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Infants are highly susceptible to invasive respiratory and gastrointestinal infections. To elucidate the age-dependent mechanism(s) that drive bacterial spread from the mucosa, we developed an infant mouse model using the prevalent pediatric respiratory pathogen, Streptococcus pneumoniae (Spn). Despite similar upper respiratory tract (URT) colonization levels, the survival rate of Spn-infected infant mice was significantly decreased compared to adults and corresponded with Spn dissemination to the bloodstream. An increased rate of pneumococcal bacteremia in early life beyond the newborn period was attributed to increased bacterial translocation across the URT barrier. Bacterial dissemination in infant mice was independent of URT monocyte or neutrophil infiltration, phagocyte-derived ROS or RNS, inflammation mediated by toll-like receptor 2 or interleukin 1 receptor signaling, or the pore-forming toxin pneumolysin. Using molecular barcoding of Spn, we found that only a minority of bacterial clones in the nasopharynx disseminated to the blood in infant mice, indicating the absence of robust URT barrier breakdown. Rather, transcriptional profiling of the URT epithelium revealed a failure of infant mice to upregulate genes involved in the tight junction pathway. Expression of many such genes was also decreased in early life in humans. Infant mice also showed increased URT barrier permeability and delayed mucociliary clearance during the first two weeks of life, which corresponded with tighter attachment of bacteria to the respiratory epithelium. Together, these results demonstrate a window of vulnerability during postnatal development when altered mucosal barrier function facilitates bacterial dissemination.

Abstract Background The airway epithelium is composed of diverse cell types with specialized functions that mediate homeostasis and protect against respiratory pathogens. Human airway epithelial (HAE) cultures at air–liquid interface are a physiologically relevant in vitro model of this heterogeneous tissue and have enabled numerous studies of airway disease. HAE cultures are classically derived from primary epithelial cells, the relatively limited passage capacity of which can limit experimental methods and study designs. BCi-NS1.1, a previously described and widely used basal cell line engineered to express hTERT, exhibits extended passage lifespan while retaining the capacity for differentiation to HAE. However, gene expression and innate immune function in BCi-NS1.1-derived versus primary-derived HAE cultures have not been fully characterized. Methods BCi-NS1.1-derived HAE cultures (n = 3 independent differentiations) and primary-derived HAE cultures (n = 3 distinct donors) were characterized by immunofluorescence and single cell RNA-Seq (scRNA-Seq). Innate immune functions were evaluated in response to interferon stimulation and to infection with viral and bacterial respiratory pathogens. Results We confirm at high resolution that BCi-NS1.1- and primary-derived HAE cultures are largely similar in morphology, cell type composition, and overall gene expression patterns. While we observed cell-type specific expression differences of several interferon stimulated genes in BCi-NS1.1-derived HAE cultures, we did not observe significant differences in susceptibility to infection with influenza A virus and Staphylococcus aureus. Conclusions Taken together, our results further support BCi-NS1.1-derived HAE cultures as a valuable tool for the study of airway infectious disease.

Abstract Chikungunya virus (CHIKV) infection has been associated with severe cardiac manifestations, yet, how CHIKV infection leads to heart disease remains unknown. Here, we leveraged both mouse models and human primary cardiac cells to define the mechanisms of CHIKV heart infection. Using an immunocompetent mouse model of CHIKV infection as well as human primary cardiac cells, we demonstrate that CHIKV directly infects and actively replicates in cardiac fibroblasts. In immunocompetent mice, CHIKV is cleared from cardiac tissue without significant damage through the induction of a local type I interferon response from both infected and non-infected cardiac cells. Using mice deficient in major innate immunity signaling components, we found that signaling through the mitochondrial antiviral-signaling protein (MAVS) is required for viral clearance from the heart. In the absence of MAVS signaling, persistent infection leads to focal myocarditis and vasculitis of the large vessels attached to the base of the heart. Large vessel vasculitis was observed for up to 60 days post infection, suggesting CHIKV can lead to vascular inflammation and potential long-lasting cardiovascular complications. This study provides a model of CHIKV cardiac infection and mechanistic insight into CHIKV-induced heart disease, underscoring the importance of monitoring cardiac function in patients with CHIKV infections.

Abstract The tumor microenvironment (TME) in pancreatic ductal adenocarcinoma (PDAC) is a complex ecosystem that drives tumor progression; however, in-depth single cell characterization of the PDAC TME and its role in response to therapy is lacking. Here, we perform single-cell RNA sequencing on freshly collected human PDAC samples either before or after chemotherapy. Overall, we find a heterogeneous mixture of basal and classical cancer cell subtypes, along with distinct cancer-associated fibroblast and macrophage subpopulations. Strikingly, classical and basal-like cancer cells exhibit similar transcriptional responses to chemotherapy and do not demonstrate a shift towards a basal-like transcriptional program among treated samples. We observe decreased ligand-receptor interactions in treated samples, particularly between TIGIT on CD8 + T cells and its receptor on cancer cells, and identify TIGIT as the major inhibitory checkpoint molecule of CD8 + T cells. Our results suggest that chemotherapy profoundly impacts the PDAC TME and may promote resistance to immunotherapy.

Despite remarkable responses to cancer immunotherapy in a subset of patients, many patients remain resistant to therapies. It is now clear that elevated levels of tumor-infiltrating T cells as well as a systemic anti-tumor immune response are requirements for successful immunotherapies. However, the tumor microenvironment imposes an additional resistance mechanism to immunotherapy. We have developed a practical and improved strategy for cancer immunotherapy using an oncolytic virus and anti-OX40. This strategy takes advantage of a preexisting T cell immune repertoire in vivo, removing the need to know about present tumor antigens. We have shown in this study that the replication-deficient oncolytic Sindbis virus vector expressing interleukin-12 (IL-12) (SV.IL12) activates immune-mediated tumor killing by inducing OX40 expression on CD4 T cells, allowing the full potential of the agonistic anti-OX40 antibody. The combination of SV.IL12 with anti-OX40 markedly changes the transcriptome signature and metabolic program of T cells, driving the development of highly activated terminally differentiated effector T cells. These metabolically reprogrammed T cells demonstrate enhanced tumor infiltration capacity as well as anti-tumor activity capable of overcoming the repressive tumor microenvironment. Our findings identify SV.IL12 in combination with anti-OX40 to be a novel and potent therapeutic strategy that can cure multiple types of low-immunogenic solid tumors.

Binding of Streptococcus pneumoniae (Spn) to nasal mucus leads to entrapment and clearance via mucociliary activity during colonization. To identify Spn factors allowing for evasion of mucus binding, we used a solid-phase adherence assay with immobilized mucus of human and murine origin. Spn bound large mucus particles through interactions with carbohydrate moieties. Mutants lacking neuraminidase A (nanA) or neuraminidase B (nanB) showed increased mucus binding that correlated with diminished removal of terminal sialic acid residues on bound mucus. The non-additive activity of the two enzymes raised the question why Spn expresses two neuraminidases and suggested they function in the same pathway. Transcriptional analysis demonstrated expression of nanA depends on the enzymatic function of NanB. As transcription of nanA is increased in the presence of sialic acid, our findings suggest that sialic acid liberated from host glycoconjugates by the secreted enzyme NanB induces the expression of the cell-associated enzyme NanA. The absence of detectable mucus desialylation in the nanA mutant, in which NanB is still expressed, suggests that NanA is responsible for the bulk of the modification of host glycoconjugates. Thus, our studies describe a functional role for NanB in sialic acid sensing in the host. The contribution of the neuraminidases in vivo was then assessed in a murine model of colonization. Although mucus-binding mutants showed an early advantage, this was only observed in a competitive infection, suggesting a complex role of neuraminidases. Histologic examination of the upper respiratory tract demonstrated that Spn stimulates mucus production in a neuraminidase-dependent manner. Thus, an increase production of mucus containing secretions appears to be balanced, in vivo, by decreased mucus binding. We postulate that through the combined activity of its neuraminidases, Spn evades mucus binding and mucociliary clearance, which is needed to counter neuraminidase-mediated stimulation of mucus secretions.

Aims: Histology, the microscopic study of normal tissues, is a crucial element of most medical curricula. Learning tools focused on histology are very important to learners who seek diagnostic competency within this important diagnostic arena. Recent developments in machine learning (ML) suggest that certain ML tools may be able to benefit this histology learning platform. Here, we aim to explore how one such tool based on a convolutional neural network, can be used to build a generalizable multi-classification model capable of classifying microscopic images of human tissue samples with the ultimate goal of providing a differential diagnosis (a list of look-alikes) for each entity. Methods: We obtained three institutional training datasets and one generalizability test dataset, each containing images of histologic tissues in 38 categories. Models were trained on data from single institutions, low quantity combinations of multiple institutions, and high quantity combinations of multiple institutions. Models were tested against withheld validation data, external institutional data, and generalizability test images obtained from Google image search. Performance was measured with macro and micro accuracy, sensitivity, specificity, and f1-score. Results: In this study, we were able to show that such a model's generalizability is dependent on both the training data source variety and the total number of training images used. Models which were trained on 760 images from only a single institution performed well on withheld internal data but poorly on external data (lower generalizability). Increasing data source diversity improved generalizability, even when decreasing data quantity: models trained on 684 images, but from three sources improved generalization accuracy between 4.05' and 18.59'. Maintaining this diversity and increasing the quantity of training images to 2280 further improved generalization accuracy between 16.51' and 32.79'. Conclusions: This pilot study highlights the significance of data diversity within such studies. As expected, optimal models are those that incorporate both diversity and quantity into their platforms.s