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.

Alcohol dependence is a heterogeneous psychiatric disorder characterized by high genetic heritability and neuroadaptations occurring from repeated drug exposure. Through an integrated systems approach we observed consistent differences in transcriptome organization within postmortem human brain tissue associated with the lifetime consumption of alcohol. Molecular networks, determined using high-throughput RNA sequencing, for drinking behavior were dominated by neurophysiological targets and signaling mechanisms of alcohol. The systematic structure of gene-sets demonstrates a novel alliance of multiple ion-channels, and related processes, underlying lifetime alcohol consumption. Coordinate expression of these transcripts was enriched for genome-wide association signals in alcohol dependence and a meta-analysis of alcohol self-administration in mice. Further dissection of genes within alcohol consumption networks revealed the potential interaction of alternatively spliced transcripts. For example, expression of a human-specific isoform of the voltage-gated sodium channel subunit SCN4B was significantly correlated to lifetime alcohol consumption. Overall, our work demonstrates novel convergent evidence for biological networks related to excessive alcohol consumption, which may prove fundamentally important in the development of pharmacotherapies for alcohol dependence.

Kaposi's sarcoma-associated herpesvirus (KSHV) is a human tumor virus that encodes 12 precursor microRNAs (pre-miRNAs) that give rise to 17 different known~22-nucleotide (nt) effector miRNAs. Like all herpesviruses, KSHV has two modes of infection: (1) a latent mode whereby only a subset of viral genes are expressed and (2) a lytic mode during which the full remaining viral genes are expressed. To date, KSHV miRNAs have been mostly identified via analysis of cells that are undergoing latent infection. Here, we developed a method to profile small RNAs (~18-75 nt) from populations of cells undergoing predominantly lytic infection. Using two different next-generation sequencing platforms, we cloned and sequenced both premiRNAs and derivative miRNAs. Our analysis shows that the vast majority of viral and host 5p miRNAs are co-terminal with the 59 end of the cloned pre-miRNAs, consistent with both being defined by microprocessor cleavage. We report the complete repertoire (25 total) of 5p and 3p derivative miRNAs from all 12 previously described KSHV pre-miRNAs. Two KSHV premiRNAs, pre-miR-K12-8 and pre-miR-K12-12, encode abundant derivative miRNAs from the previously unreported strands of the pre-miRNA. We identify several novel small RNAs of low abundance, including viral miRNA-offset-RNAs (moRNAs), and antisense viral miRNAs (miRNA-AS) that are encoded antisense to previously reported KSHV pre-miRNAs. Finally, we observe widespread antisense transcription relative to known coding sequences during lytic replication. Despite the enormous potential to form double-stranded RNA in KSHV-infected cells, we observe no evidence for the existence of abundant viral-derived small interfering RNAs (siRNAs).

BackgroundA number of publications have reported the use of microarray technology to identify gene expression signatures to infer mechanisms and pathways associated with systemic lupus erythematosus (SLE) in human peripheral blood mononuclear cells. However, meta-analysis approaches with microarray data have not been well-explored in SLE.MethodsIn this study, a pathway-based meta-analysis was applied to four independent gene expression oligonucleotide microarray data sets to identify gene expression signatures for SLE, and these data sets were confirmed by a fifth independent data set.ResultsDifferentially expressed genes (DEGs) were identified in each data set by comparing expression microarray data from control samples and SLE samples. Using Ingenuity Pathway Analysis software, pathways associated with the DEGs were identified in each of the four data sets. Using the leave one data set out pathway-based meta-analysis approach, a 37-gene metasignature was identified. This SLE metasignature clearly distinguished SLE patients from controls as observed by unsupervised learning methods. The final confirmation of the metasignature was achieved by applying the metasignature to a fifth independent data set.ConclusionsThe novel pathway-based meta-analysis approach proved to be a useful technique for grouping disparate microarray data sets. This technique allowed for validated conclusions to be drawn across four different data sets and confirmed by an independent fifth data set. The metasignature and pathways identified by using this approach may serve as a source for identifying therapeutic targets for SLE and may possibly be used for diagnostic and monitoring purposes. Moreover, the meta-analysis approach provides a simple, intuitive solution for combining disparate microarray data sets to identify a strong metasignature.Please see Research Highlight: http://genomemedicine.com/content/3/5/30