Geometry shapes evolution of early multicellularity

Organisms have increased in complexity through a series of major evolutionary transitions, in which formerly autonomous entities become parts of a novel higher-level entity. One intriguing feature of the higher-level entity after some major transitions is a division of reproductive labor among its lower-level units. Although it can have clear benefits once established, it is unknown how such reproductive division of labor originates. We consider a recent evolution experiment on the yeast Saccharomyces cerevisiae as a unique platform to address the issue of reproductive differentiation during an evolutionary transition in individuality. In the experiment, independent yeast lineages evolved a multicellular "snowflake-like'' cluster form in response to gravity selection. Shortly after the evolution of clusters, the yeast evolved higher rates of cell death. While cell death enables clusters to split apart and form new groups, it also reduces their performance in the face of gravity selection. To understand the selective value of increased cell death, we create a mathematical model of the cellular arrangement within snowflake yeast clusters. The model reveals that the mechanism of cell death and the geometry of the snowflake interact in complex, evolutionarily important ways. We find that the organization of snowflake yeast imposes powerful limitations on the available space for new cell growth. By dying more frequently, cells in clusters avoid encountering space limitations, and, paradoxically, reach higher numbers. In addition, selection for particular group sizes can explain the increased rate of apoptosis both in terms of total cell number and total numbers of collectives. Thus, by considering the geometry of a primitive multicellular organism we can gain insight into the initial emergence of reproductive division of labor during an evolutionary transition in individuality.Comment: 7 figure

Impacts of the Teach For America Investing in Innovation Scale-Up

In 2010, Teach For America (TFA) launched a major expansion effort, funded in part by a five-year Investing in Innovation (i3) scale-up grant of $50 million from the U.S. Department of Education. Using a rigorous random assignment design to examine the effectiveness of TFA elementary school teachers in the second year of the i3 scale-up, Mathematica Policy Research found that first- and second-year corps members recruited and trained during the scale-up were as effective as other teachers in the same high-poverty schools in both reading and math. To estimate the effectiveness of TFA teachers relative to the comparison teachers, we compared end-of-year test scores of students assigned to the TFA teachers and those assigned to the comparison teachers. Because students in the study were randomly assigned to teachers, we can attribute systematic differences in achievement at the end of the study school year to the relative effectiveness of TFA and comparison teachers, rather than to the types of students taught by these two different groups of teachers. In addition to the impact analysis described in this report, the evaluation included an implementation analysis that describes key features of TFA's program model and its implementation of the i3 scale-up

Assessing the Effectiveness of Teach For America's Investing in Innovation Scale-Up

In 2010, TFA launched a major expansion effort, funded in part by a five-year Investing in Innovation (i3) scale-up grant of $50 million from the U.S. Department of Education. By the 2012 -- 2013 school year -- the second year of the scale-up -- TFA had expanded its placements of first- and second-year corps members by 25 percent. This study examines the effectiveness of TFA elementary school teachers hired during the first two years of the i3 scale-up, relative to other teachers in the same grades and school

Strategic tradeoffs in competitor dynamics on adaptive networks

Recent empirical work highlights the heterogeneity of social competitions such as political campaigns: proponents of some ideologies seek debate and conversation, others create echo chambers. While symmetric and static network structure is typically used as a substrate to study such competitor dynamics, network structure can instead be interpreted as a signature of the competitor strategies, yielding competition dynamics on adaptive networks. Here we demonstrate that tradeoffs between aggressiveness and defensiveness (i.e., targeting adversaries vs. targeting like-minded individuals) creates paradoxical behaviour such as non-transitive dynamics. And while there is an optimal strategy in a two competitor system, three competitor systems have no such solution; the introduction of extreme strategies can easily affect the outcome of a competition, even if the extreme strategies have no chance of winning. Not only are these results reminiscent of classic paradoxical results from evolutionary game theory, but the structure of social networks created by our model can be mapped to particular forms of payoff matrices. Consequently, social structure can act as a measurable metric for social games which in turn allows us to provide a game theoretical perspective on online political debates.Comment: 20 pages (11 pages for the main text and 9 pages of supplementary material

Navigating the Currents of Seascape Genomics: How Spatial Analyses can Augment Population Genomic Studies

Population genomic approaches are making rapid inroads in the study of non-model organisms, including marine taxa. To date, these marine studies have predominantly focused on rudimentary metrics describing the spatial and environmental context of their study region (e.g., geographical distance, average sea surface temperature, average salinity). We contend that a more nuanced and considered approach to quantifying seascape dynamics and patterns can strengthen population genomic investigations and help identify spatial, temporal, and environmental factors associated with differing selective regimes or demographic histories. Nevertheless, approaches for quantifying marine landscapes are complicated. Characteristic features of the marine environment, including pelagic living in flowing water (experienced by most marine taxa at some point in their life cycle), require a well-designed spatial-temporal sampling strategy and analysis. Many genetic summary statistics used to describe populations may be inappropriate for marine species with large population sizes, large species ranges, stochastic recruitment, and asymmetrical gene flow. Finally, statistical approaches for testing associations between seascapes and population genomic patterns are still maturing with no single approach able to capture all relevant considerations. None of these issues are completely unique to marine systems and therefore similar issues and solutions will be shared for many organisms regardless of habitat. Here, we outline goals and spatial approaches for landscape genomics with an emphasis onmarine systems and review the growing empirical literature on seascape genomics. We review established tools and approaches and highlight promising new strategies to overcome select issues including a strategy to spatially optimize sampling. Despite the many challenges, we argue that marine systems may be especially well suited for identifying candidate genomic regions under environmentally mediated selection and that seascape genomic approaches are especially useful for identifying robust locus-by-environment associations

NMR Resonance Assignment of the Therapeutic RNA Aptamer Macugen in Complex with its in Vivo Target, the Heparin Binding Domain of VEGF165

Aptamers consist of nucleic acids that are selected from an in vitro library in order to bind a specific target or catalyze a reaction. Many aptamer-ligand structures have been solved in recent years to characterize the molecular mechanisms that explain the high affinity and specificity observed in these interactions. To this end, NMR resonance assignment experiments were performed as a necessary first step to calculating the solution structure of a therapeutically-active RNA aptamer, Macugen, bound to its in vivo target, the heparin binding domain (HBD) of vascular endothelial growth factor 165 (VEGF165). Though the resonance assignment of Macugen is challenging due to its inability to be isotopically-labeled, techniques were employed that take advantage of the unique chemical modifications of this nucleic acid that are not present in traditional, unmodified RNA. Using the information obtained from two-dimensional heteronuclear (19F, 1H) and homonuclear (1H, 1H) experiments, assignments were made for a sizable number of Macugen atoms. The assignments made in this work will be necessary to calculate a solution structure to better understand the molecular mechanisms that give rise to the high affinity and specificity observed in the Macugen-HBD complex

The evolutionary emergence of stochastic phenotype switching in bacteria

Stochastic phenotype switching – or bet hedging – is a pervasive feature of living systems and common in bacteria that experience fluctuating (unpredictable) environmental conditions. Under such conditions, the capacity to generate variable offspring spreads the risk of being maladapted in the present environment, against offspring likely to have some chance of survival in the future. While a rich subject for theoretical studies, little is known about the selective causes responsible for the evolutionary emergence of stochastic phenotype switching. Here we review recent work – both theoretical and experimental – that sheds light on ecological factors that favour switching types over non-switching types. Of particular relevance is an experiment that provided evidence for an adaptive origin of stochastic phenotype switching by subjecting bacterial populations to a selective regime that mimicked essential features of the host immune response. Central to the emergence of switching types was frequent imposition of ‘exclusion rules’ and ‘population bottlenecks’ – two complementary faces of frequency dependent selection. While features of the immune response, exclusion rules and bottlenecks are likely to operate in many natural environments. Together these factors define a set of selective conditions relevant to the evolution of stochastic switching, including antigenic variation and bacterial persistence