A dynamic model of transcriptional imprinting derived from the vitellogenesis memory effect

The Basal Level of Gene Expression Associated with Chromatin Loosening Shapes Waddington Landscapes and Controls Cell Differentiation

The baseline level of transcription, which is variable and difficult to quantify, seriously complicates the normalization of comparative transcriptomic data, but its biological importance remains unappreciated. We show that this currently neglected ingredient is essential for controlling gene network multistability and therefore cellular differentiation. Basal expression is correlated to the degree of chromatin loosening measured by DNA accessibility and systematically leads to cellular dedifferentiation as assessed by transcriptomic signatures, irrespective of the molecular and cellular tools used. Modeling gene network motifs formally involved in developmental bifurcations reveals that the epigenetic landscapes of Waddington are restructured by the level of nonspecific expression, such that the attractors of progenitor and differentiated cells can be mutually exclusive. This mechanism is universal and holds beyond the particular nature of the genes involved, provided the multistable circuits are correctly described with autonomous basal expression. These results explain the relationships long established between gene expression noise, chromatin decondensation and cellular dedifferentiation, and highlight how heterochromatin maintenance is essential for preventing pathological cellular reprogramming, age-related diseases, and cancer.

A Computational Model of the Rainbow Trout Hypothalamus-Pituitary-Ovary-Liver Axis

Reproduction in fishes and other vertebrates represents the timely coordination of many endocrine factors that culminate in the production of mature, viable gametes. In recent years there has been rapid growth in understanding fish reproductive biology, which has been motivated in part by recognition of the potential effects that climate change, habitat destruction and contaminant exposure can have on natural and cultured fish populations. New approaches to understanding the impacts of these stressors are being developed that require a systems biology approach with more biologically accurate and detailed mathematical models. We have developed a multi-scale mathematical model of the female rainbow trout hypothalamus-pituitary-ovary-liver axis to use as a tool to help understand the functioning of the system and for extrapolation of laboratory findings of stressor impacts on specific components of the axis. The model describes the essential endocrine components of the female rainbow trout reproductive axis. The model also describes the stage specific growth of maturing oocytes within the ovary and permits the presence of sub-populations of oocytes at different stages of development. Model formulation and parametrization was largely based on previously published in vivo and in vitro data in rainbow trout and new data on the synthesis of gonadotropins in the pituitary. Model predictions were validated against several previously published data sets for annual changes in gonadotropins and estradiol in rainbow trout. Estimates of select model parameters can be obtained from in vitro assays using either quantitative (direct estimation of rate constants) or qualitative (relative change from control values) approaches. This is an important aspect of mathematical models as in vitro, cell-based assays are expected to provide the bulk of experimental data for future risk assessments and will require quantitative physiological models to extrapolate across biological scales.

Reproduction in fishes and other vertebrates represents the timely coordination of many endocrine factors that culminate in the production of mature, viable gametes. Improving the ability to estimate reproductive performance in fish is important, due to the growth of the aquaculture industry and the need to maintain adequate broodstock and concerns over the effects of anthropogenic stressors on feral fish populations. We present here a quantitative, mathematical model of the female rainbow trout reproductive cycle. We show how the model is able to accurately describe experimentally measured data associated with pituitary, ovarian and liver reproductive performance. We also use the model to describe similar data sets collected in rainbow trout by other researchers. An important value of quantitative biological models is the ability to simulate various physiological conditions, real or hypothetical. We demonstrate this by predicting the effects of exposure to an endocrine disruptor on oocyte growth. The need to limit cost and animal usage will encourage future experimental studies to use in vitro methods. The model presented here can assist with the extrapolation of in vitro effects to the whole fish.

Cloning of multiple ERα mRNA variants in killifish (Fundulus heteroclitus), and differential expression by tissue type, stage of reproduction, and estrogen exposure in fish from polluted and unpolluted environments

To test the hypothesis that alternative splicing could be an adaptive mechanism for populations subject to multi-generational estrogenic exposures, we compared estrogen receptor alpha (ERα) splicing variants in two populations of killifish (Fundulus heteroclitus): one resident in an estrogenic polluted environment (New Bedford Harbor, NBH, MA, USA) and one from a relatively uncontaminated reference site (Scorton Creek, SC, MA, USA). In total we identified 19 ERα variants, each with deletions of one or more coding exons. Four of the variants with potential functional relevance were analyzed by qPCR to test for population differences in expression by tissue type, site, sex, seasonal reproductive status and estrogen treatment. Significantly, a 5'-truncated short form variant (ERαS) was highly expressed in liver and ovary, and was associated with seasonal reproductive activity in SC but not NBH fish. Both ERαS and the full-length long variant (ERαL) were estrogen-inducible (ERαS>ERαL) but the induction response was lower in NBH than in SC fish. In contrast, NBH killifish were hyper-responsive to estrogen as measured by expression of two other estrogen responsive genes: vitellogenin (Vtg) and aromatase B (AroB). Most strikingly, two ERα deletion variants (Δ6 and Δ6-8), lacking ligand binding and activation function domains, were identified in a subset of NBH fish, where they were associated with reduced responsiveness to estrogen treatment. Together, these results support the hypothesis that alternative splicing of the esr1 gene of killifish could be an autoregulatory mechanism by which estrogen modulates the differential expression of ERα, and suggests a novel and adaptive mechanistic response to xenoestrogenic exposure.

Drawing a Waddington landscape to capture dynamic epigenetics

Epigenetics is most often reduced to chromatin marking in the current literature, whereas this notion was initially defined in a more general context. This restricted view ignores that epigenetic memories are in fact more robustly ensured in living systems by steady-state mechanisms with permanent molecule renewal. This misconception is likely to result from misleading intuitions and insufficient dialogues between traditional and quantitative biologists. To demystify dynamic epigenetics, its most famous image, a Waddington landscape and its attractors, are explicitly drawn. The simple example provided, is sufficient to highlight the main requirements and characteristics of dynamic gene networks, underlying cellular differentiation, de-differentiation and trans-differentiation.

Epigenetic memories: structural marks or active circuits?

A hallmark of living systems is the management and the storage of information through genetic and epigenetic mechanisms. Although the notion of epigenetics was originally given to any regulation beyond DNA sequence, it has often been restricted to chromatin modifications, supposed to behave as cis-markers, specifying the sets of genes to be expressed or repressed. This definition does not take into account the initial view of epigenetics, based on nonlinear interaction networks whose "attractors" can remain stable without need for any chromatin mark. In addition, most chromatin modifications are the steady state resultants of highly dynamic modification and de-modification activities and, as such, seem poorly appropriate to work as long-term memory keepers. Instead, the basic support of epigenetic memory could remain the attractors, to which chromatin modifications belong as do many other components. The influence of chromatin modifications in memory is highly questionable when envisioned as static structural marks, but can be recovered under the dynamic circuitry perspective, thanks to their self-templating properties. Beside their standard repressive or permissive functions, chromatin modifications can also influence transcription in multiple ways such as: (1) by randomizing or inversely stabilizing gene expression, (2) by mediating cooperativity between pioneer and secondary transcription factors, and (3) in the hysteresis and the ultrasensitivity of gene expression switches, allowing the cells to take unambiguous transcriptional decisions.

The physics of protein-DNA interaction networks in the control of gene expression

Protein-DNA interaction networks play a central role in many fundamental cellular processes. In gene regulation, physical interactions and reactions among the molecular components together with the physical properties of DNA control how genes are turned on and off. A key player in all these processes is the inherent flexibility of DNA, which provides an avenue for long-range interactions between distal DNA elements through DNA looping. Such versatility enables multiple interactions and results in additional complexity that is remarkably difficult to address with traditional approaches. This topical review considers recent advances in statistical physics methods to study the assembly of protein-DNA complexes with loops, their effects in the control of gene expression, and their explicit application to the prototypical lac operon genetic system of the E. coli bacterium. In the last decade, it has been shown that the underlying physical properties of DNA looping can actively control transcriptional noise, cell-to-cell variability, and other properties of gene regulation, including the balance between robustness and sensitivity of the induction process. These physical properties are largely dependent on the free energy of DNA looping, which accounts for DNA bending and twisting effects. These new physical methods have also been used in reverse to uncover the actual in vivo free energy of looping double-stranded DNA in living cells, which was not possible with existing experimental techniques. The results obtained for DNA looping by the lac repressor inside the E. coli bacterium showed a more malleable DNA than expected as a result of the interplay of the simultaneous presence of two distinct conformations of looped DNA.

Optimizing information flow in small genetic networks. III. A self-interacting gene

Living cells must control the reading out or "expression" of information encoded in their genomes, and this regulation often is mediated by transcription factors--proteins that bind to DNA and either enhance or repress the expression of nearby genes. But the expression of transcription factor proteins is itself regulated, and many transcription factors regulate their own expression in addition to responding to other input signals. Here we analyze the simplest of such self-regulatory circuits, asking how parameters can be chosen to optimize information transmission from inputs to outputs in the steady state. Some nonzero level of self-regulation is almost always optimal, with self-activation dominant when transcription factor concentrations are low and self-repression dominant when concentrations are high. In steady state the optimal self-activation is never strong enough to induce bistability, although there is a limit in which the optimal parameters are very close to the critical point.