Predicting tuberculosis treatment outcome using metabolomics

Bird's Eye View on Mycobacterium tuberculosis-HIV Coinfection: Understanding the Molecular Synergism, Challenges, and New Approaches to Therapeutics

Tuberculosis (TB), caused by Mycobacterium tuberculosis (M.tb), is the most common secondary infection in the Human Immunodeficiency Virus (HIV) infected population, accounting for more than one-fourth of deaths in people living with HIV (PLWH). Reciprocally, HIV infection increases the susceptibility to primary TB or reactivation of latent TB by several folds. The synergistic interactions between M.tb and HIV not only potentiate their deleterious impact but also complicate the clinical management of both the diseases. M.tb-HIV coinfected patients have a high risk of failure of accurate diagnosis, treatment inefficiency for both TB and HIV, concurrent nontuberculous mycobacterial infections, other comorbidities such as diabetes mellitus, severe cytotoxicity due to drug overburden, and immune reconstitution inflammatory syndrome (IRIS). The need of the hour is to understand M.tb-HIV coinfection biology and their collective impact on the host immunocompetence and to think of out-of-the-box treatment perspectives, including host-directed therapy under the rising view of homeostatic medicines. This review aims to highlight the molecular players, both from the pathogens and host, that facilitate the synergistic interactions and host-associated proteins/enzymes regulating immunometabolism, underlining potential targets for designing and screening chemical inhibitors to reduce the burden of both pathogens concomitantly during M.tb-HIV coinfection. To appreciate the necessity of revisiting therapeutic approaches and research priorities, we provide a glimpse of anti-TB and antiretroviral drug-drug interactions, project the gaps in our understanding of coinfection biology, and also enlist some key research initiatives that will help us deal with the synergistic epidemic of M.tb-HIV coinfection.

Dynamic changes of respiratory microbiota associated with treatment outcome in drug-sensitive and drug-resistant pulmonary tuberculosis

BACKGROUND

Respiratory microbiota is closely related to tuberculosis (TB) initiation and progression. However, the dynamic changes of respiratory microbiota during treatment and its association with TB progression remains unclear.

METHODS

A total of 16 healthy individuals and 16 TB patients (10 drug-sensitive TB (DS-TB) and 6 drug-resistant TB (DR-TB)) were recruited. Sputum samples were collected at baseline for all anticipants and after anti-TB treatment at Month-6 for TB patients. High throughput 16 S RNA sequencing was used to characterize the respiratory microbiota composition.

RESULTS

Compared to the healthy individuals, TB patients exhibited lower respiratory microbiota diversity (p < 0.05). This disruption was alleviated after anti-TB treatment, especially for DS-TB patients. Parvimonas spp. numbers significantly increased after six months of anti-TB treatment in both DS-TB and DR-TB patients (p < 0.05). Rothia spp. increase during treatment was associated with longer sputum-culture conversion time and worse pulmonary lesion absorption (p < 0.05). Besides, Moraxella spp. prevalence was associated with longer sputum-culture conversion time, while Gemella spp. increase was associated with worsening resolving of pulmonary lesions (p < 0.05).

CONCLUSION

Dynamic changes of respiratory microbiota during anti-TB treatment is closely related to TB progression. The involvement of critical microorganisms, such as Parvimonas spp., Rothia spp., Moraxella, and Gemella spp., appears to be associated with pulmonary inflammatory conditions, particularly among DR-TB. These microorganisms could potentially serve as biomarkers or even as targets for therapeutic intervention to enhance the prognosis of tuberculosis patients.

Metabolic insights into HIV/TB co-infection: an untargeted urinary metabolomics approach

INTRODUCTION

Amid the global health crisis, HIV/TB co-infection presents significant challenges, amplifying the burden on patients and healthcare systems alike. Metabolomics offers an innovative window into the metabolic disruptions caused by co-infection, potentially improving diagnosis and treatment monitoring.

AIM

This study uses untargeted metabolomics to investigate the urinary metabolic signature of HIV/TB co-infection, enhancing understanding of the metabolic interplay between these infections.

METHODS

Urine samples from South African adults, categorised into four groups - healthy controls, TB-positive, HIV-positive, and HIV/TB co-infected - were analysed using GCxGC-TOFMS. Metabolites showing significant differences among groups were identified through Kruskal-Wallis and Wilcoxon rank sum tests.

RESULTS

Various metabolites (n = 23) were modulated across the spectrum of health and disease states represented in the cohorts. The metabolomic profiles reflect a pronounced disruption in biochemical pathways involved in energy production, amino acid metabolism, gut microbiome, and the immune response, suggesting a bidirectional exacerbation between HIV and TB. While both diseases independently perturb the host's metabolism, their co-infection leads to a unique metabolic phenotype, indicative of an intricate interplay rather than a simple additive effect.

CONCLUSION

Metabolic profiling revealed a unique metabolic landscape shaped by HIV/TB co-infection. The findings highlight the potential of urinary differential metabolites for co-infection, offering a non-invasive tool for enhancing diagnostic precision and tailoring therapeutic interventions. Future research should focus on expanding sample sizes and integrating longitudinal analyses to build upon these foundational insights, paving the way for metabolomic applications in combating these concurrent pandemics.

Development and validation of a structured questionnaire for assessing risk factors of medication non-adherence among pulmonary tuberculosis patients in Indonesia

Background: Medication non-adherence is a significant concern in tuberculosis (TB) treatment, requiring a precise understanding of the associated risk factors. However, there is a lack of appropriate means to assess the risk factors among TB patients in Indonesia, leading to the development and validation of a structured questionnaire for this purpose. Method: This study unfolded in two distinct phases, namely, the first included questionnaire construction through framework development, item generation, item screening, and pretesting (in 50 patients). The second comprised questionnaire validation with 346 participants using confirmatory factor analysis (CFA) and structural equation modeling-partial least squares (SEM-PLS). Additionally, reliability testing was conducted using Cronbach's alpha and composite reliability statistical techniques. Results: In the development phase, 168 items were defined, consisting of sociodemographic characteristics (8 items) and risk factors for medication non-adherence (160 items). Expert evaluation reduced the number of items to 60, which decreased to 22 after performing a pilot study. Subsequent SEM-PLS modeling resulted in the identification of 14 valid items, representing five major risk factors, namely, socioeconomics (4 items), healthcare team (4 items), condition (3 items), therapy (2 items), and patient (1 item). Only condition-related factors were found to influence non-adherence, and all constructs showed good reliability based on Cronbach's alpha (>0.6) and composite reliability (0.7) values. Conclusion: The final 22 items that emerged from this rigorous process indicated a valid and robust questionnaire for assessing risk factors of medication non-adherence among pulmonary tuberculosis patients in Indonesia. The developed questionnaire was positioned to be a valuable tool for healthcare professionals, policymakers, and scientists in creating patient-centered strategies and interventions to address non-adherence.

Comprehensive Two-Dimensional Gas Chromatography as a Bioanalytical Platform for Drug Discovery and Analysis

Over the last decades, comprehensive two-dimensional gas chromatography (GC×GC) has emerged as a significant separation tool for high-resolution analysis of disease-associated metabolites and pharmaceutically relevant molecules. This review highlights recent advances of GC×GC with different detection modalities for drug discovery and analysis, which ideally improve the screening and identification of disease biomarkers, as well as monitoring of therapeutic responses to treatment in complex biological matrixes. Selected recent GC×GC applications that focus on such biomarkers and metabolite profiling of the effects of drug administration are covered. In particular, the technical overview of recent GC×GC implementation with hyphenation to the key mass spectrometry (MS) technologies that provide the benefit of enhanced separation dimension analysis with MS domain differentiation is discussed. We conclude by highlighting the challenges in GC×GC for drug discovery and development with perspectives on future trends.

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.

Urine biomarkers of pulmonary tuberculosis

INTRODUCTION

Sputum-based tuberculosis diagnosis does not address the needs of certain categories of patients. Active development of a noninvasive urine-based diagnosis could provide an alternative approach. We reviewed publications covering more than 30 urine biomarkers proposed as significant for TB diagnosis. Analytical approaches were heterogeneous in design and methods; few studies on diagnostic outcome prediction described a formal specificity and sensitivity analysis.

AREAS COVERED

This review describes studies of non-sputum diagnostic approaches of pulmonary TB based on urine using specific TB biomarkers. The search was performed until December 2021, using terms [Tuberculosis] + [urine] + [biomarkers] in PubMed and Cochrane databases. Publications concerning LAM urine diagnostics were excluded as they have been described elsewhere.

EXPERT OPINION

Microbiological culture of sputum is considered to be the 'gold standard' diagnostic for pulmonary TB but the methodology is slow due to the slow growth of the TB bacteria. Urine provides a large volume of sample. Investigators have evaluated urine for either TB pathogen biomarkers or host biomarkers with some success as the review demonstrates. Detection sensitivity remains a significant problem. In future, combination of host and pathogen biomarkers could increase the sensitivity and specificity of TB diagnosis.

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.

Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults

OBJECTIVE

To systematically review and critically evaluate prediction models developed to predict tuberculosis (TB) treatment outcomes among adults with pulmonary TB.

DESIGN

Systematic review.

DATA SOURCES

PubMed, Embase, Web of Science and Google Scholar were searched for studies published from 1 January 1995 to 9 January 2020.

STUDY SELECTION AND DATA EXTRACTION

Studies that developed a model to predict pulmonary TB treatment outcomes were included. Study screening, data extraction and quality assessment were conducted independently by two reviewers. Study quality was evaluated using the Prediction model Risk Of Bias Assessment Tool. Data were synthesised with narrative review and in tables and figures.

RESULTS

14 739 articles were identified, 536 underwent full-text review and 33 studies presenting 37 prediction models were included. Model outcomes included death (n=16, 43%), treatment failure (n=6, 16%), default (n=6, 16%) or a composite outcome (n=9, 25%). Most models (n=30, 81%) measured discrimination (median c-statistic=0.75; IQR: 0.68-0.84), and 17 (46%) reported calibration, often the Hosmer-Lemeshow test (n=13). Nineteen (51%) models were internally validated, and six (16%) were externally validated. Eighteen (54%) studies mentioned missing data, and of those, half (n=9) used complete case analysis. The most common predictors included age, sex, extrapulmonary TB, body mass index, chest X-ray results, previous TB and HIV. Risk of bias varied across studies, but all studies had high risk of bias in their analysis.

CONCLUSIONS

TB outcome prediction models are heterogeneous with disparate outcome definitions, predictors and methodology. We do not recommend applying any in clinical settings without external validation, and encourage future researchers adhere to guidelines for developing and reporting of prediction models.

TRIAL REGISTRATION

The study was registered on the international prospective register of systematic reviews PROSPERO (CRD42020155782).

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.

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.

Potential anti-TB investigational compounds and drugs with repurposing potential in TB therapy: a conspectus

The latest WHO report estimates about 1.6 million global deaths annually from TB, which is further exacerbated by drug-resistant (DR) TB and comorbidities with diabetes and HIV. Exiguous dosing, incomplete treatment course, and the ability of the tuberculosis bacilli to tolerate and survive current first-line and second-line anti-TB drugs, in either their latent state or active state, has resulted in an increased prevalence of multidrug-resistant (MDR), extensively drug-resistant (XDR), and totally drug-resistant TB (TDR-TB). Although a better understanding of the TB microanatomy, genome, transcriptome, proteome, and metabolome, has resulted in the discovery of a few novel promising anti-TB drug targets and diagnostic biomarkers of late, no new anti-TB drug candidates have been approved for routine therapy in over 50 years, with only bedaquiline, delamanid, and pretomanid recently receiving tentative regulatory approval. Considering this, alternative approaches for identifying possible new anti-TB drug candidates, for effectively eradicating both replicating and non-replicating Mycobacterium tuberculosis, are still urgently required. Subsequently, several antibiotic and non-antibiotic drugs with known treatment indications (TB targeted and non-TB targeted) are now being repurposed and/or derivatized as novel antibiotics for possible use in TB therapy. Insights gathered here reveal that more studies focused on drug-drug interactions between licensed and potential lead anti-TB drug candidates need to be prioritized. This write-up encapsulates the most recent findings regarding investigational compounds with promising anti-TB potential and drugs with repurposing potential in TB therapy.

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.

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.

Metabolome analysis for investigating host-gut microbiota interactions

Dysbiosis of the gut microbiome is associated with host health conditions. Many diseases have shown to have correlations with imbalanced microbiota, including obesity, inflammatory bowel disease, cancer, and even neurodegeneration disorders. Metabolomics studies targeting small molecule metabolites that impact the host metabolome and their biochemical functions have shown promise for studying host-gut microbiota interactions. Metabolome analysis determines the metabolites being discussed for their biological implications in host-gut microbiota interactions. To facilitate understanding the critical aspects of metabolome analysis, this article reviewed (1) the sample types used in host-gut microbiome studies; (2) mass spectrometry (MS)-based analytical methods and (3) useful tools for MS-based data processing/analysis. In addition to the most frequently used sample type, feces, we also discussed others biosamples, such as urine, plasma/serum, saliva, cerebrospinal fluid, exhaled breaths, and tissues, to better understand gut metabolite systemic effects on the whole organism. Gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and capillary electrophoresis-mass spectrometry (CE-MS), three powerful tools that can be utilized to study host-gut microbiota interactions, are included with examples of their applications. After obtaining big data from MS-based instruments, noise removal, peak detection, missing value imputation, and data analysis are all important steps for acquiring valid results in host-gut microbiome research. The information provided in this review will help new researchers aiming to join this field by providing a global view of the analytical aspects involved in gut microbiota-related metabolomics studies.

Perspectives for personalized therapy for patients with multidrug-resistant tuberculosis

According to the World Health Organization (WHO), tuberculosis is the leading cause of death attributed to a single microbial pathogen worldwide. In addition to the large number of patients affected by tuberculosis, the emergence of Mycobacterium tuberculosis drug-resistance is complicating tuberculosis control in many high-burden countries. During the past 5 years, the global number of patients identified with multidrug-resistant tuberculosis (MDR-TB), defined as bacillary resistance at least against rifampicin and isoniazid, the two most active drugs in a treatment regimen, has increased by more than 20% annually. Today we experience a historical peak in the number of patients affected by MDR-TB. The management of MDR-TB is characterized by delayed diagnosis, uncertainty of the extent of bacillary drug-resistance, imprecise standardized drug regimens and dosages, very long duration of therapy and high frequency of adverse events which all translate into a poor prognosis for many of the affected patients. Major scientific and technological advances in recent years provide new perspectives through treatment regimens tailor-made to individual needs. Where available, such personalized treatment has major implications on the treatment outcomes of patients with MDR-TB. The challenge now is to bring these adances to those patients that need them most.