The role of metabolomics in tuberculosis treatment research

Comprehensive lipid profiles investigation reveals host metabolic and immune alterations during anti-tuberculosis treatment: Implications for therapeutic monitoring

In this study, we investigated the lipidome of tuberculosis patients during standard chemotherapy to discover biosignatures that could aid therapeutic monitoring. UPLC-QToF MS was used to analyze 82 baseline and treatment plasma samples of patients with pulmonary tuberculosis. Subsequently, a data-driven and knowledge-based workflow, including robust annotation, statistical analysis, and functional analysis, was applied to assess lipid profiles during treatment. Overall, the lipids species from 17 lipid subclasses were significantly altered by anti-tuberculosis chemotherapy. Cholesterol ester (CE), monoacylglycerols, and phosphatidylcholine (PC) were upregulated, whereas triacylglycerols, sphingomyelin, and ether-linked phosphatidylethanolamines (PE O-) were downregulated. Notably, PCs demonstrated a clear upward expression pattern during tuberculosis treatment. Several lipid species were identified as potential biomarkers for therapeutic monitoring, such as PC(42:6), PE(O-40:5), CE(24:6), and dihexosylceramide Hex2Cer(34:2;2 O). Functional and lipid gene enrichment analysis revealed alterations in pathways related to lipid metabolism and host immune responses. In conclusion, this study provides a foundation for the use of lipids as biomarkers for clinical management of tuberculosis.

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.

Diagnostic biomarkers for active tuberculosis: progress and challenges

Tuberculosis (TB) is a leading cause of morbidity and mortality from a single infectious agent, despite being preventable and curable. Early and accurate diagnosis of active TB is critical to both enhance patient care, improve patient outcomes, and break Mycobacterium tuberculosis (Mtb) transmission cycles. In 2020 an estimated 9.9 million people fell ill from Mtb, but only a little over half (5.8 million) received an active TB diagnosis and treatment. The World Health Organization has proposed target product profiles for biomarker- or biosignature-based diagnostics using point-of-care tests from easily accessible specimens such as urine or blood. Here we review and summarize progress made in the development of pathogen- and host-based biomarkers for active TB diagnosis. We describe several unique patient populations that have posed challenges to development of a universal diagnostic TB biomarker, such as people living with HIV, extrapulmonary TB, and children. We also review additional limitations to widespread validation and utilization of published biomarkers. We conclude with proposed solutions to enhance TB diagnostic biomarker validation and uptake.

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.

Metabolomics and microbiomes for discovering biomarkers of antituberculosis drugs-induced hepatotoxicity

Anti-tuberculosis (TB) drug-induced hepatotoxicity (ATDH) was related to metabolic and microbial dysregulation, but only limited data was available about the metabolomes and microbiomes in ATDH. We aimed at detecting the metabolic and microbial signatures of ATDH. Urine samples were obtained from ATDH (n = 33) and non-ATDH control (n = 41) and analyzed by untargeted gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). Metabolites were analyzed by orthogonal projections to latent structures-discriminate analysis (OPLS-DA) and pathway analysis. Eight ATDH and eight non-ATDH control were evaluated by sequencing of 16S rRNA genes, and the Clusters of Orthologous Groups of proteins (COG) database were used for function prediction. Linear discriminant analysis (LDA) effect size (LEfSe) was applied to detect the differential microbiotas between the two groups. The differential microbiotas were further validated by correlation analysis with differential metabolites. OPLS-DA analysis suggested 11 metabolites that differed ATDH from non-ATDH control. Pathway analysis demonstrated that metabolism of arginine and proline, metabolism of d-arginine and d-ornithine, glutathione glycine metabolism, galactose metabolism, niacin and nicotinamide metabolism, and glycine, serine and threonine metabolism were related to ATDH. LEfSe suggested significant differences in microbiotas between the two groups. The o_ Bacteroidales, f_Prevotellaceae, and g_Prevotella were significantly increased in ATDH. In contrast, the f_Chitinophagaceae, c_Gammaproteobacteria, and p_Proteobacteria were significantly increased in non-ATDH group. The biological functions of the sequenced microbiota in this study were related to amino acid transport and metabolism and defense mechanisms. Finally, we detected strong association between urine metabolites and specific urine bacteria (|r| > 0.8). d-glucoheptose showed a strong relationship to Symbiobacterium. Creatine (r = -0.901; P < 0.001) and diglycerol were strongly associated with Alishewanella. Metabolomics and microbiomes indicate ATDH characterized by metabolic and microbial profiles may differ from non-ATDH control.

Elucidating the Antimycobacterial Mechanism of Action of Ciprofloxacin Using Metabolomics

In the interest of developing more effective and safer anti-tuberculosis drugs, we used a GCxGC-TOF-MS metabolomics research approach to investigate and compare the metabolic profiles of Mtb in the presence and absence of ciprofloxacin. The metabolites that best describe the differences between the compared groups were identified as markers characterizing the changes induced by ciprofloxacin. Malic acid was ranked as the most significantly altered metabolite marker induced by ciprofloxacin, indicative of an inhibition of the tricarboxylic acid (TCA) and glyoxylate cycle of Mtb. The altered fatty acid, myo-inositol, and triacylglycerol metabolism seen in this group supports previous observations of ciprofloxacin action on the Mtb cell wall. Furthermore, the altered pentose phosphate intermediates, glycerol metabolism markers, glucose accumulation, as well as the reduction in the glucogenic amino acids specifically, indicate a flux toward DNA (as well as cell wall) repair, also supporting previous findings of DNA damage caused by ciprofloxacin. This study further provides insights useful for designing network whole-system strategies for the identification of possible modes of action of various drugs and possibly adaptations by Mtb resulting in resistance.

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.

Assessing capreomycin resistance on tlyA deficient and point mutation (G695A) Mycobacterium tuberculosis strains using multi-omics analysis

Capreomycin (CAP), a cyclic peptide antibiotic, is considered to be an ideal second-line drug for tuberculosis (TB). However, in the past few years, the emergence of more CAP-resistant (CAP^r) TB patients has limited its use. Although it has been reported that CAP resistance to Mycobacterium tuberculosis (Mtb) is associated with rrs or tlyA mutation, the exact mechanism of CAP^r Mtb strains, especially the mechanism associated with tlyA deficient or mutation, is not fully understood. Herein, we utilized a multi-omics (genome, proteome, and metabolome) approach to assess CAP resistance on tlyA deficient CAP^r Mtb strains (CAP^r1) and tlyA point mutation CAP^r Mtb strains (CAP^r2) that we established for the first time in vitro to investigate the CAP-resistant mechanism. Our results showed that the CAP^r1 strains (> 40 μg/ml) was more resistant to CAP than the CAP^r2 strains (G695A, 10 μg/ml). Furthermore, multi-omics analysis indicated that the CAP^r1 strains exhibited greater drug tolerance than the CAP^r2 strains may be associated with the weakening of S-adenosyl-L-methionine-dependent methyltransferase (AdoMet-MT) activity and abnormal membrane lipid metabolism such as suppression of fatty acid metabolism, promotion of glycolipid phospholipid and glycerolipid metabolism. As a result, these studies reveal a new mechanism for CAP resistance to tlyA deficient or mutation Mtb strains, and may be helpful in developing new therapeutic approaches to prevent Mtb resistance to CAP.

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.

T-Probe: An Integrated Microscale Device for Online In Situ Single Cell Analysis and Metabolic Profiling Using Mass Spectrometry

The exploration of single cells reveals cell heterogeneity and biological principle of cellular metabolism. Although a number of mass spectrometry (MS) based single cell MS (SCMS) techniques have been dedicatedly developed with high efficiency and sensitivity, limitations still exist. In this work, we introduced a microscale multifunctional device, the T-probe, which integrates cellular contents extraction and immediate ionization, to implement online in situ SCMS analysis at ambient conditions with minimal sample preparation. With high sensitivity and reproducibility, the T-probe was employed for MS analysis of single HeLa cells under control and anticancer drug treatment conditions. Intracellular species and xenobiotic metabolites were detected, and changes of cellular metabolic profiles induced by drug treatment were measured. Combining SCMS experiments with statistical data analyses, including Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) and two-sample t-test, we provided biological insights into cellular metabolic response to drug treatment. Online MS/MS analysis was conducted at single cell level to identify species of interest, including endogenous metabolites and the drug compound. Using the T-probe SCMS technique combined with comprehensive data analyses, we provide an approach to understanding cellular metabolism and evaluate chemotherapies at the single cell level.