High-Resolution Sequencing and Modeling Identifies Distinct Dynamic RNA Regulatory Strategies

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. Noncanonical 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 noncanonical strategy. Applying DRiLL to the regulation of noncoding RNAs and to zebrafish embryogenesis demonstrates its broad utility. Our study provides a new quantitative approach to discover transcriptional and posttranscriptional events that control dynamic changes in transcript levels using RNA sequencing data.National Human Genome Research Institute (U.S.) (Centers for Excellence in Genomics Science 1P50HG006193-01)Howard Hughes Medical InstituteNational Institutes of Health (U.S.) (Pioneer Award)Massachusetts Institute of Technology. William Asbjornsen Albert Memorial FellowshipXerox Fellowship Progra

A Large Intergenic Noncoding RNA Induced by p53 Mediates Global Gene Repression in the p53 Response

Recently, more than 1000 large intergenic noncoding 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 mediates apoptosis. We propose a model whereby transcription factors activate lincRNAs that serve as key repressors by physically associating with repressive complexes and modulate their localization to sets of previously active genes.National Institutes of Health (U.S.) (New Innovator Award)Smith Family FoundationDamon Runyon Cancer Research FoundationSearle Scholars ProgramNational Institutes of Health (U.S.) (1R01CA119176-01

Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells

available in PMC 2011 November 01.Cellular RNA levels are determined by the interplay of RNA production, processing and degradation. However, because most studies of RNA regulation do not distinguish the separate contributions of these processes, little is known about how they are temporally integrated. Here we combine metabolic labeling of RNA at high temporal resolution with advanced RNA quantification and computational modeling to estimate RNA transcription and degradation rates during the response of mouse dendritic cells to lipopolysaccharide. We find that changes in transcription rates determine the majority of temporal changes in RNA levels, but that changes in degradation rates are important for shaping sharp 'peaked' responses. We used sequencing of the newly transcribed RNA population to estimate temporally constant RNA processing and degradation rates genome wide. Degradation rates vary significantly between genes and contribute to the observed differences in the dynamic response. Certain transcripts, including those encoding cytokines and transcription factors, mature faster. Our study provides a quantitative approach to study the integrative process of RNA regulation.Human Frontier Science Program (Strasbourg, France)Howard Hughes Medical InstituteBurroughs Wellcome Fund (Career Award at the Scientific Interface

Models of dynamic Ribonucleic Acid regulation in mammalian cells

Thesis (Ph. D.)--Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 135-142).Complex molecular circuits, consisting of multiple intertwined feedback loops and non-linear interactions, are a hallmark of every living cell, and a model of a dynamic complex network. Here, I systematically study the dynamic changes in the cellular circuits that control RNA levels in mammalian cells, focusing on the model response of immune dendritic cells to pathogens, through an integration of comprehensive computational models and innovative empirical approaches. I establish a computational framework to follow the dynamics of processes for RNA birth (production, by transcription), maturation (processing), and death (degradation), and their integration in the dynamic RNA life cycle. I study the kinetics of a gene's RNA population with a model of its production and degradation, and generalize the system as an ensemble of genes. I further model genes as composite particles and study the regulation and kinetics of altering their internal structure. To allow robust statistical inference from these models, I develop innovative laboratory assays and collect extensive experimental data on the system. I directly measure RNA production rates by coupling short RNA metabolic labeling with advanced RNA quantification. I leverage recent improvements in RNA quantification by next-generation sequencing technology, to significantly increase the resolution of metabolic labeling in both time and gene-structure. Finally, I collect perturbation data, by monitoring RNA levels when specific elements of the network are disabled. In this way, I formulated several general principles of RNA regulation and its temporal evolution in mammalian cells. I find that temporal changes in production provide a dominant input in computing RNA levels by the cell over time. Yet, dynamic degradation changes contribute to shaping expression peaks, and dynamic processing changes allow a fast accumulation of mature transcripts. Static degradation and processing rates vary between genes and between individual splicing junctions, consistently with their function and expression dynamics. This study is broadly applicable to many normal as well as diseases misregulated cellular networks, and is also relevant for a more general analysis of complex systems dynamics.by Michal Rabani.Ph.D

A Massively Parallel Reporter Assay of 3' UTR Sequences Identifies In Vivo Rules for mRNA Degradation

The stability of mRNAs is regulated by signals within their sequences, but a systematic and predictive understanding of the underlying sequence rules remains elusive. Here we introduce UTR-seq, a combination of massively parallel reporter assays and regression models, to survey the dynamics of tens of thousands of 3' UTR sequences during early zebrafish embryogenesis. UTR-seq revealed two temporal degradation programs: a maternally encoded early-onset program and a late-onset program that accelerated degradation after zygotic genome activation. Three signals regulated early-onset rates: stabilizing poly-U and UUAG sequences and destabilizing GC-rich signals. Three signals explained late-onset degradation: miR-430 seeds, AU-rich sequences, and Pumilio recognition sites. Sequence-based regression models translated 3' UTRs into their unique decay patterns and predicted the in vivo effect of sequence signals on mRNA stability. Their application led to the successful design of artificial 3' UTRs that conferred specific mRNA dynamics. UTR-seq provides a general strategy to uncover the rules of RNA cis regulation

Feed-Forward Regulation of a Cell Fate Determinant by an RNA-Binding Protein Generates Asymmetry in Yeast

Saccharomyces cerevisiae can divide asymmetrically so that the mother and daughter cells have different fates. We show that the RNA-binding protein Khd1 regulates asymmetric expression of FLO11 to determine daughter cell fate during filamentous growth. Khd1 represses transcription of FLO11 indirectly through its regulation of ASH1 mRNA. Khd1 also represses FLO11 through a post-transcriptional mechanism independent of ASH1. Cross-linking immunoprecipitation (CLIP) coupled with high-throughput sequencing shows that Khd1 directly binds repetitive sequences in FLO11 mRNA. Khd1 inhibits translation through this interaction, establishing feed-forward repression of FLO11. This regulation enables changes in FLO11 expression between mother and daughter cells, which establishes the asymmetry required for the developmental transition between yeast form and filamentous growth