Background: Identifying which individuals will develop tuberculosis (TB) remains an unresolved problem due to few animal models and computational approaches that effectively address its heterogeneity. To meet these shortcomings, we show that Diversity Outbred (DO) mice reflect human-like genetic diversity and develop human-like lung granulomas when infected with Mycobacterium tuberculosis (M.tb). Methods: Following M.tb infection, a "supersusceptible" phenotype develops in approximately one-third of DO mice characterized by rapid morbidity and mortality within 8 weeks. These supersusceptible DO mice develop lung granulomas patterns akin to humans. This led us to utilize deep learning to identify supersusceptibility from hematoxylin & eosin (H&E) lung tissue sections utilizing only clinical outcomes (supersusceptible or not-supersusceptible) as labels. Findings: The proposed machine learning model diagnosed supersusceptibility with high accuracy (91.50 § 4.68%) compared to two expert pathologists using H&E stained lung sections (94.95% and 94.58%). Two nonexperts used the imaging biomarker to diagnose supersusceptibility with high accuracy (88.25% and 87.95%) and agreement (96.00%). A board-certified veterinary pathologist (GB) examined the imaging biomarker and determined the model was making diagnostic decisions using a form of granuloma necrosis (karyorrhectic and pyknotic nuclear debris). This was corroborated by one other board-certified veterinary pathologist. Finally, the imaging biomarker was quantified, providing a novel means to convert visual patterns within granulomas to data suitable for statistical analyses. Implications: Overall, our results have translatable implication to improve our understanding of TB and also to the broader field of computational pathology in which clinical outcomes alone can drive automatic identification of interpretable imaging biomarkers, knowledge discovery, and validation of existing clinical biomarkers.

Recent reports highlighting the global significance of cryptosporidiosis among children have renewed efforts to develop control measures. We evaluated the efficacy of bumped kinase inhibitor (BKI) 1369 in the gnotobiotic piglet model of acute diarrhea caused by , the species responsible for most human cases. Five-day treatment with BKI 1369 reduced signs of disease early during treatment compared to those of untreated animals. Piglets treated with BKI 1369 exhibited significant reductions of oocyst excretion, mucosal colonization by, and mucosal lesions, which resulted in considerable symptomatic improvement. BKI 1369 reduced the parasite burden and disease severity in the gnotobiotic pig model. Together these data suggest that a BKI-mediated therapeutic may be an effective treatment against cryptosporidiosis.

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.

Background Machine learning sustains successful application to many diagnostic and prognostic problems in computational histopathology. Yet, few efforts have been made to model gene expression from histopathology. This study proposes a methodology which predicts selected gene expression values (microarray) from haematoxylin and eosin whole-slide images as an intermediate data modality to identify fulminant-like pulmonary tuberculosis ('supersusceptible') in an experimentally infected cohort of Diversity Outbred mice (n=77). Methods Gradient-boosted trees were utilized as a novel feature selector to identify gene transcripts predictive of fulminant-like pulmonary tuberculosis. A novel attention-based multiple instance learning model for regression was used to predict selected genes' expression from whole-slide images. Gene expression predictions were shown to be sufficiently replicated to identify supersusceptible mice using gradient-boosted trees trained on ground truth gene expression data. Findings The model was accurate, showing high positive correlations with ground truth gene expression on both cross-validation ( n = 77, 0.63 ≤ ρ ≤ 0.84) and external testing sets ( n = 33, 0.65 ≤ ρ ≤ 0.84). The sensitivity and specificity for gene expression predictions to identify supersusceptible mice ( n =77) were 0.88 and 0.95, respectively, and for an external set of mice (n=33) 0.88 and 0.93, respectively. Implications Our methodology maps histopathology to gene expression with sufficient accuracy to predict a clinical outcome. The proposed methodology exemplifies a computational template for gene expression panels, in which relatively inexpensive and widely available tissue histopathology may be mapped to specific genes' expression to serve as a diagnostic or prognostic tool. Funding National Institutes of Health and American Lung Association.

Background Cryptosporidiosis, an enteric protozoon, causes substantial morbidity and mortality associated with diarrhea in children <2 years old in low- to middle-income countries. There is no vaccine and treatments are inadequate. A piperazine-based compound, MMV665917, has in vitro and in vivo efficacy against Cryptosporidium parvum. In this study, we evaluated the efficacy of MMV665917 in gnotobiotic piglets experimentally infected with Cryptosporidium hominis, the species responsible for >75% of diarrhea reported in these children. Methods Gnotobiotic piglets were orally challenged with C hominis oocysts, and oral treatment with MMV665917 was commenced 3 days after challenge. Oocyst excretion and diarrhea severity were observed daily, and mucosal colonization and lesions were recorded after necropsy. Results MMV665917 significantly reduced fecal oocyst excretion, parasite colonization and damage to the intestinal mucosa, and peak diarrheal symptoms, compared with infected untreated controls. A dose of 20 mg/kg twice daily for 7 days was more effective than 10 mg/kg. There were no signs of organ toxicity at either dose, but 20 mg/kg was associated with slightly elevated blood cholesterol and monocytes at euthanasia. Conclusions These results demonstrate the effectiveness of this drug against C hominis. Piperazine-derivative MMV665917 may potentially be used to treat human cryptosporidiosis; however, further investigations are required.