Promoter-dependent nuclear RNA degradation ensures cell cycle-specific gene expression

Cell-cycle-dependent mRNA localization in P-bodies

Understanding the dynamics of RNA targeting to membraneless organelles is essential to disentangle their functions. Here, we investigate how P-bodies (PBs) evolve during cell-cycle progression in HEK293 cells. PB purification across the cell cycle uncovers widespread changes in their RNA content, partly uncoupled from cell-cycle-dependent changes in RNA expression. Single-molecule fluorescence in situ hybridization (FISH) shows various mRNA localization patterns in PBs peaking in G1, S, or G2, with examples illustrating the timely capture of mRNAs in PBs when their encoded protein becomes dispensable. Rather than directly reflecting absence of translation, cyclic mRNA localization in PBs can be controlled by RBPs, such as HuR in G2, and by RNA features. Indeed, while PB mRNAs are AU rich at all cell-cycle phases, they are specifically longer in G1, possibly related to post-mitotic PB reassembly. Altogether, our study supports a model where PBs are more than a default location for excess untranslated mRNAs.

Ratcheted transport and sequential assembly of the yeast telomerase RNP

The telomerase ribonucleoprotein particle (RNP) replenishes telomeric DNA and minimally requires an RNA component and a catalytic protein subunit. However, telomerase RNP maturation is an intricate process occurring in several subcellular compartments and is incompletely understood. Here, we report how the co-transcriptional association of key telomerase components and nuclear export factors leads to an export-competent, but inactive, RNP. Export is dependent on the 5' cap, the 3' extension of unprocessed telomerase RNA, and protein associations. When the RNP reaches the cytoplasm, an extensive protein swap occurs, the RNA is trimmed to its mature length, and the essential catalytic Est2 protein joins the RNP. This mature and active complex is then reimported into the nucleus as its final destination and last processing steps. The irreversible processing events on the RNA thus support a ratchet-type model of telomerase maturation, with only a single nucleo-cytoplasmic cycle that is essential for the assembly of mature telomerase.

Dynamically regulated transcription factors are encoded by highly unstable mRNAs in the Drosophila larval brain

The level of each RNA species depends on the balance between its rates of production and decay. Although previous studies have measured RNA decay across the genome in tissue culture and single-celled organisms, few experiments have been performed in intact complex tissues and organs. It is therefore unclear whether the determinants of RNA decay found in cultured cells are preserved in an intact tissue, and whether they differ between neighboring cell types and are regulated during development. To address these questions, we measured RNA synthesis and decay rates genome wide via metabolic labeling of whole cultured Drosophila larval brains using 4-thiouridine. Our analysis revealed that decay rates span a range of more than 100-fold, and that RNA stability is linked to gene function, with mRNAs encoding transcription factors being much less stable than mRNAs involved in core metabolic functions. Surprisingly, among transcription factor mRNAs there was a clear demarcation between more widely used transcription factors and those that are expressed only transiently during development. mRNAs encoding transient transcription factors are among the least stable in the brain. These mRNAs are characterized by epigenetic silencing in most cell types, as shown by their enrichment with the histone modification H3K27me3. Our data suggest the presence of an mRNA destabilizing mechanism targeted to these transiently expressed transcription factors to allow their levels to be regulated rapidly with high precision. Our study also demonstrates a general method for measuring mRNA transcription and decay rates in intact organs or tissues, offering insights into the role of mRNA stability in the regulation of complex developmental programs.

Reliance of Host-Encoded Regulators of Retromobility on Ty1 Promoter Activity or Architecture

The Ty1 retrotransposon family is maintained in a functional but dormant state by its host, Saccharomyces cerevisiae. Several hundred RHF and RTT genes encoding co-factors and restrictors of Ty1 retromobility, respectively, have been identified. Well-characterized examples include MED3 and MED15, encoding subunits of the Mediator transcriptional co-activator complex; control of retromobility by Med3 and Med15 requires the Ty1 promoter in the U3 region of the long terminal repeat. To characterize the U3-dependence of other Ty1 regulators, we screened a library of 188 known rhf and rtt mutants for altered retromobility of Ty1his3AI expressed from the strong, TATA-less TEF1 promoter or the weak, TATA-containing U3 promoter. Two classes of genes, each including both RHFs and RTTs, were identified. The first class comprising 82 genes that regulated Ty1his3AI retromobility independently of U3 is enriched for RHF genes that restrict the G1 phase of the cell cycle and those involved in transcriptional elongation and mRNA catabolism. The second class of 51 genes regulated retromobility of Ty1his3AI driven only from the U3 promoter. Nineteen U3-dependent regulators (U3DRs) also controlled retromobility of Ty1his3AI driven by the weak, TATA-less PSP2 promoter, suggesting reliance on the low activity of U3. Thirty-one U3DRs failed to modulate P_PSP2-Ty1his3AI retromobility, suggesting dependence on the architecture of U3. To further investigate the U3-dependency of Ty1 regulators, we developed a novel fluorescence-based assay to monitor expression of p22-Gag, a restriction factor expressed from the internal Ty1i promoter. Many U3DRs had minimal effects on levels of Ty1 RNA, Ty1i RNA or p22-Gag. These findings uncover a role for the Ty1 promoter in integrating signals from diverse host factors to modulate Ty1 RNA biogenesis or fate.

Stress-induced inhibition of mRNA export triggers RNase III-mediated decay of the BDF2 mRNA

The expression of bromodomain-containing proteins that regulate chromatin structure and accessibility must be tightly controlled to ensure the appropriate regulation of gene expression. In the yeast S. cerevisiae, Bromodomain Factor 2 (BDF2) expression is extensively regulated post-transcriptionally during stress by RNase III-mediated decay (RMD), which is triggered by cleavage of the BDF2 mRNA in the nucleus by the RNase III homolog Rnt1p. Previous studies have shown that RMD-mediated down-regulation of BDF2 is hyperactivated in osmotic stress conditions, yet the mechanisms driving the enhanced nuclear cleavage of BDF2 RNA under these conditions remain unknown. Here, we show that RMD hyperactivation can be detected in multiple stress conditions that inhibit mRNA export, and that Rnt1p remains primarily localized in the nucleus during salt stress. We show that globally inhibiting mRNA nuclear export by anchoring away mRNA biogenesis or export factors out of the nucleus can recapitulate RMD hyperactivation in the absence of stress. RMD hyperactivation requires Rnt1p nuclear localization but does not depend on the BDF2 gene endogenous promoter, and its efficiency is affected by the structure of the stem-loop cleaved by Rnt1p. Because multiple stress conditions have been shown to mediate global inhibition of mRNA export, our results suggest that the hyperactivation of RMD is primarily the result of the increased nuclear retention of the BDF2 mRNA during stress.

Co-transcriptional RNA cleavage by Drosha homolog Pac1 triggers transcription termination in fission yeast

Transcription termination of protein-coding genes in eukaryotic cells usually relies on a tight coordination between the cleavage and polyadenylation of the pre-mRNA, and 5′-3′ degradation of the downstream nascent transcript. Here we investigated the contribution of the essential fission yeast endonuclease Pac1, a homolog of human Drosha that cleaves hairpin RNA structures, in triggering polyadenylation-independent transcription termination. Using ChIP-sequencing in Pac1-deficient cells, we found that Pac1 triggers transcription termination at snRNA and snoRNA genes as well as at specific protein-coding genes. Notably, we found that Pac1-dependent premature termination occurred at two genes encoding conserved transmembrane transporters whose expression were strongly repressed by Pac1. Analysis by genome editing indicated that a stem-loop structure in the nascent transcript directs Pac1-mediated cleavage and that the regions upstream and downstream of the Pac1 cleavage site in the targeted mRNAs were stabilized by mutation of nuclear 3′-5′ and 5′-3′ exonucleases, respectively. Our findings unveil a premature transcription termination pathway that uncouples co-transcriptional RNA cleavage from polyadenylation, triggering rapid nuclear RNA degradation.

Transcription coordinates histone amounts and genome content

Biochemical reactions typically depend on the concentrations of the molecules involved, and cell survival therefore critically depends on the concentration of proteins. To maintain constant protein concentrations during cell growth, global mRNA and protein synthesis rates are tightly linked to cell volume. While such regulation is appropriate for most proteins, certain cellular structures do not scale with cell volume. The most striking example of this is the genomic DNA, which doubles during the cell cycle and increases with ploidy, but is independent of cell volume. Here, we show that the amount of histone proteins is coupled to the DNA content, even though mRNA and protein synthesis globally increase with cell volume. As a consequence, and in contrast to the global trend, histone concentrations decrease with cell volume but increase with ploidy. We find that this distinct coordination of histone homeostasis and genome content is already achieved at the transcript level, and is an intrinsic property of histone promoters that does not require direct feedback mechanisms. Mathematical modeling and histone promoter truncations reveal a simple and generalizable mechanism to control the cell volume- and ploidy-dependence of a given gene through the balance of the initiation and elongation rates.

Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets

The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its molecular pathology rather than an organ. The molecular similarities among multi-omics data found in different cancer types can play several roles in both biological processes and therapeutic developments. Therefore, an integrated analysis for various genomic data is frequently used to reveal novel genetic and molecular mechanisms. However, a variety of algorithms for multi-omics clustering have been proposed in different fields. The comparison of different computational clustering methods in pan-cancer analysis performance remains unclear. To increase the utilization of current integrative methods in pan-cancer analysis, we first provide an overview of five popular computational integrative tools: similarity network fusion, integrative clustering of multiple genomic data types (iCluster), cancer integration via multi-kernel learning (CIMLR), perturbation clustering for data integration and disease subtyping (PINS) and low-rank clustering (LRACluster). Then, a priori interactions in multi-omics data were incorporated to detect prominent molecular patterns in pan-cancer data sets. Finally, we present comparative assessments of these methods, with discussion over key issues in applying these algorithms. We found that all five methods can identify distinct tumor compositions. The pan-cancer samples can be reclassified into several groups by different proportions. Interestingly, each method can classify the tumors into categories that are different from original cancer types or subtypes, especially for ovarian serous cystadenocarcinoma (OV) and breast invasive carcinoma (BRCA) tumors. In addition, all clusters of the five computational methods show notable prognostic values. Furthermore, both the 9 recurrent differential genes and the 15 common pathway characteristics were identified across all the methods. The results and discussion can help the community select appropriate integrative tools according to different research tasks or aims in pan-cancer analysis.

Stochastic simulation and statistical inference platform for visualization and estimation of transcriptional kinetics

Recent advances in single-molecule fluorescent imaging have enabled quantitative measurements of transcription at a single gene copy, yet an accurate understanding of transcriptional kinetics is still lacking due to the difficulty of solving detailed biophysical models. Here we introduce a stochastic simulation and statistical inference platform for modeling detailed transcriptional kinetics in prokaryotic systems, which has not been solved analytically. The model includes stochastic two-state gene activation, mRNA synthesis initiation and stepwise elongation, release to the cytoplasm, and stepwise co-transcriptional degradation. Using the Gillespie algorithm, the platform simulates nascent and mature mRNA kinetics of a single gene copy and predicts fluorescent signals measurable by time-lapse single-cell mRNA imaging, for different experimental conditions. To approach the inverse problem of estimating the kinetic parameters of the model from experimental data, we develop a heuristic optimization method based on the genetic algorithm and the empirical distribution of mRNA generated by simulation. As a demonstration, we show that the optimization algorithm can successfully recover the transcriptional kinetics of simulated and experimental gene expression data. The platform is available as a MATLAB software package at https://data.caltech.edu/records/1287.