Smell-led metabolomics analyses for rapid, early diagnosis and stratification of tuberculosis infection

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One million children (<14 yrs) contracted tuberculosis (TB) in 2017[1]. While the global efforts to eliminate the spread of TB by 2035 are focused on prevention and treatment of TB, it is important to diagnose TB infections in children, in very early stages in order to decrease the risk of spread of the disease. Current diagnostic tests for TB in children include bacteriological tests (culture, molecular tests, microscopy) on sputum or gastric aspirates. While sputum is non-invasively available, children often have difficulties in producing sufficient quantity of sputum. Delay in diagnosis will increase the risk of spread of disease to contacts, although in many high-prevalence areas in the developing world, access to equipment and expertise for culture is not even available. Thus, a non-invasive, rapid and affordable test that can accurately detect TB early in its onset is required.In an ongoing pilot study with my collaborators Prof Perdita Barran (The University of Manchester) and Apopo, a non-profit in Tanzania, Joy Milne, a super smeller [2] has described a unique smell associated with TB in sputum. Joy has successfully demonstrated that smell of biofluids can be linked to one's disease state [3]. This project will investigate sputum along with breath and sebum to identify early diagnostic biomarkers of TB on skin. Sebum is an unexplored bio-fluid for disease diagnostics. We have hypothesised sebum as a sink to odourous compounds that can be a hallmark of many diseases such as Parkinson's disease [4] and also TB. These odorous molecules obtained non-invasively will be ideal candidates for early diagnostic test since we have demonstrated that body odour changes before presentation of clinical symptoms in disease such as Parkinson's [5]. A longitudinal study will be performed on samples from adults with suspected TB, confirmed TB and healthy participants. Using chromatography hyphenated to high resolution mass spectrometry, metabolomics and volatilomics profiles in sputum, breath and sebum will be generated along with qualitative analysis of skin microbiome. Advanced chemometrics and machine learning approaches will be employed to build data driven models for classification and prediction of TB leading to its early diagnosis. Integration of microbiome and metabolome data along with patient information, metadata and other clinical observations will be performed for stratification of TB.