scholarly journals The biochemical basis of microRNA targeting efficacy

2018 ◽  
Author(s):  
Sean E. McGeary ◽  
Kathy S. Lin ◽  
Charlie Y. Shi ◽  
Namita Bisaria ◽  
David P. Bartel
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Binding Affinities ◽  
Biochemical Basis ◽  
Mrna Targets ◽  
Argonaute Proteins ◽  
Relative Binding ◽  
Target Sites ◽  
Gene Regulatory ◽  
Insight Into

MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of mRNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA–target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute–miRNA complexes and all ≤12-nucleotide sequences. This approach revealed noncanonical target sites unique to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks.

scholarly journals The biochemical basis of microRNA targeting efficacy

Science ◽  
2019 ◽  
Vol 366 (6472) ◽  
pp. eaav1741 ◽  
Author(s):  
Sean E. McGeary ◽  
Kathy S. Lin ◽  
Charlie Y. Shi ◽  
Thy M. Pham ◽  
Namita Bisaria ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Messenger Rna ◽  
Regulatory Networks ◽  
Binding Affinities ◽  
Biochemical Basis ◽  
Argonaute Proteins ◽  
Relative Binding ◽  
Rna Targets ◽  
Gene Regulatory ◽  
Insight Into

MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of messenger RNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA-target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute-miRNA complexes and all sequences ≤12 nucleotides in length. This approach revealed noncanonical target sites specific to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks.

scholarly journals Predicting microRNA targeting efficacy in Drosophila

2017 ◽  
Author(s):  
Vikram Agarwal ◽  
Alexander O. Subtelny ◽  
Prathapan Thiru ◽  
Igor Ulitsky ◽  
David P. Bartel
Keyword(s):  
Quantitative Study ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Quantitative Model ◽  
Valuable Resource ◽  
Mrna Targets ◽  
Experimental Organism ◽  
Gene Regulatory ◽  
Better Than

ABSTRACTImportant for understanding the regulatory roles of miRNAs is the ability to predict the mRNA targets most responsive to each miRNA. Here, we acquired datasets needed for the quantitative study of microRNA targeting in Drosophila. Analyses of these data expanded the types of sites known to be effective in flies, expanded the mRNA regions with detectable targeting to include 5′ UTRs, and identified features of site context that correlate with targeting efficacy. Updated evolutionary analyses evaluated the probability of conserved targeting for each predicted site and indicated that more than a third of the Drosophila genes are preferentially conserved targets of miRNAs. Based on these results, a quantitative model was developed to predict targeting efficacy in insects. This model performed better than existing models and will drive the next version of TargetScanFly (v7.0; targetscan.org), thereby providing a valuable resource for placing miRNAs into gene-regulatory networks of this important experimental organism.

scholarly journals Dynamics and predicted drug response of a gene network linking dedifferentiation with β-catenin dysfunction in hepatocellular carcinoma

2018 ◽  
Author(s):  
Claude Gérard ◽  
Mickaël Di-Luoffo ◽  
Léolo Gonay ◽  
Stefano Caruso ◽  
Gabrielle Couchy ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
The Cancer Genome Atlas ◽  
Pharmacological Agents ◽  
Distinct Cell ◽  
Cancer Genome Atlas ◽  
Gene Regulatory ◽  
Using Data ◽  
Insight Into

AbstractAlterations of individual genes variably affect development of hepatocellular carcinoma (HCC), prompting the need to characterize the function of tumor-promoting genes in the context of gene regulatory networks (GRN). Here, we identify a GRN which functionally links LIN28B-dependent dedifferentiation with dysfunction of CTNNB1 (β-CATENIN). LIN28B and CTNNB1 form a functional GRN with SMARCA4 (BRG1), Let-7b, SOX9, TP53 and MYC. GRN activity is detected in HCC and gastrointestinal cancers; it negatively correlates with HCC prognosis and contributes to a transcriptomic profile typical of the proliferative class of HCC. Using data from The Cancer Genome Atlas and from transcriptomic, transfection and mouse transgenic experiments, we generated and validated a quantitative mathematical model of the GRN. The model predicts how the expression of GRN components changes when the expression of another GRN member varies or is inhibited by a pharmacological drug. The dynamics of GRN component expression reveal distinct cell states that can switch reversibly in normal condition, and irreversibly in HCC. We conclude that identification and modelling of the GRN provides insight into prognosis, mechanisms of tumor-promoting genes and response to pharmacological agents in HCC.

scholarly journals Robust dynamics in minimal hybrid models of genetic networks

2010 ◽  
Vol 368 (1930) ◽  
pp. 4961-4975 ◽  
Author(s):  
Theodore J. Perkins ◽  
Roy Wilds ◽  
Leon Glass
Keyword(s):  
Cell Cycle ◽  
Transcription Factor ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Genetic Networks ◽  
Protein Molecules ◽  
Symbolic Sequences ◽  
Gene Regulatory ◽  
Logical Functions ◽  
Insight Into

Many gene-regulatory networks necessarily display robust dynamics that are insensitive to noise and stable under evolution. We propose that a class of hybrid systems can be used to relate the structure of these networks to their dynamics and provide insight into the origin of robustness. In these systems, the genes are represented by logical functions, and the controlling transcription factor protein molecules are real variables, which are produced and destroyed. As the transcription factor concentrations cross thresholds, they control the production of other transcription factors. We discuss mathematical analysis of these systems and show how the concepts of robustness and minimality can be used to generate putative logical organizations based on observed symbolic sequences. We apply the methods to control of the cell cycle in yeast.

scholarly journals Gene regulatory networks for compatible versus incompatible grafts identify a role for SlWOX4 during junction formation

2021 ◽  
Author(s):  
Hannah Thomas ◽  
Lisa Van den Broeck ◽  
Ryan Spurney ◽  
Rosangela Sozzani ◽  
Margaret Frank
Keyword(s):  
Gene Regulatory Networks ◽  
Crop Production ◽  
Regulatory Networks ◽  
Vegetable Crop ◽  
Perennial Crop ◽  
Junction Formation ◽  
Genetic Mechanisms ◽  
Gene Regulatory ◽  
Insight Into

AbstractGraft incompatibility is a poorly understood phenomenon that presents a serious agricultural challenge. Unlike immediate incompatibility that results in rapid death, delayed incompatibility can take months or even years to manifest, creating a significant economic burden for perennial crop production. To gain insight into the genetic mechanisms underlying this phenomenon, we developed a model system with Solanum lycopersicum ‘tomato’ and Capsicum annuum ‘pepper’ heterografting, which expresses signs of anatomical junction failure within the first week of grafting. By generating a detailed timeline for junction formation we were able to pinpoint the cellular basis for this delayed incompatibility. Furthermore, we infer gene regulatory networks for compatible self-grafts versus incompatible heterografts based on these key anatomical events, which predict core regulators for grafting. Finally, we delve into the role of vascular development in graft formation and validate SlWOX4 as a regulator for grafting in tomato. Notably, SlWOX4 is the first gene to be functionally implicated in vegetable crop grafting.

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