (AUC) values of these DEGs enriched in pathways mostly were greater than 0.8. As a result, a connected network of metabolites and genes in the pathways were established, which provides insights into the credibility of selected metabolites.

Conclusion

The newly identified metabolic biomarkers display a high potential to be developed into a promising tool for TB screening, diagnosis, and therapeutic effect monitoring.

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.

Evolutionary Views of Tuberculosis: Indoleamine 2,3-Dioxygenase Catalyzed Nicotinamide Synthesis Reflects Shifts in Macrophage Metabolism: Indoleamine 2,3-Dioxygenase Reflects Altered Macrophage Metabolism During Tuberculosis Pathogenesis

Indoleamine 2,3-dioxygenase (IDO) is the rate-limiting enzyme in conversion of tryptophan to kynurenines, feeding de novo nicotinamide synthesis. IDO orchestrates materno-foetal tolerance, increasing human reproductive fitness. IDO mediates immune suppression through depletion of tryptophan required by T lymphocytes and other mechanisms. IDO is expressed by alternatively activated macrophages, suspected to play a key role in tuberculosis (TB) pathogenesis. Unlike its human host, Mycobacterium tuberculosis can synthesize tryptophan, suggesting possible benefit to the host from infection with the microbe. Intriguingly, nicotinamide analogues are used to treat TB. In reviewing this field, it is postulated that flux through the nicotinamide synthesis pathway reflects switching between aerobic glycolysis and oxidative phosphorylation in M. tuberculosis-infected macrophages. The evolutionary cause of such shifts may be ancient mitochondrial behavior related to reproductive fitness. Evolutionary perspectives on the IDO pathway may elucidate why, after centuries of co-existence with the Tubercle bacillus, humans still remain susceptible to TB disease.

Urine NMR-based TB metabolic fingerprinting for the diagnosis of TB in children

Tuberculosis (TB) is a major cause of morbidity and mortality in children, and early diagnosis and treatment are crucial to reduce long-term morbidity and mortality. In this study, we explore whether urine nuclear magnetic resonance (NMR)-based metabolomics could be used to identify differences in the metabolic response of children with different diagnostic certainty of TB. We included 62 children with signs and symptoms of TB and 55 apparently healthy children. Six of the children with presumptive TB had bacteriologically confirmed TB, 52 children with unconfirmed TB, and 4 children with unlikely TB. Urine metabolic fingerprints were identified using high- and low-field proton NMR platforms and assessed with pattern recognition techniques such as principal components analysis and partial least squares discriminant analysis. We observed differences in the metabolic fingerprint of children with bacteriologically confirmed and unconfirmed TB compared to children with unlikely TB (p = 0.041 and p = 0.013, respectively). Moreover, children with unconfirmed TB with X-rays compatible with TB showed differences in the metabolic fingerprint compared to children with non-pathological X-rays (p = 0.009). Differences in the metabolic fingerprint in children with different diagnostic certainty of TB could contribute to a more accurate characterisation of TB in the paediatric population. The use of metabolomics could be useful to improve the prediction of TB progression and diagnosis in children.

Understanding Metabolic Regulation Between Host and Pathogens: New Opportunities for the Development of Improved Therapeutic Strategies Against Mycobacterium tuberculosis Infection

Mycobacterium tuberculosis (Mtb) causes chronic granulomatous lung disease in humans. Recently, novel strategies such as host-directed therapeutics and adjunctive therapies that enhance the effect of existing antibiotics have emerged to better control Mtb infection. Recent advances in understanding the metabolic interplay between host immune cells and pathogens have provided new insights into how their interactions ultimately influence disease outcomes and antibiotic-treatment efficacy. In this review, we describe how metabolic cascades in immune environments and relevant metabolites produced from immune cells during Mtb infection play critical roles in the progression of diseases and induction of anti-Mtb protective immunity. In addition, we introduce how metabolic alterations in Mtb itself can lead to the development of persister cells that are resistant to host immunity and can eventually evade antibiotic attacks. Further understanding of the metabolic link between host cells and Mtb may contribute to not only the prevention of Mtb persister development but also the optimization of host anti-Mtb immunity together with enhanced efficacy of existing antibiotics. Overall, this review highlights novel approaches to improve and develop host-mediated therapeutic strategies against Mtb infection by restoring and switching pathogen-favoring metabolic conditions with host-favoring conditions.

Metabolic Regulation of Immune Responses to Mycobacterium tuberculosis: A Spotlight on L-Arginine and L-Tryptophan Metabolism

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), is a leading cause of death worldwide. Despite decades of research, there is still much to be uncovered regarding the immune response to Mtb infection. Here, we summarize the current knowledge on anti-Mtb immunity, with a spotlight on immune cell amino acid metabolism. Specifically, we discuss L-arginine and L-tryptophan, focusing on their requirements, regulatory roles, and potential use as adjunctive therapy in TB patients. By continuing to uncover the immune cell contribution during Mtb infection and how amino acid utilization regulates their functions, it is anticipated that novel host-directed therapies may be developed and/or refined, helping to eradicate TB.

Human tuberculosis and Mycobacterium tuberculosis complex: A review on genetic diversity, pathogenesis and omics approaches in host biomarkers discovery

Mycobacterium tuberculosis complex (MTBC) refers to a group of mycobacteria encompassing nine members of closely related species that causes tuberculosis in animals and humans. Among the nine members, Mycobacterium tuberculosis (M. tuberculosis) remains the main causative agent for human tuberculosis that results in high mortality and morbidity globally. In general, MTBC species are low in diversity but exhibit distinctive biological differences and phenotypes among different MTBC lineages. MTBC species are likely to have evolved from a common ancestor through insertions/deletions processes resulting in species speciation with different degrees of pathogenicity. The pathogenesis of human tuberculosis is complex and remains poorly understood. It involves multi-interactions or evolutionary co-options between host factors and bacterial determinants for survival of the MTBC. Granuloma formation as a protection or survival mechanism in hosts by MTBC remains controversial. Additionally, MTBC species are capable of modulating host immune response and have adopted several mechanisms to evade from host immune attack in order to survive in humans. On the other hand, current diagnostic tools for human tuberculosis are inadequate and have several shortcomings. Numerous studies have suggested the potential of host biomarkers in early diagnosis of tuberculosis, in disease differentiation and in treatment monitoring. "Multi-omics" approaches provide holistic views to dissect the association of MTBC species with humans and offer great advantages in host biomarkers discovery. Thus, in this review, we seek to understand how the genetic variations in MTBC lead to species speciation with different pathogenicity. Furthermore, we also discuss how the host and bacterial players contribute to the pathogenesis of human tuberculosis. Lastly, we provide an overview of the journey of "omics" approaches in host biomarkers discovery in human tuberculosis and provide some interesting insights on the challenges and directions of "omics" approaches in host biomarkers innovation and clinical implementation.

Novel therapeutic evaluation biomarkers of lipid metabolism targets in uncomplicated pulmonary tuberculosis patients

Currently, the management of pulmonary tuberculosis (TB) lacks potent medications and accurate efficacy evaluation biomarkers. In view of the fact that the host lipids are the important energy source of Mycobacterium tuberculosis (Mtb), UPLC-MS/MS based on lipid metabolism was used to monitor the plasma lipid spectrum of TB patients from the initial diagnosis to cured. The analysis showed that TB patients presented aberrant metabolism of phospholipids, glycerides, and sphingolipids. Upon the treatment, the abnormal expression of Cer (d18:1/24:0), CerP (d18:1/20:3), LPE (0:0/22:0), LPA (0:0/16:0), and LPA (0:0/18:0) in TB patients were gradually normalized, indicating that the intervention of lipid metabolism could block energy metabolism and inhibit the cell wall synthesis of Mtb. Furthermore, the increase in ceramide (Cer) levels could promote autophagosome-lysosome fusion. LPA (0:0/16:0) and LPA (0:0/18:0) had a great potential in the early diagnosis (both sensitivity and specificity were 100%) and efficacy evaluation (both sensitivity and specificity were 100%) of TB, indicating that the above lipid metabolites could be used as potential biomarkers for TB.

Discovery and validation of an NMR-based metabolomic profile in urine as TB biomarker

Despite efforts to improve tuberculosis (TB) detection, limitations in access, quality and timeliness of diagnostic services in low- and middle-income countries are challenging for current TB diagnostics. This study aimed to identify and characterise a metabolic profile of TB in urine by high-field nuclear magnetic resonance (NMR) spectrometry and assess whether the TB metabolic profile is also detected by a low-field benchtop NMR spectrometer. We included 189 patients with tuberculosis, 42 patients with pneumococcal pneumonia, 61 individuals infected with latent tuberculosis and 40 uninfected individuals. We acquired the urine spectra from high and low-field NMR. We characterised a TB metabolic fingerprint from the Principal Component Analysis. We developed a classification model from the Partial Least Squares-Discriminant Analysis and evaluated its performance. We identified a metabolic fingerprint of 31 chemical shift regions assigned to eight metabolites (aminoadipic acid, citrate, creatine, creatinine, glucose, mannitol, phenylalanine, and hippurate). The model developed using low-field NMR urine spectra correctly classified 87.32%, 85.21% and 100% of the TB patients compared to pneumococcal pneumonia patients, LTBI and uninfected individuals, respectively. The model validation correctly classified 84.10% of the TB patients. We have identified and characterised a metabolic profile of TB in urine from a high-field NMR spectrometer and have also detected it using a low-field benchtop NMR spectrometer. The models developed from the metabolic profile of TB identified by both NMR technologies were able to discriminate TB patients from the rest of the study groups and the results were not influenced by anti-TB treatment or TB location. This provides a new approach in the search for possible biomarkers for the diagnosis of TB.

Integration of metabolomics and transcriptomics reveals novel biomarkers in the blood for tuberculosis diagnosis in children

Pediatric tuberculosis (TB) remains a major global health problem. Improved pediatric diagnostics using readily available biosources are urgently needed. We used liquid chromatography-mass spectrometry to analyze plasma metabolite profiles of Indian children with active TB (n = 16) and age- and sex-matched, Mycobacterium tuberculosis-exposed but uninfected household contacts (n = 32). Metabolomic data were integrated with whole blood transcriptomic data for each participant at diagnosis and throughout treatment for drug-susceptible TB. A decision tree algorithm identified 3 metabolites that correctly identified TB status at distinct times during treatment. N-acetylneuraminate achieved an area under the receiver operating characteristic curve (AUC) of 0.66 at diagnosis. Quinolinate achieved an AUC of 0.77 after 1 month of treatment, and pyridoxate achieved an AUC of 0.87 after successful treatment completion. A set of 4 metabolites (gamma-glutamylalanine, gamma-glutamylglycine, glutamine, and pyridoxate) identified treatment response with an AUC of 0.86. Pathway enrichment analyses of these metabolites and corresponding transcriptional data correlated N-acetylneuraminate with immunoregulatory interactions between lymphoid and non-lymphoid cells, and correlated pyridoxate with p53-regulated metabolic genes and mitochondrial translation. Our findings shed new light on metabolic dysregulation in children with TB and pave the way for new diagnostic and treatment response markers in pediatric TB.

Diagnostic accuracy of plasma kynurenine/tryptophan ratio, measured by enzyme-linked immunosorbent assay, for pulmonary tuberculosis

INTRODUCTION

The World Health Organization has identified the need for a non-sputum-based test capable of detecting active tuberculosis (TB) as a priority. The plasma kynurenine-to-tryptophan (K/T) ratio, largely mediated by activity of the enzyme indoleamine 2,3-dioxygenase, may have potential as a suitable biomarker for active TB.

METHOD

We evaluated a commercial enzyme-linked immunosorbent assay (ELISA) in comparison to mass spectrometry for measuring the K/T ratio. We also used ELISA to determine the K/T ratio in plasma from patients with active TB compared to latently infected controls, with and without HIV.

RESULTS

The two methods showed good agreement, with a mean bias of 0.01 (limit of agreement from -0.06 to 0.10). Using ELISA, it was found that HIV-infected patients with active TB disease had higher K/T ratios than those without TB (median, 0.101 [interquartile range (IQR), 0.091-0.140] versus 0.061 [IQR, 0.034-0.077], P<0.0001). At a cutoff of 0.080, the K/T ratio produced a sensitivity of 90%, a specificity of 80%, a positive predictive value (PPV) of 82%, and a negative predictive value (NPV) of 90%. In a receiver operating characteristics analysis, the K/T ratio had an area under the curve of 0.93. HIV-uninfected patients with active TB also had higher K/T ratios than those with latent TB infections (median, 0.064 [IQR, 0.040-0.088] versus 0.022 [IQR, 0.016-0.027], P<0.0001). A cutoff of 0.040 gave a sensitivity of 85%, a specificity of 92%, a PPV of 91%, and an NPV of 84%.

CONCLUSION

The plasma K/T ratio is a sensitive biomarker for active TB. The K/T ratio can be measured from blood using ELISA. The K/T ratio should be evaluated as an initial test for TB.

Tuberculosis causes highly conserved metabolic changes in human patients, mycobacteria-infected mice and zebrafish larvae

Tuberculosis is a highly infectious and potentially fatal disease accompanied by wasting symptoms, which cause severe metabolic changes in infected people. In this study we have compared the effect of mycobacteria infection on the level of metabolites in blood of humans and mice and whole zebrafish larvae using one highly standardized mass spectrometry pipeline, ensuring technical comparability of the results. Quantification of a range of circulating small amines showed that the levels of the majority of these compounds were significantly decreased in all three groups of infected organisms. Ten of these metabolites were common between the three different organisms comprising: methionine, asparagine, cysteine, threonine, serine, tryptophan, leucine, citrulline, ethanolamine and phenylalanine. The metabolomic changes of zebrafish larvae after infection were confirmed by nuclear magnetic resonance spectroscopy. Our study identified common biomarkers for tuberculosis disease in humans, mice and zebrafish, showing across species conservation of metabolic reprogramming processes as a result of disease. Apparently, the mechanisms underlying these processes are independent of environmental, developmental and vertebrate evolutionary factors. The zebrafish larval model is highly suited to further investigate the mechanism of metabolic reprogramming and the connection with wasting syndrome due to infection by mycobacteria.

The Echo of Pulmonary Tuberculosis: Mechanisms of Clinical Symptoms and Other Disease-Induced Systemic Complications

Clinical symptoms of active tuberculosis (TB) can range from a simple cough to more severe reactions, such as irreversible lung damage and, eventually, death, depending on disease progression. In addition to its clinical presentation, TB has been associated with several other disease-induced systemic complications, such as hyponatremia and glucose intolerance. Here, we provide an overview of the known, although ill-described, underlying biochemical mechanisms responsible for the clinical and systemic presentations associated with this disease and discuss novel hypotheses recently generated by various omics technologies. This summative update can assist clinicians to improve the tentative diagnosis of TB based on a patient's clinical presentation and aid in the development of improved treatment protocols specifically aimed at restoring the disease-induced imbalance for overall homeostasis while simultaneously eradicating the pathogen. Furthermore, future applications of this knowledge could be applied to personalized diagnostic and therapeutic options, bettering the treatment outcome and quality of life of TB patients.

The application of metabolomics analysis in the research of gestational diabetes mellitus and preeclampsia

AIM

The aim of the study was to investigate the difference of the serum metabolic profile between gestational diabetes mellitus (GDM) patients and preeclampsia (PE) patients, to establish the disease differentiation model and to find characteristic metabolites, in order to provide a new idea for the occurrence, development and treatment of the disease.

METHODS

Twenty-nine patients with GDM group and 29 PE group who were examined in Tianjin No. 3 Central Hospital from March 2018 to August 2018 were enrolled as case group, and 29 normal pregnant women were selected as control group. All the serum samples were analyzed by using the ultra-performance liquid chromatography and mass spectrometry. Based on the multivariate statistical analysis method of pattern recognition, we screened out and identified the differential characteristic metabolites.

RESULTS

The serum metabolic profile model of GDM group and PE group was successfully constructed. A total of nine characteristic metabolites were screened and identified in this study, including LPC 18:0, LPC 22:6, LPC 16:0, (S)-14-methylhexadecanoic acid, behenic acid, palmitic acid, sphingosine, phytosphingosine and 1,25-dihydroxyvitamin D3-26,23-lactone. Among them, six characteristic metabolites which were LPC 18:0, LPC 22:6, behenic acid, palmitic acid, sphingosine and 1,25-dihydroxyvitamin D3-26,23-lactone all had a significant statistical difference among GDM, PE and normal pregnancy groups (P < 0.05).

CONCLUSION

The construction of metabolic profile discriminant model has a strong ability to differentiate GDM patients from PE pregnant women. The screened characteristic metabolites can early reflect the disorder of lipid, calcium and phosphorus metabolism of patients, and provide reference and help for the discussion of the occurrence, development and treatment of diseases.

Identification of serum biomarkers for active pulmonary tuberculosis using a targeted metabolomics approach

Although tuberculosis (TB) is a severe health problem worldwide, the current diagnostic methods are far from optimal. Metabolomics is increasingly being used in the study of infectious diseases. We performed metabolome profiling to identify potential biomarkers in patients with active TB. Serum samples from 21 patients with active pulmonary TB, 20 subjects with latent TB infection (LTBI), and 28 healthy controls were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by multivariate and univariate analyses. Metabolic profiles indicated higher serum levels of glutamate, sulfoxy methionine, and aspartate and lower serum levels of glutamine, methionine, and asparagine in active TB patients than in LTBI subjects or healthy controls. The ratios between metabolically related partners (glutamate/glutamine, sulfoxy methionine/methionine, and aspartate/asparagine) were also elevated in the active TB group. There was no significant difference in the serum concentration of these metabolites according to the disease extent or risk of relapse in active TB patients. Novel serum biomarkers such as glutamate, sulfoxy methionine, aspartate, glutamine, methionine, and asparagine are potentially useful for adjunctive, rapid, and noninvasive pulmonary TB diagnosis.

Plasma metabolomics in tuberculosis patients with and without concurrent type 2 diabetes at diagnosis and during antibiotic treatment

Tuberculosis (TB) and type 2 diabetes mellitus (DM), a major TB risk factor, are both accompanied by marked alterations in metabolic processes. Dissecting the specific metabolic changes induced by disease through metabolomics has shown potential to improve our understanding of relevant pathophysiological mechanisms of disease, which could lead to improved treatment. Targeted tandem liquid chromatography–mass spectrometry (LC-MS/MS) was used to compare amine and acylcarnitine levels in plasma samples of patients with TB or TB-DM from Indonesia at time of diagnosis and during antibiotic treatment. Partial least squares discrimination analysis (PLS-DA) showed good separation of patient groups. Amine levels were strongly altered in both disease groups compared to healthy controls, including low concentrations of citrulline and ornithine. Several amino acid ratios discriminated TB from controls (phenylalanine/histidine; citrulline/arginine; kynurenine/tryptophan), possibly reflecting changes in indoleamine-pyrrole 2,3-dioxygenase (IDO) and nitric oxide synthase (NOS) activity. Choline, glycine, serine, threonine and homoserine levels were lower in TB-DM compared to TB, and, in contrast to other analytes, did not normalize to healthy control levels during antibiotic treatment. Our results not only provide important validation of previous studies but also identify novel biomarkers, and significantly enhance our understanding of metabolic changes in human TB and TB-DM.

n-Butylamine for Improving the Efficiency of Untargeted Mass Spectrometry Analysis of Plasma Metabolite Composition

A comparative study of the impact of n-butylamine and traditionally used additives (ammonium hydroxide and formic acid) on the efficiency of the electrospray ionization (ESI) process for the enhancement of metabolite coverage was performed by direct injection mass spectrometry (MS) analysis in negative mode. Evaluation of obtained MS data showed that n-butylamine is one of the most effective additives for the analysis of metabolite composition in ESI in negative ion mode (ESI(-)) The limitations of the use of n-butylamine and other alkylamines in the analysis of metabolic composition and a decontamination procedure that can reduce MS device contamination after their application are discussed. The proposed procedure allows the performance of high-sensitivity analysis of low-molecular-weight compounds on the same MS device in both polarities.

Indoleamine 2, 3-Dioxygenase-Mediated Tryptophan Catabolism: A Leading Star or Supporting Act in the Tuberculosis and HIV Pas-de-Deux?

Progression from latency to active Tuberculosis (TB) disease is mediated by incompletely understood host immune factors. The definitive characteristic of progressive human immunodeficiency virus (HIV) disease is a severe loss in number and function of T lymphocytes. Among the many possible mediators of T lymphocyte loss and ineffective function is the activity of the immune-modulatory enzyme indoleamine 2,3-dioxygenase (IDO). IDO is the rate-limiting enzyme converting tryptophan to kynurenine. IDO activity was initially recognized to mediate tolerance at the foeto-maternal interface. Recently, IDO activity has also been noted to play a critical role in immune tolerance to pathogens. Studies of host immune and metabolic mediators have found IDO activity significantly elevated in HIV and TB disease. In this review, we explore the link between IDO-mediated tryptophan catabolism and the presence of active TB disease in HIV-infected patients. We draw attention to increased IDO activity as a key factor marking the progression from latent to active TB disease in HIV-infected patients.

Kynurenine Pathway Metabolites as Biomarkers for Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) currently lacks a robust and well-defined biomarker that can 1) assess the progression of the disease, 2) predict and/or delineate the various clinical subtypes, and 3) evaluate or predict a patient's response to treatments. The kynurenine Pathway (KP) of tryptophan degradation represent a promising candidate as it is involved with several neuropathological features present in ALS including neuroinflammation, excitotoxicity, oxidative stress, immune system activation and dysregulation of energy metabolism. Some of the KP metabolites (KPMs) can cross the blood brain barrier, and many studies have shown their levels are dysregulated in major neurodegenerative diseases including ALS. The KPMs can be easily analyzed in body fluids and tissue and as they are small molecules, and are stable. KPMs have a Janus face action, they can be either or both neurotoxic and/or neuroprotective depending of their levels. This mini review examines and presents evidence supporting the use of KPMs as a relevant set of biomarkers for ALS, and highlights the criteria required to achieve a valid biomarker set for ALS.

Application of model systems to study adaptive responses of Mycobacterium tuberculosis during infection and disease

Tuberculosis (TB) claims more human lives than any other infectious organism. The lethal synergy between TB-HIV infection and the rapid emergence of drug resistant strains has created a global public health threat that requires urgent attention. Mycobacterium tuberculosis, the causative agent of TB is an exquisitely well-adapted human pathogen, displaying the ability to promptly remodel metabolism when encountering stressful environments during pathogenesis. A careful study of the mechanisms that enable this adaptation will enhance the understanding of key aspects related to the microbiology of TB disease. However, these efforts require microbiological model systems that mimic host conditions in the laboratory. Herein, we describe several in vitro model systems that generate non-replicating and differentially culturable mycobacteria. The changes that occur in the metabolism of M. tuberculosis in some of these models and how these relate to those reported for human TB disease are discussed. We describe mechanisms that tubercle bacteria use to resuscitate from these non-replicating conditions, together with phenotypic heterogeneity in terms of culturabiliy of M. tuberculosis in sputum. Transcriptional changes in M. tuberculosis that allow for adaptation of the organism to the lung environment are also summarized. Finally, given the emerging importance of the microbiome in various infectious diseases, we provide a description of how the lung and gut microbiome affect susceptibility to TB infection and response to treatment. Consideration of these collective aspects will enhance the understanding of basic metabolism, physiology, drug tolerance and persistence in M. tuberculosis to enable development of new therapeutic interventions.

Metabolomic-Based Methods in Diagnosis and Monitoring Infection Progression

A robust biomarker screening and validation is crucial for overcoming the current limits in the clinical management of infectious diseases. In this chapter, a general workflow for metabolomics is summarized. Subsequently, an overview of the major contributions of this omics science to the field of biomarkers of infectious diseases is discussed. Different approaches using a variety of analytical platforms can be distinguished to unveil the key metabolites for the diagnosis, prognosis, response to treatment and susceptibility for infectious diseases. To allow the implementation of such biomarkers into the clinics, the performance of large-scale studies employing solid validation criteria becomes essential. Focusing on the etiological agents and after an extensive review of the field, we present a comprehensive revision of the main metabolic biomarkers of viral, bacterial, fungal, and parasitic diseases. Finally, we discussed several articles which show the strongest validation criteria. Following these research avenues, precious clinical resources will be revealed, allowing for reduced misdiagnosis, more efficient therapies, and affordable costs, ultimately leading to a better patient management.

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.

Metabolite changes in blood predict the onset of tuberculosis

New biomarkers of tuberculosis (TB) risk and disease are critical for the urgently needed control of the ongoing TB pandemic. In a prospective multisite study across Subsaharan Africa, we analyzed metabolic profiles in serum and plasma from HIV-negative, TB-exposed individuals who either progressed to TB 3–24 months post-exposure (progressors) or remained healthy (controls). We generated a trans-African metabolic biosignature for TB, which identifies future progressors both on blinded test samples and in external data sets and shows a performance of 69% sensitivity at 75% specificity in samples within 5 months of diagnosis. These prognostic metabolic signatures are consistent with development of subclinical disease prior to manifestation of active TB. Metabolic changes associated with pre-symptomatic disease are observed as early as 12 months prior to TB diagnosis, thus enabling timely interventions to prevent disease progression and transmission.

The tuberculosis pandemic requires new methods for diagnosing and containing infections prior to active disease. Here, the authors performed a multi-site observational study within sub-Saharan Africa and present serum and plasma metabolic signatures that can predict the onset of active TB with a high degree of sensitivity and specificity.

Metabolomics applied to the discovery of tuberculosis and diabetes mellitus biomarkers

Tuberculosis (TB) and diabetes mellitus Type 2 (DM2) are two diseases as ancient as they are harmful to human health. The outcome for both diseases in part depends on immune and metabolic individual responses. DM2 is increasing yearly, mainly due to environmental, genetic and lifestyle habits. There are multiple evidence that DM2 is one of the most important risk factor of becoming infected with TB or reactivating latent TB. Mass spectrometry-based metabolomics is an important tool for elucidating the metabolites and metabolic pathways that influence the immune responses to M. tuberculosis infection during diabetes. We provide an up-to-date review highlighting the importance and benefit of metabolomics for identifying biomarkers as candidate molecules for diagnosis, disease activity or prognosis.

Lysophosphatidylcholine Promotes Phagosome Maturation and Regulates Inflammatory Mediator Production Through the Protein Kinase A-Phosphatidylinositol 3 Kinase-p38 Mitogen-Activated Protein Kinase Signaling Pathway During Mycobacterium tuberculosis Infection in Mouse Macrophages

Tuberculosis is caused by the infectious agent Mycobacterium tuberculosis (Mtb). Mtb has various survival strategies, including blockade of phagosome maturation and inhibition of antigen presentation. Lysophosphatidylcholine (LPC) is a major phospholipid component of oxidized low-density lipoprotein and is involved in various cellular responses, such as activation of second messengers and bactericidal activity in neutrophils. In this study, macrophages were infected with a low infectious dose of Mtb and treated with LPC to investigate the bactericidal activity of LPC against Mtb. In macrophages infected with Mtb strain, H37Ra or H37Rv, LPC suppressed bacterial growth; however, this effect was suppressed in bone marrow-derived macrophages (BMDMs) isolated from G2A (a G protein-coupled receptor involved in some LPC actions) knockout mice. LPC also promoted phagosome maturation via phosphatidylinositol 3 kinase (PI3K)–p38 mitogen-activated protein kinase (MAPK)-mediated reactive oxygen species production and intracellular Ca²⁺ release during Mtb infection. In addition, LPC induced increased levels of intracellular cyclic adenosine monophosphate (cAMP) and phosphorylated glycogen synthase kinase 3 beta (GSK3β) in Mtb-infected macrophages. Protein kinase A (PKA)-induced phosphorylation of GSK3β suppressed activation of NF-κB in LPC-treated macrophages during Mtb infection, leading to decreased secretion of pro-inflammatory cytokines and increased secretion of anti-inflammatory cytokines. These results suggest that LPC can effectively control Mtb growth by promoting phagosome maturation via cAMP-induced activation of the PKA–PI3K–p38 MAPK pathway. Moreover, LPC can regulate excessive production of pro-inflammatory cytokines associated with bacterial infection of macrophages.

Integration of genome-scale metabolic networks into whole-body PBPK models shows phenotype-specific cases of drug-induced metabolic perturbation

Drug-induced perturbations of the endogenous metabolic network are a potential root cause of cellular toxicity. A mechanistic understanding of such unwanted side effects during drug therapy is therefore vital for patient safety. The comprehensive assessment of such drug-induced injuries requires the simultaneous consideration of both drug exposure at the whole-body and resulting biochemical responses at the cellular level. We here present a computational multi-scale workflow that combines whole-body physiologically based pharmacokinetic (PBPK) models and organ-specific genome-scale metabolic network (GSMN) models through shared reactions of the xenobiotic metabolism. The applicability of the proposed workflow is illustrated for isoniazid, a first-line antibacterial agent against Mycobacterium tuberculosis, which is known to cause idiosyncratic drug-induced liver injuries (DILI). We combined GSMN models of a human liver with N-acetyl transferase 2 (NAT2)-phenotype-specific PBPK models of isoniazid. The combined PBPK-GSMN models quantitatively describe isoniazid pharmacokinetics, as well as intracellular responses, and changes in the exometabolome in a human liver following isoniazid administration. Notably, intracellular and extracellular responses identified with the PBPK-GSMN models are in line with experimental and clinical findings. Moreover, the drug-induced metabolic perturbations are distributed and attenuated in the metabolic network in a phenotype-dependent manner. Our simulation results show that a simultaneous consideration of both drug pharmacokinetics at the whole-body and metabolism at the cellular level is mandatory to explain drug-induced injuries at the patient level. The proposed workflow extends our mechanistic understanding of the biochemistry underlying adverse events and may be used to prevent drug-induced injuries in the future.

The genotype of a patient determines the extent of drug-induced metabolic perturbations on the endogenous cellular network of the liver. A team around Lars Kuepfer at Germany’s RWTH Aachen University developed a computational workflow that links drug pharmacokinetics at the whole-body level with a cellular network of the liver. The authors used the competitive cofactor and energy demands in endogenous and drug metabolism to establish a multi-scale model for the antibiotic isoniazid. Their model quantitatively describes how isoniazid pharmacokinetics alter the intracellular liver biochemistry and the utilization of extracellular metabolites in different patient genotypes. The study outlines how a mechanistic understanding of genotype-dependent drug-induced metabolic perturbations may help to explain diverging incidence rates of toxic events in different patient subgroups. This could reduce the occurrence of toxic side effects during drug treatments in the future.

Plasma lipidomics of tuberculosis patients: altered phosphatidylcholine remodeling

Aim:

Decreased circulating levels of lysophosphatidylcholines have been monitored in the serum of tuberculosis (TB) patients. However, the etiology of these findings has not been explored and other critical lung surfactant lipids have not been examined.

Materials & methods:

We undertook a lipidomics analysis of 30 controls and 30 TB patients, utilizing a high-resolution mass spectrometric analytical platform that assays over 1800 lipids.

Findings:

As previously reported, we found decrements in the plasma levels of lysophosphatidylcholines in TB patients. In addition, we report for the first time that there are increases in the plasma levels of phosphatidylcholines and phosphatidylglycerols in TB patients.

Conclusion:

These data suggest that TB results in altered glycerophosphocholine remodeling involving deacylation–reacylation reactions at sn-2 of the glycerol backbone. Such alterations in lipid remodeling have the potential to exert negative effects on the function of lung surfactant, on signal transduction mechanisms and membrane structural lipid architecture in TB patients.

On investigating tuberculosis patients, we found altered metabolism of structural lipids that are essential to lung function. These new biomarkers may be useful to monitor the efficacy of current and new patient treatment strategies in the future.

Mass spectrometry applied to the identification of Mycobacterium tuberculosis and biomarker discovery

An adequate and effective tuberculosis (TB) diagnosis system has been identified by the World Health Organization as a priority in the fight against this disease. Over the years, several methods have been developed to identify the bacillus, but bacterial culture remains one of the most affordable methods for most countries. For rapid and accurate identification, however, it is more feasible to implement molecular techniques, taking advantage of the availability of public databases containing protein sequences. Mass spectrometry (MS) has become an interesting technique for the identification of TB. Here, we review some of the most widely employed methods for identifying Mycobacterium tuberculosis and present an update on MS applied for the identification of mycobacterial species.

Metabolomics biomarkers for tuberculosis diagnostics: current status and future objectives

Numerous studies have contributed to our current understanding of the complex biology of pulmonary tuberculosis and subsequently provided solutions to its control or eradication. Metabolomics, a newcomer to the Omics research domain, has significantly contributed to this understanding by identifying biomarkers originating from the disease-associated metabolome adaptations of both the microbe and host. These biomarkers have shed light on previously unknown disease mechanisms, many of which have been implemented toward the development of improved diagnostic strategies. In this review, we will discuss the role that metabolomics has played in tuberculosis research to date, with a specific focus on new biomarker identification, and how these have contributed to improved disease characterization and diagnostics, and their potential clinical applications.

Utility of Novel Plasma Metabolic Markers in the Diagnosis of Pediatric Tuberculosis: A Classification and Regression Tree Analysis Approach

Although tuberculosis (TB) has been the greatest killer due to a single infectious disease, pediatric TB is still hard to diagnose because of the lack of sensitive biomarkers. Metabolomics is increasingly being applied in infectious diseases. But little is known regarding metabolic biomarkers in children with TB. A combination of a NMR-based plasma metabolic method and classification and regression tree (CART) analysis was used to provide a broader range of applications in TB diagnosis in our study. Plasma samples obtained from 28 active TB children and 37 non-TB controls (including 21 RTIs and 16 healthy children) were analyzed by an orthogonal partial least-squares discriminant analysis (OPLS-DA) model, and 17 metabolites were identified that can separate children with TB from non-TB controls. CART analysis was then used to choose 3 of the markers, l-valine, pyruvic acid, and betaine, with the least error. The sensitivity, specificity, and area under the curve (AUC) of the 3 metabolites is 85.7% (24/28, 95% CI, 66.4%, 95.3%), 94.6% (35/37, 95% CI, 80.5%, 99.1%), and 0.984(95% CI, 0.917, 1.000), respectively. The 3 metabolites demonstrated sensitivity of 82.4% (14/17, 95% CI, 55.8%, 95.3%) and specificity of 83.9% (26/31, 95% CI, 65.5%, 93.9%), respectively, in 48 blinded subjects in an independent cohort. Taken together, the novel plasma metabolites are potentially useful for diagnosis of pediatric TB and would provide insights into the disease mechanism.

Metabolomics: Applications and Promise in Mycobacterial Disease

Until recently, the study of mycobacterial diseases was trapped in culture-based technology that is more than a century old. The use of nucleic acid amplification is changing this, and powerful new technologies are on the horizon. Metabolomics, which is the study of sets of metabolites of both the bacteria and host, is being used to clarify mechanisms of disease, and can identify changes leading to better diagnosis, treatment, and prognostication of mycobacterial diseases. Metabolomic profiles are arrays of biochemical products of genes in their environment. These complex patterns are biomarkers that can allow a more complete understanding of cell function, dysfunction, and perturbation than genomics or proteomics. Metabolomics could herald sweeping advances in personalized medicine and clinical trial design, but the challenges in metabolomics are also great. Measured metabolite concentrations vary with the timing within a condition, the intrinsic biology, the instruments, and the sample preparation. Metabolism profoundly changes with age, sex, variations in gut microbial flora, and lifestyle. Validation of biomarkers is complicated by measurement accuracy, selectivity, linearity, reproducibility, robustness, and limits of detection. The statistical challenges include analysis, interpretation, and description of the vast amount of data generated. Despite these drawbacks, metabolomics provides great opportunity and the potential to understand and manage mycobacterial diseases.

Diagnostic 'omics' for active tuberculosis

The decision to treat active tuberculosis (TB) is dependent on microbiological tests for the organism or evidence of disease compatible with TB in people with a high demographic risk of exposure. The tuberculin skin test and peripheral blood interferon-γ release assays do not distinguish active TB from a cleared or latent infection. Microbiological culture of mycobacteria is slow. Moreover, the sensitivities of culture and microscopy for acid-fast bacilli and nucleic acid detection by PCR are often compromised by difficulty in obtaining samples from the site of disease. Consequently, we need sensitive and rapid tests for easily obtained clinical samples, which can be deployed to assess patients exposed to TB, discriminate TB from other infectious, inflammatory or autoimmune diseases, and to identify subclinical TB in HIV-1 infected patients prior to commencing antiretroviral therapy. We discuss the evaluation of peripheral blood transcriptomics, proteomics and metabolomics to develop the next generation of rapid diagnostics for active TB. We catalogue the studies published to date seeking to discriminate active TB from healthy volunteers, patients with latent infection and those with other diseases. We identify the limitations of these studies and the barriers to their adoption in clinical practice. In so doing, we aim to develop a framework to guide our approach to discovery and development of diagnostic biomarkers for active TB.