Epigenetic epistatic interactions constrain the evolution of gene expression

Reduced activity of two genes in combination often has a more detrimental effect than expected. Such epistatic interactions not only occur when genes are mutated but also due to variation in gene expression, including among isogenic individuals in a controlled environment. We hypothesized that these ‘epigenetic’ epistatic interactions could place important constraints on the evolution of gene expression. Consistent with this, we show here that yeast genes with many epistatic interaction partners typically show low expression variation among isogenic individuals and low variation across different conditions. In addition, their expression tends to remain stable in response to the accumulation of mutations and only diverges slowly between strains and species. Yeast promoter architectures, the retention of gene duplicates, and the divergence of expression between humans and chimps are also consistent with selective pressure to reduce the likelihood of harmful epigenetic epistatic interactions. Based on these and previous analyses, we propose that the tight regulation of epistatic interaction network hubs makes an important contribution to the maintenance of a robust, ‘canalized’ phenotype. Moreover, that epigenetic epistatic interactions may contribute substantially to fitness defects when single genes are deleted.     

Parallel evolution of segmentation by co‐option of ancestral gene regulatory networks

Different sources of data on the evolution of segmentation lead to very different conclusions. Molecular similarities in the developmental pathways generating a segmented body plan tend to suggest a segmented common ancestor for all bilaterally symmetrical animals. Data from paleontology and comparative morphology suggest that this is unlikely. A possible solution to this conundrum is that throughout evolution there was a parallel co‐option of gene regulatory networks that had conserved ancestral roles in determining body axes and in elongating the anterior‐posterior axis. Inherent properties in some of these networks made them easily recruitable for generating repeated patterns and for determining segmental boundaries. Phyla where this process happened are among the most successful in the animal kingdom, as the modular nature of the segmental body organization allowed them to diverge and radiate into a bewildering array of variations on a common theme.     

Selection for distinct gene expression properties favours the evolution of mutational robustness in gene regulatory networks

Mutational robustness is a genotype's tendency to keep a phenotypic trait with little and few changes in the face of mutations. Mutational robustness is both ubiquitous and evolutionarily important as it affects in different ways the probability that new phenotypic variation arises. Understanding the origins of robustness is specially relevant for systems of development that are phylogenetically widespread and that construct phenotypic traits with a strong impact on fitness. Gene regulatory networks are examples of this class of systems. They comprise sets of genes that, through cross‐regulation, build the gene activity patterns that define cellular responses, different tissues or distinct cell types. Several empirical observations, such as a greater robustness of wild‐type phenotypes, suggest that stabilizing selection underlies the evolution of mutational robustness. However, the role of selection in the evolution of robustness is still under debate. Computer simulations of the dynamics and evolution of gene regulatory networks have shown that selection for any gene activity pattern that is steady and self‐sustaining is sufficient to promote the evolution of mutational robustness. Here, I generalize this scenario using a computational model to show that selection for different aspects of a gene activity phenotype increases mutational robustness. Mutational robustness evolves even when selection favours properties that conflict with the stationarity of a gene activity pattern. The results that I present support an important role for stabilizing selection in the evolution of robustness in gene regulatory networks.     

Algebraic connectivity may explain the evolution of gene regulatory networks

Gene expression is a result of the interplay between the structure, type, kinetics, and specificity of gene regulatory interactions, whose diversity gives rise to the variety of life forms. As the dynamic behavior of gene regulatory networks depends on their structure, here we attempt to determine structural reasons which, despite the similarities in global network properties, may explain the large differences in organismal complexity. We demonstrate that the algebraic connectivity, the smallest non-trivial eigenvalue of the Laplacian, of the directed gene regulatory networks decreases with the increase of organismal complexity, and may therefore explain the difference between the variety of analyzed regulatory networks. In addition, our results point out that, for the species considered in this study, evolution favours decreasing concentration of strategically positioned feed forward loops, so that the network as a whole can increase the specificity towards changing environments. Moreover, contrary to the existing results, we show that the average degree, the length of the longest cascade, and the average cascade length of gene regulatory networks cannot recover the evolutionary relationships between organisms. Whereas the dynamical properties of special subnetworks are relatively well understood, there is still limited knowledge about the evolutionary reasons for the already identified design principles pertaining to these special subnetworks, underlying the global quantitative features of gene regulatory networks of different organisms. The behavior of the algebraic connectivity, which we show valid on gene regulatory networks extracted from curated databases, can serve as an additional evolutionary principle of organism-specific regulatory networks.     

Dynamical approaches to modeling developmental gene regulatory networks

The network of interacting regulatory signals within a cell comprises one of the most complex and powerful computational systems in biology. Gene regulatory networks (GRNs) play a key role in transforming the information encoded in a genome into morphological form. To achieve this feat, GRNs must respond to and integrate environmental signals with their internal dynamics in a robust and coordinated fashion. The highly dynamic nature of this process lends itself to interpretation and analysis in the language of dynamical models. Modeling provides a means of systematically untangling the complicated structure of GRNs, a framework within which to simulate the behavior of reconstructed systems and, in some cases, suites of analytic tools for exploring that behavior and its implications. This review provides a general background to the idea of treating a regulatory network as a dynamical system, and describes a variety of different approaches that have been taken to the dynamical modeling of GRNs. Birth Defects Research (Part C) 87:131–142, 2009. © 2009 Wiley‐Liss, Inc.     

重建基因调控网络

分子生物学的主要挑战是如何更好的理解基因间的调控机理。重建基因网络有助于探索生命系统的本质问题。这里对研究基因调控网络的起源、发展动向、目的和方法及目前所面临的挑战进行了综述。     

A novel model-free approach for reconstruction of time-delayed gene regulatory networks

Many important biological processes (e.g. cellular differentiation during development, aging, disease etiology etc.) are very unlikely controlled by a single gene instead by the underlying complex regulatory interactions between thousands of genes within …     

Reliability of regulatory networks and its evolution

The problem of reliability of the dynamics in biological regulatory networks is studied in the framework of a generalized Boolean network model with continuous timing and noise. Using well-known artificial genetic networks such as the repressilator, we discuss concepts of reliability of rhythmic attractors. In a simple evolution process we investigate how overall network structure affects the reliability of the dynamics. In the course of the evolution, networks are selected for reliable dynamics. We find that most networks can be easily evolved towards reliable functioning while preserving the original function.     

固醇调节元件结合蛋白1及其靶基因网络

固醇调节元件结合蛋白1(Sterol regulatory element-binding protein 1, SREBP-1)是重要的核转录因子之一, 能调控内源性胆固醇、脂肪酸、甘油三酯和磷脂合成所需酶的表达, 以维持血脂动态平衡。研究表明, SREBP-1及其靶基因网络的异常可引起胰岛素抵抗、Ⅱ型糖尿病、心功能紊乱、血管并发症和肝脂肪变等一系列代谢性疾病。近年高通量组学技术的发展极大扩展了对SREBP-1靶基因及其转录调控模式的了解。文章对SREBP-1蛋白结构、活化过程、DNA结合位点及其调控的靶基因等方面的研究进展进行了综述, 并着重介绍了基于组学数据的转录调控网络的构建, 这将有助于更好的认识SREBP-1在脂类代谢中的作用, 为深入探讨脂质代谢性疾病的治疗提供新线索。     

Gene regulatory networks reused to build novel traits: co-option of an eye-related gene regulatory network in eye-like organs and red wing patches on insect wings is suggested by optix expression

Co-option of the eye developmental gene regulatory network may have led to the appearance of novel functional traits on the wings of flies and butterflies. The first trait is a recently described wing organ in a species of extinct midge resembling the outer layers of the midge's own compound eye. The second trait is red pigment patches on Heliconius butterfly wings connected to the expression of an eye selector gene, optix. These examples, as well as others, are discussed regarding the type of empirical evidence and burden of proof that have been used to infer gene network co-option underlying the origin of novel traits. A conceptual framework describing increasing confidence in inference of network co-option is proposed. Novel research directions to facilitate inference of network co-option are also highlighted, especially in cases where the pre-existent and novel traits do not resemble each other.     

基于合成基因线路的智能药物

合成生物学自诞生以来对生物学领域的研究产生了重要的影响。利用工程学思维与方法,合成生物学揭开了生命系统许多调控机制,改造并扩展了一系列生物元件,同时带来了广泛的生物医学应用,为疾病诊断与治疗提供了新的思路。本文综述了适用于哺乳动物细胞或者细菌的合成基因线路并用于疾病诊断与治疗领域的最新进展,为未来智能药物设计提供新的思路。     

Exact reconstruction of gene regulatory networks using compressive sensing

Background

We consider the problem of reconstructing a gene regulatory network structure from limited time series gene expression data, without any a priori knowledge of connectivity. We assume that the network is sparse, meaning the connectivity among genes is much less than full connectivity. We develop a method for network reconstruction based on compressive sensing, which takes advantage of the network’s sparseness.

Results

For the case in which all genes are accessible for measurement, and there is no measurement noise, we show that our method can be used to exactly reconstruct the network. For the more general problem, in which hidden genes exist and all measurements are contaminated by noise, we show that our method leads to reliable reconstruction. In both cases, coherence of the model is used to assess the ability to reconstruct the network and to design new experiments. We demonstrate that it is possible to use the coherence distribution to guide biological experiment design effectively. By collecting a more informative dataset, the proposed method helps reduce the cost of experiments. For each problem, a set of numerical examples is presented.

Conclusions

The method provides a guarantee on how well the inferred graph structure represents the underlying system, reveals deficiencies in the data and model, and suggests experimental directions to remedy the deficiencies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0400-4) contains supplementary material, which is available to authorized users.     

Modelling the evolution of genetic regulatory networks

An evolutionary model of genetic regulatory networks is developed, based on a model of network encoding and dynamics called the Artificial Genome (AG). This model derives a number of specific genes and their interactions from a string of (initially random) bases in an idealized manner analogous to that employed by natural DNA. The gene expression dynamics are determined by updating the gene network as if it were a simple Boolean network. The generic behaviour of the AG model is investigated in detail. In particular, we explore the characteristic network topologies generated by the model, their dynamical behaviours, and the typical variance of network connectivities and network structures. These properties are demonstrated to agree with a probabilistic analysis of the model, and the typical network structures generated by the model are shown to lie between those of random networks and scale-free networks in terms of their degree distribution. Evolutionary processes are simulated using a genetic algorithm, with selection acting on a range of properties from gene number and degree of connectivity through periodic behaviour to specific patterns of gene expression. The evolvability of increasingly complex patterns of gene expression is examined in detail. When a degree of redundancy is introduced, the average number of generations required to evolve given targets is reduced, but limits on evolution of complex gene expression patterns remain. In addition, cyclic gene expression patterns with periods that are multiples of shorter expression patterns are shown to be inherently easier to evolve than others. Constraints imposed by the template-matching nature of the AG model generate similar biases towards such expression patterns in networks in initial populations, in addition to the somewhat scale-free nature of these networks. The significance of these results on current understanding of biological evolution is discussed.     

Molecular regulatory mechanisms underlying the adaptability of polyploid plants

Polyploidization influences the genetic composition and gene expression of an organism. This multi-level genetic change allows the formation of new regulatory pathways leading to increased adaptability. Although both forms of polyploidization provide advantages, autopolyploids were long thought to have little impact on plant divergence compared to allopolyploids due to their formation through genome duplication only, rather than in combination with hybridization. Recent advances have begun to clarify the molecular regulatory mechanisms such as microRNAs, alternative splicing, RNA-binding proteins, histone modifications, chromatin remodelling, DNA methylation, and N⁶-methyladenosine (m6A) RNA methylation underlying the evolutionary success of polyploids. Such research is expanding our understanding of the evolutionary adaptability of polyploids and the regulatory pathways that allow adaptive plasticity in a variety of plant species. Herein we review the roles of individual molecular regulatory mechanisms and their potential synergistic pathways underlying plant evolution and adaptation. Notably, increasing interest in m6A methylation has provided a new component in potential mechanistic coordination that is still predominantly unexplored. Future research should attempt to identify and functionally characterize the evolutionary impact of both individual and synergistic pathways in polyploid plant species.     

Receptors rather than signals change in expression in four physiological regulatory networks during evolutionary divergence in threespine stickleback

The molecular mechanisms underlying behavioural evolution following colonization of novel environments are largely unknown. Molecules that interact to control equilibrium within an organism form physiological regulatory networks. It is essential to determine whether particular components of physiological regulatory networks evolve or if the network as a whole is affected in populations diverging in behavioural responses, as this may affect the nature, amplitude and number of impacted traits. We studied the regulation of four physiological regulatory networks in freshwater and marine populations of threespine stickleback raised in a common environment, which were previously characterized as showing evolutionary divergence in behaviour and stress reactivity. We measured nineteen components of these networks (ligands and receptors) using mRNA and monoamine levels in the brain, pituitary and interrenal gland, as well as hormone levels. Freshwater fish showed higher expression in the brain of adrenergic (adrb2a), serotonergic (htr2a) and dopaminergic (DRD2) receptors, but lower expression of the htr2b receptor. Freshwater fish also showed higher expression of the mc2r receptor of the glucocorticoid axis in the interrenals. Collectively, our results suggest that the inheritance of the regulation of these networks may be implicated in the evolution of behaviour and stress reactivity in association with population divergence. Our results also suggest that evolutionary change in freshwater threespine stickleback may be more associated with the expression of specific receptors rather than with global changes of all the measured constituents of the physiological regulatory networks.     

Dynamical roles of biological regulatory circuits

Regulatory circuits are found at the basis of all non-trivial dynamical properties of biological networks. More specifically, positive circuits are involved in the generation of multiple differentiated states, whereas negative circuits can generate cyclic or homeostatic behaviours. These notions are briefly reviewed, from initial biological formulations to mathematical formalisations, further encompassing their application to the design of synthetic regulatory systems. Finally, current challenges for the analysis of increasingly complex regulatory networks are indicated, as well as prospects for our understanding of development and evolution.