scholarly journals Gene regulatory network rewiring by an adaptively evolving microRNA cluster in Drosophila

2017 ◽  
Author(s):  
Yang Lyu ◽  
Zhongqi Liufu ◽  
Juan Xiao ◽  
Yuxin Chen ◽  
Chung-I Wu ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Biological Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
De Novo ◽  
Target Prediction ◽  
New Members ◽  
Microrna Cluster ◽  
Gene Regulatory ◽  
Network Rewiring

AbstractNew miRNAs are evolutionarily important but their impact on existing biological networks remains unclear. We report the evolution of a microRNA cluster, mir-972C, that arose de novo and the subsequently rewired gene regulatory networks in Drosophila. Molecular evolution analyses revealed that mir-972C originated in the common ancestor of Drosophila where it comprises five old miRNAs. It subsequently recruited five new members in the melanogaster subgroup after conservative evolution for at least 50 million years. Population genetics analyses reveal that young and old mir-972C miRNAs evolved rapidly under positive selection in both seed and non-seed regions. Combining target prediction and cell transfection experiments, we find that sequence changes in individual mir-972C members resulted in extensive gene regulatory network divergence among D. melanogaster, D. simulans, and D. virilis, whereas the target pool of the cluster as a whole remains relatively conserved. Our results suggest that clustering of young and old miRNAs at the same locus broadens target repertoires, resulting in the gain of new targets without losing many old ones. This may facilitate the establishment of new miRNAs within existing regulatory networks.

scholarly journals Developmental transcriptomics of the brittle star Amphiura filiformis reveals gene regulatory network rewiring in echinoderm larval skeleton evolution

2017 ◽  
Author(s):  
David Dylus ◽  
Liisa M. Blowes ◽  
Anna Czarkwiani ◽  
Maurice R. Elphick ◽  
Paola Oliveri
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
De Novo ◽  
Sea Urchins ◽  
Brittle Star ◽  
Stages Of Development ◽  
Sequence Alignments ◽  
Amphiura Filiformis ◽  
Gene Regulatory ◽  
Network Rewiring

ABSTRACTAmongst the echinoderms the class Ophiuroidea is of particular interest for its phylogenetic position, ecological importance, developmental and regenerative biology. However, compared to other echinoderms, notably echinoids (sea urchins), relatively little is known about developmental changes in gene expression in ophiuroids. To address this issue we have generated and assembled a large RNAseq data set of four key stages of development in the brittle star Amphiura filiformis and a de novo reference transcriptome of comparable quality to that of a model echinoderm - the sea urchin Strongyloncentrotus purpuratus. Furthermore, we provide access to the new data via a web interface: http://www.echinonet.eu/shiny/Amphiura_filiformis/. With a focus on skeleton development, we have identified highly conserved genes associated with the development of a biomineralized skeleton. We also identify important class-specific characters, including the independent duplication of the msp130 class of genes in different echinoderm classes and the unique occurrence of spicule matrix (sm) genes in echinoids. Using a new quantification pipeline for our de novo transcriptome, validated with other methodologies, we find major differences between brittle stars and sea urchins in the temporal expression of many transcription factor genes. This divergence in developmental regulatory states is more evident in early stages of development when cell specification begins, than when cells initiate differentiation. Our findings indicate that there has been a high degree of gene regulatory network rewiring in the evolution of echinoderm larval development.Data DepositionsAll sequence reads are available at Genbank SRR4436669 - SRR4436674. Any sequence alignments used are available by the corresponding author upon request.

scholarly journals Improving gene regulatory network structure using redundancy reduction in the MRNET algorithm

RSC Advances ◽  
2017 ◽  
Vol 7 (37) ◽  
pp. 23222-23233 ◽  
Author(s):  
Wei Liu ◽  
Wen Zhu ◽  
Bo Liao ◽  
Haowen Chen ◽  
Siqi Ren ◽  
...  
Keyword(s):  
Systems Biology ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Network Structure ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Central Problem ◽  
Expression Data ◽  
Redundancy Reduction ◽  
Gene Regulatory

Inferring gene regulatory networks from expression data is a central problem in systems biology.

scholarly journals Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

2019 ◽  
Author(s):  
Daniel Morgan ◽  
Matthew Studham ◽  
Andreas Tjärnberg ◽  
Holger Weishaupt ◽  
Fredrik J. Swartling ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Inference ◽  
Gene Regulatory Network Inference ◽  
Validation Data ◽  
Regulatory Interactions ◽  
Link Type ◽  
Gene Regulatory ◽  
Novel Interactions

AbstractThe gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. Reliable inference of GRNs is however still a major challenge in systems biology.To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in crossvalidated benchmarks and for an independent dataset of the same genes under a different perturbation design. It agrees with many known links, in addition to predicting a large number of novel interactions from which a subset was experimentally validated. The inferred GRN captures regulatory interactions central to cancer-relevant processes and thus provides mechanistic insights that are useful for future cancer research.Data available at GSE125958Inferred GRNs and inference statistics available at https://dcolin.shinyapps.io/CancerGRN/ Software available at https://bitbucket.org/sonnhammergrni/genespider/src/BFECV/Author SummaryCancer is the second most common cause of death globally, and although cancer treatments have improved in recent years, we need to understand how regulatory mechanisms are altered in cancer to combat the disease efficiently. By applying gene perturbations and inference of gene regulatory networks to 40 genes known or suspected to have a role in cancer due to interactions with the oncogene MYC, we deduce their underlying regulatory interactions. Using a recent computational framework for inference together with a novel method for cross validation, we infer a reliable regulatory model of this system in a completely data driven manner, not reliant on literature or priors. The novel interactions add to the understanding of the progressive oncogenic regulatory process and may provide new targets for therapy.

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 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 A Rapid Evolving microRNA Cluster Rewires Its Target Regulatory Networks in Drosophila

2021 ◽  
Vol 12 ◽  
Author(s):  
Yang Lyu ◽  
Zhongqi Liufu ◽  
Juan Xiao ◽  
Tian Tang
Keyword(s):  
Regulatory Networks ◽  
Common Ancestor ◽  
Genomic Analysis ◽  
Target Prediction ◽  
Cell Transfection ◽  
Functional Evolution ◽  
New Members ◽  
New Targets ◽  
Microrna Cluster ◽  
The Common

New miRNAs are evolutionarily important but their functional evolution remains unclear. Here we report that the evolution of a microRNA cluster, mir-972C rewires its downstream regulatory networks in Drosophila. Genomic analysis reveals that mir-972C originated in the common ancestor of Drosophila where it comprises six old miRNAs. It has subsequently recruited six new members in the melanogaster subgroup after evolving for at least 50 million years. Both the young and the old mir-972C members evolved rapidly in seed and non-seed regions. Combining target prediction and cell transfection experiments, we found that the seed and non-seed changes in individual mir-972C members cause extensive target divergence among D. melanogaster, D. simulans, and D. virilis, consistent with the functional evolution of mir-972C reported recently. Intriguingly, the target pool of the cluster as a whole remains relatively conserved. Our results suggest that clustering of young and old miRNAs broadens the target repertoires by acquiring new targets without losing many old ones. This may facilitate the establishment of new miRNAs in existing regulatory networks.

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.

scholarly journals Gene regulatory network stabilized by pervasive weak repressions: microRNA functions revealed by the May–Wigner theory

2019 ◽  
Vol 6 (6) ◽  
pp. 1176-1188 ◽  
Author(s):  
Yuxin Chen ◽  
Yang Shen ◽  
Pei Lin ◽  
Ding Tong ◽  
Yixin Zhao ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Biological Networks ◽  
Regulatory Network ◽  
Mammalian Cells ◽  
Regulatory Networks ◽  
Yeast Cells ◽  
Gene Products ◽  
Mathematical Solution ◽  
Gene Regulatory ◽  
The Stability

Abstract Food web and gene regulatory networks (GRNs) are large biological networks, both of which can be analyzed using the May–Wigner theory. According to the theory, networks as large as mammalian GRNs would require dedicated gene products for stabilization. We propose that microRNAs (miRNAs) are those products. More than 30% of genes are repressed by miRNAs, but most repressions are too weak to have a phenotypic consequence. The theory shows that (i) weak repressions cumulatively enhance the stability of GRNs, and (ii) broad and weak repressions confer greater stability than a few strong ones. Hence, the diffuse actions of miRNAs in mammalian cells appear to function mainly in stabilizing GRNs. The postulated link between mRNA repression and GRN stability can be seen in a different light in yeast, which do not have miRNAs. Yeast cells rely on non-specific RNA nucleases to strongly degrade mRNAs for GRN stability. The strategy is suited to GRNs of small and rapidly dividing yeast cells, but not the larger mammalian cells. In conclusion, the May–Wigner theory, supplanting the analysis of small motifs, provides a mathematical solution to GRN stability, thus linking miRNAs explicitly to ‘developmental canalization’.

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