Genomic mutation rates that neutralize adaptive evolution and natural selection

When mutation rates are low, natural selection remains effective, and increasing the mutation rate can give rise to an increase in adaptation rate. When mutation rates are high to begin with, however, increasing the mutation rate may have a detrimental effect because of the overwhelming presence of deleterious mutations. Indeed, if mutation rates are high enough: (i) adaptive evolution may be neutralized, resulting in a zero (or negative) adaptation rate despite the continued availability of adaptive and/or compensatory mutations, or (ii) natural selection may be neutralized, because the fitness of lineages bearing adaptive and/or compensatory mutations—whether established or newly arising—is eroded by excessive mutation, causing such lineages to decline in frequency. We apply these two criteria to a standard model of asexual adaptive evolution and derive mathematical expressions—some new, some old in new guise—delineating the mutation rates under which either adaptive evolution or natural selection is neutralized. The expressions are simple and require no a priori knowledge of organism- and/or environment-specific parameters. Our discussion connects these results to each other and to previous theory, showing convergence or equivalence of the different results in most cases.     

Ferrous iron formation following the co-aggregation of ferric iron and the Alzheimer's disease peptide β-amyloid (1–42)

For decades, a link between increased levels of iron and areas of Alzheimer''s disease (AD) pathology has been recognized, including AD lesions comprised of the peptide β-amyloid (Aβ). Despite many observations of this association, the relationship between Aβ and iron is poorly understood. Using X-ray microspectroscopy, X-ray absorption spectroscopy, electron microscopy and spectrophotometric iron(II) quantification techniques, we examine the interaction between Aβ(1–42) and synthetic iron(III), reminiscent of ferric iron stores in the brain. We report Aβ to be capable of accumulating iron(III) within amyloid aggregates, with this process resulting in Aβ-mediated reduction of iron(III) to a redox-active iron(II) phase. Additionally, we show that the presence of aluminium increases the reductive capacity of Aβ, enabling the redox cycling of the iron. These results demonstrate the ability of Aβ to accumulate iron, offering an explanation for previously observed local increases in iron concentration associated with AD lesions. Furthermore, the ability of iron to form redox-active iron phases from ferric precursors provides an origin both for the redox-active iron previously witnessed in AD tissue, and the increased levels of oxidative stress characteristic of AD. These interactions between Aβ and iron deliver valuable insights into the process of AD progression, which may ultimately provide targets for disease therapies.     

Future climates: Markov blankets and active inference in the biosphere

We formalize the Gaia hypothesis about the Earth climate system using advances in theoretical biology based on the minimization of variational free energy. This amounts to the claim that non-equilibrium steady-state dynamics—that underwrite our climate—depend on the Earth system possessing a Markov blanket. Our formalization rests on how the metabolic rates of the biosphere (understood as Markov blanket''s internal states) change with respect to solar radiation at the Earth''s surface (i.e. external states), through the changes in greenhouse and albedo effects (i.e. active states) and ocean-driven global temperature changes (i.e. sensory states). Describing the interaction between the metabolic rates and solar radiation as climatic states—in a Markov blanket—amounts to describing the dynamics of the internal states as actively inferring external states. This underwrites climatic non-equilibrium steady-state through free energy minimization and thus a form of planetary autopoiesis.     

Constructing and characterizing a bioactive small molecule and microRNA association network for Alzheimer's disease

Alzheimer''s disease (AD) is an incurable neurodegenerative disorder. Much effort has been devoted to developing effective therapeutic agents. Recently, targeting microRNAs (miRNAs) with small molecules has become a novel therapy for human diseases. In this study, we present a systematic computational approach to construct a bioactive Small molecule and miRNA association Network in AD (SmiRN-AD), which is based on the gene expression signatures of bioactive small molecule perturbation and AD-related miRNA regulation. We also performed topological and functional analysis of the SmiRN-AD from multiple perspectives. At the significance level of p ≤ 0.01, 496 small molecule–miRNA associations, including 25 AD-related miRNAs and 275 small molecules, were recognized and used to construct the SmiRN-AD. The drugs that were connected with the same miRNA tended to share common drug targets (p = 1.72 × 10⁻⁴) and belong to the same therapeutic category (p = 4.22 × 10⁻⁸). The miRNAs that were linked to the same small molecule regulated more common miRNA targets (p = 6.07 × 10⁻³). Further analysis of the positive connections (quinostatin and miR-148b, amantadine and miR-15a) and the negative connections (melatonin and miR-30e-5p) indicated that our large-scale predictions afforded specific biological insights into AD pathogenesis and therapy. This study proposes a holistic strategy for deciphering the associations between small molecules and miRNAs in AD, which may be helpful for developing a novel effective miRNA-associated therapeutic strategy for AD. A comprehensive database for the SmiRN-AD and the differential expression patterns of the miRNA targets in AD is freely available at http://bioinfo.hrbmu.edu.cn/SmiRN-AD/.     

Predator confusion is sufficient to evolve swarming behaviour

Swarming behaviours in animals have been extensively studied owing to their implications for the evolution of cooperation, social cognition and predator–prey dynamics. An important goal of these studies is discerning which evolutionary pressures favour the formation of swarms. One hypothesis is that swarms arise because the presence of multiple moving prey in swarms causes confusion for attacking predators, but it remains unclear how important this selective force is. Using an evolutionary model of a predator–prey system, we show that predator confusion provides a sufficient selection pressure to evolve swarming behaviour in prey. Furthermore, we demonstrate that the evolutionary effect of predator confusion on prey could in turn exert pressure on the structure of the predator''s visual field, favouring the frontally oriented, high-resolution visual systems commonly observed in predators that feed on swarming animals. Finally, we provide evidence that when prey evolve swarming in response to predator confusion, there is a change in the shape of the functional response curve describing the predator''s consumption rate as prey density increases. Thus, we show that a relatively simple perceptual constraint—predator confusion—could have pervasive evolutionary effects on prey behaviour, predator sensory mechanisms and the ecological interactions between predators and prey.     

Alzheimer's disease: rapid and slow progression

The variability in the progression of Alzheimer''s disease (AD) across patients has made identification of disease-delaying treatments difficult. Quantitative analysis of this variability has important implications in understanding the pathophysiology of AD and identifying disease-delaying treatments. The functional assessment staging (FAST) procedure characterizes seven stages in the course of AD from normal ageing to severe dementia. The present study applied statistical methods to analyse FAST stage durations from a dataset of 648 AD patients. These methods uncovered two distinct types of disease progression, characterized by different mean progression rates. We identified two separate distributions of FAST stage progression times differing by up to 2 years in mean duration within each stage. These results further indicate that if a patient progresses rapidly through a given FAST stage, then their further progression is also likely to be rapid. These findings support the hypothesis that progression of AD can occur via two different pathophysiological mechanisms that lead to distinct average rates of decline.     

An analytical approach to bistable biological circuit discrimination using real algebraic geometry

Biomolecular circuits with two distinct and stable steady states have been identified as essential components in a wide range of biological networks, with a variety of mechanisms and topologies giving rise to their important bistable property. Understanding the differences between circuit implementations is an important question, particularly for the synthetic biologist faced with determining which bistable circuit design out of many is best for their specific application. In this work we explore the applicability of Sturm''s theorem—a tool from nineteenth-century real algebraic geometry—to comparing ‘functionally equivalent’ bistable circuits without the need for numerical simulation. We first consider two genetic toggle variants and two different positive feedback circuits, and show how specific topological properties present in each type of circuit can serve to increase the size of the regions of parameter space in which they function as switches. We then demonstrate that a single competitive monomeric activator added to a purely monomeric (and otherwise monostable) mutual repressor circuit is sufficient for bistability. Finally, we compare our approach with the Routh–Hurwitz method and derive consistent, yet more powerful, parametric conditions. The predictive power and ease of use of Sturm''s theorem demonstrated in this work suggest that algebraic geometric techniques may be underused in biomolecular circuit analysis.     

Mechanisms of self-organization for the collagen fibril lattice in the human cornea

The transparency of the human cornea depends on the regular lattice arrangement of the collagen fibrils and on the maintenance of an optimal hydration—the achievement of both depends on the presence of stromal proteoglycans (PGs) and their linear sidechains of negatively charged glycosaminoglycans (GAGs). Although the GAGs produce osmotic pressure by the Donnan effect, the means by which they exert positional control of the lattice is less clear. In this study, a theoretical model based on equilibrium thermodynamics is used to describe restoring force mechanisms that may control and maintain the fibril lattice and underlie corneal transparency. Electrostatic-based restoring forces that result from local charge density changes induced by fibril motion, and entropic elastic restoring forces that arise from duplexed GAG structures that bridge neighbouring fibrils, are described. The model allows for the possibility that fibrils have a GAG-dense coating that adds an additional fibril force mechanism preventing fibril aggregation. Swelling pressure predictions are used to validate the model with results showing excellent agreement with experimental data over a range of hydration from 30 to 200% of normal. The model suggests that the electrostatic restoring force is dominant, with the entropic forces from GAG duplexes being an order or more smaller. The effect of a random GAG organization, as observed in recent imaging, is considered in a dynamic model of the lattice that incorporates randomness in both the spatial distribution of GAG charge and the topology of the GAG duplexes. A striking result is that the electrostatic restoring forces alone are able to reproduce the image-based lattice distribution function for the human cornea, and thus dynamically maintain the short-range order of the lattice.     

Dual‐Modal NIR‐Fluorophore Conjugated Magnetic Nanoparticle for Imaging Amyloid‐β Species In Vivo

Senile plaques, the extracellular deposit of amyloid‐β (Aβ) peptides, are one of the neuropathological hallmarks found in Alzheimer's disease (AD) brain. The current method of brain imaging of amyloid plaques based on positron emission tomography (PET) is expensive and invasive with low spatial resolution. Thus, the development of sensitive and nonradiative amyloid‐β (Aβ)‐specific contrast agents is highly important and beneficial to achieve early AD detection, monitor the disease progression, and evaluate the effectiveness of potential AD drugs. Here a neuroprotective dual‐modal nanoprobe developed by integrating highly Aβ‐specific and turn‐on fluorescence cyanine sensors with superparamagnetic iron oxide nanoparticles as an effective near‐infrared imaging (NIRI)/magnetic resonance imaging (MRI) contrast agent for imaging of Aβ species in vivo is reported. This Aβ‐specific probe is found not only nontoxic and noninvasive, but also highly blood brain barrier permeable. It also shows a potent neuroprotective effect against Aβ‐induced toxicities. This nanoprobe is successfully applied for in vivo fluorescence imaging with high sensitivity and selectivity to Aβ species, and MRI with high spatial resolution in an APP/PS1 transgenic mice model. Its potential as a powerful in vivo dual‐modal imaging tool for early detection and diagnosis of AD in humans is affirmed.     

Learning dynamics explains human behaviour in Prisoner's Dilemma on networks

Cooperative behaviour lies at the very basis of human societies, yet its evolutionary origin remains a key unsolved puzzle. Whereas reciprocity or conditional cooperation is one of the most prominent mechanisms proposed to explain the emergence of cooperation in social dilemmas, recent experimental findings on networked Prisoner''s Dilemma games suggest that conditional cooperation also depends on the previous action of the player—namely on the ‘mood’ in which the player is currently in. Roughly, a majority of people behave as conditional cooperators if they cooperated in the past, whereas they ignore the context and free ride with high probability if they did not. However, the ultimate origin of this behaviour represents a conundrum itself. Here, we aim specifically to provide an evolutionary explanation of moody conditional cooperation (MCC). To this end, we perform an extensive analysis of different evolutionary dynamics for players'' behavioural traits—ranging from standard processes used in game theory based on pay-off comparison to others that include non-economic or social factors. Our results show that only a dynamic built upon reinforcement learning is able to give rise to evolutionarily stable MCC, and at the end to reproduce the human behaviours observed in the experiments.     

The entropic basis of collective behaviour

We identify a unique viewpoint on the collective behaviour of intelligent agents. We first develop a highly general abstract model for the possible future lives these agents may encounter as a result of their decisions. In the context of these possibilities, we show that the causal entropic principle, whereby agents follow behavioural rules that maximize their entropy over all paths through the future, predicts many of the observed features of social interactions among both human and animal groups. Our results indicate that agents are often able to maximize their future path entropy by remaining cohesive as a group and that this cohesion leads to collectively intelligent outcomes that depend strongly on the distribution of the number of possible future paths. We derive social interaction rules that are consistent with maximum entropy group behaviour for both discrete and continuous decision spaces. Our analysis further predicts that social interactions are likely to be fundamentally based on Weber''s law of response to proportional stimuli, supporting many studies that find a neurological basis for this stimulus–response mechanism and providing a novel basis for the common assumption of linearly additive ‘social forces’ in simulation studies of collective behaviour.     

The aerodynamic cost of flight in the short-tailed fruit bat (Carollia perspicillata): comparing theory with measurement

Aerodynamic theory has long been used to predict the power required for animal flight, but widely used models contain many simplifications. It has been difficult to ascertain how closely biological reality matches model predictions, largely because of the technical challenges of accurately measuring the power expended when an animal flies. We designed a study to measure flight speed-dependent aerodynamic power directly from the kinetic energy contained in the wake of bats flying in a wind tunnel. We compared these measurements with two theoretical predictions that have been used for several decades in diverse fields of vertebrate biology and to metabolic measurements from a previous study using the same individuals. A high-accuracy displaced laser sheet stereo particle image velocimetry experimental design measured the wake velocities in the Trefftz plane behind four bats flying over a range of speeds (3–7 m s⁻¹). We computed the aerodynamic power contained in the wake using a novel interpolation method and compared these results with the power predicted by Pennycuick''s and Rayner''s models. The measured aerodynamic power falls between the two theoretical predictions, demonstrating that the models effectively predict the appropriate range of flight power, but the models do not accurately predict minimum power or maximum range speeds. Mechanical efficiency—the ratio of aerodynamic power output to metabolic power input—varied from 5.9% to 9.8% for the same individuals, changing with flight speed.     

Crowdsourcing contest dilemma

Crowdsourcing offers unprecedented potential for solving tasks efficiently by tapping into the skills of large groups of people. A salient feature of crowdsourcing—its openness of entry—makes it vulnerable to malicious behaviour. Such behaviour took place in a number of recent popular crowdsourcing competitions. We provide game-theoretic analysis of a fundamental trade-off between the potential for increased productivity and the possibility of being set back by malicious behaviour. Our results show that in crowdsourcing competitions malicious behaviour is the norm, not the anomaly—a result contrary to the conventional wisdom in the area. Counterintuitively, making the attacks more costly does not deter them but leads to a less desirable outcome. These findings have cautionary implications for the design of crowdsourcing competitions.     

Near‐Infrared Activated Black Phosphorus as a Nontoxic Photo‐Oxidant for Alzheimer's Amyloid‑β Peptide

The inhibition of amyloid‐β (Aβ) aggregation by photo‐oxygenation has become an effective way of treating Alzheimer's disease (AD). New near‐infrared (NIR) activated treatment agents, which not only possess high photo‐oxygenation efficiency, but also show low biotoxicity, are urgently needed. Herein, for the first time, it is demonstrated that NIR activated black phosphorus (BP) could serve as an effective nontoxic photo‐oxidant for amyloid?β peptide in vitro and in vivo. The nanoplatform BP@BTA (BTA: one of thioflavin‐T derivatives) possesses high affinity to the Aβ peptide due to specific amyloid selectivity of BTA. Importantly, under NIR light, BP@BTA can significantly generate a high quantum yield of singlet oxygen (¹O₂) to oxygenate Aβ, thereby resulting in inhibiting the aggregation and attenuating Aβ‐induced cytotoxicity. In addition, BP could finally degrade into nontoxic phosphate, which guarantees the biosafety. Using transgenic Caenorhabditis elegans CL2006 as AD model, the results demonstrate that the ¹O₂‐generation system could dramatically promote life‐span extension of CL2006 strain by decreasing the neurotoxicity of Aβ.     

Microfluidic Synthesis of Ultrasmall Chitosan/Graphene Quantum Dots Particles for Intranasal Delivery in Alzheimer's Disease Treatment

Nanoparticles (NPs) based therapies for Alzheimer's disease (AD) attract interest due to their ability to pass across or bypass the blood-brain barrier. Chitosan (CS) NPs or graphene quantum dots (GQDs) are promising drug carriers with excellent physicochemical and electrical properties. The current study proposes the combination of CS and GQDs in ultrasmall NP form not as drug carriers but as theranostic agents for AD. The microfluidic-based synthesis of the CS/GQD NPs with optimized characteristics makes them ideal for transcellular transfer and brain targeting after intranasal (IN) delivery. The NPs have the ability to enter the cytoplasm of C6 glioma cells in vitro and show dose and time-dependent effects on the viability of the cells. IN administration of the NPs to streptozotocin (STZ) induced AD-like models lead to a significant number of entrances of the treated rats to the target arm in the radial arm water maze (RAWM) test. It shows the positive effect of the NPs on the memory recovery of the treated rats. The NPs are detectable in the brain via in vivo bioimaging due to GQDs as diagnostic markers. The noncytotoxic NPs localize in the myelinated axons of hippocampal neurons. They do not affect the clearance of amyloid β (Aβ) plaques at intercellular space. Moreover, they showed no positive impact on the enhancement of MAP2 and NeuN expression as markers of neural regeneration. The memory improvement in treated AD rats may be due to neuroprotection via the anti-inflammation effect and regulation of the brain tissue microenvironment that needs to be studied.     

Selection methods regulate evolution of cooperation in digital evolution

A key, yet often neglected, component of digital evolution and evolutionary models is the ‘selection method’ which assigns fitness (number of offspring) to individuals based on their performance scores (efficiency in performing tasks). Here, we study with formal analysis and numerical experiments the evolution of cooperation under the five most common selection methods (proportionate, rank, truncation-proportionate, truncation-uniform and tournament). We consider related individuals engaging in a Prisoner''s Dilemma game where individuals can either cooperate or defect. A cooperator pays a cost, whereas its partner receives a benefit, which affect their performance scores. These performance scores are translated into fitness by one of the five selection methods. We show that cooperation is positively associated with the relatedness between individuals under all selection methods. By contrast, the change in the performance benefit of cooperation affects the populations’ average level of cooperation only under the proportionate methods. We also demonstrate that the truncation and tournament methods may introduce negative frequency-dependence and lead to the evolution of polymorphic populations. Using the example of the evolution of cooperation, we show that the choice of selection method, though it is often marginalized, can considerably affect the evolutionary dynamics.     

The effect of microbial selection on the occurrence-abundance patterns of microbiomes

Theoretical models are useful to investigate the drivers of community dynamics. In the simplest case of neutral models, the events of death, birth and immigration of individuals are assumed to only depend on their abundance—thus, all types share the same parameters. The community level expectations arising from these simple models and their agreement to empirical data have been discussed extensively, often suggesting that in nature, rates might indeed be neutral or their differences might not be important. However, how robust are these model predictions to type-specific rates? Also, what are the consequences at the level of types? Here, we address these questions moving from simple neutral communities to heterogeneous communities. For this, we build a model where types are differently adapted to the environment. We compute the equilibrium distribution of the abundances. Then, we look into the occurrence-abundance pattern often reported in microbial communities. We observe that large immigration and biodiversity—common in microbial systems—lead to such patterns, regardless of whether the rates are neutral or non-neutral. We conclude by discussing the implications to interpret and test empirical data.     

Self‐Assembled Peptide–Polyoxometalate Hybrid Nanospheres: Two in One Enhances Targeted Inhibition of Amyloid β‐Peptide Aggregation Associated with Alzheimer's Disease

Amyloid fibril formation is a critical step in Alzheimer's disease (AD) pathogenesis. Inhibition of Aβ aggregation has shown promising against AD and has been used in clinic trials. Here, a novel strategy is reported for the self‐assembly of polyoxometalate–peptide (POM@P) hybrid particles as bifunctional Aβ inhibitors. The two‐in‐one bifunctional POM@P nanoparticles show an enhanced inhibition effect on amyloid aggregation in mice cerebrospinal fluid. Incorporating a clinically used Aβ fibril‐staining dye, congo red (CR), into the hybrid colloidal spheres, the nanoparticles can also act as an effective fluorescent probe to monitor the inhibition process of POM@P via CR fluorescence change in real time. It is believed that such flexible organic–inorganic hybrid systems may prompt the design of new multifunctional materials for AD treatment.