The modern evolutionary synthesis assumes that mutations occur at random, independently of the environment in which they confer an advantage. However, there are indications that cells facing challenging conditions can adapt rapidly, utilizing processes beyond selection of pre-existing genetic variation. Here, we show that a strong regulatory challenge can induce mutations in many independent yeast cells, in the absence of general mutagenesis. Whole genome sequencing of cell lineages reveals a repertoire of independent mutations within a single lineage that arose only after the cells were exposed to the challenging environment, while other cells in the same lineage adapted without any mutation in their genomes. Thus, our experiments uncovered multiple alternative routes for heritable adaptation that were all induced in the same lineage during a short time period. Our results demonstrate the existence of adaptation mechanisms beyond random mutation, suggesting a tight connection between physiological and genetic processes.
The cell-cycle progression is regulated by a specific network enabling its ordered dynamics. Recent experiments supported by computational models have shown that a core of genes ensures this robust cycle dynamics. However, much less is known about the direct interaction of the cell-cycle regulators with genes outside of the cell-cycle network, in particular those of the metabolic system. Following our recent experimental work, we present here a model focusing on the dynamics of the cell-cycle core network under rewiring perturbations. Rewiring is achieved by placing an essential metabolic gene exclusively under the regulation of a cell-cycles promoter, forcing the cell-cycle network to function under a multitasking challenging condition; operating in parallel the cell-cycle progression and a metabolic essential gene. Our model relies on simple rate equations that capture the dynamics of the relevant protein-DNA and protein-protein interactions, while making a clear distinction between these two different types of processes. In particular, we treat the cell-cycle transcription factors as limited resources and focus on the redistribution of resources in the network during its dynamics. This elucidates the sensitivity of its various nodes to rewiring interactions. The basic model produces the correct cycle dynamics for a wide range of parameters. The simplicity of the model enables us to study the interface between the cell-cycle regulation and other cellular processes. Rewiring a promoter of the network to regulate a foreign gene, forces a multitasking regulatory load. The higher the load on the promoter, the longer is the cell-cycle period. Moreover, in agreement with our experimental results, the model shows that different nodes of the network exhibit variable susceptibilities to the rewiring perturbations. Our model suggests that the topology of the cell-cycle core network ensures its plasticity and flexible interface with other cellular processes, without a need for an optimal setting of the kinetic parameters.
Frequently during evolution, new phenotypes evolved due to novelty in gene regulation, such as that caused by genome rewiring. This has been demonstrated by comparing common regulatory sequences among species and by identifying single regulatory mutations that are associated with new phenotypes. However, while a single mutation changes a single element, gene regulation is accomplished by a regulatory network involving multiple interactive elements. Therefore, to better understand regulatory evolution, we have studied how mutations contributed to the adaptation of cells to a regulatory challenge. We created a synthetic genome rewiring in yeast cells, challenged their gene regulation, and studied their adaptation. HIS3, an essential enzyme for histidine biosynthesis, was placed exclusively under a GAL promoter, which is induced by galactose and strongly repressed in glucose. Such rewired cells were faced with significant regulatory challenges in a repressive glucose medium. We identified several independent mutations in elements of the GAL system associated with the rapid adaptation of cells, such as the repressor GAL80 and the binding sites of the activator GAL4. Consistent with the extraordinarily high rate of cell adaptation, new regulation emerged during adaptation via multiple trajectories, including those involving mutations in elements of the GAL system. The new regulation of HIS3 tuned its expression according to histidine requirements with or without these significant mutations, indicating that additional factors participated in this regulation and that the regulatory network could reorganize in multiple ways to accommodate different mutations. This study, therefore, stresses network plasticity as an important property for regulatory adaptation and evolution.
Neo-Darwinian evolution has presented a paradigm for population dynamics built on random mutations and selection with a clear separation of time-scales between single-cell mutation rates and the rate of reproduction. Laboratory experiments on evolving populations until now have concentrated on the fixation of beneficial mutations. Following the Darwinian paradigm, these experiments probed populations at low temporal resolution dictated by the rate of rare mutations, ignoring the intermediate evolving phenotypes. Selection however, works on phenotypes rather than genotypes. Research in recent years has uncovered the complexity of genotype-to-phenotype transformation and a wealth of intracellular processes including epigenetic inheritance, which operate on a wide range of time-scales. Here, by studying the adaptation dynamics of genetically rewired yeast cells, we show a novel type of population dynamics in which the intracellular processes intervene in shaping the population structure. Under constant environmental conditions, we measure a wide distribution of growth rates that coexist in the population for very long durations (>100 generations). Remarkably, the fastest growing cells do not take over the population on the time-scale dictated by the width of the growth-rate distributions and simple selection. Additionally, we measure significant fluctuations in the population distribution of various phenotypes: the fraction of exponentially-growing cells, the distributions of single-cell growth-rates and protein content. The observed fluctuations relax on time-scales of many generations and thus do not reflect noisy processes. Rather, our data show that the phenotypic state of the cells, including the growth-rate, for large populations in a constant environment is metastable and varies on time-scales that reflect the importance of long-term intracellular processes in shaping the population structure. This lack of time-scale separation between the intracellular and population processes calls for a new framework for population dynamics which is likely to be significant in a wide range of biological contexts, from evolution to cancer.
The phenotypic state of the cell is commonly thought to be determined by the set of expressed genes. However, given the apparent complexity of genetic networks, it remains open what processes stabilize a particular phenotypic state. Moreover, it is not clear how unique is the mapping between the vector of expressed genes and the cells phenotypic state. To gain insight on these issues, we study here the expression dynamics of metabolically essential genes in twin cell populations. We show that two yeast cell populations derived from a single steady-state mother population and exhibiting a similar growth phenotype in response to an environmental challenge, displayed diverse expression patterns of essential genes. The observed diversity in the mean expression between populations could not result from stochastic cell-to-cell variability, which would be averaged out in our large cell populations. Remarkably, within a population, sets of expressed genes exhibited coherent dynamics over many generations. Thus, the emerging gene expression patterns resulted from collective population dynamics. It suggests that in a wide range of biological contexts, gene expression reflects a self-organization process coupled to population-environment dynamics.
Despite their evolutionary significance, little is known about the adaptation dynamics of genomically rewired cells in evolution. We have confronted yeast cells carrying a rewired regulatory circuit with a severe and unforeseen challenge. The essential HIS3 gene from the histidine biosynthesis pathway was placed under the exclusive regulation of the galactose utilization system. Glucose containing medium strongly represses the GAL genes including HIS3 and these rewired cells are required to operate this essential gene. We show here that although there were no adapted cells prior to the encounter with glucose, a large fraction of cells adapted to grow in this medium and this adaptation was stably inherited. The adaptation relied on individual cells that switched into an adapted state and, thus, the adaptation was due to a response of many individual cells to the change in environment and not due to selection of rare advantageous phenotypes. The adaptation of numerous individual cells by heritable phenotypic switching in response to a challenge extends the common evolutionary framework and attests to the adaptive potential of regulatory circuits.
In this perspective we provide an example for the limits of reverse engineering in neuroscience. We demonstrate that application of reverse engineering to the study of the design principle of a functional neuro-system with a known mechanism, may result in a perfectly valid but wrong induction of the systems design principle. If in the very simple setup we bring here (static environment, primitive task and practically unlimited access to every piece of relevant information), it is difficult to induce a design principle, what are our chances of exposing biological design principles when more realistic conditions are examined? Implications to the way we do Biology are discussed.
The fundamental dynamics of the cell cycle, underlying cell growth and reproduction, were previously found to be robust under a wide range of environmental and internal perturbations. This property was commonly attributed to its network structure, which enables the coordinated interactions among hundreds of proteins. Despite significant advances in deciphering the components and autonomous interactions of this network, understanding the interfaces of the cell cycle with other major cellular processes is still lacking. To gain insight into these interfaces, we used the process of genome-rewiring in yeast by placing an essential metabolic gene HIS3 from the histidine biosynthesis pathway, under the exclusive regulation of different cell-cycle promoters. In a medium lacking histidine and under partial inhibition of the HIS3p, the rewired cells encountered an unforeseen multitasking challenge; the cell-cycle regulatory genes were required to regulate the essential histidine-pathway gene in concert with the other metabolic demands, while simultaneously driving the cell cycle through its proper temporal phases. We show here that chemostat cell populations with rewired cell-cycle promoters adapted within a short time to accommodate the inhibition of HIS3p and stabilized a new phenotypic state. Furthermore, a significant fraction of the population was able to adapt and grow into mature colonies on plates under such inhibiting conditions. The adapted state was shown to be stably inherited across generations. These adaptation dynamics were accompanied by a non-specific and irreproducible genome-wide transcriptional response. Adaptation of the cell-cycle attests to its multitasking capabilities and flexible interface with cellular metabolic processes and requirements. Similar adaptation features were found in our previous work when rewiring HIS3 to the GAL system and switching cells from galactose to glucose. Thus, at the basis of cellular plasticity is the emergence of a yet-unknown general, non-specific mechanism allowing fast inherited adaptation to unforeseen challenges.
The copy number of any protein fluctuates among cells in a population; characterizing and understanding these fluctuations is a fundamental problem in biophysics. We show here that protein distributions measured under a broad range of biological realizations collapse to a single non-gaussian curve under scaling by the first two moments. Moreover, in all experiments the variance is found to depend quadratically on the mean, showing that a single degree of freedom determines the entire distribution. Our results imply that protein fluctuations do not reflect any specific molecular or cellular mechanism, and suggest that some buffering process masks these details and induces universality.
Developing organisms have evolved a wide range of mechanisms for coping with recurrent environmental challenges. How they cope with rare or unforeseen challenges is, however, unclear as are the implications to their unchallenged offspring. Here, we investigate these questions by confronting the development of the fly, D. melanogaster, with artificial tissue distributions of toxic stress that are not expected to occur during fly development. We show that under a wide range of toxic scenarios, this challenge can lead to modified development that may coincide with increased tolerance to an otherwise lethal condition. Part of this response was mediated by suppression of Polycomb group genes, which in turn leads to derepression of developmental regulators and their expression in new domains. Importantly, some of the developmental alterations were epigenetically inherited by subsequent generations of unchallenged offspring. These results show that the environment can induce alternative patterns of development that are stable across multiple generations.
Related JoVE Video
Journal of Visualized Experiments
What is Visualize?
JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
How does it work?
We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.
Video X seems to be unrelated to Abstract Y...
In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.