E-cigarette Vapor Extract Alters Human Eosinophil Gene Expression in an Effect Mediated by Propylene glycol, Glycerin, and Nicotine.

E-cigarette use has become widespread, and its effects on airway inflammation and disease are not fully delineated. E-cigarette vapor extract (EVE) profoundly affects neutrophil function. We hypothesized that EVE also alters eosinophil function and thus could impact allergic airways disease. We employed RNA-sequencing to measure the ex vivo effect of EVE components on human eosinophil transcription. Blood eosinophils from 9 non-vaping subjects without asthma were isolated by negative selection. Cells were incubated for 48 hours with EVE consisting of glycerin, propylene glycol and nicotine (EVE+), EVE without nicotine ("EVE-"), air-exposed media termed Extract Buffer (EB), or untreated media. Bulk RNA-sequencing was performed. Transcriptomic analysis revealed that the EB, EVE-, and EVE+ conditions showed highly variable gene expression with respect to No Treatment and each other. Differential gene expression analysis comparing a combination of EVE+, EVE-, and EB revealed 3,030 differentially expressed genes (DEG) with adjusted p value < 0.05 and log2 fold change >0.5 or <0.5. There were 645 DEG between EB and EVE-, 1,713 between EB and EVE+, and 404 between EVE- and EVE+. Gene set enrichment analysis demonstrated that DEG between both EVE+ and EVE- and the EB control were positively enriched for heme metabolism and apoptosis and negatively enriched TNFα signaling, IFNγ signaling, and inflammatory response. Thus, EVE significantly alters eosinophil metabolic and inflammatory pathways, mediated by propylene glycol and glycerin with both enhancing and unique effects of nicotine. This study motivates further research into the pathogenic effects of vaping on airway eosinophils and allergic airways disease.

A functional identification platform reveals frequent, spontaneous neoantigen-specific T cell responses in patients with cancer.

The clinical impact of tumor-specific neoantigens as both immunotherapeutic targets and biomarkers has been impeded by the lack of efficient methods for their identification and validation from routine samples. We have developed a platform that combines bioinformatic analysis of tumor exomes and transcriptional data with functional testing of autologous peripheral blood mononuclear cells (PBMCs) to simultaneously identify and validate neoantigens recognized by naturally primed CD4+ and CD8+ T cell responses across a range of tumor types and mutational burdens. The method features a human leukocyte antigen (HLA)-agnostic bioinformatic algorithm that prioritizes mutations recognized by patient PBMCs at a greater than 40% positive predictive value followed by a short-term in vitro functional assay, which allows interrogation of 50 to 75 expressed mutations from a single 50-ml blood sample. Neoantigens validated by this method include both driver and passenger mutations, and this method identified neoantigens that would not have been otherwise detected using an in silico prediction approach. These findings reveal an efficient approach to systematically validate clinically actionable neoantigens and the T cell receptors that recognize them and demonstrate that patients across a variety of human cancers have a diverse repertoire of neoantigen-specific T cells.

Transcriptome and chromatin landscape of iNKT cells are shaped by subset differentiation and antigen exposure.

Invariant natural killer T cells (iNKT cells) differentiate into thymic and peripheral NKT1, NKT2 and NKT17 subsets. Here we use RNA-seq and ATAC-seq analyses and show iNKT subsets are similar, regardless of tissue location. Lung iNKT cell subsets possess the most distinct location-specific features, shared with other innate lymphocytes in the lung, possibly consistent with increased activation. Following antigenic stimulation, iNKT cells undergo chromatin and transcriptional changes delineating two populations: one similar to follicular helper T cells and the other NK or effector like. Phenotypic analysis indicates these changes are observed long-term, suggesting that iNKT cells gene programs are not fixed, but they are capable of chromatin remodeling after antigen to give rise to additional subsets.

Predicting T cell recognition of MHC class I restricted neoepitopes.

Epitopes that arise from a somatic mutation, also called neoepitopes, are now known to play a key role in cancer immunology and immunotherapy. Recent advances in high-throughput sequencing have made it possible to identify all mutations and thereby all potential neoepitope candidates in an individual cancer. However, most of these neoepitope candidates are not recognized by T cells of cancer patients when tested in vivo or in vitro, meaning they are not immunogenic. Especially in patients with a high mutational load, usually hundreds of potential neoepitopes are detected, highlighting the need to further narrow down this candidate list. In our study, we assembled a dataset of known, naturally processed, immunogenic neoepitopes to dissect the properties that make these neoepitopes immunogenic. The tools to use and thresholds to apply for prioritizing neoepitopes have so far been largely based on experience with epitope identification in other settings such as infectious disease and allergy. Here, we performed a detailed analysis on our dataset of curated immunogenic neoepitopes to establish the appropriate tools and thresholds in the cancer setting. To this end, we evaluated different predictors for parameters that play a role in a neoepitope's immunogenicity and suggest that using binding predictions and length-rescaling yields the best performance in discriminating immunogenic neoepitopes from a background set of mutated peptides. We furthermore show that almost all neoepitopes had strong predicted binding affinities (as expected), but more surprisingly, the corresponding non-mutated peptides had nearly as high affinities. Our results provide a rational basis for parameters in neoepitope filtering approaches that are being commonly used. Abbreviations: SNV: single nucleotide variant; nsSNV: nonsynonymous single nucleotide variant; ROC: receiver operating characteristic; AUC: area under ROC curve; HLA: human leukocyte antigen; MHC: major histocompatibility complex; PD-1: Programmed cell death protein 1; PD-L1 or CTLA-4: cytotoxic T-lymphocyte associated protein 4.