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.

TREX1 is an exonuclease that digests DNA in the cytoplasm. Loss-of-function mutations of TREX1 are linked to Aicardi–Goutieres Syndrome (AGS) and systemic lupus erythematosus (SLE) in humans. Trex1−/− mice exhibit autoimmune and inflammatory phenotypes that are associated with elevated expression of interferon (IFN)-induced genes (ISGs). Cyclic GMP-AMP (cGAMP) synthase (cGAS) is a cytosolic DNA sensor that activates the IFN pathway. Upon binding to DNA, cGAS is activated to catalyze the synthesis of cGAMP, which functions as a second messenger that binds and activates the adaptor protein STING to induce IFNs and other cytokines. Here we show that genetic ablation of cGas in Trex1−/− mice eliminated all detectable pathological and molecular phenotypes, including ISG induction, autoantibody production, aberrant T-cell activation, and lethality. Even deletion of just one allele of cGas largely rescued the phenotypes of Trex1−/− mice. Similarly, deletion of cGas in mice lacking DNaseII, a lysosomal enzyme that digests DNA, rescued the lethal autoimmune phenotypes of the DNaseII−/− mice. Through quantitative mass spectrometry, we found that cGAMP accumulated in mouse tissues deficient in Trex1 or DNaseII and that this accumulation was dependent on cGAS. These results demonstrate that cGAS activation causes the autoimmune diseases in Trex1−/− and DNaseII−/− mice and suggest that inhibition of cGAS may lead to prevention and treatment of some human autoimmune diseases caused by self-DNA.

The y-linked autoimmune accelerating (yaa) locus is a potent autoimmune disease allele. Transcription profiling of yaa-bearing B cells revealed the overexpression of a cluster of X-linked genes that included Tlr7. FISH analysis demonstrated the translocation of this segment onto the yaa chromosome. The resulting overexpression of Tlr7 increased in vitro responses to Toll-like receptor (TLR) 7 signaling in all yaa-bearing males. B6.yaa mice are not overtly autoimmune, but the addition of Sle1, which contains the autoimmune-predisposing Slam͞Cd2 haplotype, causes the development of fatal lupus with numerous immunological aberrations. B6.Sle1yaa CD4 T cells develop the molecular signature for T FH cells and also show expression changes in numerous cytokines and chemokines. Disease development and all component autoimmune phenotypes were inhibited by Sles1, a potent suppressor locus. Sles1 had no effect on yaa-enhanced TLR7 signaling in vitro, and these data place Sles1 downstream from the lesion in innate immune responses mediated by TLR7, suggesting that Sles1 modulates the activation of adaptive immunity in response to innate immune signaling.Sle1 ͉ Sles1 ͉ Toll-like receptor 7 ͉ yaa

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.