Dynamic modeling of cooperative protein secretion in microorganism populations

Interactions between microorganisms can have a crucial effect on their population dynamics. Typically, interactions are mediated through the environment by molecules and proteins that are products of cell metabolism and physiology; they therefore reflect the internal dynamics of the single cell. In this work we aim to integrate single-cell properties of gene expression that affect indirect interactions between microorganisms under challenging conditions, into a quantitative model of population dynamics. Specifically we address the problem of a microbial population secreting a protein that can actively extract a growth-limiting resource, such as a simple sugar or iron, from the environment. The genes coding for the protein can undergo random epigenetic transitions between active and silenced states, and can be repressed by the product of their reaction. We model cooperative and competitive interactions between protein producing and non-producing phenotypes by nonlinear dynamical systems and analyze them both in terms of asymptotic states and of transient dynamics. Our model shows that phenotypic transitions allow a stable coexistence of the two phenotypes, and enables us to make predictions regarding the conditions required for such coexistence and the typical timescales of transient dynamics. It also shows how repression by the reaction product induces a feedback at the population-environment level that can result in limit cycle dynamics. The relation of these results to experiments are discussed.     

Expertise in Musical Improvisation and Creativity: The Mediation of Idea Evaluation

The current study explored the influence of musical expertise, and specifically training in improvisation on creativity, using the framework of the twofold model, according to which creativity involves a process of idea generation and idea evaluation. Based on the hypothesis that a strict evaluation phase may have an inhibiting effect over the generation phase, we predicted that training in improvisation may have a “releasing effect” on the evaluation system, leading to greater creativity. To examine this hypothesis, we compared performance among three groups - musicians trained in improvisation, musicians not trained in improvisation, and non-musicians - on divergent thinking tasks and on their evaluation of creativity. The improvisation group scored higher on fluency and originality compared to the other two groups. Among the musicians, evaluation of creativity mediated how experience in improvisation was related to originality and fluency scores. It is concluded that deliberate practice of improvisation may have a “releasing effect” on creativity.     

Identification of Neonatal White Matter on DTI: Influence of More Inclusive Thresholds for Atlas Segmentation

Purpose

Semi-automated diffusion tensor imaging (DTI) analysis of white matter (WM) microstructure offers a clinically feasible technique to assess neonatal brain development and provide early prognosis, but is limited by variable methods and insufficient evidence regarding optimal parameters. The purpose of this research was to investigate the influence of threshold values on semi-automated, atlas-based brain segmentation in very-low-birth-weight (VLBW) preterm infants at near-term age.

Materials and Methods

DTI scans were analyzed from 45 VLBW preterm neonates at near-term-age with no brain abnormalities evident on MRI. Brain regions were selected with a neonatal brain atlas and threshold values: trace <0.006 mm²/s, fractional anisotropy (FA)>0.15, FA>0.20, and FA>0.25. Relative regional volumes, FA, axial diffusivity (AD), and radial diffusivity (RD) were compared for twelve WM regions.

Results

Near-term brain regions demonstrated differential effects from segmentation with the three FA thresholds. Regional DTI values and volumes selected in the PLIC, CereP, and RLC varied the least with the application of different FA thresholds. Overall, application of higher FA thresholds significantly reduced brain region volume selected, increased variability, and resulted in higher FA and lower RD values. The lower threshold FA>0.15 selected 78±21% of original volumes segmented by the atlas, compared to 38±12% using threshold FA>0.25.

Conclusion

Results indicate substantial and differential effects of atlas-based DTI threshold parameters on regional volume and diffusion scalars. A lower, more inclusive FA threshold than typically applied for adults is suggested for consistent analysis of WM regions in neonates.     

Divergence and Selectivity of Expression-Coupled Histone Modifications in Budding Yeasts

Various histone modifications are widely associated with gene expression, but their functional selectivity at individual genes remains to be characterized. Here, we identify widespread differences between genome-wide patterns of two prominent marks, H3K9ac and H3K4me3, in budding yeasts. As well as characteristic gene profiles, relative modification levels vary significantly amongst genes, irrespective of expression. Interestingly, we show that these differences couple to contrasting features: higher methylation to essential, periodically expressed, ‘DPN’ (Depleted Proximal Nucleosome) genes, and higher acetylation to non-essential, responsive, ‘OPN’ (Occupied Proximal Nucleosome) genes. Thus, H3K4me3 may generally associate with expression stability, and H3K9ac, with variability. To evaluate this notion, we examine their association with expression divergence between the closely related species, S. cerevisiae and S. paradoxus. Although individually well conserved at orthologous genes, changes between modifications are mostly uncorrelated, indicating largely non-overlapping regulatory mechanisms. Notably, we find that inter-species differences in methylation, but not acetylation, are well correlated with expression changes, thereby proposing H3K4me3 as a candidate regulator of expression divergence. Taken together, our results suggest distinct evolutionary roles for expression-linked modifications, wherein H3K4me3 may contribute to stabilize average expression, whilst H3K9ac associates with more indirect aspects such as responsiveness.     

Optical Detection of E. coli Bacteria by Mesoporous Silicon Biosensors

A label-free optical biosensor based on a nanostructured porous Si is designed for rapid capture and detection of Escherichia coli K12 bacteria, as a model microorganism. The biosensor relies on direct binding of the target bacteria cells onto its surface, while no pretreatment (e.g. by cell lysis) of the studied sample is required. A mesoporous Si thin film is used as the optical transducer element of the biosensor. Under white light illumination, the porous layer displays well-resolved Fabry-Pérot fringe patterns in its reflectivity spectrum. Applying a fast Fourier transform (FFT) to reflectivity data results in a single peak. Changes in the intensity of the FFT peak are monitored. Thus, target bacteria capture onto the biosensor surface, through antibody-antigen interactions, induces measurable changes in the intensity of the FFT peaks, allowing for a ''real time'' observation of bacteria attachment.The mesoporous Si film, fabricated by an electrochemical anodization process, is conjugated with monoclonal antibodies, specific to the target bacteria. The immobilization, immunoactivity and specificity of the antibodies are confirmed by fluorescent labeling experiments. Once the biosensor is exposed to the target bacteria, the cells are directly captured onto the antibody-modified porous Si surface. These specific capturing events result in intensity changes in the thin-film optical interference spectrum of the biosensor. We demonstrate that these biosensors can detect relatively low bacteria concentrations (detection limit of 10⁴ cells/ml) in less than an hour.     

Computational design of auxotrophy-dependent microbial biosensors for combinatorial metabolic engineering experiments

Combinatorial approaches in metabolic engineering work by generating genetic diversity in a microbial population followed by screening for strains with improved phenotypes. One of the most common goals in this field is the generation of a high rate chemical producing strain. A major hurdle with this approach is that many chemicals do not have easy to recognize attributes, making their screening expensive and time consuming. To address this problem, it was previously suggested to use microbial biosensors to facilitate the detection and quantification of chemicals of interest. Here, we present novel computational methods to: (i) rationally design microbial biosensors for chemicals of interest based on substrate auxotrophy that would enable their high-throughput screening; (ii) predict engineering strategies for coupling the synthesis of a chemical of interest with the production of a proxy metabolite for which high-throughput screening is possible via a designed bio-sensor. The biosensor design method is validated based on known genetic modifications in an array of E. coli strains auxotrophic to various amino-acids. Predicted chemical production rates achievable via the biosensor-based approach are shown to potentially improve upon those predicted by current rational strain design approaches. (A Matlab implementation of the biosensor design method is available via http://www.cs.technion.ac.il/~tomersh/tools).     

Dynamic proteomics in individual human cells uncovers widespread cell-cycle dependence of nuclear proteins

We examined cell cycle-dependent changes in the proteome of human cells by systematically measuring protein dynamics in individual living cells. We used time-lapse microscopy to measure the dynamics of a random subset of 20 nuclear proteins, each tagged with yellow fluorescent protein (YFP) at its endogenous chromosomal location. We synchronized the cells in silico by aligning protein dynamics in each cell between consecutive divisions. We observed widespread (40%) cell-cycle dependence of nuclear protein levels and detected previously unknown cell cycle-dependent localization changes. This approach to dynamic proteomics can aid in discovery and accurate quantification of the extensive regulation of protein concentration and localization in individual living cells.     

Loss of growth homeostasis by genetic decoupling of cell division from biomass growth: implication for size control mechanisms

Growing cells adjust their division time with biomass accumulation to maintain growth homeostasis. Size control mechanisms, such as the size checkpoint, provide an inherent coupling of growth and division by gating certain cell cycle transitions based on cell size. We describe genetic manipulations that decouple cell division from cell size, leading to the loss of growth homeostasis, with cells becoming progressively smaller or progressively larger until arresting. This was achieved by modulating glucose influx independently of external glucose. Division rate followed glucose influx, while volume growth was largely defined by external glucose. Therefore, the coordination of size and division observed in wild‐type cells reflects tuning of two parallel processes, which is only refined by an inherent feedback‐dependent coupling. We present a class of size control models explaining the observed breakdowns of growth homeostasis.