scholarly journals Gene regulatory network plasticity predates a switch in function of a conserved transcription regulator

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Isabel Nocedal ◽  
Eugenio Mancera ◽  
Alexander D Johnson
Keyword(s):  
Regulatory Networks ◽  
Large Scale ◽  
Target Gene ◽  
Regulatory Function ◽  
Transcription Regulator ◽  
Ancestral Function ◽  
Gene Regulatory ◽  
Network Plasticity ◽  
Open Question ◽  
Phenotypic Novelty

The rewiring of gene regulatory networks can generate phenotypic novelty. It remains an open question, however, how the large number of connections needed to form a novel network arise over evolutionary time. Here, we address this question using the network controlled by the fungal transcription regulator Ndt80. This conserved protein has undergone a dramatic switch in function—from an ancestral role regulating sporulation to a derived role regulating biofilm formation. This switch in function corresponded to a large-scale rewiring of the genes regulated by Ndt80. However, we demonstrate that the Ndt80-target gene connections were undergoing extensive rewiring prior to the switch in Ndt80’s regulatory function. We propose that extensive drift in the Ndt80 regulon allowed for the exploration of alternative network structures without a loss of ancestral function, thereby facilitating the formation of a network with a new function.

scholarly journals Mapping Gene Regulatory Networks in Drosophila Eye Development by Large-Scale Transcriptome Perturbations and Motif Inference

Cell Reports ◽  
2014 ◽  
Vol 9 (6) ◽  
pp. 2290-2303 ◽  
Author(s):  
Delphine Potier ◽  
Kristofer Davie ◽  
Gert Hulselmans ◽  
Marina Naval Sanchez ◽  
Lotte Haagen ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Eye Development ◽  
Drosophila Eye ◽  
Mapping Gene ◽  
Gene Regulatory

MicroRNA profiling reveals dysregulated microRNAs and their target gene regulatory networks in cemento-ossifying fibroma

2017 ◽  
Vol 47 (1) ◽  
pp. 78-85 ◽  
Author(s):  
Thaís dos Santos Fontes Pereira ◽  
João Artur Ricieri Brito ◽  
André Luiz Sena Guimarães ◽  
Carolina Cavaliéri Gomes ◽  
Júlio Cesar Tanos de Lacerda ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Gene ◽  
Ossifying Fibroma ◽  
Microrna Profiling ◽  
Gene Regulatory

scholarly journals Compensatory mutation can drive gene regulatory network evolution

2019 ◽  
Author(s):  
Yifei Wang ◽  
Marios Richards ◽  
Steve Dorus ◽  
Nicholas K. Priest ◽  
Joanna J. Bryson
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Network Evolution ◽  
Regulatory Function ◽  
Compensatory Mutation ◽  
Relaxed Selection ◽  
Mutation Site ◽  
Regulatory Pathways ◽  
Gene Regulatory

AbstractGene regulatory networks underlie every aspect of life; better understanding their assembly would better our understanding of evolution more generally. For example, evolutionary theory typically assumed that low-fitness intermediary pathways are not a significant factor in evolution, yet there is substantial empirical evidence of compensatory mutation. Here we revise theoretical assumptions to explore the possibility that compensatory mutation may drive rapid evolutionary recovery. Using a well-established in silico model of gene regulatory networks, we show that assuming only that deleterious mutations are not fatal, compensatory mutation is surprisingly frequent. Further, we find that it entails biases that drive the evolution of regulatory pathways. In our simulations, we find compensatory mutation to be common during periods of relaxed selection, with 8-15% of degraded networks having regulatory function restored by a single randomly-generated additional mutation. Though this process reduces average robustness, proportionally higher robustness is found in networks where compensatory mutations occur close to the deleterious mutation site, or where the compensatory mutation results in a large regulatory effect size. This location- and size-specific robustness systematically biases which networks are purged by selection for network stability, producing emergent changes to the population of regulatory networks. We show that over time, large-effect and co-located mutations accumulate, assuming only that episodes of relaxed selection occur, even very rarely. This accumulation results in an increase in regulatory complexity. Our findings help explain a process by which large-effect mutations structure complex regulatory networks, and may account for the speed and pervasiveness of observed occurrence of compensatory mutation, for example in the context of antibiotic resistance, which we discuss. If sustained by in vitro experiments, these results promise a significant breakthrough in the understanding of evolutionary and regulatory processes.

INFERENCE OF LARGE-SCALE GENE REGULATORY NETWORKS USING REGRESSION-BASED NETWORK APPROACH

2009 ◽  
Vol 07 (04) ◽  
pp. 717-735 ◽  
Author(s):  
HASEONG KIM ◽  
JAE K. LEE ◽  
TAESUNG PARK
Keyword(s):  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Simple Procedure ◽  
Simulated Data ◽  
Computation Time ◽  
Network Approach ◽  
Global Regulators ◽  
Gene Regulatory

The gene regulatory network modeling plays a key role in search for relationships among genes. Many modeling approaches have been introduced to find the causal relationship between genes using time series microarray data. However, they have been suffering from high dimensionality, overfitting, and heavy computation time. Further, the selection of a best model among several possible competing models is not guaranteed that it is the best one. In this study, we propose a simple procedure for constructing large scale gene regulatory networks using a regression-based network approach. We determine the optimal out-degree of network structure by using the sum of squared coefficients which are obtained from all appropriate regression models. Through the simulated data, accuracy of estimation and robustness against noise are computed in order to compare with the vector autoregressive regression model. Our method shows high accuracy and robustness for inferring large-scale gene networks. Also it is applied to Caulobacter crecentus cell cycle data consisting of 1472 genes. It shows that many genes are regulated by two transcription factors, ctrA and gcrA, that are known for global regulators.

scholarly journals DCI: learning causal differences between gene regulatory networks

2021 ◽  
Author(s):  
Anastasiya Belyaeva ◽  
Chandler Squires ◽  
Caroline Uhler
Keyword(s):  
Gene Expression ◽  
Causal Inference ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Developmental Time ◽  
Supplementary Information ◽  
Causal Graph ◽  
Causal Graphs ◽  
Gene Regulatory

Abstract Summary Designing interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. Currently, large-scale gene expression datasets from different conditions, cell types, disease states, and developmental time points are being collected. However, application of classical causal inference algorithms to infer gene regulatory networks based on such data is still challenging, requiring high sample sizes and computational resources. Here, we describe an algorithm that efficiently learns the differences in gene regulatory mechanisms between different conditions. Our difference causal inference (DCI) algorithm infers changes (i.e. edges that appeared, disappeared, or changed weight) between two causal graphs given gene expression data from the two conditions. This algorithm is efficient in its use of samples and computation since it infers the differences between causal graphs directly without estimating each possibly large causal graph separately. We provide a user-friendly Python implementation of DCI and also enable the user to learn the most robust difference causal graph across different tuning parameters via stability selection. Finally, we show how to apply DCI to single-cell RNA-seq data from different conditions and cell states, and we also validate our algorithm by predicting the effects of interventions. Availability and implementation Python package freely available at http://uhlerlab.github.io/causaldag/dci. Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document