Gene coexpression networks reveal molecular interactions underlying cichlid jaw modularity

Abstract Background The oral and pharyngeal jaw of cichlid fishes are a classic example of evolutionary modularity as their functional decoupling boosted trophic diversification and contributed to the success of cichlid adaptive radiations. Most studies until now have focused on the functional, morphological, or genetic aspects of cichlid jaw modularity. Here we extend this concept to include transcriptional modularity by sequencing whole transcriptomes of the two jaws and comparing their gene coexpression networks. Results We show that transcriptional decoupling of gene expression underlies the functional decoupling of cichlid oral and pharyngeal jaw apparatus and the two units are evolving independently in recently diverged cichlid species from Lake Tanganyika. Oral and pharyngeal jaw coexpression networks reflect the common origin of the jaw regulatory program as there is high preservation of gene coexpression modules between the two sets of jaws. However, there is substantial rewiring of genetic architecture within those modules. We define a global jaw coexpression network and highlight jaw-specific and species-specific modules within it. Furthermore, we annotate a comprehensive in silico gene regulatory network linking the Wnt and AHR signalling pathways to jaw morphogenesis and response to environmental cues, respectively. Components of these pathways are significantly differentially expressed between the oral and pharyngeal jaw apparatus. Conclusion This study describes the concerted expression of many genes in cichlid oral and pharyngeal jaw apparatus at the onset of the independent life of cichlid fishes. Our findings suggest that – on the basis of an ancestral gill arch network—transcriptional rewiring may have driven the modular evolution of the oral and pharyngeal jaws, highlighting the evolutionary significance of gene network reuse. The gene coexpression and in silico regulatory networks presented here are intended as resource for future studies on the genetics of vertebrate jaw morphogenesis and trophic adaptation.

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Transcriptomic Analysis Reveals Regulatory Networks for Osmotic Water Stress and Rewatering Response in the Leaves of Ginkgo biloba

Forests ◽

10.3390/f12121705 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1705

Author(s):

Wanwen Yu ◽

Jinfeng Cai ◽

Huimin Liu ◽

Zhiguo Lu ◽

Jingjing Hu ◽

...

Keyword(s):

Cell Wall ◽

Water Stress ◽

Water Transport ◽

Ginkgo Biloba ◽

Regulatory Networks ◽

Differentially Expressed ◽

Control Group ◽

Gene Coexpression ◽

Osmotic Water ◽

Coexpression Networks

To elucidate the transcriptomic regulation mechanisms that underlie the response of Ginkgo biloba to dehydration and rehydration, we used ginkgo saplings exposed to osmotically driven water stress and subsequent rewatering. When compared with a control group, 137, 1453, 1148, and 679 genes were differentially expressed in ginkgo leaves responding to 2, 6, 12, and 24 h of water deficit, and 796 and 1530 genes were differentially expressed responding to 24 and 48 h of rewatering. Upregulated genes participated in the biosynthesis of abscisic acid, eliminating reactive oxygen species (ROS), and biosynthesis of flavonoids and bilobalide, and downregulated genes were involved in water transport and cell wall enlargement in water stress-treated ginkgo leaves. Under rehydration conditions, the genes associated with water transport and cell wall enlargement were upregulated, and the genes that participated in eliminating ROS and the biosynthesis of flavonoids and bilobalide were downregulated in the leaves of G. biloba. Furthermore, the weighted gene coexpression networks were established and correlated with distinct water stress and rewatering time-point samples. Hub genes that act as key players in the networks were identified. Overall, these results indicate that the gene coexpression networks play essential roles in the transcriptional reconfiguration of ginkgo leaves in response to water stress and rewatering.

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Estimating Linear and Nonlinear Gene Coexpression Networks by Semiparametric Neighborhood Selection

Genetics ◽

10.1534/genetics.120.303186 ◽

2020 ◽

Vol 215 (3) ◽

pp. 597-607

Author(s):

Juho A. J. Kontio ◽

Marko J. Rinta-aho ◽

Mikko J. Sillanpää

Keyword(s):

Regulatory Networks ◽

Selection Procedure ◽

Critical Issue ◽

Small Sample ◽

Estimation Methods ◽

Gene Coexpression ◽

Neighborhood Selection ◽

Nonlinear Associations ◽

Linear And Nonlinear ◽

Coexpression Networks

Whereas nonlinear relationships between genes are acknowledged, there exist only a few methods for estimating nonlinear gene coexpression networks or gene regulatory networks (GCNs/GRNs) with common deficiencies. These methods often consider only pairwise associations between genes, and are, therefore, poorly capable of identifying higher-order regulatory patterns when multiple genes should be considered simultaneously. Another critical issue in current nonlinear GCN/GRN estimation approaches is that they consider linear and nonlinear dependencies at the same time in confounded form nonparametrically. This severely undermines the possibilities for nonlinear associations to be found, since the power of detecting nonlinear dependencies is lower compared to linear dependencies, and the sparsity-inducing procedures might favor linear relationships over nonlinear ones only due to small sample sizes. In this paper, we propose a method to estimate undirected nonlinear GCNs independently from the linear associations between genes based on a novel semiparametric neighborhood selection procedure capable of identifying complex nonlinear associations between genes. Simulation studies using the common DREAM3 and DREAM9 datasets show that the proposed method compares superiorly to the current nonlinear GCN/GRN estimation methods.

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Comparative transcriptomics method to infer gene coexpression networks and its applications to maize and rice leaf transcriptomes

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1817621116 ◽

2019 ◽

Vol 116 (8) ◽

pp. 3091-3099 ◽

Cited By ~ 9

Author(s):

Yao-Ming Chang ◽

Hsin-Hung Lin ◽

Wen-Yu Liu ◽

Chun-Ping Yu ◽

Hsiang-June Chen ◽

...

Keyword(s):

Time Series ◽

Regulatory Networks ◽

High Efficiency ◽

Kranz Anatomy ◽

Rice Leaf ◽

Regulatory Cascade ◽

Gene Coexpression ◽

Coexpression Networks ◽

Analyze Time Series ◽

Upstream Regulators

Time-series transcriptomes of a biological process obtained under different conditions are useful for identifying the regulators of the process and their regulatory networks. However, such data are 3D (gene expression, time, and condition), and there is currently no method that can deal with their full complexity. Here, we developed a method that avoids time-point alignment and normalization between conditions. We applied it to analyze time-series transcriptomes of developing maize leaves under light–dark cycles and under total darkness and obtained eight time-ordered gene coexpression networks (TO-GCNs), which can be used to predict upstream regulators of any genes in the GCNs. One of the eight TO-GCNs is light-independent and likely includes all genes involved in the development of Kranz anatomy, which is a structure crucial for the high efficiency of photosynthesis in C4 plants. Using this TO-GCN, we predicted and experimentally validated a regulatory cascade upstream of SHORTROOT1, a key Kranz anatomy regulator. Moreover, we applied the method to compare transcriptomes from maize and rice leaf segments and identified regulators of maize C4 enzyme genes and RUBISCO SMALL SUBUNIT2. Our study provides not only a powerful method but also novel insights into the regulatory networks underlying Kranz anatomy development and C4 photosynthesis.

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Faculty Opinions recommendation of Gene Coexpression Networks in Human Brain Developmental Transcriptomes Implicate the Association of Long Noncoding RNAs with Intellectual Disability.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.725902848.793515876 ◽

2016 ◽

Author(s):

Pietro Chiurazzi

Keyword(s):

Intellectual Disability ◽

Human Brain ◽

Noncoding Rnas ◽

Long Noncoding Rnas ◽

Gene Coexpression ◽

Coexpression Networks

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The state of the art in soybean transcriptomics resources and gene coexpression networks

in silico Plants ◽

10.1093/insilicoplants/diab005 ◽

2021 ◽

Author(s):

Fabricio Almeida-Silva ◽

Kanhu C Moharana ◽

Thiago M Venancio

Keyword(s):

State Of The Art ◽

The State ◽

Gene Coexpression Network ◽

Rna Seq ◽

Transcriptomic Data ◽

The Past ◽

Gene Coexpression ◽

Genomics Research ◽

Public Repositories ◽

Coexpression Networks

Abstract In the past decade, over 3000 samples of soybean transcriptomic data have accumulated in public repositories. Here, we review the state of the art in soybean transcriptomics, highlighting the major microarray and RNA-seq studies that investigated soybean transcriptional programs in different tissues and conditions. Further, we propose approaches for integrating such big data using gene coexpression network and outline important web resources that may facilitate soybean data acquisition and analysis, contributing to the acceleration of soybean breeding and functional genomics research.

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