No abstract

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the United States Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed, for these and qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.

Determination of a 28,793-base-pair DNA sequence of a region from the Azotobacter vinelandii genome that includes and flanks the nitrogenase structural gene region was completed. This information was used to revise the previously proposed organization of the major nifcluster. The major nif cluster from A. vinelandii encodes 15 nif-specific genes whose products bear significant structural identity to the corresponding nif-specific gene products from Klebsiella pneumoniae. These genes include nifH, nifD, nipK, nipT, nifY, nijE, nifV, nijX, nifU, niS, niJfV, nimW, nijZ, nfJM, and nipF. Although there are significant spatial differences, the identified A.vinelandii nif-specific genes have the same sequential arrangement as the corresponding nif-specific genes from K. pneumoniae. Twelve other potential genes whose expression could be subject to nif-specific regulation were also found interspersed among the identified nif-specific genes. These potential genes do not encode products that are structurally related to the identified nif-specific gene products. Eleven potential nif-specific promoters were identified within the major nif cluster, and nine of these are preceded by an appropriate upstream activator sequence. A+T-rich regions were identified between 8 of the 11 proposed nif promoter sequences and their upstream activator sequences. Site-directed deletion-and-insertion mutagenesis was used to establish a genetic map of the major nif cluster.

Background Bronchial airway expression profiling has identified inflammatory subphenotypes of asthma, but invasiveness of this technique has limited its application to childhood asthma. Objectives To determine if the nasal transcriptome can proxy expression changes in the lung airway transcriptome in asthma. To determine if the nasal transcriptome can distinguish subphenotypes of asthma. Methods Whole transcriptome RNA-sequencing (RNA-seq) was performed on nasal airway brushings from 10 controls and 10 subjects with asthma, which was compared to established bronchial and small airway transcriptomes. Targeted RNA-seq nasal expression analysis was used to profile 105 genes in 50 subjects with asthma and 50 controls for differential expression and clustering analyses. Results We found 90.2% overlap in expressed genes and strong correlation in gene expression (ρ=0.87) between the nasal and bronchial transcriptomes. Previously observed asthmatic bronchial differential expression was strongly correlated with asthmatic nasal differential expression (ρ=0.77, p=5.6×10−9). Clustering analysis identified Th2-high and Th2-low subjects differentiated by expression of 70 genes including IL-13, IL-5, POSTN, CLCA1, and SERPINB2. Th2-high subjects were more likely to have atopy (O.R.=10.3, p=3.5×10−6), atopic asthma (OR=32.6, p=6.9×10−7), high blood eosinophils (OR=9.1, 2.6×10−6), and rhinitis (OR=8.3, p=4.1×10−6) compared to Th2-low subjects. Nasal IL-13 expression levels were 3.9-fold higher in asthmatic participants who experienced asthma exacerbation in the past year (p=0.01). Several differentially expressed nasal genes were specific to asthma and independent of atopic status. Conclusion Nasal airway gene expression profiles largely recapitulate expression profiles in the lung airways. Nasal expression profiling can be used to identify individuals with IL13-driven asthma and a Th2-skewed systemic immune response. Clinical Implications Nasal airway gene expression profiling can be used to easily identify the Th2-high subphenotype of asthma in children and also other genes dyregulated in the asthmatic airway but independent of atopic status.