Faculty Bibliography

Young age is a risk factor for respiratory and gastrointestinal infections. Here, we compared infant and adult mice to identify age-dependent mechanisms that drive susceptibility to mucosal infections during early life. Transcriptional profiling of the upper respiratory tract (URT) epithelium revealed significant dampening of early life innate mucosal defenses. Epithelial-mediated production of the most abundant antimicrobial molecules, lysozyme, and lactoferrin, and the polymeric immunoglobulin receptor (pIgR), responsible for IgA transcytosis, was expressed in an age-dependent manner. This was attributed to delayed functional development of serous cells. Absence of epithelial-derived lysozyme and the pIgR was also observed in the small intestine during early life. Infection of infant mice with lysozyme-susceptible strains of Streptococcus pneumoniae or Staphylococcus aureus in the URT or gastrointestinal tract, respectively, demonstrated an age-dependent regulation of lysozyme enzymatic activity. Lysozyme derived from maternal milk partially compensated for the reduction in URT lysozyme activity of infant mice. Similar to our observations in mice, expression of lysozyme and the pIgR in nasopharyngeal samples collected from healthy human infants during the first year of life followed an age-dependent regulation. Thus, a global pattern of reduced antimicrobial and IgA-mediated defenses may contribute to increased susceptibility of young children to mucosal infections.

Rationale Cross-sectional human data suggest that enrichment of oral anaerobic bacteria in the lung is associated with increased Th17 inflammatory phenotype. In this study we evaluated the microbial and host immune response dynamics after aspiration with a oral commensals using a preclinical mouse model. Methods Aspiration with a mixture of human oral commensals (MOC; Prevotella melaninogenica, Veillonella parvula, and Streptococcus mitis) was modeled in mice followed by variable time of sacrifice. Genetic background of mice included WT, MyD88 knock out and STAT3C. Measurements 16S rRNA gene sequencing characterized changes in microbiota. Flow cytometry, cytokine measurement via Luminex and RNA host transcriptome sequencing was used to characterize host immune phenotype. Main Results While MOC aspiration correlated with lower airway dysbiosis that resolved within five days, it induced an extended inflammatory response associated with IL17-producing T-cells lasting at least 14 days. MyD88 expression was required for the IL-17 response to MOC aspiration, but not for T-cell activation or IFN-γ expression. MOC aspiration prior to a respiratory challenge with S. pneumoniae led to a decreased in host's susceptibility to this pathogen. Conclusions Thus, in otherwise healthy mice, a single aspiration event with oral commensals are rapidly cleared from the lower airways, but induce a prolonged Th17 response that secondarily decreased susceptibility to respiratory pathogens. Translationally, these data implicate an immuno-protective role of episodic microaspiration of oral microbes in the regulation of the lung immune phenotype and mitigation of host susceptibility to infection with lower airway pathogens.

Rationale:Malignant pleural mesothelioma(MPM) has a poor prognosis with median survival of 12-24 months. We are not aware of prior studies examining the immune microenvironment in tumor draining lymph nodes (TDLN) in MPM. Our aim is to compare the tumor microenvironment(TME) and the microenvironment of TDLN. We hypothesize that the TME will display an immunosuppressive phenotype reflected in the TDLN.
Method(s):We performed digital spatial profiling(DSP) using the GeoMx (NanoString) platform on stored primary tumor and nodal biopsy specimens from 3 patients from our tumor bank. Samples from both primary tumor and lymph nodes were sectioned and labeled with pancytokeratin (CK). Tissue was then classified as "tumor" or "nontumor" using semi-automated segmentation based on pan-Cytokeratin (panK) labeling. The slides were then labeled with antibodies to 58 selected markers, with each unique antibody attached to a respective oligonucleotide. The tissue was exposed to UV light separately for tumor and non-tumor regions, cleaving the oligonucleotides from the attached antibodies. The oligonucleotides from the separate tumor and non-tumor regions were quantified using nCounter (NanoString).
Result(s):The non-neoplastic regions of the primary tumor contained higher expression of proteins associated with inflammatory cells including helper T-cells, cytotoxic T-cells, B-cells, macrophages, neutrophils, natural killer cells(Table 1). Furthermore, there was greater expression of immune checkpoint proteins, PD-L1 and CTLA-4, and CD163 and CD14, proteins associated with immunosuppressive macrophages, in the non-neoplastic region compared to the neoplastic region of the tumoe(Table 1). TDLNs contained similar levels of expression of lymphocyte markers, including those delineating cytotoxic T-cells and helper T-cells, as the primary tumor(Table 1). Despite this, TME expressed higher levels of T-cell exhaustion and immunsupression markers (FOXP3, LAG3, PD-1, CTLA-4) than TDLN(Table 1).
Conclusion(s):DSP is feasible in Formalin-fixed paraffin embedded (FFPE) mesothelioma specimens, providing a method for using quantitative immunopathology to study corresponding immune microenvironments. In our study, the non-tumor region of the primary tumor contained macrophages, lymphocytes, natural killer cells, and cancer-associated fibroblasts consistent with prior descriptions of the mesothelioma TME. Increased expression of immune checkpoint molecules in the non-tumor region suggests an immunosuppressive TME. TDLNs demonstrated similar lymphocyte markers, but without corresponding immune checkpoint expression of t suggesting the immunosuppressive phenotype of the TME may not be reflected in TDLNs. This pilot study is the first to use DSP to preliminarily characterize TDLNs in mesothelioma. We plan to apply this approach to stored additional MPM and NSCLC specimens to gain an in-depth understanding of the relationship between TME and TDLN

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.

In lung cancer, enrichment of the lower airway microbiota with oral commensals commonly occurs and ex vivo models support that some of these bacteria can trigger host transcriptomic signatures associated with carcinogenesis. Here, we show that this lower airway dysbiotic signature was more prevalent in group IIIB-IV TNM stage lung cancer and is associated with poor prognosis, as shown by decreased survival among subjects with early stage disease (I-IIIA) and worse tumor progression as measured by RECIST scores among subjects with IIIB-IV stage disease. In addition, this lower airway microbiota signature was associated with upregulation of IL-17, PI3K, MAPK and ERK pathways in airway transcriptome, and we identified Veillonella parvula as the most abundant taxon driving this association. In a KP lung cancer model, lower airway dysbiosis with V. parvula led to decreased survival, increased tumor burden, IL-17 inflammatory phenotype and activation of checkpoint inhibitor markers.

The paucity of genetically informed, immune-competent tumor models impedes evaluation of conventional, targeted, and immune therapies. By engineering mouse fallopian tube epithelial organoids using lentiviral gene transduction and/or CRISPR/Cas9 mutagenesis, we generated multiple high grade serous tubo-ovarian carcinoma (HGSC) models exhibiting mutational combinations seen in HGSC patients. Detailed analysis of homologous recombination (HR)-proficient (Tp53-/-;Ccne1OE;Akt2OE ;KrasOE), HR-deficient (Tp53-/-;Brca1-/-;MycOE), and unclassified (Tp53-/-;Pten-/-;Nf1-/-) organoids revealed differences in in vitro properties (proliferation, differentiation, "secretome"), copy number aberrations, and tumorigenicity. Tumorigenic organoids had variable sensitivity to HGSC chemotherapeutics, evoked distinct immune microenvironments that could be modulated by neutralizing organoid-produced chemokines/cytokines. These findings enabled development of a chemotherapy/immunotherapy regimen that yielded durable, T-cell dependent responses in Tp53-/-;Ccne1OE;Akt2OE;Kras HGSC; by contrast, Tp53-/-;Pten-/-;Nf1-/- tumors failed to respond. Mouse and human HGSC models showed genotype-dependent similarities in chemosensitivity, secretome, and immune microenvironment. Genotype-informed, syngeneic organoid models could provide a platform for the rapid evaluation of tumor biology and therapeutics.

Aims/UNASSIGNED: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/UNASSIGNED: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/UNASSIGNED: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/UNASSIGNED: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.

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the United States. Glycans, such as carbohydrate antigen 19-9, are biomarkers of PDAC and are emerging as important modulators of cancer phenotypes. Herein, we used a systems-based approach integrating glycomic analysis of the well-established KC mouse, which models early events in transformation, and analysis of samples from human pancreatic cancer patients to identify glycans with potential roles in cancer formation. We observed both common and distinct patterns of glycosylation in pancreatic cancer across species. Common alterations included increased levels of α-2,3-sialic acid and α-2,6-sialic acid, bisecting GlcNAc and poly-N-acetyllactosamine. However, core fucose, which was increased in human PDAC, was not seen in the mouse, indicating that not all human glycomic changes are observed in the KC mouse model. In silico analysis of bulk and single-cell sequencing data identified ST6 beta-galactoside alpha-2,6-sialyltransferase 1, which underlies α-2,6-sialic acid, as overexpressed in human PDAC, concordant with histological data showing higher levels of this enzyme at the earliest stages. To test whether ST6 beta-galactoside alpha-2,6-sialyltransferase 1 promotes pancreatic cancer, we created a novel mouse in which a pancreas-specific genetic deletion of this enzyme overlays the KC mouse model. The analysis of our new model showed delayed cancer formation and a significant reduction in fibrosis. Our results highlight the importance of a strategic systems approach to identifying glycans whose functions can be modeled in mouse, a crucial step in the development of therapeutics targeting glycosylation in pancreatic cancer.