The evolutionary scaling of cellular traits imposed by the drift barrier

Population size interacts with reproductive longevity to shape the germline mutation rate

Mutation rates vary across the tree of life by many orders of magnitude, with fewer mutations occurring each generation in species that reproduce quickly and maintain large effective population sizes. A compelling explanation is that large effective population sizes facilitate selection against weakly deleterious "mutator alleles" such as variants that modulate cell division or interfere with the molecular efficacy of DNA repair. However, while the fidelity of a single cell division largely determines microorganisms' mutation rates, the relationship of the mutation rate to the molecular determinants of DNA damage and repair is more complex in multicellular species with long generation times. Since long generations leave more time for mutations to accrue each generation, we posit that a long generation time likely amplifies the fitness consequences of any damage agent or DNA repair defect that creates extra mutations in the spermatogonia or oocytes. This leads to the counterintuitive prediction that the species with the highest germline mutation rates per generation are also the species with most effective mechanisms for avoiding and repairing mutations in their reproductive cells. Consistent with this, we show that mutation rates in the reproductive cells are inversely correlated with generation time; in contrast, the number of germline mutations that occur during prepuberty development trends weakly upward as generation time increases. Our results parallel recent findings that the longest-lived species have the lowest mutation rates in adult somatic tissues, potentially due to selection to keep the lifetime mutation load below a harmful threshold.

Constraints on the optimization of gene product diversity

RNA and proteins can have diverse isoforms due to post-transcriptional and post-translational modifications. A fundamental question is whether these isoforms are mostly beneficial or the result of noisy molecular processes. To assess the plausibility of these explanations, we developed mathematical models depicting different regulatory architectures and investigated isoform evolution under multiple population genetic regimes. We found that factors beyond selection, such as effective population size and the number of cis-acting loci, significantly influence evolutionary outcomes. We found that sub-optimal phenotypes are more likely to evolve when populations are small and/or when the number of cis-loci is large. We also discovered that opposing selection on cis- and trans-acting loci can constrain adaptation, leading to a non-monotonic relationship between effective population size and optimization. More generally, our models provide a quantitative framework for developing statistical tests to analyze empirical data; as a demonstration of this, we analyzed A-to-I RNA editing levels in coleoids and found these to be largely consistent with non-adaptive explanations.

The divergence of mean phenotypes under persistent Gaussian selection

Although multigenic traits are often assumed to be under some form of stabilizing selection, numerous aspects of the population-genetic environment can cause mean phenotypes to deviate from presumed optima, often in ways that effectively transform the fitness landscape to one of directional selection. Focusing on an asexual population, we consider the ways in which such deviations scale with the relative power of selection and genetic drift, the number of linked genomic sites, the magnitude of mutation bias, and the location of optima with respect to possible genotypic space. Even in the absence of mutation bias, mutation will influence evolved mean phenotypes unless the optimum happens to coincide exactly with the mean expected under neutrality. In the case of directional mutation bias and large numbers of selected sites, effective population sizes (Ne) can be dramatically reduced by selective interference effects, leading to further mismatches between phenotypic means and optima. Situations in which the optimum is outside or near the limits of possible genotypic space (e.g. a half-Gaussian fitness function) can lead to particularly pronounced gradients of phenotypic means with respect to Ne, but such gradients can also occur when optima are well within the bounds of attainable phenotypes. These results help clarify the degree to which mean phenotypes can vary among populations experiencing identical mutation and selection pressures but differing in Ne, and yield insight into how the expected scaling relationships depend on the underlying features of the genetic system.

Population size interacts with reproductive longevity to shape the germline mutation rate

Mutation rates vary across the tree of life by many orders of magnitude, with lower mutation rates in species that reproduce quickly and maintain large effective population sizes. A compelling explanation for this trend is that large effective population sizes facilitate selection against weakly deleterious "mutator alleles" such as variants that interfere with the molecular efficacy of DNA repair. However, in multicellular organisms, the relationship of the mutation rate to DNA repair efficacy is complicated by variation in reproductive age. Long generation times leave more time for mutations to accrue each generation, and late reproduction likely amplifies the fitness consequences of any DNA repair defect that creates extra mutations in the sperm or eggs. Here, we present theoretical and empirical evidence that a long generation time amplifies the strength of selection for low mutation rates in the spermatocytes and oocytes. This leads to the counterintuitive prediction that the species with the highest germline mutation rates per generation are also the species with most effective mechanisms for DNA proofreading and repair in their germ cells. In contrast, species with different generation times accumulate similar mutation loads during embryonic development. Our results parallel recent findings that the longest-lived species have the lowest mutation rates in adult somatic tissues, potentially due to selection to keep the lifetime mutation load below a harmful threshold.

Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these 2 fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we lay out a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., genome-wide association studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur analytically and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study, we re-examine an analysis testing for coevolution of expression levels between genes across a fungal phylogeny and show that including eigenvectors of the covariance matrix as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.

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.

The substrate quality of CK2 target sites has a determinant role on their function and evolution

Most biological processes are regulated by signaling modules that bind to short linear motifs. For protein kinases, substrates may have full or only partial matches to the kinase recognition motif, a property known as "substrate quality." However, it is not clear whether differences in substrate quality represent neutral variation or if they have functional consequences. We examine this question for the kinase CK2, which has many fundamental functions. We show that optimal CK2 sites are phosphorylated at maximal stoichiometries and found in many conditions, whereas minimal substrates are more weakly phosphorylated and have regulatory functions. Optimal CK2 sites tend to be more conserved, and substrate quality is often tuned by selection. For intermediate sites, increases or decreases in substrate quality may be deleterious, as we demonstrate for a CK2 substrate at the kinetochore. The results together suggest a strong role for substrate quality in phosphosite function and evolution. A record of this paper's transparent peer review process is included in the supplemental information.

Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these two fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we derive a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., Genome-Wide Association Studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur using analytical theory and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate this by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study of this, we re-examine an analysis testing for co-evolution of expression levels between genes across a fungal phylogeny, and show that including covariance matrix eigenvectors as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.

Scaling of Protein Function across the Tree of Life

Scaling laws are a powerful way to compare genomes because they put all organisms onto a single curve and reveal nontrivial generalities as genomes change in size. The abundance of functional categories across genomes has previously been found to show power law scaling with respect to the total number of functional categories, suggesting that universal constraints shape genomic category abundance. Here, we look across the tree of life to understand how genome evolution may be related to functional scaling. We revisit previous observations of functional genome scaling with an expanded taxonomy by analyzing 3,726 bacterial, 220 archaeal, and 79 unicellular eukaryotic genomes. We find that for some functional classes, scaling is best described by multiple exponents, revealing previously unobserved shifts in scaling as genome-encoded protein annotations increase or decrease. Furthermore, we find that scaling varies between phyletic groups at both the domain and phyla levels and is less universal than previously thought. This variability in functional scaling is not related to taxonomic phylogeny resolved at the phyla level, suggesting that differences in cell plan or physiology outweigh broad patterns of taxonomic evolution. Since genomes are maintained and replicated by the functional proteins encoded by them, these results point to functional degeneracy between taxonomic groups and unique evolutionary trajectories toward these. We also find that individual phyla frequently span scaling exponents of functional classes, revealing that individual clades can move across scaling exponents. Together, our results reveal unique shifts in functions across the tree of life and highlight that as genomes grow or shrink, proteins of various functions may be added or lost.

Evolutionary Insights from a Large-Scale Survey of Population-Genomic Variation

The field of genomics has ushered in new methods for studying molecular-genetic variation in natural populations. However, most population-genomic studies still rely on small sample sizes (typically, <100 individuals) from single time points, leaving considerable uncertainties with respect to the behavior of relatively young (and rare) alleles and, owing to the large sampling variance of measures of variation, to the specific gene targets of unusually strong selection. Genomic sequences of ∼1,700 haplotypes distributed over a 10-year period from a natural population of the microcrustacean Daphnia pulex reveal evolutionary-genomic features at a refined scale, including previously hidden information on the behavior of rare alleles predicted by recent theory. Background selection, resulting from the recurrent introduction of deleterious alleles, appears to strongly influence the dynamics of neutral alleles, inducing indirect negative selection on rare variants and positive selection on common variants. Temporally fluctuating selection increases the persistence of nonsynonymous alleles with intermediate frequencies, while reducing standing levels of variation at linked silent sites. Combined with the results from an equally large metapopulation survey of the study species, classes of genes that are under strong positive selection can now be confidently identified in this key model organism. Most notable among rapidly evolving Daphnia genes are those associated with ribosomes, mitochondrial functions, sensory systems, and lifespan determination.

Validation of machine learning approach for direct mutation rate estimation

Mutations are the primary source of all genetic variation. Knowledge about their rates is critical for any evolutionary genetic analyses, but for a long time, that knowledge has remained elusive and indirectly inferred. In recent years, parent-offspring comparisons have yielded the first direct mutation rate estimates. The analyses are, however, challenging due to high rate of false positives and no consensus regarding standardized filtering of candidate de novo mutations. Here, we validate the application of a machine learning approach for such a task and estimate the mutation rate for the guppy (Poecilia reticulata), a model species in eco-evolutionary studies. We sequenced 4 parents and 20 offspring, followed by screening their genomes for de novo mutations. The initial large number of candidate de novo mutations was hard-filtered to remove false-positive results. These results were compared with mutation rate estimated with a supervised machine learning approach. Both approaches were followed by molecular validation of all candidate de novo mutations and yielded similar results. The ML method uniquely identified three mutations, but overall required more hands-on curation and had higher rates of false positives and false negatives. Both methods concordantly showed no difference in mutation rates between families. Estimated here the guppy mutation rate is among the lowest directly estimated mutation rates in vertebrates; however, previous research has also found low estimated rates in other teleost fishes. We discuss potential explanations for such a pattern, as well as future utility and limitations of machine learning approaches.

On the Decoupling of Evolutionary Changes in mRNA and Protein Levels

Variation in gene expression across lineages is thought to explain much of the observed phenotypic variation and adaptation. The protein is closer to the target of natural selection but gene expression is typically measured as the amount of mRNA. The broad assumption that mRNA levels are good proxies for protein levels has been undermined by a number of studies reporting moderate or weak correlations between the two measures across species. One biological explanation for this discrepancy is that there has been compensatory evolution between the mRNA level and regulation of translation. However, we do not understand the evolutionary conditions necessary for this to occur nor the expected strength of the correlation between mRNA and protein levels. Here, we develop a theoretical model for the coevolution of mRNA and protein levels and investigate the dynamics of the model over time. We find that compensatory evolution is widespread when there is stabilizing selection on the protein level; this observation held true across a variety of regulatory pathways. When the protein level is under directional selection, the mRNA level of a gene and the translation rate of the same gene were negatively correlated across lineages but positively correlated across genes. These findings help explain results from comparative studies of gene expression and potentially enable researchers to disentangle biological and statistical hypotheses for the mismatch between transcriptomic and proteomic data.

The divergence of mean phenotypes under persistent directional selection

Numerous organismal traits, particularly at the cellular level, are likely to be under persistent directional selection across phylogenetic lineages. Unless all mutations affecting such traits have large enough effects to be efficiently selected in all species, gradients in mean phenotypes are expected to arise as a consequence of differences in the power of random genetic drift, which varies by approximately five orders of magnitude across the Tree of Life. Prior theoretical work examining the conditions under which such gradients can arise focused on the simple situation in which all genomic sites affecting the trait have identical and constant mutational effects. Here, we extend this theory to incorporate the more biologically realistic situation in which mutational effects on a trait differ among nucleotide sites. Pursuit of such modifications leads to the development of semi-analytic expressions for the ways in which selective interference arises via linkage effects in single-effects models, which then extend to more complex scenarios. The theory developed clarifies the conditions under which mutations of different selective effects mutually interfere with each others' fixation and shows how variance in effects among sites can substantially modify and extend the expected scaling relationships between mean phenotypes and effective population sizes.

Evolutionary Insights from a Large-scale Survey of Population-genomic Variation

Results from data on > 1000 haplotypes distributed over a nine-year period from a natural population of the microcrustacean Daphnia pulex reveal evolutionary-genomic features at a refined scale, including key population-genetic properties that are obscured in studies with smaller sample sizes. Background selection, resulting from the recurrent introduction of deleterious alleles, appears to strongly influence the dynamics of neutral alleles, inducing indirect negative selection on rare variants and positive selection on common variants. Fluctuating selection increases the persistence of nonsynonymous alleles with intermediate frequencies, while reducing standing levels of variation at linked silent sites. Combined with the results from an equally large metapopulation survey of the study species, regions of gene structure that are under strong purifying selection and classes of genes that are under strong positive selection in this key species can be confidently identified. Most notable among rapidly evolving Daphnia genes are those associated with ribosomes, mitochondrial functions, sensory systems, and lifespan determination.

Decoupling of evolutionary changes in mRNA and protein levels

Variation in gene expression across lineages is thought to explain much of the observed phenotypic variation and adaptation. The protein is closer to the target of natural selection but gene expression is typically measured as the amount of mRNA. The broad assumption that mRNA levels are good proxies for protein levels has been undermined by a number of studies reporting moderate or weak correlations between the two measures across species. One biological explanation for this discrepancy is that there has been compensatory evolution between the mRNA level and regulation of translation. However, we do not understand the evolutionary conditions necessary for this to occur nor the expected strength of the correlation between mRNA and protein levels. Here we develop a theoretical model for the coevolution of mRNA and protein levels and investigate the dynamics of the model over time. We find that compensatory evolution is widespread when there is stabilizing selection on the protein level, which is true across a variety of regulatory pathways. When the protein level is under directional selection, the mRNA level of a gene and its translation rate of the same gene were negatively correlated across lineages but positively correlated across genes. These findings help explain results from comparative studies of gene expression and potentially enable researchers to disentangle biological and statistical hypotheses for the mismatch between transcriptomic and proteomic studies.

Experimental approaches to study evolutionary cell biology using yeasts

The past century has witnessed tremendous advances in understanding how cells function. Nevertheless, how cellular processes have evolved is still poorly understood. Many studies have highlighted surprising molecular diversity in how cells from diverse species execute the same processes, and advances in comparative genomics are likely to reveal much more molecular diversity than was believed possible until recently. Extant cells remain therefore the product of an evolutionary history that we vastly ignore. Evolutionary cell biology has emerged as a discipline aiming to address this knowledge gap by combining evolutionary, molecular, and cellular biology thinking. Recent studies have shown how even essential molecular processes, such as DNA replication, can undergo fast adaptive evolution under certain laboratory conditions. These developments open new lines of research where the evolution of cellular processes can be investigated experimentally. Yeasts naturally find themselves at the forefront of this research line. Not only do they allow the observation of fast evolutionary adaptation, but they also provide numerous genomic, synthetic, and cellular biology tools already developed by a large community. Here we propose that yeasts can serve as an "evolutionary cell lab" to test hypotheses, principles, and ideas in evolutionary cell biology. We discuss various experimental approaches available for this purpose, and how biology at large can benefit from them.

Endosymbiotic ratchet accelerates divergence after organelle origin: The Paulinella model for plastid evolution: The Paulinella model for plastid evolution

We hypothesize that as one of the most consequential events in evolution, primary endosymbiosis accelerates lineage divergence, a process we refer to as the endosymbiotic ratchet. Our proposal is supported by recent work on the photosynthetic amoeba, Paulinella, that underwent primary plastid endosymbiosis about 124 Mya. This amoeba model allows us to explore the early impacts of photosynthetic organelle (plastid) origin on the host lineage. The current data point to a central role for effective population size (N_e ) in accelerating divergence post-endosymbiosis due to limits to dispersal and reproductive isolation that reduce N_e , leading to local adaptation. We posit that isolated populations exploit different strategies and behaviors and assort themselves in non-overlapping niches to minimize competition during the early, rapid evolutionary phase of organelle integration. The endosymbiotic ratchet provides a general framework for interpreting post-endosymbiosis lineage evolution that is driven by disruptive selection and demographic and population shifts. Also see the video abstract here: https://youtu.be/gYXrFM6Zz6Q.

Evolutionary scaling of maximum growth rate with organism size

Data from nearly 1000 species reveal the upper bound to rates of biomass production achievable by natural selection across the Tree of Life. For heterotrophs, maximum growth rates scale positively with organism size in bacteria but negatively in eukaryotes, whereas for phototrophs, the scaling is negligible for cyanobacteria and weakly negative for eukaryotes. These results have significant implications for understanding the bioenergetic consequences of the transition from prokaryotes to eukaryotes, and of the expansion of some groups of the latter into multicellularity. The magnitudes of the scaling coefficients for eukaryotes are significantly lower than expected under any proposed physical-constraint model. Supported by genomic, bioenergetic, and population-genetic data and theory, an alternative hypothesis for the observed negative scaling in eukaryotes postulates that growth-diminishing mutations with small effects passively accumulate with increasing organism size as a consequence of associated increases in the power of random genetic drift. In contrast, conditional on the structural and functional features of ribosomes, natural selection has been able to promote bacteria with the fastest possible growth rates, implying minimal conflicts with both bioenergetic constraints and random genetic drift. If this extension of the drift-barrier hypothesis is correct, the interpretations of comparative studies of biological traits that have traditionally ignored differences in population-genetic environments will require revisiting.

Cell types as species: Exploring a metaphor

The concept of "cell type," though fundamental to cell biology, is controversial. Cells have historically been classified into types based on morphology, physiology, or location. More recently, single cell transcriptomic studies have revealed fine-scale differences among cells with similar gross phenotypes. Transcriptomic snapshots of cells at various stages of differentiation, and of cells under different physiological conditions, have shown that in many cases variation is more continuous than discrete, raising questions about the relationship between cell type and cell state. Some researchers have rejected the notion of fixed types altogether. Throughout the history of discussions on cell type, cell biologists have compared the problem of defining cell type with the interminable and often contentious debate over the definition of arguably the most important concept in systematics and evolutionary biology, "species." In the last decades, systematics, like cell biology, has been transformed by the increasing availability of molecular data, and the fine-grained resolution of genetic relationships have generated new ideas about how that variation should be classified. There are numerous parallels between the two fields that make exploration of the "cell types as species" metaphor timely. These parallels begin with philosophy, with discussion of both cell types and species as being either individuals, groups, or something in between (e.g., homeostatic property clusters). In each field there are various different types of lineages that form trees or networks that can (and in some cases do) provide criteria for grouping. Developing and refining models for evolutionary divergence of species and for cell type differentiation are parallel goals of the two fields. The goal of this essay is to highlight such parallels with the hope of inspiring biologists in both fields to look for new solutions to similar problems outside of their own field.

Whence Blobs? Phylogenetics of functional protein condensates

What do we know about the molecular evolution of functional protein condensation? The capacity of proteins to form biomolecular condensates (compact, protein-rich states, not bound by membranes, but still separated from the rest of the contents of the cell) appears in many cases to be bestowed by weak, transient interactions within one or between proteins. Natural selection is expected to remove or fix amino acid changes, insertions or deletions that preserve and change this condensation capacity when doing so is beneficial to the cell. A few recent studies have begun to explore this frontier of phylogenetics at the intersection of biophysics and cell biology.

Expression Partitioning of Duplicate Genes at Single Cell Resolution in Arabidopsis Roots

Gene duplication is a key evolutionary phenomenon, prevalent in all organisms but particularly so in plants, where whole genome duplication (WGD; polyploidy) is a major force in genome evolution. Much effort has been expended in attempting to understand the evolution of duplicate genes, addressing such questions as why some paralog pairs rapidly return to single copy status whereas, in other pairs, both paralogs are retained and may diverge in expression pattern or function. The effect of a gene - its site of expression and thus the initial locus of its function - occurs at the level of a cell comprising a single cell type at a given state of the cell's development. Using Arabidopsis thaliana single cell transcriptomic data we categorized patterns of expression for 11,470 duplicate gene pairs across 36 cell clusters comprising nine cell types and their developmental states. Among these 11,470 pairs, 10,187 (88.8%) had at least one copy expressed in at least one of the 36 cell clusters. Pairs produced by WGD more often had both paralogs expressed in root cells than did pairs produced by small scale duplications. Three quarters of gene pairs expressed in the 36 cell clusters (7,608/10,187) showed extreme expression bias in at least one cluster, including 352 cases of reciprocal bias, a pattern consistent with expression subfunctionalization. More than twice as many pairs showed reciprocal expression bias between cell states than between cell types or between roots and leaves. A group of 33 gene pairs with reciprocal expression bias showed evidence of concerted divergence of gene networks in stele vs. epidermis. Pairs with both paralogs expressed without bias were less likely to have paralogs with divergent mutant phenotypes; such bias-free pairs showed evidence of preservation by maintenance of dosage balance. Overall, we found considerable evidence of shifts in gene expression following duplication, including in >80% of pairs encoding 7,653 genes expressed ubiquitously in all root cell types and states for which we inferred the polarity of change.

Independent accretion of TIM22 complex subunits in the animal and fungal lineages

Background: The mitochondrial protein import complexes arose early in eukaryogenesis. Most of the components of the protein import pathways predate the last eukaryotic common ancestor. For example, the carrier-insertase TIM22 complex comprises the widely conserved Tim22 channel core. However, the auxiliary components of fungal and animal TIM22 complexes are exceptions to this ancient conservation. Methods: Using comparative genomics and phylogenetic approaches, we identified precisely when each TIM22 accretion occurred. Results: In animals, we demonstrate that Tim29 and Tim10b arose early in the holozoan lineage. Tim29 predates the metazoan lineage being present in the animal sister lineages, choanoflagellate and filastereans, whereas the erroneously named Tim10b arose from a duplication of Tim9 at the base of metazoans. In fungi, we show that Tim54 has representatives present in every holomycotan lineage including microsporidians and fonticulids, whereas Tim18 and Tim12 appeared much later in fungal evolution. Specifically, Tim18 and Tim12 arose from duplications of Sdh3 and Tim10, respectively, early in the Saccharomycotina. Surprisingly, we show that Tim54 is distantly related to AGK suggesting that AGK and Tim54 are extremely divergent orthologues and the origin of AGK/Tim54 interaction with Tim22 predates the divergence of animals and fungi. Conclusions: We argue that the evolutionary history of the TIM22 complex is best understood as the neutral structural divergence of an otherwise strongly functionally conserved protein complex. This view suggests that many of the differences in structure/subunit composition of multi-protein complexes are non-adaptive. Instead, most of the phylogenetic variation of functionally conserved molecular machines, which have been under stable selective pressures for vast phylogenetic spans, such as the TIM22 complex, is most likely the outcome of the interplay of random genetic drift and mutation pressure.