Crossing the blood-brain barrier with carbon dots: uptake mechanism and in vivo cargo delivery.

The blood-brain barrier (BBB) is a major obstacle for drug delivery to the central nervous system (CNS) such that most therapeutics lack efficacy against brain tumors or neurological disorders due to their inability to cross the BBB. Therefore, developing new drug delivery platforms to facilitate drug transport to the CNS and understanding their mechanism of transport are crucial for the efficacy of therapeutics. Here, we report (i) carbon dots prepared from glucose and conjugated to fluorescein (GluCD-F) cross the BBB in zebrafish and rats without the need of an additional targeting ligand and (ii) uptake mechanism of GluCDs is glucose transporter-dependent in budding yeast. Glucose transporter-negative strain of yeast showed undetectable GluCD accumulation unlike the glucose transporter-positive yeast, suggesting glucose-transporter-dependent GluCD uptake. We tested GluCDs' ability to cross the BBB using both zebrafish and rat models. Following the injection to the heart, wild-type zebrafish showed GluCD-F accumulation in the central canal consistent with the transport of GluCD-F across the BBB. In rats, following intravenous administration, GluCD-F was observed in the CNS. GluCD-F was localized in the gray matter (e.g. ventral horn, dorsal horn, and middle grey) of the cervical spinal cord consistent with neuronal accumulation. Therefore, neuron targeting GluCDs hold tremendous potential as a drug delivery platform in neurodegenerative disease, traumatic injury, and malignancies of the CNS.

An inexpensive, high-throughput µPAD assay of microbial growth rate and motility on solid surfaces using Saccharomyces cerevisiae and Escherichia coli as model organisms.

Many microbial phenotypes are differentially or exclusively expressed on agar surfaces, including biofilms, motility, and sociality. However, agar-based assays are limited by their low throughput, which increases costs, lab waste, space requirements, and the time required to conduct experiments. Here, we demonstrate the use of wax-printed microfluidic paper-based analytical devices (µPADs) to measure linear growth rate of microbes on an agar growth media as a means of circumventing the aforementioned limitations. The main production materials of the proposed µPAD design are a wax printer, filter paper, and empty pipet boxes. A single wax-printed µPAD allowing 8 independent, agar-grown colonies costs $0.07, compared to $0.20 and $9.37 for the same number of replicates on traditional microtiter/spectrophotometry and Petri dish assays, respectively. We optimized the µPAD design for channel width (3 mm), agar volume (780 µL/channel), and microbe inoculation method (razor-blade). Comparative analyses of the traditional and proposed µPAD methods for measuring growth rate of nonmotile (Saccharomyces cerevisiae) and motile (flagellated Escherichia coli) microorganisms suggested the µPAD assays conferred a comparable degree of accuracy and reliability to growth rate measurements as their traditional counterparts. We substantiated this claim with strong, positive correlations between the traditional and µPAD assay, a significant nonzero slope in the model relating the two assays, a nonsignificant difference between the relative standard errors of the two techniques, and an analysis of inter-device reliability. Therefore, µPAD designs merit consideration for the development of enhanced-throughput, low-cost microbial growth and motility assays.

Effect of Stressors on the Carrying Capacity of Spatially Distributed Metapopulations.

Stressors such as antibiotics, herbicides, and pollutants are becoming increasingly common in the environment. The effects of stressors on populations are typically studied in homogeneous, nonspatial settings. However, most populations in nature are spatially distributed over environmentally heterogeneous landscapes with spatially restricted dispersal. Little is known about the effects of stressors in these more realistic settings. Here, we combine laboratory experiments with novel mathematical theory to rigorously investigate how a stressor's physiological effect and spatial distribution interact with dispersal to influence population dynamics. We prove mathematically that if a stressor increases the death rate and/or simultaneously decreases the population growth rate and yield, a homogeneous distribution of the stressor leads to a lower total population size than if the same amount of the stressor was heterogeneously distributed. We experimentally test this prediction on spatially distributed populations of budding yeast (Saccharomyces cerevisiae). We find that the antibiotic cycloheximide increases the yeast death rate but reduces the growth rate and yield. Consistent with our mathematical predictions, we observe that a homogeneous spatial distribution of cycloheximide minimizes the total equilibrium size of experimental metapopulations, with the magnitude of the effect depending predictably on the dispersal rate and the geographic pattern of antibiotic heterogeneity. Our study has implications for assessing the population risk posed by pollutants, antibiotics, and global change and for the rational design of strategies for employing toxins to control pathogens and pests.

Evolutionary Rescue from a Wave of Biological Invasion.

Evolution can potentially rescue populations from being driven extinct by biological invasions, but predictions for this occurrence are generally lacking. Here I derive theoretical predictions for evolutionary rescue of a resident population experiencing invasion from an introduced competitor that spreads over its introduced range as a traveling spatial wave that displaces residents. I compare the likelihood of evolutionary rescue from invasion for two modes of competition: exploitation and interference competition. I find that, all else equal, evolutionary rescue is less effective at preventing extinction caused by interference-driven invasions than by exploitation-driven invasions. Rescue from interference-driven invasions is, surprisingly, independent of invader dispersal rate or the speed of invasion and is more weakly dependent on range size than in the exploitation-driven case. In contrast, rescue from exploitation-driven invasions strongly depends on range size and is less likely during fast invasions. The results presented here have potential applications for conserving endemic species from nonnative invaders and for ensuring extinction of pests using intentionally introduced biocontrol agents.

Carrying capacity of a spatially-structured population: Disentangling the effects of dispersal, growth parameters, habitat heterogeneity and habitat clustering.

Carrying capacity, K, is a fundamental quantity in theoretical and applied ecology. When populations are distributed over space, carrying capacity becomes a complicated function of local, global and nearby environments, dispersal rate, and the relationship between population growth parameters, e.g., r and K. Expressions for the total carrying capacity, Ktotal, in an n-patch model that explicitly disentangle all of these factors are currently lacking. Therefore, here we derive Ktotal for a linear spatial array of n habitat patches with logistic growth and strong or weak random dispersal of individuals between adjacent patches. With strong dispersal, Ktotal depends on the mean r and K over all patches (〈r〉 and 〈K〉), the among-patch variance in K, and the linear regression coefficient of r on K, ßr,K. Strong dispersal increases Ktotal only if ßr, K > 〈r〉/〈K〉, which requires a positive convex or negative concave association between r and K, and decreases Ktotal if ßr, K < 〈r〉/〈K〉. Alternatively, weak dispersal increases Ktotal only if the within-patch covariance of r and K, cov(r, K) is greater than the spatial covariance between r and K, cov(r, Km), defined as the average covariance between r in a focal patch and K in neighboring patches. Unlike the strong dispersal limit, this condition depends not only on the magnitude of environmental heterogeneity, but explicitly on the spatial distribution of heterogeneity (i.e., habitat clustering). This work clarifies how the interaction between dispersal, habitat heterogeneity, and population growth parameters shape carrying capacity in spatial populations, with implications for species management, conservation and evolution.

Microbial expansion-collision dynamics promote cooperation and coexistence on surfaces.

Microbes colonizing a surface often experience colony growth dynamics characterized by an initial phase of spatial clonal expansion followed by collision between neighboring colonies to form potentially genetically heterogeneous boundaries. For species with life cycles consisting of repeated surface colonization and dispersal, these spatially explicit "expansion-collision dynamics" generate periodic transitions between two distinct selective regimes, "expansion competition" and "boundary competition," each one favoring a different growth strategy. We hypothesized that this dynamic could promote stable coexistence of expansion- and boundary-competition specialists by generating time-varying, negative frequency-dependent selection that insulates both types from extinction. We tested this experimentally in budding yeast by competing an exoenzyme secreting "cooperator" strain (expansion-competition specialists) against nonsecreting "defectors" (boundary-competition specialists). As predicted, we observed cooperator-defector coexistence or cooperator dominance with expansion-collision dynamics, but only defector dominance otherwise. Also as predicted, the steady-state frequency of cooperators was determined by colonization density (the average initial cell-cell distance) and cost of cooperation. Lattice-based spatial simulations give good qualitative agreement with experiments, supporting our hypothesis that expansion-collision dynamics with costly public goods production is sufficient to generate stable cooperator-defector coexistence. This mechanism may be important for maintaining public-goods cooperation and conflict in microbial pioneer species living on surfaces.

Noise slows the rate of Michaelis-Menten reactions.

Microscopic randomness and the small volumes of living cells combine to generate random fluctuations in molecule concentrations called "noise". Here I investigate the effect of noise on biochemical reactions obeying Michaelis-Menten kinetics, concluding that substrate noise causes these reactions to slow. I derive a general expression for the time evolution of the joint probability density of chemical species in arbitrarily connected networks of non-linear chemical reactions in small volumes. This equation is a generalization of the chemical master equation (CME), a common tool for investigating stochastic chemical kinetics, extended to reaction networks occurring in small volumes, such as living cells. I apply this equation to a generalized Michaelis-Menten reaction in an open system, deriving the following general result: 〈p〉≤p¯ and 〈s〉≥s¯, where s¯ and p¯ denote the deterministic steady-state concentration of reactant and product species, respectively, and 〈s〉 and 〈p〉 denote the steady-state ensemble average over independent realizations of a stochastic reaction. Under biologically realistic conditions, namely when substrate is degraded or diluted by cell division, 〈p〉≤p¯. Consequently, noise slows the rate of in vivo Michaelis-Menten reactions. These predictions are validated by extensive stochastic simulations using Gillespie's exact stochastic simulation algorithm. I specify the conditions under which these effects occur and when they vanish, therefore reconciling discrepancies among previous theoretical investigations of stochastic biochemical reactions. Stochastic slowdown of reaction flux caused by molecular noise in living cells may have functional consequences, which the present theory may be used to quantify.

Carrying capacity in a heterogeneous environment with habitat connectivity.

A large body of theory predicts that populations diffusing in heterogeneous environments reach higher total size than if non-diffusing, and, paradoxically, higher size than in a corresponding homogeneous environment. However, this theory and its assumptions have not been rigorously tested. Here, we extended previous theory to include exploitable resources, proving qualitatively novel results, which we tested experimentally using spatially diffusing laboratory populations of yeast. Consistent with previous theory, we predicted and experimentally observed that spatial diffusion increased total equilibrium population abundance in heterogeneous environments, with the effect size depending on the relationship between r and K. Refuting previous theory, however, we discovered that homogeneously distributed resources support higher total carrying capacity than heterogeneously distributed resources, even with species diffusion. Our results provide rigorous experimental tests of new and old theory, demonstrating how the traditional notion of carrying capacity is ambiguous for populations diffusing in spatially heterogeneous environments.

Propagation and control of gene expression noise with non-linear translation kinetics.

Gene expression is a stochastic process involving small numbers of molecules. As a consequence, cells in a clonal population vary randomly and sometimes substantially from one another in the concentration of mRNA and protein species, a phenomenon known as gene expression noise. Previous theoretical models of gene expression noise assumed that translation is first-order (linear) in mRNA concentration, leading to unfiltered propagation of mRNA noise to the protein level. Here I consider the biological ramifications of relaxing this assumption. Specifically, I solve for the noise statistics of gene expression assuming that translation obeys hyperbolic, Michaelis-Menten kinetics with respect to mRNA concentration. I generalize previous stochastic gene expression models by allowing the kinetic order of translation, denoted here by a, to vary continuously from zero-order (a = 0), where ribosomes are fully saturated with mRNA, to first order (a = 1), where ribosomes are unsaturated and mRNA is limiting for translation. In general, hyperbolic translation acts as a high-amplitude filter of mRNA noise. This hyperbolic filtering greatly attenuates the propagation of transcriptional noise to the protein level and qualitatively changes the selective and synthetic targets of noise control. In principle, natural selection or synthetic biologists could exploit this feature to limit or amplify gene expression noise by tuning mRNA and ribosome levels to control the kinetic order of translation.

Crowded growth leads to the spontaneous evolution of semistable coexistence in laboratory yeast populations.

Identifying the mechanisms that create and maintain biodiversity is a central challenge in biology. Stable diversification of microbial populations often requires the evolution of differences in resource utilization. Alternatively, coexistence can be maintained by specialization to exploit spatial heterogeneity in the environment. Here, we report spontaneous diversification maintained by a related but distinct mechanism: crowding avoidance. During experimental evolution of laboratory Saccharomyces cerevisiae populations, we observed the repeated appearance of "adherent" (A) lineages able to grow as a dispersed film, in contrast to their crowded "bottom-dweller" (B) ancestors. These two types stably coexist because dispersal reduces interference competition for nutrients among kin, at the cost of a slower maximum growth rate. This tradeoff causes the frequencies of the two types to oscillate around equilibrium over the course of repeated cycles of growth, crowding, and dispersal. However, further coevolution of the A and B types can perturb and eventually destroy their coexistence over longer time scales. We introduce a simple mathematical model of this "semistable" coexistence, which explains the interplay between ecological and evolutionary dynamics. Because crowded growth generally limits nutrient access in biofilms, the mechanism we report here may be broadly important in maintaining diversity in these natural environments.

Nonadaptive processes can create the appearance of facultative cheating in microbes.

Adaptations to social life may take the form of facultative cheating, in which organisms cooperate with genetically similar individuals but exploit others. Consistent with this possibility, many strains of social microbes like Myxococcus bacteria and Dictyostelium amoebae have equal fitness in single-genotype social groups but outcompete other strains in mixed-genotype groups. Here we show that these observations are also consistent with an alternative, nonadaptive scenario: kin selection-mutation balance under local competition. Using simple mathematical models, we show that deleterious mutations that reduce competitiveness within social groups (growth rate, e.g.) without affecting group productivity can create fitness effects that are only expressed in the presence of other strains. In Myxococcus, mutations that delay sporulation may strongly reduce developmental competitiveness. Deleterious mutations are expected to accumulate when high levels of kin selection relatedness relax selection within groups. Interestingly, local resource competition can create nonzero "cost" and "benefit" terms in Hamilton's rule even in the absence of any cooperative trait. Our results show how deleterious mutations can play a significant role even in organisms with large populations and highlight the need to test evolutionary causes of social competition among microbes.

Spatial population expansion promotes the evolution of cooperation in an experimental Prisoner's Dilemma.

Cooperation is ubiquitous in nature, but explaining its existence remains a central interdisciplinary challenge. Cooperation is most difficult to explain in the Prisoner's Dilemma game, where cooperators always lose in direct competition with defectors despite increasing mean fitness. Here we demonstrate how spatial population expansion, a widespread natural phenomenon, promotes the evolution of cooperation. We engineer an experimental Prisoner's Dilemma game in the budding yeast Saccharomyces cerevisiae to show that, despite losing to defectors in nonexpanding conditions, cooperators increase in frequency in spatially expanding populations. Fluorescently labeled colonies show genetic demixing of cooperators and defectors, followed by increase in cooperator frequency as cooperator sectors overtake neighboring defector sectors. Together with lattice-based spatial simulations, our results suggest that spatial population expansion drives the evolution of cooperation by (1) increasing positive genetic assortment at population frontiers and (2) selecting for phenotypes maximizing local deme productivity. Spatial expansion thus creates a selective force whereby cooperator-enriched demes overtake neighboring defector-enriched demes in a "survival of the fastest." We conclude that colony growth alone can promote cooperation and prevent defection in microbes. Our results extend to other species with spatially restricted dispersal undergoing range expansion, including pathogens, invasive species, and humans.

Origins of altruism diversity I: The diverse ecological roles of altruistic strategies and their evolutionary responses to local competition.

Nature abounds with a rich variety of altruistic strategies, including public resource enhancement, resource provisioning, communal foraging, alarm calling, and nest defense. Yet, despite their vastly different ecological roles, current theory typically treats diverse altruistic traits as being favored under the same general conditions. Here, we introduce greater ecological realism into social evolution theory and find evidence of at least four distinct modes of altruism. Contrary to existing theory, we find that altruistic traits contributing to "resource-enhancement" (e.g., siderophore production, provisioning, agriculture) and "resource-efficiency" (e.g., pack hunting, communication) are most strongly favored when there is strong local competition. These resource-based modes of helping are "K-strategies" that increase a social group's growth yield, and should characterize species with scarce resources and/or high local crowding caused by low mortality, high fecundity, and/or mortality occurring late in the process of resource-acquisition. The opposite conditions, namely weak local competition (abundant resource, low crowding), favor survival (e.g., nest defense) and fecundity (e.g., nurse workers) altruism, which are "r-strategies" that increase a social group's growth rate. We find that survival altruism is uniquely favored by a novel evolutionary force that we call "sunk cost selection." Sunk cost selection favors helping that prevents resources from being wasted on individuals destined to die before reproduction. Our results contribute to explaining the observed natural diversity of altruistic strategies, reveal the necessary connection between the evolution and the ecology of sociality, and correct the widespread but inaccurate view that local competition uniformly impedes the evolution of altruism.

Origins of altruism diversity II: Runaway coevolution of altruistic strategies via "reciprocal niche construction".

Understanding the evolution of altruism requires knowledge of both its constraints and its drivers. Here we show that, paradoxically, ecological constraints on altruism may ultimately be its strongest driver. We construct a two-trait, coevolutionary adaptive dynamics model of social evolution in a genetically structured population with local resource competition. The intensity of local resource competition, which influences the direction and strength of social selection and which is typically treated as a static parameter, is here allowed to be an evolvable trait. Evolution of survival/fecundity altruism, which requires weak local competition, increases local competition as it evolves, creating negative environmental feedback that ultimately inhibits its further evolutionary advance. Alternatively, evolution of resource-based altruism, which requires strong local competition, weakens local competition as it evolves, also ultimately causing its own evolution to stall. When evolving independently, these altruistic strategies are intrinsically self-limiting. However, the coexistence of these two altruism types transforms the negative ecoevolutionary feedback generated by each strategy on itself into positive feedback on the other, allowing the presence of one trait to drive the evolution of the other. We call this feedback conversion "reciprocal niche construction." In the absence of constraints, this process leads to runaway coevolution of altruism types. We discuss applications to the origins and evolution of eusociality, division of labor, the inordinate ecological success of eusocial species, and the interaction between technology and demography in human evolution. Our theory suggests that the evolution of extreme sociality may often be an autocatalytic process.

Detecting the molecular signature of social conflict: theory and a test with bacterial quorum sensing genes.

Extending social evolution theory to the molecular level opens the door to an unparalleled abundance of data and statistical tools for testing alternative hypotheses about the long-term evolutionary dynamics of cooperation and conflict. To this end, we take a collection of known sociality genes (bacterial quorum sensing [QS] genes), model their evolution in terms of patterns that are detectable using gene sequence data, and then test model predictions using available genetic data sets. Specifically, we test two alternative hypotheses of social conflict: (1) the "adaptive" hypothesis that cheaters are maintained in natural populations by frequency-dependent balancing selection as an evolutionarily stable strategy and (2) the "evolutionary null" hypothesis that cheaters are opposed by purifying kin selection yet exist transiently because of their recurrent introduction into populations by mutation (i.e., kin selection-mutation balance). We find that QS genes have elevated within- and among-species sequence variation, nonsignificant signatures of natural selection, and putatively small effect sizes of mutant alleles, all patterns predicted by our evolutionary null model but not by the stable cheater hypothesis. These empirical findings support our theoretical prediction that QS genes experience relaxed selection due to nonclonality of social groups, conditional expression, and the individual-level advantage enjoyed by cheaters. Furthermore, cheaters are evolutionarily transient, persisting in populations because of their recurrent introduction by mutation and not because they enjoy a frequency-dependent fitness advantage.

Kin selection-mutation balance: a model for the origin, maintenance, and consequences of social cheating.

Social conflict, in the form of intraspecific selfish "cheating," has been observed in a number of natural systems. However, a formal, evolutionary genetic theory of social cheating that provides an explanatory, predictive framework for these observations is lacking. Here we derive the kin selection-mutation balance, which provides an evolutionary null hypothesis for the statics and dynamics of cheating. When social interactions have linear fitness effects and Hamilton's rule is satisfied, selection is never strong enough to eliminate recurrent cheater mutants from a population, but cheater lineages are transient and do not invade. Instead, cheating lineages are eliminated by kin selection but are constantly reintroduced by mutation, maintaining a stable equilibrium frequency of cheaters. The presence of cheaters at equilibrium creates a "cheater load" that selects for mechanisms of cheater control, such as policing. We find that increasing relatedness reduces the cheater load more efficiently than does policing the costs and benefits of cooperation. Our results provide new insight into the effects of genetic systems, mating systems, ecology, and patterns of sex-limited expression on social evolution. We offer an explanation for the widespread cheater/altruist polymorphism found in nature and suggest that the common fear of conflict-induced social collapse is unwarranted.

A generalization of Hamilton's rule for the evolution of microbial cooperation.

Hamilton's rule states that cooperation will evolve if the fitness cost to actors is less than the benefit to recipients multiplied by their genetic relatedness. This rule makes many simplifying assumptions, however, and does not accurately describe social evolution in organisms such as microbes where selection is both strong and nonadditive. We derived a generalization of Hamilton's rule and measured its parameters in Myxococcus xanthus bacteria. Nonadditivity made cooperative sporulation remarkably resistant to exploitation by cheater strains. Selection was driven by higher-order moments of population structure, not relatedness. These results provide an empirically testable cooperation principle applicable to both microbes and multicellular organisms and show how nonlinear interactions among cells insulate bacteria against cheaters.

The components of kin competition.

It is well known that competition among kin alters the rate and often the direction of evolution in subdivided populations. Yet much remains unclear about the ecological and demographic causes of kin competition, or what role life cycle plays in promoting or ameliorating its effects. Using the multilevel Price equation, I derive a general equation for evolution in structured populations under an arbitrary intensity of kin competition. This equation partitions the effects of selection and demography, and recovers numerous previous models as special cases. I quantify the degree of kin competition, α, which explicitly depends on life cycle. I show how life cycle and demographic assumptions can be incorporated into kin selection models via α, revealing life cycles that are more or less permissive of altruism. As an example, I give closed-form results for Hamilton's rule in a three-stage life cycle. Although results are sensitive to life cycle in general, I identify three demographic conditions that give life cycle invariant results. Under the infinite island model, α is a function of the scale of density regulation and dispersal rate, effectively disentangling these two phenomena. Population viscosity per se does not impede kin selection.

Multilevel and kin selection in a connected world.

Wild et al. argue that the evolution of reduced virulence can be understood from the perspective of inclusive fitness, obviating the need to evoke group selection as a contributing causal factor. Although they acknowledge the mathematical equivalence of the inclusive fitness and multilevel selection approaches, they conclude that reduced virulence can be viewed entirely as an individual-level adaptation by the parasite. Here we show that their model is a well-known special case of the more general theory of multilevel selection, and that the cause of reduced virulence resides in the opposition of two processes: within-group and among-group selection. This distinction is important in light of the current controversy among evolutionary biologists in which some continue to affirm that natural selection centres only and always at the level of the individual organism or gene, despite mathematical demonstrations that evolutionary dynamics must be described by selection at various levels in the hierarchy of biological organization.

The genetic signature of conditional expression.

Conditionally expressed genes have the property that every individual in a population carries and transmits the gene, but only a fraction, , expresses the gene and exposes it to natural selection. We show that a consequence of this pattern of inheritance and expression is a weakening of the strength of natural selection, allowing deleterious mutations to accumulate within and between species and inhibiting the spread of beneficial mutations. We extend previous theory to show that conditional expression in space and time have approximately equivalent effects on relaxing the strength of selection and that the effect holds in a spatially heterogeneous environment even with low migration rates among patches. We support our analytical approximations with computer simulations and delineate the parameter range under which the approximations fail. We model the effects of conditional expression on sequence polymorphism at mutation-selection-drift equilibrium, allowing for neutral sites, and show that sequence variation within and between species is inflated by conditional expression, with the effect being strongest in populations with large effective size. As decreases, more sites are recruited into neutrality, leading to pseudogenization and increased drift load. Mutation accumulation diminishes the degree of adaptation of conditionally expressed genes to rare environments, and the mutational cost of phenotypic plasticity, which we quantify as the plasticity load, is greater for more rarely expressed genes. Our theory connects gene-level relative polymorphism and divergence with the spatial and temporal frequency of environments inducing gene expression. Our theory suggests that null hypotheses for levels of standing genetic variation and sequence divergence must be corrected to account for the frequency of expression of the genes under study.