Biology subjects

Kosiol, C.

Publications and source records attributed to Kosiol, C..

13 recordsLinked to original sources

bioRxiv2023

Development and patterning of a highly versatile visual system in spiders

Visual systems provide a key interface between organisms and their surroundings, and have evolved in many forms to perform diverse functions across the animal kingdom. Spiders exhibit a range of visual abilities and ecologies, the diversity of which is underpinned by a highly versatile, modular visual system architecture. This typically includes eight eyes of two developmentally distinct types, but the number, size, location, and function of the eyes can vary dramatically between lineages. Previous studies of visual system development in spiders have confirmed that many components of the retinal determination gene (RDG) network are conserved with other arthropods, but so far, comparative studies among spiders are lacking. We characterised visual system development in eight species of spiders representing a range of morphologies, visual ecologies, and phylogenetic positions, to determine how these diverse configurations are formed, and how they might evolve. Combining synchrotron radiation tomography, transcriptomics, in situ hybridisation, and selection analyses, we characterise the repertoires and expression of key RDGs in relation to adult morphology. We identify key molecular players, timepoints, and developmental events that may contribute to adult diversity, in particular the molecular and developmental underpinnings of eye size, number, position, and identity across spiders.

developmental biology↗

bioRxiv2023

Polymorphism-aware models in RevBayes: Species trees, disentangling Balancing Selection and CG-biased gene conversion

The role of balancing selection is a long-standing evolutionary puzzle. Balancing selection is a crucial evolutionary process that maintains genetic variation (polymorphism) over extended periods of time; however, detecting it poses a significant challenge. Building upon the polymorphismaware phylogenetic models (PoMos) framework rooted in the Moran model, we introduce Po-MoBalance model. This novel approach is designed to disentangle the interplay of mutation, genetic drift, directional selection (GC-biased gene conversion), along with the previously unexplored balancing selection pressures on ultra-long timescales comparable with species divergence times by analysing multi-individual genomic and phylogenetic divergence data. Implemented in the open-source RevBayes Bayesian framework, PoMoBalance offers a versatile tool for inferring phylogenetic trees as well as quantifying various selective pressures. The novel aspect of our approach in studying balancing selection lies in PoMos ability to account for ancestral polymorphisms and incorporate parameters that measure frequency-dependent selection, allowing us to determine the strength of the effect and exact frequencies under selection. We implemented validation tests and assessed the model on the data simulated with SLiM and a custom Moran model simulator. Real sequence analysis of Drosophila populations reveals insights into the evolutionary dynamics of regions subject to frequency-dependent balancing selection, particularly in the context of sex-limited colour dimorphism in Drosophila erecta.

evolutionary biology↗

bioRxiv2023

Functional Adaptations of Endogenous Retroviruses to the Drosophila Host Underlie their Evolutionary Diversification

Transposable elements profoundly affect the biology and evolution of their hosts, yet their own evolutionary dynamics remain poorly understood. Here, we investigate insect endogenous retroviruses (iERVs), a monophyletic group of LTR retrotransposons that have acquired the trait of infectivity, likely through capture of a Baculovirus envelope gene. In Drosophila ovaries, iERVs with functional envelope have adapted their cis-regulatory sequences to be expressed in any somatic cell type, from where they infect the germline. Strikingly, related retroviruses show distinct expression patterns, indicating niche partitioning. In contrast, all non-infectious iERVs that emerged through secondary envelope-loss are specifically expressed in the germline. Co-evolving with iERVs, the genome-protecting piRNA pathway has assimilated iERV promoter and sequence information into piRNA clusters, underscoring the functional significance of iERV expression in somatic niches. We propose that the evolutionary innovation of cell-to-cell infectivity has triggered the adaptive radiation of iERVs through trait diversification and antagonistic virus-host interactions, processes that likely underpin niche-specific expression of endogenous retroviruses in vertebrates as well.

evolutionary biology↗

bioRxiv2023

Inference of Genomic Landscapes using Ordered Hidden Markov Models with Emission Densities (oHMMed)

BackgroundGenomes are inherently inhomogeneous, with features such as base composition, recombination, gene density, and gene expression varying along chromosomes. Evolutionary, biological, and biomedical analyses aim to quantify this variation, account for it during inference procedures, and ultimately determine the causal processes behind it. Since sequential observations along chromosomes are not independent, it is unsurprising that autocorrelation patterns have been observed e.g., in human base composition. In this article, we develop a class of Hidden Markov Models (HMMs) called oHMMed (ordered HMM with emission densities, the corresponding R package of the same name is available on CRAN): They identify the number of comparably homogeneous regions within autocorrelated observed sequences. These are modelled as discrete hidden states; the observed data points are realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are inferred. ResultsWe apply our oHMMed algorithms to the proportion of G and C bases (modelled as a mixture of normal distributions) and the number of genes (modelled as a mixture of poisson-gamma distributions) in windows along the human, mouse, and fruit fly genomes. This results in a partitioning of the genomes into regions by statistically distinguishable averages of these features, and in a characterisation of their continuous patterns of variation. In regard to the genomic G and C proportion, this latter result distinguishes oHMMed from segmentation algorithms based in isochore or compositional domain theory. We further use oHMMed to conduct a detailed analysis of variation of chromatin accessibility (ATAC-seq) and epigenetic markers H3K27ac and H3K27me3 (modelled as a mixture of poisson-gamma distributions) along the human chromosome 1 and their correlations. ConclusionsOur algorithms provide a biologically assumption-free approach to characterising genomic landscapes shaped by continuous, autocorrelated patterns of variation. Despite this, the resulting genome segmentation enables extraction of compositionally distinct regions for further downstream analyses.

bioinformatics↗

bioRxiv2023

The patterns of codon usage between chordatesand arthropods are different but co-evolving withmutational biases

Different frequencies amongst codons that encode the same amino acid (i.e. synonymous codons) have been observed in multiple species. Studies focused on uncovering the forces that drive such codon usage showed that a combined effect of mutational biases and translational selection works to produce different frequencies of synonymous codons. However, only few have been able to measure and distinguish between these forces that may leave similar traces on the coding regions. Here, we have developed a codon model that allows the disentangling of mutation, selection on amino acids and synonymous codons, and GC-biased gene conversion (gBGC) which we employed on an extensive dataset of 415 chordates and 191 arthropods. We found that chordates need 15 more synonymous codon categories than arthropods to explain the empirical codon frequencies, which suggests that the extent of codon usage can vary greatly between animal phyla. Moreover, methylation at CpG sites seems to partially explain these patterns of codon usage in chordates but not in arthropods. Despite the differences between the two phyla, our findings demonstrate that in both, GC-rich codons are disfavoured when mutations are GC-biased, and the opposite is true when mutations are AT-biased. This indicates that selection on the genomic coding regions might act primarily to stabilise its GC/AT content on a genome-wide level. Our study shows that the degree of synonymous codon usage varies considerably among animals, but is likely governed by a common underlying dynamic. Significance statementThe reasons for the differential usage of codons encoding for the same amino acid has puzzled scientist for decades. By examining the frequencies of synonymous codons in different species, this study presents a novel model that sheds light on the underlying factors that drive differences in codon usage between chordates and arthropods. Our analysis unveiled more extensive codon usage patterns in chordates compared to arthropods. Despite differences between the phyla, the study highlights that genome- wide selection acts to balance mutational biases, as GC-rich codons are less favoured under GC-biased mutations, while the opposite holds true for AT-biased mutations. This research provides valuable insights into our understanding of the complex interplay between mutational biases and selection forces in shaping the variation at the synonymous sites, and has important implications for future studies of genome evolution and adaptation.

evolutionary biology↗

bioRxiv2022

Traditional phylogenetic models are insensitive to variations in the effective population size

A substitution represents the emergence and fixation of an allele in a population or species and is the fundamental event from which phylogenetic models of sequence evolution are devised. Because of the increasing availability of genomic sequences, we are now able to take advantage of intraspecific variability when reconstructing the tree of life. As a result, substitutions can be more realistically modeled as the product of mutation, selection, and genetic drift. However, it is still unclear whether this increased complexity affects our measures of evolutionary times and rates. This study seeks to answer this question by contrasting the traditional substitution model with a population genetic equivalent using data from 4385 individuals distributed across 179 populations and representing 17 species of animals, plants, and fungi. We found that when the population genetics dynamic is modeled via the substitution rates, the evolutionary times and rates of the two models are well correlated, suggesting that the phylogenetic model is able to capture the time and pace of its population counterpart. However, a closer inspection of this result showed that the traditional models largely ignore the effect of the effective population size, even when it is explicitly accounted for in the substitution rates. Our findings suggest that superimposing population-genetics results on the substitution rates is an effective strategy to study mutation and selection biases, while other data sources (e.g., life history traits or polymorphisms) may need to be additionally integrated to make the traditional substitution models sensitive to the impact of genetic drift. When combined with the known effect of ancestral population size on generating phylogenomic incongruence due to incomplete lineage sorting, our findings provide further evidence that unaccounted-for variations in the effective population size may be one of the primary causes of errors in phylogenetic analyses at shorter time scales.

evolutionary biology↗

bioRxiv2022

Selection on the fly: short term adaptation to an altered sexual selection regime in Drosophila pseudoobscura

Experimental evolution studies are powerful approaches to unveil the evolutionary history of lab populations. Such studies have shed light on how selection changes phenotypes and genotypes. Most of these studies have not examined the time course of adaptation under sexual selection manipulation, by resequencing the populations genomes at multiple time points. Here, we analyse allele frequency trajectories in Drosophila pseudoobscura where we altered their sexual selection regime for 200 generations and sequenced pooled populations at 5 time points. The intensity of sexual selection was either relaxed in monogamous populations (M) or elevated in polyandrous lines (E). We present a comprehensive study of how selection alters population genetics parameters at the chromosome and gene level. We investigate differences in the effective population size - Ne - between the treatments, and perform a genome-wide scan to identify signatures of selection from the time-series data. We found genomic signatures of adaptation to both regimes in D. pseudoobscura. There are more significant variants on E lines as expected from stronger sexual selection. However, we found that the response on the X chromosome was substantial in both treatments, only more marked in E and restricted to chromosome arm XR in M. Ne is lower on the X at the start of the experiment, which might indicate a swift adaptive response at the onset of selection. Additionally, we show that the third chromosome was also affected by elevated polyandry. Its distal end harbours a region showing a strong signal of adaptive divergence in E lines.

evolutionary biology↗

bioRxiv2021

Polymorphism-aware estimation of species trees and evolutionary forces from genomic sequences with RevBayes

O_LIThe availability of population genomic data through new sequencing technologies gives unprecedented opportunities for estimating important evolutionary forces such as genetic drift, selection, and mutation biases across organisms. Yet, analytical methods that can handle polymorphisms jointly with sequence divergence across species are rare and not easily accessible to empiricists. C_LIO_LIWe implemented polymorphism-aware phylogenetic models (PoMos), an alternative approach for species tree estimation, in the Bayesian phylogenetic software RevBayes. PoMos naturally account for incomplete lineage sorting, which is known to cause difficulties for phylogenetic inference in species radiations, and scale well with genome-wide data. Simultaneously, PoMos can estimate mutation and selection biases. C_LIO_LIWe have applied our methods to resolve the complex phylogenetic relationships of a young radiation of Chorthippus grasshoppers, based on coding sequences. In addition to establishing a well-supported species tree, we found a mutation bias favoring AT alleles and selection bias promoting the fixation of GC alleles, the latter consistent with GC-biased gene conversion. The selection bias is two orders of magnitude lower than genetic drift, validating the critical role of nearly neutral evolutionary processes in species radiation. C_LIO_LIPoMos offer a wide range of models to reconstruct phylogenies and can be easily combined with existing models in RevBayes -- e.g., relaxed clock and divergence time estimation -- offering new insights into the evolutionary processes underlying molecular evolution and, ultimately, species diversification. C_LI

bioinformatics↗

bioRxiv2020

Bait-ER: a Bayesian method to detect targets of selection in Evolve-and-Resequence experiments

For over a decade, experimental evolution has been combined with high-throughput sequencing techniques in so-called Evolve-and-Resequence (E&R) experiments. This allows testing for selection in populations kept in the laboratory under given experimental conditions. However, identifying signatures of adaptation in E&R datasets is far from trivial, and it is still necessary to develop more efficient and statistically sound methods for detecting selection in genome-wide data. Here, we present Bait-ER - a fully Bayesian approach based on the Moran model of allele evolution to estimate selection coefficients from E&R experiments. The model has overlapping generations, a feature that describes several experimental designs found in the literature. We tested our method under several different demographic and experimental conditions to assess its accuracy and precision, and it performs well in most scenarios. Nevertheless, some care must be taken when analysing trajectories where drift largely dominates and starting frequencies are low. We compare our method with other available software and report that ours has generally high accuracy even for trajectories whose complexity goes beyond a classical sweep model. Furthermore, our approach avoids the computational burden of simulating an empirical null distribution, outperforming available software in terms of computational time and facilitating its use on genome-wide data. We implemented and released our method in a new open-source software package that can be accessed at https://github.com/mrborges23/Bait-ER.

evolutionary biology↗

bioRxiv2020

Pervasive selection biases inferences of the species tree

Despite the importance of natural selection in species evolutionary history, phylogenetic methods that take into account population-level processes typically ignore selection. The assumption of neutrality is often based on the idea that selection occurs at a minority of loci in the genome and is unlikely to compromise phylogenetic inferences significantly. However, genome-wide processes like GC-bias and some variation segregating at the coding regions are known to evolve in the nearly neutral range. As we are now using genome-wide data to estimate species trees, it is natural to ask whether weak but pervasive selection is likely to blur species tree inferences. We developed a polymorphism-aware phylogenetic model tailored for measuring signatures of nucleotide usage biases to test the impact of selection in the species tree. Our analyses indicate that while the inferred relationships among species are not significantly compromised, the genetic distances are systematically underestimated in a node-height dependent manner: i.e., the deeper nodes tend to be more underestimated than the shallow ones. Such biases have implications for molecular dating. We dated the evolutionary history of 30 worldwide fruit fly populations, and we found signatures of GC-bias considerably affecting the estimated divergence times (up to 23%) in the neutral model. Our findings call for the need to account for selection when quantifying divergence or dating species evolution. Significance statementAlthough little is known about the impact of natural selection on species tree estimation, expectations are that it occurs at a minority of loci in eukaryotic genomes and is thus unlikely to affect the divergence process. However, growing evidence suggests that a large amount of the genomic variation evolves under weak but pervasive selection (e.g., fixation biases created by GC-bias gene conversion). We tested the impact of unaccounted-for nearly neutral selection on species tree estimation and found that the estimated branch lengths are systematically biased. Our results highlight the need for selection-aware models in species tree estimation and molecular dating.

evolutionary biology↗

bioRxiv2019

Consistency and identifiability of the polymorphism-aware phylogenetic models

Polymorphism-aware phylogenetic models (PoMo) constitute an alternative approach for species tree estimation from genome-wide data. PoMo builds on the standard substitution models of DNA evolution but expands the classic alphabet of the four nucleotide bases to include polymorphic states. By doing so, PoMo accounts for ancestral and current intra-population variation, while also accommodating population-level processes ruling the substitution process (e.g. genetic drift, mutations, allelic selection). PoMo has shown to be a valuable tool in several phylogenetic applications but a proof of statistical consistency (and identifiability, a necessary condition for consistency) is lacking. Here, we prove that PoMo is identifiable and, using this result, we further show that the maximum a posteriori (MAP) tree estimator of PoMo is a consistent estimator of the species tree. We complement our theoretical results with a simulated data set mimicking the diversity observed in natural populations exhibiting incomplete lineage sorting. We implemented PoMo in a Bayesian framework and show that the MAP tree easily recovers the true tree for typical numbers of sites that are sampled in genome-wide analyses.

evolutionary biology↗

bioRxiv2018

Polymorphism-aware species trees with advanced mutation models, bootstrap and rate heterogeneity

Molecular phylogenetics has neglected polymorphisms within present and ancestral populations for a long time. Recently, multispecies coalescent based methods have increased in popularity, however, their application is limited to a small number of species and individuals. We introduced a polymorphism-aware phylogenetic model (PoMo), which overcomes this limitation and scales well with the increasing amount of sequence data while accounting for present and ancestral polymorphisms. PoMo circumvents handling of gene trees and directly infers species trees from allele frequency data. Here, we extend the PoMo implementation in IQ-TREE and integrate search for the statistically best-fit mutation model, the ability to infer mutation rate variation across sites, and assessment of branch support values. We exemplify an analysis of a hundred species with ten haploid individuals each, showing that PoMo can perform inference on large data sets. While PoMo is more accurate than standard substitution models applied to concatenated alignments, it is almost as fast. We also provide bmm-simulate, a software package that allows simulation of sequences evolving under PoMo. The new options consolidate the value of PoMo for phylogenetic analyses with population data.

bioinformatics↗

bioRxiv2018

Quantifying GC-biased gene conversion in great ape genomes using polymorphism-aware models

As multi-individual population-scale data is becoming available, more-complex modeling strategies are needed to quantify the genome-wide patterns of nucleotide usage and associated mechanisms of evolution. Recently, the multivariate neutral Moran model was proposed. However, it was shown insufficient to explain the distribution of alleles in great apes. Here, we propose a new model that includes allelic selection. Our theoretical results constitute the basis of a new Bayesian framework to estimate mutation rates and selection coefficients from population data. We employ the new framework to quantify the patterns of genome-wide GC-biased gene conversion (gBCG) in great apes. In particular, we show that great apes have patterns of allelic selection that vary in intensity, a feature that we correlated with the great apes distinct demographies. We also demonstrate that the AT/GC toggling effect decreases the probability of a substitution, which promotes more polymorphisms in the base composition of great ape genomes. We further assess the impact of CG-bias in molecular analysis and we find that mutation rates and genetic distances are estimated under bias when gBGC is not properly accounted. Our results contribute to the discussion on the tempo and mode of gBGC evolution, while stressing the need for gBGC-aware models in population genetics and phylogenetics.

genetics↗