Global Urine Metabolomics in Patients Treated with First-Line Tuberculosis Drugs and Identification of a Novel Metabolite of Ethambutol

Personalized Medicine Approach to Proteomics and Metabolomics of Cytochrome P450 Enzymes: A Narrative Review

Cytochrome P450 enzymes (CYPs) represent a diverse family of heme-thiolate proteins involved in the metabolism of a wide range of endogenous compounds and xenobiotics. In recent years, proteomics and metabolomics have been used to obtain a comprehensive insight into the role of CYPs in health and disease aspects. The objective of the present work is to better understand the status of proteomics and metabolomics in CYP research in optimizing therapeutics and patient safety from a personalized medicine approach. The literature used in this narrative review was procured by electronic search of PubMed, Medline, Embase, and Google Scholar databases. The following keywords were used in combination to identify related literature: "proteomics," "metabolomics," "cytochrome P450," "drug metabolism," "disease conditions," "proteome," "liquid chromatography-mass spectrometry," "integration," "metabolites," "pathological conditions." We reviewed studies that utilized proteomics and metabolomics approaches to explore the multifaceted roles of CYPs in identifying disease markers and determining the contribution of CYP enzymes in developing treatment strategies. The applications of various cutting-edge analytical techniques, including liquid chromatography-mass spectrometry, nuclear magnetic resonance, and bioinformatics analyses in CYP proteomics and metabolomics studies, have been highlighted. The identification of CYP enzymes through metabolomics and/or proteomics in various disease conditions provides key information in the diagnostic and therapeutic landscape. Leveraging both proteomics and metabolomics presents a powerful approach for an exhaustive exploration of the multifaceted roles played by CYP enzymes in personalized medicine. Proteomics and metabolomics have enabled researchers to unravel the complex connection between CYP enzymes and metabolic markers associated with specific diseases. As technology and methodologies evolve, an integrated approach promises to further elucidate the role of CYPs in human health and disease, potentially ushering in a new era of personalized medicine.

Cancer metabolites: promising biomarkers for cancer liquid biopsy

Cancer exerts a multitude of effects on metabolism, including the reprogramming of cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation of cancer cells and adaptation to the tumor microenvironment. There is a growing body of evidence suggesting that aberrant metabolites play pivotal roles in tumorigenesis and metastasis, and have the potential to serve as biomarkers for personalized cancer therapy. Importantly, high-throughput metabolomics detection techniques and machine learning approaches offer tremendous potential for clinical oncology by enabling the identification of cancer-specific metabolites. Emerging research indicates that circulating metabolites have great promise as noninvasive biomarkers for cancer detection. Therefore, this review summarizes reported abnormal cancer-related metabolites in the last decade and highlights the application of metabolomics in liquid biopsy, including detection specimens, technologies, methods, and challenges. The review provides insights into cancer metabolites as a promising tool for clinical applications.

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

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

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.

Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds

The recently emerged 2019 Novel Coronavirus (SARS-CoV-2) and associated COVID-19 disease cause serious or even fatal respiratory tract infection and yet no approved therapeutics or effective treatment is currently available to effectively combat the outbreak. This urgent situation is pressing the world to respond with the development of novel vaccine or a small molecule therapeutics for SARS-CoV-2. Along these efforts, the structure of SARS-CoV-2 main protease (Mpro) has been rapidly resolved and made publicly available to facilitate global efforts to develop novel drug candidates. Recently, our group has developed a novel deep learning platform - Deep Docking (DD) which provides fast prediction of docking scores of Glide (or any other docking program) and, hence, enables structure-based virtual screening of billions of purchasable molecules in a short time. In the current study we applied DD to all 1.3 billion compounds from ZINC15 library to identify top 1,000 potential ligands for SARS-CoV-2 Mpro protein. The compounds are made publicly available for further characterization and development by scientific community.

The Many Hosts of Mycobacteria 8 (MHM8): A conference report

Mycobacteria are important causes of disease in human and animal hosts. Diseases caused by mycobacteria include leprosy, tuberculosis (TB), nontuberculous mycobacteria (NTM) infections and Buruli Ulcer. To better understand and treat mycobacterial disease, clinicians, veterinarians and scientists use a range of discipline-specific approaches to conduct basic and applied research, including conducting epidemiological surveys, patient studies, wildlife sampling, animal models, genetic studies and computational simulations. To foster the exchange of knowledge and collaboration across disciplines, the Many Hosts of Mycobacteria (MHM) conference series brings together clinical, veterinary and basic scientists who are dedicated to advancing mycobacterial disease research. Started in 2007, the MHM series recently held its 8th conference at the Albert Einstein College of Medicine (Bronx, NY). Here, we review the diseases discussed at MHM8 and summarize the presentations on research advances in leprosy, NTM and Buruli Ulcer, human and animal TB, mycobacterial disease comorbidities, mycobacterial genetics and 'omics, and animal models. A mouse models workshop, which was held immediately after MHM8, is also summarized. In addition to being a resource for those who were unable to attend MHM8, we anticipate this review will provide a benchmark to gauge the progress of future research concerning mycobacteria and their many hosts.

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.

Mass spectrometry-based metabolomics for tuberculosis meningitis

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

First-line anti-tuberculosis drugs induce hepatotoxicity: A novel mechanism based on a urinary metabolomics platform

Tuberculosis (TB) has become a global public health and social threat. As clinical first-line drugs, rifampicin and isoniazid used in combination with pyrazinamide and ethambutol (the HRZE regimen) usually induce hepatotoxicity. However, the mechanisms underlying this phenomenon remain unclear, and studying the metabolic impact of co-treating TB patients with the HRZE regimen can provide new hepatotoxicity evidence. In this study, urine metabolites from TB patients were profiled using a high-resolution ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) platform. The tricarboxylic acid circulation, arginine and proline metabolism and purine metabolic pathways were found to be affected by anti-TB drugs. The levels of pyroglutamate, isocitrate, citrate, and xanthine were significantly decreased after the administration of HRZE. The above mentioned pathways were also different between drug-induced liver injury (DILI) and non-DILI patients. Urate and cis-4-octenedioic acid levels in the DILI group were significantly increased compared to those in the non-DILI group, while the cis-aconitate and hypoxanthine levels were significantly decreased. These results highlight that superoxide generation can aggravate the hepatotoxic effects of the HRZE regimen. In addition, our metabolomic approach had the ability to predict hepatotoxicity for clinical applications.

Vaccine and Therapeutic Options To Control Chikungunya Virus

Beginning in 2004, chikungunya virus (CHIKV) went from an endemic pathogen limited to Africa and Asia that caused periodic outbreaks to a global pathogen. Given that outbreaks caused by CHIKV have continued and expanded, serious consideration must be given to identifying potential options for vaccines and therapeutics. Currently, there are no licensed products in this realm, and control relies completely on the use of personal protective measures and integrated vector control, which are only minimally effective. Therefore, it is prudent to urgently examine further possibilities for control. Vaccines have been shown to be highly effective against vector-borne diseases. However, as CHIKV is known to rapidly spread and generate high attack rates, therapeutics would also be highly valuable. Several candidates are currently being developed; this review describes the multiple options under consideration for future development and assesses their relative advantages and disadvantages.

The role of metabolomics in tuberculosis treatment research

Despite the fact that tuberculosis (TB) is a curable disease, it still results in approximately 1.8 million deaths annually. Various inadequacies in the current TB treatment strategies are major contributors to this high disease prevalence, including the long duration of therapy, the severe side effects associated with TB drugs, treatment failure due to drug resistance, post-treatment disease relapse, and HIV co-infection. In this review, we describe how metabolomics has contributed toward better explaining/elucidating the mechanisms of drug action/metabolism, drug toxicity and microbial drug resistance, and how metabolite biomarkers may serve as prognostic indicators for predicting treatment outcome as well as for the development of new TB drugs. We also discuss possible future contributions that metabolomics can make toward more efficient, less toxic TB treatment strategies.

Novel Applications of Metabolomics in Personalized Medicine: A Mini-Review

Interindividual variability in drug responses and disease susceptibility is common in the clinic. Currently, personalized medicine is highly valued, the idea being to prescribe the right medicine to the right patient. Metabolomics has been increasingly applied in evaluating the therapeutic outcomes of clinical drugs by correlating the baseline metabolic profiles of patients with their responses, i.e., pharmacometabonomics, as well as prediction of disease susceptibility among population in advance, i.e., patient stratification. The accelerated advance in metabolomics technology pinpoints the huge potential of its application in personalized medicine. In current review, we discussed the novel applications of metabolomics with typical examples in evaluating drug therapy and patient stratification, and underlined the potential of metabolomics in personalized medicine in the future.