Ecology and signal evolution in lizards

Current models of signal evolution explain diversity by invoking a variety of social, perceptual and environmental factors. Social systems and spacing patterns determine the active space of signals and their function. Receiver sensory systems and habitat characteristics interact to constrain signal design. These factors are traditionally implicated in promoting directional evolutionary change, leading to increases or decreases in signal complexity. We examine macro-evolutionary trends in signal design, as reflected by display modifier repertoire size, for 124 species of iguanian lizards to identify the importance of ecological factors in display evolution. Possessing a small home range, being arboreal and feeding on moving prey are all correlated with the evolution of large repertoires. However, living in closed habitats is associated with increased evolutionary change in repertoire size, producing greater signal diversity. Ecological factors can thus act either directionally or to promote evolutionary lability.   © 2002 The Linnean Society of London, Biological Journal of the Linnean Society , 2002, 77 , 127–148.     

Explaining the geographical origins of seasonal influenza A (H3N2)

Most antigenically novel and evolutionarily successful strains of seasonal influenza A (H3N2) originate in East, South and Southeast Asia. To understand this pattern, we simulated the ecological and evolutionary dynamics of influenza in a host metapopulation representing the temperate north, tropics and temperate south. Although seasonality and air traffic are frequently used to explain global migratory patterns of influenza, we find that other factors may have a comparable or greater impact. Notably, a region''s basic reproductive number (R₀) strongly affects the antigenic evolution of its viral population and the probability that its strains will spread and fix globally: a 17–28% higher R₀ in one region can explain the observed patterns. Seasonality, in contrast, increases the probability that a tropical (less seasonal) population will export evolutionarily successful strains but alone does not predict that these strains will be antigenically advanced. The relative sizes of different host populations, their birth and death rates, and the region in which H3N2 first appears affect influenza''s phylogeography in different but relatively minor ways. These results suggest general principles that dictate the spatial dynamics of antigenically evolving pathogens and offer predictions for how changes in human ecology might affect influenza evolution.     

Modularity in the evolution of yeast protein interaction network

Protein interaction networks are known to exhibit remarkable structures: scale-free and small-world and modular structures. To explain the evolutionary processes of protein interaction networks possessing scale-free and small-world structures, preferential attachment and duplication-divergence models have been proposed as mathematical models. Protein interaction networks are also known to exhibit another remarkable structural characteristic, modular structure. How the protein interaction networks became to exhibit modularity in their evolution? Here, we propose a hypothesis of modularity in the evolution of yeast protein interaction network based on molecular evolutionary evidence. We assigned yeast proteins into six evolutionary ages by constructing a phylogenetic profile. We found that all the almost half of hub proteins are evolutionarily new. Examining the evolutionary processes of protein complexes, functional modules and topological modules, we also found that member proteins of these modules tend to appear in one or two evolutionary ages. Moreover, proteins in protein complexes and topological modules show significantly low evolutionary rates than those not in these modules. Our results suggest a hypothesis of modularity in the evolution of yeast protein interaction network as systems evolution.     

Effects of allometry,productivity and lifestyle on rates and limits of body size evolution

Body size affects nearly all aspects of organismal biology, so it is important to understand the constraints and dynamics of body size evolution. Despite empirical work on the macroevolution and macroecology of minimum and maximum size, there is little general quantitative theory on rates and limits of body size evolution. We present a general theory that integrates individual productivity, the lifestyle component of the slow–fast life-history continuum, and the allometric scaling of generation time to predict a clade''s evolutionary rate and asymptotic maximum body size, and the shape of macroevolutionary trajectories during diversifying phases of size evolution. We evaluate this theory using data on the evolution of clade maximum body sizes in mammals during the Cenozoic. As predicted, clade evolutionary rates and asymptotic maximum sizes are larger in more productive clades (e.g. baleen whales), which represent the fast end of the slow–fast lifestyle continuum, and smaller in less productive clades (e.g. primates). The allometric scaling exponent for generation time fundamentally alters the shape of evolutionary trajectories, so allometric effects should be accounted for in models of phenotypic evolution and interpretations of macroevolutionary body size patterns. This work highlights the intimate interplay between the macroecological and macroevolutionary dynamics underlying the generation and maintenance of morphological diversity.     

Beyond classical theories: An integrative mathematical model of mating dynamics and parental care

Classical theories, such as Bateman's principle and Trivers' parental investment theory, attempted to explain the coevolution of sexual selection and parental care through simple verbal arguments. Since then, quantitative models have demonstrated that it is rarely that simple because many non-intuitive structures and non-linear relationships are actually at play. In this study, we propose a new standard for models of mating dynamics and parental care, emphasizing the clarity and use of mathematical and probabilistic arguments, the meaning of consistency conditions, and the key role of spatial densities and the law of mass action. We used adaptive dynamics to calculate the evolutionary trajectory of the total care duration. Our results clearly show how the outcomes of parental care evolution can be diverse, depending on the quantitative balance between a set of dynamical forces arising from relevant differences and conditions in the male and female populations. The intensity of sexual selection, synergy of care, care quality, and relative mortality rates during mating interactions and caring activities act as forces driving evolutionary transitions between uniparental and biparental care. Sexual selection reduces the care duration of the selected sex, uniparental care evolves in the sex that offers the higher care quality, higher mortality during mating interactions of one sex leads to more care by that sex, and higher mortality during caring activities of one sex favours the evolution of uniparental care in the other sex. Both synergy and higher overall mortality during mating interactions can stabilize biparental care when sexual selection reduces the care duration of the selected sex. We discuss how the interaction between these forces influences the evolution of care patterns, and how sex ratios can vary and be interpreted in these contexts. We also propose new directions for future developments of our integrative model, creating new comparable analyses that share the same underlying assumptions and dynamical frameworks.     

Modeling genome evolution with a diffusion approximation of a birth-and-death process

MOTIVATION: In our previous studies, we developed discrete-space birth, death and innovation models (BDIMs) of genome evolution. These models explain the origin of the characteristic Pareto distribution of paralogous gene family sizes in genomes, and model parameters that provide for the evolution of these distributions within a realistic time frame have been identified. However, extracting the temporal dynamics of genome evolution from discrete-space BDIM was not technically feasible. We were interested in obtaining dynamic portraits of the genome evolution process by developing a diffusion approximation of BDIM. RESULTS: The diffusion version of BDIM belongs to a class of continuous-state models whose dynamics is described by the Fokker-Plank equation and the stationary solution could be any specified Pareto function. The diffusion models have time-dependent solutions of a special kind, namely, generalized self-similar solutions, which describe the transition from one stationary distribution of the system to another; this provides for the possibility of examining the temporal dynamics of genome evolution. Analysis of the generalized self-similar solutions of the diffusion BDIM reveals a biphasic curve of genome growth in which the initial, relatively short, self-accelerating phase is followed by a prolonged phase of slow deceleration. This evolutionary dynamics was observed both when genome growth started from zero and proceeded via innovation (a potential model of primordial evolution), and when evolution proceeded from one stationary state to another. In biological terms, this regime of evolution can be tentatively interpreted as a punctuated-equilibrium-like phenomenon whereby evolutionary transitions are accompanied by rapid gene amplification and innovation, followed by slow relaxation to a new stationary state.     

Predicting rates of interspecific interaction from phylogenetic trees

Integrating phylogenetic information can potentially improve our ability to explain species' traits, patterns of community assembly, the network structure of communities, and ecosystem function. In this study, we use mathematical models to explore the ecological and evolutionary factors that modulate the explanatory power of phylogenetic information for communities of species that interact within a single trophic level. We find that phylogenetic relationships among species can influence trait evolution and rates of interaction among species, but only under particular models of species interaction. For example, when interactions within communities are mediated by a mechanism of phenotype matching, phylogenetic trees make specific predictions about trait evolution and rates of interaction. In contrast, if interactions within a community depend on a mechanism of phenotype differences, phylogenetic information has little, if any, predictive power for trait evolution and interaction rate. Together, these results make clear and testable predictions for when and how evolutionary history is expected to influence contemporary rates of species interaction.     

CRDB: database of chemosensory receptor gene families in vertebrate

Chemosensory receptors (CR) are crucial for animals to sense the environmental changes and survive on earth. The emergence of whole-genome sequences provides us an opportunity to identify the entire CR gene repertoires. To completely gain more insight into the evolution of CR genes in vertebrates, we identified the nearly all CR genes in 25 vertebrates using homology-based approaches. Among these CR gene repertoires, nearly half of them were identified for the first time in those previously uncharacterized species, such as the guinea pig, giant panda and elephant, etc. Consistent with previous findings, we found that the numbers of CR genes vary extensively among different species, suggesting an extreme form of 'birth-and-death' evolution. For the purpose of facilitating CR gene analysis, we constructed a database with the goals to provide a resource for CR genes annotation and a web tool for exploring their evolutionary patterns. Besides a search engine for the gene extraction from a specific chromosome region, an easy-to-use phylogenetic analysis tool was also provided to facilitate online phylogeny study of CR genes. Our work can provide a rigorous platform for further study on the evolution of CR genes in vertebrates.     

Associated evolution of fruit size,fruit colour and spines in Neotropical palms

Understanding how ecological interactions have shaped the evolutionary dynamics of species traits remains a challenge in evolutionary ecology. Combining trait evolution models and phylogenies, we analysed the evolution of characters associated with seed dispersal (fruit size and colour) and herbivory (spines) in Neotropical palms to infer the role of these opposing animal–plant interactions in driving evolutionary patterns. We found that the evolution of fruit colour and fruit size was associated in Neotropical palms, supporting the adaptive interpretation of seed‐dispersal syndromes and highlighting the role of frugivores in shaping plant evolution. Furthermore, we revealed a positive association between fruit size and the presence of spines on palm leaves, bracteas and stems. We hypothesize that interactions between palms and large‐bodied frugivores/herbivores may explain the evolutionary relationship between fruit size and spines. Large‐bodied frugivores, such as extinct megafauna, besides consuming the fruits and dispersing large seeds, may also have consumed the leaves or damaged the plants, thus simultaneously favouring the evolution of large fruits and defensive structures. Our findings show how current trait patterns can be understood as the result of the interplay between antagonistic and mutualistic interactions that have happened throughout the evolutionary history of a clade.     

Elastic,not plastic species: Frozen plasticity theory and the origin of adaptive evolution in sexually reproducing organisms

Background  

Darwin's evolutionary theory could easily explain the evolution of adaptive traits (organs and behavioral patterns) in asexual but not in sexual organisms. Two models, the selfish gene theory and frozen plasticity theory were suggested to explain evolution of adaptive traits in sexual organisms in past 30 years.     

Divergent evolution of a structural proteome: phenomenological models

We develop models of the divergent evolution of genomes; the elementary object of sequence dynamics is the protein structural domain. To identify patterns of organization that reflect mechanisms of evolution, we consider the individual genomes of many procaryote species, studying the arrangement of protein structural domains in the space of all polypeptide structures. We view the network of structural similarities as a graph, called the organismal Protein Domain Universe Graph (oPDUG); vertices represent types of structural domains and edges represent strong structural similarity. As observed before, each oPDUG is a highly nonrandom graph, as evidenced in the vertex degree distribution, which resembles a Pareto law (which has a power-law asymptotic). To explain this and other peculiar properties of the oPDUGs, we construct an evolving-graph model for the long-timescale evolutionary dynamics of oPDUGs, containing only divergent mechanisms of domain discovery. The model generates degree distributions (resembling Pareto laws) and clustering-coefficient distributions that are characteristic of the oPDUGs. In the infinite-graph limit, we analytically compute the exponent for specific biological parameters, as well as the complete phase diagram of the model, finding two distinct regimes of domain innovation dynamics. Thus, divergent evolutionary dynamics quantitatively explains the nonrandom organization of oPDUGs.     

Delineating the longitudinal tumor evolution using organoid models

Cancer is an evolutionary process fueled by genetic or epigenetic alterations in the genome. Understanding the evolutionary dynamics that are operative at different stages of tumor progression might inform effective strategies in early detection, diagnosis, and treatment of cancer. However, our understanding on the dynamics of tumor evolution through time is very limited since it is usually impossible to sample patient tumors repeatedly. The recent advances in in vitro 3D organoid culture technologies have opened new avenues for the development of more realistic human cancer models that mimic many in vivo biological characteristics in human tumors. Here, we review recent progresses and challenges in cancer genomic evolution studies and advantages of using tumor organoids to study cancer evolution. We propose to establish an experimental evolution model based on continuous passages of patient-derived organoids and longitudinal sampling to study clonal dynamics and evolutionary patterns over time. Development and integration of population genetic theories and computational models into time-course genomic data in tumor organoids will help to pinpoint the key cellular mechanisms underlying cancer evolutionary dynamics, thus providing novel insights on therapeutic strategies for highly dynamic and heterogeneous tumors.     

A demographic model for sex ratio evolution and the effects of sex‐biased offspring costs

The evolution of the primary sex ratio, the proportion of male births in an individual's offspring production strategy, is a frequency‐dependent process that selects against the more common sex. Because reproduction is shaped by the entire life cycle, sex ratio theory would benefit from explicitly two‐sex models that include some form of life cycle structure. We present a demographic approach to sex ratio evolution that combines adaptive dynamics with nonlinear matrix population models. We also determine the evolutionary and convergence stability of singular strategies using matrix calculus. These methods allow the incorporation of any population structure, including multiple sexes and stages, into evolutionary projections. Using this framework, we compare how four different interpretations of sex‐biased offspring costs affect sex ratio evolution. We find that demographic differences affect evolutionary outcomes and that, contrary to prior belief, sex‐biased mortality after parental investment can bias the primary sex ratio (but not the corresponding reproductive value ratio). These results differ qualitatively from the widely held conclusions of previous models that neglect demographic structure.     

Studying Patterns of Recent Evolution at Synonymous Sites and Intronic Sites in Drosophila melanogaster

Most previous studies of the evolution of codon usage bias (CUB) and intronic GC content (iGC) in Drosophila melanogaster were based on between-species comparisons, reflecting long-term evolutionary events. However, a complete picture of the evolution of CUB and iGC cannot be drawn without knowledge of their more recent evolutionary history. Here, we used a polymorphism dataset collected from Zimbabwe to study patterns of the recent evolution of CUB and iGC. Analyzing coding and intronic data jointly with a model which can simultaneously estimate selection, mutational, and demographic parameters, we have found that: (1) natural selection is probably acting on synonymous codons; (2) a constant population size model seems to be sufficient to explain most of the observed synonymous polymorphism patterns; (3) GC is favored over AT in introns. In agreement with the long-term evolutionary patterns, ongoing selection acting on X-linked synonymous codons is stronger than that acting on autosomal codons. The selective differences between preferred and unpreferred codons tend to be greater than the differences between GC and AT in introns, suggesting that natural selection, not just biased gene conversion, may have influenced the evolution of CUB. Interestingly, evidence for non-equilibrium evolution comes exclusively from the intronic data. However, three different models, an equilibrium model with two classes of selected sites and two non-equilibrium models with changes in either population size or mutational parameters, fit the intronic data equally well. These results show that using inadequate selection (or demographic) models can result in incorrect estimates of demographic (or selection) parameters.     

The evolution of cultural adaptations: Fijian food taboos protect against dangerous marine toxins

The application of evolutionary theory to understanding the origins of our species'' capacities for social learning has generated key insights into cultural evolution. By focusing on how our psychology has evolved to adaptively extract beliefs and practices by observing others, theorists have hypothesized how social learning can, over generations, give rise to culturally evolved adaptations. While much field research documents the subtle ways in which culturally transmitted beliefs and practices adapt people to their local environments, and much experimental work reveals the predicted patterns of social learning, little research connects real-world adaptive cultural traits to the patterns of transmission predicted by these theories. Addressing this gap, we show how food taboos for pregnant and lactating women in Fiji selectively target the most toxic marine species, effectively reducing a woman''s chances of fish poisoning by 30 per cent during pregnancy and 60 per cent during breastfeeding. We further analyse how these taboos are transmitted, showing support for cultural evolutionary models that combine familial transmission with selective learning from locally prestigious individuals. In addition, we explore how particular aspects of human cognitive processes increase the frequency of some non-adaptive taboos. This case demonstrates how evolutionary theory can be deployed to explain both adaptive and non-adaptive behavioural patterns.     

The Evolutionary Dynamics of Protein-Protein Interaction Networks Inferred from the Reconstruction of Ancient Networks

Cellular functions are based on the complex interplay of proteins, therefore the structure and dynamics of these protein-protein interaction (PPI) networks are the key to the functional understanding of cells. In the last years, large-scale PPI networks of several model organisms were investigated. A number of theoretical models have been developed to explain both the network formation and the current structure. Favored are models based on duplication and divergence of genes, as they most closely represent the biological foundation of network evolution. However, studies are often based on simulated instead of empirical data or they cover only single organisms. Methodological improvements now allow the analysis of PPI networks of multiple organisms simultaneously as well as the direct modeling of ancestral networks. This provides the opportunity to challenge existing assumptions on network evolution. We utilized present-day PPI networks from integrated datasets of seven model organisms and developed a theoretical and bioinformatic framework for studying the evolutionary dynamics of PPI networks. A novel filtering approach using percolation analysis was developed to remove low confidence interactions based on topological constraints. We then reconstructed the ancient PPI networks of different ancestors, for which the ancestral proteomes, as well as the ancestral interactions, were inferred. Ancestral proteins were reconstructed using orthologous groups on different evolutionary levels. A stochastic approach, using the duplication-divergence model, was developed for estimating the probabilities of ancient interactions from today''s PPI networks. The growth rates for nodes, edges, sizes and modularities of the networks indicate multiplicative growth and are consistent with the results from independent static analysis. Our results support the duplication-divergence model of evolution and indicate fractality and multiplicative growth as general properties of the PPI network structure and dynamics.     

Nest shape explains variation in sexual dichromatism in New World blackbirds

Following Charles Darwin, research on sexual dichromatism has long focused on sexual selection driving ornamentation in males. However, Alfred Russel Wallace proposed another explanation – that dichromatism evolves as a result of selection favoring crypsis in incubating females. Many recent studies suggest that evolutionary changes in sexual dichromatism often result from changes in female, in addition to male, plumage, yet the evolutionary mechanisms driving changes in female plumage remain largely unexplained. To test Wallace's hypothesis, we examined variation in sexual dichromatism and nest shape, a proxy for predation risk, among New World blackbirds (Aves: Icteridae). Phylogenetic models reveal an evolutionary correlation between sexual dichromatism and nest exposure. Specifically, we found that transitions in monochromatic lineages with exposed nests toward either concealed nests or dichromatism were common. Although this evidence supports Wallace's hypothesis that female incubation leads to selection for crypsis or concealment, we also found that transitions to monomorphism were common, even in lineages with exposed nests – a result suggestive of a role for positive selection on female ornamentation. These patterns of plumage evolution support a growing body of work emphasizing the importance of developing and testing hypotheses to explain evolutionary changes in female, as well as male, ornamentation.     

Spatial processes and evolutionary models: a critical review

Evolution is a fundamentally population level process in which variation, drift and selection produce both temporal and spatial patterns of change. Statistical model fitting is now commonly used to estimate which kind of evolutionary process best explains patterns of change through time using models like Brownian motion, stabilizing selection (Ornstein–Uhlenbeck) and directional selection on traits measured from stratigraphic sequences or on phylogenetic trees. But these models assume that the traits possessed by a species are homogeneous. Spatial processes such as dispersal, gene flow and geographical range changes can produce patterns of trait evolution that do not fit the expectations of standard models, even when evolution at the local‐population level is governed by drift or a typical OU model of selection. The basic properties of population level processes (variation, drift, selection and population size) are reviewed and the relationship between their spatial and temporal dynamics is discussed. Typical evolutionary models used in palaeontology incorporate the temporal component of these dynamics, but not the spatial. Range expansions and contractions introduce rate variability into drift processes, range expansion under a drift model can drive directional change in trait evolution, and spatial selection gradients can create spatial variation in traits that can produce long‐term directional trends and punctuation events depending on the balance between selection strength, gene flow, extirpation probability and model of speciation. Using computational modelling that spatial processes can create evolutionary outcomes that depart from basic population‐level notions from these standard macroevolutionary models.     

抗CD20单克隆抗体治疗B 细胞淋巴瘤的效应机制

B细胞淋巴瘤是一种主要的非霍奇金淋巴瘤(non-Hodgkin’s lymphoma,NHL),95%以上的B细胞淋巴瘤均表达B细胞分化抗原CD20。尽管目前CD20在B细胞分化发育过程中的具体功能尚未阐明,但其特有的表达方式和在细胞膜上的分布特点决定了其成为B细胞淋巴瘤靶向治疗的主要靶点。近十年来,随着抗CD20单克隆抗体的不断发展和进步,联合传统的CHOP化疗方案,在NHL的治疗中显示出良好的效果。虽然,近年来抗CD20单抗逐渐被用于B细胞相关的自身免疫病的治疗,但相关作用机制尚不明确。在针对NHL的临床治疗中,抗CD20单抗的疗效被认为依赖于效应器机制,主要有ADCC、CDC和细胞凋亡。虽然抗CD20在淋巴瘤的治疗中具有显著的免疫疗效,但部分瘤荷较大的病人出现了耐药和复发,循环中大量B细胞由于单抗的结合而被机体的单核巨噬细胞吞噬或NK细胞杀伤,机体出现成熟B细胞空缺期,同时单核巨噬细胞、NK细胞和补体大量消耗,造成机体免疫效应功能饱和和效应器耗竭。本文就抗CD20单克隆抗体在治疗淋巴瘤中的具体作用机制及可能造成的机体效应器耗竭问题做一简要的概述。