Vertebrate retinas are generally composed of rod (dim-light) and cone (bright-light) photoreceptors with distinct morphologies that evolved as adaptations to nocturnal/crepuscular and diurnal light environments. Over 70 years ago, the "transmutation" theory was proposed to explain some of the rare exceptions in which a photoreceptor type is missing, suggesting that photoreceptors could evolutionarily transition between cell types. Although studies have shown support for this theory in nocturnal geckos, the origins of allcone retinas, such as those found in diurnal colubrid snakes, remain a mystery. Here we investigate the evolutionary fate of the rods in a diurnal garter snake and test two competing hypotheses: (i) that the rods, and their corresponding molecular machinery, were lost or (ii) that the rods were evolutionarily modified to resemble, and function, as cones. Using multiple approaches, we find evidence for a functional and unusually blue-shifted rhodopsin that is expressed in small single "cones." Moreover, these cones express rod transducin and have rod ultrastructural features, providing strong support for the hypothesis that they are not true cones, as previously thought, but rather are modified rods. Several intriguing features of garter snake rhodopsin are suggestive of a more cone-like function. We propose that these cone-like rods may have evolved to regain spectral sensitivity and chromatic discrimination as a result of ancestral losses of middle-wavelength cone opsins in early snake evolution. This study illustrates how sensory evolution can be shaped not only by environmental constraints but also by historical contingency in forming new cell types with convergent functionality. rhodopsin evolution | visual evolution | reptile vision | snake photoreceptors | visual pigment H ow complex structures can arise has long fascinated evolutionary biologists, and the evolution of the eye, as noted by Charles Darwin (1), is perhaps the most famous example. Within the vertebrate eye, the light-sensing photoreceptors are complex, highly specialized cellular structures that can be divided into two general types based on their distinct morphologies and functions: cones, which are active during the day and contain cone opsin pigments, and rods, which mediate dim-light vision and contain rhodopsin (RH1) (2-4). The visual pigments contained in cone photoreceptors are classified into four different subtypes that mediate vision across the visible spectrum from the UV to the red (2). Although most vertebrate retinas are duplex, containing both cones and rods, squamate reptiles (lizards and snakes) are unusual, not only in having highly variable photoreceptor morphologies, but also for several instances of the absence of an entire class of photoreceptors, resulting in simplex retinas composed of only cones or rods (4).In a seminal book published in 1942, Walls (4) hypothesized that, during evolution, vertebrate photoreceptors could transform from one type to another, a process that he termed photoreceptor "transm...
The characterization of the distribution of mutational effects is a key goal in evolutionary biology. Recently developed deepsequencing approaches allow for accurate and simultaneous estimation of the fitness effects of hundreds of engineered mutations by monitoring their relative abundance across time points in a single bulk competition. Naturally, the achievable resolution of the estimated fitness effects depends on the specific experimental setup, the organism and type of mutations studied, and the sequencing technology utilized, among other factors. By means of analytical approximations and simulations, we provide guidelines for optimizing time-sampled deep-sequencing bulk competition experiments, focusing on the number of mutants, the sequencing depth, and the number of sampled time points. Our analytical results show that sampling more time points together with extending the duration of the experiment improves the achievable precision disproportionately compared with increasing the sequencing depth or reducing the number of competing mutants. Even if the duration of the experiment is fixed, sampling more time points and clustering these at the beginning and the end of the experiment increase experimental power and allow for efficient and precise assessment of the entire range of selection coefficients. Finally, we provide a formula for calculating the 95%-confidence interval for the measurement error estimate, which we implement as an interactive web tool. This allows for quantification of the maximum expected a priori precision of the experimental setup, as well as for a statistical threshold for determining deviations from neutrality for specific selection coefficient estimates.KEYWORDS experimental design; experimental evolution; distribution of fitness effects; mutation; population genetics M UTATIONS provide the fuel for evolutionary change, and their fitness effects critically influence the course and dynamics of evolution. The distribution of fitness effects (DFE) lies at the heart of many evolutionary concepts, such as the genetic basis of complex traits (Eyre-Walker 2010) and diseases (Keightley and Eyre-Walker 2010), the rate of adaptation to a new environment (Gerrish and Lenski 1998;Orr 1998Orr , 2005b, the maintenance of genetic variation (Charlesworth et al. 1995), and the relative importance of selection and drift in molecular evolution (Ohta 1977(Ohta , 1992Kimura 1979). Unsurprisingly, considerable effort has been devoted, both empirically (e.g., Sawyer et al. 2003;Sousa et al. 2012;Gordo and Campos 2013;Bernet and Elena 2015) and theoretically (e.g., Gillespie 1983;Orr 2005a;Martin and Lenormand 2006b;Connallon and Clark 2015;Rice et al. 2015), to assess the fraction of all possible mutations that are beneficial, neutral, or deleterious. Until recently, the two main approaches for assessing the DFE have been based either on the analysis of polymorphism and divergence data (Jensen et al. 2008;Keightley and Eyre-Walker 2010;Schneider et al. 2011) or on laboratory evolution studies ...
Background:The primate Y chromosome is distinguished by a lack of inter-chromosomal recombination along most of its length, extensive gene loss, and a prevalence of repetitive elements. A group of genes on the male-specific portion of the Y chromosome known as the "ampliconic genes" are present in multiple copies that are sometimes part of palindromes, and that undergo a form of intra-chromosomal recombination called gene conversion, wherein the nucleotides of one copy are homogenized by those of another. With the aim of further understanding gene family evolution of these genes, we collected nucleotide sequence and gene copy number information for several species of papionin monkey. We then tested for evidence of gene conversion, and developed a novel statistical framework to evaluate alternative models of gene family evolution using our data combined with other information from a human, a chimpanzee, and a rhesus macaque. Results: Our results (i) recovered evidence for several novel examples of gene conversion in papionin monkeys and indicate that (ii) ampliconic gene families evolve faster than autosomal gene families and than single-copy genes on the Y chromosome and that (iii) Y-linked singleton and autosomal gene families evolved faster in humans and chimps than they do in the other Old World Monkey lineages we studied. Conclusions: Rapid evolution of ampliconic genes cannot be attributed solely to residence on the Y chromosome, nor to variation between primate lineages in the rate of gene family evolution. Instead other factors, such as natural selection and gene conversion, appear to play a role in driving temporal and genomic evolutionary heterogeneity in primate gene families.
The characterization of the distribution of mutational effects is a key goal in evolutionary biology. Recently developed deep-sequencing approaches allow for accurate and simultaneous estimation of the fitness effects of hundreds of engineered mutations by monitoring their relative abundance across time points in a single bulk competition. Naturally, the achievable resolution of the estimated fitness effects depends on the specific experimental setup, the organism and type of mutations studied, and the sequencing technology utilized, among other factors. By means of analytical approximations and simulations, we provide guidelines for optimizing time-sampled deep-sequencing bulk competition experiments, focusing on the number of mutants, the sequencing depth, and the number of sampled time points. Our analytical results show that sampling more time points together with extending the duration of the experiment improves the achievable precision disproportionately as compared with increasing the sequencing depth, or reducing the number of competing mutants. Even if the duration of the experiment is fixed, sampling more time points and clustering these at the beginning and the end of the experiment increases experimental power, and allows for efficient and precise assessment of the entire range of selection coefficients. Finally, we provide a formula for calculating the 95%-confidence interval for the measurement error estimate, which we implement as an interactive web tool. This allows for quantification of the maximum expected a priori precision of the experimental setup, as well as for a statistical threshold for determining deviations from neutrality for specific selection coefficient estimates.
After more than 100 years of generating monoculture batch culture growth curves, microbial ecologists and evolutionary biologists still lack a reference method for inferring growth rates. Our work highlights the challenges of estimating the growth rate from growth curve data and shows that inaccurate estimates of growth rates significantly impact the estimated relative fitness, a principal quantity in evolution and ecology. First, we conducted a literature review and found which different types of methods are currently used to estimate growth rates. These methods differ in the meaning of the estimated growth rate parameter. Kinetic models estimate the intrinsic growth rate μ whereas statistical methods — both model-based and model-free — estimate the maximum per capita growth rate μmax. Using math and simulations, we show the conditions in which μmax is not a good estimator of μ. Then, we demonstrate that inaccurate absolute estimates of μ is not overcome by calculating relative values. Importantly, we find that poor approximations for μ sometimes lead to wrongly classifying a beneficial mutant as deleterious. Finally, we re-analyzed four published data-sets using most of the methods found by our literature review. We detected no single best-fitting model across all experiments within a data-set and found that the Gompertz models, which were among the most commonly used, were often among the worst fitting. Our study provides suggestions for how experimenters can improve their growth rate and associated relative fitness estimates and highlights a neglected but fundamental problem for nearly everyone who studies microbial populations in the lab.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.