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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2016

Pan-retinal characterisation of Light Responses from Ganglion Cells in the Developing Mouse Retina

We have investigated the ontogeny of light-driven responses in mouse retinal ganglion cells (RGCs). Using a large-scale, high-density multielectrode array, we recorded from hundreds to thousands of RGCs simultaneously at pan-retinal level, including dorsal and ventral locations. Responses to different contrasts not only revealed a complex developmental profile for ON, OFF and ON-OFF RGC types, but also unveiled differences between dorsal and ventral RGCs. At eye-opening, dorsal RGCs of all types were more responsive to light, perhaps indicating an environmental priority to nest viewing for pre-weaning pups. The developmental profile of ON and OFF RGCs exhibited antagonistic behavior, with the strongest ON responses shortly after eye-opening, followed by an increase in the strength of OFF responses later on. Further, we found that with maturation receptive field (RF) center sizes decrease, responses to light get stronger, and centers become more circular while seeing differences in all of them between RGC types. These findings show that retinal functionality is not spatially homogeneous, likely reflecting ecological requirements that favour the early development of dorsal retina, and reflecting different roles in vision in the mature animal.

Neuroscience↗

bioRxiv2016

A Model for Brain Life History Evolution

Mathematical modeling of brain evolution is scarce, possibly due in part to the difficulty of describing how brain relates to fitness. Yet such modeling is needed to formalize verbal arguments and deepen our understanding of brain evolution. To address this issue, we combine elements of life history and metabolic theories to formulate a metabolically explicit mathematical model for brain life history evolution. We assume that some of the brains energetic expense is due to production (learning) and maintenance (memory) of skills (or cognitive abilities, knowledge, information, etc.). We also assume that individuals use skills to extract energy from the environment, and can allocate this energy to grow and maintain the body, including brain and reproductive tissues. Our model can be used to ask what fraction of growth energy should be allocated to the growth of brain and other tissues at each age under various biological settings as a result of natural selection. We apply the model to find uninvadable allocation strategies under a \"me-against-nature\" setting, namely when overcoming environmentally determined energy-extraction challenges does not involve any interactions with other individuals (possibly except caregivers), and using parameter values for modern humans. The uninvadable strategies yield predictions for brain and body mass throughout ontogeny, as well as for the ages at maturity, adulthood, and brain growth arrest. We find that (1) a me-against-nature setting is enough to generate adult brain and body mass of ancient human scale, (2) large brains are favored by intermediately challenging environments, moderately effective skills, and metabolically expensive memory, and (3) adult skill number is proportional to brain mass when metabolic costs of memory saturate the brain metabolic rate allocated to skills. Overall, our model is a step towards a quantitative theory of brain life history evolution yielding testable quantitative predictions as ecological, demographic, and social factors vary.\n\nAuthor SummaryUnderstanding what promotes the evolution of a given feature is often helped by mathematical modeling. However, mathematical modeling of brain evolution has remained scarce, possibly because of difficulties describing mathematically how the brain relates to reproductive success, which is the currency of evolution. Here we combine elements of two research fields that have previously been successful at detailing how a feature impacts reproductive success (life history theory) and at predicting the individuals body mass throughout its life without the need to describe in detail the inner workings of the body (metabolic theory). We apply the model to a setting where individuals must extract energy from the environment without interacting with other individuals except caregivers (\"me-against-nature\") and parameterize the model with data from humans. In this setting, the model can correctly predict a variety of human features, including large brain sizes. Our model can be used to obtain testable quantitative predictions in terms of brain mass throughout an individuals life from assumed hypotheses promoting brain evolution, such as harsh environments or plentiful social interactions.

Evolutionary Biology↗

bioRxiv2016

Adaptation in isolated populations: when does it happen and when can we tell?

Isolated populations with novel phenotypes present an exciting opportunity to uncover the genetic basis of ecologically significant adaptation, and genomic scans for positive selection in such populations have often, but not always, led to candidate genes directly related to an adaptive phenotype. However, in many cases these populations were established by a severe bottleneck, which can make identifying targets of selection problematic. Here we simulate severe bottlenecks and subsequent selection on standing variation, mimicking adaptation after establishment of a new small population, such as an island or an artificial selection experiment. Using simulations of single loci under positive selection and population genetics theory, we examine how population size and age of the population isolate affects the ability of outlier scans for selection to identify adaptive alleles using both single site measures and haplotype structure. We find and explain an optimal combination of selection strength, starting frequency, and age of the adaptive allele, which we refer to as a Goldilocks zone, where adaptation is likely to occur, and yet the adaptive variants are most likely to derive from a single ancestor (a \"hard\" selective sweep); in this zone, four commonly used statistics detect selection with high power. Real-world examples of both island colonization and experimental evolution studies are discussed. Our study provides concrete considerations to be made before embarking on whole genome sequencing of differentiated populations.

Evolutionary Biology↗

bioRxiv2016

Rescuing perishable neuroanatomical information from a threatened biodiversity hotspot: Remote field methods for brain tissue preservation validated by cytoarchitectonic analysis, immunohistochemistry, and X-ray microcomputed tomography

Biodiversity hotspots, which harbor more endemic species than elsewhere on Earth, are increasingly threatened. There is a need to accelerate collection efforts in these regions before threatened or endangered species become extinct. The diverse geographical, ecological, genetic, morphological, and behavioral data generated from the on-site collection of an individual specimen are useful for many scientific purposes. However, traditional methods for specimen preparation in the field do not permit researchers to retrieve neuroanatomical data, disregarding potentially useful data for increasing our understanding of brain diversity. These data have helped clarify brain evolution, deciphered relationships between structure and function, and revealed constraints and selective pressures that provide context about the evolution of complex behavior. Here, we report our field-testing of two commonly used laboratory-based techniques for brain preservation while on a collecting expedition in the Congo Basin and Albertine Rift, two poorly known regions associated with the Eastern Afromontane biodiversity hotspot.\n\nFirst, we found that transcardial perfusion fixation and long-term brain storage, conducted in remote field conditions with no access to cold storage laboratory equipment, had no observable impact on cytoarchitectural features of lizard brain tissue when compared to lizard brain tissue processed under laboratory conditions. Second, field-perfused brain tissue subjected to prolonged post-fixation remained readily compatible with subsequent immunohistochemical detection of neural antigens, with immunostaining that was comparable to that of laboratory-perfused brain tissue. Third, immersion-fixation of lizard brains, prepared under identical environmental conditions, was readily compatible with subsequent iodine-enhanced X-ray microcomputed tomography, which facilitated the non-destructive imaging of the intact brain within its skull.\n\nIn summary, we have validated multiple approaches to preserving intact lizard brains in remote field conditions with limited access to supplies and a high degree of environmental exposure. This protocol should serve as a malleable framework for researchers attempting to rescue perishable and irreplaceable morphological and molecular data from regions of disappearing biodiversity. Our approach can be harnessed to extend the numbers of species being actively studied by the neuroscience community, by reducing some of the difficulty associated with acquiring brains of animal species that are not readily available in captivity.

Neuroscience↗

bioRxiv2016

DIVERGENT IMMUNE PRIMING RESPONSES ACROSS FLOUR BEETLE LIFE STAGES AND POPULATIONS

Growing evidence shows that low doses of pathogens may prime the immune response in many insects, conferring subsequent protection against infection in the same developmental stage (within life stage priming), across life stages (ontogenic priming), or to offspring (trans-generational priming). Recent work also suggests that immune priming is a costly response. Thus, depending on host and pathogen ecology and evolutionary history, tradeoffs with other fitness components may constrain the evolution of priming. However, the relative impacts of priming at different life stages and across natural populations remain unknown. We quantified immune priming responses of 10 natural populations of the red flour beetle Tribolium castaneum, primed and infected with the natural insect pathogen Bacillus thuringiensis. We found that priming responses were highly variable both across life stages and populations, ranging from no detectable response to a 13-fold survival benefit. Comparing across stages, we found that ontogenic immune priming at the larval stage conferred maximum protection against infection. Finally, we found that various forms of priming showed sex-specific associations that may represent tradeoffs or shared mechanisms. These results suggest that sex-, life stage-, and pathogen-specific selective pressures can cause substantial divergence in priming responses even within a species. Our work highlights the necessity of further work to understand the mechanistic basis of this variability.

Evolutionary Biology↗

bioRxiv2016

Soft selective sweeps in evolutionary rescue

Evolutionary rescue occurs when a population that is declining in size because of an environmental change is rescued by genetic adaptation. Evolutionary rescue is an important phenomenon at the intersection of ecology and population genetics. While most population genetic models of evolutionary rescue focus on estimating the probability of rescue, we focus on whether one or more adaptive lineages contribute to evolutionary rescue. We find that when evolutionary rescue is likely, it is often driven by soft selective sweeps where multiple adaptive mutations spread through the population simultaneously. We give full analytic results for the probability of evolutionary rescue and the probability that evolutionary rescue occurs via soft selective sweeps in our model. We expect that these results will find utility in understanding the genetic signatures associated with various evolutionary rescue scenarios in large populations, such as the evolution of drug resistance in viral, bacterial, or eukaryotic pathogens.

Evolutionary Biology↗

bioRxiv2016

Resource limitation reveals a twofold benefit of eusociality

Explaining the evolution and maintenance of cooperative breeding or eusociality remains a challenge. Surprisingly, fundamental ecological factors, specifically competition for limited resources and resource variance, are frequently ignored in models of animal sociality. We here develop a mathematical model that includes density-dependent population growth and quantify the influence of cooperative foraging on resource use efficiency. We derive optimal resource sharing strategies, ranging from egalitarian to cooperatively breeding and eusocial groups. We find that, while egalitarian resource sharing is a risk-reducing foraging strategy, eusociality yields additional benefits: like egalitarian strategies, eusocial groups can reduce their members starvation risk by reducing resource variance. Additionally, eusocial groups increase their reproductive output by increasing intra-group variance in resources allocated to reproduction. This allows reproduction even when resources are so scarce that solitary animals would not be able to reproduce. In a majority of environmental situations and life-histories, this twofold benefit of eusociality increased resource use efficiency and led to supersaturation, that is, to a strong increase in carrying capacity. Supersaturation provides indirect benefits to group members even for low intra-group relatedness and represents one potential explanation for the evolution and maintenance of eusociality and cooperative breeding.

Evolutionary Biology↗

bioRxiv2016

Seed size and its rate of evolution correlate with species diversification across angiosperms

Species diversity varies greatly across the different taxonomic groups that comprise the Tree of Life (ToL). This imbalance is particularly conspicuous within angiosperms, but is largely unexplained. Seed mass is one trait that may help clarify why some lineages diversify more than others because it confers adaptation to different environments, which can subsequently influence speciation and extinction. The rate at which seed mass changes across the angiosperm phylogeny may also be linked to diversification by increasing reproductive isolation and allowing access to novel ecological niches. However, the magnitude and direction of the association between seed mass and diversification has not been assessed across the angiosperm phylogeny. Here, we show that absolute seed size and the rate of change in seed size are both associated with variation in diversification rates. Based on the largest available angiosperm phylogenetic tree, we found that smaller-seeded plants had higher rates of diversification, possibly due to improved colonisation potential. The rate of phenotypic change in seed size was also strongly positively correlated with speciation rates, providing rare, large-scale evidence that rapid morphological change is associated with species divergence. Our study now reveals that variation in morphological traits and, importantly, the rate at which they evolve can contribute to explaining the extremely uneven distribution of diversity across the ToL.

Evolutionary Biology↗

bioRxiv2016

Synthesizer: Expediting synthesis studies from context-free data with natural language processing

Todays low cost digital data provides unprecedented opportunities for scientific discovery from synthesis studies. For example, the medical field is revolutionizing patient care by creating personalized treatment plans based upon mining electronic medical records, imaging, and genomics data. Standardized annotations are essential to subsequent analyses for synthesis studies. However, accurately combining records from diverse studies requires tedious and error-prone human curation, posing a significant barrier to synthesis studies. We propose a novel natural language processing (NLP) algorithm, Synthesize, to merge data annotations automatically. Application to patient characteristics for diverse human cancers and ecological datasets demonstrates the accuracy of Synthesize in diverse scientific disciplines. This NLP approach is implemented in an open-source software package, Synthesizer. Synthesizer is a generalized, user-friendly system for error-free data merging.

Bioinformatics↗

bioRxiv2016

A brush fire feedback loop for the ramp-like rise in hominin brain size that began 2.4 million years ago

The rapid three-fold enlargement of the hominin brain1,2 began about 2.3 million years ago (myr) as Africa dried and grass replaced brush, creating great savannas3. Seeking an amplifying feedback loop, I analyzed the lightning-brush-fire ecology for grazing animals in a grassy burn scar4. Discovering the new grass by exploring brush byways could promote a population boom-but only after grass-specialized herbivores evolved from mixed feeders5 at 2.4 myr. When the brush returned several decades later, the grazer boom would turn to bust, squeezing numerous descendants back into the core grasslands. Meat-eating Homo species would boom and bust when grazers did, enriching the core in whatever alleles were earlier concentrated in the brush fringe catchment zone for that boom. This return migration for Homo is what creates the amplifying feedback loop that speeds brain enlargement rate, likely up to the mutation rate limit. It also promotes trait hitchhiking: any brush-relevant allele, not just those for hunting, can experience amplifying feedback merely by hanging out in the catchment zone4. The shade offered by brush would have been the default location for cooperative nurseries, time-consuming food preparation, and toolmaking. Increased behavioral versatility correlates with larger brain size and the more versatile brains of a current generation need only spend more-than-average time in the booms catchment zone for this recursive evolutionary process to keep average brain size increasing via assortative mating. This helps account for the time when enlargement began, why it was linear, when it ended, and why it slowed in Neanderthals and in Asian Homo erectus. Without utilizing Darwins selective survival, the feedback loop makes advance room for \"free\" future functionality in the cerebral cortex, likely relevant to the evolutionary emergence of our structured intellectual functions6 such as syntax, contingent planning, games, and logic.

Neuroscience↗

bioRxiv2016

Metagenomic covariation along densely sampled environmental gradients in the Red Sea

Oceanic microbial diversity covaries with physicochemical parameters. Temperature, for example, explains approximately half of global variation in surface taxonomic abundance. It is unknown, however, whether covariation patterns hold over narrower parameter gradients and spatial scales, and extending to mesopelagic depths. We collected and sequenced 45 epipelagic and mesopelagic microbial metagenomes on a meridional transect through the eastern Red Sea. We asked which environmental parameters explain the most variation in relative abundances of taxonomic groups, gene ortholog groups, and pathways--at a spatial scale of <2000 km, along narrow but well-defined latitudinal and depth-dependent gradients. We also asked how microbes are adapted to gradients and extremes in irradiance, temperature, salinity, and nutrients, examining the responses of individual gene ortholog groups to these parameters. Functional and taxonomic metrics were equally well explained (75-79%) by environmental parameters. However, only functional and not taxonomic covariation patterns were conserved when comparing with an intruding water mass with different physicochemical properties. Temperature explained the most variation in each metric, followed by nitrate, chlorophyll, phosphate, and salinity. That nitrate explained more variation than phosphate suggested nitrogen limitation, consistent with low surface N:P ratios. Covariation of gene ortholog groups with environmental parameters revealed patterns of functional adaptation to the challenging Red Sea environment: high irradiance, temperature, salinity, and low nutrients. Nutrient acquisition gene ortholog groups were anticorrelated with concentrations of their respective nutrient species, recapturing trends previously observed across much larger distances and environmental gradients. This dataset of metagenomic covariation along densely sampled environmental gradients includes online data exploration supplements, serving as a community resource for marine microbial ecology.

Microbiology↗

bioRxiv2016

Information use shapes range expansion dynamics into environmental gradients

Globally, geographic distributions of species are dynamic and strongly influenced by dispersal. Vice versa, range dynamics feed back and may select for increased dispersal. This interplay almost universally happens across environmental gradients which can directly impact the fitness of organisms, but also provide individuals with information on the environmental changes. However, the organisms; ability to subsequently adjust dispersal decisions plastically has been largely ignored and the (macro)ecological consequences remain unclear. Using modeling and controlled experiments in replicated microcosm landscapes, we show that information on environmental gradients severely impacted range dynamics and inverted the spatial distribution of population densities in comparison to controls where this information was not provided. Additionally, information use prevented evolutionary changes in dispersal and an acceleration of range expansions. We demonstrate the strong impact of informed dispersal and subsequent behavioral changes on range dynamics in environmental gradients and spatial dynamics in general.

Evolutionary Biology↗

bioRxiv2016

When local means local: Polygenic signatures of local adaptation within whitebark pine (Pinus albicaulis Engelm.) across the Lake Tahoe Basin, USA

Patterns of local adaptation at fine spatial scales are central to understanding how evolution proceeds, and are essential to the effective management of economically and ecologically important forest tree species. Here, we employ single and multilocus analyses of genetic data (n = 116,231 SNPs) to describe signatures of fine-scale adaptation within eight whitebark pine (Pinus albicaulis Engelm.) populations across the local extent of the environmentally heterogeneous Lake Tahoe Basin, USA. We show that despite highly shared genetic variation (FST = 0.0069) there is strong evidence for adaptation to the rain shadow experienced across the eastern Sierra Nevada. Specifically, we build upon evidence from a common garden study and find that allele frequencies of loci associated with four phenotypes (mean = 236 SNPs), 18 environmental variables (mean = 99 SNPs), and those detected through genetic differentiation (n = 110 SNPs) exhibit significantly higher signals of selection (covariance of allele frequencies) than could be expected to arise, given the data. We also provide evidence that this covariance tracks environmental measures related to soil water availability through subtle allele frequency shifts across populations. Our results replicate empirical support for theoretical expectations of local adaptation for populations exhibiting strong gene flow and high selective pressures, and suggest that ongoing adaptation of many P. albicaulis populations within the Lake Tahoe Basin will not be constrained by the lack of genetic variation. Even so, some populations exhibit low levels of heritability for the traits presumed to be related to fitness. These instances could be used to prioritize management to maintain adaptive potential. Overall, we suggest that established practices regarding whitebark pine conservation be maintained, with the additional context of fine-scale adaptation.

Evolutionary Biology↗

bioRxiv2016

The evolutionary basis of premature migration in Pacific salmon highlights the utility of genomics for informing conservation

The delineation of conservation units (CUs) is a challenging issue that has profound implications for minimizing the loss of biodiversity and ecosystem services. CU delineation typically seeks to prioritize evolutionary significance and genetic methods play a pivotal role in the delineation process by quantifying overall differentiation between populations. While CUs that primarily reflect overall genetic differentiation do protect adaptive differences between distant populations, they do not necessarily protect adaptive variation within highly connected populations. Advances in genomic methodology facilitate the characterization of adaptive genetic variation, but the potential utility of this information for CU delineation is unclear. Here we use genomic methods to investigate the evolutionary basis of premature migration in Pacific salmon, a complex behavioral and physiological adaptation that exists within highly-connected populations and has experienced severe declines. Strikingly, we find that premature migration is associated with the same single locus across multiple populations in each of two different species. Patterns of variation at this locus suggest that the premature migration alleles arose from a single evolutionary event within each species and were subsequently spread to distant populations through straying and positive selection. Our results reveal that complex adaptive variation can depend on rare mutational events at a single locus, demonstrate that CUs reflecting overall genetic differentiation can fail to protect evolutionarily significant variation that has substantial ecological and societal benefits, and suggest that a supplemental framework for protecting specific adaptive variation will sometimes be necessary to prevent the loss of significant biodiversity and ecosystem services.

Genetics↗

bioRxiv2016

Evolutionary dynamics of CRISPR gene drives

The alteration of wild populations has been discussed as a solution to a number of humanitys most pressing ecological and public health concerns. Enabled by the recent revolution in genome editing, CRISPR gene drives, selfish genetic elements which can spread through populations even if they confer no advantage to their host organism, are rapidly emerging as the most promising approach. But before real-world applications are considered, it is imperative to develop a clear understanding of the outcomes of drive release in nature. Toward this aim, we mathematically study the evolutionary dynamics of CRISPR gene drives. We demonstrate that the emergence of drive-resistant alleles presents a major challenge to previously reported constructs, and we show that an alternative design which selects against resistant alleles greatly improves evolutionary stability. We discuss all results in the context of CRISPR technology and provide insights which inform the engineering of practical gene drive systems.

Synthetic Biology↗

bioRxiv2016

Daisy-chain gene drives for the alteration of local populations

RNA-guided gene drive elements could address many ecological problems by altering the traits of wild organisms, but the likelihood of global spread tremendously complicates ethical development and use. Here we detail a localized form of CRISPR-based gene drive composed of genetic elements arranged in a daisy-chain such that each element drives the next. \"Daisy drive\" systems can duplicate any effect achievable using an equivalent global drive system, but their capacity to spread is limited by the successive loss of non-driving elements from the base of the chain. Releasing daisy drive organisms constituting a small fraction of the local wild population can drive a useful genetic element to local fixation for a wide range of fitness parameters without resulting in global spread. We additionally report numerous highly active guide RNA sequences sharing minimal homology that may enable evolutionary stable daisy drive as well as global CRISPR-based gene drive. Daisy drives could simplify decision-making and promote ethical use by enabling local communities to decide whether, when, and how to alter local ecosystems.\n\nAuthors Summary Global gene drive systems based on CRISPR are likely to spread to every population of the target species, hampering safe and ethical use. Daisy drive systems offer a way to alter the traits of only local populations in a temporary manner. Because they can exactly duplicate the activity of any global CRISPR-based drive at a local level, daisy drives may enable safe field trials and empower local communities to make decisions concerning their own shared environments.\n\nFor more details and an animation intended for a general audience, see the summary at Sculpting Evolution.

Synthetic Biology↗

bioRxiv2016

Oligogenic Adaptation, Soft Sweeps, and Parallel Melanic Evolution in Drosophila melanogaster

Unraveling the genetic architecture of adaptive phenotypic divergence is a fundamental quest in evolutionary biology. In Drosophila melanogaster, high-altitude melanism has evolved in separate mountain ranges in sub-Saharan Africa, potentially as an adaptation to UV intensity. We investigated the genetic basis of this melanism in three populations using a new bulk segregant analysis mapping method. Although hundreds of genes are known to affect cuticular pigmentation in D. melanogaster, we identified only 19 distinct QTLs from 9 mapping crosses, with several QTL peaks being shared among two or all populations. Surprisingly, we did not find wide signals of genetic differentiation (Fst) between lightly and darkly pigmented populations at these QTLs, in spite of the pronounced phenotypic difference in pigmentation. Instead, we found small numbers of highly differentiated SNPs at the probable causative genes. A simulation analysis showed that these patterns of polymorphism are consistent with selection on standing genetic variation (leading to \"soft sweeps\"). Our results thus support a role for oligogenic selection on standing genetic variation in driving parallel ecological adaptation.

Genetics↗

bioRxiv2016

Exposing the diversity of multiple infection patterns

Natural populations often have to cope with genetically distinct parasites that can coexist, or not, within the same hosts. Theoretical models addressing the evolution of virulence have considered two within host infection outcomes, namely superinfection and coinfection. The field somehow became limited by this dichotomy that does not correspond to an empirical reality, as other infection patterns, namely sets of within-host infection outcomes, are possible. We indeed formally prove there are 114 different infection patterns for the sole recoverable chronic infections caused by horizontally-transmitted microparasites. We afterwards highlight eight infection patterns using an explicit modelling of within-host dynamics that captures a large range of ecological interactions, five of which have been neglected so far. To clarify the terminology related to multiple infections, we introduce terms describing these new relevant patterns and illustrate them with existing biological systems. This characterisation of infection patterns opens new perspectives for understanding the epidemiology and the evolution of parasites.

Epidemiology↗