Transient responses and adaptation to steady state in a eukaryotic gene regulation system

Post-Transcriptional Dynamics is Involved in Rapid Adaptation to Hypergravity in Jurkat T Cells

The transcriptome of human immune cells rapidly reacts to altered gravity in a highly dynamic way. We could show in previous experiments that transcriptional patterns show profound adaption after seconds to minutes of altered gravity. To gain further insight into these transcriptional alteration and adaption dynamics, we conducted a highly standardized RNA-Seq experiment with human Jurkat T cells exposed to 9xg hypergravity for 3 and 15 min, respectively. We investigated the frequency with which individual exons were used during transcription and discovered that differential exon usage broadly appeared after 3 min and became less pronounced after 15 min. Additionally, we observed a shift in the transcript pool from coding towards non-coding transcripts. Thus, adaption of gravity-sensitive differentially expressed genes followed a dynamic transcriptional rebound effect. The general dynamics were compatible with previous studies on the transcriptional effects of short hypergravity on human immune cells and suggest that initial up-regulatory changes mostly result from increased elongation rates. The shift correlated with a general downregulation of the affected genes. All chromosome bands carried homogenous numbers of gravity-sensitive genes but showed a specific tendency towards up- or downregulation. Altered gravity affected transcriptional regulation throughout the entire genome, whereby the direction of differential expression was strongly dependent on the structural location in the genome. A correlation analysis with potential mediators of the early transcriptional response identified a link between initially upregulated genes with certain transcription factors. Based on these findings, we have been able to further develop our model of the transcriptional response to altered gravity.

microRNA-Mediated Encoding and Decoding of Time-Dependent Signals in Tumorigenesis

microRNAs, pivotal post-transcriptional regulators of gene expression, in the past decades have caught the attention of researchers for their involvement in different biological processes, ranging from cell development to cancer. Although lots of effort has been devoted to elucidate the topological features and the equilibrium properties of microRNA-mediated motifs, little is known about how the information encoded in frequency, amplitude, duration, and other features of their regulatory signals can affect the resulting gene expression patterns. Here, we review the current knowledge about microRNA-mediated gene regulatory networks characterized by time-dependent input signals, such as pulses, transient inputs, and oscillations. First, we identify the general characteristic of the main motifs underlying temporal patterns. Then, we analyze their impact on two commonly studied oncogenic networks, showing how their dysfunction can lead to tumorigenesis.

Metabolic cost of rapid adaptation of single yeast cells

Cells can rapidly adapt to changing environments through nongenetic processes; however, the metabolic cost of such adaptation has never been considered. Here we demonstrate metabolic coupling in a remarkable, rapid adaptation process (1 in 1,000 cells adapt per hour) by simultaneously measuring metabolism and division of thousands of individual Saccharomyces cerevisiae cells using a droplet microfluidic system: droplets containing single cells are immobilized in a two-dimensional (2D) array, with osmotically induced changes in droplet volume being used to measure cell metabolism, while simultaneously imaging the cells to measure division. Following a severe challenge, most cells, while not dividing, continue to metabolize, displaying a remarkably wide diversity of metabolic trajectories from which adaptation events can be anticipated. Adaptation requires a characteristic amount of energy, indicating that it is an active process. The demonstration that metabolic trajectories predict a priori adaptation events provides evidence of tight energetic coupling between metabolism and regulatory reorganization in adaptation. This process allows S. cerevisiae to adapt on a physiological timescale, but related phenomena may also be important in other processes, such as cellular differentiation, cellular reprogramming, and the emergence of drug resistance in cancer.

A living vector field reveals constraints on galactose network induction in yeast

When a cell encounters a new environment, its transcriptional response can be constrained by its history. For example, yeast cells in galactose induce GAL genes with a speed and unanimity that depends on previous nutrient conditions. Cellular memory of long-term glucose exposure delays GAL induction and makes it highly variable with in a cell population, while other nutrient histories lead to rapid, uniform responses. To investigate how cell-level gene expression dynamics produce population-level phenotypes, we built living vector fields from thousands of single-cell time courses of the proteins Gal3p and Gal1p as cells switched to galactose from various nutrient histories. We show that, after sustained glucose exposure, the lack of these GAL transducers leads to induction delays that are long but also variable; that cellular resources constrain induction; and that bimodally distributed expression levels arise from lineage selection-a subpopulation of cells induces more quickly and outcompetes the rest. Our results illuminate cellular memory in this important model system and illustrate how resources and randomness interact to shape the response of a population to a new environment.

The unforeseen challenge: from genotype-to-phenotype in cell populations

Biological cells present a paradox, in that they show simultaneous stability and flexibility, allowing them to adapt to new environments and to evolve over time. The emergence of stable cell states depends on genotype-to-phenotype associations, which essentially reflect the organization of gene regulatory modes. The view taken here is that cell-state organization is a dynamical process in which the molecular disorder manifests itself in a macroscopic order. The genome does not determine the ordered cell state; rather, it participates in this process by providing a set of constraints on the spectrum of regulatory modes, analogous to boundary conditions in physical dynamical systems. We have developed an experimental framework, in which cell populations are exposed to unforeseen challenges; novel perturbations they had not encountered before along their evolutionary history. This approach allows an unbiased view of cell dynamics, uncovering the potential of cells to evolve and develop adapted stable states. In the last decade, our experiments have revealed a coherent set of observations within this framework, painting a picture of the living cell that in many ways is not aligned with the conventional one. Of particular importance here, is our finding that adaptation of cell-state organization is essentially an efficient exploratory dynamical process rather than one founded on random mutations. Based on our framework, a set of concepts underlying cell-state organization-exploration evolving by global, non-specific, dynamics of gene activity-is presented here. These concepts have significant consequences for our understanding of the emergence and stabilization of a cell phenotype in diverse biological contexts. Their implications are discussed for three major areas of biological inquiry: evolution, cell differentiation and cancer. There is currently no unified theoretical framework encompassing the emergence of order, a stable state, in the living cell. Hopefully, the integrated picture described here will provide a modest contribution towards a physics theory of the cell.

GENETIC MUTATIONS ARE CHARACTERIZED BY INCREASE IN ENTROPY AT THE TRANSCRIPTIONAL LEVEL

Predicting the genomic and phenotypic re-programming in organisms undergoing genetic perturbations is a challenging task in modern biology. It is hypothesized that genomic alterations perturb the dynamics of biological information flow. In the present study, a statistical data analysis framework was designed and the network entropy concept was employed to quantify the level of disorder at the transcriptional level as a result of the genomic re-programming of S. cerevisiae cells under genetic perturbations. The customized re-programming in transcription levels to different genetic modifications was observed and genetic mutations were characterized by enhanced network entropies, which revealed higher degree of randomness in mRNA expression levels. To our knowledge, this study constitutes the first numerical demonstration on the conservative energetic state of the microorganisms against genetic perturbations.

An allele of an ancestral transcription factor dependent on a horizontally acquired gene product

Changes in gene regulatory circuits often give rise to phenotypic differences among closely related organisms. In bacteria, these changes can result from alterations in the ancestral genome and/or be brought about by genes acquired by horizontal transfer. Here, we identify an allele of the ancestral transcription factor PmrA that requires the horizontally acquired pmrD gene product to promote gene expression. We determined that a single amino acid difference between the PmrA proteins from the human adapted Salmonella enterica serovar Paratyphi B and the broad host range S. enterica serovar Typhimurium rendered transcription of PmrA-activated genes dependent on the PmrD protein in the former but not the latter serovar. Bacteria harboring the serovar Typhimurium allele exhibited polymyxin B resistance under PmrA- or under PmrA- and PmrD-inducing conditions. By contrast, isogenic strains with the serovar Paratyphi B allele displayed PmrA-regulated polymyxin B resistance only when experiencing activating conditions for both PmrA and PmrD. We establish that the two PmrA orthologs display quantitative differences in several biochemical properties. Strains harboring the serovar Paratyphi B allele showed enhanced biofilm formation, a property that might promote serovar Paratyphi B's chronic infection of the gallbladder. Our findings illustrate how subtle differences in ancestral genes can impact the ability of horizontally acquired genes to confer new properties.

Studying and modelling dynamic biological processes using time-series gene expression data

Biological processes are often dynamic, thus researchers must monitor their activity at multiple time points. The most abundant source of information regarding such dynamic activity is time-series gene expression data. These data are used to identify the complete set of activated genes in a biological process, to infer their rates of change, their order and their causal effects and to model dynamic systems in the cell. In this Review we discuss the basic patterns that have been observed in time-series experiments, how these patterns are combined to form expression programs, and the computational analysis, visualization and integration of these data to infer models of dynamic biological systems.

Short- and long-term dynamic responses of the metabolic network and gene expression in yeast to a transient change in the nutrient environment

Quantitative data on the dynamic changes in the transcriptome and the metabolome of yeast in response to an impulse-like perturbation in nutrient availability was integrated with the metabolic pathway information in order to elucidate the long-term dynamic re-organization of the cells. This study revealed that, in addition to the dynamic re-organization of the de novo biosynthetic pathways, salvage pathways were also re-organized in a time-dependent manner upon catabolite repression. The transcriptional and the metabolic responses observed for nitrogen catabolite repression were not as severe as those observed for carbon catabolite repression. Selective up- or down regulation of a single member of a paralogous gene pair during the response to the relaxation from nutritional limitation was identified indicating a differentiation of functions among paralogs. Our study highlighted the role of inosine accumulation and recycling in energy homeostasis and indicated possible bottlenecks in the process.

Classes of fast and specific search mechanisms for proteins on DNA

Problems of search and recognition appear over different scales in biological systems. In this review we focus on the challenges posed by interactions between proteins, in particular transcription factors, and DNA and possible mechanisms which allow for fast and selective target location. Initially we argue that DNA-binding proteins can be classified, broadly, into three distinct classes which we illustrate using experimental data. Each class calls for a different search process and we discuss the possible application of different search mechanisms proposed over the years to each class. The main thrust of this review is a new mechanism which is based on barrier discrimination. We introduce the model and analyze in detail its consequences. It is shown that this mechanism applies to all classes of transcription factors and can lead to a fast and specific search. Moreover, it is shown that the mechanism has interesting transient features which allow for stability at the target despite rapid binding and unbinding of the transcription factor from the target.

Collective dynamics of gene expression in cell populations

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 cell's 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.

Impulse control: temporal dynamics in gene transcription

Regulatory circuits controlling gene expression constantly rewire to adapt to environmental stimuli, differentiation cues, and disease. We review our current understanding of the temporal dynamics of gene expression in eukaryotes and prokaryotes and the molecular mechanisms that shape them. We delineate several prototypical temporal patterns, including "impulse" (or single-pulse) patterns in response to transient environmental stimuli, sustained (or state-transitioning) patterns in response to developmental cues, and oscillating patterns. We focus on impulse responses and their higher-order temporal organization in regulons and cascades and describe how core protein circuits and cis-regulatory sequences in promoters integrate with chromatin architecture to generate these responses.

Adaptive prediction of environmental changes by microorganisms

Natural habitats of some microorganisms may fluctuate erratically, whereas others, which are more predictable, offer the opportunity to prepare in advance for the next environmental change. In analogy to classical Pavlovian conditioning, microorganisms may have evolved to anticipate environmental stimuli by adapting to their temporal order of appearance. Here we present evidence for environmental change anticipation in two model microorganisms, Escherichia coli and Saccharomyces cerevisiae. We show that anticipation is an adaptive trait, because pre-exposure to the stimulus that typically appears early in the ecology improves the organism's fitness when encountered with a second stimulus. Additionally, we observe loss of the conditioned response in E. coli strains that were repeatedly exposed in a laboratory evolution experiment only to the first stimulus. Focusing on the molecular level reveals that the natural temporal order of stimuli is embedded in the wiring of the regulatory network-early stimuli pre-induce genes that would be needed for later ones, yet later stimuli only induce genes needed to cope with them. Our work indicates that environmental anticipation is an adaptive trait that was repeatedly selected for during evolution and thus may be ubiquitous in biology.

Stochastic analysis of the GAL genetic switch in Saccharomyces cerevisiae: modeling and experiments reveal hierarchy in glucose repression

Background

Transcriptional regulation involves protein-DNA and protein-protein interactions. Protein-DNA interactions involve reactants that are present in low concentrations, leading to stochastic behavior. In addition, multiple regulatory mechanisms are typically involved in transcriptional regulation. In the GAL regulatory system of Saccharomyces cerevisiae, the inhibition of glucose is accomplished through two regulatory mechanisms: one through the transcriptional repressor Mig1p, and the other through regulating the amount of transcriptional activator Gal4p. However, the impact of stochasticity in gene expression and hierarchy in regulatory mechanisms on the phenotypic level is not clearly understood.

Results

We address the question of quantifying the effect of stochasticity inherent in these regulatory mechanisms on the performance of various genes under the regulation of Mig1p and Gal4p using a dynamic stochastic model. The stochastic analysis reveals the importance of both the mechanisms of regulation for tight expression of genes in the GAL network. The mechanism involving Gal4p is the dominant mechanism, yielding low variability in the expression of GAL genes. The mechanism involving Mig1p is necessary to maintain the switch-like response of certain GAL genes. The number of binding sites for Mig1p and Gal4p further influences the expression of the genes, with extra binding sites lowering the variability of expression. Our experiments involving growth on various substrates show that the trends predicted in mean expression and its variability are transmitted to the phenotypic level.

Conclusion

The mechanisms involved in the transcriptional regulation and their variability set up a hierarchy in the phenotypic response to growth on various substrates. Structural motifs, such as the number of binding sites and the mechanism of regulation, determine the level of stochasticity and eventually, the phenotypic response.

Cellular properties and population asymptotics in the population balance equation

Proliferating cell populations at steady-state growth often exhibit broad protein distributions with exponential tails. The sources of this variation and its universality are of much theoretical interest. Here we address the problem by asymptotic analysis of the population balance equation. We show that the steady-state distribution tail is determined by a combination of protein production and cell division and is insensitive to other model details. Under general conditions this tail is exponential with a dependence on parameters consistent with experiment. We discuss the conditions for this effect to be dominant over other sources of variation and the relation to experiments.

Nonequilibrium statistical mechanics of dividing cell populations

We present and study a model for the nonequilibrium statistical mechanics of protein distributions in a proliferating cell population. Our model describes how the total protein variation is shaped by two processes: variation in protein production internal to the cells and variation in division and inheritance at the population level. It enables us to assess the contribution of each of these components separately. We find that, even if production is deterministic, cell division can generate a large variation in protein distribution. In this limit we solve exactly a special case and draw an analogy between protein distribution along cell generations and stress distribution in layers of granular material. At the other limit of extremely noisy protein production, we find that the population structure restrains variation and that the details of division do not affect the tail of the distribution.

Robustness and state-space structure of Boolean gene regulatory models

Robustness to perturbation is an important characteristic of genetic regulatory systems, but the relationship between robustness and model dynamics has not been clearly quantified. We propose a method for quantifying both robustness and dynamics in terms of state-space structures, for Boolean models of genetic regulatory systems. By investigating existing models of the Drosophila melanogaster segment polarity network and the Saccharomyces cerevisiae cell-cycle network, we show that the structure of attractor basins can yield insight into the underlying decision making required of the system, and also the way in which the system maximises its robustness. In particular, gene networks implementing decisions based on a few genes have simple state-space structures, and their attractors are robust by virtue of their simplicity. Gene networks with decisions that involve many interacting genes have correspondingly more complicated state-space structures, and robustness cannot be achieved through the structure of the attractor basins, but is achieved by larger attractor basins that dominate the state space. These different types of robustness are demonstrated by the two models: the D. melanogaster segment polarity network is robust due to simple attractor basins that implement decisions based on spatial signals; the S. cerevisiae cell-cycle network has a complicated state-space structure, and is robust only due to a giant attractor basin that dominates the state space.

Thermal Field-Flow Fractionation of Charged Submicrometer Particles in Aqueous Media

Thermal field-flow fractionation (ThFFF) of various types of submicrometer silica particles in aqueous media is experimentally investigated under an extended range of medium ionic strengths with and without the presence of surfactant. The experiments were designed to examine the applicability to submicrometer particles of the theory of charged nanoparticles thermodiffusion recently proposed by Parola and Piazza (Parola, A.; Piazza, R. Eur. Phys. J. E. 2004, 15, 255-263). In particular, the expression for the calibration function in terms of particle radius and channel temperature is derived and experimentally verified. Moreover, retention is expected to be dependent on particle surface potential and charge, and on ionic strength. These dependences are experimentally investigated and the pertinent relationships and correlations derived. The effect of heavy metal adsorption on the silica surface was investigated, and significant ThFFF retention changes were measured. Independent measurements of the zeta potential (zeta-potential) indicated that a decrease in the surface charge of a silica particle is a consequence of heavy metal adsorption, which is, in turn, correlated to the observed decrease in ThFFF retention.

Genome-wide transcriptional plasticity underlies cellular adaptation to novel challenge

By recruiting the essential HIS3 gene to the GAL regulatory system and switching to a repressing glucose medium, we confronted yeast cells with a novel challenge they had not encountered before along their history in evolution.

Adaptation to this challenge involved a global transcriptional response of a sizeable fraction of the genome, which relaxed on the time scale of the population adaptation, of order of 10 generations.

For a large fraction of the responding genes there is no simple biological interpretation, connecting them to the specific cellular demands imposed by the novel challenge.

Strikingly, repeating the experiment did not reproduce similar transcription patterns neither in the transient phase nor in the adapted state in glucose.

These results suggest that physiological selection operates on the new metabolic configurations generated by the non-specific large scale transcriptional response to eventually stabilize an adaptive state.

Cells adjust their transcriptional state to accommodate environmental and genetic perturbations. Some common perturbations, such as changes in nutrient composition, elicit well-characterized transcriptional responses that can be understood by simple engineering-like design principles as satisfying specific demands imposed by the perturbation. However, cells also have the ability to adapt to novel and unforeseen challenges. This ability is central in realizing the evolvability potential of cells as they respond to dramatic genetic or environmental changes along evolution. Little is known about the mechanisms underlying such adaptations to novel challenges; in particular, the role of the transcriptional regulatory network in such adaptations has not been characterized. Genome-wide measurements have revealed that, in many cases, perturbations lead to a global transcriptional response involving a sizeable fraction of the genome (Gasch et al, 2000; Jelinsky et al, 2000; Causton et al, 2001; Ideker et al, 2001; Lai et al, 2005). Such global behavior suggests that general collective properties of the genetic network, rather than specific pre-designed pathways, determine an important part of the transcriptional response. It is not known however what fraction of genes within such massive transcriptional responses is essential to the specific cellular demands. It is also unknown whether the non-pre-designed part of the response can have a functional role in adaptation to novel challenges.

To study these questions, we confronted yeast cells with a novel challenge they had not encountered before along their history in evolution. A strain of the yeast Saccharomyces cerevisiae was engineered to recruit the gene HIS3, an essential enzyme from the histidine biosynthesis pathway (Hinnebusch, 1992), to the GAL regulatory system, responsible for galactose utilization (Stolovicki et al, 2006). The GAL system is known to be strongly repressed when the cells are exposed to glucose. Therefore, upon switching to a medium containing glucose and lacking histidine, the GAL system and with it HIS3 are highly repressed immediately following the switch and the cells encounter a severe challenge. We have recently shown that a cell population carrying this rewired genome can adapt to grow competitively in a chemostat in a medium containing pure glucose (Stolovicki et al, 2006). This adaptation occurred on a timescale of ∼10 generations; applying a stronger environmental pressure in the form of a competitive inhibitor to HIS3 (3AT) resulted in a similar adaptation albeit with a longer timescale. Figure 1 shows the dynamics of the population's cell density (blue lines, measured by OD) following a medium switch from galactose to glucose in the chemostat without (A) and with (B) 3AT. The experiments revealed that adaptation occurs on physiological timescales (much shorter than required by spontaneous random mutations), but the mechanisms underlying this adaptation have remained unclear (Stolovicki et al, 2006).

Yeast cells had not encountered recruitment of HIS3 to the GAL system along their evolutionary history, and their genome could not possibly have been selected to specifically address glucose repression of HIS3. This experiment, therefore, provides a unique opportunity to characterize the spontaneous transcriptional response during adaptation to a novel challenge and to assess the functional role of the regulatory system in this adaptation. We used DNA microarrays to measure the genome-wide expression levels at time points along the adaptation process, with and without 3AT. These measurements revealed that a sizeable fraction of the genome responded by induction or repression to the switch into glucose. Superimposed on the OD traces, Figure 1 shows the results of a clustering analysis of the expression of genes as measured by the arrays along time in the experiments. This analysis revealed two dominant clusters, each containing hundreds of genes in each experiment, which responded to the medium switch to glucose by a strong transient induction or repression followed by relaxation to steady state on the timescale of the adaptation process, ∼ 10 generations. The two clusters in each experiment show similar but opposite dynamics.

A detailed analysis of the gene content in the two clusters revealed that only a small portion of the response was induced by a change in carbon source (15% overlap between the corresponding clusters in the two experiments, with and without 3AT). Moreover, it revealed a very low overlap with the universal stress response observed for a wide range of environmental stresses (Gasch et al, 2000; Causton et al, 2001) and with the typical response to amino-acid starvation (Natarajan et al, 2001). Additionally, all known specific responses to stress in the literature are characterized by transient induction or repression with relaxation to steady state within a generation time (Gasch et al, 2000; Koerkamp et al, 2002; Wu et al, 2004), whereas in our experiments relaxation of the transcriptional response occurs over many generations. Taken together, these results show that the transcriptional response observed here is neither a metabolic response to the change in carbon source nor is it a standard response to stress or amino-acid starvation. This raises the possibility that it is a spontaneous collective response that is largely composed of genes that do not have a specific function. This possibility was tested directly by repeating the experiment with different populations and comparing their responses. This procedure revealed reproducible adaptation dynamics and steady states in terms of population density, but showed significantly different transcriptional transient responses and steady states for the two repeated experiments. Thus, a significant portion of the genes that changed their expression during the adaptation process do not have a well-defined and reproducible function in the challenging environment.

The application of a stronger environmental pressure in the form of 3AT had a dramatic effect on the global characteristics of the transcriptional response: it induced a markedly higher correlation among the hundreds of responding genes. Figure 3A compares the array data in color code for the two experiments. It is seen that the emergent pattern of transcription exhibits a higher degree of order by the introduction of high external pressure. Observation of the transcriptional patterns for specific metabolic pathways illustrates the different contributions to the correlated dynamics (Figure 3B–D). A general energetic module such as glycolysis exhibited similar patterns of induction and relaxation in experiments with and without 3AT (Figure 3B). However, in general, we found that more than one-third of the known metabolic modules (30 out of 88 modules described in KEGG) exhibited high expression correlation among their genes when the environmental pressure was high but not when it was low. As an example, Figure 3C shows the histidine biosynthesis pathway and Figure 3D the purine pathway. Note the highly ordered trajectories in the lower panels (with 3AT) compared to the disordered ones in the upper panels (no 3AT). This order extends also between genes belonging to different and even distant metabolic modules. It indicates that a global transcriptional regulatory mechanism is in operation, rather than a local specific one. Surprisingly, genes belonging to the same metabolic pathway exhibited simultaneous positively and negatively correlated dynamics. Thus, an important conclusion of this work is that the global transcriptional response to a novel challenge cannot be explained by a simple cellular or metabolic logic. This is to be expected if the response had not been specifically selected in evolution and was not pre-designed for the challenge.

Our data clearly reveal that the massive transcriptional response underlies the adaptation process to a novel challenge. The novelty of the challenge presented to the cells excludes the possibility that this response has been specifically selected toward this challenge. Thus, transcriptional regulation has dynamic properties resulting in a general massive nonspecific response to a novel perturbation. Such a response in turn allows for metabolic rearrangements, which by feeding back on transcription lead to adaptation of the cells to the unforeseen situation. The drastic change in the expression state of the cell opens multiple new metabolic pathways. Physiological selection works then on these multiple metabolic pathways to stabilize an adaptive state that causes relaxation of the perturbed expression pattern. This scenario, involving the creation of a library of possibilities and physiological selection over this library, is compatible with our understanding of a broad class of biological systems, placing the cellular metabolic/regulatory networks on the same footing as the neural or the immune systems (Gerhart and Kirschner, 1997).

Cells adjust their transcriptional state to accommodate environmental and genetic perturbations. An open question is to what extent transcriptional response to perturbations has been specifically selected along evolution. To test the possibility that transcriptional reprogramming does not need to be ‘pre-designed' to lead to an adaptive metabolic state on physiological timescales, we confronted yeast cells with a novel challenge they had not previously encountered. We rewired the genome by recruiting an essential gene, HIS3, from the histidine biosynthesis pathway to a foreign regulatory system, the GAL network responsible for galactose utilization. Switching medium to glucose in a chemostat caused repression of the essential gene and presented the cells with a severe challenge to which they adapted over approximately 10 generations. Using genome-wide expression arrays, we show here that a global transcriptional reprogramming (>1200 genes) underlies the adaptation. A large fraction of the responding genes is nonreproducible in repeated experiments. These results show that a nonspecific transcriptional response reflecting the natural plasticity of the regulatory network supports adaptation of cells to novel challenges.

Mammalian mRNA splice-isoform selection is tightly controlled

Post-transcriptional RNA processing is an important regulatory control mechanism for determining the phenotype of eukaryotic cells. The processing of a transcribed RNA species into alternative splice isoforms yields products that can perform different functions. Each type of cell in a multi-cellular organism is presumed to actively control the relative quantities of alternative splice isoforms. In this study, the alternatively spliced isoforms of five mRNA transcription units were examined by quantitative reverse transcription-PCR amplification. We show that interindividual variation in splice-isoform selection is very highly constrained when measured in a large population of genetically diverse mice (i.e., full siblings; N = 150). Remarkably, splice-isoform ratios are among the most invariant phenotypes measured in this population and are confirmed in a second, genetically distinct population. In addition, the patterns of splice-isoform selection show tissue-specific and age-related changes. We propose that splice-isoform selection is exceptionally robust to genetic and environmental variability and may provide a control point for cellular homeostasis. As a consequence, splice-isoform ratios may be useful as a practical quantitative measure of the physiological status of cells and tissues.

Dynamics of protein distributions in cell populations

A population of cells exhibits wide phenotypic variation even if it is genetically homogeneous. In particular, individual cells differ from one another in the amount of protein they express under a given regulatory system under fixed conditions. Here we study how protein distributions in a population of the yeast S. cerevisiae are shaped by a balance of processes: protein production--an intracellular process--and protein dilution due to cell division--a population process. We measure protein distributions by employing reporter green fluorescence protein (gfp) under the regulation of the yeast GAL system under conditions where it is metabolically essential. Cell populations are grown in chemostats, thus allowing control of the environment and stable measurements of distribution dynamics over many generations. Despite the essential functional role of the GAL system in a pure galactose medium, steady-state distributions are found to be universally broad, with exponential tails and a large standard-deviation-to-mean ratio. Under several different perturbations the dynamics of the distribution is observed to be asymmetric, with a much longer time to build a wide expression distribution from below compared with a fast relaxation of the distribution toward steady state from above. These results show that the main features of the protein distributions are largely determined by population effects and are less sensitive to the intracellular biochemical noise.

Impact of RNA interference on gene networks

Small endogenous RNAs such as microRNAs (miRNAs) and small interfering RNAs (siRNAs) have been found to post-transcriptionally control cellular gene networks by targeting complementary mRNAs for translation impairment (miRNA) or destruction (siRNA). We have developed a computational model, coordinated to molecular and biochemical parameters of RNA interference pathways, to provide (semi-) quantitative insight into the molecular events managing siRNA-mediated gene expression silencing in native and synthetic gene networks. Based on mass-conservation principles and kinetic rate laws, we converted biochemical RNA interference pathways into a set of ordinary differential equations that describe the dynamics of siRNA-mediated translation-regulation in mammalian cells. Capitalizing on mechanistic details of synthetic transactivator operation, we wired this model into a transcription control circuitry in which the siRNA and its target mRNA are independently regulated at the transcriptional level. In this context, we studied the impact of siRNA transcription timing on the onset of target gene transcription and production kinetics of target mRNA-encoded proteins. We also simulated the rate of siRNA-induced mRNA depletion and demonstrated that the relative concentrations of interacting siRNAs/mRNAs and the number of siRNA-specific target sites on a transcript modulate (i) the rate of target mRNA disappearance, (ii) the steady-state mRNA levels and (iii) induction dynamics of mRNA-encoded protein production. As our model predictions are consistent with available biochemical parameters, extrapolations may improve our understanding of how complex regulatory gene networks are impacted by small endogenous RNAs.

Conservation of expression and sequence of metabolic genes is reflected by activity across metabolic states

Variation in gene expression levels on a genomic scale has been detected among different strains, among closely related species, and within populations of genetically identical cells. What are the driving forces that lead to expression divergence in some genes and conserved expression in others? Here we employ flux balance analysis to address this question for metabolic genes. We consider the genome-scale metabolic model of Saccharomyces cerevisiae, and its entire space of optimal and near-optimal flux distributions. We show that this space reveals underlying evolutionary constraints on expression regulation, as well as on the conservation of the underlying gene sequences. Genes that have a high range of optimal flux levels tend to display divergent expression levels among different yeast strains and species. This suggests that gene regulation has diverged in those parts of the metabolic network that are less constrained. In addition, we show that genes that are active in a large fraction of the space of optimal solutions tend to have conserved sequences. This supports the possibility that there is less selective pressure to maintain genes that are relevant for only a small number of metabolic states.

The regulation of gene product activity allows cells to efficiently cope with various tasks under varying conditions. Given that, one may have expected that striving for efficiency would cause genetically similar cells to have similar regulation. However, in reality, high variations in gene expression levels are detected between different strains and even between genetically identical cells taken from the same culture. What are the driving forces that lead to expression divergence in some genes and conserved expression in others?

To address this question, the authors study the conservation of regulation in yeast metabolism, using a computational model. They find that genes coding for reactions whose flux rates are narrowly constrained by the cellular need to maximize growth rate tend to have strictly conserved regulation and expression. However, when a wide range of flux rates is compatible with high cellular growth rates, the corresponding regulation and expression patterns are free to diverge. Furthermore, enzymes that participate in a large number of alternative metabolic behaviors tend to be encoded by genes with a highly conserved sequence. Taken together, these findings support the hypothesis that maintaining large variability in the overall expression and metabolic repertoire of the cell is under marked evolutionary selection.

Synthetic gene recruitment reveals adaptive reprogramming of gene regulation in yeast

The recruitment of a gene to a foreign regulatory system is a major evolutionary event that can lead to novel phenotypes. However, the evolvability potential of cells depends on their ability to cope with challenges presented by gene recruitment. To study this ability, we combined synthetic gene recruitment with continuous culture and online measurements of the metabolic and regulatory dynamics over long timescales. The gene HIS3 from the histidine synthesis pathway was recruited to the GAL system, responsible for galactose utilization in the yeast S. cerevisiae. Following a switch from galactose to glucose--from induced to repressed conditions of the GAL system--in histidine-lacking chemostats (where the recruited HIS3 is essential), the regulatory system reprogrammed to adaptively tune HIS3 expression, allowing the cells to grow competitively in pure glucose. The adapted state was maintained for hundreds of generations in various environments. The timescales involved and the reproducibility of separate experiments render spontaneous mutations an unlikely underlying mechanism. Essentially all cells could adapt, excluding selection over a genetically variable population. The results reveal heritable adaptation induced by the exposure to glucose. They demonstrate that genetic regulatory networks have the potential to support highly demanding events of gene recruitment.

Reconstructing the pathways of a cellular system from genome-scale signals by using matrix and tensor computations

We describe the use of the matrix eigenvalue decomposition (EVD) and pseudoinverse projection and a tensor higher-order EVD (HOEVD) in reconstructing the pathways that compose a cellular system from genome-scale nondirectional networks of correlations among the genes of the system. The EVD formulates a genes x genes network as a linear superposition of genes x genes decorrelated and decoupled rank-1 subnetworks, which can be associated with functionally independent pathways. The integrative pseudoinverse projection of a network computed from a "data" signal onto a designated "basis" signal approximates the network as a linear superposition of only the subnetworks that are common to both signals and simulates observation of only the pathways that are manifest in both experiments. We define a comparative HOEVD that formulates a series of networks as linear superpositions of decorrelated rank-1 subnetworks and the rank-2 couplings among these subnetworks, which can be associated with independent pathways and the transitions among them common to all networks in the series or exclusive to a subset of the networks. Boolean functions of the discretized subnetworks and couplings highlight differential, i.e., pathway-dependent, relations among genes. We illustrate the EVD, pseudoinverse projection, and HOEVD of genome-scale networks with analyses of yeast DNA microarray data.

Is the regulation of galactose 1-phosphate tuned against gene expression noise?

The average number of mRNA molecules per active gene in yeast can be remarkably low. Consequently, the relative number of copies of each transcript per cell can vary greatly from moment to moment. When these transcripts are encoding metabolic enzymes, how do the resulting variations in enzyme concentrations affect the regulation of metabolic intermediates? Using a kinetic model of galactose utilization in yeast, we analysed the transmission of noise from transcription and translation on metabolic intermediate regulation. In particular, the effect of the kinetic properties of the galactose-1-phosphate uridylyltransferase reaction on the transmission of noise was analysed.

Regulatory on/off minimization of metabolic flux changes after genetic perturbations

Predicting the metabolic state of an organism after a gene knockout is a challenging task, because the regulatory system governs a series of transient metabolic changes that converge to a steady-state condition. Regulatory on/off minimization (ROOM) is a constraint-based algorithm for predicting the metabolic steady state after gene knockouts. It aims to minimize the number of significant flux changes (hence on/off) with respect to the wild type. ROOM is shown to accurately predict steady-state metabolic fluxes that maintain flux linearity, in agreement with experimental flux measurements, and to correctly identify short alternative pathways used for rerouting metabolic flux in response to gene knockouts. ROOM's growth rate and flux predictions are compared with previously suggested algorithms, minimization of metabolic adjustment, and flux balance analysis (FBA). We find that minimization of metabolic adjustment provides accurate predictions for the initial transient growth rates observed during the early postperturbation state, whereas ROOM and FBA more successfully predict final higher steady-state growth rates. Although FBA explicitly maximizes the growth rate, ROOM does not, and only implicitly favors flux distributions having high growth rates. This indicates that, even though the cell has not evolved to cope with specific mutations, regulatory mechanisms aiming to minimize flux changes after genetic perturbations may indeed work to this effect. Further work is needed to identify metrics that characterize the complete trajectory from the initial to the final metabolic steady states after genetic perturbations.