Network-dosage compensation topologies as recurrent network motifs in natural gene networks

The duplication of genomes and genetic networks and its potential for evolutionary adaptation and survival during environmental turmoil

The importance of whole-genome duplication (WGD) for evolution is controversial. Whereas some view WGD mainly as detrimental and an evolutionary dead end, there is growing evidence that polyploidization can help overcome environmental change, stressful conditions, or periods of extinction. However, despite much research, the mechanistic underpinnings of why and how polyploids might be able to outcompete or outlive nonpolyploids at times of environmental upheaval remain elusive, especially for autopolyploids, in which heterosis effects are limited. On the longer term, WGD might increase both mutational and environmental robustness due to redundancy and increased genetic variation, but on the short-or even immediate-term, selective advantages of WGDs are harder to explain. Here, by duplicating artificially generated Gene Regulatory Networks (GRNs), we show that duplicated GRNs-and thus duplicated genomes-show higher signal output variation than nonduplicated GRNs. This increased variation leads to niche expansion and can provide polyploid populations with substantial advantages to survive environmental turmoil. In contrast, under stable environments, GRNs might be maladaptive to changes, a phenomenon that is exacerbated in duplicated GRNs. We believe that these results provide insights into how genome duplication and (auto)polyploidy might help organisms to adapt quickly to novel conditions and to survive ecological uproar or even cataclysmic events.

Modeling aging and its impact on cellular function and organismal behavior

Aging is a complex phenomenon of functional decay in a biological organism. Although the effects of aging are readily recognizable in a wide range of organisms, the cause(s) of aging are ill defined and poorly understood. Experimental methods on model organisms have driven significant insight into aging as a process, but have not provided a complete model of aging. Computational biology offers a unique opportunity to resolve this gap in our knowledge by generating extensive and testable models that can help us understand the fundamental nature of aging, identify the presence and characteristics of unaccounted aging factor(s), demonstrate the mechanics of particular factor(s) in driving aging, and understand the secondary effects of aging on biological function. In this review, we will address each of the above roles for computational biology in aging research. Concurrently, we will explore the different applications of computational biology to aging in single-celled versus multicellular organisms. Given the long history of computational biogerontological research on lower eukaryotes, we emphasize the key future goals of gradually integrating prior models into a holistic map of aging and translating successful models to higher-complexity organisms.

A synthetic circuit for buffering gene dosage variation between individual mammalian cells

Precise control of gene expression is critical for biological research and biotechnology. However, transient plasmid transfections in mammalian cells produce a wide distribution of copy numbers per cell, and consequently, high expression heterogeneity. Here, we report plasmid-based synthetic circuits - Equalizers - that buffer copy-number variation at the single-cell level. Equalizers couple a transcriptional negative feedback loop with post-transcriptional incoherent feedforward control. Computational modeling suggests that the combination of these two topologies enables Equalizers to operate over a wide range of plasmid copy numbers. We demonstrate experimentally that Equalizers outperform other gene dosage compensation topologies and produce as low cell-to-cell variation as chromosomally integrated genes. We also show that episome-encoded Equalizers enable the rapid generation of extrachromosomal cell lines with stable and uniform expression. Overall, Equalizers are simple and versatile devices for homogeneous gene expression and can facilitate the engineering of synthetic circuits that function reliably in every cell.

The Systems Biology of Single-Cell Aging

Aging is a leading cause of human morbidity and mortality, but efforts to slow or reverse its effects are hampered by an incomplete understanding of its multi-faceted origins. Systems biology, the use of quantitative and computational methods to understand complex biological systems, offers a toolkit well suited to elucidating the root cause of aging. We describe the known components of the aging network and outline innovative techniques that open new avenues of investigation to the aging research community. We propose integration of the systems biology and aging fields, identifying areas of complementarity based on existing and impending technological capabilities.

Multi-component gene network design as a survival strategy in diverse environments

Background

Gene-environment interactions are often mediated though gene networks in which gene expression products interact with other network components to dictate network activity levels, which in turn determines the fitness of the host cell in specific environments. Even though a gene network is the right context for studying gene-environment interactions, we have little understanding on how systematic genetic perturbations affects fitness in the context of a gene network.

Results

Here we examine the effect of combinatorial gene dosage alterations on gene network activity and cellular fitness. Using the galactose utilization pathway as a model network in diploid yeast, we reduce the copy number of four regulatory genes (GAL2, GAL3, GAL4, GAL80) from two to one, and measure the activity of the perturbed networks. We integrate these results with competitive fitness measurements made in six different rationally-designed environments containing different galactose concentrations representing the natural induction spectrum of the galactose network. In the lowest galactose environment, we find a nonlinear relationship between gene expression and fitness while high galactose environments lead to a linear relationship between the two with a saturation regime reached at a sufficiently high galactose concentration. We further uncover environment-specific relevance of the different network components for dictating the relationship between the network activity and organismal fitness, indicating that none of the network components are redundant.

Conclusions

These results provide experimental support to the hypothesis that dynamic changes in the environment throughout natural evolution is key to structuring natural gene networks in a multi-component fashion, which robustly provides protection against population extinction in different environments.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0609-3) contains supplementary material, which is available to authorized users.

High-resolution studies of lysis-lysogeny decision-making in bacteriophage lambda

Cellular decision-making guides complex development such as cell differentiation and disease progression. Much of our knowledge about decision-making is derived from simple models, such as bacteriophage lambda infection, in which lambda chooses between the vegetative lytic fate and the dormant lysogenic fate. This paradigmatic system is broadly understood but lacking mechanistic details, partly due to limited resolution of past studies. Here, we discuss how modern technologies have enabled high-resolution examination of lambda decision-making to provide new insights and exciting possibilities in studying this classical system. The advent of techniques for labeling specific DNA, RNA, and proteins in cells allows for molecular-level characterization of events in lambda development. These capabilities yield both new answers and new questions regarding how the isolated lambda genetic circuit acts, what biological events transpire among phages in their natural context, and how the synergy of simple phage macromolecules brings about complex behaviors.

Coupling of DNA Replication and Negative Feedback Controls Gene Expression for Cell-Fate Decisions

Cellular decision-making arises from the expression of genes along a regulatory cascade, which leads to a choice between distinct phenotypic states. DNA dosage variations, often introduced by replication, can significantly affect gene expression to ultimately bias decision outcomes. The bacteriophage lambda system has long served as a paradigm for cell-fate determination, yet the effect of DNA replication remains largely unknown. Here, through single-cell studies and mathematical modeling we show that DNA replication drastically boosts cI expression to allow lysogenic commitment by providing more templates. Conversely, expression of CII, the upstream regulator of cI, is surprisingly robust to DNA replication due to the negative autoregulation of the Cro repressor. Our study exemplifies how living organisms can not only utilize DNA replication for gene expression control but also implement mechanisms such as negative feedback to allow the expression of certain genes to be robust to dosage changes resulting from DNA replication.

Noise reduction as an emergent property of single-cell aging

Noise-induced heterogeneity in gene expression is an inherent reality for cells. However, it is not well understood how noise strength changes for a single gene while the host cell is aging. Using a state-of-the-art microfluidic platform, we measure noise dynamics in aging yeast cells by tracking the generation-specific activity of the canonical GAL1 promoter. We observe noise reduction during normal aging of a cell, followed by a short catastrophe phase in which noise increased. We hypothesize that aging-associated increases in chromatin state transitions are behind the observed noise reduction and a stochastic model provides quantitative support to the proposed mechanism. Noise trends measured from strains with altered GAL1 promoter dynamics (constitutively active, synthetic with nucleosome-disfavoring sequences, and in the absence of RPD3, a global remodeling regulator) lend further support to our hypothesis. Observing similar noise dynamics from a different promoter (HHF2) provides support to the generality of our findings.

Gene expression is a noisy process, but it is not known how noise in gene expression changes during the aging of single cells. Here the authors show that noise decreases during normal aging, and provide support for aging-associated increases in chromatin state transitions governing noise reduction.

Noise reduction facilitated by dosage compensation in gene networks

Genetic noise together with genome duplication and volume changes during cell cycle are significant contributors to cell-to-cell heterogeneity. How can cells buffer the effects of these unavoidable epigenetic and genetic variations on phenotypes that are sensitive to such variations? Here we show that a simple network motif that is essential for network-dosage compensation can reduce the effects of extrinsic noise on the network output. Using natural and synthetic gene networks with and without the network motif, we measure gene network activity in single yeast cells and find that the activity of the compensated network is significantly lower in noise compared with the non-compensated network. A mathematical analysis provides intuitive insights into these results and a novel stochastic model tracking cell-volume and cell-cycle predicts the experimental results. Our work implies that noise is a selectable trait tunable by evolution.

A cell size- and cell cycle-aware stochastic model for predicting time-dynamic gene network activity in individual cells

Background

Despite the development of various modeling approaches to predict gene network activity, a time dynamic stochastic model taking into account real-time changes in cell volume and cell cycle stages is still missing.

Results

Here we present a stochastic single-cell model that can be applied to any eukaryotic gene network with any number of components. The model tracks changes in cell volume, DNA replication, and cell division, and dynamically adjusts rates of stochastic reactions based on this information. By tracking cell division, the model can maintain cell lineage information, allowing the researcher to trace the descendants of any single cell and therefore study cell lineage effects. To test the predictive power of our model, we applied it to the canonical galactose network of the yeast Saccharomyces cerevisiae. Using a minimal set of free parameters and across several galactose induction conditions, the model effectively captured several details of the experimentally-obtained single-cell network activity levels as well as phenotypic switching rates.

Conclusion

Our model can readily be customized to model any gene network in any of the commonly used cells types, offering a novel and user-friendly stochastic modeling capability to the systems biology field.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0240-5) contains supplementary material, which is available to authorized users.

Impact of the cell division cycle on gene circuits

In growing cells, protein synthesis and cell growth are typically not synchronous, and, thus, protein concentrations vary over the cell division cycle. We have developed a theoretical description of genetic regulatory systems in bacteria that explicitly considers the cell division cycle to investigate its impact on gene expression. We calculate the cell-to-cell variations arising from cells being at different stages in the division cycle for unregulated genes and for basic regulatory mechanisms. These variations contribute to the extrinsic noise observed in single-cell experiments, and are most significant for proteins with short lifetimes. Negative autoregulation buffers against variation of protein concentration over the division cycle, but the effect is found to be relatively weak. Stronger buffering is achieved by an increased protein lifetime. Positive autoregulation can strongly amplify such variation if the parameters are set to values that lead to resonance-like behaviour. For cooperative positive autoregulation, the concentration variation over the division cycle diminishes the parameter region of bistability and modulates the switching times between the two stable states. The same effects are seen for a two-gene mutual-repression toggle switch. By contrast, an oscillatory circuit, the repressilator, is only weakly affected by the division cycle.