A genome wide dosage suppressor network reveals genomic robustness.

Genomic robustness is the extent to which an organism has evolved to withstand the effects of deleterious mutations. We explored the extent of genomic robustness in budding yeast by genome wide dosage suppressor analysis of 53 conditional lethal mutations in cell division cycle and RNA synthesis related genes, revealing 660 suppressor interactions of which 642 are novel. This collection has several distinctive features, including high co-occurrence of mutant-suppressor pairs within protein modules, highly correlated functions between the pairs and higher diversity of functions among the co-suppressors than previously observed. Dosage suppression of essential genes encoding RNA polymerase subunits and chromosome cohesion complex suggests a surprising degree of functional plasticity of macromolecular complexes, and the existence of numerous degenerate pathways for circumventing the effects of potentially lethal mutations. These results imply that organisms and cancer are likely able to exploit the genomic robustness properties, due the persistence of cryptic gene and pathway functions, to generate variation and adapt to selective pressures.

The Evolution of Neuroplasticity and the Effect on Integrated Information.

Information integration theory has been developed to quantify consciousness. Since conscious thought requires the integration of information, the degree of this integration can be used as a neural correlate (Φ) with the intent to measure degree of consciousness. Previous research has shown that the ability to integrate information can be improved by Darwinian evolution. The value Φ can change over many generations, and complex tasks require systems with at least a minimum Φ . This work was done using simple animats that were able to remember previous sensory inputs, but were incapable of fundamental change during their lifetime: actions were predetermined or instinctual. Here, we are interested in changes to Φ due to lifetime learning (also known as neuroplasticity). During lifetime learning, the system adapts to perform a task and necessitates a functional change, which in turn could change Φ . One can find arguments to expect one of three possible outcomes: Φ might remain constant, increase, or decrease due to learning. To resolve this, we need to observe systems that learn, but also improve their ability to learn over the many generations that Darwinian evolution requires. Quantifying Φ over the course of evolution, and over the course of their lifetimes, allows us to investigate how the ability to integrate information changes. To measure Φ , the internal states of the system must be experimentally observable. However, these states are notoriously difficult to observe in a natural system. Therefore, we use a computational model that not only evolves virtual agents (animats), but evolves animats to learn during their lifetime. We use this approach to show that a system that improves its performance due to feedback learning increases its ability to integrate information. In addition, we show that a system's ability to increase Φ correlates with its ability to increase in performance. This suggests that systems that are very plastic regarding Φ learn better than those that are not.

Robust Min-system oscillation in the presence of internal photosynthetic membranes in cyanobacteria.

The oscillatory Min system of Escherichia coli defines the cell division plane by regulating the site of FtsZ-ring formation and represents one of the best-understood examples of emergent protein self-organization in nature. The oscillatory patterns of the Min-system proteins MinC, MinD and MinE (MinCDE) are strongly dependent on the geometry of membranes they bind. Complex internal membranes within cyanobacteria could disrupt this self-organization by sterically occluding or sequestering MinCDE from the plasma membrane. Here, it was shown that the Min system in the cyanobacterium Synechococcus elongatus PCC 7942 oscillates from pole-to-pole despite the potential spatial constraints imposed by their extensive thylakoid network. Moreover, reaction-diffusion simulations predict robust oscillations in modeled cyanobacterial cells provided that thylakoid network permeability is maintained to facilitate diffusion, and suggest that Min proteins require preferential affinity for the plasma membrane over thylakoids to correctly position the FtsZ ring. Interestingly, in addition to oscillating, MinC exhibits a midcell localization dependent on MinD and the DivIVA-like protein Cdv3, indicating that two distinct pools of MinC are coordinated in S. elongatus. Our results provide the first direct evidence for Min oscillation outside of E. coli and have broader implications for Min-system function in bacteria and organelles with internal membrane systems.

A putative design for the electromagnetic activation of split proteins for molecular and cellular manipulation.

The ability to manipulate cellular function using an external stimulus is a powerful strategy for studying complex biological phenomena. One approach to modulate the function of the cellular environment is split proteins. In this method, a biologically active protein or an enzyme is fragmented so that it reassembles only upon a specific stimulus. Although many tools are available to induce these systems, nature has provided other mechanisms to expand the split protein toolbox. Here, we show a novel method for reconstituting split proteins using magnetic stimulation. We found that the electromagnetic perceptive gene (EPG) changes conformation due to magnetic field stimulation. By fusing split fragments of a certain protein to both termini of the EPG, the fragments can be reassembled into a functional protein under magnetic stimulation due to conformational change. We show this effect with three separate split proteins: NanoLuc, APEX2, and herpes simplex virus type-1 thymidine kinase. Our results show, for the first time, that reconstitution of split proteins can be achieved only with magnetic fields. We anticipate that this study will be a starting point for future magnetically inducible split protein designs for cellular perturbation and manipulation. With this technology, we can help expand the toolbox of the split protein platform and allow better elucidation of complex biological systems.

Academic ecosystems must evolve to support a sustainable postdoc workforce.

The postdoctoral workforce comprises a growing proportion of the science, technology, engineering and mathematics (STEM) community, and plays a vital role in advancing science. Postdoc professional development, however, remains rooted in outdated realities. We propose enhancements to postdoc-centred policies and practices to better align this career stage with contemporary job markets and work life. By facilitating productivity, wellness and career advancement, the proposed changes will benefit all stakeholders in postdoc success-including research teams, institutions, professional societies and the scientific community as a whole. To catalyse reform, we outline recommendations for (1) skills-based training tailored to the current career landscape, and (2) supportive policies and tools outlined in postdoc handbooks. We also invite the ecology and evolution community to lead further progressive reform.

Evolutionary game theory using agent-based methods.

Evolutionary game theory is a successful mathematical framework geared towards understanding the selective pressures that affect the evolution of the strategies of agents engaged in interactions with potential conflicts. While a mathematical treatment of the costs and benefits of decisions can predict the optimal strategy in simple settings, more realistic settings such as finite populations, non-vanishing mutations rates, stochastic decisions, communication between agents, and spatial interactions, require agent-based methods where each agent is modeled as an individual, carries its own genes that determine its decisions, and where the evolutionary outcome can only be ascertained by evolving the population of agents forward in time. While highlighting standard mathematical results, we compare those to agent-based methods that can go beyond the limitations of equations and simulate the complexity of heterogeneous populations and an ever-changing set of interactors. We conclude that agent-based methods can predict evolutionary outcomes where purely mathematical treatments cannot tread (for example in the weak selection-strong mutation limit), but that mathematics is crucial to validate the computational simulations.

Evolution and stability of altruist strategies in microbial games.

When microbes compete for limited resources, they often engage in chemical warfare using bacterial toxins. This competition can be understood in terms of evolutionary game theory (EGT). We study the predictions of EGT for the bacterial "suicide bomber" game in terms of the phase portraits of population dynamics, for parameter combinations that cover all interesting games for two-players, and seven of the 38 possible phase portraits of the three-player game. We compare these predictions to simulations of these competitions in finite well-mixed populations, but also allowing for probabilistic rather than pure strategies, as well as Darwinian adaptation over tens of thousands of generations. We find that Darwinian evolution of probabilistic strategies stabilizes games of the rock-paper-scissors type that emerge for parameters describing realistic bacterial populations, and point to ways in which the population fixed point can be selected by changing those parameters.