Characterization of stem cells and cancer cells on the basis of gene expression profile stability, plasticity, and robustness: dynamical systems theory of gene expressions under cell-cell interaction explains mutational robustness of differentiated cells and suggests how cancer cells emerge

Here I present and discuss a model that, among other things, appears able to describe the dynamics of cancer cell origin from the perspective of stable and unstable gene expression profiles. In identifying such aberrant gene expression profiles as lying outside the normal stable states attracted through development and normal cell differentiation, the hypothesis explains why cancer cells accumulate mutations, to which they are not robust, and why these mutations create a new stable state far from the normal gene expression profile space. Such cells are in strong contrast with normal cell types that appeared as an attractor state in the gene expression dynamical system under cell-cell interaction and achieved robustness to noise through evolution, which in turn also conferred robustness to mutation. In complex gene regulation networks, other aberrant cellular states lacking such high robustness are expected to remain, which would correspond to cancer cells.     

Developmental evolution in social insects: regulatory networks from genes to societies

The evolution and development of complex phenotypes in social insect colonies, such as queen-worker dimorphism or division of labor, can, in our opinion, only be fully understood within an expanded mechanistic framework of Developmental Evolution. Conversely, social insects offer a fertile research area in which fundamental questions of Developmental Evolution can be addressed empirically. We review the concept of gene regulatory networks (GRNs) that aims to fully describe the battery of interacting genomic modules that are differentially expressed during the development of individual organisms. We discuss how distinct types of network models have been used to study different levels of biological organization in social insects, from GRNs to social networks. We propose that these hierarchical networks spanning different organizational levels from genes to societies should be integrated and incorporated into full GRN models to elucidate the evolutionary and developmental mechanisms underlying social insect phenotypes. Finally, we discuss prospects and approaches to achieve such an integration.     

Use with caution: Developmental systems divergence and potential pitfalls of animal models

Transgenic animal models have played an important role in elucidating gene functions and the molecular basis development, physiology, behavior, and pathogenesis. Transgenic models have been so successful that they have become a standard tool in molecular genetics and biomedical studies and are being used to fulfill one of the main goals of the post-genomic era: to assign functions to each gene in the genome. However, the assumption that gene functions and genetic systems are conserved between models and humans is taken for granted, often in spite of evidence that gene functions and networks diverge during evolution. In this review, I discuss some mechanisms that generate functional divergence and highlight recent examples demonstrating that gene functions and regulatory networks diverge through time. These examples suggest that annotation of gene functions based solely on mutant phenotypes in animal models, as well as assumptions of conserved functions between species, can be wrong. Therefore, animal models of gene function and human disease may not provide appropriate information, particularly for rapidly evolving genes and systems.     

Impact of gene gains,losses and duplication modes on the origin and diversification of vertebrates

The study of the evolutionary origin of vertebrates has been linked to the study of genome duplications since Susumo Ohno suggested that the successful diversification of vertebrate innovations was facilitated by two rounds of whole-genome duplication (2R-WGD) in the stem vertebrate. Since then, studies on the functional evolution of many genes duplicated in the vertebrate lineage have provided the grounds to support experimentally this link. This article reviews cases of gene duplications derived either from the 2R-WGD or from local gene duplication events in vertebrates, analyzing their impact on the evolution of developmental innovations. We analyze how gene regulatory networks can be rewired by the activity of transposable elements after genome duplications, discuss how different mechanisms of duplication might affect the fate of duplicated genes, and how the loss of gene duplicates might influence the fate of surviving paralogs. We also discuss the evolutionary relationships between gene duplication and alternative splicing, in particular in the vertebrate lineage. Finally, we discuss the role that the 2R-WGD might have played in the evolution of vertebrate developmental gene networks, paying special attention to those related to vertebrate key features such as neural crest cells, placodes, and the complex tripartite brain. In this context, we argue that current evidences points that the 2R-WGD may not be linked to the origin of vertebrate innovations, but to their subsequent diversification in a broad variety of complex structures and functions that facilitated the successful transition from peaceful filter-feeding non-vertebrate ancestors to voracious vertebrate predators.     

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.     

The evolution of developmental gene networks: lessons from comparative studies on holometabolous insects

Recent comparative studies have revealed significant differences in the developmental gene networks operating in three holometabolous insects: the beetle Tribolium castaneum, the parasitic wasp Nasonia vitripennis and the fruitfly Drosophila melanogaster. I discuss these differences in relation to divergent and convergent changes in cellular embryology. I speculate on how segmentation gene networks have evolved to operate in divergent embryological contexts, and highlight the role that co-option might have played in this process. I argue that insects represent an important example of how diversification in life-history strategies between lineages can lead to divergence in the genetic and cellular mechanisms controlling the development of homologous adult structures.     

Gene regulatory networks in the early ascidian embryo

Ascidians, or sea squirts, are tunicates that diverged from the vertebrate lineage early in the chordate evolution. The compact and simple organization of the ascidian genome makes this organism an ideal model system for analyzing gene regulatory networks in embryonic development. Embryos contain relatively few cells and gene activities by individual cells have been determined. Here we review and discuss advances in our understanding of the ascidian embryogenesis emerging from genomic expression studies and analyses at the single cell level.     

Tumorigenesis as a process of gradual loss of original cell identity and gain of properties of neural precursor/progenitor cells

Cancer is a complex disease without a unified explanation for its cause so far. Our recent work demonstrates that cancer cells share similar regulatory networks and characteristics with embryonic neural cells. Based on the study, I will address the relationship between tumor and neural cells in more details. I collected the evidence from various aspects of cancer development in many other studies, and integrated the information from studies on cancer cell properties, cell fate specification during embryonic development and evolution. Synthesis of the information strongly supports that cancer cells share much more similarities with neural progenitor/stem cells than with mesenchymal-type cells and that tumorigenesis represents a process of gradual loss of cell or lineage identity and gain of characteristics of neural cells. I also discuss cancer EMT, a concept having been under intense debate, and possibly the true meaning of EMT in cancer initiation and development. This synthesis provides fresh insights into a unified explanation for and a previously unrecognized nature of tumorigenesis, which might not be revealed by studies on individual molecular events. The review will also present some brief suggestions for cancer research based on the proposed model of tumorigenesis.     

Network design meets in silico evolutionary biology

Cell fate is programmed through gene regulatory networks that perform several calculations to take the appropriate decision. In silico evolutionary optimization mimics the way Nature has designed such gene regulatory networks. In this review we discuss the basic principles of these evolutionary approaches and how they can be applied to engineer synthetic networks. We summarize the basic guidelines to implement an in silico evolutionary design method, the operators for mutation and selection that iteratively drive the network architecture towards a specified dynamical behavior. Interestingly, as it happens in natural evolution, we show the existence of patterns of punctuated evolution. In addition, we highlight several examples of models that have been designed using automated procedures, together with different objective functions to select for the proper behavior. Finally, we briefly discuss the modular designability of gene regulatory networks and its potential application in biotechnology.     

Efficient reverse-engineering of a developmental gene regulatory network

Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to discover whether there are rules or regularities governing development and evolution of complex multi-cellular organisms.     

Sex-chromosome evolution: recent progress and the influence of male and female heterogamety

It is now clear that sex chromosomes differ from autosomes in many aspects of genome biology, such as organization, gene content and gene expression. Moreover, sex linkage has numerous evolutionary genetic implications. Here, I provide a coherent overview of sex-chromosome evolution and function based on recent data. Heteromorphic sex chromosomes are almost as widespread across the animal and plant kingdoms as sexual reproduction itself and an accumulating body of genetic data reveals interesting similarities, as well as dissimilarities, between organisms with XY or ZW sex-determination systems. Therefore, I discuss how patterns and processes associated with sex linkage in male- and female-heterogametic systems offer a useful contrast in the study of sex-chromosome evolution.     

MULTIDIMENSIONAL ADAPTIVE EVOLUTION OF A FEED‐FORWARD NETWORK AND THE ILLUSION OF COMPENSATION

When multiple substitutions affect a trait in opposing ways, they are often assumed to be compensatory, not only with respect to the trait, but also with respect to fitness. This type of compensatory evolution has been suggested to underlie the evolution of protein structures and interactions, RNA secondary structures, and gene regulatory modules and networks. The possibility for compensatory evolution results from epistasis. Yet if epistasis is widespread, then it is also possible that the opposing substitutions are individually adaptive. I term this possibility an adaptive reversal. Although possible for arbitrary phenotype‐fitness mappings, it has not yet been investigated whether such epistasis is prevalent in a biologically realistic setting. I investigate a particular regulatory circuit, the type I coherent feed‐forward loop, which is ubiquitous in natural systems and is accurately described by a simple mathematical model. I show that such reversals are common during adaptive evolution, can result solely from the topology of the fitness landscape, and can occur even when adaptation follows a modest environmental change and the network was well adapted to the original environment. The possibility of adaptive reversals warrants a systems perspective when interpreting substitution patterns in gene regulatory networks.     

Robustness to noise in gene expression evolves despite epistatic constraints in a model of gene networks

Stochastic noise in gene expression causes variation in the development of phenotypes, making such noise a potential target of stabilizing selection. Here, we develop a new simulation model of gene networks to study the adaptive landscape underlying the evolution of robustness to noise. We find that epistatic interactions between the determinants of the expression of a gene and its downstream effect impose significant constraints on evolution, but these interactions do allow the gradual evolution of increased robustness. Despite strong sign epistasis, adaptation rarely proceeds via deleterious intermediate steps, but instead occurs primarily through small beneficial mutations. A simple mathematical model captures the relevant features of the single‐gene fitness landscape and explains counterintuitive patterns, such as a correlation between the mean and standard deviation of phenotypes. In more complex networks, mutations in regulatory regions provide evolutionary pathways to increased robustness. These results chart the constraints and possibilities of adaptation to reduce expression noise and demonstrate the potential of a novel modeling framework for gene networks.     

Synthetic circuits reveal how mechanisms of gene regulatory networks constrain evolution

Phenotypic variation is the raw material of adaptive Darwinian evolution. The phenotypic variation found in organismal development is biased towards certain phenotypes, but the molecular mechanisms behind such biases are still poorly understood. Gene regulatory networks have been proposed as one cause of constrained phenotypic variation. However, most pertinent evidence is theoretical rather than experimental. Here, we study evolutionary biases in two synthetic gene regulatory circuits expressed in Escherichia coli that produce a gene expression stripe—a pivotal pattern in embryonic development. The two parental circuits produce the same phenotype, but create it through different regulatory mechanisms. We show that mutations cause distinct novel phenotypes in the two networks and use a combination of experimental measurements, mathematical modelling and DNA sequencing to understand why mutations bring forth only some but not other novel gene expression phenotypes. Our results reveal that the regulatory mechanisms of networks restrict the possible phenotypic variation upon mutation. Consequently, seemingly equivalent networks can indeed be distinct in how they constrain the outcome of further evolution.     

Gene duplication: past, present and future

Gene duplication is of central interest to evolutionary developmental biology, having been implicated in evolutionary increases in complexity. These ideas stem principally from the Lewis model for the evolution of the BX-C and Ohno's proposal for genome duplications during chordate evolution. Here I revisit these models and show how recent data have confirmed their essential features, but forced some important revisions. These include revised dates for homeotic gene duplications and for widespread gene duplication in vertebrate evolution. I also outline the major unresolved questions in the study of gene duplication, and its relevance to evolution and development.