Objective Mucociliary clearance sustains a baseline functionality and an “on demand” capability to upregulate clearance upon irritant exposure involving mucus hypersecretion and accelerated ciliary beat frequency (CBF) modulated by nitric oxide (NO). This study characterized these elements as well as cellular and exogenous NO concentrations subsequent to a single exposure to tobacco smoke (TS) or e-cigarette vapor (EV) on cultured human airway epithelium. Materials and methods Air-liquid interface (ALI) airway epithelial cultures per nonsmoking human subjects were subjected to single TS or EV exposures. Measures of ciliary function and secretion were performed and cellular and exogenous NO concentrations under control and experimental conditions were assessed. Results Both TS and EV exposures resulted similar patterns of decline in CBF within 1 min of the completion of exposure followed by a gradual return often exceeding baseline within 1 h. Post-exposure examination of exposed cultures suggested morphologic differences in secretory function relative to controls. The relative NO concentrations of TS and EV chamber air were sharply different with EV NO being only slightly elevated relative to cellular NO production. Discussion and conclusions Epithelial remodeling and mucociliary dysfunction have been clearly associated with TS exposure. However, information contrasting epithelial structure/function following a single acute TS or EV exposure is limited. This study demonstrates a similar pattern of epithelial response to acute TS or EV exposure. Inasmuch as NO may contribute to an inflammatory milieu and generation of toxic metabolites, it is plausible that recurrent exposures over time may be contributory to chronic pathologies.

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BACKGROUND:No methods exist to rapidly and accurately quantify the immune insult created by burn injuries. The development of a rapid, noninvasive clinical biomarker assay that evaluates a burn patient's underlying immune dysfunction and predicts clinical outcomes could transform burn care. We aimed to determine a set of peripheral biomarkers that correlates with clinical outcomes of burn patients. METHODS:This prospective observational study enrolled two patient cohorts within a single burn center into an institutionally approved institutional review board study. Blood draws were performed <48 hours after injury. Initial unbiased immune gene expression analysis compared 23 burn patients and 6 healthy controls using multiplex immune gene expression analysis of RNA from peripheral blood mononuclear cells. We then performed confirmatory outcomes analysis in 109 burn patients and 19 healthy controls using a targeted rapid quantitative polymerase chain reaction. Findings were validated and modeled associations with clinical outcomes using a regression model. RESULTS:A total of 149 genes with a significant difference in expression from burn patients compared with controls were identified. Pathway analysis identified pathways related to interleukin (IL)-10 and inducible nitric oxide synthase signaling to have significant z scores. quantitative polymerase chain reaction analysis of IL-10, IL-12, arginase 1 (ARG1), and inducible nitric oxide synthase demonstrated that burn injury was associated with increased expression of ARG1 and IL-10, and decreased expression of nitric oxide synthase 2 (NOS2) and IL-12. Burn severity, acute lung injury, development of infection, failure of skin autograft, and mortality significantly correlated with expression of one or more of these genes. Ratios of IL-10/IL-12, ARG1/NOS2, and (ARG1-IL-10)/ (NOS2-IL-12) transcript levels further improved the correlation with outcomes. Using a multivariate regression model, adjusting for patient confounders demonstrated that (ARG1-IL-10)/(NOS2-IL-12) significantly correlated with burn severity and development of acute lung injury. CONCLUSION:We present a means to predict patient outcomes early after burn injury using peripheral blood, allowing early identification of underlying immune dysfunction.

Burn patients are subject to significant acute immune and metabolic dysfunction. Concomitant inhalation injury increases mortality by 20%. In order to identify specific immune and metabolic signaling pathways in burn (B), inhalation (I), and combined burn-inhalation (BI) injury, unbiased nanoString multiplex technology was used to investigate gene expression within peripheral blood mononuclear cells (PBMCs) from burn patients, with and without inhalation injury. PBMCs were collected from 36 injured patients and 12 healthy, non-burned controls within 72 h of injury. mRNA was isolated and hybridized with probes for 1342 genes related to general immunology and cellular metabolism. From these specific gene patterns, specific cellular perturbations and signaling pathways were inferred using robust bioinformatic tools. In both B and BI injuries, elements of mTOR, PPARγ, TLR, and NF-kB signaling pathways were significantly altered within PBMC after injury compared to PBMC from the healthy control group. Using linear regression modeling, (1) DEPTOR, LAMTOR5, PPARγ, and RPTOR significantly correlated with patient BMI; (2) RPTOR significantly correlated with patient length of stay, and (3) MRC1 significantly correlated with the eventual risk of patient mortality. Identification of mediators of this immunometabolic response that can act as biomarkers and/or therapeutic targets could ultimately aid the management of burn patients.

Cancer (treatment) vaccines that are made of neoantigens, or peptides unique to tumor cells due to somatic mutations, have emerged as a promising method to reinvigorate the immune response against cancer. A key step to prioritizing neoantigens for cancer vaccines is computationally predicting which neoantigens are presented on the cell surface by a human leukocyte antigen (HLA). We propose to address this challenge by training a neural network using mass spectrometry (MS) data composed of peptides presented by at least one of several HLAs of a subject. We embed the neural network within a mixture model and train the neural network by maximizing the likelihood of the mixture model. After evaluating our method using data sets where the peptide presentation status was known, we applied it to analyze somatic mutations of 60 melanoma patients and identified a group of neoantigens more immunogenic in tumor cells than in normal cells. Moreover, neoantigen burden estimated by our method was significantly associated with a measurement of the immune system activity, suggesting these neoantigens could induce an immune response.