Biology subjects

Boussau, B.

Publications and source records attributed to Boussau, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Horizontal transfers can reveal ghost birth events and complete species trees

Extinct and unsampled species vastly outnumber the species typically included in phylogenetic studies. Most of these so-called ``ghost lineages'' are unknown and, among them, those that are extinct may have left no trace in the fossil record. Horizontal gene transfers (HGTs), however, provide a potential source of information about these missing lineages, because transfers can connect sampled ancestral species to lineages outside the sampled species tree. Such transfers may leave observable signatures in gene trees. Here, we formalize this signal under idealized models of duplication, transfer, and loss, and characterize the information that can be retrieved about the complete species tree (containing ghost and non-ghost lineages) thanks to sampled gene trees and sampled species trees (containing only the non-ghost lineages). We establish four identifiability results, assuming an infinite number of gene families and errorless dated gene trees. First, transfers identify the lineage through time (LTT) curve of the complete tree, averaged over each time interval induced by the nodes of the sampled species tree. Second, in the presence of transfers, the distribution of node heights in sampled gene trees uniquely determines the internal speciation times of the complete species tree, including ghost speciations. Third, these hidden speciation times suffice to restore identifiability of birth-death-sampling parameters, through a reduction to a recent theorem of Dieselhorst and Stadler. Finally, when duplication, transfer, and loss rates are known constants and the transfer rate is non-zero, the distribution of sampled gene trees almost surely uniquely determines the topology and branch lengths of the complete species tree, including all ghost branches. These results provide a theoretical basis for using signatures left by HGTs in gene trees to recover information about extinct and unsampled lineages, and suggest ways in which this signal could be exploited in practice.

bioinformatics↗

bioRxiv2026

Improved ancestral genome reconstruction using a learned gene-content grammar

Ancestral gene content inferences allow inferring the set of genes - and by extension, the cellular features and metabolic capabilities - of ancestral organisms, based on data from modern genomes. Current methods differ in their approach to ancestral inferences and the kinds of errors they make: reconciliation methods map gene trees onto species trees, and tend to under-estimate ancestral contents due to phylogenetic noise; profile methods model the evolution of phylogenetic profiles (presence-absence or count data) on the species tree, and tend to return inflated ancestors because they ignore gene trees and as a result can only account for horizontal gene transfer (HGT) in a limited manner. For reasons of tractability, both approaches also share a core limitation: neither uses the fact that genes do not act alone but belong to operons, protein complexes, and metabolic pathways that may be gained and lost together or experience shared selective constraints. Here, we show that this context - the grammar of gene content - provides a rich source of information that can be used to greatly improve ancestral gene content inference and metabolic reconstruction under both the reconciliation- and profile-based approaches. We model this structure as an Ising model and infer its parameters from 113,104 bacterial and archaeal genomes (one per species representative in GTDB). We validate the model on extant taxa using phylum-level holdout (i.e. using test data from different prokaryotic phyla than training data), showing that it can accurately "denoise", i.e., reconstruct gene repertoires from highly fragmented and noisy input data, learning about protein-protein interactions and gene essentiality during the training process. When applied to ancestral reconstructions, the denoiser fills gaps in conservative reconstructions and removes excess genes from overly-generous ones, such that different reconstruction methods converge to broadly concordant conclusions. By using this gene content grammar, patchy method-dependent ancestral reconstructions can be turned into organism-like ones, and yield agreement on the gene families, cell-biological features, and metabolic capabilities of the deepest nodes in the tree of life.

evolutionary biology↗

bioRxiv2025

Statistical detection of protein sites associated with continuous traits

Comparative genomic data can be used to look for substitutions in coding sequences that are associated with the variation of a particular phenotypic trait. A few statistical methods have been proposed to do so for phenotypes represented by discrete values. For continuous traits, no such statistical approach has been proposed, and researchers have resorted to sensible but uncharacterized criteria. Here, we investigate a phylogenetic model for coding sequences where amino acid preferences at a site are given by a continuous function of a quantitative trait. This function is inferred from the amino acids and the trait values in extant species and requires inferred point estimates of ancestral values of the trait at internal nodes. For detecting sites whose evolution is associated with this trait, we use a significance test against the hypothesis that amino acid preference does not depend on the trait. This procedure is compared to simpler strategies on simulated alignments. It displays an increased recall for low false positive rates, which is of special importance for performing whole-genome scans. This comes however at a much higher computational cost, and we suggest using a simple test to filter promising candidate sites. We then revisit a dataset of alignments for 62 species of mammals, using longevity as a phenotypic trait. We apply our method to three protein families that have previously been proposed to display sites associated with variation in lifespan in mammals. Using a graphical representation extracted from the detailed phylogenetic analysis of candidate sites, we suggest that the evidence for this in the sequence data alone is weak. The proposed method has been added to our Pelican software. It is available at https://gitlab.in2p3.fr/phoogle/pelican and can now be used with both discrete and continuous phenotypes to search for sites associated with phenotypic variation, on data sets with thousands of alignments.

bioinformatics↗

bioRxiv2025

Characterization of selective pressures acting on protein sites with Deep Learning

It is often useful, in the field of molecular evolution, to identify the selective pressures acting on a particular site of a protein to better understand its function. This is typically done with likelihood-based approaches applied to codon sequences in a phylogenetic context. However, these approaches are computationally costly. Here we adapt a linear transformer neural network architecture, which has been shown to be able to reconstruct accurate pairwise distances from sequence alignments, to identify selective pressures acting on individual amino acid sites. We design different versions of the architecture and train and test them on simulations. We compare the results of one of our best models to state-of-the-art likelihood-based methods and find that it outperforms it when it is applied to data that resemble its training data, but that it performs less well when applied to datasets that do not resemble the ones the model has been trained on. In all cases, our approach operates at a fraction of the computational cost of likelihood-based methods. These results suggest that such a neural network architecture can compare very favorably to state-of-the-art approaches to characterize selection pressures acting on coding sequences, but that it must be trained on datasets representative of empirical data.

bioinformatics↗

bioRxiv2024

Gene flow can reveal ghost lineages

Ghost species, encompassing extinct, unknown, and unsampled taxa, vastly outnumber those typically included in phylogenetic analyses. This hidden diversity has been shown to influence the study of horizontal gene flow (e.g., introgression and horizontal gene transfer) by complicating the phylogenetic signals commonly used for their detection. In this work, we explore the potential of horizontal gene transfer (HGT) detection methods based on phylogenies (i.e., reconciliation methods) to detect and quantify ghost diversity . We briefly outline the theoretical framework underpinning our approach. Then, using simple simulations and empirical data from Cyanobacteria, we show that HGT signals, as interpreted by reconciliation methods, can reveal the presence and phylogenetic position of ghost clades, despite the absence of genomic data for these taxa. We anticipate possible limitations and difficulties in using HGT detection to explore ghost diversity and suggest promising approaches to address or circumvent them. Altogether, this proof of concept opens new lines of research for the future: a scarce fossil record and a large proportion of unknown lineages, especially in Archaea and Bacteria, does not equate to an absence of information for evolutionary studies.

evolutionary biology↗

bioRxiv2024

Convergent transcriptomic and genomic adaptation in xeric rodents

Repeated adaptations rely in part on convergent genetic changes. The extent of convergent changes at the genomic scale is debated and may depend on the interplay between different factors. Rodents have repeatedly adapted to life in arid conditions, notably with altered renal morphology and physiology. This occurred at different time periods, allowing us to test the importance of time in convergent genomic evolution. We analyzed kidney transcriptomes from 34 species to quantify and characterize convergent evolution at the level of gene expression, tissue composition, and coding sequences. We found that several genes showed convergent expression changes, some of which also carried convergent changes in their coding sequence. We then subdivided these data to test the influence of evolutionary history. First, within the subfamily Murinae, we found more convergent gene expression, reflecting convergent changes in cell proportions. Second, we compared data for recent (within genera) and ancient (between genera) adaptations, and observed more convergent changes in the latter. Our study shows that adaptation to xeric environments in rodents involves repeated changes in tissue composition, gene expression and coding sequences, and that the degree of convergent evolution increases with both the age of the adaptations and species relatedness.

evolutionary biology↗

bioRxiv2024

Phyloformer: Fast, accurate and versatile phylogenetic reconstruction with deep neural networks

Phylogenetic inference aims at reconstructing the tree describing the evolution of a set of sequences descending from a common ancestor. The high computational cost of state-of-the-art Maximum likelihood and Bayesian inference methods limits their usability under realistic evolutionary models. Harnessing recent advances in likelihood-free inference and geometric deep learning, we introduce Phyloformer, a fast and accurate method for evolutionary distance estimation and phylogenetic reconstruction. Sampling many trees and sequences under an evolutionary model, we train the network to learn a function that enables predicting the former from the latter. Under a commonly used model of protein sequence evolution and exploiting GPU acceleration, it outpaces fast distance methods while matching maximum likelihood accuracy on simulated and empirical data. Under more complex models, some of which include dependencies between sites, it outperforms other methods. Our results pave the way for the adoption of sophisticated realistic models for phylogenetic inference.

bioinformatics↗

bioRxiv2023

Evolution of sex-biased gene expression during transitions to separate sexes in the Silene genus

Sexual dimorphism is widespread among species with separate sexes and its extent is thought to be governed by the differential expression of thousands of genes between males and females (known as Sex-Biased Genes, hereafter SBGs). SBGs have been studied in numerous species, but rarely in a comparative way, which curtails our understanding of their evolution, especially during multiple independent transitions to separate sexes. We sequenced the transcriptomes of nine dioecious species, two gynodioecious species (separate females and hermaphrodites) and two hermaphrodite species from the Silene genus. Our dataset provides access to three independent transitions to dioecy (dating from less than 1 Myo to about 11 Myo). We demonstrated that male-biased expression emerges first during a transition to separate sexes, later followed by female-biased genes. Furthermore, we showed that, despite a mixture of selective regimes, positive selection significantly affects the evolution of some SBGs. Overall, this study provides new insights on the causes of SBG evolution during transitions to separate sexes. TeaserThis study describes the evolution of sex-biased gene expression during a transition to separate sexes in plants.

evolutionary biology↗

bioRxiv2023

An evolutionary timescale for Bacteria calibrated using the Great Oxidation Event

Most of lifes diversity and history is microbial but it has left a meagre fossil record, greatly hindering understanding of evolution in deep time. However, the co-evolution of life and the Earth system has left signatures of bacterial metabolism in the geochemical record, most conspicuously the Great Oxidation Event (GOE) [~]2.33 billion years ago (Ga, (Poulton et al. 2021)), in which oxygenic photosynthesis and tectonism (Eguchi, Seales, and Dasgupta 2019) transformed Earths biosphere from dominantly anaerobic to aerobic. Here, we combine machine learning and phylogenetic reconciliation to infer ancestral transitions to aerobic lifestyles during bacterial evolution. Linking these transitions to the GOE provides new constraints to infer the timetree of Bacteria. We find that extant bacterial phyla are truly ancient, having radiated in the Archaean and the Proterozoic: the oldest include Bacillota (Firmicutes), which radiated 3.1-3.7 Ga, Cyanobacteria (3.3-3.5 Ga) and Patescibacteria (3-3.5 Ga). We show that most bacterial phyla were ancestrally anaerobic and that most transitions to an aerobic lifestyle post-dated the GOE. Our analyses trace oxygen production and consumption back to Cyanobacteria. From that starting point, horizontal transfer seeded aerobic lifestyles across bacterial diversity over hundreds of millions of years. Our analyses demonstrate that the diversification of aerobes proceeded in two waves corresponding to the GOE and to a second sustained rise in atmospheric O2 at the dawn of the Palezoic (Krause et al. 2022).

evolutionary biology↗

bioRxiv2023

Simulations of sequence evolution: how (un)realistic they really are and why

MotivationSimulating Multiple Sequence Alignments (MSAs) using probabilistic models of sequence evolution plays an important role in the evaluation of phylogenetic inference tools, and is crucial to the development of novel learning-based approaches for phylogenetic reconstruction, for instance, neural networks. These models and the resulting simulated data need to be as realistic as possible to be indicative of the performance of the developed tools on empirical data and to ensure that neural networks trained on simulations perform well on empirical data. Over the years, numerous models of evolution have been published with the goal to represent as faithfully as possible the sequence evolution process and thus simulate empirical-like data. In this study, we simulated DNA and protein MSAs under increasingly complex models of evolution with and without insertion/deletion (indel) events using a state-of-the-art sequence simulator. We assessed their realism by quantifying how accurately supervised learning methods are able to predict whether a given MSA is simulated or empirical. ResultsOur results show that we can distinguish between empirical and simulated MSAs with high accuracy using two distinct and independently developed classification approaches across all tested models of sequence evolution. Our findings suggest that the current state-of-the-art models fail to accurately replicate several aspects of empirical MSAs, including site-wise rates as well as amino acid and nucleotide composition. Data and Code AvailabilityAll simulated and empirical MSAs, as well as all analysis results, are available at https://cme.h-its.org/exelixis/material/simulation_study.tar.gz. All scripts required to reproduce our results are available at https://github.com/tschuelia/SimulationStudy and https://github.com/JohannaTrost/seqsharp. Contactjulia.haag@h-its.org

bioinformatics↗

bioRxiv2022

Endo-parasitoid lifestyle promotes endogenization and domestication of dsDNA viruses

The accidental endogenization of viral elements within eukaryotic genomes can occasionally provide significant evolutionary benefits, giving rise to their long-term retention, that is, to viral domestication. For instance, in some endoparasitoid wasps (whose immature stages develop inside their hosts), the membrane-fusion property of double-stranded DNA viruses has been repeatedly domesticated following ancestral endogenizations. The endogenized genes provide female wasps with a delivery tool to inject virulence factors that are essential to the developmental success of their offspring. Because all known cases of viral domestication involve endoparasitic wasps, we hypothesized that this lifestyle, relying on a close interaction between individuals, may have promoted the endogenization and domestication of viruses. By analyzing the composition of 124 Hymenoptera genomes, spread over the diversity of this clade and including free-living, ecto- and endoparasitoid species, we tested this hypothesis. Our analysis first revealed that double-stranded DNA viruses, in comparisons with other viral genomic structures (ssDNA, dsRNA, ssRNA), are more often endogenized and domesticated (that is, retained by selection) than expected from their estimated abundance in insect viral communities. Secondly, our analysis indicates that the rate at which dsDNA viruses are endogenized is higher in endoparasitoids than in ectoparasitoids or free-living hymenopterans, which also translates into more frequent events of domestication. Hence, these results are consistent with the hypothesis that the endoparasitoid lifestyle has facilitated the endogenization of dsDNA viruses, in turn increasing the opportunities of domestications that now play a central role in the biology of many endoparasitoid lineages.

genomics↗

bioRxiv2022

Phyloformer: towards fast and accurate phylogeny estimation with self-attention networks

AO_SCPLOWBSTRACTC_SCPLOWAn important problem in molecular evolution is that of phylogenetic reconstruction, that is, given a set of sequences descending from a common ancestor, the reconstruction of the binary tree describing their evolution from the latter. State-of-the-art methods for the task, namely Maximum likelihood and Bayesian inference, have a high computational cost, which limits their usability on large datasets. Recently researchers have begun investigating deep learning approaches to the problem but so far these attempts have been limited to the reconstruction of quartet tree topologies, addressing phylogenetic reconstruction as a classification problem. We present here a radically different approach with a transformer-based network architecture that, given a multiple sequence alignment, predicts all the pairwise evolutionary distances between the sequences, which in turn allow us to accurately reconstruct the tree topology with standard distance-based algorithms. The architecture and its high degree of parameter sharing allow us to apply the same network to alignments of arbitrary size, both in the number of sequences and in their length. We evaluate our network Phyloformer on two types of simulations and find that its accuracy matches that of a Maximum Likelihood method on datasets that resemble training data, while being significantly faster.

evolutionary biology↗

bioRxiv2022

Evaluation of methods to detect shifts in directional selection at the genome scale

Identifying the footprints of selection in coding sequences can inform about the importance and function of individual sites. Analyses of the ratio of non-synonymous to synonymous sub-stitutions (dN /dS) have been widely used to pinpoint changes in the intensity of selection, but cannot distinguish them from changes in the direction of selection, i.e., changes in the fitness of specific amino acids at a given position. A few methods that rely on amino acid profiles to detect changes in directional selection have been designed, but their performance have not been well characterized. In this paper, we investigate the performance of 6 of these methods. We evaluate them on simulations along empirical phylogenies in which transition events have been annotated, and compare their ability to detect sites that have undergone changes in the direction or intensity of selection to that of a widely used dN /dS approach, codemls branch-site model A. We show that all methods have reduced performance in the presence of biased gene conversion but not CpG hypermutability. The best profile method, Pelican, a new implementation of [Tamuri et al., 2009], performs as well as codeml in a range of conditions except for detecting relaxations of selection, and performs better when tree length increases, or in the presence of persistent positive selection. It is fast, enabling genome-scale searches for site-wise changes in the direction of selection associated with phenotypic changes.

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

Adaptation to host cell environment during experimental evolution of Zika virus

Zika virus (ZIKV) infection can cause important developmental and neurological defects in Humans. Type I/III interferon responses control ZIKV infection and pathological processes, yet the virus has evolved various mechanisms to defeat these host responses. Here, we established a pipeline to delineate at high-resolution the genetic evolution of ZIKV in a controlled host cell environment. We uncovered that serially passaged ZIKV acquired increased infectivity and simultaneously developed a resistance to TLR3-induced restriction. We built a mathematical model that suggests that the increased infectivity is due to a reduced time-lag between infection and viral replication. We found that this adaptation is cell-type specific, suggesting that different cell environments may drive viral evolution along different routes. Deep-sequencing of ZIKV populations pinpointed mutations whose increased frequencies temporally coincide with the acquisition of the adapted phenotype. We functionally validated S455L, a substitution in ZIKV envelope (E) protein, recapitulating the adapted phenotype. Its positioning on the E structure suggests a putative function in protein refolding/stability. Taken together, our results uncovered ZIKV adaptations to the cellular environment leading to accelerated replication onset coupled with resistance to TLR3-induced antiviral response. Our work provides insights into Zika virus adaptation to host cells and immune escape mechanisms.

microbiology↗

bioRxiv2020

Relative time constraints improve molecular dating

Dating the tree of life is central to understanding the evolution of life on Earth. Molecular clocks calibrated with fossils represent the state of the art for inferring the ages of major groups. Yet, other information on the timing of species diversification can be used to date the tree of life. For example, horizontal gene transfer events and ancient coevolutionary relationships such as (endo)symbioses can imply temporal relationships between two nodes in a phylogeny (Davin et al. 2018). These alternative sources of temporal constraints can be particularly helpful when the geological record is sparse, e.g. for microorganisms, which represent the vast majority of extant and extinct biodiversity. Here, we present a new method to combine fossil calibrations and relative age constraints to estimate chronograms. We provide an implementation of relative age constraints in RevBayes (Hohna et al. 2016) that can be combined in a modular manner with the wide range of molecular dating methods available in the software. We use both realistic simulations and empirical datasets of 40 Cyanobacteria and 62 Archaea to evaluate our method. We show that the combination of relative age constraints with fossil calibrations significantly improves the estimation of node ages.

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

Treerecs: an integrated phylogenetic tool, from sequences to reconciliations

MotivationGene and species tree reconciliation methods are used to interpret gene trees, root them and correct uncertainties that are due to scarcity of signal in multiple sequence alignments. So far, reconciliation tools have not been integrated in standard phylogenetic software and they either lack performance on certain functions, or usability for biologists. ResultsWe present Treerecs, a phylogenetic software based on duplication-loss reconciliation. Treerecs is simple to install and to use. It is fast and versatile, has a graphic output, and can be used along with methods for phylogenetic inference on multiple alignments like PLL and Seaview. AvailabilityTreerecs is open-source. Its source code (C++, AGPLv3) and manuals are available from https://project.inria.fr/treerecs/ Contacteric.tannier@inria.fr or david.parsons@inria.fr

bioinformatics↗