Prion Replication in the Mammalian Cytosol: Functional Regions within a Prion Domain Driving Induction, Propagation, and Inheritance

Prions of lower eukaryotes are transmissible protein particles that propagate by converting homotypic soluble proteins into growing protein assemblies. Prion activity is conferred by so-called prion domains, regions of low complexity that are often enriched in glutamines and asparagines (Q/N). The compositional similarity of fungal prion domains with intrinsically disordered domains found in many mammalian proteins raises the question of whether similar sequence elements can drive prion-like phenomena in mammals. Here, we define sequence features of the prototype Saccharomyces cerevisiae Sup35 prion domain that govern prion activities in mammalian cells by testing the ability of deletion mutants to assemble into self-perpetuating particles. Interestingly, the amino-terminal Q/N-rich tract crucially important for prion induction in yeast was dispensable for the prion life cycle in mammalian cells. Spontaneous and template-assisted prion induction, growth, and maintenance were preferentially driven by the carboxy-terminal region of the prion domain that contains a putative soft amyloid stretch recently proposed to act as a nucleation site for prion assembly. Our data demonstrate that preferred prion nucleation domains can differ between lower and higher eukaryotes, resulting in the formation of prions with strikingly different amyloid cores

A self-organized model for cell-differentiation based on variations of molecular decay rates

Systemic properties of living cells are the result of molecular dynamics governed by so-called genetic regulatory networks (GRN). These networks capture all possible features of cells and are responsible for the immense levels of adaptation characteristic to living systems. At any point in time only small subsets of these networks are active. Any active subset of the GRN leads to the expression of particular sets of molecules (expression modes). The subsets of active networks change over time, leading to the observed complex dynamics of expression patterns. Understanding of this dynamics becomes increasingly important in systems biology and medicine. While the importance of transcription rates and catalytic interactions has been widely recognized in modeling genetic regulatory systems, the understanding of the role of degradation of biochemical agents (mRNA, protein) in regulatory dynamics remains limited. Recent experimental data suggests that there exists a functional relation between mRNA and protein decay rates and expression modes. In this paper we propose a model for the dynamics of successions of sequences of active subnetworks of the GRN. The model is able to reproduce key characteristics of molecular dynamics, including homeostasis, multi-stability, periodic dynamics, alternating activity, differentiability, and self-organized critical dynamics. Moreover the model allows to naturally understand the mechanism behind the relation between decay rates and expression modes. The model explains recent experimental observations that decay-rates (or turnovers) vary between differentiated tissue-classes at a general systemic level and highlights the role of intracellular decay rate control mechanisms in cell differentiation.Comment: 16 pages, 5 figure

Protein complex formation: computational clarification of the sequential versus probabilistic recruitment puzzle.

Our current view on how protein complexes assemble and disassemble at promoter sites is based on experimental data. For instance this data is provided by biochemical methods (e.g. ChIP-on-chip assays) or GFP-based assays. These two approaches suggest very different characteristics for protein recruitment processes that regulate and initiate gene transcription. Biochemical methods suggest a strictly ordered and consecutive protein recruitment while GFP-based assays draw a picture much closer to chaotic or stochastic recruitment. To understand the reason for these conflicting results, we design a generalized recruitment model (GRM) that allows to simulate all possible scenarios between strictly sequential recruitment and completely probabilistic recruitment. With this model we show that probabilistic, transient binding events that are visible in GFP experiments can not be detected by ChIP experiments. We demonstrate that sequential recruitment processes and probabilistic recruitment processes that contain "shortcuts" exhibit periodic dynamics and are hard to distinguish with standard ChIP measurements. Therefore we propose a simple experimental method that can be used to discriminate sequential from probabilistic recruitment processes. We discuss the limitations of this method

Schematic visualization landmarks and intermediate steps.

<p>(a) Using ChIP only a subset of events (landmarks) can be detected. (b) Binding events of proteins, for instance, are accompanied by conformational changes. ChIP is not sensible to these binding events and thus these intermediate processes are not reflected in the ChIP signal. (c,d) Transitions between landmarks modeled without (c) and with (d) intermediate steps: If several intermediate steps are included in the model, the transitions between landmarks become Poissonian instead of exponentially distributed. For a large number of intermediate states – as suggested by experimental ChIP data (see text) – this converges against a delta distribution.</p

Results for the PR for transcription of the gene (same setup as in Fig. 3).

<p>(a,e–j) (red), (green), (blue). The predicted ChIP signal (f) and the experimental ChIP (see Fig. 9a for details) have the same characteristics.</p

Results for the PR with 100 shortcuts (same setup as in Fig. 3).

<p>(a) The shortcuts branching backward and forward is shown in blue and orange, respectively. The thickness of the lines corresponds to the transition rate of the shortcuts (). (e-g) The ChIP signal converges rapidly to a constant value for all initial conditions.</p

Results for the PR with one shortcut (same setup as in Fig. 3).

<p>(a) The shortcut (branching backward, ) is shown in blue. (f) In case of a synchronized cell population (Gaussian initial condition) the ChIP signal exhibits periodical dynamics similar to the case of SR (Fig. 3f). (h,j) The initial conditions that represent de-synchronized cell populations show initial oscillating dynamics.</p

Result of ChIP experiments for -induced transcription of the gene.

<p>(a) ChIP signal of in vitro experiment (data extracted from Metivier et al. (2003)) (b) ChIP signal in silico experiment using the GRM (see Fig. 8). The ChIP signals of both experiments exhibit the same characteristics.</p

Results for the PR with 100 transitions with low transition rates (same setup as in Fig. 3).

<p>(a) The transitions branching backward and forward are shown in blue and orange, respectively. A distinction with the ChIP signal of a SR process (Fig. 3) is practically not possible.</p