Metabolomics: Applications and Promise in Mycobacterial Disease

Abstract

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.

Reply: Metabolomics and Mycobacterial Disease: Don’t Forget the Bioinformatics

“…We agree with Ryerson and colleagues that the classification of bronchiolitis is problematic (1,2). Terms such as "bronchiolitis obliterans" and "obliterative bronchiolitis" have been used to describe both proliferative bronchiolitis and constrictive bronchiolitis (5), thus grouping under one umbrella entities that have distinctive radiologic findings, histopathologic features, and therapeutic responses.…”

“…The greater the complexity of the system, resulting from increasing the number of compounds studied, the more difficult it is to obtain meaningful data (1). Collins and colleagues make insightful comments on the role of bioinformatics in metabolomics, for which we thank them.…”

Individualizing management of extensively drug-resistant tuberculosis: diagnostics, treatment, and biomarkers

“…Related to this aim, it is worth noting that 'successfully treated' TB patients are one of the primary groups at risk of developing active TB again, with a risk of around 2000 per 100 000 person years [94]. Disease-associated host biomarkers, useful for treatment monitoring based on transcriptomic [95], metabolic [96], or immunological markers [97] could thus also have a role in screening high-risk groups including patients post treatment or latently infected individuals to monitor increased chance of break down to active disease.…”

Metabolomics and Mycobacterial Disease: Don’t Forget the Bioinformatics

“…We read with interest the review by Mirsaeidi and colleagues on the applications of metabolomics in the diagnosis and management of mycobacterial diseases (1). Although we agree with the authors' conclusions that "the great sensitivity and enormous data produced" present a significant analytical challenge, a more complete discussion of recent advances in ultra-high-resolution mass spectrometry and bioinformatics is essential to fully understand potential applications for these technologies.…”

“…Although we look to bioinformatics to unlock the potential of metabolomics, that field is currently technically limited and does not provide strong enough clues to differentiate informative variation from noninformative variation. The greater the complexity of the system, resulting from increasing the number of compounds studied, the more difficult it is to obtain meaningful data (1). Increasing the number of identified analytes (to the thousands) with high-resolution metabolomics requires a large sample size to avoid the bias of data mining and decreased statistical power (2).…”