Biology subjects

Pennell, M.

Publications and source records attributed to Pennell, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Unicellular and Multicellular Modes of Selection Impose Distinct Constraints on Cellular Phenotype Evolution

Single-cell sequencing data have revealed that cellular phenotypes, such as gene expression states, are often low-dimensional, suggesting that cellular variation may arise from combinations of a smaller set of gene expression programs. A genome therefore defines a repertoire of cellular phenotypes that can be configured through different combinations of programs. However, organisms vary in how much of this repertoire is exposed to selection. In unicellular organisms, different phenotypes are often expressed across environments or life-cycle stages, so selection in a given context acts primarily through the phenotype expressed there. In multicellular organisms, multiple phenotypes can coexist within an individual and contribute jointly to fitness. Here, we use a geometric model to ask how selection acting through cellular phenotypes separately or jointly constrains the ability of a shared genome to evolve and maintain differentiated phenotypes across multiple functional demands. We vary the number of functional demands and how many corresponding phenotypes contribute jointly to fitness. We find similar evolutionary outcomes when demands are weakly divergent. Under strongly divergent demands, however, selection on one phenotype at a time leads to reduced differentiation as demands accumulate, even when sufficient programs are available. As more phenotypes contribute jointly to fitness, differentiation and performance improve. When all phenotypes contribute jointly, differentiation is maintained until demands outnumber programs. Our results suggest that how cellular phenotypes are organized in time and space can impose distinct constraints on the evolution of differentiation from a shared genome.

evolutionary biology↗

bioRxiv2026

Gene conversion facilitates rapid evolution of inversions across avian immunoglobulin loci

The genomic architecture of immunoglobulin (IG) loci in birds has received remarkably little attention, despite their relevance to infectious disease susceptibility. One of the few exceptions is the domestic chicken, which has been found to use a completely different mechanism to generate a diverse IG repertoire than most vertebrates; rather than relying on V(D)J recombination, chickens primarily use somatic gene conversion. Whether this is true of all birds has remained unknown and untestable at scale until now. And importantly, it is not known how this alternative mechanism for antibody generation shapes, and is shaped by, genome evolution in birds. Leveraging IG locus annotations from 122 bird species generated through the Vertebrate Genomes Project and 17 species from the California Conservation Genomics Project, we show that avian IGH loci display a striking, previously unreported architecture of recurrent inverted duplications that generate direct and inverted copies of the same repeat unit, found in no other vertebrate lineage. Inversion density varies considerably across species, and population-level analyses reveal that these inversions evolve rapidly. We propose a model in which these inversions are actively maintained because they continuously replenish a pool of highly similar pseudogenes that serve as donors for somatic gene conversion, substituting for the large functional V gene repertoires other vertebrates use to generate IG diversity. This model makes a direct prediction: IGH loci should harbor few functional genes and many pseudogenes, while IGL loci, which typically lack this inversion architecture, should show the opposite pattern. Our cross-species analysis confirms this. To test the model at the level of the expressed repertoire, we generated paired whole-genome and Iso-seq data from a single wild-caught Red-winged Blackbird. Consistent with our predictions, a single terminal IGLV gene is diversified through gene conversion from surrounding pseudogenes, while IGH carries a large donor pool at which we also detect gene conversion. Together, these findings reveal that the molecular evolution of IG in birds is governed by fundamentally different constraints and processes than in the rest of known vertebrates.

evolutionary biology↗

bioRxiv2026

Disruption of a CCR5-like immunoglobulin gene is linked to plague susceptibility in black-footed ferrets

Black-footed ferrets (Mustela nigripes) are among the worlds most endangered mammals and remain highly vulnerable to sylvatic plague caused by Yersinia pestis, yet the genetic basis of this susceptibility has remained unknown. Current conservation strategies rely on vaccination of captive-bred animals and large-scale flea control with insecticides, approaches that are costly, labor-intensive, and difficult to implement across the species natural range. Several closely related mustelid species, including the domestic ferret, are substantially more resistant to plague, providing an opportunity to identify naturally evolved immune mechanisms through comparative immunogenomics. Here we identify a conserved class of immunoglobulin lambda variable genes encoding unusually long antigen-binding loops with CCR5-mimicking sequence features that are widespread among Caniformia species. Because CCR5 has been implicated in host interactions with Yersinia species, we hypothesized that antibodies encoded by these germline genes contribute to plague resistance through receptor-like molecular mimicry. Consistent with this hypothesis, we show that these genes are under strong purifying selection in mustelids, are actively expressed in antibody repertoires, and monoclonal antibodies encoded by them reduced intracellular Y. pestis survival in macrophages. In contrast, all analyzed black-footed ferrets carried a frameshifting deletion resulting in loss of gene expression. These findings identify a naturally disrupted germline antibody gene as a candidate determinant of plague susceptibility in black-footed ferrets, demonstrating that variation in germline immunoglobulin genes can influence susceptibility to a lethal infectious disease. Ultimately, these findings lay the groundwork for genetically informed conservation management and the development of new antibody-based anti-plague strategies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/734856v2_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@19d8ac1org.highwire.dtl.DTLVardef@a37aorg.highwire.dtl.DTLVardef@1ecd1e0org.highwire.dtl.DTLVardef@c6fd67_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2026

Convergent genome- and gene-level constraints shape repeated environmental adaptation in grasses

Grasses (Poaceae) dominate terrestrial ecosystems and sustain global food security, yet the genomic principles enabling their repeated adaptation to extreme environments remain unresolved. Combining dense phylogenomic sampling, global environmental data, and genomic large language models (gLLMs), we characterize the mutational targets underlying environmental adaptation across 707 genomes from 569 species spanning 17 climate zones. We identify 19-30 phylogenetically independent transitions into extreme temperature, water, and soil environments, accompanied by convergent shifts in genome-scale molecular properties, including the Nitrogen-to-Carbon balance and the biosynthetic cost of the proteome. Our gLLMs-informed phylogenetic mixed modeling framework identifies 330 genes that repeatedly underlie distinct axes of environmental adaptation, highlighting the importance of protein modification and localization within extracellular and organellar compartments. Overlaying independent convergent adaptation tests identifies 17 high-confidence candidates for further characterization. Together, our results show that grass adaptation is canalized by layered constraints at genome-wide and gene-specific scales, producing predictable evolutionary trajectories.

evolutionary biology↗

bioRxiv2026

Collective behavior drives diversification across the tree of ray-finned fishes

Trait evolution and lineage diversification are thought to be driven by selection on the phenotypes of individuals. However, many important phenotypes are only manifested when individuals interact. Collective behavior is one such phenotype. During collective behavior, interactions among individuals lead to emergent group-level phenotypes that can govern individual fitness. Yet, whether collective behaviors exhibit similar patterns of trait evolution to individual traits and whether these group phenotypes have influenced macroevolutionary diversification is unknown. In ray-finned fishes, the most species-rich of all vertebrate classes, many species exhibit striking displays of collective behavior in the form of collective movement. Here, we use state-of-the-art machine learning methods for text and image analysis to quantify collective movement phenotypes for 2,839 Actinopterygian fishes, comprising nearly 10% of all extant species. We find that collective movement is highly evolutionarily stable in some clades and extremely labile in others, exhibiting over 300 evolutionary transitions. Consistent with theoretical predictions, collective movement is associated with more patchy diets and higher predation pressure. Finally, we find that the evolution of collective movement coincides with an increase in net macroevolutionary diversification rate. Our analyses reveal that collective behavior is deeply intertwined with ecological and evolutionary dynamics across a vast phylogenetic scale, unlocking new questions about the role of collective phenotypes in shaping the diversity of life on Earth. SignificanceCollective behavior is thought to contribute to the fitness of individuals that live in groups, but we still do not understand why some species exhibit collective behavior and others do not. We developed machine learning methods to quantify collective phenotypes for thousands of species spanning the phylogeny of ray-finned fishes and found collective behavior to be widespread and consistently correlated with experiencing higher predation pressure and foraging for grouping prey. Lineages that evolve collective movement show a higher net diversification rate. Our results illustrate that collective behavior is foundational to the broad-scale patterns of evolution and diversification in the largest vertebrate class.

evolutionary biology↗

bioRxiv2026

Phenotypic complexity determines the predictability of molecular convergence

Whether the outcomes of evolution are predictable remains a central question in evolutionary biology. Convergent evolution, in which similar phenotypes arise independently in distinct lineages, is often interpreted as evidence of predictability under shared selective pressures. However, empirical studies have shown that convergent phenotypes can exhibit similar or disparate molecular bases, and no general frame-work predicts when convergence should occur. Here, we show that phenotypic complexity determines the extent of convergence under shared conditions. Using a deliberately minimal abstraction linking genotype and phenotype and evolutionary simulations, we investigated how regulatory gene expression programs evolve under selection for shared phenotypic optima. We find that phenotypes of low complexity can be produced by diverse regulator expression programs, permitting phenotypic convergence despite molecular divergence. However, as the complexity of the target phenotype increases, the variety of viable regulatory solutions decreases, funneling independently evolving lineages towards similar expression programs. In this regime, molecular convergence emerges as a predictable consequence of selection acting on finite regulatory repertoires, consistent with the phenomenon of "deep homology" observed in structures like eyes and limbs. Allowing evolutionary changes in regulator-target interactions relaxes these constraints and enhances divergence in expression between phenotypically convergent species. Together, our results provide general expectations for predicting when phenotypic convergence should be accompanied by convergent genetic mechanisms, and shed light on the causes of convergence in a broad range of phenotypic traits.

evolutionary biology↗

bioRxiv2025

Characterizing and mitigating confounding by unreplicated evolutionary events in phylogenetic regression

When researchers want to test for a direct effect of one attribute on another using interspecific data, they need to account for the confounding influence of other attributes that have phylogenetic structure. Accordingly, a suite of approaches have been developed for this purpose, mostly notably the use of phylogenetic regression. However, it has been shown that such methods can be misled by evolutionary events with large effects. We suggest that one widely-applicable solution to this problem can be found by borrowing both from the statistical genetics literature and by digging into the past of phylogenetic comparative methods. In conventional phylogenetic regression, the phylogenetic variance-covariance matrix is included as a random-effect term; here, we argue that it can be advantageous to also include the leading eigenvectors of this matrix as fixed effects. Building on the recent results of Schraiber et al. (2024), we use mathematical analysis and simulations to show under which scenarios this hybrid strategy is effective. We develop a novel approach for visualizing the contributions of different branches of the phylogeny to the eigenvectors of the variance-covariance matrix. We also illustrate how quantile-quantile plots can be used to assess whether the phylogenetic structure has been effectively controlled. We then apply the mixed approach to investigate the co-evolution of gene expression levels of different genes across the phylogeny of cichlid fishes; we show that including the leading eigenvectors likely reduces the false positive rate. To facilitate the use of our visualization approach and of including eigenvectors in phylogenetic regression models, we present a new R package called EIGER. We argue that the approaches we explore here can both help address the statistical problems that arise from large, historical events and more generally, provide richer insights into the nature of phylogenetic confounding.

evolutionary biology↗

bioRxiv2025

Observational epidemiological studies can mitigate genetic confounding with the genetic relatedness matrix

Observational studies are commonly used in psychology and epidemiology to identify risk factors correlated with health outcomes. However, these studies are vulnerable to confounding when shared genetic variation influences both the putative risk factor and outcome. Researchers have historically controlled for this type of genetic confounding using polygenic scores, but these scores are often noisy and biased estimators of a traits genetic component. Here, we develop a method that leverages a genetic relatedness matrix (GRM) to control genetic confounding when testing for non-genetic risk factors. In simulations, we find that our method outperforms existing approaches, particularly at sample sizes that are large by the standards of much human research but smaller than datasets often used in human genetics. We also demonstrate that existing methods are susceptible to poor GWAS portability, whereas our method is inherently robust to such concerns. Finally, we apply our method to the UK Biobank to re-analyze social risk factors for health outcomes in previously understudied cohorts. Significance statementLarge observational datasets are often used in epidemiology or social sciences to identify risk factors associated with health outcomes. However, these studies can be misleading when the putative risk factor and outcome are both complex genetic traits: a correlated genetic basis can create spurious associations between a putative risk factor and outcome. Existing approaches to address this problem typically rely on GWAS summary statistics, which require biobank-scale datasets. In this work, we introduce an alternative method that uses a genetic relatedness matrix (GRM) to control for genetic confounding directly. We show that our approach is closely related to existing methods but outperforms them in smaller datasets, making it an especially valuable tool for researchers who work on understudied traits and populations.

genetics↗

bioRxiv2025

Biophysical constraints on mRNA decay rates shape macroevolutionary divergence in steady-state abundances

Interspecific comparisons of cell-type-specific gene expression levels can provide information about the evolutionary processes that drove divergence between species. From these comparisons, it is now evident that the predominant mode of gene expression evolution has been stabilizing selection, both on the steady-state (mean) protein levels as well as on the mRNA levels with additional lineage-specific shifts resulting from directional selection. However, as all previous work has used bulk RNA measurements, it has been impossible to determine which of the many cellular processes that contribute to mean abundances are highly constrained and which are more evolutionary labile. Assessing this is further complicated by the expectation that components of complex systems will evolve over time independent of changes in the selective regime so long as the net output of a system (i.e., mean expression) remains near the evolutionary optima. This process, known as evolutionary systems drift (ESD), has been frequently invoked as a non-adaptive explanation for changes in cellular phenotypes but has never been quantitatively tested or accounted for in any statistical test for selective constraints. Here, we develop a new paradigm that addresses both of these open problems simultaneously. Using single-cell expression data and biophysical models, we estimate mRNA transcriptional bursting rates, splicing rates, and decay rates across multiple vertebrate species. We then derive new mathematical results that describe how these various biophysical parameters are expected to co-evolve under ESD and then test whether we need additional evolutionary constraints to explain the divergences in these parameters. We find evidence that the biophysical parameters are indeed evolving in a coordinated manner as predicted by ESD and that there are additional strong constraints on transcriptional bursting, likely as a consequence of selection to reduce noise in expression. More broadly, this work opens up a whole new approach for studying the evolutionary dynamics of complex cellular systems.

evolutionary biology↗

bioRxiv2025

GeneCAD: Plant Genome Annotation with a DNA Foundation Model

Accurate genome annotation is fundamental to biological discovery, yet identifying gene structures directly from DNA sequence remains a major challenge in complex genomes. We introduce GeneCAD, a sequence-only framework that predicts biologically coherent gene models without requiring species-matched transcriptomic or proteomic evidence. GeneCAD integrates lineage-specific DNA representations from the PlantCAD2 foundation model with a transformer encoder and a chromosome-scale conditional random field (CRF) to enforce structural constraints, such as splice-phase and feature order. To ensure high-quality supervision, we implement a curation strategy using a sequence-based masked-motif score to filter reference transcripts. As a primary validation across diverse angiosperms, including a complex allotetraploid, GeneCAD improves transcript F1 by approximately 9% over current tools like Helixer and BRAKER3, while sharpening boundary precision and achieving a best-in-class recovery of 86% of classical coding sequences. Furthermore, we demonstrate the frameworks modularity by adapting it to animal lineages through the substitution of the underlying DNA foundation model. While the long introns of vertebrates challenge full transcript reconstruction, the model remains highly effective at identifying individual exons. By connecting evolutionary signals with structured decoding, GeneCAD provides a versatile and scalable solution for high-fidelity genome annotation across the Tree of Life. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/685877v3_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@5dd348org.highwire.dtl.DTLVardef@1da37daorg.highwire.dtl.DTLVardef@188ef17org.highwire.dtl.DTLVardef@108da0c_HPS_FORMAT_FIGEXP M_FIG C_FIG Lay SummaryIdentifying where genes are located within a genome is a major challenge in biology, especially in plants with large and repetitive DNA. Current methods often rely on expensive laboratory data or struggle to find genes in "noisy" regions. We developed GeneCAD, a deep-learning tool that identifies genes using only the raw DNA sequence. By using AI models that recognize patterns shaped by millions of years of evolution, GeneCAD predicts gene structures with high accuracy and consistency. Our tests show that GeneCAD outperforms existing tools in plants and can be easily adapted for use in other species, including animals. By removing the need for costly lab experiments, GeneCAD provides an affordable way for researchers to quickly map the genomes of everything from rare wild plants to essential food crops.

bioinformatics↗

bioRxiv2025

Discovery of additional ancient genome duplications in yeasts

Whole genome duplication (WGD) has had profound macroevolutionary impacts on diverse lineages1,2, preceding adaptive radiations in vertebrates3-5, teleost fish6,7, and angiosperms8,9. In contrast to the many known ancient WGDs in animals10,11 and especially plants12-14, we are aware of evidence for only four in fungi15,16. The oldest of these occurred [~]100 million years ago (mya) and is shared by [~]60 extant Saccharomycetales species17,18, including the bakers yeast Saccharomyces cerevisiae (Fig. 1). Notably, this is the only known ancient WGD in the yeast subphylum Saccharomycotina. The dearth of ancient WGD events in fungi remains a mystery15. Some studies have suggested that fungal lineages that experience chromosome19 and genome15 duplication quickly go extinct, leaving no trace in the genomic record, while others contend that the lack of known WGD is due to an absence of data15,16. Under the second hypothesis, additional sampling and deeper sequencing of fungal genomes should lead to the discovery of more WGD events. Coupling hundreds of recently published genomes from nearly every described Saccharomycotina species with three additional long-read assemblies, we discovered three novel WGD events. While the functions of retained duplicate genes originating from these events are broad, they bear many similarities to the well-known WGD that occurred in the Saccharomycetales17. Our results suggest that WGD may be a more common evolutionary force in fungi than previously believed. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/673279v1_fig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@6a8f47org.highwire.dtl.DTLVardef@1e4a790org.highwire.dtl.DTLVardef@b4e0eforg.highwire.dtl.DTLVardef@11b7795_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Three additional whole genome duplications in Saccharomycotina yeasts. Rates of gene duplication are mapped onto Saccharomycetales (left) and Dipodascales (right) phylogenies. Whole genome duplications indicated along the lineages in which they are predicted to occur. Trees have been pruned for visualization purposes. C_FIG HighlightsO_LIEvidence for three whole genome duplications (WGDs) in Dipodascales yeasts C_LIO_LIImpacts of WGD are broad but bear many similarities to the known WGD in Saccharomycetales yeasts C_LIO_LIDuplicates with many protein-protein interactions are more likely to be retained over long timescales C_LIO_LIWGD in fungi is likely underreported C_LI

evolutionary biology↗

bioRxiv2025

PlantCAD2: A Long-Context DNA Language Model for Cross-Species Functional Annotation in Angiosperms

Understanding how DNA sequence encodes biological function remains a fundamental challenge in biology. Flowering plants (angiosperms), the dominant terrestrial clade, exhibit maximal biochemical complexity, extraordinary species diversity (over 100,000 species), relatively recent origins ([~]160 million years), [~]200-fold variation in genome size and relative compact coding regions compared with other eukaryotes. These features present both a unique challenge and opportunity for pre-training DNA language models to understand plant-specific evolutionary conservation, regulatory architectures and genomic functions. Here, we introduce PlantCAD2, an extended context, plant-specific DNA language model with single-nucleotide resolution, pre-trained on 65 angiosperm genomes, together with a series of public benchmarks for evaluation. Comprehensive zero-shot testing shows that PlantCAD2 (676 million parameters) efficiently captures evolutionary conservation, surpassing the 7-billion-parameter Evo2 model in 10 of 12 tasks. With parameter-efficient fine-tuning, PlantCAD2 also outperforms the 1-billion-parameter AgroNT across seven cross-species tasks including chromatin accessible region, gene expression and protein translation. Moreover, its 8,192bp context window substantially improves accessible chromatin prediction in large genomes such as maize (AUPRC increasing from 0.587 to 0.711), underscoring the importance of long-range context for modeling distal regulation. Together, these results establish PlantCAD2 as a powerful, efficient, and versatile foundation model for plant genomics, enabling accurate genome annotation across diverse species.

bioinformatics↗

bioRxiv2025

Comparative analysis of mammalian adaptive immune loci revealed spectacular divergence and common genetic patterns

Adaptive immune responses are mediated by the production of adaptive immune receptors, antibodies and T-cell receptors, that bind antigens, thus causing their neutralization. Unlike other proteins, adaptive immune receptors are not fully encoded in the germline genome and result from a complex of somatic processes collectively called V(D)J recombination affecting germline immunoglobulin (IG) and T-cell receptor (TR) loci consisting of template genes. While various existing studies report extreme diversity of antibodies and T-cell receptors, little is known about the diversity of germline IG and TR loci. To overcome this gap, the first comparative analysis of full-length sequences of IG/TR loci across 44 mammalian species from 13 taxonomic orders was performed. First, germline genes counts were shown to correlate in IGH/IGL and TRA/TRB and anticorrelate in IGK/IGL, possibly indicating co-evolution between corresponding chains. Second, structures of IG/TR loci were analyzed, and it was shown that IG/TR loci formed by long arrays of high multiplicity repeats are more common for species that have experienced population bottlenecks. Finally, haplotypes of IG/TR loci with little or no sequence similarity within a species were found, suggesting that they may have a limited potential for homologous recombination. These results demonstrate that IG/TR loci are rapidly evolving genomic regions whose structural variation is shaped by the population history of the species and open new perspectives for immunogenomics studies.

genomics↗

bioRxiv2025

A general evolutionary model for the emergence of novel characters from serial homologs

Repeated structures are widespread across multicellular organisms, such as vertebrae, segments, and cell types. These structures, also known as serial homologs, share ancestral states and developmental underpinnings yet also provide the materials for novel adaptive phenotypes. It remains largely unclear why some repeated structures diverge quickly, while others remain constrained. One reason for this uncertainty is the lack of a generalized model for the evolution of repeated structures under different scenarios that links developmental, genetic, and selective constraints to expected and observed patterns of evolution. Here, we introduce a model that incorporates key structural features of gene regulatory networks and selection and investigate how responses to multivariate selection depend on developmental constraints. We show structural features of developmental networks determine when repeats can respond independently to selection and when divergence is limited. Simulations recover broad expectations of phenotypic evolution under directional selection inferred from empirical data. We further show that, in the face of fluctuating selection, strong developmental constraints lead to reduced fitness over time and attenuated fitness fluctuations. Together, our results provide general insights into the principles of evolution of repeated structures and offer a modeling framework for the evolution of a broad range of key phenotypic characters.

evolutionary biology↗

bioRxiv2024

Unveiling Assembly Errors in Immunoglobulin Loci: A Comprehensive Evaluation of Long-read Genome Assemblies Across Vertebrates

Long-read sequencing technologies have revolutionized genome assembly producing near-complete chromosome assemblies for numerous organisms, which are invaluable to research in many fields. However, regions with complex repetitive structure continue to represent a challenge for genome assembly algorithms, particularly in areas with high heterozygosity. Robust and comprehensive solutions for the assessment of assembly accuracy and completeness in these regions do not exist. In this study we focus on the assembly of biomedically important antibody-encoding immunoglobulin (IG) loci, which are characterized by complex duplications and repeat structures. High-quality full-length assemblies for these loci are critical for resolving haplotype-level annotations of IG genes, without which, functional and evolutionary studies of antibody immunity across vertebrates are not tractable. To address these challenges, we developed a pipeline, "CloseRead", that generates multiple assembly verification metrics for analysis and visualization. These metrics expand upon those of existing quality assessment tools and specifically target complex and highly heterozygous regions. Using CloseRead, we systematically assessed the accuracy and completeness of IG loci in publicly available assemblies of 74 vertebrate species, identifying problematic regions. We also demonstrated that inspecting assembly graphs for problematic regions can both identify the root cause of assembly errors and illuminate solutions for improving erroneous assemblies. For a subset of species, we were able to correct assembly errors through targeted reassembly. Together, our analysis demonstrated the utility of assembly assessment in improving the completeness and accuracy of IG loci across species.

genomics↗

bioRxiv2024

Convergent expansions of keystone gene families drive metabolic innovation in a major eukaryotic clade

Many remarkable innovations have repeatedly occurred across vast evolutionary distances. When convergent traits emerge on the tree of life, they are sometimes driven by the same underlying gene families, while other times many different gene families are involved. Conversely, a gene family may be repeatedly recruited for a single trait or many different traits. To understand the general rules governing convergence at both genomic and phenotypic levels, we systematically tested associations between 56 binary metabolic traits and gene count in 14,710 gene families from 993 species of Saccharomycotina yeasts. Using a recently developed phylogenetic approach that reduces spurious correlations, we discovered that gene family expansion and contraction was significantly linked to trait gain and loss in 45/56 (80%) of traits. While 601/746 (81%) of significant gene families were associated with only one trait, we also identified several keystone gene families that were significantly associated with up to 13/56 (23%) of all traits. These results indicate that metabolic innovations in yeasts are governed by a narrow set of major genetic elements and mechanisms.

evolutionary biology↗

bioRxiv2024

Genetic and selective constraints on the optimization of gene product diversity

RNA and protein expressed from the same gene can have diverse isoforms due to various post-transcriptional and post-translational modifications. For the vast majority of alternative isoforms, It is unknown whether they are adaptive or simply biological noise. As we cannot experimentally probe the function of each isoform, we can ask whether the distribution of isoforms across genes and across species is consistent with expectations from different evolutionary processes. However, there is currently no theoretical framework that can generate such predictions. To address this, we developed a mathematical model where isoform abundances are determined collectively by cis-acting loci, trans-acting factors, gene expression levels, and isoform decay rates to predict isoform abundance distributions across species and genes in the face of mutation, genetic drift, and selection. We found that factors beyond selection, such as effective population size and the number of cis-acting loci, significantly influence evolutionary outcomes. Notably, suboptimal phenotypes are more likely to evolve when the population is small and/or when the number of cis-loci is large. We also explored scenarios where modification processes have both beneficial and detrimental effects, revealing a non-monotonic relationship between effective population size and optimization, demonstrating how opposing selection pressures on cis- and trans-acting loci can constrain the optimization of gene product diversity. As a demonstration of the power of our theory, we compared the expected distribution of A-to-I RNA editing levels in coleoids and found this to be largely consistent with non-adaptive explanations.

evolutionary biology↗

bioRxiv2024

Phylogenetic analysis reveals how selection and mutation shape the coevolution of mRNA and protein abundances

The regulatory mechanisms that shape mRNA and protein abundances are intensely studied. Much less is known about the evolutionary processes that shape the relationship between these two levels of gene expression. To disentangle the contributions of mutational and selective processes, we derive a novel phylogenetic model and fit it to multi-species data from mammalian skin tissue. We find that over macroevolutionary time: 1) there has been strong stabilizing selection on protein abundances; 2) mutations impacting mRNA abundances have minimal influence on protein abundances; 3) mRNA abundances are under selection to track protein abundances, and 4) mRNA abundances adapt more quickly than protein abundances due to increased mutational opportunity. We find additional support for these findings by comparing gene-specific parameter estimates from our model to human functional genomic data. More broadly, our new phylogenetic approach provides a foundation for testing hypotheses about the processes that led to divergence in gene expression.

evolutionary biology↗