scholarly journals Distinct regulation of hippocampal neuroplasticity and ciliary genes by corticosteroid receptors

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Karen R. Mifsud ◽  
Clare L. M. Kennedy ◽  
Silvia Salatino ◽  
Eshita Sharma ◽  
Emily M. Price ◽  
...  
Keyword(s):  
Dna Sequences ◽  
Glucocorticoid Receptors ◽  
Acute Stress ◽  
Circadian Variation ◽  
Rna Seq ◽  
Physiological Regulation ◽  
Behavioural Adaptation ◽  
Neuronal Progenitor ◽  
Genome Wide ◽  
Transcriptional Changes

AbstractGlucocorticoid hormones (GCs) — acting through hippocampal mineralocorticoid receptors (MRs) and glucocorticoid receptors (GRs) — are critical to physiological regulation and behavioural adaptation. We conducted genome-wide MR and GR ChIP-seq and Ribo-Zero RNA-seq studies on rat hippocampus to elucidate MR- and GR-regulated genes under circadian variation or acute stress. In a subset of genes, these physiological conditions resulted in enhanced MR and/or GR binding to DNA sequences and associated transcriptional changes. Binding of MR at a substantial number of sites however remained unchanged. MR and GR binding occur at overlapping as well as distinct loci. Moreover, although the GC response element (GRE) was the predominant motif, the transcription factor recognition site composition within MR and GR binding peaks show marked differences. Pathway analysis uncovered that MR and GR regulate a substantial number of genes involved in synaptic/neuro-plasticity, cell morphology and development, behavior, and neuropsychiatric disorders. We find that MR, not GR, is the predominant receptor binding to >50 ciliary genes; and that MR function is linked to neuronal differentiation and ciliogenesis in human fetal neuronal progenitor cells. These results show that hippocampal MRs and GRs constitutively and dynamically regulate genomic activities underpinning neuronal plasticity and behavioral adaptation to changing environments.

scholarly journals Genome-wide RNA sequencing as a method for assessing the effects of synthetic derivatives of melanocortin under normal conditions and under acute stress

2020 ◽  
pp. 101-102
Author(s):  
И.Б. Филиппенков ◽  
В.В. Ставчанский ◽  
Н.Ю. Глазова ◽  
Е.А. Себенцова ◽  
Н.Г. Левицкая ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Acute Stress ◽  
Regulation Of Gene Expression ◽  
Rna Seq ◽  
Structural Analogue ◽  
Specific Effects ◽  
Genome Wide ◽  
Transcriptome Response ◽  
Synthetic Derivatives ◽  
Derivatives Of

В целях исследования молекулярных механизмов действия пептидов меланокортинового ряда был проведен сравнительный анализ действия семакса и его структурного аналога АКТГ(6-9)PGP на транскриптом гиппокампа крыс в норме и условиях острого стресса с помощью метода RNA-Seq. Полученные результаты выявили общие и специфические эффекты пептидов меланокортинового ряда в регулировании экспрессии генов в условиях острого стресса и нормы. Предложена модель формирования ответа транскриптома на введение пептидов, основанная на аллостерическом взаимодействии пептидов с рецепторами. In order to study the molecular mechanisms of action of the peptides of the melanocortin series, a comparative analysis of the effect of Semax and its structural analogue of ACTH(6-9)PGP on transcripts of rat hippocampus under normal and acute stress conditions was performed using the RNA-Seq method. The results revealed common and specific effects of peptides in the regulation of gene expression under acute stress and normal conditions. We proposed a model for the formation of transcriptome response to peptide administration based on allosteric interaction of peptides with receptors.

scholarly journals DeeReCT-TSS: A novel meta-learning-based method annotates TSS in multiple cell types based on DNA sequences and RNA-seq data

2021 ◽  
Author(s):  
Juexiao Zhou ◽  
Bin Zhang ◽  
Haoyang Li ◽  
Longxi Zhou ◽  
Zhongxiao Li ◽  
...  
Keyword(s):  
Dna Sequences ◽  
Cell Types ◽  
Rna Seq ◽  
Sources Of Information ◽  
Genome Wide ◽  
A Genome ◽  
Meta Learning ◽  
Multiple Cell ◽  
Different Cell Types ◽  
Genome Scale

The accurate annotation of TSSs and their usage is critical for the mechanistic understanding of gene regulation under different biological contexts. To fulfill this, specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner. Various computational tools have also been developed for in silico prediction of TSSs solely based on genomic sequences. Most of these tools have drastic false positive predictions when applied on the genome-scale. Here, we present DeeReCT-TSS, a deep-learning-based method that is capable of TSSs identification across the whole genome based on DNA sequences and conventional RNA-seq data. We show that by effectively incorporating these two sources of information, DeeReCT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell types. Furthermore, we develop a meta-learning-based extension for simultaneous transcription start site (TSS) annotation on 10 cell types, which enables the identification of cell-type-specific TSS. Finally, we demonstrate the high precision of DeeReCT-TSS on two independent datasets from the ENCODE project by correlating our predicted TSSs with experimentally defined TSS chromatin states.

scholarly journals Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Amit Blumberg ◽  
Yixin Zhao ◽  
Yi-Fei Huang ◽  
Noah Dukler ◽  
Edward J. Rice ◽  
...  
Keyword(s):  
Dna Sequences ◽  
Structural Equation ◽  
Dynamic Equilibrium ◽  
Rna Stability ◽  
Estimation Method ◽  
Decay Rates ◽  
Rna Decay ◽  
Rna Seq ◽  
Simple Method ◽  
Genome Wide

Abstract Background The concentrations of distinct types of RNA in cells result from a dynamic equilibrium between RNA synthesis and decay. Despite the critical importance of RNA decay rates, current approaches for measuring them are generally labor-intensive, limited in sensitivity, and/or disruptive to normal cellular processes. Here, we introduce a simple method for estimating relative RNA half-lives that is based on two standard and widely available high-throughput assays: Precision Run-On sequencing (PRO-seq) and RNA sequencing (RNA-seq). Results Our method treats PRO-seq as a measure of transcription rate and RNA-seq as a measure of RNA concentration, and estimates the rate of RNA decay required for a steady-state equilibrium. We show that this approach can be used to assay relative RNA half-lives genome-wide, with good accuracy and sensitivity for both coding and noncoding transcription units. Using a structural equation model (SEM), we test several features of transcription units, nearby DNA sequences, and nearby epigenomic marks for associations with RNA stability after controlling for their effects on transcription. We find that RNA splicing-related features are positively correlated with RNA stability, whereas features related to miRNA binding and DNA methylation are negatively correlated with RNA stability. Furthermore, we find that a measure based on U1 binding and polyadenylation sites distinguishes between unstable noncoding and stable coding transcripts but is not predictive of relative stability within the mRNA or lincRNA classes. We also identify several histone modifications that are associated with RNA stability. Conclusion We introduce an approach for estimating the relative half-lives of individual RNAs. Together, our estimation method and systematic analysis shed light on the pervasive impacts of RNA stability on cellular RNA concentrations.

scholarly journals Predicting chromatin interactions between open chromatin regions from DNA sequences

2019 ◽  
Author(s):  
Fan Cao ◽  
Ying Zhang ◽  
Yan Ping Loh ◽  
Yichao Cai ◽  
Melissa J. Fullwood
Keyword(s):  
Rna Polymerase Ii ◽  
Dna Sequences ◽  
State Of The Art ◽  
Lymphocytic Leukemia ◽  
Computational Method ◽  
Chromatin Interaction ◽  
Open Chromatin ◽  
Rna Seq ◽  
Chromatin Interactions ◽  
Genome Wide

AbstractChromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is very limited. Various computational methods have been developed to predict chromatin interactions. Most of these methods rely on large collections of ChIP-Seq/RNA-Seq/DNase-Seq datasets and predict only enhancer-promoter interactions. Some of the ‘state-of-the-art’ methods have poor experimental designs, leading to over-exaggerated performances and misleading conclusions. Here we developed a computational method, Chromatin Interaction Neural Network (CHINN), to predict chromatin interactions between open chromatin regions by using only DNA sequences of the interacting open chromatin regions. CHINN is able to predict CTCF- and RNA polymerase II-associated chromatin interactions between open chromatin regions. CHINN also shows good across-sample performances and captures various sequence features that are predictive of chromatin interactions. We applied CHINN to 84 chronic lymphocytic leukemia (CLL) samples and detected systematic differences in the chromatin interactome between IGVH-mutated and IGVH-unmutated CLL samples.

scholarly journals Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data

2019 ◽  
Author(s):  
Amit Blumberg ◽  
Yixin Zhao ◽  
Yi-Fei Huang ◽  
Noah Dukler ◽  
Edward J. Rice ◽  
...  
Keyword(s):  
Dna Sequences ◽  
Rna Splicing ◽  
Structural Equation ◽  
Rna Stability ◽  
Estimation Method ◽  
Equation Model ◽  
Rna Seq ◽  
Simple Method ◽  
Systematic Analysis ◽  
Genome Wide

AbstractThe rate at which RNA molecules decay is a key determinant of cellular RNA concentrations, yet current approaches for measuring RNA half-lives are generally labor-intensive, limited in sensitivity, and/or disruptive to normal cellular processes. Here we introduce a simple method for estimating relative RNA half-lives that is based on two standard and widely available high-throughput assays: Precision Run-On and sequencing (PRO-seq) and RNA sequencing (RNA-seq). Our method treats PRO-seq as a measure of transcription rate and RNA-seq as a measure of RNA concentration, and estimates the rate of RNA decay required for a steady-state equilibrium. We show that this approach can be used to assay relative RNA half-lives genome-wide, with good accuracy and sensitivity for both coding and noncoding transcription units. Using a structural equation model (SEM), we test several features of transcription units, nearby DNA sequences, and nearby epigenomic marks for associations with RNA stability after controlling for their effects on transcription. We find that RNA splicing-related features are positively correlated with RNA stability, whereas features related to miRNA binding, DNA methylation, and G+C-richness are negatively correlated with RNA stability. Furthermore, we find that a measure based on U1-binding and polyadenylation sites distinguishes between unstable noncoding and stable coding transcripts but is not predictive of relative stability within the mRNA or lincRNA classes. We also identify several histone modifications that are associated with RNA stability. Together, our estimation method and systematic analysis shed light on the pervasive impacts of RNA stability on cellular RNA concentrations.

scholarly journals H3K9me2 genome-wide distribution in the holocentric insect Spodoptera frugiperda (Lepidoptera: Noctuidae)

2021 ◽  
Author(s):  
Sandra NHIM ◽  
Sylvie GIMENEZ ◽  
Rima NAIT SAIDI ◽  
Dany Severac ◽  
Kiwoong Nam ◽  
...  
Keyword(s):  
Dna Sequences ◽  
Spodoptera Frugiperda ◽  
Transcriptional Repression ◽  
Active Regions ◽  
Wide Distribution ◽  
Model Organisms ◽  
Rna Seq ◽  
Protein Coding ◽  
Genome Wide ◽  
Lepidoptera Noctuidae

Eukaryotic genomes are packaged by Histone proteins in a structure called chromatin. There are different chromatin types. Euchromatin is typically associated with decondensed, transcriptionally active regions and heterochromatin to more condensed regions of the chromosomes. Methylation of Lysine 9 of Histone H3 (H3K9me) is a conserved biochemical marker of heterochromatin. In many organisms, heterochromatin is usually localized at telomeric as well as pericentromeric regions but can also be found at interstitial chromosomal loci. This distribution may vary in different species depending on their general chromosomal organization. Holocentric species such as Spodoptera frugiperda (Lepidoptera: Noctuidae) possess dispersed centromeres instead of a monocentric one and thus no observable pericentromeric compartment. To identify the localization of heterochromatin in such species we performed ChIP-Seq experiments and analyzed the distribution of the heterochromatin marker H3K9me2 in the Sf9 cell line and whole 4th instar larvae (L4) in relation to RNA-Seq data. In both samples we measured an enrichment of H3K9me2 at the (sub) telomeres, rDNA loci, and satellite DNA sequences, which could represent dispersed centromeric regions. We also observed that density of H3K9me2 is positively correlated with transposable elements and protein-coding genes. But contrary to most model organisms, H3K9me2 density is not correlated with transcriptional repression. This is the first genome-wide ChIP-Seq analysis conducted in S. frugiperda for H3K9me2. Compared to model organisms, this mark is found in expected chromosomal compartments such as rDNA and telomeres. However, it is also localized at numerous dispersed regions, instead of the well described large pericentromeric domains, indicating that H3K9me2 might not represent a classical heterochromatin marker in Lepidoptera.

scholarly journals DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome

2021 ◽  
Author(s):  
Yanrong Ji ◽  
Zhihan Zhou ◽  
Han Liu ◽  
Ramana V Davuluri
Keyword(s):  
Dna Sequences ◽  
Regulatory Elements ◽  
Ease Of Use ◽  
Fine Tuning ◽  
Supplementary Information ◽  
Sequence Motifs ◽  
Semantic Relationship ◽  
Accurate Identification ◽  
Conserved Sequence ◽  
Genome Wide

Abstract Motivation Deciphering the language of non-coding DNA is one of the fundamental problems in genome research. Gene regulatory code is highly complex due to the existence of polysemy and distant semantic relationship, which previous informatics methods often fail to capture especially in data-scarce scenarios. Results To address this challenge, we developed a novel pre-trained bidirectional encoder representation, named DNABERT, to capture global and transferrable understanding of genomic DNA sequences based on up and downstream nucleotide contexts. We compared DNABERT to the most widely used programs for genome-wide regulatory elements prediction and demonstrate its ease of use, accuracy and efficiency. We show that the single pre-trained transformers model can simultaneously achieve state-of-the-art performance on prediction of promoters, splice sites and transcription factor binding sites, after easy fine-tuning using small task-specific labeled data. Further, DNABERT enables direct visualization of nucleotide-level importance and semantic relationship within input sequences for better interpretability and accurate identification of conserved sequence motifs and functional genetic variant candidates. Finally, we demonstrate that pre-trained DNABERT with human genome can even be readily applied to other organisms with exceptional performance. We anticipate that the pre-trained DNABERT model can be fined tuned to many other sequence analyses tasks. Availability and implementation The source code, pretrained and finetuned model for DNABERT are available at GitHub (https://github.com/jerryji1993/DNABERT). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals VELCRO-IP RNA-seq reveals ribosome expansion segment function in translation genome-wide

Cell Reports ◽  
2021 ◽  
Vol 34 (3) ◽  
pp. 108629
Author(s):  
Kathrin Leppek ◽  
Gun Woo Byeon ◽  
Kotaro Fujii ◽  
Maria Barna
Keyword(s):  
Rna Seq ◽  
Expansion Segment ◽  
Genome Wide

scholarly journals SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Verônica R. de Melo Costa ◽  
Julianus Pfeuffer ◽  
Annita Louloupi ◽  
Ulf A. V. Ørom ◽  
Rosario M. Piro
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Time Course ◽  
Progressive Increase ◽  
Rna Seq ◽  
Transcript Processing ◽  
Aberrant Transcript ◽  
Genome Wide ◽  
Splicing Efficiency ◽  
Nascent Rna

Abstract Background Introns are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Thus, to better understand the dynamics of this process and the perturbations that might be caused by aberrant transcript processing it is important to quantify splicing efficiency. Results Here, we introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. SPLICE-q uses aligned reads from strand-specific RNA-seq to quantify splicing efficiency for each intron individually and allows the user to select different levels of restrictiveness concerning the introns’ overlap with other genomic elements such as exons of other genes. We applied SPLICE-q to globally assess the dynamics of intron excision in yeast and human nascent RNA-seq. We also show its application using total RNA-seq from a patient-matched prostate cancer sample. Conclusions Our analyses illustrate that SPLICE-q is suitable to detect a progressive increase of splicing efficiency throughout a time course of nascent RNA-seq and it might be useful when it comes to understanding cancer progression beyond mere gene expression levels. SPLICE-q is available at: https://github.com/vrmelo/SPLICE-q

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