Transcriptional Classification and Functional Characterization of Human Airway Macrophage and Dendritic Cell Subsets.

The respiratory system is a complex network of many cell types, including subsets of macrophages and dendritic cells that work together to maintain steady-state respiration. Owing to limitations in acquiring cells from healthy human lung, these subsets remain poorly characterized transcriptionally and phenotypically. We set out to systematically identify these subsets in human airways by developing a schema of isolating large numbers of cells by whole-lung bronchoalveolar lavage. Six subsets of phagocytic APC (HLA-DR+) were consistently observed. Aside from alveolar macrophages, subsets of Langerin+, BDCA1-CD14+, BDCA1+CD14+, BDCA1+CD14-, and BDCA1-CD14- cells were identified. These subsets varied in their ability to internalize Escherichia coli, Staphylococcus aureus, and Bacillus anthracis particles. All subsets were more efficient at internalizing S. aureus and B. anthracis compared with E. coli Alveolar macrophages and CD14+ cells were overall more efficient at particle internalization compared with the four other populations. Subsets were further separated into two groups based on their inherent capacities to upregulate surface CD83, CD86, and CCR7 expression levels. Whole-genome transcriptional profiling revealed a clade of "true dendritic cells" consisting of Langerin+, BDCA1+CD14+, and BDCA1+CD14- cells. The dendritic cell clade was distinct from a macrophage/monocyte clade, as supported by higher mRNA expression levels of several dendritic cell-associated genes, including CD1, FLT3, CX3CR1, and CCR6 Each clade, and each member of both clades, was discerned by specific upregulated genes, which can serve as markers for future studies in healthy and diseased states.

Exploring gene expression biomarker candidates for neurobehavioral impairment from total sleep deprivation.

BACKGROUND: Although sleep deprivation is associated with neurobehavioral impairment that may underlie significant risks to performance and safety, there is no reliable biomarker test to detect dangerous levels of impairment from sleep loss in humans. This study employs microarrays and bioinformatics analyses to explore candidate gene expression biomarkers associated with total sleep deprivation (TSD), and more specifically, the phenotype of neurobehavioral impairment from TSD. Healthy adult volunteers were recruited to a sleep laboratory for seven consecutive days (six nights). After two Baseline nights of 10 h time in bed, 11 subjects underwent an Experimental phase of 62 h of continuous wakefulness, followed by two Recovery nights of 10 h time in bed. Another six subjects underwent a well-rested Control condition of 10 h time in bed for all six nights. Blood was drawn for measuring gene expression on days two, four, and six at 4 h intervals from 08:00 to 20:00 h, corresponding to 12 timepoints across one Baseline, one Experimental, and one Recovery day. RESULTS: Altogether 212 genes changed expression in response to the TSD Treatment, with most genes exhibiting down-regulation during TSD. Also, 28 genes were associated with neurobehavioral impairment as measured by the Psychomotor Vigilance Test. The results support previous findings associating TSD with the immune response and ion signaling, and reveal novel candidate biomarkers such as the Speedy/RINGO family of cell cycle regulators. CONCLUSIONS: This study serves as an important step toward understanding gene expression changes during sleep deprivation. In addition to exploring potential biomarkers for TSD, this report presents novel candidate biomarkers associated with lapses of attention during TSD. Although further work is required for biomarker validation, analysis of these genes may aid fundamental understanding of the impact of TSD on neurobehavioral performance.

Gene expression profiling of primary human type I alveolar epithelial cells exposed to Bacillus anthracis spores reveals induction of neutrophil and monocyte chemokines.

The lung is the entry site for Bacillus anthracis in inhalation anthrax, the most deadly form of the disease. Spores must escape through the alveolar epithelial cell (AEC) barrier and migrate to regional lymph nodes, germinate and enter the circulatory system to cause disease. Several mechanisms to explain alveolar escape have been postulated, and all these tacitly involve the AEC barrier. In this study, we incorporate our primary human type I AEC model, microarray and gene enrichment analysis, qRT-PCR, multiplex ELISA, and neutrophil and monocyte chemotaxis assays to study the response of AEC to B. anthracis, (Sterne) spores at 4 and 24 h post-exposure. Spore exposure altered gene expression in AEC after 4 and 24 h and differentially expressed genes (±1.3 fold, p ≤ 0.05) included CCL4/MIP-1ß (4 h), CXCL8/IL-8 (4 and 24 h) and CXCL5/ENA-78 (24 h). Gene enrichment analysis revealed that pathways involving cytokine or chemokine activity, receptor binding, and innate immune responses to infection were prominent. Microarray results were confirmed by qRT-PCR and multiplex ELISA assays. Chemotaxis assays demonstrated that spores induced the release of biologically active neutrophil and monocyte chemokines, and that CXCL8/IL-8 was the major neutrophil chemokine. The small or sub-chemotactic doses of CXCL5/ENA-78, CXCL2/GROß and CCL20/MIP-3α may contribute to chemotaxis by priming effects. These data provide the first whole transcriptomic description of the human type I AEC initial response to B. anthracis spore exposure. Taken together, our findings contribute to an increased understanding of the role of AEC in the pathogenesis of inhalational anthrax.

Human primary airway epithelial cells isolated from active smokers have epigenetically impaired antiviral responses.

BACKGROUND: Cigarette smoking (CS) is the main risk factor for the development of chronic obstructive pulmonary disease (COPD) and most COPD exacerbations are caused by respiratory infections including influenza. Influenza infections are more severe in smokers. The mechanism of the increased risk and severity of infections in smokers is likely multifactorial, but certainly includes changes in immunologic host defenses. METHODS: We investigated retinoic acid-inducible protein I (RIG-I) and interferon (IFN) induction by influenza A virus (IAV) in human bronchial epithelial cells (HBEC) isolated from smokers or nonsmokers. Subcultured HBEC cells were infected with A/Puerto Rico/8/1934 (PR8) IAV at an MOI of 1. After 24 h of infection, cells and supernatants were collected for qRT-PCR, immunoblot or ELISA to determine RIG-I, Toll-like receptor3 (TLR3) and IFN expression levels. RESULTS: IAV exposure induced a vigorous IFN-ß, IFN-λ 1 and IFN-λ 2/3 antiviral response in HBEC from nonsmokers and significant induction of RIG-I and TLR3. In cells from smokers, viral RIG-I and TLR3 mRNA induction was reduced 87 and 79 % compared to the response from nonsmokers. CS exposure history was associated with inhibition of viral induction of the IFN-ß, IFN-λ1 and IFN-λ 2/3 mRNA response by 85, 96 and 95 %, respectively, from that seen in HBEC from nonsmokers. The demethylating agent 5-Aza-2-deoxycytidine reversed the immunosuppressive effects of CS exposure in HBEC since viral induction of all three IFNs was restored. IFN-ß induction of RIG-I and TLR3 was also suppressed in the cells from smokers. CONCLUSION: Our results suggest that active smoking reduces expression of antiviral cytokines in primary HBEC cells. This effect likely occurs via downregulation of RIG-I and TLR3 due to smoke-induced epigenetic modifications. Reduction in lung epithelial cell RIG-I and TLR3 responses may be a major mechanism contributing to the increased risk and severity of viral respiratory infections in smokers and to viral-mediated acute exacerbations of COPD.

Microarray characterization of gene expression changes in blood during acute ethanol exposure.

BACKGROUND: As part of the civil aviation safety program to define the adverse effects of ethanol on flying performance, we performed a DNA microarray analysis of human whole blood samples from a five-time point study of subjects administered ethanol orally, followed by breathalyzer analysis, to monitor blood alcohol concentration (BAC) to discover significant gene expression changes in response to the ethanol exposure. METHODS: Subjects were administered either orange juice or orange juice with ethanol. Blood samples were taken based on BAC and total RNA was isolated from PaxGene™ blood tubes. The amplified cDNA was used in microarray and quantitative real-time polymerase chain reaction (RT-qPCR) analyses to evaluate differential gene expression. Microarray data was analyzed in a pipeline fashion to summarize and normalize and the results evaluated for relative expression across time points with multiple methods. Candidate genes showing distinctive expression patterns in response to ethanol were clustered by pattern and further analyzed for related function, pathway membership and common transcription factor binding within and across clusters. RT-qPCR was used with representative genes to confirm relative transcript levels across time to those detected in microarrays. RESULTS: Microarray analysis of samples representing 0%, 0.04%, 0.08%, return to 0.04%, and 0.02% wt/vol BAC showed that changes in gene expression could be detected across the time course. The expression changes were verified by qRT-PCR.The candidate genes of interest (GOI) identified from the microarray analysis and clustered by expression pattern across the five BAC points showed seven coordinately expressed groups. Analysis showed function-based networks, shared transcription factor binding sites and signaling pathways for members of the clusters. These include hematological functions, innate immunity and inflammation functions, metabolic functions expected of ethanol metabolism, and pancreatic and hepatic function. Five of the seven clusters showed links to the p38 MAPK pathway. CONCLUSIONS: The results of this study provide a first look at changing gene expression patterns in human blood during an acute rise in blood ethanol concentration and its depletion because of metabolism and excretion, and demonstrate that it is possible to detect changes in gene expression using total RNA isolated from whole blood. The analysis approach for this study serves as a workflow to investigate the biology linked to expression changes across a time course and from these changes, to identify target genes that could serve as biomarkers linked to pilot performance.

Examining smoking-induced differential gene expression changes in buccal mucosa.

BACKGROUND: Gene expression changes resulting from conditions such as disease, environmental stimuli, and drug use, can be monitored in the blood. However, a less invasive method of sample collection is of interest because of the discomfort and specialized personnel necessary for blood sampling especially if multiple samples are being collected. Buccal mucosa cells are easily collected and may be an alternative sample material for biomarker testing. A limited number of studies, primarily in the smoker/oral cancer literature, address this tissue's efficacy as an RNA source for expression analysis. The current study was undertaken to determine if total RNA isolated from buccal mucosa could be used as an alternative tissue source to assay relative gene expression. METHODS: Total RNA was isolated from swabs, reverse transcribed and amplified. The amplified cDNA was used in RT-qPCR and microarray analyses to evaluate gene expression in buccal cells. Initially, RT-qPCR was used to assess relative transcript levels of four genes from whole blood and buccal cells collected from the same seven individuals, concurrently. Second, buccal cell RNA was used for microarray-based differential gene expression studies by comparing gene expression between a group of female smokers and nonsmokers. RESULTS: An amplification protocol allowed use of less buccal cell total RNA (50 ng) than had been reported previously with human microarrays. Total RNA isolated from buccal cells was degraded but was of sufficient quality to be used with RT-qPCR to detect expression of specific genes. We report here the finding of a small number of statistically significant differentially expressed genes between smokers and nonsmokers, using buccal cells as starting material. Gene Set Enrichment Analysis confirmed that these genes had a similar expression pattern to results from another study. CONCLUSIONS: Our results suggest that despite a high degree of degradation, RNA from buccal cells from cheek mucosa could be used to detect differential gene expression between smokers and nonsmokers. However the RNA degradation, increase in sample variability and microarray failure rate show that buccal samples should be used with caution as source material in expression studies.