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.

Introduction

Tuberculosis meningitis (TBM) is an infectious disease of the nervous system caused by Mycobacterium tuberculosis (Mtb). TBM accounts for nearly 5% of all cases of extra-pulmonary tuberculosis [1], and it is associated with the highest rates of morbidity and mortality among all forms of tuberculosis [2]. Outcomes of patients often include substantial neurological sequelae or death [3, 4]. Early diagnosis and initiation of appropriate treatment at an optimal dose can greatly improve the clinical outcomes of TBM [5]; however, it is difficult to make a definitive diagnosis of TBM, because of its variable and nonspecific clinical presentation, and the low sensitivity of current diagnostic laboratory tests. Hence, there is an urgent need for diagnostic variables that can accurately identify TBM. Metabolomics, an emerging science of the omics era, provides a new tool for the identification of novel diagnostic markers of TBM. This methodology is used to identify and quantify low-molecular-weight metabolites, and produces metabolite profiles which reflect the state of a given biological system [6]. Metabolites can change rapidly in response to even slight alterations in environmental stimuli [7], and key metabolites and related metabolic pathways that correlate with specific human diseases have potential for use as indicators or biomarkers. Recently, metabolomics has been applied in the identification of biomarkers for disease processes including Alzheimer’s disease [8], Parkinson’s disease [9], and multiple sclerosis [10]. Metabolomics is an advanced analytical tool that relies on the specific technique used for its implementation. A high throughput and sensitive analytical platform is needed to identify and quantify metabolites, and the primary approaches that have been applied are nuclear magnetic resonance (NMR) spectrometry and mass spectrometry (MS) [11-13] (Table 1). Although NMR is highly quantitative and reproducible, its low sensitivity has greatly limited its application in metabolomics. MS is commonly integrated with various chromatography systems, providing superior sensitivity and resolution compared with NMR [14]. MS can profile more metabolites in one analytical run; therefore, relative to NMR, MS is the dominant metabolomics platform. Metabolomics has been widely used in tuberculosis research, and MS has become the technology of choice for this approach. To date, the metabolomics approach has been applied successfully to the investigation of TBM diagnosis; however, the number of studies remains limited. Although the majority of such investigations have used NMR, rather than MS, some have begun to take an MS-based approach to obtain more specific metabolic markers. The high sensitivity and resolution of MS-based metabolomics will lead to its wide application in TBM research; for example, for identification of indicators or biomarkers of TBM. The objectives of this review were to introduce the uses and limitations of current MS-based metabolomic approaches for TBM research, describe recent advances in MS-based metabolomics for Mtb-related research, and discuss the prospects for the application of MS-based metabolomics for the development of TBM diagnostic approaches.