Rule-based modeling and simulations of the inner kinetochore structure

Background

Combinatorial complexity is a central problem when modeling biochemical reaction networks, since the association of a few components can give rise to a large variation of protein complexes. Available classical modeling approaches are often insufficient for the analysis of very large and complex networks in detail. Recently, we developed a new rule-based modeling approach that facilitates the analysis of spatial and combinatorially complex problems. Here, we explore for the first time how this approach can be applied to a specific biological system, the human kinetochore, which is a multi-protein complex involving over 100 proteins.

Results

Applying our freely available SRSim software to a large data set on kinetochore proteins in human cells, we construct a spatial rule-based simulation model of the human inner kinetochore. The model generates an estimation of the probability distribution of the inner kinetochore 3D architecture and we show how to analyze this distribution using information theory. In our model, the formation of a bridge between CenpA and an H3 containing nucleosome only occurs efficiently for higher protein concentration realized during S-phase but may be not in G1. Above a certain nucleosome distance the protein bridge barely formed pointing towards the importance of chromatin structure for kinetochore complex formation. We define a metric for the distance between structures that allow us to identify structural clusters. Using this modeling technique, we explore different hypothetical chromatin layouts.

Conclusions

Applying a rule-based network analysis to the spatial kinetochore complex geometry allowed us to integrate experimental data on kinetochore proteins, suggesting a 3D model of the human inner kinetochore architecture that is governed by a combinatorial algebraic reaction network. This reaction network can serve as bridge between multiple scales of modeling. Our approach can be applied to other systems beyond kinetochores.     

Postembryonic lineages of the Drosophila brain: I. Development of the lineage-associated fiber tracts

Neurons of the Drosophila central brain fall into approximately 100 paired groups, termed lineages. Each lineage is derived from a single asymmetrically-dividing neuroblast. Embryonic neuroblasts produce 1,500 primary neurons (per hemisphere) that make up the larval CNS followed by a second mitotic period in the larva that generates approximately 10,000 secondary, adult-specific neurons. Clonal analyses based on previous works using lineage-specific Gal4 drivers have established that such lineages form highly invariant morphological units. All neurons of a lineage project as one or a few axon tracts (secondary axon tracts, SATs) with characteristic trajectories, thereby representing unique hallmarks. In the neuropil, SATs assemble into larger fiber bundles (fascicles) which interconnect different neuropil compartments. We have analyzed the SATs and fascicles formed by lineages during larval, pupal, and adult stages using antibodies against membrane molecules (Neurotactin/Neuroglian) and synaptic proteins (Bruchpilot/N-Cadherin). The use of these markers allows one to identify fiber bundles of the adult brain and associate them with SATs and fascicles of the larval brain. This work lays the foundation for assigning the lineage identity of GFP-labeled MARCM clones on the basis of their close association with specific SATs and neuropil fascicles, as described in the accompanying paper (Wong et al., 2013. Postembryonic lineages of the Drosophila brain: II. Identification of lineage projection patterns based on MARCM clones. Submitted.).     

Predictive systems ecology

Human societies, and their well-being, depend to a significant extent on the state of the ecosystems that surround them. These ecosystems are changing rapidly usually in response to anthropogenic changes in the environment. To determine the likely impact of environmental change on ecosystems and the best ways to manage them, it would be desirable to be able to predict their future states. We present a proposal to develop the paradigm of predictive systems ecology, explicitly to understand and predict the properties and behaviour of ecological systems. We discuss the necessary and desirable features of predictive systems ecology models. There are places where predictive systems ecology is already being practised and we summarize a range of terrestrial and marine examples. Significant challenges remain but we suggest that ecology would benefit both as a scientific discipline and increase its impact in society if it were to embrace the need to become more predictive.     

Impaired Recognition of Facially Expressed Emotions in Different Groups of Patients with Sleep Disorders

IntroductionRecently it has been shown that acute sleep loss has a direct impact on emotional processing in healthy individuals. Here we studied the effect of chronically disturbed sleep on emotional processing by investigating two samples of patients with sleep disorders.Methods25 patients with psychophysiologic insomnia (23 women and 2 men, mean age: 51.6 SD; 10.9 years), 19 patients with sleep apnea syndrome (4 women and 15 men, mean age: 51.9; SD 11.1) and a control sample of 24 subjects with normal sleep (15women and 9 men, mean age 45.3; SD 8.8) completed a Facial Expressed Emotion Labelling (FEEL) task, requiring participants to categorize and rate the intensity of six emotional expression categories: anger, anxiety, fear, happiness, disgust and sadness. Differences in FEEL score and its subscales among the three samples were analysed using ANOVA with gender as a covariate.ResultsBoth patients with psychophysiologic insomnia and patients with sleep apnea showed significantly lower performance in the FEEL test as compared to the control group. Differences were seen in the scales happiness and sadness. Patient groups did not differ from each other.ConclusionBy demonstrating that previously known effects of acute sleep deprivation on emotional processing can be extended to persons experiencing chronically disturbed sleep, our data contribute to a deeper understanding of the relationship between sleep loss and emotions.     

Intraspecific trait variation and colonization sequence alter community assembly and disease epidemics

When individuals from multiple populations colonize a new habitat patch, intraspecific trait variation can make the arrival order of colonists an important factor for subsequent population and community dynamics. In particular, intraspecific priority effects (IPEs) allow early arrivers to limit the growth or establishment of later arrivers, even when competitively inferior on a per‐capita basis. Through their effects on genes and traits, IPEs can alter short‐term growth and long‐term evolutionary change in single species metapopulations. Given their importance for intraspecific interactions, IPEs in a dominant species have the potential to affect the composition of entire communities. We conducted an experiment to determine whether and how arrival order and IPEs in the zooplankter Daphnia pulex affected its interactions with both competitors (the cladoceran Simocephalus vetulus) and parasites (the virulent fungus Metschnikowia bicuspidata). We found strong evidence for IPEs in Daphnia, as early arrivers inhibited late arrivers even when competitively inferior. These IPEs in Daphnia altered both the establishment success of interspecific competitors and the size of disease epidemics: early colonization by fast‐growing D. pulex led to large Daphnia populations and low competitor establishment, but large disease epidemics. Early colonization by slow‐growing D. pulex, on the other hand, resulted in small Daphnia populations with high competitor establishment, but smaller disease epidemics. Overall, our results demonstrate the importance of intraspecific variation and arrival order for community dynamics, and highlight IPEs as a general mechanism driving variation in natural communities.     

Multiple stressors: using the honeybee model BEEHAVE to explore how spatial and temporal forage stress affects colony resilience

The causes underlying the increased mortality of honeybee Apis mellifera colonies observed over the past decade remain unclear. Since so far the evidence for monocausal explanations is equivocal, involvement of multiple stressors is generally assumed. We here focus on various aspects of forage availability, which have received less attention than other stressors because it is virtually impossible to explore them empirically. We applied the colony model BEEHAVE, which links within‐hive dynamics and foraging, to stylized landscape settings to explore how foraging distance, forage supply, and “forage gaps”, i.e. periods in which honeybees cannot find any nectar and pollen, affect colony resilience and the mechanisms behind. We found that colony extinction was mainly driven by foraging distance, but the timing of forage gaps had strongest effects on time to extinction. Sensitivity to forage gaps of 15 days was highest in June or July even if otherwise forage availability was sufficient to survive. Forage availability affected colonies via cascading effects on queen's egg‐laying rate, reduction of new‐emerging brood stages developing into adult workers, pollen debt, lack of workforce for nursing, and reduced foraging activity. Forage gaps in July led to reduction in egg‐laying and increased mortality of brood stages at a time when the queen's seasonal egg‐laying rate is at its maximum, leading to colony failure over time. Our results demonstrate that badly timed forage gaps interacting with poor overall forage supply reduce honeybee colony resilience. Existing regulation mechanisms which in principle enable colonies to cope with varying forage supply in a given landscape and year, such as a reduction in egg‐laying, have only a certain capacity. Our results are hypothetical, as they are obtained from simplified landscape settings, but they are consistent with existing empirical knowledge. They offer ample opportunities for testing the predicted effects of forage stress in controlled experiments.