Mads Kærn

Stochasticity in gene expression: from theories to phenotypes

Genetically identical cells exposed to the same environmental conditions can show significant variation in molecular content and marked differences in phenotypic characteristics. This variability is linked to stochasticity in gene expression, which is generally viewed as having detrimental effects on cellular function with potential implications for disease. However, stochasticity in gene expression can also be advantageous. It can provide the flexibility needed by cells to adapt to fluctuating environments or respond to sudden stresses, and a mechanism by which population heterogeneity can be established during cellular differentiation and development.

Noise in eukaryotic gene expression.

Transcription in eukaryotic cells has been described as quantal, with pulses of messenger RNA produced in a probabilistic manner. This description reflects the inherently stochastic nature of gene expression, known to be a major factor in the heterogeneous response of individual cells within a clonal population to an inducing stimulus. Here we show in Saccharomyces cerevisiae that stochasticity (noise) arising from transcription contributes significantly to the level of heterogeneity within a eukaryotic clonal population, in contrast to observations in prokaryotes, and that such noise can be modulated at the translational level. We use a stochastic model of transcription initiation specific to eukaryotes to show that pulsatile mRNA production, through reinitiation, is crucial for the dependence of noise on transcriptional efficiency, highlighting a key difference between eukaryotic and prokaryotic sources of noise. Furthermore, we explore the propagation of noise in a gene cascade network and demonstrate experimentally that increased noise in the transcription of a regulatory protein leads to increased cell–cell variability in the target gene output, resulting in prolonged bistable expression states. This result has implications for the role of noise in phenotypic variation and cellular differentiation.

A chance at survival: gene expression noise and phenotypic diversification strategies

Phenotypic diversification plays a central role in evolution and provides species with a capacity to survive environmental adversity. The profound impact of random molecular events on the shaping of life is well accepted in the context of chance mutations and genetic drift; however, the evolution of the regulatory networks encoding microorganismal stress response and survival strategies might also have been significantly influenced by gene expression noise. This likelihood has inspired numerous investigations to characterize the sources of phenotypic diversity within isogenic populations, and to explore their direct and potential biological implications. Here, we discuss different scenarios where gene expression noise might bestow a selective advantage under stress, highlighting a potentially fundamental role of stochastic mechanisms in the evolution of microbial survival strategies.

A Non-Death Role of the Yeast Metacaspase: Yca1p Alters Cell Cycle Dynamics

Caspase proteases are a conserved protein family predominantly known for engaging and executing apoptotic cell death. Nevertheless, in higher eukaryotes, caspases also influence a variety of cell behaviors including differentiation, proliferation and growth control. S. cerevisiae expresses a primordial caspase, yca1, and exhibits apoptosis-like death under certain stresses; however, the benefit of a dedicated death program to single cell organisms is controversial. In the absence of a clear rationale to justify the evolutionary retention of a death only pathway, we hypothesize that yca1 also influences non-apoptotic events. We report that genetic ablation and/or catalytic inactivation of Yca1p leads to a longer G1/S transition accompanied by slower growth in fermentation conditions. Downregulation of Yca1p proteolytic activity also results in failure to arrest during nocodazole treatment, indicating that Yca1p participates in the G2/M mitotic checkpoint. 20s proteasome activity and ROS staining of the Δyca1 strain is indistinguishable from its isogenic control suggesting that putative regulation of the oxidative stress response by Yca1p does not instigate the cell cycle phenotype. Our results demonstrate multiple non-death roles for yca1 in the cell cycle.

The Circadian Molecular Clock Regulates Adult Hippocampal Neurogenesis by Controlling the Timing of Cell-Cycle Entry and Exit

The subgranular zone (SGZ) of the adult hippocampus contains a pool of quiescent neural progenitor cells (QNPs) that are capable of entering the cell cycle and producing newborn neurons. The mechanisms that control the timing and extent of adult neurogenesis are not well understood. Here, we show that QNPs of the adult SGZ express molecular-clock components and proliferate in a rhythmic fashion. The clock proteins PERIOD2 and BMAL1 are critical for proper control of neurogenesis. The absence of PERIOD2 abolishes the gating of cell-cycle entrance of QNPs, whereas genetic ablation of bmal1 results in constitutively high levels of proliferation and delayed cell-cycle exit. We use mathematical model simulations to show that these observations may arise from clock-driven expression of a cell-cycle inhibitor that targets the cyclin D/Cdk4-6 complex. Our findings may have broad implications for the circadian clock in timing cell-cycle events of other stem cell populations throughout the body.

Estimations of intrinsic and extrinsic noise in models of nonlinear genetic networks

We discuss two methods that can be used to estimate the impact of internal and external variability on nonlinear systems, and demonstrate their utility by comparing two experimentally implemented oscillatory genetic networks with different designs. The methods allow for rapid estimations of intrinsic and extrinsic noise and should prove useful in the analysis of natural genetic networks and when constructing synthetic gene regulatory systems.

Model-based rational design of an oncolytic virus with improved therapeutic potential

Oncolytic viruses are complex biological agents that interact at multiple levels with both tumour and normal tissues. Antiviral pathways induced by interferon are known to have a critical role in determining tumour cell sensitivity and normal cell resistance to infection with oncolytic viruses. Here we pursue a synthetic biology approach to identify methods that enhance antitumour activity of oncolytic viruses through suppression of interferon signalling. On the basis of the mathematical analysis of multiple strategies, we hypothesize that a positive feedback loop, established by virus-mediated expression of a soluble interferon-binding decoy receptor, increases tumour cytotoxicity without compromising normal cells. Oncolytic rhabdoviruses engineered to express a secreted interferon antagonist have improved oncolytic potential in cellular cancer models, and display improved therapeutic potential in tumour-bearing mice. Our results demonstrate the potential of this methodology in evaluating potential caveats of viral immune-evasion strategies and improving the design oncolytic viruses.

Gene expression noise facilitates adaptation and drug resistance independently of mutation.

We show that the effect of stress on the reproductive fitness of noisy cell populations can be modeled as a first-passage time problem, and demonstrate that even relatively short-lived fluctuations in gene expression can ensure the long-term survival of a drug-resistant population. We examine how this effect contributes to the development of drug-resistant cancer cells, and demonstrate that permanent immunity can arise independently of mutations.

Estimating the stochastic bifurcation structure of cellular networks.

High throughput measurement of gene expression at single-cell resolution, combined with systematic perturbation of environmental or cellular variables, provides information that can be used to generate novel insight into the properties of gene regulatory networks by linking cellular responses to external parameters. In dynamical systems theory, this information is the subject of bifurcation analysis, which establishes how system-level behaviour changes as a function of parameter values within a given deterministic mathematical model. Since cellular networks are inherently noisy, we generalize the traditional bifurcation diagram of deterministic systems theory to stochastic dynamical systems. We demonstrate how statistical methods for density estimation, in particular, mixture density and conditional mixture density estimators, can be employed to establish empirical bifurcation diagrams describing the bistable genetic switch network controlling galactose utilization in yeast Saccharomyces cerevisiae. These approaches allow us to make novel qualitative and quantitative observations about the switching behavior of the galactose network, and provide a framework that might be useful to extract information needed for the development of quantitative network models.

Chromosomal Position Effects Are Linked to Sir2-Mediated Variation in Transcriptional Burst Size

Gene expression noise varies with genomic position and is a driving force in the evolution of chromosome organization. Nevertheless, position effects remain poorly characterized. Here, we present a systematic analysis of chromosomal position effects by characterizing single-cell gene expression from euchromatic positions spanning the length of a eukaryotic chromosome. We demonstrate that position affects gene expression by modulating the size of transcriptional bursts, rather than their frequency, and that the histone deacetylase Sir2 plays a role in this process across the chromosome.