scholarly journals CircHIPK3 dysregulation of the miR-30c/DLL4 axis is essential for KSHV lytic replication

2021 ◽  
Author(s):  
Katherine L Harper ◽  
Timothy J Mottram ◽  
Chinedu A Arene ◽  
Becky Foster ◽  
Molly R Patterson ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Cell Cycle Control ◽  
Lytic Replication ◽  
Protein Coding ◽  
Non Coding Rna ◽  
Novel Host ◽  
Cellular Pathways

Non coding RNA (ncRNA) regulatory networks are emerging as critical regulators of gene expression. These intricate networks of ncRNA-ncRNA interactions modulate multiple cellular pathways and impact the development and progression of multiple diseases. Herpesviruses, including Kaposi's sarcoma-associated herpesvirus, are adept at utilising ncRNAs, encoding their own as well as dysregulating host ncRNAs to modulate virus gene expression and the host response to infection. Research has mainly focused on unidirectional ncRNA-mediated regulation of target protein-coding transcripts; however, we have identified a novel host ncRNA regulatory network essential for KSHV lytic replication in B cells. KSHV-mediated upregulation of the host cell circRNA, circHIPK3, is a key component of this network, functioning as a competing endogenous RNA of miR-30c, leading to increased levels of the miR-30c target, DLL4. Dysregulation of this network highlights a novel mechanism of cell cycle control during KSHV lytic replication in B cells. Importantly, disruption at any point within this novel ncRNA regulatory network has a detrimental effect on KSHV lytic replication, highlighting the essential nature of this network and potential for therapeutic intervention.

scholarly journals Highly Divergent Neuropeptide - non-coding RNA regulatory networks underpin variant host-finding behaviours in Steinernema species infective juveniles

2020 ◽  
Author(s):  
Neil D. Warnock ◽  
Erwan Atcheson ◽  
Ciaran McCoy ◽  
Johnathan J. Dalzell
Keyword(s):  
Gene Expression ◽  
Small Rna ◽  
Regulatory Networks ◽  
Host Finding ◽  
Protein Coding ◽  
Rna Analysis ◽  
New Family ◽  
Non Coding Rna ◽  
Non Coding Rnas ◽  
Shared Gene

AbstractWe conducted a transcriptomic and small RNA analysis of infective juveniles (IJs) from three behaviourally distinct Steinernema species. Substantial variation was found in the expression of shared gene orthologues, revealing gene expression signatures that correlate with behavioural states. 97% of predicted microRNAs are novel to each species. Surprisingly, our data provide evidence that isoform variation can effectively convert protein-coding neuropeptide genes into non-coding transcripts, which may represent a new family of long non-coding RNAs. These data suggest that differences in neuropeptide gene expression, isoform variation, and small RNA interactions could contribute to behavioural differences within the Steinernema genus.

The origins and evolutionary history of human non-coding RNA regulatory networks

2017 ◽  
Vol 15 (02) ◽  
pp. 1750005 ◽  
Author(s):  
Masih Sherafatian ◽  
Seyed Javad Mowla
Keyword(s):  
Cell Cycle ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Evolutionary History ◽  
Regulatory Function ◽  
Protein Coding ◽  
Evolutionary Pattern ◽  
Non Coding Rna ◽  
History Of ◽  
Non Coding Rnas

The evolutionary history and origin of the regulatory function of animal non-coding RNAs are not well understood. Lack of conservation of long non-coding RNAs and small sizes of microRNAs has been major obstacles in their phylogenetic analysis. In this study, we tried to shed more light on the evolution of ncRNA regulatory networks by changing our phylogenetic strategy to focus on the evolutionary pattern of their protein coding targets. We used available target databases of miRNAs and lncRNAs to find their protein coding targets in human. We were able to recognize evolutionary hallmarks of ncRNA targets by phylostratigraphic analysis. We found the conventional 3′-UTR and lesser known 5′-UTR targets of miRNAs to be enriched at three consecutive phylostrata. Firstly, in eukaryata phylostratum corresponding to the emergence of miRNAs, our study revealed that miRNA targets function primarily in cell cycle processes. Moreover, the same overrepresentation of the targets observed in the next two consecutive phylostrata, opisthokonta and eumetazoa, corresponded to the expansion periods of miRNAs in animals evolution. Coding sequence targets of miRNAs showed a delayed rise at opisthokonta phylostratum, compared to the 3′ and 5′ UTR targets of miRNAs. LncRNA regulatory network was the latest to evolve at eumetazoa.

scholarly journals Commuting to Work: Nucleolar Long Non-Coding RNA Control Ribosome Biogenesis from Near and Far

2021 ◽  
Vol 7 (3) ◽  
pp. 42
Author(s):  
Victoria Mamontova ◽  
Barbara Trifault ◽  
Lea Boten ◽  
Kaspar Burger
Keyword(s):  
Gene Expression ◽  
Rna Polymerase Ii ◽  
Ribosome Biogenesis ◽  
Intergenic Spacer ◽  
Cellular Growth ◽  
Rrna Synthesis ◽  
Protein Coding ◽  
Non Coding Rna ◽  
And Function ◽  
In Cis

Gene expression is an essential process for cellular growth, proliferation, and differentiation. The transcription of protein-coding genes and non-coding loci depends on RNA polymerases. Interestingly, numerous loci encode long non-coding (lnc)RNA transcripts that are transcribed by RNA polymerase II (RNAPII) and fine-tune the RNA metabolism. The nucleolus is a prime example of how different lncRNA species concomitantly regulate gene expression by facilitating the production and processing of ribosomal (r)RNA for ribosome biogenesis. Here, we summarise the current findings on how RNAPII influences nucleolar structure and function. We describe how RNAPII-dependent lncRNA can both promote nucleolar integrity and inhibit ribosomal (r)RNA synthesis by modulating the availability of rRNA synthesis factors in trans. Surprisingly, some lncRNA transcripts can directly originate from nucleolar loci and function in cis. The nucleolar intergenic spacer (IGS), for example, encodes nucleolar transcripts that counteract spurious rRNA synthesis in unperturbed cells. In response to DNA damage, RNAPII-dependent lncRNA originates directly at broken ribosomal (r)DNA loci and is processed into small ncRNA, possibly to modulate DNA repair. Thus, lncRNA-mediated regulation of nucleolar biology occurs by several modes of action and is more direct than anticipated, pointing to an intimate crosstalk of RNA metabolic events.

PAGeneRN

2020 ◽  
pp. 1052-1075 ◽  
Author(s):  
Dina Elsayad ◽  
A. Ali ◽  
Howida A. Shedeed ◽  
Mohamed F. Tolba
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Network Analysis ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Expression Data ◽  
Gene Expression Data Analysis ◽  
Gene Regulatory

The gene expression analysis is an important research area of Bioinformatics. The gene expression data analysis aims to understand the genes interacting phenomena, gene functionality and the genes mutations effect. The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network aims to study the genes interactions topological organization. The regulatory network is critical for understanding the pathological phenotypes and the normal cell physiology. There are many researches that focus on gene regulatory network analysis but unfortunately some algorithms are affected by data size. Where, the algorithm runtime is proportional to the data size, therefore, some parallel algorithms are presented to enhance the algorithms runtime and efficiency. This work presents a background, mathematical models and comparisons about gene regulatory networks analysis different techniques. In addition, this work proposes Parallel Architecture for Gene Regulatory Network (PAGeneRN).

scholarly journals Methods of MicroRNA Promoter Prediction and Transcription Factor Mediated Regulatory Network

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yuming Zhao ◽  
Fang Wang ◽  
Su Chen ◽  
Jun Wan ◽  
Guohua Wang
Keyword(s):  
Transcription Factor ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
High Throughput Sequencing ◽  
Prediction Models ◽  
Promoter Prediction ◽  
Sequencing Data ◽  
Protein Coding ◽  
Mirna Genes ◽  
Intergenic Regions

MicroRNAs (miRNAs) are short (~22 nucleotides) noncoding RNAs and disseminated throughout the genome, either in the intergenic regions or in the intronic sequences of protein-coding genes. MiRNAs have been proved to play important roles in regulating gene expression. Hence, understanding the transcriptional mechanism of miRNA genes is a very critical step to uncover the whole regulatory network. A number of miRNA promoter prediction models have been proposed in the past decade. This review summarized several most popular miRNA promoter prediction models which used genome sequence features, or other features, for example, histone markers, RNA Pol II binding sites, and nucleosome-free regions, achieved by high-throughput sequencing data. Some databases were described as resources for miRNA promoter information. We then performed comprehensive discussion on prediction and identification of transcription factor mediated microRNA regulatory networks.

scholarly journals BCL6 positively regulates AID and germinal center gene expression via repression of miR-155

2012 ◽  
Vol 209 (13) ◽  
pp. 2455-2465 ◽  
Author(s):  
Katia Basso ◽  
Christof Schneider ◽  
Qiong Shen ◽  
Antony B. Holmes ◽  
Manu Setty ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
Germinal Center ◽  
Potential Role ◽  
Transcriptional Repressor ◽  
Substantial Evidence ◽  
Protein Coding ◽  
Protein Coding Genes

The BCL6 proto-oncogene encodes a transcriptional repressor that is required for germinal center (GC) formation and whose de-regulation is involved in lymphomagenesis. Although substantial evidence indicates that BCL6 exerts its function by repressing the transcription of hundreds of protein-coding genes, its potential role in regulating gene expression via microRNAs (miRNAs) is not known. We have identified a core of 15 miRNAs that show binding of BCL6 in their genomic loci and are down-regulated in GC B cells. Among BCL6 validated targets, miR-155 and miR-361 directly modulate AID expression, indicating that via repression of these miRNAs, BCL6 up-regulates AID. Similarly, the expression of additional genes relevant for the GC phenotype, including SPI1, IRF8, and MYB, appears to be sustained via BCL6-mediated repression of miR-155. These findings identify a novel mechanism by which BCL6, in addition to repressing protein coding genes, promotes the expression of important GC functions by repressing specific miRNAs.

scholarly journals Histone demethylome map reveals combinatorial gene regulatory functions in embryonic stem cells

2020 ◽  
Author(s):  
Yogesh Kumar ◽  
Pratibha Tripathi ◽  
Majid Mehravar ◽  
Michael J. Bullen ◽  
Varun K. Pandey ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Gene Regulation ◽  
Embryonic Stem Cells ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Target Gene ◽  
Embryonic Stem ◽  
Epigenetic Regulators ◽  
Regulatory Functions

SUMMARYEpigenetic regulators and transcription factors establish distinct regulatory networks for gene regulation to maintain the embryonic stem cells (ESC) state. Although much has been learned regarding individual epigenetic regulators, their combinatorial functions remain elusive. Here, we report combinatorial functions of histone demethylases (HDMs) in gene regulation of mouse ESCs that are currently unknown. We generated a histone demethylome (HDMome) map of 20 well-characterized HDMs based on their genome-wide binding. This revealed co-occupancy of HDMs in different combinations; predominantly, KDM1A-KDM4B-KDM6A and JARID2-KDM4A-KDM4C-KDM5B co-occupy at enhancers and promoters, respectively. Comprehensive mechanistic studies uncover that KDM1A-KDM6A combinatorially modulates P300/H3K27ac, H3K4me1, H3K4me2 deposition and OCT4 recruitment that eventually directs the OCT4/CORE regulatory network for target gene expression; while co-operative actions of JARID2-KDM4A-KDM4C-KDM5B control H2AK119ub1 and bivalent marks of polycomb-repressive complexes that facilitates the PRC regulatory network for target gene repression. Thus, combinatorial functions of HDMs impact gene expression programs to maintain the ESC state.

scholarly journals Predicting genotype-specific gene regulatory networks

2021 ◽  
Author(s):  
Deborah Weighill ◽  
Marouen Ben Guebila ◽  
Kimberly Glass ◽  
John Quackenbush ◽  
John Platig
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Genetic Variants ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Specific Gene ◽  
Disease Etiology ◽  
Gene Regulatory ◽  
The Impact

AbstractThe majority of disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding and the alteration of downstream gene expression. Identifying how a person’s genotype affects their individual gene regulatory network has the potential to provide important insights into disease etiology and to enable improved genotype-specific disease risk assessments and treatments. However, the impact of genetic variants is generally not considered when constructing gene regulatory networks. To address this unmet need, we developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network (GRN) for each individual in a study population by using message passing to integrate genotype-informed TF motif predictions - derived from individual genotype data, the predicted effects of variants on TF binding and gene expression, and TF motif predictions - with TF protein-protein interactions and gene expression. Comparing EGRET networks for two blood-derived cell lines identified genotype-associated cell-line specific regulatory differences which were subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential TF binding from ChIP-seq. In addition, EGRET GRNs for three cell types across 119 individuals captured regulatory differences associated with disease in a cell-type-specific manner. Our analyses demonstrate that EGRET networks can capture the impact of genetic variants on complex phenotypes, supporting a novel fine-scale stratification of individuals based on their genetic background. EGRET is available through the Network Zoo R package (netZooR v0.9; netzoo.github.io).

scholarly journals Widespread dysregulation of long non-coding genes associated with fatty acid metabolism, cell division, and immune response gene networks in xenobiotic-exposed rat liver

2019 ◽  
Author(s):  
Kritika Karri ◽  
David J. Waxman
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
Fatty Acid ◽  
Cell Division ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Acid Metabolism ◽  
Protein Coding ◽  
Xenobiotic Exposure ◽  
Gene Regulatory

AbstractXenobiotic exposure activates or inhibits transcription of hundreds of protein-coding genes in mammalian liver, impacting many physiological processes and inducing diverse toxicological responses. Little is known about the effects of xenobiotic exposure on long noncoding RNAs (lncRNAs), many of which play critical roles in regulating gene expression. Objective: to develop a computational framework to discover liver-expressed, xenobiotic-responsive lncRNAs (xeno-lncs) with strong functional, gene regulatory potential and elucidate the impact of xenobiotic exposure on their gene regulatory networks. We analyzed 115 liver RNA-seq data sets from male rats treated with 27 individual chemicals representing seven mechanisms of action (MOAs) to assemble the long non-coding transcriptome of xenobiotic-exposed rat liver. Ortholog analysis was combined with co-expression data and causal inference methods to infer lncRNA function and deduce gene regulatory networks, including causal effects of lncRNAs on protein-coding gene expression and biological pathways. We discovered >1,400 liver-expressed xeno-lncs, many with human and/or mouse orthologs. Xenobiotics representing different MOAs were often regulated common xeno-lnc targets: 123 xeno-lncs were dysregulated by at least 10 chemicals, and 5 xeno-lncs responded to at least 20 of the 27 chemicals investigated. 81 other xeno-lncs served as MOA-selective markers of xenobiotic exposure. Xeno-lnc–protein-coding gene co-expression regulatory network analysis identified xeno-lncs closely associated with exposure-induced perturbations of hepatic fatty acid metabolism, cell division, and immune response pathways. We also identified hub and bottleneck lncRNAs, which are expected to be key regulators of gene expression in cis or in trans. This work elucidates extensive networks of xeno-lnc–protein-coding gene interactions and provides a framework for understanding the extensive transcriptome-altering actions of diverse foreign chemicals in a key responsive mammalian tissue.

scholarly journals Neural Gene Network Constructor: A Neural Based Model for Reconstructing Gene Regulatory Network

2019 ◽  
Author(s):  
Zhang Zhang ◽  
Lifei Wang ◽  
Shuo Wang ◽  
Ruyi Tao ◽  
Jingshu Xiao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Network Structure ◽  
Regulatory Network ◽  
Gene Network ◽  
Regulatory Networks ◽  
Expression Data ◽  
Gene Regulatory

SummaryReconstructing gene regulatory networks (GRNs) and inferring the gene dynamics are important to understand the behavior and the fate of the normal and abnormal cells. Gene regulatory networks could be reconstructed by experimental methods or from gene expression data. Recent advances in Single Cell RNA sequencing technology and the computational method to reconstruct trajectory have generated huge scRNA-seq data tagged with additional time labels. Here, we present a deep learning model “Neural Gene Network Constructor” (NGNC), for inferring gene regulatory network and reconstructing the gene dynamics simultaneously from time series gene expression data. NGNC is a model-free heterogenous model, which can reconstruct any network structure and non-linear dynamics. It consists of two parts: a network generator which incorporating gumbel softmax technique to generate candidate network structure, and a dynamics learner which adopting multiple feedforward neural networks to predict the dynamics. We compare our model with other well-known frameworks on the data set generated by GeneNetWeaver, and achieve the state of the arts results both on network reconstruction and dynamics learning.

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