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bioRxiv2015

Stochastic evolutionary games in dynamic populations

Frequency dependent selection and demographic fluctuations play important roles in evolutionary and ecological processes. Under frequency dependent selection, the average fitness of the population may increase or decrease based on interactions between individuals within the population. This should be reflected in fluctuations of the population size even in constant environ ments. Here, we propose a stochastic model, which naturally combines these two evolutionary ingredients by assuming frequency dependent competition between different types in an individual-based model. In contrast to previous game theoretic models, the carrying capacity of the population and thus the population size is determined by pairwise competition of individuals mediated by evolutionary games and demographic stochasticity. In the limit of infinite population size, the averaged stochastic dynamics is captured by the deterministic competitive LotkaVolterra equations. In small populations, demographic stochasticity may instead lead to the extinction of the entire population. As the population size is driven by the fitness in evolutionary games, a population of cooperators is less prone to go extinct than a population of defectors, whereas in the usual systems of fixed size, the population would thrive regardless of its average payoff.

Evolutionary Biology↗

bioRxiv2015

Approaches to estimating inbreeding coefficients in clinical isolates of Plasmodium falciparum from genomic sequence data

The advent of whole-genome sequencing has generated increased interest in modeling the structure of strain mixture within clinicial infections of Plasmodium falciparum (Pf). The life cycle of the parasite implies that the mixture of multiple strains within an infected individual is related to the out-crossing rate across populations, making methods for measuring this process in situ central to understanding the genetic epidemiology of the disease. In this paper, we show how to estimate inbreeding coefficients using genomic data from Pf clinical samples, providing a simple metric for assessing within-sample mixture that connects to an extensive literature in population genetics and conservation ecology. Features of the P. falciparum genome mean that some standard methods for inbreeding coefficients and related F-statistics cannot be used directly. Here, we review an initial effort to estimate the inbreeding coefficient within clinical isolates of P. falciparum and provide several generalizations using both frequentist and Bayesian approaches. The Bayesian approach connects these estimates to the Balding-Nichols model, a mainstay within genetic epidemiology. We provide simulation results on the performance of the estimators and show their use on ~ 1500 samples from the PF3K data set. We also compare the results to output from a recent mixture model for within-sample strain mixture, showing that inbreeding coefficients provide a strong proxy for the results of these more complex models. We provide the methods described within an open-source R package pfmix.

Genomics↗

bioRxiv2015

Cellular Population Dynamics Control the Robustness of the Stem Cell Niche

Within populations of cells, fate decisions are controlled by an indeterminate combination of cell-intrinsic and cell-extrinsic factors. In the case of stem cells, the stem cell niche is believed to maintain \"stemness\" through communication and interactions between the stem cells and one or more other cell-types that contribute to the niche conditions. To investigate the robustness of cell fate decisions in the stem cell hierarchy and the role that the niche plays, we introduce simple mathematical models of stem and progenitor cells, their progeny and their interplay in the niche. These models capture the fundamental processes of proliferation and differentiation and allow us to consider alternative possibilities regarding how niche-mediated signalling feedback regulates the niche dynamics. Generalised stability analysis of these stem cell niche systems enables us to describe the stability properties of each model. We find that although the number of feasible states depends on the model, their probabilities of stability in general do not: stem cell-niche models are stable across a wide range of parameters. We demonstrate that niche-mediated feedback increases the number of stable steady states, and show how distinct cell states have distinct branching characteristics. The ecological feedback and interactions mediated by the stem cell niche thus lend (surprisingly) high levels of robustness to the stem and progenitor cell population dynamics. Furthermore, cell-cell interactions are sufficient for populations of stem cells and their progeny to achieve stability and maintain homeostasis. We show that the robustness of the niche -- and hence of the stem cell pool in the niche -- depends only weakly, if at all, on the complexity of the niche make-up: simple as well as complicated niche systems are capable of supporting robust and stable stem cell dynamics.

Systems Biology↗

bioRxiv2015

Low but significant genetic differentiation underlies biologically meaningful phenotypic divergence in a large Atlantic salmon population

Despite decades of research assessing the genetic structure of natural populations, the biological meaning of low yet significant genetic divergence often remains unclear due to a lack of associated phenotypic and ecological information. At the same time, structured populations with low genetic divergence and overlapping boundaries can potentially provide excellent models to study adaptation and reproductive isolation in cases where high resolution genetic markers and relevant phenotypic and life history information are available. Here, we combined SNP-based population inference with extensive phenotypic and life history data to identify potential biological mechanisms driving fine scale sub-population differentiation in Atlantic salmon (Salmo salar) from the Teno River, a major salmon river in Europe. Two sympatrically occurring sub-populations had low but significant genetic differentiation (FST = 0.018) and displayed marked differences in the distribution of life history strategies, including variation in juvenile growth rate, age at maturity and size within age classes. Large, late-maturing individuals were virtually absent from one of the two sub-populations and there were significant differences in juvenile growth rates and size-at-age after oceanic migration between individuals in the respective sub-populations. Our findings suggest that different evolutionary processes affect each sub-population and that hybridization and subsequent selection may maintain low genetic differentiation without hindering adaptive divergence.

Evolutionary Biology↗

bioRxiv2015

Predicting microbial relative growth in a mixed culture from growth curve data

Estimates of microbial fitness from growth curves are inaccurate. Rather, competition experiments are necessary for accurate estimation. But competition experiments require unique markers and are difficult to perform with isolates derived from a common ancestor or non-model organisms. Here we describe a new approach for predicting relative growth of microbes in a mixed culture utilizing mono- and mixed culture growth curve data. We validated this approach using growth curve and competition experiments with E. coli. Our approach provides an effective way to predict growth in a mixed culture and infer relative fitness. Furthermore, by integrating several growth phases, it provides an ecological interpretation for microbial fitness.

Evolutionary Biology↗

bioRxiv2015

Maximum Likelihood Estimation of Biological Relatedness from Low Coverage Sequencing Data

1The inference of biological relatedness from DNA sequence data has a wide array of applications, such as in the study of human disease, anthropology and ecology. One of the most common analytical frameworks for performing this inference is to genotype individuals for large numbers of independent genomewide markers and use population allele frequencies to infer the probability of identity-by-descent (IBD) given observed genotypes. Current implementations of this class of methods assume genotypes are known without error. However, with the advent of 2nd generation sequencing data there are now an increasing number of situations where the confidence attached to a particular genotype may be poor because of low coverage. Such scenarios may lead to biased estimates of the kinship coefficient,{varepsilon} We describe an approach that utilizes genotype likelihoods rather than a single observed best genotype to estimate{phi} and demonstrate that we can accurately infer relatedness in both simulated and real 2nd generation sequencing data from a wide variety of human populations down to at least the third degree when coverage is as low as 2x for both individuals, while other commonly used methods such as PLINK exhibit large biases in such situations. In addition the method appears to be robust when the assumed population allele frequencies are diverged from the true frequencies for realistic levels of genetic drift. This approach has been implemented in the C++ software lcMLkin.

Genetics↗

bioRxiv2015

Genome-Wide Scan for Adaptive Divergence and Association with Population-Specific Covariates

In population genomics studies, accounting for the neutral covariance structure across population allele frequencies is critical to improve the robustness of genome-wide scan approaches. Elaborating on the BO_SCPLOWAYC_SCPLOWEO_SCPLOWNVC_SCPLOW model, this study investigates several modeling extensions i) to improve the estimation accuracy of the population covariance matrix and all the related measures; ii) to identify significantly overly differentiated SNPs based on a calibration procedure of the XtX statistics; and iii) to consider alternative covariate models for analyses of association with population-specific covariables. In particular, the auxiliary variable model allows to deal with multiple testing issues and, providing the relative marker positions are available, to capture some Linkage Disequilibrium information. A comprehensive simulation study was carried out to evaluate the performances of these different models. Also, when compared in terms of power, robustness and computational efficiency, to five other state-of-the-art genome scan methods (BO_SCPLOWAYC_SCPLOWEO_SCPLOWNVC_SCPLOW2, BO_SCPLOWAYC_SCPLOWSO_SCPLOWCC_SCPLOWEO_SCPLOWNVC_SCPLOW, BO_SCPLOWAYC_SCPLOWSO_SCPLOWCANC_SCPLOW, FLK and LFMM) the proposed approaches proved highly effective. For illustration purpose, genotyping data on 18 French cattle breeds were analyzed leading to the identification of thirteen strong signatures of selection. Among these, four (surrounding the KITLG, KIT, EDN3 and ALB genes) contained SNPs strongly associated with the piebald coloration pattern while a fifth (surrounding PLAG1) could be associated to morphological differences across the populations. Finally, analysis of Pool-Seq data from 12 populations of Littorina saxatilis living in two different ecotypes illustrates how the proposed framework might help addressing relevant ecological issue in non-model species. Overall, the proposed methods define a robust Bayesian framework to characterize adaptive genetic differentiation across populations. The BO_SCPLOWAYC_SCPLOWPO_SCPLOWASSC_SCPLOW program implementing the different models is available at http://www1.montpellier.inra.fr/CBGP/software/baypass/.

Genetics↗

bioRxiv2015

Detection of adaptive shifts on phylogenies using shifted stochastic processes on a tree

Comparative and evolutive ecologists are interested in the distribution of quantitative traits among related species. The classical framework for these distributions consists of a random process running along the branches of a phylogenetic tree relating the species. We consider shifts in the process parameters, which reveal fast adaptation to changes of ecological niches. We show that models with shifts are not identifiable in general. Constraining the models to be parsimonious in the number of shifts partially alleviates the problem but several evolutionary scenarios can still provide the same joint distribution for the extant species. We provide a recursive algorithm to enumerate all the equivalent scenarios and to count the effectively different scenarios. We introduce an incomplete-data framework and develop a maximum likelihood estimation procedure based on the EM algorithm. Finally, we propose a model selection procedure, based on the cardinal of effective scenarios, to estimate the number of shifts and prove an oracle inequality.

Evolutionary Biology↗

bioRxiv2015

Landscape structure and management alter the outcome of a pesticide ERA: evaluating impacts of endocrine disruption using the ALMaSS European Brown Hare model

There is a gradual change towards explicitly considering landscapes in regulatory risk assessment. To realise the objective of developing representative scenarios for risk assessment it is necessary to know how detailed a landscape representation is needed to generate a realistic risk assessment, and indeed how to generate such landscapes. This paper evaluates the contribution of landscape and farming components to a model based risk assessment of a fictitious endocrine disruptor on hares. In addition, we present methods and code examples for generation of landscape structures and farming simulation from data collected primarily for EU agricultural subsidy support and GIS map data.\n\nTen different Danish landscapes were generated and the ERA carried out for each landscape using two different assumed toxicities. The results showed negative impacts in all cases, but the extent and form in terms of impacts on abundance or occupancy differed greatly between landscapes. A meta-model was created, predicting impact from landscape and farming characteristics. Scenarios based on all combinations of farming and landscape for five landscapes representing extreme and middle impacts were created. The meta-models developed from the 10 real landscapes failed to predict impacts for these 25 scenarios. Landscape, farming, and the emergent density of hares all influenced the results of the risk assessment considerably.\n\nThe study indicates that prediction of a reasonable worst case scenario is difficult from structural, farming or population metrics; rather the emergent properties generated from interactions between landscape, management and ecology are needed. Meta-modelling may also fail to predict impacts, even when restricting inputs to combinations of those used to create the model. Future ERA may therefore need to make use of multiple scenarios representing a wide range of conditions to avoid locally unacceptable risks. This approach could now be feasible Europe wide given the landscape generation methods presented.

Systems Biology↗

bioRxiv2015

Extreme Mitogenomic Variation Without Cryptic Speciation in Chaetognaths

The extent of within-species genetic variation across the diversity of animal life is a fundamental but largely unexplored problem in ecology and evolution. The neutral theory of molecular evolution predicts that genetic variation scales positively with population size. However, the genetic diversity of mitochondrial DNA, a prominent marker used in DNA barcoding studies, shows very little variation across animal species. Here, we report an unprecedented case of extreme mitochondrial variation within natural populations of two species of chaetognaths (arrow worms). We determined that this diversity is composed of deep intraspecific mitochondrial lineages within single populations that could be as divergent as human and newt. This mitochondrial diversity is the highest ever reported in animals without evidence of cryptic speciation or allopatric divergence as supported by nuclear evidence. We sequenced 54 complete mitogenomes revealing gene order rearrangements between these intraspecific lineages. Such structural differences have never previously been reported within single species. We confirm that this divergence was not driven by positive selection, and conversely show that these lineages evolved under purifying selection, consistently with neutral expectations. Our findings question the generally accepted narrow range of genetic variation in animal mitochondria and argue for a reappraisal of DNA barcoding techniques. Furthermore, extreme levels of mitogenomic variation in chaetognaths challenge classical views regarding mitochondrial evolution and cytonuclear co-evolution.

Evolutionary Biology↗

bioRxiv2015

Comparing RADseq and microsatellites to infer complex phylogeographic patterns, a real data informed perspective in the Crucian carp, Carassius carassius, L.

The conservation of threatened species must be underpinned by phylogeographic knowledge in order to be effective. This need is epitomised by the freshwater fish Carassius carassius, which has recently undergone drastic declines across much of its European range. Restriction Site Associated DNA sequencing (RADseq) is being increasingly used for such phylogeographic questions, however RADseq is expensive, and limitations on sample number must be weighed against the benefit of large numbers of markers. Such tradeoffs have predominantly been addressed using simulated data. Here we compare the results generated from microsatellites and RADseq to the phylogeography of C. carassius, to add real-data-informed perspectives to this important debate. These datasets, along with data from the mitochondrial cytochrome b gene, agree on broad phylogeographic patterns; showing the existence of two previously unidentified C. carassius lineages in Europe. These lineages have been isolated for approximately 2.2-2.3 M years, and should arguably be considered as separate conservation units. RADseq recovered finer population structure and stronger patterns of IBD than microsatellites, despite including only 17.6% of samples (38% of populations and 52% of samples per population). RADseq was also used along with Approximate Bayesian Computation to show that the postglacial colonisation routes of C. carassius differ from the general patterns of freshwater fish in Europe, likely as a result of their distinctive ecology.

Evolutionary Biology↗

bioRxiv2015

Characterizing and comparing phylogenies from their Laplacian spectrum

Phylogenetic trees are central to many areas of biology, ranging from population genetics and epidemiology to microbiology, ecology, and macroevolution. The ability to summarize properties of trees, compare different trees, and identify distinct modes of division within trees is essential to all these research areas. But despite wide-ranging applications, there currently exists no common, comprehensive framework for such analyses. Here we present a graph-theoretical approach that provides such a framework. We show how to construct the spectral density profiles of phylogenetic trees from their Laplacian graphs. Using ultrametric simulated trees as well as non-ultrametric empirical trees, we demonstrate that the spectral density successfully identifies various properties of the trees and clusters them into meaningful groups. Finally, we illustrate how the eigengap can identify modes of division within a given tree. As phylogenetic data continue to accumulate and to be integrated into various areas of the life sciences, we expect that this spectral graph-theoretical framework to phylogenetics will have powerful and long-lasting applications.

Systems Biology↗

bioRxiv2015

Suboptimal larval habitats modulate oviposition of the malaria vector mosquito Anopheles gambiae

Selection of oviposition sites by gravid females is a critical behavioural preference in the reproductive cycle of Anopheles gambiae, the principal Afrotropical malaria vector mosquito. Several studies suggest this decision is mediated by semiochemicals associated with potential oviposition sites. To better understand the chemosensory basis of this behaviour and identify compounds that can modulate oviposition, we examined the generally held hypothesis that suboptimal larval habitats give rise to semiochemicals that negatively influence the oviposition preference of gravid females. Dual-choice bioassays indicated that oviposition sites conditioned in this manner do indeed foster significant and concentration dependent aversive effects on the oviposition site selection of gravid females. Headspace analyses derived from aversive habitats consistently noted the presence of dimethyl disulphide (DMDS), dimethyl trisulphide (DMTS) and 6-methyl-5-hepten-2-one (sulcatone) each of which unitarily affected An. gambiae oviposition preference. Electrophysiological assays across the antennae, maxillary palp, and labellum of gravid An. gambiae revealed differential responses to these semiochemicals. Taken together, these findings validate the hypothesis in question and suggest that suboptimal environments for An. gambiae larval development results in the release of DMDS, DMTS and sulcatone that impact the response valence of gravid females to directly modulate the chemical ecology of oviposition site selection.

Physiology↗

bioRxiv2015

Summer temperature can predict the distribution of wild yeast populations

The wine yeast, Saccharomyces cerevisiae, is the best understood microbial eukaryote at the molecular and cellular level, yet its natural geographic distribution is unknown. Here we report the results of a field survey for S. cerevisiae, S. paradoxus and other budding yeast on oak trees in Europe. We show that yeast species differ in their geographic distributions, and investigated which ecological variables can predict the isolation rate of S. paradoxus, the most abundant species. We find a positive association between trunk girth and S. paradoxus abundance suggesting that older trees harbour more yeast. S. paradoxus isolation frequency is also associated with summer temperature, showing highest isolation rates at intermediate temperatures. Using our statistical model, we estimated a range of summer temperatures at which we expect high S. paradoxus isolation rates, and show that the geographic distribution predicted by this optimum temperature range is consistent with the worldwide distribution of sites where S. paradoxus has been isolated. Using laboratory estimates of optimal growth temperatures for S. cerevisiae relative to S. paradoxus, we also estimated an optimum range of summer temperatures for S. cerevisiae. The geographical distribution of these optimum temperatures are consistent with the locations where wild S. cerevisiae have been reported, and can explain why only human-associated S. cerevisiae strains are isolated at northernmost latitudes. Our results provide a starting point for targeted isolation of S. cerevisiae from natural habitats, which could lead to a better understanding of climate associations and natural history in this important model microbe.

Evolutionary Biology↗

bioRxiv2015

Parental effects and the evolution of phenotypic memory

Despite growing evidence for nongenetic inheritance, the ecological conditions that favor the evolution of heritable parental or grandparental effects remain poorly understood. Here, we systematically explore the evolution of parental effects in a patch-structured population with locally changing environments. When selection favors the production of a mix of offspring types, this mix differs according to the parental phenotype, implying that parental effects are favored over selection for bet-hedging in which the mixture of offspring phenotypes produced does not depend on the parental phenotype. Positive parental effects (generating a positive correlation between parental and offspring phenotype) are favored in relatively stable habitats and when different types of local environment are roughly equally abundant, and can give rise to long-term parental inheritance of phenotypes. By contrast, unstable habitats can favor negative parental effects (generating a negative correlation between parental and offspring phenotype), and under these circumstances even slight asymmetries in the abundance of local environmental states select for marked asymmetries in transmission fidelity.

Evolutionary Biology↗

bioRxiv2015

One Codex: A Sensitive and Accurate Data Platform for Genomic Microbial Identification

High-throughput sequencing (HTS) is increasingly being used for broad applications of microbial characterization, such as microbial ecology, clinical diagnosis, and outbreak epidemiology. However, the analytical task of comparing short sequence reads against the known diversity of microbial life has proved to be computationally challenging. The One Codex data platform was created with the dual goals of analyzing microbial data against the largest possible collection of microbial reference genomes, as well as presenting those results in a format that is consumable by applied end-users. One Codex identifies microbial sequences using a \"k-mer based\" taxonomic classification algorithm through a web-based data platform, using a reference database that currently includes approximately 40,000 bacterial, viral, fungal, and protozoan genomes. In order to evaluate whether this classification method and associated database provided quantitatively different performance for microbial identification, we created a large and diverse evaluation dataset containing 50 million reads from 10,639 genomes, as well as sequences from six organisms novel species not be included in the reference databases of any of the tested classifiers. Quantitative evaluation of several published microbial detection methods shows that One Codex has the highest degree of sensitivity and specificity (AUC = 0.97, compared to 0.82-0.88 for other methods), both when detecting well-characterized species as well as newly sequenced, \"taxonomically novel\" organisms.

Bioinformatics↗

bioRxiv2015

Beyond Animals and Plants: Dynamic Maternal Effects in the Fungus Neurospora crassa

Maternal effects are widely documented in animals and plants, but not in fungi or other eukaryotes. A principal cause of maternal effects is asymmetrical parental investment in a zygote, creating greater maternal versus paternal influence on offspring phenotypes. Asymmetrical investments are not limited to animals and plants, but are also prevalent in fungi and groups including apicocomplexans, dinoflagellates, and red algae. Evidence suggesting maternal effects among fungi is sparse and anecdotal. In an experiment designed to test for maternal effects across sexual reproduction in the model fungus Neurospora crassa, we measured offspring phenotypes from crosses of all possible pairs of 22 individuals. Crosses encompassed reciprocals of 11 mating-type \"A\" and 11 mating-type \"a\" wild strains. After controlling for the genetic and geographic distances between strains in any individual cross, we found strong evidence for maternal control of sporocarp production, as well as maternal effects on spore numbers and spore germination. However, both parents exert equal influence on the percentage of spores that are pigmented, and size of pigmented spores. We propose a model linking the stage-specific presence or absence of maternal effects to cellular developmental processes: effects appear to be mediated primarily through the maternal cytoplasm, and, after spore cell walls form, maternal influence on spore development is limited. Maternal effects in fungi, thus far largely ignored, are likely to shape species evolution and ecology. The association of anisogamy and maternal effects in a fungus suggests maternal effects may also influence the biology of other anisogamous eukaryotes.

Genetics↗

bioRxiv2015

Dietary Breadth is Positively Correlated with Venom Complexity in Cone Snails

Although diet is believed to be a major factor underlying the evolution of venom, few comparative studies examine both venom composition and diet across a radiation of venomous species. Cone snails within the family, Conidae, comprise more than 700 species of carnivorous marine snails that capture their prey by using a cocktail of venomous neurotoxins (conotoxins or conopeptides). Venom composition across species has been previously hypothesized to be shaped by (a) prey taxonomic class (i.e., worms, molluscs, or fish) and (b) dietary breadth. We tested these hypotheses under a comparative phylogenetic framework using ecological data in conjunction with venom duct transcriptomes sequenced from 12 phylogenetically disparate cone snail species, including 10 vermivores (worm-eating), one molluscivore, and one generalist. We discovered 2223 unique conotoxin precursor peptides that encoded 1864 unique mature toxins across all species, >90% of which are new to this study. In addition, we identified two novel gene superfamilies and 16 novel cysteine frameworks. Each species exhibited unique venom profiles, with venom composition and expression patterns among species dominated by a restricted set of gene superfamilies and mature toxins. In contrast with the dominant paradigm for interpreting Conidae venom evolution, prey taxonomic class did not predict venom composition patterns among species. Our results suggests that cone snails have either evolved species-specific expression patterns likely as a consequence of the rapid evolution of conotoxin genes, or that traditional means of categorizing prey type (i.e., worms, mollusc, or fish) and conotoxins (i.e., by gene superfamily) do not accurately encapsulate evolutionary dynamics between diet and venom composition. We also found a significant positive relationship between dietary breadth and measures of conotoxin complexity. These results indicate that species with more generalized diets tend to have more complex venoms and utilize a greater number of venom genes for prey capture. Whether this increased gene diversity confers an increased capacity for evolutionary change remains to be tested. Overall, our results corroborate the key role of diet in influencing patterns of venom evolution in cone snails and other venomous radiations.

Evolutionary Biology↗