Person:

Kelley, David Roy

RNA is known to be an abundant and important structural component of the nuclear matrix, including long noncoding RNAs (lncRNA). Yet the molecular identities, functional roles, and localization dynamics of lncRNAs that influence nuclear architecture remain poorly understood. Here, we describe one lncRNA, Firre, that interacts with the nuclear matrix factor hnRNPU, through a 156 bp repeating sequence and Firre localizes across a ~5 Mb domain on the X-chromosome. We further observed Firre localization across at least five distinct trans-chromosomal loci, which reside in spatial proximity to the Firre genomic locus on the X-chromosome. Both genetic deletion of the Firre locus or knockdown of hnRNPU resulted in loss of co-localization of these trans-chromosomal interacting loci. Thus, our data suggest a model in which lncRNAs such as Firre can interface with and modulate nuclear architecture across chromosomes.

Differentiation of human embryonic stem cells (hESCs) provides a unique opportunity to study the regulatory mechanisms that facilitate cellular transitions in a human context. To that end, we performed comprehensive transcriptional and epigenetic profiling of populations derived through directed differentiation of hESCs representing each of the three embryonic germ layers. Integration of whole-genome bisulfite sequencing, chromatin immunoprecipitation sequencing, and RNA sequencing reveals unique events associated with specification toward each lineage. Lineage-specific dynamic alterations in DNA methylation and H3K4me1 are evident at putative distal regulatory elements that are frequently bound by pluripotency factors in the undifferentiated hESCs. In addition, we identified germ-layer-specific H3K27me3 enrichment at sites exhibiting high DNA methylation in the undifferentiated state. A better understanding of these initial specification events will facilitate identification of deficiencies in current approaches, leading to more faithful differentiation strategies as well as providing insights into the rewiring of human regulatory programs during cellular transitions.

The prevalence of obesity has led to a surge of interest in understanding the detailed mechanisms underlying adipocyte development. Many protein-coding genes, mRNAs, and microRNAs have been implicated in adipocyte development, but the global expression patterns and functional contributions of long noncoding RNA (lncRNA) during adipogenesis have not been explored. Here we profiled the transcriptome of primary brown and white adipocytes, preadipocytes, and cultured adipocytes and identified 175 lncRNAs that are specifically regulated during adipogenesis. Many lncRNAs are adipose-enriched, strongly induced during adipogenesis, and bound at their promoters by key transcription factors such as peroxisome proliferator-activated receptor γ (PPARγ) and CCAAT/enhancer-binding protein α (CEBPα). RNAi-mediated loss of function screens identified functional lncRNAs with varying impact on adipogenesis. Collectively, we have identified numerous lncRNAs that are functionally required for proper adipogenesis.

Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ~1 h of hands-on time.