Robust phylogenetic regression.

Modern comparative biology owes much to phylogenetic regression. At its conception, this technique sparked a revolution that armed biologists with phylogenetic comparative methods (PCMs) for disentangling evolutionary correlations from those arising from hierarchical phylogenetic relationships. Over the past few decades, the phylogenetic regression framework has become a paradigm of modern comparative biology that has been widely embraced as a remedy for shared ancestry. However, recent evidence has sown doubt over the efficacy of phylogenetic regression, and PCMs more generally, with the suggestion that many of these methods fail to provide an adequate defense against unreplicated evolution-the primary justification for using them in the first place. Importantly, some of the most compelling examples of biological innovation in nature result from abrupt lineage-specific evolutionary shifts, which current regression models are largely ill-equipped to deal with. Here we explore a solution to this problem by applying robust linear regression to comparative trait data. We formally introduce robust phylogenetic regression to the PCM toolkit with linear estimators that are less sensitive to model violations than the standard least-squares estimator, while still retaining high power to detect true trait associations. Our analyses also highlight an ingenuity of the original algorithm for phylogenetic regression based on independent contrasts, whereby robust estimators are particularly effective. Collectively, we find that robust estimators hold promise for improving tests of trait associations and offer a path forward in scenarios where classical approaches may fail. Our study joins recent arguments for increased vigilance against unreplicated evolution and a better understanding of evolutionary model performance in challenging-yet biologically important-settings.

Learning Retention Mechanisms and Evolutionary Parameters of Duplicate Genes from Their Expression Data.

Learning about the roles that duplicate genes play in the origins of novel phenotypes requires an understanding of how their functions evolve. A previous method for achieving this goal, CDROM, employs gene expression distances as proxies for functional divergence and then classifies the evolutionary mechanisms retaining duplicate genes from comparisons of these distances in a decision tree framework. However, CDROM does not account for stochastic shifts in gene expression or leverage advances in contemporary statistical learning for performing classification, nor is it capable of predicting the parameters driving duplicate gene evolution. Thus, here we develop CLOUD, a multi-layer neural network built on a model of gene expression evolution that can both classify duplicate gene retention mechanisms and predict their underlying evolutionary parameters. We show that not only is the CLOUD classifier substantially more powerful and accurate than CDROM, but that it also yields accurate parameter predictions, enabling a better understanding of the specific forces driving the evolution and long-term retention of duplicate genes. Further, application of the CLOUD classifier and predictor to empirical data from Drosophila recapitulates many previous findings about gene duplication in this lineage, showing that new functions often emerge rapidly and asymmetrically in younger duplicate gene copies, and that functional divergence is driven by strong natural selection. Hence, CLOUD represents a major advancement in classifying retention mechanisms and predicting evolutionary parameters of duplicate genes, thereby highlighting the utility of incorporating sophisticated statistical learning techniques to address long-standing questions about evolution after gene duplication.

Epistasis-Driven Evolution of the SARS-CoV-2 Secondary Structure.

Epistasis is an evolutionary phenomenon whereby the fitness effect of a mutation depends on the genetic background in which it arises. A key source of epistasis in an RNA molecule is its secondary structure, which contains functionally important topological motifs held together by hydrogen bonds between Watson-Crick (WC) base pairs. Here we study epistasis in the secondary structure of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by examining properties of derived alleles arising from substitution mutations at ancestral WC base-paired and unpaired (UP) sites in 15 conserved topological motifs across the genome. We uncover fewer derived alleles and lower derived allele frequencies at WC than at UP sites, supporting the hypothesis that modifications to the secondary structure are often deleterious. At WC sites, we also find lower derived allele frequencies for mutations that abolish base pairing than for those that yield G·U "wobbles," illustrating that weak base pairing can partially preserve the integrity of the secondary structure. Last, we show that WC sites under the strongest epistatic constraint reside in a three-stemmed pseudoknot motif that plays an essential role in programmed ribosomal frameshifting, whereas those under the weakest epistatic constraint are located in 3' UTR motifs that regulate viral replication and pathogenicity. Our findings demonstrate the importance of epistasis in the evolution of the SARS-CoV-2 secondary structure, as well as highlight putative structural and functional targets of different forms of natural selection.

Rapid functional divergence after small-scale gene duplication in grasses.

BACKGROUND: Gene duplication has played an important role in the evolution and domestication of flowering plants. Yet little is known about how plant duplicate genes evolve and are retained over long timescales, particularly those arising from small-scale duplication (SSD) rather than whole-genome duplication (WGD) events. RESULTS: We address this question in the Poaceae (grass) family by analyzing gene expression data from nine tissues of Brachypodium distachyon, Oryza sativa japonica (rice), and Sorghum bicolor (sorghum). Consistent with theoretical predictions, expression profiles of most grass genes are conserved after SSD, suggesting that functional conservation is the primary outcome of SSD in grasses. However, we also uncover support for widespread functional divergence, much of which occurs asymmetrically via the process of neofunctionalization. Moreover, neofunctionalization preferentially targets younger (child) duplicate gene copies, is associated with RNA-mediated duplication, and occurs quickly after duplication. Further analysis reveals that functional divergence of SSD-derived genes is positively correlated with both sequence divergence and tissue specificity in all three grass species, and particularly with anther expression in B. distachyon. CONCLUSIONS: Our results suggest that SSD-derived grass genes often undergo rapid functional divergence that may be driven by natural selection on male-specific phenotypes. These observations are consistent with those in several animal species, suggesting that duplicate genes take similar evolutionary trajectories in plants and animals.

Natural Selection Drives Rapid Functional Evolution of Young Drosophila Duplicate Genes.

Gene duplication is thought to play a major role in phenotypic evolution. Yet the forces involved in the functional divergence of young duplicate genes remain unclear. Here, we use population-genetic inference to elucidate the role of natural selection in the functional evolution of young duplicate genes in Drosophila melanogaster. We find that negative selection acts on young duplicates with ancestral functions, and positive selection on those with novel functions, suggesting that natural selection may determine whether and how young duplicate genes are retained. Moreover, evidence of natural selection is strongest in protein-coding regions and 3' UTRs of young duplicates, indicating that selection may primarily target encoded proteins and regulatory sequences specific to 3' UTRs. Further analysis reveals that natural selection acts immediately after duplication and weakens over time, possibly explaining the observed bias toward the acquisition of new functions by young, rather than old, duplicate gene copies. Last, we find an enrichment of testis-related functions in young duplicates that underwent recent positive selection, but not in young duplicates that did not undergo recent positive selection, or in old duplicates that either did or did not undergo recent positive selection. Thus, our findings reveal that natural selection is a key player in the functional evolution of young duplicate genes, acts rapidly and in a region-specific manner, and may underlie the origin of novel testis-specific phenotypes in Drosophila.

CDROM: Classification of Duplicate gene RetentiOn Mechanisms.

BACKGROUND: Gene duplication is a major source of new genes that is thought to play an important role in phenotypic innovation. Though several mechanisms have been hypothesized to drive the functional evolution and long-term retention of duplicate genes, there are currently no software tools for assessing their genome-wide contributions. Thus, the evolutionary mechanisms by which duplicate genes acquire novel functions remain unclear in a number of taxa. RESULTS: In a recent study, researchers developed a phylogenetic approach that uses gene expression data from two species to classify the mechanisms underlying the retention of duplicate genes (Proc Natl Acad Sci USA 110:1740917414, 2013). We have implemented their classification method, as well as a more generalized method, in the R package CDROM, enabling users to apply these methods to their data and gain insights into the origin of novel biological functions after gene duplication. The CDROM R package, source code, and user manual for the R package are available for download from CRAN at https://cran.rstudio.com/web/packages/CDROM/ . Additionally, the CDROM R source code, user manual for running CDROM from the source code, and sample dataset used in this manuscript can be accessed at www.personal.psu.edu/rua15/software.html . CONCLUSIONS: CDROM is the first software package that enables genome-wide classification of the mechanisms driving the long-term retention of duplicate genes. It is user-friendly and flexible, providing researchers with a tool for studying the functional evolution of duplicate genes in a variety of taxa.

Strong epistatic selection on the RNA secondary structure of HIV.

A key question in evolutionary genomics is how populations navigate the adaptive landscape in the presence of epistasis, or interactions among loci. This problem can be directly addressed by studying the evolution of RNA secondary structures, for which there is constraint to maintain pairing between Watson-Crick (WC) sites. Replacement of a nucleotide at one site of a WC pair reduces fitness by disrupting binding, which can be restored via a compensatory replacement at the interacting site. Here, I present the first genome-scale analysis of epistasis on the RNA secondary structure of human immunodeficiency virus type 1 (HIV-1). Comparison of polymorphism frequencies at ancestrally conserved sites reveals that selection against replacements is ∼ 2.7 times stronger at WC than at non-WC sites, such that nearly 50% of constraint can be attributed to epistasis. However, almost all epistatic constraint is due to selection against conversions of WC pairs to unpaired (UP) nucleotides, whereas conversions to GU wobbles are only slightly deleterious. This disparity is also evident in pairs with second-site compensatory replacements; conversions from UP nucleotides to WC pairs increase median fitness by ∼ 4.2%, whereas conversions from GU wobbles to WC pairs only increase median fitness by ∼ 0.3%. Moreover, second-site replacements that convert UP nucleotides to GU wobbles also increase median fitness by ∼ 4%, indicating that such replacements are nearly as compensatory as those that restore WC pairing. Thus, WC peaks of the HIV-1 epistatic adaptive landscape are connected by high GU ridges, enabling the viral population to rapidly explore distant peaks without traversing deep UP valleys.

Neofunctionalization of young duplicate genes in Drosophila.

Gene duplication is a key source of genetic innovation that plays a role in the evolution of phenotypic complexity. Although several evolutionary processes can result in the long-term retention of duplicate genes, their relative contributions in nature are unknown. Here we develop a phylogenetic approach for comparing genome-wide expression profiles of closely related species to quantify the roles of conservation, neofunctionalization, subfunctionalization, and specialization in the preservation of duplicate genes. Application of our method to pairs of young duplicates in Drosophila shows that neofunctionalization, the gain of a novel function in one copy, accounts for the retention of almost two-thirds of duplicate genes. Surprisingly, novel functions nearly always originate in younger (child) copies, whereas older (parent) copies possess functions similar to those of ancestral genes. Further examination of such pairs reveals a strong bias toward RNA-mediated duplication events, implicating asymmetric duplication and positive selection in the evolution of new functions. Moreover, we show that young duplicate genes are expressed primarily in testes and that their expression breadth increases over evolutionary time. This finding supports the "out-of-testes" hypothesis, which posits that testes are a catalyst for the emergence of new genes that ultimately evolve functions in other tissues. Thus, our study highlights the importance of neofunctionalization and positive selection in the retention of young duplicates in Drosophila and illustrates how duplicates become incorporated into novel functional networks over evolutionary time.

Rapid divergence and diversification of mammalian duplicate gene functions.

BACKGROUND: Gene duplication provides raw material for the evolution of functional innovation. We recently developed a phylogenetic method that classifies evolutionary processes driving the retention of duplicate genes by quantifying divergence between their spatial gene expression profiles and that of their single-copy orthologous gene in a closely related sister species. RESULTS: Here, we apply our classification method to pairs of duplicate genes in eight mammalian genomes, using data from 11 tissues to construct spatial gene expression profiles. We find that young mammalian duplicates are often functionally conserved, and that expression divergence rapidly increases over evolutionary time. Moreover, expression divergence results in increased tissue specificity, with an overrepresentation of expression in male kidney, underrepresentation of expression in female liver, and strong underrepresentation of expression in testis. Thus, duplicate genes acquire a diversity of new tissue-specific functions outside of the testis, possibly contributing to the origin of a multitude of complex phenotypes during mammalian evolution. CONCLUSIONS: Our findings reveal that mammalian duplicate genes are initially functionally conserved, and then undergo rapid functional divergence over evolutionary time, acquiring diverse tissue-specific biological roles. These observations are in stark contrast to the much faster expression divergence and acquisition of broad housekeeping roles we previously observed in Drosophila duplicate genes. Due to the smaller effective population sizes of mammals relative to Drosophila, these analyses implicate natural selection in the functional evolution of duplicate genes.

Conserved proteins are fragile.

Levels of selective constraint vary among proteins. Although strong constraint on a protein is often attributed to its functional importance, evolutionary rate may also be limited if a protein is fragile, such that a large proportion of amino acid replacements reduce its fitness. To determine the relative contributions of essentiality and fragility to selective constraint, we compared relationships of selection against nonsense mutations (snon) and selection against missense mutations (smis) to protein sequence conservation (Ka). As expected, snon is greater than smis; however, the correlation between smis and Ka is nearly three times stronger than the correlation between snon and Ka. Moreover, examination of relationships to gene expression level, tissue specificity, and number of protein-protein interactions shows that smis is more strongly correlated than snon to all three measures of biological function. Thus, our analysis reveals that slowly evolving proteins are under strong selective constraint primarily because they are fragile, and that this association likely exists because allowing a protein to function improperly, rather than removing it from a biological network, can negatively affect the functions of other molecules it interacts with and their downstream products.

A strong deletion bias in nonallelic gene conversion.

Gene conversion is the unidirectional transfer of genetic information between orthologous (allelic) or paralogous (nonallelic) genomic segments. Though a number of studies have examined nucleotide replacements, little is known about length difference mutations produced by gene conversion. Here, we investigate insertions and deletions produced by nonallelic gene conversion in 338 Drosophila and 10,149 primate paralogs. Using a direct phylogenetic approach, we identify 179 insertions and 614 deletions in Drosophila paralogs, and 132 insertions and 455 deletions in primate paralogs. Thus, nonallelic gene conversion is strongly deletion-biased in both lineages, with almost 3.5 times as many conversion-induced deletions as insertions. In primates, the deletion bias is considerably stronger for long indels and, in both lineages, the per-site rate of gene conversion is orders of magnitudes higher than that of ordinary mutation. Due to this high rate, deletion-biased nonallelic gene conversion plays a key role in genome size evolution, leading to the cooperative shrinkage and eventual disappearance of selectively neutral paralogs.

Predicting evolutionary targets and parameters of gene deletion from expression data.

Motivation: Gene deletion is traditionally thought of as a nonadaptive process that removes functional redundancy from genomes, such that it generally receives less attention than duplication in evolutionary turnover studies. Yet, mounting evidence suggests that deletion may promote adaptation via the "less-is-more" evolutionary hypothesis, as it often targets genes harboring unique sequences, expression profiles, and molecular functions. Hence, predicting the relative prevalence of redundant and unique functions among genes targeted by deletion, as well as the parameters underlying their evolution, can shed light on the role of gene deletion in adaptation. Results: Here, we present CLOUDe, a suite of machine learning methods for predicting evolutionary targets of gene deletion events from expression data. Specifically, CLOUDe models expression evolution as an Ornstein-Uhlenbeck process, and uses multi-layer neural network, extreme gradient boosting, random forest, and support vector machine architectures to predict whether deleted genes are "redundant" or "unique", as well as several parameters underlying their evolution. We show that CLOUDe boasts high power and accuracy in differentiating between classes, and high accuracy and precision in estimating evolutionary parameters, with optimal performance achieved by its neural network architecture. Application of CLOUDe to empirical data from Drosophila suggests that deletion primarily targets genes with unique functions, with further analysis showing these functions to be enriched for protein deubiquitination. Thus, CLOUDe represents a key advance in learning about the role of gene deletion in functional evolution and adaptation. Availability and implementation: CLOUDe is freely available on GitHub (https://github.com/anddssan/CLOUDe).

Nonallelic gene conversion is not GC-biased in Drosophila or primates.

Gene conversion is the unidirectional transfer of genetic information between allelic (orthologous) or nonallelic (paralogous) DNA segments. Recently, there has been much interest in understanding how gene conversion shapes the nucleotide composition of the genomic landscape. A widely held hypothesis is that gene conversion is universally GC-biased. However, direct experimental evidence of this hypothesis is limited to a single study of meiotic crossovers in yeast. Although there have been a number of indirect studies of gene conversion, evidence of GC-biased replacements gathered from such studies can also be attributed to positive selection, which has the same evolutionary dynamics as biased gene conversion. Here, we apply a direct phylogenetic approach to examine nucleotide replacements produced by nonallelic gene conversion in Drosophila and primate genomes. We find no evidence for GC-biased gene conversion in either lineage, suggesting that previously observed GC biases may be due to positive selection rather than to biased gene conversion.

Predicting gene expression divergence between single-copy orthologs in two species.

Predicting gene expression divergence is integral to understanding the emergence of new biological functions and associated traits. Whereas several sophisticated methods have been developed for this task, their applications are either limited to duplicate genes or require expression data from more than two species. Thus, here we present PiXi, the first machine learning framework for predicting gene expression divergence between single-copy orthologs in two species. PiXi models gene expression evolution as an Ornstein-Uhlenbeck process, and overlays this model with multi-layer neural network, random forest, and support vector machine architectures for making predictions. It outputs the predicted class "conserved" or "diverged" for each pair of orthologs, as well as their predicted expression optima in the two species. We show that PiXi has high power and accuracy in predicting gene expression divergence between single-copy orthologs, as well as high accuracy and precision in estimating their expression optima in the two species, across a wide range of evolutionary scenarios, with the globally best performance achieved by a multi-layer neural network. Moreover, application of our best performing PiXi predictor to empirical gene expression data from single-copy orthologs residing at different loci in two species of Drosophila reveals that approximately 23% underwent expression divergence after positional relocation. Further analysis shows that several of these "diverged" genes are involved in the electron transport chain of the mitochondrial membrane, suggesting that new chromatin environments may impact energy production in Drosophila. Thus, by providing a toolkit for predicting gene expression divergence between single-copy orthologs in two species, PiXi can shed light on the origins of novel phenotypes across diverse biological processes and study systems.

Models for the retention of duplicate genes and their biological underpinnings.

Gene content in genomes changes through several different processes, with gene duplication being an important contributor to such changes. Gene duplication occurs over a range of scales from individual genes to whole genomes, and the dynamics of this process can be context dependent. Still, there are rules by which genes are retained or lost from genomes after duplication, and probabilistic modeling has enabled characterization of these rules, including their context-dependence. Here, we describe the biology and corresponding mathematical models that are used to understand duplicate gene retention and its contribution to the set of biochemical functions encoded in a genome.

Rapid repetitive element-mediated expansion of piRNA clusters in mammalian evolution.

Piwi-interacting RNAs (piRNAs) are approximately 30 nucleotide noncoding RNAs that may be involved in transposon silencing in mammalian germline cells. Most piRNA sequences are found in a small number of genomic regions referred to as clusters, which range from 1 to hundreds of kilobases. We studied the evolution of 140 rodent piRNA clusters, 103 of which do not overlap protein-coding genes. Phylogenetic analysis revealed that 14 clusters were acquired after rat-mouse divergence and another 44 after rodent-primate divergence. Most clusters originated in a process analogous to the duplication of protein-coding genes by ectopic recombination, via insertions of long sequences that were mediated by flanking chromosome-specific repetitive elements (REs). Source sequences for such insertions are often located on the same chromosomes and also harbor clusters. The rate of piRNA cluster expansion is higher than that of any known gene family and, in contrast to other large gene families, there was not a single cluster loss. These observations suggest that piRNA cluster expansion is driven by positive selection, perhaps caused by the need to silence the ever-expanding repertoire of mammalian transposons.

Nested genes and increasing organizational complexity of metazoan genomes.

The most common form of protein-coding gene overlap in eukaryotes is a simple nested structure, whereby one gene is embedded in an intron of another. Analysis of nested protein-coding genes in vertebrates, fruit flies and nematodes revealed substantially higher rates of evolutionary gains than losses. The accumulation of nested gene structures could not be attributed to any obvious functional relationships between the genes involved and represents an increase of the organizational complexity of animal genomes via a neutral process.

Bridges: a tool for identifying local similarities in long sequences.

SUMMARY: Bridges is a heuristic search tool that uses short word matches to rapidly identify local similarities between sequences. It consists of three stages: filtering input sequences, identifying local similarities and post-processing local similarities. As input sequence data are released from memory after the filtering stage, genome-scale datasets can be efficiently compared in a single run. Bridges also includes 20 parameters, which enable the user to dictate the sensitivity and specificity of a search. AVAILABILITY: Bridges is implemented in the C programming language and can be run on all platforms. Source code and documentation are available at http://github.com/rassis/bridges.

No Expression Divergence despite Transcriptional Interference between Nested Protein-Coding Genes in Mammals.

Nested protein-coding genes accumulated throughout metazoan evolution, with early analyses of human and Drosophila microarray data indicating that this phenomenon was simply due to the presence of large introns. However, a recent study employing RNA-seq data uncovered evidence of transcriptional interference driving rapid expression divergence between Drosophila nested genes, illustrating that accurate expression estimation of overlapping genes can enhance detection of their relationships. Hence, here I apply an analogous approach to strand-specific RNA-seq data from human and mouse to revisit the role of transcriptional interference in the evolution of mammalian nested genes. A genomic survey reveals that whereas mammalian nested genes indeed accrued over evolutionary time, they are retained at lower frequencies than in Drosophila. Though several properties of mammalian nested genes align with observations in Drosophila and with expectations under transcriptional interference, contrary to both, their expression divergence is not statistically different from that between unnested genes, and also does not increase after nesting. Together, these results support the hypothesis that lower selection efficiencies limit rates of gene expression evolution in mammals, leading to their reliance on immediate eradication of deleterious nested genes to avoid transcriptional interference.

BLAST from the Past: Impacts of Evolving Approaches on Studies of Evolution by Gene Duplication.

In 1970, Susumu Ohno hypothesized that gene duplication was a major reservoir of adaptive innovation. However, it was not until over two decades later that DNA sequencing studies uncovered the ubiquity of gene duplication across all domains of life, highlighting its global importance in the evolution of phenotypic complexity and species diversification. Today, it seems that there are no limits to the study of evolution by gene duplication, as it has rapidly coevolved with numerous experimental and computational advances in genomics. In this perspective, we examine word stem usage in PubMed abstracts to infer how evolving discoveries and technologies have shaped the landscape of studying evolution by gene duplication, leading to a more refined understanding of its role in the emergence of novel phenotypes.