Summary Recently, more than a thousand large intergenic non-coding RNAs (lincRNAs) have been reported. These RNAs are evolutionarily conserved in mammalian genomes and thus presumably function in diverse biological processes. Here, we report the identification of lincRNAs that are regulated by p53. One of these lincRNAs (lincRNA-p21) serves as a repressor in p53-dependent transcriptional responses. Inhibition of lincRNA-p21 affects the expression of hundreds of gene targets enriched for genes normally repressed by p53. The observed transcriptional repression by lincRNA-p21 is mediated through the physical association with hnRNP-K. This interaction is required for proper genomic localization of hnRNP-K at repressed genes and regulation of p53 mediated apoptosis. We propose a model whereby transcription factors activate lincRNAs that serve as key repressors by physically associating with repressive complexes and modulating their localization to sets of previously active genes.

Regulation of RNA levels is determined through the interplay between RNA production, processing and degradation. However, since most global studies of RNA regulation do not distinguish the separate contributions of these processes, relatively little is known about how they are temporally integrated to determine changes in RNA levels. In particular, while some studies emphasize the role of changes in the rate of transcription, others suggest a prominent involvement of time-varying degradation rates. Here, we combine metabolic labeling of RNA at high temporal resolution with advanced RNA quantification assays and computational modeling to estimate RNA transcription and degradation rates during the model response of immune dendritic cells (DCs) to pathogens. We find that changes in transcription rates determine the majority of temporal changes in RNA levels, but that changes in degradation rate are important for shaping sharp ‘peaked’ responses. Furthermore, transcription rate changes precede corresponding changes in RNA level by a small lag (15-30 min), which is shorter for induced than for repressed genes. We used massively parallel sequencing of the newly-transcribed RNA population – including non-polyadenylated transcripts – to estimate constant RNA degradation and processing rates. We find that temporally constant degradation rates vary significantly between genes and contribute substantially to the observed differences in the dynamic response, and that specific groups of transcripts, mostly cytokines and transcription factors, are undergoing faster mRNA maturation. Our study provides a new quantitative approach to study key steps in the integrative process of RNA regulation.

Protein expression is regulated by production and degradation of mRNAs and proteins, but their specific relationships remain unknown. We combine measurements of protein production and degradation and mRNA dynamics to build a quantitative genomic model of the differential regulation of gene expression in LPS-stimulated mouse dendritic cells. Changes in mRNA abundance play a dominant role in determining most dynamic fold changes in protein levels. Conversely, the preexisting proteome of proteins performing basic cellular functions is remodeled primarily through changes in protein production or degradation, accounting for over half of the absolute change in protein molecules in the cell. Thus, the proteome is regulated by transcriptional induction of novel cellular functions and remodeling of preexisting functions through the protein life cycle.

Summary Cells control dynamic transitions in transcript levels by regulating transcription, processing and/or degradation through an integrated regulatory strategy. Here, we combine RNA metabolic labeling, rRNA-depleted RNA-seq, and DRiLL, a novel computational framework, to quantify the level, editing sites, and transcription, processing and degradation rates of each transcript at a splice junction resolution during the LPS response of mouse dendritic cells. Four key regulatory strategies, dominated by RNA transcription changes, generate most temporal gene expression patterns. Non-canonical strategies that also employ dynamic posttranscriptional regulation control only a minority of genes, but provide unique signal processing features. We validate Tristetraprolin (TTP) as a major regulator of RNA degradation in one non-canonical strategy. Applying DRiLL to the regulation of non-coding RNAs and to zebrafish embryogenesis demonstrates its broad utility. Our study provides a new quantitative approach to discover transcriptional and post-transcriptional events that control dynamic changes in transcript levels using RNA-Seq data.