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Prader-Willi syndrome (PWS), a genetic disorder of obesity, intellectual disability and sleep abnormalities, is caused by loss of non-coding RNAs on paternal chromosome 15q11-q13. The imprinted minimal PWS locus encompasses a long non-coding RNA (lncRNA) transcript processed into multiple SNORD116 small nucleolar RNAs and the spliced exons of the host gene, 116HG. However, both the molecular function and the disease relevance of the spliced lncRNA 116HG are unknown. Here, we show that 116HG forms a subnuclear RNA cloud that co-purifies with the transcriptional activator RBBP5 and active metabolic genes, remains tethered to the site of its transcription and increases in size in post-natal neurons and during sleep. Snord116del mice lacking 116HG exhibited increased energy expenditure corresponding to the dysregulation of diurnally expressed Mtor and circadian genes Clock, Cry1 and Per2. These combined genomic and metabolic analyses demonstrate that 116HG regulates the diurnal energy expenditure of the brain. These novel molecular insights into the energy imbalance in PWS should lead to improved therapies and understanding of lncRNA roles in complex neurodevelopmental and metabolic disorders.

Prader-Willi syndrome (PWS) and Angelman syndrome (AS) are oppositely imprinted autism-spectrum disorders with known genetic bases, but complex epigenetic mechanisms underlie their pathogenesis. The PWS/AS locus on 15q11-q13 is regulated by an imprinting control region that is maternally methylated and silenced. The PWS imprinting control region is the promoter for a one megabase paternal transcript encoding the ubiquitous protein-coding Snrpn gene and multiple neuron-specific noncoding RNAs, including the PWS-related Snord116 repetitive locus of small nucleolar RNAs and host genes, and the antisense transcript to AS-causing ubiquitin ligase encoding Ube3a (Ube3a-ATS). Neuron-specific transcriptional progression through Ube3a-ATS correlates with paternal Ube3a silencing and chromatin decondensation. Interestingly, topoisomerase inhibitors, including topotecan, were recently identified in an unbiased drug screen for compounds that could reverse the silent paternal allele of Ube3a in neurons, but the mechanism of topotecan action on the PWS/AS locus is unknown. Here, we demonstrate that topotecan treatment stabilizes the formation of RNA:DNA hybrids (R loops) at G-skewed repeat elements within paternal Snord116, corresponding to increased chromatin decondensation and inhibition of Ube3a-ATS expression. Neural precursor cells from paternal Snord116 deletion mice exhibit increased Ube3a-ATS levels in differentiated neurons and show a reduced effect of topotecan compared with wild-type neurons. These results demonstrate that the AS candidate drug topotecan acts predominantly through stabilizing R loops and chromatin decondensation at the paternally expressed PWS Snord116 locus. Our study holds promise for targeted therapies to the Snord116 locus for both AS and PWS.

Although thousands of large intergenic non-coding RNAs (lincRNAs) have been identified in mammals, few have been functionally characterized, leading to debate about their biological role. To address this, we performed loss-of- function studies on most lincRNAs expressed in mouse embryonic stem (ES) cells and characterized the effects on gene expression. Here we show that knockdown of lincRNAs has major consequences on gene expression patterns, comparable to knockdown of well-known ES cell regulators. Notably, lincRNAs primarily affect gene expression in trans. Knockdown of dozens of lincRNAs causes either exit from the pluripotent state or upregulation of lineage commitment programs. We integrate lincRNAs into the molecular circuitry of ES cells and show that lincRNA genes are regulated by key transcription factors and that lincRNA transcripts bind to multiple chromatin regulatory proteins to affect shared gene expression programs. Together, the results demonstrate that lincRNAs have key roles in the circuitry controlling ES cell state., The mammalian genome encodes many thousands of large noncoding transcripts1 including a class of ~3,500 lincRNAs identified using a chromatin signature of actively transcribed genes2-4. These lincRNA genes have been [...]

Abstract Background Determining the expression levels of microRNAs (miRNAs) is of great interest to researchers in many areas of biology, given the significant roles these molecules play in cellular regulation. Two common methods for measuring miRNAs in a total RNA sample are microarrays and quantitative RT-PCR (qPCR). To understand the results of studies that use these two different techniques to measure miRNAs, it is important to understand how well the results of these two analysis methods correlate. Since both methods use total RNA as a starting material, it is also critical to understand how measurement of miRNAs might be affected by the particular method of total RNA preparation used. Results We measured the expression of 470 human miRNAs in nine human tissues using Agilent microarrays, and compared these results to qPCR profiles of 61 miRNAs in the same tissues. Most expressed miRNAs (53/60) correlated well (R > 0.9) between the two methods. Using spiked-in synthetic miRNAs, we further examined the two miRNAs with the lowest correlations, and found the differences cannot be attributed to differential sensitivity of the two methods. We also tested three widely-used total RNA sample prep methods using miRNA microarrays. We found that while almost all miRNA levels correspond between the three methods, there were a few miRNAs whose levels consistently differed between the different prep techniques when measured by microarray analysis. These differences were corroborated by qPCR measurements. Conclusion The correlations between Agilent miRNA microarray results and qPCR results are generally excellent, as are the correlations between different total RNA prep methods. However, there are a few miRNAs whose levels do not correlate between the microarray and qPCR measurements, or between different sample prep methods. Researchers should therefore take care when comparing results obtained using different analysis or sample preparation methods.

Abstract Background The increasing use of DNA microarrays in biomedical research, toxicogenomics, pharmaceutical development, and diagnostics has focused attention on the reproducibility and reliability of microarray measurements. While the reproducibility of microarray gene expression measurements has been the subject of several recent reports, there is still a need for systematic investigation into what factors most contribute to variability of measured expression levels observed among different laboratories and different experimenters. Results We report the results of an interlaboratory comparison of gene expression array measurements on the same microarray platform, in which the RNA amplification and labeling, hybridization and wash, and slide scanning were each individually varied. Identical input RNA was used for all experiments. While some sources of variation have measurable influence on individual microarray signals, they showed very low influence on sample-to-reference ratios based on averaged triplicate measurements in the two-color experiments. RNA labeling was the largest contributor to interlaboratory variation. Conclusion Despite this variation, measurement of one particular breast cancer gene expression signature in three different laboratories was found to be highly robust, showing a high intralaboratory and interlaboratory reproducibility when using strictly controlled standard operating procedures.