A key aspect of TB pathogenesis that maintains Mycobacterium tuberculosis in the human population is the ability to cause necrosis in pulmonary lesions. As co-evolution shaped M . tuberculosis (M.tb) and human responses, the complete TB disease profile and lesion manifestation are not fully reproduced by any animal model. However, animal models are absolutely critical to understand how infection with virulent M.tb generates outcomes necessary for the pathogen transmission and evolutionary success. In humans, a wide spectrum of TB outcomes has been recognized based on clinical and epidemiological data. In mice, there is clear genetic basis for susceptibility. Although the spectra of human and mouse TB do not completely overlap, comparison of human TB with mouse lesions across genetically diverse strains firmly establishes points of convergence. By embracing the genetic heterogeneity of the mouse population, we gain tremendous advantage in the quest for suitable in vivo models. Below, we review genetically defined mouse models that recapitulate a key element of M.tb pathogenesis—induction of necrotic TB lesions in the lungs—and discuss how these models may reflect TB stratification and pathogenesis in humans. The approach ensures that roles that mouse models play in basic and translational TB research will continue to increase allowing researchers to address fundamental questions of TB pathogenesis and bacterial physiology in vivo using this well-defined, reproducible, and cost-efficient system. Combination of the new generation mouse models with advanced imaging technologies will also allow rapid and inexpensive assessment of experimental vaccines and therapies prior to testing in larger animals and clinical trials.

Pulmonary tuberculosis (TB) is caused by Mycobacterium tuberculosis in susceptible humans. Here, we infected Diversity Outbred (DO) mice with ∼100 bacilli by aerosol to model responses in a highly heterogeneous population. Following infection, ‘supersusceptible’, ‘susceptible’ and ‘resistant’ phenotypes emerged. TB disease (reduced survival, weight loss, high bacterial load) correlated strongly with neutrophils, neutrophil chemokines, tumor necrosis factor (TNF) and cell death. By contrast, immune cytokines were weak correlates of disease. We next applied statistical and machine learning approaches to our dataset of cytokines and chemokines from lungs and blood. Six molecules from the lung: TNF, CXCL1, CXCL2, CXCL5, interferon-γ (IFN-γ), interleukin 12 (IL-12); and two molecules from blood – IL-2 and TNF – were identified as being important by applying both statistical and machine learning methods. Using molecular features to generate tree classifiers, CXCL1, CXCL2 and CXCL5 distinguished four classes (supersusceptible, susceptible, resistant and non-infected) from each other with approximately 77% accuracy using completely independent experimental data. By contrast, models based on other molecules were less accurate. Low to no IFN-γ, IL-12, IL-2 and IL-10 successfully discriminated non-infected mice from infected mice but failed to discriminate disease status amongst supersusceptible, susceptible and resistant M.-tuberculosis-infected DO mice. Additional analyses identified CXCL1 as a promising peripheral biomarker of disease and of CXCL1 production in the lungs. From these results, we conclude that: (1) DO mice respond variably to M. tuberculosis infection and will be useful to identify pathways involving necrosis and neutrophils; (2) data from DO mice is suited for machine learning methods to build, validate and test models with independent data based solely on molecular biomarkers; (3) low levels of immunological cytokines best indicate a lack of exposure to M. tuberculosis but cannot distinguish infection from disease.

During tuberculosis (TB), some Mycobacterium tuberculosis bacilli persist in the presence of an active immunity and antibiotics that are used to treat the disease. Herein, by using the Cornell model of TB persistence, we further explored our recent finding that suggested that M. tuberculosis can escape therapy by residing in the bone marrow (BM) mesenchymal stem cells. We initially showed that M. tuberculosis rapidly disseminates to the mouse BM after aerosol exposure and maintained a stable burden for at least 220 days. In contrast, in the lungs, the M. tuberculosis burden peaked at 28 days and subsequently declined approximately 10-fold. More important, treatment of the mice with the antibiotics rifampicin and isoniazid, as expected, resulted in effective clearance of M. tuberculosis from the lungs and spleen. In contrast, M. tuberculosis persisted, albeit at low numbers, in the BM of antibiotic-treated mice. Moreover, most viable M. tuberculosis was recovered from the bone marrow CD271(+)CD45(-)-enriched cell fraction, and only few viable bacteria could be isolated from the CD271(-)CD45(+) cell fraction. These results clearly show that BM mesenchymal stem cells provide an antibiotic-protective niche for M. tuberculosis and suggest that unraveling the mechanisms underlying this phenomenon will enhance our understanding of M. tuberculosis persistence in treated TB patients.

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.

Tuberculosis, the disease due to Mycobacterium tuberculosis, is an important cause of morbidity and mortality in the elderly. Use of mouse models may accelerate insight into the disease and tests of therapies since mice age thirty times faster than humans. However, the majority of TB research relies on inbred mouse strains, and these results might not extrapolate well to the genetically diverse human population. We report here the first tests of M. tuberculosis infection in genetically heterogeneous aging mice, testing if old mice benefit from rapamycin. We find that genetically diverse aging mice are much more susceptible than young mice to M. tuberculosis, as are aging human beings. We also find that rapamycin boosts immune responses during primary infection but fails to increase survival. Genetically diverse mouse models provide a valuable resource to study how age influences responses and susceptibility to pathogens and to test interventions. Additionally, surrogate markers such as immune measures may not predict whether interventions improve survival.

Tuberculosis (TB), primarily affecting the lungs, is caused by the bacterium Mycobacterium tuberculosis and poses a significant health risk. Detecting acid-fast bacilli (AFB) in stained samples is critical for TB diagnosis. Whole Slide (WS) Imaging allows for digitally examining these stained samples. However, current deep-learning approaches to analyzing large-sized whole slide images (WSIs) often employ patch-wise analysis, potentially missing the complex spatial patterns observed in the granuloma essential for accurate TB classification. To address this limitation, we propose an approach that models cell characteristics and interactions as a graph, capturing both cell-level information and the overall tissue micro-architecture. This method differs from the strategies in related cell graph-based works that rely on edge thresholds based on sparsity/density in cell graph construction, emphasizing a biologically informed threshold determination instead. We introduce a cell graph-based jumping knowledge neural network (CG-JKNN) that operates on the cell graphs where the edge thresholds are selected based on the length of the mycobacteria’s cords and the activated macrophage nucleus’s size to reflect the actual biological interactions observed in the tissue. The primary process involves training a Convolutional Neural Network (CNN) to segment AFBs and macrophage nuclei, followed by converting large (42831*41159 pixels) lung histology images into cell graphs where an activated macrophage nucleus/AFB represents each node within the graph and their interactions are denoted as edges. To enhance the interpretability of our model, we employ Integrated Gradients and Shapely Additive Explanations (SHAP). Our analysis incorporated a combination of 33 graph metrics and 20 cell morphology features. In terms of traditional machine learning models, Extreme Gradient Boosting (XGBoost) was the best performer, achieving an F1 score of 0.9813 and an Area under the Precision-Recall Curve (AUPRC) of 0.9848 on the test set. Among graph-based models, our CG-JKNN was the top performer, attaining an F1 score of 0.9549 and an AUPRC of 0.9846 on the held-out test set. The integration of graph-based and morphological features proved highly effective, with CG-JKNN and XGBoost showing promising results in classifying instances into AFB and activated macrophage nucleus. The features identified as significant by our models closely align with the criteria used by pathologists in practice, highlighting the clinical applicability of our approach. Future work will explore knowledge distillation techniques and graph-level classification into distinct TB progression categories.