More humans have died of tuberculosis (TB) than any other infectious disease and millions still die each year. Experts advocate for blood-based, serum protein biomarkers to help diagnose TB, which afflicts millions of people in high-burden countries. However, the protein biomarker pipeline is small. Here, we used the Diversity Outbred (DO) mouse population to address this gap, identifying five protein biomarker candidates. One protein biomarker, serum CXCL1, met the World Health Organization’s Targeted Product Profile for a triage test to diagnose active TB from latent M.tb infection (LTBI), non-TB lung disease, and normal sera in HIV-negative, adults from South Africa and Vietnam. To find the biomarker candidates, we quantified seven immune cytokines and four inflammatory proteins corresponding to highly expressed genes unique to progressor DO mice. Next, we applied statistical and machine learning methods to the data, i.e., 11 proteins in lungs from 453 infected and 29 non-infected mice. After searching all combinations of five algorithms and 239 protein subsets, validating, and testing the findings on independent data, two combinations accurately diagnosed progressor DO mice: Logistic Regression using MMP8; and Gradient Tree Boosting using a panel of 4: CXCL1, CXCL2, TNF, IL-10. Of those five protein biomarker candidates, two (MMP8 and CXCL1) were crucial for classifying DO mice; were above the limit of detection in most human serum samples; and had not been widely assessed for diagnostic performance in humans before. In patient sera, CXCL1 exceeded the triage diagnostic test criteria (>90% sensitivity; >70% specificity), while MMP8 did not. Using Area Under the Curve analyses, CXCL1 averaged 94.5% sensitivity and 88.8% specificity for active pulmonary TB (ATB) vs LTBI; 90.9% sensitivity and 71.4% specificity for ATB vs non-TB; and 100.0% sensitivity and 98.4% specificity for ATB vs normal sera. Our findings overall show that the DO mouse population can discover diagnostic-quality, serum protein biomarkers of human TB.
BackgroundBiomarker discovery for pulmonary tuberculosis (TB) may be accelerated by modeling human genotypic diversity and phenotypic responses to Mycobacterium tuberculosis (Mtb). To meet these objectives, we use the Diversity Outbred (DO) mouse population and apply novel classifiers to identify informative biomarkers from multidimensional data sets.MethodTo identify biomarkers, we infected DO mice with aerosolized Mtb confirmed a human-like spectrum of phenotypes, examined gene expression, and inflammatory and immune mediators in the lungs. We measured 11 proteins in 453 Mtb-infected and 29 non-infected mice. We have searched all combinations of six classification algorithms and 239 biomarker subsets and independently validated the selected classifiers. Finally, we selected two mouse lung biomarkers to test as candidate biomarkers of active TB, measuring their diagnostic performance in human sera acquired from the Foundation for Innovative New Diagnostics.FindingsDO mice discovered two translationally relevant biomarkers, CXCL1 and MMP8 that accurately diagnosed active TB in humans with > 90% sensitivity and specificity compared to controls. We identified them through the two classifiers that accurately diagnosed supersusceptible DO mice with early-onset TB: Logistic Regression using MMP8 as a single biomarker, and Gradient Tree Boosting using a panel of 4 biomarkers (CXCL1, CXCL2, TNF, IL-10).InterpretationThis work confirms that the DO population models human responses and can accelerate discovery of translationally relevant TB biomarkers.FundingSupport was provided by NIH R21 AI115038; NIH R01 HL145411; NIH UL1-TR001430; and the American Lung Association Biomedical Research Grant RG-349504.
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