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RNA-Seq Quantification of the Human Small Airway Epithelium Transcriptome

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RNA-Seq Quantification of the Human Small Airway Epithelium Transcriptome Hackett et al. BMC Genomics 2012, 13:82 http://www.biomedcentral.com/1471-2164/13/82 RESEARCHARTICLE Open Access RNA-Seq quantification of the human small airway epithelium transcriptome Neil R Hackett1*†, Marcus W Butler1*†, Renat Shaykhiev1*†, Jacqueline Salit1*, Larsson Omberg2, Juan L Rodriguez-Flores1*, Jason G Mezey1,2*, Yael Strulovici-Barel1*, Guoqing Wang1*, Lukas Didon1* and Ronald G Crystal1* Abstract Background: The small airway epithelium (SAE), the cell population that covers the human airway surface from the 6th generation of airway branching to the alveoli, is the major site of lung disease caused by smoking. The focus of this study is to provide quantitative assessment of the SAE transcriptome in the resting state and in response to chronic cigarette smoking using massive parallel mRNA sequencing (RNA-Seq). Results: The data demonstrate that 48% of SAE expressed genes are ubiquitous, shared with many tissues, with 52% enriched in this cell population. The most highly expressed gene, SCGB1A1, is characteristic of Clara cells, thecelltypeuniquetothehumanSAE.Amongothergenes expressed by the SAE are those related to Clara cell differentiation, secretory mucosal defense, and mucociliary differentiation. The high sensitivity of RNA-Seq permitted quantification of gene expression related to infrequent cell populations such as neuroendocrine cells and epithelial stem/progenitor cells. Quantification of the absolute smoking-induced changes in SAE gene expression revealed that, compared to ubiquitous genes, more SAE-enriched genes responded to smoking with up-regulation, and those with the highest basal expression levels showed most dramatic changes. Smoking had no effect on SAE gene splicing, but was associated with a shift in molecular pattern from Clara cell-associated towards the mucus-secreting cell differentiation pathway with multiple features of cancer-associated molecular phenotype. Conclusions: These observations provide insights into the unique biology of human SAE by providing quantit- ative assessment of the global transcriptome under physiological conditions and in response to the stress of chronic cigarette smoking. Background cigarette smoking, with its 4000 xenobiotics and > 1014 The tracheobronchial tree, a dichotomous branching oxidants per puff, is a major cause of airway disease, structure that begins at the larynx and ends after 23 including chronic obstructive pulmonary disease branches at the alveoli, is lined by an epithelium com- (COPD) and bronchogenic carcinoma [4-6]. It is the air- prised of 4 major cell types, including ciliated, secretory, way epithelium that exhibits the first abnormalities rele- undifferentiated columnar and basal cells [1,2]. The air- vant to COPD and lung cancer, and it is the small way epithelium is exposed directly to environmental airway epithelium (SAE; ≥ 6th generation) that is the pri- xenobiotics, particulates, pathogens and other toxic sub- mary site of the early manifestations of the majority of stances suspended in inhaled air [2-4]. Of these, chronic smoking-induced lung disease [7]. As compared to prox- imal airways, the small airway epithelium has unique * Correspondence: [email protected]; [email protected]; morphologic features with a decrease in the frequency [email protected]; [email protected]; [email protected]. of basal cells and mucus-secreting cells accompanied by edu; [email protected]; [email protected]; [email protected]. edu; [email protected]; [email protected] increased numbers of Clara cells, a secretory cell sub- † Contributed equally type critical for the maintenance of the structural and 1 Department of Genetic Medicine, Weill Cornell Medical College, New York, functional integrity at the airway-alveoli interface New York, USA Full list of author information is available at the end of the article [1,8-10]. © 2012 Hackett et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Hackett et al. BMC Genomics 2012, 13:82 Page 2 of 31 http://www.biomedcentral.com/1471-2164/13/82 Our group [11-13] and others [14-18] have carried out coverage, raw reads were converted into reads per kilo- several studies using gene expression microarrays to base of exon per million mapped reads (RPKM) [23]. assess the transcriptome of the human airway epithe- RPKM was then assessed across the entirety of reads lium, demonstrating that smoking modulates the expres- with reference to exons, introns and intergenic regions. sion of hundreds of genes. The advent of RNA-Seq A comparison was made between the expression levels technology, in which the entire polyadenylated tran- of exons and intergenic regions to define a threshold scriptome is sequenced [19-24], is capable of building valueabovewhichtherewasthe highest confidence in on this microarray data to provide additional insights thevalidityoftheexpressionvalue(Figure1A,B).This into the transcriptome of the airway epithelium and its was performed by generating a false discovery rate for a response to cigarette smoke. Because RNA-Seq provides range of expression values across all subjects, resulting direct sequencing information of all polyadenylated in the adoption of a cut-off value of 0.125 RPKM, repre- mRNAs and is not limited by probe design, RNA-Seq senting an optimal compromise between false positive data has inherently less noise and higher specificity, and, and false negative values (see Methods). All subsequent importantly, provides quantitative information on analyses were based on the application of this expression mRNA transcript number [19]. With high sensitivity threshold. and low background, RNA-Seq has a dynamic range of Of the 21,475 annotated genes in the Human Genome > 8,000-fold, is highly reproducible, yields digital infor- version 19 reference [26], 15,877 (73.9%) were expressed mation not requiring normalization, and can distinguish in SAE at greater than the RPKM cut-off value of 0.125. individual members of highly homologous gene families The average expression level was 13.8 RPKM (Figure [25]. In the context of this background, the focus of this 1C) and the average among subject coefficient of varia- study is to utilize massive parallel sequencing to quan- tion in RKPM was 0.25. This cut off may be conserva- tify the complete transcriptome of the human SAE in tive due to overestimation of the number of intronic healthy nonsmokers and healthy smokers. and intergenic reads. Based on a survey of a random intergenic domain on the genome, we estimate that Results ~50% of the reads mapped to intergenic and intronic Study Population and SAE sampling regions correspond to repetitive elements. These prob- SAE samples from 5 healthy nonsmokers and 6 healthy ably represent mismapping of reads that properly belong smokers were analyzed using mRNA-Seq (Additional polyadenylated mRNAs that contain the same repetitive file 1, Table S1). All individuals had no significant prior elements. If the impact was to drop the threshold from medical history and a normal physical examination. To 0.125, as used here, to 0.05, the number of expressed minimize the influence of potential confounding vari- genes would increase from 15,877 to 16,844 (a 6.1% ables, only males of African-American ancestry were increase). It is estimated that 1 RPKM corresponds to assessed. The nonsmokers were younger (p < 0.02). one mRNA per cell. As a result, we believe there is little There were no differences be-tween the two groups significant biology lost by using a conservative cut off with respect to pulmonary function criteria (p > 0.1, all (consistent with literature precedent) of 0.125 (i.e., one variables). The smoking status was confirmed by urinary mRNA per 8 cells) vs amorestringentcutoffof0.05 tobacco metabolites (see Additional Data Methods and (one mRNA per 20 cells). Additional file 1, Table S1). The number of cells recov- A subset of 12 genes with RPKM differing by > 4 logs ered ranged from 5.0 to 9.7 × 106, with > 97% epithelial was confirmed by TaqMan realtime quantitative PCR cells in all cases (Additional file 1, Table S1). There was with relative quantitation, using rRNA for normaliza- no difference between the two groups with respect to tion. The overall correlation between expression levels the proportions of each of the four major cell types, determined by these 2 methods was very strong (r2 = with the exception of ciliated cells, which were signifi- 0.81) (Additional file 1, Figure S1). cantly lower in the healthy smokers compared to the healthy nonsmokers (p < 0.04). Comparison of SAE Gene Expression to Other Tissue Transcriptomes Data Processing and Quality Control RNA-Seq allows absolute quantitation of mRNA levels The cDNA generated from SAE samples was run in a and for the fractional contribution of individual tran- single lane per subject on Illumina flow cells. A total of scripts to the total mRNA population to be assessed 182 million, 43 base pair single end reads were gener- [24]. In some tissues, transcripts from a relatively small ated, yielding 7.8 gigabases of sequence. These number of genes account for much of the total cellular sequences were aligned using Bowtie version 0.12 (see poly(A)+ RNA pool (Figure 2A). In the case of liver, the Additional file 1, Table S2 for a summary of mapping single most highly expressed gene contributes 10% of details). To correct for transcript length and depth of the total mRNA molecules and the top ten collectively Hackett et al. BMC Genomics 2012, 13:82 Page 3 of 31 http://www.biomedcentral.com/1471-2164/13/82 A. 0.10 Intergenic 0.08 0.06 Intron 0.04 Exon 0.02 Fraction of per genes bin 0.00 10-2 10-1 100 101 102 Expression level (RPKM) B. 0.125 1.0 False discovery 0.8 rate False 0.6 negative expressed expressed f rate regions 0.4 0.2 Fraction o 0.0 10-2 10-1 100 101 102 Expression level (RPKM) C.
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