Tetsushi Yada

Long Noncoding RNA NEAT1-Dependent SFPQ Relocation from Promoter Region to Paraspeckle Mediates IL8 Expression upon Immune Stimuli

Although thousands of long noncoding RNAs (lncRNAs) are localized in the nucleus, only a few dozen have been functionally characterized. Here we show that nuclear enriched abundant transcript 1 (NEAT1), an essential lncRNA for the formation of nuclear body paraspeckles, is induced by influenza virus and herpes simplex virus infection as well as by Toll-like receptor3-p38 pathway-triggered poly I:C stimulation, resulting in excess formation of paraspeckles. We found that NEAT1 facilitates the expression of antiviral genes including cytokines such as interleukin-8 (IL8). We found that splicing factor proline/glutamine-rich (SFPQ), a NEAT1-binding paraspeckle protein, is a repressor of IL8 transcription, and that NEAT1 induction relocates SFPQ from the IL8 promoter to the paraspeckles, leading to transcriptional activation of IL8. Together, our data show that NEAT1 plays an important role in the innate immune response through the transcriptional regulation of antiviral genes by the stimulus-responsive cooperative action of NEAT1 and SFPQ.

Identification of hundreds of novel UPF1 target transcripts by direct determination of whole transcriptome stability

UPF1 eliminates aberrant mRNAs harboring premature termination codons, and regulates the steady-state levels of normal physiological mRNAs. Although genome-wide studies of UPF1 targets performed, previous studies did not distinguish indirect UPF1 targets because they could not determine UPF1-dependent altered RNA stabilities. Here, we measured the decay rates of the whole transcriptome in UPF1-depleted HeLa cells using BRIC-seq, an inhibitor-free method for directly measuring RNA stability. We determined the half-lives and expression levels of 9,229 transcripts. An amount of 785 transcripts were stabilized in UPF1-depleted cells. Among these, the expression levels of 76 transcripts were increased, but those of the other 709 transcripts were not altered. RNA immunoprecipitation showed UPF1 bound to the stabilized transcripts, suggesting that UPF1 directly degrades the 709 transcripts. Many UPF1 targets in this study were newly identified. This study clearly demonstrates that direct determination of RNA stability is a powerful approach for identifying targets of RNA degradation factors.

Analysis of RNA decay factor mediated RNA stability contributions on RNA abundance.

BackgroundHistone epigenome data determined by chromatin immunoprecipitation sequencing (ChIP-seq) is used in identifying transcript regions and estimating expression levels. However, this estimation does not always correlate with eventual RNA expression levels measured by RNA sequencing (RNA-seq). Part of the inconsistency may arise from the variance in RNA stability, where the transcripts that are more or less abundant than predicted RNA expression from histone epigenome data are inferred to be more or less stable. However, there is little systematic analysis to validate this assumption. Here, we used stability data of whole transcriptome measured by 5′-bromouridine immunoprecipitation chase sequencing (BRIC-seq), which enabled us to determine the half-lives of whole transcripts including lincRNAs, and we integrated BRIC-seq with ChIP-seq to achieve better estimation of the eventual transcript levels and to understand the importance of post-transcriptional regulation that determine the eventual transcript levels.ResultsWe identified discrepancies between the RNA abundance estimated by ChIP-seq and measured RNA expression from RNA-seq; for number of genes and estimated that the expression level of 865 genes was controlled at the level of RNA stability in HeLa cells. ENCODE data analysis supported the idea that RNA stability control aids to determine transcript levels in multiple cell types. We identified UPF1, EXOSC5 and STAU1, well-studied RNA degradation factors, as controlling factors for 8% of cases. Computational simulations reasonably explained the changes of eventual mRNA levels attributable to the changes in the rates of mRNA half-lives. In addition, we propose a feedback circuit that includes the regulated degradation of mRNAs encoding transcription factors to maintain the steady state level of RNA abundance. Intriguingly, these regulatory mechanisms were distinct between mRNAs and lincRNAs.ConclusionsIntegrative analysis of ChIP-seq, RNA-seq and our BRIC-seq showed that transcriptional regulation and RNA degradation are independently regulated. In addition, RNA stability is an important determinant of eventual transcript levels. RNA binding proteins, such as UPF1, STAU1 and EXOSC5 may play active roles in such controls.

Large-scale motif discovery using DNA Gray code and equiprobable oligomers

Motivation: How to find motifs from genome-scale functional sequences, such as all the promoters in a genome, is a challenging problem. Word-based methods count the occurrences of oligomers to detect excessively represented ones. This approach is known to be fast and accurate compared with other methods. However, two problems have hampered the application of such methods to large-scale data. One is the computational cost necessary for clustering similar oligomers, and the other is the bias in the frequency of fixed-length oligomers, which complicates the detection of significant words. Results: We introduce a method that uses a DNA Gray code and equiprobable oligomers, which solve the clustering problem and the oligomer bias, respectively. Our method can analyze 18 000 sequences of ~1 kbp long in 30 s. We also show that the accuracy of our method is superior to that of a leading method, especially for large-scale data and small fractions of motif-containing sequences. Availability: The online and stand-alone versions of the application, named Hegma, are available at our website: http://www.genome.ist.i.kyoto-u.ac.jp/~ichinose/hegma/ Contact: ichinose@i.kyoto-u.ac.jp; o.gotoh@i.kyoto-u.ac.jp

Reconstruction of transcription-translation dynamics with a model of gene networks.

A transcription-translation model of gene networks and a method to reconstruct it from gene expression data are proposed. The model is a hybrid system based on the Glass network with continuous-time dynamics and logical interactions. Transcription-translation dynamics is introduced into the Glass network. The reconstruction of gene networks is reduced to the problem of estimating logical functions from binary representations of quantities of mRNAs and proteins. The reconstruction method is applied to the gene expression data of circadian rhythms. The response characteristics of the reconstructed gene network to periodic stimuli are analyzed. The results suggest the existence of a receiver gene that responds to an external signal, consistently with biological knowledge.

miRNA-target prediction based on transcriptional regulation.

BackgroundmicroRNAs (miRNAs) are tiny endogenous RNAs that have been discovered in animals and plants, and direct the post-transcriptional regulation of target mRNAs for degradation or translational repression via binding to the 3UTRs and the coding exons. To gain insight into the biological role of miRNAs, it is essential to identify the full repertoire of mRNA targets (target genes). A number of computer programs have been developed for miRNA-target prediction. These programs essentially focus on potential binding sites in 3UTRs, which are recognized by miRNAs according to specific base-pairing rules.ResultsHere, we introduce a novel method for miRNA-target prediction that is entirely independent of existing approaches. The method is based on the hypothesis that transcription of a miRNA and its target genes tend to be co-regulated by common transcription factors. This hypothesis predicts the frequent occurrence of common cis-elements between promoters of a miRNA and its target genes. That is, our proposed method first identifies putative cis-elements in a promoter of a given miRNA, and then identifies genes that contain common putative cis-elements in their promoters. In this paper, we show that a significant number of common cis-elements occur in ~28% of experimentally supported human miRNA-target data. Moreover, we show that the prediction of human miRNA-targets based on our method is statistically significant. Further, we discuss the random incidence of common cis-elements, their consensus sequences, and the advantages and disadvantages of our method.ConclusionsThis is the first report indicating prevalence of transcriptional regulation of a miRNA and its target genes by common transcription factors and the predictive ability of miRNA-targets based on this property.

Differential display analysis of mutants for the transcription factor Pdr1p regulating multidrug resistance in the budding yeast

The transcription factor Pdr1p recognizes Pdr1p/Pdr3p‐response element (PDRE) to activate genes involved in multidrug resistance of the budding yeast. To identify novel targets of Pdr1p, we compared transcriptomes among the yeast cells bearing wild, disrupted and gain‐of‐function alleles of PDR1 using a high‐throughput fluorescent differential display PCR. Consequently, we identified 20 transcripts apparently regulated by Pdr1p, which are derived from well‐known target genes as well as those that have never been described in the context of drug resistance. Intriguingly, among the latter, a previously unrecognized gene bearing a small putative open reading frame preceded by a functional PDRE was found.

Predicting promoter activities of primary human DNA sequences

We developed a computer program that can predict the intrinsic promoter activities of primary human DNA sequences. We observed promoter activity using a quantitative luciferase assay and generated a prediction model using multiple linear regression. Our program achieved a prediction accuracy correlation coefficient of 0.87 between the predicted and observed promoter activities. We evaluated the prediction accuracy of the program using massive sequencing analysis of transcriptional start sites in vivo. We found that it is still difficult to predict transcript levels in a strictly quantitative manner in vivo; however, it was possible to select active promoters in a given cell from the other silent promoters. Using this program, we analyzed the transcriptional landscape of the entire human genome. We demonstrate that many human genomic regions have potential promoter activity, and the expression of some previously uncharacterized putatively non-protein-coding transcripts can be explained by our prediction model. Furthermore, we found that nucleosomes occasionally formed open chromatin structures with RNA polymerase II recruitment where the program predicted significant promoter activities, although no transcripts were observed.

Identification of Minimal p53 Promoter Region Regulated by MALAT1 in Human Lung Adenocarcinoma Cells

The MALAT1 long noncoding RNA is strongly linked to cancer progression. Here we report a MALAT1 function in repressing the promoter of p53 (TP53) tumor suppressor gene. p21 and FAS, well-known p53 targets, were upregulated by MALAT1 knockdown in A549 human lung adenocarcinoma cells. We found that these upregulations were mediated by transcriptional activation of p53 through MALAT1 depletion. In addition, we identified a minimal MALAT1-responsive region in the P1 promoter of p53 gene. Flow cytometry analysis revealed that MALAT1-depleted cells exhibited G1 cell cycle arrest. These results suggest that MALAT1 affects the expression of p53 target genes through repressing p53 promoter activity, leading to influence the cell cycle progression.

Tetrahedral Gray Code for Visualization of Genome Information

We propose a tetrahedral Gray code that facilitates visualization of genome information on the surfaces of a tetrahedron, where the relative abundance of each -mer in the genomic sequence is represented by a color of the corresponding cell of a triangular lattice. For biological significance, the code is designed such that the -mers corresponding to any adjacent pair of cells differ from each other by only one nucleotide. We present a simple procedure to draw such a pattern on the development surfaces of a tetrahedron. The thus constructed tetrahedral Gray code can demonstrate evolutionary conservation and variation of the genome information of many organisms at a glance. We also apply the tetrahedral Gray code to the honey bee (Apis mellifera) genome to analyze its methylation structure. The results indicate that the honey bee genome exhibits CpG overrepresentation in spite of its methylation ability and that two conserved motifs, CTCGAG and CGCGCG, in the unmethylated regions are responsible for the overrepresentation of CpG.