Systems genetics uncover new loci containing functional gene candidates in Mycobacterium tuberculosis-infected Diversity Outbred mice.

Mycobacterium tuberculosis infects two billion people across the globe, and results in 8-9 million new tuberculosis (TB) cases and 1-1.5 million deaths each year. Most patients have no known genetic basis that predisposes them to disease. Here, we investigate the complex genetic basis of pulmonary TB by modelling human genetic diversity with the Diversity Outbred mouse population. When infected with M. tuberculosis, one-third develop early onset, rapidly progressive, necrotizing granulomas and succumb within 60 days. The remaining develop non-necrotizing granulomas and survive longer than 60 days. Genetic mapping using immune and inflammatory mediators; and clinical, microbiological, and granuloma correlates of disease identified five new loci on mouse chromosomes 1, 2, 4, 16; and three known loci on chromosomes 3 and 17. Further, multiple positively correlated traits shared loci on chromosomes 1, 16, and 17 and had similar patterns of allele effects, suggesting these loci contain critical genetic regulators of inflammatory responses to M. tuberculosis. To narrow the list of candidate genes, we used a machine learning strategy that integrated gene expression signatures from lungs of M. tuberculosis-infected Diversity Outbred mice with gene interaction networks to generate scores representing functional relationships. The scores were used to rank candidates for each mapped trait, resulting in 11 candidate genes: Ncf2, Fam20b, S100a8, S100a9, Itgb5, Fstl1, Zbtb20, Ddr1, Ier3, Vegfa, and Zfp318. Although all candidates have roles in infection, inflammation, cell migration, extracellular matrix remodeling, or intracellular signaling, and all contain single nucleotide polymorphisms (SNPs), SNPs in only four genes (S100a8, Itgb5, Fstl1, Zfp318) are predicted to have deleterious effects on protein functions. We performed methodological and candidate validations to (i) assess biological relevance of predicted allele effects by showing that Diversity Outbred mice carrying PWH/PhJ alleles at the H-2 locus on chromosome 17 QTL have shorter survival; (ii) confirm accuracy of predicted allele effects by quantifying S100A8 protein in inbred founder strains; and (iii) infection of C57BL/6 mice deficient for the S100a8 gene. Overall, this body of work demonstrates that systems genetics using Diversity Outbred mice can identify new (and known) QTLs and functionally relevant gene candidates that may be major regulators of complex host-pathogens interactions contributing to granuloma necrosis and acute inflammation in pulmonary TB.

B cells in perivascular and peribronchiolar granuloma-associated lymphoid tissue and B-cell signatures identify asymptomatic Mycobacterium tuberculosis lung infection in Diversity Outbred mice.

Because most humans resist Mycobacterium tuberculosis infection, there is a paucity of lung samples to study. To address this gap, we infected Diversity Outbred mice with M. tuberculosis and studied the lungs of mice in different disease states. After a low-dose aerosol infection, progressors succumbed to acute, inflammatory lung disease within 60 days, while controllers maintained asymptomatic infection for at least 60 days, and then developed chronic pulmonary tuberculosis (TB) lasting months to more than 1 year. Here, we identified features of asymptomatic M. tuberculosis infection by applying computational and statistical approaches to multimodal data sets. Cytokines and anti-M. tuberculosis cell wall antibodies discriminated progressors vs controllers with chronic pulmonary TB but could not classify mice with asymptomatic infection. However, a novel deep-learning neural network trained on lung granuloma images was able to accurately classify asymptomatically infected lungs vs acute pulmonary TB in progressors vs chronic pulmonary TB in controllers, and discrimination was based on perivascular and peribronchiolar lymphocytes. Because the discriminatory lesion was rich in lymphocytes and CD4 T cell-mediated immunity is required for resistance, we expected CD4 T-cell genes would be elevated in asymptomatic infection. However, the significantly different, highly expressed genes were from B-cell pathways (e.g., Bank1, Cd19, Cd79, Fcmr, Ms4a1, Pax5, and H2-Ob), and CD20+ B cells were enriched in the perivascular and peribronchiolar regions of mice with asymptomatic M. tuberculosis infection. Together, these results indicate that genetically controlled B-cell responses are important for establishing asymptomatic M. tuberculosis lung infection.

Democratizing Artificial Intelligence in Anatomic Pathology.

CONTEXT.­: Artificial intelligence is a transforming technology for anatomic pathology. Involvement within the workforce will foster support for algorithm development and implementation. OBJECTIVE.­: To develop a supportive ecosystem that enables pathologists with variable expertise in artificial intelligence to create algorithms in a development environment with seamless transition to a production environment. RESULTS.­: The development team considered internal development and vended solutions. Because of the extended timeline and resource requirements for internal development, a decision was made to use a vended solution. Vendor proposals were solicited and reviewed by pathologists, IT, and security groups. A vendor was selected and pipelines for development and production were established. Proposals for development were solicited from the pathology department. Eighty-four investigators were selected for the initial cohort, receiving training and access to dedicated subject matter experts. A total of 30 of 31 projects progressed through the model development process of annotating, training, and validation. Based on these projects, 15 abstracts were submitted to national meetings. CONCLUSIONS.­: Democratizing artificial intelligence by creating an ecosystem to support pathologists with varying levels of expertise can break down entry barriers, reduce overall cost of algorithm development, improve algorithm quality, and enhance the speed of adoption.

Host Genetic Background Influences BCG-Induced Antibodies Cross-Reactive to SARS-CoV-2 Spike Protein.

Mycobacterium bovis Bacillus Calmette-Guérin (BCG) protects against childhood tuberculosis; and unlike most vaccines, BCG broadly impacts immunity to other pathogens and even some cancers. Early in the COVID-19 pandemic, epidemiological studies identified a protective association between BCG vaccination and outcomes of SARS-CoV-2, but the associations in later studies were inconsistent. We sought possible reasons and noticed the study populations often lived in the same country. Since individuals from the same regions can share common ancestors, we hypothesized that genetic background could influence associations between BCG and SARS-CoV-2. To explore this hypothesis in a controlled environment, we performed a pilot study using Diversity Outbred mice. First, we identified amino acid sequences shared by BCG and SARS-CoV-2 spike protein. Next, we tested for IgG reactive to spike protein from BCG-vaccinated mice. Sera from some, but not all, BCG-vaccinated Diversity Outbred mice contained higher levels of IgG cross-reactive to SARS-CoV-2 spike protein than sera from BCG-vaccinated C57BL/6J inbred mice and unvaccinated mice. Although larger experimental studies are needed to obtain mechanistic insight, these findings suggest that genetic background may be an important variable contributing to different associations observed in human randomized clinical trials evaluating BCG vaccination on SARS-CoV-2 and COVID-19.

Prediction of Tuberculosis From Lung Tissue Images of Diversity Outbred Mice Using Jump Knowledge Based Cell Graph Neural Network.

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.

Multiple genetic loci influence vaccine-induced protection against Mycobacterium tuberculosis in genetically diverse mice.

Mycobacterium tuberculosis (M.tb.) infection leads to over 1.5 million deaths annually, despite widespread vaccination with BCG at birth. Causes for the ongoing tuberculosis endemic are complex and include the failure of BCG to protect many against progressive pulmonary disease. Host genetics is one of the known factors implicated in susceptibility to primary tuberculosis, but less is known about the role that host genetics plays in controlling host responses to vaccination against M.tb. Here, we addressed this gap by utilizing Diversity Outbred (DO) mice as a small animal model to query genetic drivers of vaccine-induced protection against M.tb. DO mice are a highly genetically and phenotypically diverse outbred population that is well suited for fine genetic mapping. Similar to outcomes in people, our previous studies demonstrated that DO mice have a wide range of disease outcomes following BCG vaccination and M.tb. challenge. In the current study, we used a large population of BCG-vaccinated/M.tb.-challenged mice to perform quantitative trait loci mapping of complex infection traits; these included lung and spleen M.tb. burdens, as well as lung cytokines measured at necropsy. We found sixteen chromosomal loci associated with complex infection traits and cytokine production. QTL associated with bacterial burdens included a region encoding major histocompatibility antigens that are known to affect susceptibility to tuberculosis, supporting validity of the approach. Most of the other QTL represent novel associations with immune responses to M.tb. and novel pathways of cytokine regulation. Most importantly, we discovered that protection induced by BCG is a multigenic trait, in which genetic loci harboring functionally-distinct candidate genes influence different aspects of immune responses that are crucial collectively for successful protection. These data provide exciting new avenues to explore and exploit in developing new vaccines against M.tb.

The Many Hosts of Mycobacteria 9 (MHM9): A conference report.

The Many Hosts of Mycobacteria (MHM) meeting series brings together basic scientists, clinicians and veterinarians to promote robust discussion and dissemination of recent advances in our knowledge of numerous mycobacterial diseases, including human and bovine tuberculosis (TB), nontuberculous mycobacteria (NTM) infection, Hansen's disease (leprosy), Buruli ulcer and Johne's disease. The 9th MHM conference (MHM9) was held in July 2022 at The Ohio State University (OSU) and centered around the theme of "Confounders of Mycobacterial Disease." Confounders can and often do drive the transmission of mycobacterial diseases, as well as impact surveillance and treatment outcomes. Various confounders were presented and discussed at MHM9 including those that originate from the host (comorbidities and coinfections) as well as those arising from the environment (e.g., zoonotic exposures), economic inequality (e.g. healthcare disparities), stigma (a confounder of leprosy and TB for millennia), and historical neglect (a confounder in Native American Nations). This conference report summarizes select talks given at MHM9 highlighting recent research advances, as well as talks regarding the historic and ongoing impact of TB and other infectious diseases on Native American Nations, including those in Southwestern Alaska where the regional TB incidence rate is among the highest in the Western hemisphere.

Core Needle Biopsy Guidance Based on Tissue Morphology Assessment with AI-OCT Imaging.

This paper presents a combined optical imaging/artificial intelligence (OI/AI) technique for the real-time analysis of tissue morphology at the tip of the biopsy needle, prior to collecting a biopsy specimen. This is an important clinical problem as up to 40% of collected biopsy cores provide low diagnostic value due to high adipose or necrotic content. Micron-scale-resolution optical coherence tomography (OCT) images can be collected with a minimally invasive needle probe and automatically analyzed using a computer neural network (CNN)-based AI software. The results can be conveyed to the clinician in real time and used to select the biopsy location more adequately. This technology was evaluated on a rabbit model of cancer. OCT images were collected with a hand-held custom-made OCT probe. Annotated OCT images were used as ground truth for AI algorithm training. The overall performance of the AI model was very close to that of the humans performing the same classification tasks. Specifically, tissue segmentation was excellent (~99% accuracy) and provided segmentation that closely mimicked the ground truth provided by the human annotations, while over 84% correlation accuracy was obtained for tumor and non-tumor classification.

IL-10 Modulation Increases Pyrazinamide's Antimycobacterial Efficacy against Mycobacterium tuberculosis Infection in Mice.

Mechanisms to shorten the duration of tuberculosis (TB) treatment include new drug formulations or schedules and the development of host-directed therapies (HDTs) that better enable the host immune system to eliminate Mycobacterium tuberculosis. Previous studies have shown that pyrazinamide, a first-line antibiotic, can also modulate immune function, making it an attractive target for combinatorial HDT/antibiotic therapy, with the goal to accelerate clearance of M. tuberculosis. In this study, we assessed the value of anti-IL-10R1 as an HDT along with pyrazinamide and show that short-term anti-IL-10R1 blockade during pyrazinamide treatment enhanced the antimycobacterial efficacy of pyrazinamide, resulting in faster clearance of M. tuberculosis in mice. Furthermore, 45 d of pyrazinamide treatment in a functionally IL-10-deficient environment resulted in sterilizing clearance of M. tuberculosis. Our data suggest that short-term IL-10 blockade with standard TB drugs has the potential to improve clinical outcome by reducing the treatment duration.

Host genetic background is a barrier to broadly effective vaccine-mediated protection against tuberculosis.

Heterogeneity in human immune responses is difficult to model in standard laboratory mice. To understand how host variation affects Bacillus Calmette Guerin-induced (BCG-induced) immunity against Mycobacterium tuberculosis, we studied 24 unique collaborative cross (CC) mouse strains, which differ primarily in the genes and alleles they inherit from founder strains. The CC strains were vaccinated with or without BCG and challenged with aerosolized M. tuberculosis. Since BCG protects only half of the CC strains tested, we concluded that host genetics has a major influence on BCG-induced immunity against M. tuberculosis infection, making it an important barrier to vaccine-mediated protection. Importantly, BCG efficacy is dissociable from inherent susceptibility to tuberculosis (TB). T cell immunity was extensively characterized to identify components associated with protection that were stimulated by BCG and recalled after M. tuberculosis infection. Although considerable diversity is observed, BCG has little impact on the composition of T cells in the lung after infection. Instead, variability is largely shaped by host genetics. BCG-elicited protection against TB correlated with changes in immune function. Thus, CC mice can be used to define correlates of protection and to identify vaccine strategies that protect a larger fraction of genetically diverse individuals instead of optimizing protection for a single genotype.

Potential diagnostic biomarkers for pulmonary tuberculosis in humans are not elevated in Mycobacterium tuberculosis culture-positive Asian elephants (Elephas maximus).

OBJECTIVE: To determine (1) if chemokine (C-X-C motif) ligand 1 (CXCL1), matrix metalloproteinase 8 (MMP8), interleukin-10 (IL-10), interferon-γ (IFN-γ), and tumor necrosis factor-α (TNF-α) can be detected in serum from Asian elephants, and (2) if their concentrations are significantly elevated in Mycobacterium tuberculosis (M.tb) culture-positive elephants compared to -negative elephants. CXCL1, MMP8, IL-10, IFN-γ, and TNF-α were recently identified as potential diagnostic biomarkers for pulmonary tuberculosis in experimental studies in animals and humans. Therefore, we hypothesized that they would be detectable and significantly elevated in M.tb culture-positive elephants compared to M.tb culture-negative elephants. SAMPLE: 101 Asian elephant serum samples, including 91 samples from 6 M.tb-negative elephants and 10 samples from 5 M.tb-positive elephants (none of which exhibited clinical signs of disease). M.tb status was determined by trunk wash culture. PROCEDURES: Commercially available ELISA kits were used to determine the concentrations of each biomarker in serum samples. RESULTS: Biomarker concentrations were below the limit of detection for the assay in 100/101 (99%) samples for CXCL1, 98/101 (97%) samples for MMP8, 85/101 (84%) samples for IL-10, 75/101 (74%) samples for IFN-γ, and 45/101 (45%) samples for TNF-α. Multiple M.tb culture-positive elephants did not have detectable levels of any of the 5 biomarkers. CLINICAL RELEVANCE: CXCL1, MMP8, IL-10, IFN-γ, and TNF-α were not elevated in M.tb culture-positive Asian elephants compared to M.tb culture-negative Asian elephants. This may be related to disease state (ie, clinically asymptomatic). More sensitive assays are needed to better understand the role of these biomarkers in M.tb infection in Asian elephants.

Recent developments in systems biology and genetic engineering toward design of vaccines for TB.

Tuberculosis (TB) is one of the most prevalent diseases worldwide. The currently available Bacillus Calmette-Guérin vaccine is not sufficient in protecting against pulmonary TB. Although many vaccines have been evaluated in clinical trials, but none of them yet has proven to be more successful. Thus, new strategies are urgently needed to design more effective TB vaccines. The emergence of new technologies will undoubtedly accelerate the process of vaccine development. This review summarizes the potential and validated applications of emerging technologies, including: systems biology (genomics, proteomics, and transcriptomics), genetic engineering, and other computational tools to discover and develop novel vaccines against TB. It also discussed that the significant implementation of these approaches will play crucial roles in the development of novel vaccines to cure and control TB.

CXCL1: A new diagnostic biomarker for human tuberculosis discovered using Diversity Outbred mice.

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.

Deep learning predicts gene expression as an intermediate data modality to identify susceptibility patterns in Mycobacterium tuberculosis infected Diversity Outbred mice.

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.

Recombinant Paraprobiotics as a New Paradigm for Treating Gastrointestinal Nematode Parasites of Humans.

Gastrointestinal nematodes (GINs) of humans, e.g., hookworms, negatively impact childhood growth, cognition, nutrition, educational attainment, income, productivity, and pregnancy. Hundreds of millions of people are targeted with mass drug administration (MDA) of donated benzimidazole anthelmintics. However, benzimidazole efficacy against GINs is suboptimal, and reduced/low efficacy has been seen. Developing an anthelmintic for human MDA is daunting: it must be safe, effective, inexpensive, stable without a cold chain, and massively scalable. Bacillus thuringiensis crystal protein 5B (Cry5B) has anthelmintic properties that could fill this void. Here, we developed an active pharmaceutical ingredient (API) containing B. thuringiensis Cry5B compatible with MDA. We expressed Cry5B in asporogenous B. thuringiensis during vegetative phase, forming cytosolic crystals. These bacteria with cytosolic crystals (BaCC) were rendered inviable (inactivated BaCC [IBaCC]) with food-grade essential oils. IBaCC potency was validated in vitro against nematodes. IBaCC was also potent in vivo against human hookworm infections in hamsters. IBaCC production was successfully scaled to 350 liters at a contract manufacturing facility. A simple fit-for-purpose formulation to protect against stomach digestion and powdered IBaCC were successfully made and used against GINs in hamsters and mice. A pilot histopathology study and blood chemistry workup showed that five daily consecutive doses of 200 mg/kg body weight Cry5B IBaCC (the curative single dose is 40 mg/kg) was nontoxic to hamsters and completely safe. IBaCC is a safe, inexpensive, highly effective, easy-to-manufacture, and scalable anthelmintic that is practical for MDA and represents a new paradigm for treating human GINs.

Automatic discovery of clinically interpretable imaging biomarkers for Mycobacterium tuberculosis supersusceptibility using deep learning.

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 non-experts 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. FUNDING: National Institutes of Health and American Lung Association.

The Diversity Outbred Mouse Population Is an Improved Animal Model of Vaccination against Tuberculosis That Reflects Heterogeneity of Protection.

Many studies of Mycobacterium tuberculosis infection and immunity have used mouse models. However, outcomes of vaccination and challenge with M. tuberculosis in inbred mouse strains do not reflect the full range of outcomes seen in people. Previous studies indicated that the novel Diversity Outbred (DO) mouse population exhibited a spectrum of outcomes after primary aerosol infection with M. tuberculosis Here, we demonstrate the value of this novel mouse population for studies of vaccination against M. tuberculosis aerosol challenge. Using the only currently licensed tuberculosis vaccine, we found that the DO population readily controlled systemic Mycobacterium bovis BCG bacterial burdens and that BCG vaccination significantly improved survival across the DO population upon challenge with M. tuberculosis Many individual DO mice that were vaccinated with BCG and then challenged with M. tuberculosis exhibited low bacterial burdens, low or even no systemic dissemination, little weight loss, and only minor lung pathology. In contrast, some BCG-vaccinated DO mice progressed quickly to fulminant disease upon M. tuberculosis challenge. Across the population, most of these disease parameters were at most modestly correlated with each other and were often discordant. This result suggests the need for a multiparameter metric to better characterize "disease" and "protection," with closer similarity to the complex case definitions used in people. Taken together, these results demonstrate that DO mice provide a novel small-animal model of vaccination against tuberculosis that better reflects the wide spectrum of outcomes seen in people.IMPORTANCE We vaccinated the Diversity Outbred (DO) population of mice with BCG, the only vaccine currently used to protect against tuberculosis, and then challenged them with M. tuberculosis by aerosol. We found that the BCG-vaccinated DO mouse population exhibited a wide range of outcomes, in which outcomes in individual mice ranged from minimal respiratory or systemic disease to fulminant disease and death. The breadth of these outcomes appears similar to the range seen in people, indicating that DO mice may serve as an improved small-animal model to study tuberculosis infection and immunity. Moreover, sophisticated tools are available for the use of these mice to map genes contributing to control of vaccination. Thus, the present studies provided an important new tool in the fight against tuberculosis.

Traffic-related particulate matter affects behavior, inflammation, and neural integrity in a developmental rodent model.

Recent studies indicate that exposure to airborne particulate matter (PM) is associated with cognitive delay, depression, anxiety, autism, and neurodegenerative diseases; however, the role of PM in the etiology of these outcomes is not well-understood. Therefore, there is a need for controlled animal studies to better elucidate the causes and mechanisms by which PM impacts these health outcomes. We assessed the effects of gestational and early life exposure to traffic-related PM on social- and anxiety-related behaviors, cognition, inflammatory markers, and neural integrity in juvenile male rats. Gestating and lactating rats were exposed to PM from a Boston (MA, USA) traffic tunnel for 5 h/day, 5 days/week for 6 weeks (3 weeks gestation, 3 weeks lactation). The target exposure concentration for the fine fraction of nebulized PM, measured as PM2.5, was 200 µg/m3. To assess anxiety and cognitive function, F1 male juveniles underwent elevated platform, cricket predation, nest building, social behavior and marble burying tests at 32-60 days of age. Upon completion of behavioral testing, multiple cytokines and growth factors were measured in these animals and their brains were analyzed with diffusion tensor MRI to assess neural integrity. PM exposure had no effect on litter size or weight, or offspring growth; however, F1 litters developmentally exposed to PM exhibited significantly increased anxiety (p = 0.04), decreased cognition reflected in poorer nest-organization (p = 0.04), and decreased social play and allogrooming (p = 0.003). MRI analysis of ex vivo brains revealed decreased structural integrity of neural tissues in the anterior cingulate and hippocampus in F1 juveniles exposed to PM (p < 0.01, p = 0.03, respectively). F1 juvenile males exposed to PM also exhibited significantly decreased plasma levels of both IL-18 (p = 0.03) and VEGF (p = 0.04), and these changes were inversely correlated with anxiety-related behavior. Chronic exposure of rat dams and their offspring to traffic-related PM during gestation and lactation decreases social behavior, increases anxiety, impairs cognition, decreases levels of inflammatory and growth factors (which are correlated with behavioral changes), and disrupts neural integrity in the juvenile male offspring. Our findings add evidence that exposure to traffic-related air pollution during gestation and lactation is involved in the etiology of autism spectrum disorder and other disorders which include social and cognitive deficits and/or increased anxiety.

The Impact of Actotoxumab Treatment of Gnotobiotic Piglets Infected With Different Clostridium difficile Isogenic Mutants.

Nosocomial infections with Clostridium difficile are on the rise in the Unites States, attributed to emergence of antibiotic-resistant and hypervirulent strains associated with greater likelihood of recurrent infections. In addition to antibiotics, treatment with Merck anti-toxin B (TcdB) antibody bezlotoxumab is reported to reduce recurrent infections. However, treatment with anti-toxin A (TcdA) antibody actotoxumab was associated with dramatically increased disease severity and mortality rates in humans and gnotobiotic piglets. Using isogenic mutants of C. difficile strain NAPI/BI/027 deficient in TcdA (A-B+) or TcdB (A+B-), and the wild type, we investigated how and why treatment of infected animals with anti-TcdA dramatically increased disease severity. Contrary to the hypothesis, among piglets treated with anti-TcdA, those with A+B- infection were disease free, in contrast to the disease enhancement seen in those with wild-type or A-B+ infection. It seems that the lack of TcdA, through either deletion or neutralization with anti-TcdA, reduces a competitive pressure, allowing TcdB to freely exert its profound effect, leading to increased mucosal injury and disease severity.

An immunocompetent rat model of infection with Cryptosporidium hominis and Cryptosporidium parvum.

A major obstacle to developing vaccines against cryptosporidiosis, a serious diarrheal disease of children in developing countries, is the lack of rodent models essential to identify and screen protective immunogens. Rodent models commonly used for drug discovery are unsuitable for vaccine development because they either are purposefully immunodeficient or immunosuppressed. Here, we describe the development and optimization of an immunocompetent intratracheal (IT) rat model susceptible to infections with sporozoites of Cryptosporidium parvum and Cryptosporidium hominis - the primary causes of human cryptosporidiosis. A model suitable for screening of parasite immunogens is a prerequisite for immunogen screening and vaccine development.